diff --git a/book/EDS_paper/BayesianClassification.ipynb b/book/EDS_paper/BayesianClassification.ipynb new file mode 100644 index 0000000..f25c378 --- /dev/null +++ b/book/EDS_paper/BayesianClassification.ipynb @@ -0,0 +1,1246 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d1bb37a6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-26 13:57:39.992405: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-11-26 13:57:40.529813: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import tensorflow_probability as tfp\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "from tensorflow.keras.optimizers import Adam\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "d9e19465", + "metadata": {}, + "source": [ + "### Setup and Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "68eb4db6", + "metadata": {}, + "outputs": [], + "source": [ + "# North Carolina data\n", + "url = \"https://github.com/geo-smart/flood-risk-ml-tutorial/blob/main/North_Carolina_Buildings.zip?raw=true\"\n", + "\n", + "# read data into a Pandas dataframe\n", + "rawDf = pd.read_csv(url, compression=\"zip\")\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "rawDf = rawDf.iloc[:, 3:] \n", + "df = rawDf.copy( deep=True )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "999bfa70", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Residential 976690\n", + "Commercial 64029\n", + "Other 37425\n", + "Name: BuildingType, dtype: int64\n", + "\n", + "Residential 1.000000\n", + "Commercial 15.253869\n", + "Other 26.097261\n", + "Name: BuildingType, dtype: float64\n", + "\n" + ] + } + ], + "source": [ + "# classify a building as \"Residential\", \"Commercial\", or \"Other\"\n", + "df.loc[df[\"BuildingType\"] == \"Industrial\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Assembly\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Education\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Government\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Agriculture\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Utility and Misc\", \"BuildingType\"] = 'Other'\n", + "\n", + "# building type distributions\n", + "print()\n", + "print( df['BuildingType'].value_counts() )\n", + "print()\n", + "print( 976690/df['BuildingType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1e299cf1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_60006/1358776107.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " df.iloc[:, nCols-1] = le.transform( df.iloc[:, nCols-1] )\n", + "/tmp/ipykernel_60006/1358776107.py:23: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " df.iloc[:, nCols-2] = le2.transform( df.iloc[:, nCols-2] )\n" + ] + } + ], + "source": [ + "from sklearn import preprocessing\n", + "\n", + "df = df.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "# preprocess the data - scaling\n", + "scaler = preprocessing.StandardScaler()\n", + " \n", + "columns = ['X', 'Y', 'Area', 'MedianIncomeCounty', \n", + " 'HousingUnitsCounty', 'HousingDensityCounty',\n", + " 'Impervious', 'AgCount', 'CmCount', 'GvCount',\n", + " 'EdCount', 'InCount']\n", + "df[columns] = scaler.fit_transform(df[columns])\n", + "\n", + "df = df.dropna()\n", + "\n", + "nCols = df.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( df.iloc[:, nCols-1] ) # ornl type\n", + "df.iloc[:, nCols-1] = le.transform( df.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( df.iloc[:, nCols-2] ) # nearest road type\n", + "df.iloc[:, nCols-2] = le2.transform( df.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "958d6b5e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadBuildingType
8678590.9288150.630400-0.2047131.3022840.963812-0.3396210.5835031.6657310.8805030.6435561.0978171.04193442
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571914-1.157729-0.751705-0.0803100.3372531.0518500.031186-0.311114-0.3377991.1401851.2781100.9492440.97965442
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" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty HousingUnitsCounty \\\n", + "867859 0.928815 0.630400 -0.204713 1.302284 0.963812 \n", + "445392 -0.718482 0.325763 -0.164892 -1.567594 -1.182225 \n", + "876314 0.862746 0.742963 0.166077 1.302284 0.963812 \n", + "8612 -0.834396 -0.081805 -0.160557 -0.099105 -1.052135 \n", + "571914 -1.157729 -0.751705 -0.080310 0.337253 1.051850 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount \\\n", + "867859 -0.339621 0.583503 1.665731 0.880503 0.643556 \n", + "445392 0.048369 0.866798 -0.585148 -1.195094 -1.048588 \n", + "876314 -0.339621 0.315118 1.665731 0.880503 0.643556 \n", + "8612 -0.760445 0.829522 -0.807762 -1.045352 -1.048588 \n", + "571914 0.031186 -0.311114 -0.337799 1.140185 1.278110 \n", + "\n", + " EdCount InCount OsmNearestRoad BuildingType \n", + "867859 1.097817 1.041934 4 2 \n", + "445392 -1.201147 -1.217191 4 2 \n", + "876314 1.097817 1.041934 9 2 \n", + "8612 -1.064738 -0.999500 4 2 \n", + "571914 0.949244 0.979654 4 2 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "61b9c6ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of classes: 3\n" + ] + } + ], + "source": [ + "nClasses = len(df['BuildingType'].unique())\n", + "print(\"Number of classes:\", nClasses)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f2ae35fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Size: (862513, 13)\n", + "Testing Size: (215629, 13)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "train, test = train_test_split(df, test_size=0.2, random_state=42, stratify=df[['BuildingType']])\n", + "\n", + "nCols = test.shape[1]\n", + "xTest = test.iloc[:, :nCols-1]\n", + "yTest = test.iloc[:, nCols-1]\n", + "\n", + "xTrain = train.iloc[:, :nCols-1]\n", + "yTrain = train.iloc[:, nCols-1]\n", + "\n", + "print(\"Training Size:\", xTrain.shape)\n", + "print(\"Testing Size:\", xTest.shape)\n", + "\n", + "# save test/train split\n", + "xTrain.to_pickle(\"./xTrain.pkl\") \n", + "yTrain.to_pickle(\"./yTrain.pkl\") \n", + "xTest.to_pickle(\"./xTest.pkl\") \n", + "yTest.to_pickle(\"./yTest.pkl\") " + ] + }, + { + "cell_type": "markdown", + "id": "6167606a", + "metadata": {}, + "source": [ + "### Bayesian Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "78574d2f", + "metadata": {}, + "outputs": [], + "source": [ + "xTrain = pd.read_pickle(\"./xTrain.pkl\") \n", + "yTrain = pd.read_pickle(\"./yTrain.pkl\") \n", + "xTest = pd.read_pickle(\"./xTest.pkl\") \n", + "yTest = pd.read_pickle(\"./yTest.pkl\") " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b9fb60d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weight for class 0: 8.42\n", + "Weight for class 1: 14.40\n", + "Weight for class 2: 0.55\n" + ] + } + ], + "source": [ + "# Scaling by total/2 helps keep the loss to a similar magnitude.\n", + "total = xTrain.shape[0]\n", + "ix = np.where( yTrain == 0 )\n", + "zero = len(ix[0])\n", + "ix = np.where( yTrain == 1 )\n", + "one = len(ix[0])\n", + "ix = np.where( yTrain == 2 )\n", + "two = len(ix[0])\n", + "\n", + "weight_for_0 = (1 / zero) * (total / 2.0)\n", + "weight_for_1 = (1 / one) * (total / 2.0)\n", + "weight_for_2 = (1 / two) * (total / 2.0)\n", + "\n", + "class_weight = {0: weight_for_0, 1: weight_for_1, 2: weight_for_2}\n", + "\n", + "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n", + "print('Weight for class 1: {:.2f}'.format(weight_for_1))\n", + "print('Weight for class 2: {:.2f}'.format(weight_for_2))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "63f8937e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jupyter-narock/.local/lib/python3.9/site-packages/tensorflow_probability/python/layers/util.py:98: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use the `layer.add_weight()` method instead.\n", + " loc = add_variable_fn(\n", + "2023-11-26 13:58:26.592861: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5337 MB memory: -> device: 0, name: NVIDIA GeForce GTX TITAN Black, pci bus id: 0000:65:00.0, compute capability: 3.5\n", + "/home/jupyter-narock/.local/lib/python3.9/site-packages/tensorflow_probability/python/layers/util.py:108: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use the `layer.add_weight()` method instead.\n", + " untransformed_scale = add_variable_fn(\n" + ] + } + ], + "source": [ + "from keras import backend as K \n", + "\n", + "# Keras keeps models hanging around in memory. If we retrain a model, Keras will\n", + "# start from the previously concluded weight values. This resets everything.\n", + "K.clear_session()\n", + "\n", + "# KL divergence weighted by the number of training samples, using\n", + "# lambda function to pass as input to the kernel_divergence_fn on\n", + "# flipout layers.\n", + "kl_divergence_function = (lambda q, p, _: tfd.kl_divergence(q, p) / \n", + " tf.cast(xTrain.shape[0], dtype=tf.float32))\n", + "\n", + "tfd = tfp.distributions\n", + "\n", + "# Define a logistic regression model as a Bernoulli distribution\n", + "# parameterized by logits from a single linear layer. We use the Flipout\n", + "# Monte Carlo estimator for the layer: this enables lower variance\n", + "# stochastic gradients than naive reparameterization.\n", + "input_layer = tf.keras.layers.Input(shape=xTrain.shape[1])\n", + "\n", + "#dense_layer = tfp.layers.DenseFlipout(\n", + "# units=1,\n", + "# activation='sigmoid',\n", + "# kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + "# bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + "# kernel_divergence_fn=kl_divergence_function)(input_layer)\n", + "\n", + "layer1 = tfp.layers.DenseFlipout(\n", + " units=26,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(input_layer)\n", + "\n", + "layer2 = tfp.layers.DenseFlipout(\n", + " units=13,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer1)\n", + "\n", + "layer3 = tfp.layers.DenseFlipout(\n", + " units=8,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer2)\n", + "\n", + "layer4 = tfp.layers.DenseFlipout(\n", + " units=4,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer3)\n", + "\n", + "out = tfp.layers.DenseFlipout(\n", + " units=3,\n", + " activation='softmax',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer4)\n", + "\n", + "# Model compilation\n", + "#bnn = tf.keras.Model(inputs=input_layer, outputs=dense_layer)\n", + "bnn = tf.keras.Model(inputs=input_layer, outputs=out)\n", + "optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\n", + " \n", + "# We use the binary_crossentropy loss since this toy example contains\n", + "# two labels. The Keras API will then automatically add the\n", + "# Kullback-Leibler divergence (contained on the individual layers of\n", + "# the model), to the cross entropy loss, effectively\n", + "# calcuating the (negated) Evidence Lower Bound Loss (ELBO)\n", + "bnn.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6c70029b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 13)] 0 \n", + " \n", + " dense_flipout (DenseFlipout (None, 26) 728 \n", + " ) \n", + " \n", + " dense_flipout_1 (DenseFlipo (None, 13) 702 \n", + " ut) \n", + " \n", + " dense_flipout_2 (DenseFlipo (None, 8) 224 \n", + " ut) \n", + " \n", + " dense_flipout_3 (DenseFlipo (None, 4) 72 \n", + " ut) \n", + " \n", + " dense_flipout_4 (DenseFlipo (None, 3) 30 \n", + " ut) \n", + " \n", + "=================================================================\n", + "Total params: 1,756\n", + "Trainable params: 1,756\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "bnn.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "494e0dfa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/25\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-26 13:58:36.154323: I tensorflow/compiler/xla/service/service.cc:169] XLA service 0x7f7e6797aa50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2023-11-26 13:58:36.154356: I tensorflow/compiler/xla/service/service.cc:177] StreamExecutor device (0): NVIDIA GeForce GTX TITAN Black, Compute Capability 3.5\n", + "2023-11-26 13:58:36.159337: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2023-11-26 13:58:36.327544: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8600\n", + "2023-11-26 13:58:36.385677: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n", + "2023-11-26 13:58:36.452123: I ./tensorflow/compiler/jit/device_compiler.h:180] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5391/5391 [==============================] - 88s 15ms/step - loss: 1.1961 - accuracy: 0.8490 - val_loss: 0.4975 - val_accuracy: 0.8726\n", + "Epoch 2/25\n", + "5391/5391 [==============================] - 81s 15ms/step - loss: 1.1239 - accuracy: 0.8645 - val_loss: 0.4582 - val_accuracy: 0.8769\n", + "Epoch 3/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0964 - accuracy: 0.8600 - val_loss: 0.4051 - val_accuracy: 0.8877\n", + "Epoch 4/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0867 - accuracy: 0.8582 - val_loss: 0.4280 - val_accuracy: 0.8923\n", + "Epoch 5/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0799 - accuracy: 0.8595 - val_loss: 0.4699 - val_accuracy: 0.8752\n", + "Epoch 6/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0756 - accuracy: 0.8612 - val_loss: 0.5146 - val_accuracy: 0.8549\n", + "Epoch 7/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0733 - accuracy: 0.8617 - val_loss: 0.4419 - val_accuracy: 0.8811\n", + "Epoch 8/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0707 - accuracy: 0.8610 - val_loss: 0.4395 - val_accuracy: 0.8745\n", + "Epoch 9/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0694 - accuracy: 0.8608 - val_loss: 0.4489 - val_accuracy: 0.8745\n", + "Epoch 10/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0666 - accuracy: 0.8600 - val_loss: 0.3907 - val_accuracy: 0.8926\n", + "Epoch 11/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0657 - accuracy: 0.8604 - val_loss: 0.4543 - val_accuracy: 0.8723\n", + "Epoch 12/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0651 - accuracy: 0.8594 - val_loss: 0.4728 - val_accuracy: 0.8649\n", + "Epoch 13/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0647 - accuracy: 0.8601 - val_loss: 0.4836 - val_accuracy: 0.8689\n", + "Epoch 14/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0631 - accuracy: 0.8597 - val_loss: 0.4511 - val_accuracy: 0.8704\n", + "Epoch 15/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0624 - accuracy: 0.8593 - val_loss: 0.4543 - val_accuracy: 0.8699\n", + "Epoch 16/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0621 - accuracy: 0.8593 - val_loss: 0.4764 - val_accuracy: 0.8608\n", + "Epoch 17/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0614 - accuracy: 0.8583 - val_loss: 0.5215 - val_accuracy: 0.8496\n", + "Epoch 18/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0615 - accuracy: 0.8581 - val_loss: 0.3928 - val_accuracy: 0.8919\n", + "Epoch 19/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0606 - accuracy: 0.8584 - val_loss: 0.4359 - val_accuracy: 0.8795\n", + "Epoch 20/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0608 - accuracy: 0.8566 - val_loss: 0.5177 - val_accuracy: 0.8340\n", + "Epoch 21/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0591 - accuracy: 0.8572 - val_loss: 0.4310 - val_accuracy: 0.8820\n", + "Epoch 22/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0592 - accuracy: 0.8583 - val_loss: 0.4852 - val_accuracy: 0.8565\n", + "Epoch 23/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0589 - accuracy: 0.8558 - val_loss: 0.4575 - val_accuracy: 0.8659\n", + "Epoch 24/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0577 - accuracy: 0.8572 - val_loss: 0.4718 - val_accuracy: 0.8596\n", + "Epoch 25/25\n", + "5391/5391 [==============================] - 83s 15ms/step - loss: 1.0588 - accuracy: 0.8580 - val_loss: 0.5208 - val_accuracy: 0.8497\n" + ] + } + ], + "source": [ + "history = bnn.fit( xTrain, tf.one_hot(yTrain,nClasses), \n", + " epochs=25, verbose=1, batch_size=128, class_weight=class_weight,\n", + " use_multiprocessing=True, validation_split=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "119724ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Epoch')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1ZFiXpkEuuTGmqtky8kHgySvwa3ykWEpyI/ILvayvxhMT//XRRjbuPsQj1/ThoynnMqJ7s1MOIsUa1o3ir5f14K1Jg6gXHcnEF5Zx56xV7PXkBanUxphwsBZJEHhyCytcHsXXTwPuB+mXXP3GC1bu+JHXl+/kV+e152f9gr8mZv82jfnwjnN46rOtPPV5Ou+uzqJnUhxDOicytEtT+rVOIMqPbkJjTPVggSQIcnILaFGJW3mbuRMTV+74kZtoF8KSVV6RV/nzu2tpHhfDHcM7hex16kZFcteIzlzeL4m5abtZtCmbqYsyePKzrcTGRHFupyYM6ZzIkM5NT9vbpI2pLSyQBIEnr4CuzSs3T6K6TkycueR71mV5ePIXKTSogsHwdk0aMHlYRyYP64gnr4Cvt+xn0eZsPt+Uzdy0PQB0bR7LkC6JDO3clP5tGhEdZa0VY6oTCyRBkFNBdsTS9KuGExP3Hz7Gfz7exDkdm3BRr+ZV/vpxMXUY3asFo3u1QFXZtPcQizY5QWX6V9t4ZlEGDaIjObtjE+4Y3omeSfEVX9QYE3IWSALk9SqHjxVWOpCkJCcAsGrHj4yuJvNJHvhoI3kFRfz1sh4BD6wHSkTo2jyOrs3j+NWQDhw+Vsg36U5rZd7aPVzx1Df8flQXbjy7HRG2BIsxYWV9BAE6lFeIqn/Lo/jq0TKe6GqUMXH59gO8tSKTm89tT8emDcNdnJM0rBvFyB7N+cfPevHJ3UMY0iWRv3+4gRteXMa+Q3bXlzHhZIEkQP7mIinpp4mJB0NQqsopLPLyp3fX0jI+htvPr3ZrYp6kUYNopl3fn79f3pMlGT8w+r9f8tnGfeEuljG1lgWSAFVmeZSSUpITSNuVQ36hN9jFqpSXF3/Pxj2H+PMl3akffXr0dooI153Vhg9uP4fE2LpMfHEZf3t/HXkFgWefPHAkn+lfbWPe2t1BKKkxNZ8FkgB5/ExqVZp+7sTEdVk5wS6W3/YdyuPh+Zs5r3Mio3pW/QB7oDo1i+XdyWdzw+C2vPD1di5/8mu27D1U6euoKoszfmDKrFWc9c+F/O8H67l15kreWpEZglIbU7NYIAnQqXZtwU8ZE8OZ6OqBuRs5Vujlb9VggP1UxdSJ5K+X9WD6DalkHzrGJY9/xSuLv/crU+OPR/J57ssMLnh4EWOnLebTjfv4xZnJzLntbM7u0ITfvbWGd1ftqoJaGHP6Oj36Maqxn7q2Kv+nbB4fQ8v4GFbu+JEbwzAxcUnGD8xetYvbhnWkXZMGVf76wXZ+12Z8dOe5/OaNNfzp3bV8sTmbf/28N40aRJ9wnKqybPuPvLrke+au3UN+oZeU5AT+c2VvLundknrRzvpnz45P5cYXl3H3G6uJjBAu7dMyHNUyptoLaYtEREaJyCYRSReRe0vZHy8i74vIGhFZJyITffYliMhbIrJRRDaIyCB3e2MRWSAiW9zfYV1jpDK5SErTr02jsLRICoq8/OW9dSQl1GPysOo/wO6vprExzJg4kD9d3I3PNu1j1KNf8E26k7354NF8nv9qGyMe+YKrn/mWhRv2MXZAaz6aci6zf302V6W2Ph5EAOpFR/L8DamktmnMna+vZm5acMdMNuz28ORn6WzbfySo1zWmqoWsRSIikcCTwAggE1gmInNUdb3PYZOB9ap6qYgkAptEZKaq5gOPAvNU9Uo3b3txAvB7gYWq+oAbnO4F7glVPSriySsgQqDBKQ5SpyQ34sPvdrPXk0ezuKqbmDjjm+1s2nuIZ67vf8KHZ00QESHcfG57zmp/BnfMWsW1zy/hnI5NWLLtAPmFXvq6y+Ff0rtFhTcX1I+OYvrEAUyYvpQ7XltFVIQwskdgY0ler/LcVxk8+PFm8ou8PDh/EyO7N+OW89rTv03jgK5tTDiEskUyEEhX1Qw3MMwCxpQ4RoFYcTrnGwIHgEIRiQPOA54HUNV8VT3onjMGmOE+ngFcHsI6VKh4VvupToob2Nb54Hh/TVYwi1WuvZ48/vvJFoZ1SWRk92ZV9rpVrWdSPB/cfg5jBySzac8hrk5txdw7zuXdyWdzdWprv+9Qa1g3ihcnDqBnUjyTX13Jwg17T7lMWQdzufa5Jfxz7kaGdklk/l3ncduwjizZdoCfP/0tVzz1NfPW7qbIW/H4jjHVRSjHSJKAnT7PM4EzSxzzBDAHyAJigWtU1Ssi7YFs4AUR6QOsAKao6hGgmaruBlDV3SJSakILEbkFuAUgOTk5eLUqwVPJXCQl9WoVz7mdmvD4p+lc1b818fVP/Vr++ufcDeQXeavFDPZQqx8dxf9d0QvoFdB1YmPqMOPGgVz//BJufWUl08b3Z2glc6m8vyaLP76TRqFX+ffPe3NVaitEhN+M7MKtQzvw1opMnvtyG5NeWUmbM+pz8zntuLJ/6xrXYjQ1TyhbJKV9QpX8mnUhsBpoCfQFnnBbI1FACvC0qvYDjuB0YflNVaepaqqqpiYmJlay6P5zWiSBxeP7LuqGJ6+AJz9PD1Kpyvbt1h94b3UWk4Z0oM0Zp/8Ae1WKr1eHl24cSMemDbnl5RV8uSXbr/M8eQXc9fpqbn9tFR2aNuSjKedy9YDWJwTx+tFRjB/Uls9+O5Snrk0hoX40f35vHYMfWMjD8zex//CxUFXLmICFMpBkAq19nrfCaXn4mgjMVkc6sA3o6p6bqapL3OPewgksAHtFpAWA+zusU5o9eYWnPNBerFuLOH6e0ooXv97OzgNHg1SykzkD7Gtp1agevx7aIWSvU5Ml1I9m5s1n0r5JA26esZxvtu4v9/jimfdz1mRx1wWdefNXg8oN4JERwkW9WvDurwfz5qRBpLZtzOOfpTP4gU/5w+w0tmYfDnaVjAlYKAPJMqCTiLRzB8vH4nRj+doBDAcQkWZAFyBDVfcAO0Wki3vccKB4kH4OMMF9PAF4L3RVqFhl0+yW5TcjOxMRAQ/O3xSEUpXuha+3sWXfYf56aQ9i6lh3yalq1MAJJm3OqM9NLy5nScYPJx2TX+jlX/M2MvbZxURFCm9OGsSUCzr5nbBLRBjQtjHPjk/lk7uHcGX/Vry9MpPhDy3ihheW8v6arKDM4jcmGEIWSFS1ELgN+BjYALyhqutEZJKITHIPux8YLCJpwELgHlUt/op3OzBTRL7D6fb6p7v9AWCEiGzBuSPsgVDVwR+e3Mql2S1Li/h63HROO95bncV3mQcDL1gJu3Ny+e8nWxjetSkX1OAB9qpyRsO6zLz5LFomxDDxxWUs337g+L70fYf42VNf8/TnW7kmtTVz7zj3+OTTU9EhsSH//Fkvvrn3fKYM78TmPYe4/bVVDPj7J9z79ncs3XbAr8mXxoSK1IZ/gKmpqbp8+fKQXLvrnz9i/KC23HdRt4CvdSivgKH/+ZxOzRry2i/PCtpAuKry65kr+XTjPj65ewitG9ev+CTjl32ePK6ZtpjsQ8d46aaBrN2Vwz8+3ED96Ege+HlvLgzwVuHSeL3Oci5vr9zFR2t3czS/iNaN63FFv1ZckZJkY18maERkhaqmVnSczWwPwLHCIvIKvEFpkYBzZ9CUCzrxl/fW8enGfQzvFpyWwytLdvDR2j38flQXCyJB1jQuhld/eSbXPLOYK5/+Bq/CkM6J/OfK3jQN0bygiAhhcMcmDO7YhPsv78G8tXuYvXIXj326hUcXbiG1TSOuSGnFxb1bBO3fpjHlsRZJALIPHWPAPz7h/jE9uH5Q26Bcs6DIy4WPfEFEhDBvyrl+96mXZfXOg1w19RvO6diE5ycMsCRQIbLrYC73vv0dF3RrxvhBbcJyW/XunFzeXZXF2yszSd93mOioCEZ0a8bP+iXRtkl9RIRIESIjBBFnYD9Cin9wtzv760ZFUCfAf3vm9GctkioQyBLyZakTGcHvR3Vl0isreGN5Jr8489TnwBw4ks+vX1lBs7gYHrmmrwWREEpKqMfLN5WcJlW1WsTX49ahHZg0pD1pu3KYvXIX763exYensLRLw7pRTL2uP+d0ahKCkpqaxgJJAIpX/g1mIAG4sEczUts04uEFmxnTtyUN6lb+bSryKlNmrWL/kXzenjSYhPrRFZ9kagQRoXerBHq3SuC+i7qxZNsPHDxagFcVrypFXmecxatKkar72Pk3U3zM7JW7uPmlZbw4cSBntT8j3FUy1ZwFkgDkBJCLpDwiwn0Xd+OKp75h2hcZ3DWic6Wv8egnm/lyy34euKIXvVrFB7V85vQRHRXBuZ0qPyH3ipRWjJ22mBtfXMbLNw20NcBMuawTNADFSa1CMaCZktyIi3u1YNoXGezzVC4n+acb9/LYp+lc1b8V1wxoXfEJxpTQpGFdXr35TJrG1uWG6ctYs/NguItkqjELJAHw5DlLyAe6REpZfj+qC4VeL498stnvc3YeOMpdr6+hW4s47r+8Z41fS8uEjnNH2lkkNKjD+OlLw5rJ01RvFkgCEEiaXX+0OaMB153VhteX7WSzH+lj8wqKuHXmCryqTL0uxWavm4C1TKjHqzefRYPoSK5/fimb9lQ+jbGp+SyQBMCTW0DdqIiQfmDfcX4nGtSN4v/mbqjw2L+9v461uzw8fHVfm5RmgqZ14/q8+suziIoQrn1uia33ZU5igSQAxblIQqlRg2gmD+vIZ5uyj2f6K80by3fy2tKd/HpoB0bYEigmyNo2acCrvzwLUH7x7GK+/8GyOpqfWCAJgCcvOOtsVeSGwW1JSqjHP+ZuwFtKwqN1WTn8+d21DO5wBnefwh1exvijY9OGvHLzmeQXevnFs0vI/DF0K1Wb04sFkgA4K/+G/g7qmDqR/PbCzqzL8vDeml0nluFoAbe+spJG9aN5bFy/gGfCG1Oers3jePmmMzmUV8Avnl3C7pzccBfJVAP2qRMAT27guUj8NaZPEj2T4njw483Hlw/3epXfvLmarIO5PHltCk0a1q2SspjarWdSPDNuHMiBI/lc++wS9h2q3O3ppuaxQBIAT17ox0iKRUQI943uxq6Dubz4zXYAnl60lU827ONPF3ejf5tTX6bcmMrql9yIFyYOYI8nj2ufXcIPVZDBMfvQMb7d+oMtmV8NWSAJQE6QcpH4a3DHJgzrksiTn6XzwXdZPDR/E5f2acmEwW2rrAzGFBvQtjHPTUhlx4GjXPf8Ug4ezQ/q9VWVLXsP8dTn6Vzx1NcM/OcnjHt2MXPWlEy0Gl7Lth/g6qnfhjS7aXVXYSARkUtExAJOCaqKJ0jZESvjDxd148ixQm57dRXtExvywBW9bNKhCZvBHZowbXwqW/cdZtiDnzPxhaU8vGAzC9bvZU9OXqVbD4VFXpZk/MA/PlzPsAc/Z8QjX/DveZsoKFLuuqAzXZvH8u95m6pNdsiNezzc9OIylm4/wMuLvw93ccLGn5HiscCjIvI28IKqVjyhwSUio4BHgUjgOVV9oMT+eOAVINkty4Oq+oK7bztwCCgCCouXMhaRvsBUIAYoBH6tqkv9LVOwHD5WiFdDN6u9LJ2bxXLtmW14d9Uupl6XckoLOhoTTEM6J/LyTQN5fflO1u7KYdHmbIpvLmzSsC69kuLolRRPz6R4erWKp3lczAlffo4cK+TLLdnMX7+Xzzbu48ejBURHRjCowxncdG57LujWlBbx9QBIbdOIXzy3hBe/2c6kIR3CUd3jMn88yoTpS6kXHUnXFnG8vSKT347sQnRU7fveXeGnkKpeJyJxwDjgBRFR4AXgNVUtc5qriEQCT+Kkw80ElonIHFVd73PYZGC9ql4qIonAJhGZqarFbeRhPql3i/0b+JuqfiQiF7nPh/pV2yAqXh4lHImD/nZZD34/qguxVdwaMqYsZ7Y/gzPdVYKP5heyYbeHtMwc0nZ5Sgku0fRMiqdr8zg27fHw9dYfyC90EsSd37UpI7o349xOTUr99z24YxPO79qUJ9215M4I0w0mPx7JZ/z0pRzNL+LNSYPYnZPHxBeWsWD9Xi7u3SIsZSqN16tVkj7Cr6+zqupxWyT1gDuBnwG/E5HHVPXxMk4bCKSragaAiMwCxgC+gUSBWHG+njQEDuC0MsotDhDnPo4HwtJhmnM0tMujlCciQiyImGqrfnQU/ds0PmHF4Nz8ItbvdoJK2q4c1u7K4cst+0lKqMf1Z7Xhgm7NGNC2kV+3r993UVcu/O+XPLZwC38b0zOUVSnV0fxCbpyxjMwfc3n5xoF0bR5Hp6axtIyPYdayHdUmkGzc4+HOWat5fFw/OjWLDelrVRhIRORS4EagA/AyMFBV94lIfWADUFYgSQJ2+jzPBEpm/nkCmIMTDGKBa1TV6+5TYL7bAnpGVae52+8EPhaRB3HGeAaXUe5bgFsAkpNPPTlUWYpzkVgqU2MqVi86kv5tGp1wd2FBkZcoNytjZXRsGsvYAa2ZuWQH4we3pUNiw2AXt0wFRV5ue3UVa3Ye5Klr+x9vhUVGCFeltuaxT7ew88DRsKe0/mzTPm5/dRUN6kZyrNBb8QkB8qcz7yrgEVXtrar/UdV9AKp6FCfAlKW0fx0lR94uBFYDLYG+wBNuNxrA2aqaAowGJovIee72W4G7VLU1cBfwfGkvrqrTVDVVVVMTEyufj6EinhBkRzSmNqkTGXHKN4rcNaIzMXUieeCjjUEuVdlUlftmp/Hpxn3cf3lPRvVsfsL+q92UDW8s31na6VXmpW+3c9OLy0huXJ93J59Nz6TQ5yPyJ5D8D3B8MFtE6olIWwBVXVjOeZmAbzKMVpzcDTURmK2OdGAb0NW9dpb7ex/wDk5XGcAEYLb7+E2f7VUqJ4S5SIwx5WvSsC63Du3AgvV7WZzxQ5W85n8+3sSbKzKZMrwT157Z5qT9SQn1GNI5kTeW76SwKPStgJKKvMpf56zjL++t4/yuTXlz0qDjNymEmj+B5E3A969S5G6ryDKgk4i0E5FonLu/5pQ4ZgcwHEBEmgFdgAwRaSAise72BsBIYK17ThYwxH18PrDFj7IE3fFcJDZWYUxY3HROO1rGx/D3D9eXugZdML349Tae+nwr4wYmc+cFnco8buyAZPZ6jrFoc3ZIy1PS4WOF/PKl5bz4zXZuOqcdz1yfWqV3dPoTSKJ87qLCfVxhAnBVLQRuAz7GGUt5Q1XXicgkEZnkHnY/MFhE0oCFwD3uXVrNgK9EZA1Oa+hDVZ3nnvNL4CF33z9xx0GqWnGLpGEVrLVljDlZTJ1IfjeqC2t3nbwGXTB98F0Wf/tgPRf2aMbfK0gWN7xbU5o0rMtrS6uueyvrYC5XPv0NizZnc//lPfnzJd2JrII7tXz58ymYLSKXqeocABEZA5S9nrkPVZ0LzC2xbarP4yyc1kbJ8zKAPmVc8yugvz+vH0qe3AJiY6Kq/A0zxvxkTJ8kpn+1nf/M28Toni2Cnhvom/T93P36Gga0acyjY/tV+P+9TmQEV/ZvxbNfZrDXk0ezuJiglqek7zIPctOM5eTlFzH9hgEM6Rz88WB/+NMimQTcJyI7RGQncA/wq9AWq/oLx6x2Y8yJIiKEP17cjaycPJ7/altQr712Vw63vLyCdk0a8OyEVL+D1NgBrSnyKm+GeNB93to9XP3Mt0RHRvDWrYPDFkTAj0CiqltV9SygO9BdVQe7A+O1WlXlIjHGlO+s9mcwonsznv58K/uDtHjkjh+OcsMLy4ivV4cZNw6s1P/1tk0aMKj9Gby+fGdIxm5UlWcWbeXWmSvo2jyOdyefTZfmoZ0nUhG/5vKLyMXAr4G7ROQvIvKX0Bar+vPkFlb58ijGmNLdO7oreQVFPLJgc8DX2n/4GOOnL6HQ62XGjQNpHl/57qmxA1uz80Au32wN7h1lBUVe/jA7jf/7aCMX9WrBrFvOIjE2/Okj/Fm0cSpwDXA7ztyQq4CT732rZap65V9jTNk6JDbk2jOTmbVsJ1v2lrlyU4U27z3Edc8tYY8nj+k3DKBj01Ob7Hhhj+Yk1K/Da8t2nHJZSso5WsANLyxl1rKd3DasI4+P7Rf0MaFT5U+LZLCqjgd+VNW/AYM4cX5IreTJszESY6qTKRd0pn6dSP7vFCYp5hd6efSTLVz82JfsO3SMadenkpJ86jl+YupE8rN+ScxftycouVpy84u4Ztq3LN12gAev6sNvL+xSJWto+cufQFKc/uyoiLQECoB2oSvS6SEnt+qSWhljKta4QTSTz+/Ipxv38XW6XzeWApCWmcNlT3zFI59sZlTPFiy46zzOC8LA9biByRQUKe+sCvzW5P/9YB2b9h5i2vWpXNm/VcDXCzZ/Asn7IpIA/AdYCWwHXgthmaq9giIvR/OLrGvLmGrmhsFtSUqoxz8+3EBRBQPdeQVFPPDRRi5/6msOHMnn2fGpPD6uX9BWFO7cLJaU5AReW7ojoKyO76/J4rWlO7l1SAeGdW0alLIFW7mBxE1otVBVD6rq2zhjI11VtVYPth86PqvdBtuNqU5i6kTy+1FdWL/bw+yVmWUet2z7AS569EumLtrKlSmtWHD3EEZ0bxb08owdkMzW7CMs//7HUzp/54Gj3Dc7jZTkBO4a0TnIpQuecgOJuxLvQz7Pj6lqTshLVc0dX2ervrVIjKluLuvTkj6tE3hw/iZy80/MpHjkWCH/895arn7mW/KLvLxy05n868reIetduKRPCxrWjeK1pZUfdC8o8nLba6tA4NGx/ajjxxL74eJPyeaLyM/F8rked3zlXxtsN6baERH+dHE39nqO8eyXGce3f7E5m5GPfMFLi79nwqC2fHzneZzTqUlIy1I/OorL+rZkbtru419A/fXg/E2s2XmQf/28d9iXpa+IP4HkbpxFGo+JiEdEDomIJ8TlqtZs5V9jqrcBbRszqkdzpi7aSvq+Q/zuzTWMn76UunUiePNXg/jrZT2qbFHDcQOSySvwMme1/4PuizZn88yiDK49M5mLelWPRFnl8SfVbninTFZDxUmt7K4tY6qve0d3ZeHGvYx85AtEhF8P7cAdwztV+dyLnklxdG8Rx2tLd3LdWW0qzMGyz5PH3a+vpkuzWP58SfcqKmVg/MmQeF5p21X1i+AX5/SQY11bxlR7bZs04I7zO/HFlmz+59IeVZLgqTQiwriBrfnze+tI25VD71YJZR7r9Sp3vbGaI/mFzPrFWdVmwmFF/Gnb/c7ncQxOIqkVOLlAaiVPrnPXlnVtGVO93T68E7cPLzt/SFUZ0y+Jf8zdwGtLd5YbSJ5etJWv03/ggSt6hTzPejD5s2jjpT4/I4CewN7QF6368uQVUCdSiKlTfe+iMMZUH3Exdbi4V0vmrN7FkWOFpR6z4vsfeXjBZi7p3YJrBpxei4ecyidhJk4wqbWK19myG9mMMf4aO7A1R/KL+PC73SftyzlawB2vraJlQgz/vKLXaffZ4s+ijY+LyGPuzxPAl8Aafy4uIqNEZJOIpIvIvaXsjxeR90VkjYisE5GJPvu2i0iaiKwWkeUlzrvdve46Efm3P2UJJstFYoyprNQ2jejYtOFJCzmqKvfO/o69njweH5dyWn62+DNG4vshXgi8pqpfV3SSiEQCTwIjcFoxy0Rkjqqu9zlsMrBeVS8VkURgk4jM9EntO8xNvet73WHAGKC3qh4TkSpfMyAnt4BYGx8xxlSCiDB2QGv+/uEGNu05dDyHyMwlO/ho7R7+MLorfVsnhLeQp8ifrq23gFdUdYaqzgQWi4g/s2MGAumqmuEGhlk4AcCXArHuZMeGwAGcYFWeW4EHVPUYgKru86MsQeXJK7SBdmNMpV2R0oo6kcIst1WycY+H//1gPed1TuSX57YPc+lOnT+BZCFQz+d5PeATP85LAnxzTWa623w9AXQDsoA0YIq7LAs4QWa+iKwQkVt8zukMnCsiS0RkkYgM8KMsQeV0bdk6W8aYymncIJqRPZrzzqpdHDyaz22vriK+Xh0evrpPtVoWvrL8CSQxqnq4+In72J8WSWl/lZJLYF4IrAZaAn2BJ0Qkzt13tqqmAKOByT7zWaKARsBZOLcmv1Ha8i0icouILBeR5dnZ2X4U138eS2pljDlF4wYkc/BoAVdO/Zat2Yd55Oq+NAnSisPh4k8gOSIiKcVPRKQ/kOvHeZmcmACrFU7Lw9dEYLY60oFtQFcAVc1yf+8D3sHpKiu+bvE5SwEvcNKCOao6TVVTVTU1MTHw3AI+13WSWlkgMcacgsEdzqB143qk7zvMrUM6hHy9r6rgTyC5E3hTRL4UkS+B14Hb/DhvGdBJRNqJSDQwFphT4pgdwHAAEWkGdAEyRKSBiMS62xsAI4G17jnv4k6GFJHOQDTgfxabAOUWFFFQpNYiMcackogI4b7R3bg6tVW1Xhq+MvxZa2uZiHTF+ZAXYKOqVriMpaoWishtwMdAJDBdVdeJyCR3/1TgfuBFEUlzr32Pqu4XkfbAO26PVRTwqqrOcy89HZguImuBfGCCBpI1ppKKZ7WfjrfoGWOqh9G9WjD6NFiM0V/+rLU1GZipqmvd541EZJyqPlXRuao6F5hbYttUn8dZOK2NkudlAH3KuGY+cF1Frx0qx9fZqmeD7cYYA/51bf1SVQ8WP1HVH4FfhqxE1Vzxyr/WtWWMMQ5/AkmE711R7kTD6NAVqXrLOWor/xpjjC9/+mc+xrnFdirO7buTgI9CWqpqzFokxhhzIn8CyT3ALTgzygVYBdScUaJKOp5m1wKJMcYA/i0j7wUWAxlAKs7tuhtCXK5qK+f4XVs22G6MMVBOi8SdozEWGAf8gDN/BFUdVjVFq548eQU0iI4kKtJykRhjDJTftbURZ8n4S91Z54jIXVVSqmosJ9dmtRtjjK/yvlb/HNgDfCYiz4rIcEpfP6tWsXW2jDHmRGUGElV9R1WvwVn76nPgLqCZiDwtIidNIqwtciyplTHGnMCfwfYjqjpTVS/BWXhxNXBStsPawpNXaF1bxhjjo1Ijxqp6QFWfUdXzQ1Wg6s6TW2DLoxhjjA+79aiSbIzEGGNOZIGkEoq8yqFjhTZGYowxPiyQVMKhPJvVbowxJVkgqYTiXCTWtWWMMT+xQFIJxQs22vIoxhjzEwsklVCc1MpaJMYY85OQBhIRGSUim0QkXUROmnsiIvEi8r6IrBGRdSIy0WffdhFJE5HVIrK8lHN/KyIqIk1CWQdftvKvMcacLGR9NG4CrCeBEUAmsExE5qjqep/DJgPrVfVSEUkENonITDedLsAwVd1fyrVbu9fdEaryl8ZaJMYYc7JQtkgGAumqmuEGhlnAmBLHKBDrZmBsCBwACv249iPA793zq4zH7toyxpiThDKQJAE7fZ5nutt8PQF0A7KANGCKm/8EnCAxX0RWiMgtxSeIyGXALlVdU96Li8gtIrJcRJZnZ2cHWBVHTm4BkRFCg+jIoFzPGGNqglDeflTaSsElWxAX4qzddT7QAVggIl+qqgc4W1WzRKSpu30jsBz4I1DhopGqOg2YBpCamhqUlosnt5C4mCh8UtgbY0ytF8oWSSbQ2ud5K5yWh6+JwGx1pAPbcFYbRlWz3N/7gHdwuso6AO2ANSKy3b3mShFpHsJ6HOfJs1wkxhhTUigDyTKgk4i0E5FonGyLc0ocswMndS8i0gzoAmSISAMRiXW3N8BpgaxV1TRVbaqqbVW1LU6wSlHVPSGsx3E5ts6WMcacJGRdW6paKCK3AR8DkcB0VV0nIpPc/VOB+4EXRSQNpyvsHlXdLyLtgXfcLqQo4FVVnReqsvrLY7lIjDHmJCGdoq2qc4G5JbZN9XmcRSnjHaqaAfTx4/ptAy+l/3JyC2gRX68qX9IYY6o9m9leCU5SK1sexRhjfFkgqQRLs2uMMSezQOKnvIIi8gu9dteWMcaUYIHETzar3RhjSmeBxE8eW2fLGGNKZYHETzluUivLRWKMMSeyQOIna5EYY0zpLJD4ycZIjDGmdBZI/FSci8Ru/zXGmBNZIPHTT9kRbYzEGGN8WSDxkyevkJg6EdSNslwkxhjjywKJn3KO2sq/xhhTGgskfvLk2fIoxhhTGgskfrJcJMYYUzoLJH6y7IjGGFM6CyR+clb+tTu2jDGmpJAGEhEZJSKbRCRdRO4tZX+8iLwvImtEZJ2ITPTZt11E0kRktYgs99n+HxHZKCLficg7IpIQyjoU8+QWWteWMcaUImSBREQigSeB0UB3YJyIdC9x2GRgvar2AYYCD7n53YsNU9W+qprqs20B0FNVewObgT+Eqg7FvF7lkHVtGWNMqULZIhkIpKtqhqrmA7OAMSWOUSBWnOTsDYEDQGF5F1XV+apafMxioFVwi32yw/mFeNXW2TLGmNKEMpAkATt9nme623w9AXQDsoA0YIqqet19CswXkRUicksZr3Ej8FHwilw6jy2PYowxZQplIJFStmmJ5xcCq4GWQF/gCRGJc/edraopOF1jk0XkvBMuLvJHnNbLzFJfXOQWEVkuIsuzs7NPuRLgs86WtUiMMeYkoQwkmUBrn+etcFoeviYCs9WRDmwDugKoapb7ex/wDk5XGQAiMgG4BLhWVUsGJ9zzpqlqqqqmJiYmBlQRT3EuEltnyxhjThLKQLIM6CQi7dwB9LHAnBLH7ACGA4hIM6ALkCEiDUQk1t3eABgJrHWfjwLuAS5T1aMhLP9xtvKvMcaULWRfsVW1UERuAz4GIoHpqrpORCa5+6cC9wMvikgaTlfYPaq6X0TaA+84Y/BEAa+q6jz30k8AdYEF7v7FqjopVPWAn3KR2GC7McacLKR9Nao6F5hbYttUn8dZOK2NkudlAH3KuGbHIBezQh4bIzHGmDLZzHY/eHILEIHYujZGYowxJVkg8YMnr5DYulFERJR2I5oxxtRuFkj8kJNbQHx969YyxpjSWCDxgyfXcpEYY0xZLJD4wZJaGWNM2SyQ+MGSWhljTNkskPjBk1tos9qNMaYMFkj8YC0SY4wpmwWSCuQXesktKLIxEmOMKYMFkgocXx7Fbv81xphSWSCpgOUiMcaY8lkgqYAnz5aQN8aY8lggqUDxEvI22G6MMaWzQFIB69oyxpjyWSCpgLVIjDGmfBZIKlB815blIjHGmNJZIKlATm4B0VERxNSJDHdRjDGmWgppIBGRUSKySUTSReTeUvbHi8j7IrJGRNaJyESffdtFJE1EVovIcp/tjUVkgYhscX83CmUdPLmFNj5ijDHlCFkgEZFI4ElgNNAdGCci3UscNhlYr6p9gKHAQyIS7bN/mKr2VdVUn233AgtVtROw0H0eMp68Arv11xhjyhHKFslAIF1VM1Q1H5gFjClxjAKxIiJAQ+AAUFjBdccAM9zHM4DLg1biUnhsnS1jjClXKANJErDT53mmu83XE0A3IAtIA6aoqtfdp8B8EVkhIrf4nNNMVXcDuL+blvbiInKLiCwXkeXZ2dmnXAlLamWMMeULZSApLcG5lnh+IbAaaAn0BZ4QkTh339mqmoLTNTZZRM6rzIur6jRVTVXV1MTExEoV3Jet/GuMMeULZSDJBFr7PG+F0/LwNRGYrY50YBvQFUBVs9zf+4B3cLrKAPaKSAsA9/e+kNUAZ4kUGyMxxpiyhTKQLAM6iUg7dwB9LDCnxDE7gOEAItIM6AJkiEgDEYl1tzcARgJr3XPmABPcxxOA90JVAVW1FokxxlQgZF+1VbVQRG4DPgYigemquk5EJrn7pwL3Ay+KSBpOV9g9qrpfRNoD7zhj8EQBr6rqPPfSDwBviMhNOIHoqlDV4Wh+EUVetTESY4wpR0j7bFR1LjC3xLapPo+zcFobJc/LAPqUcc0fcFsxoWaz2o0xpmI2s70cts6WMcZUzAJJOTy5bi4S69oyxpgyWSAph7VIjDGmYhZIynE8F4nd/muMMWWyQFIOa5EYY0zFLJCUo/iurYZ1rUVijDFlsUBSDk9uIQ3rRhEVaX8mY4wpi31ClqNzs4Zc3KtFuIthjDHVmvXZlGPswGTGDkwOdzGMMaZasxaJMcaYgFggMcYYExALJMYYYwJigcQYY0xALJAYY4wJiAUSY4wxAbFAYowxJiAWSIwxxgREVDXcZQg5EckGvj/F05sA+4NYnNNNba6/1b32qs319617G1VNrOiEWhFIAiEiy1U1NdzlCJfaXH+re+2sO9Tu+p9K3a1ryxhjTEAskBhjjAmIBZKKTQt3AcKsNtff6l571eb6V7ruNkZijDEmINYiMcYYExALJMYYYwJigaQcIjJKRDaJSLqI3Bvu8lQlEdkuImkislpEloe7PKEmItNFZJ+IrPXZ1lhEFojIFvd3o3CWMVTKqPtfRWSX+/6vFpGLwlnGUBGR1iLymYhsEJF1IjLF3V5b3vuy6l+p99/GSMogIpHAZmAEkAksA8ap6vqwFqyKiMh2IFVVa8WkLBE5DzgMvKSqPd1t/wYOqOoD7heJRqp6TzjLGQpl1P2vwGFVfTCcZQs1EWkBtFDVlSISC6wALgduoHa892XV/2oq8f5bi6RsA4F0Vc1Q1XxgFjAmzGUyIaKqXwAHSmweA8xwH8/A+Q9W45RR91pBVXer6kr38SFgA5BE7Xnvy6p/pVggKVsSsNPneSan8Ac+jSkwX0RWiMgt4S5MmDRT1d3g/IcDmoa5PFXtNhH5zu36qpFdO75EpC3QD1hCLXzvS9QfKvH+WyApm5SyrTb1A56tqinAaGCy2/1hao+ngQ5AX2A38FBYSxNiItIQeBu4U1U94S5PVSul/pV6/y2QlC0TaO3zvBWQFaayVDlVzXJ/7wPewenqq232un3IxX3J+8JcniqjqntVtUhVvcCz1OD3X0Tq4HyIzlTV2e7mWvPel1b/yr7/FkjKtgzoJCLtRCQaGAvMCXOZqoSINHAH3hCRBsBIYG35Z9VIc4AJ7uMJwHthLEuVKv4Qdf2MGvr+i4gAzwMbVPVhn1214r0vq/6Vff/trq1yuLe8/ReIBKar6j/CW6KqISLtcVohAFHAqzW97iLyGjAUZwntvcD/AO8CbwDJwA7gKlWtcYPSZdR9KE63hgLbgV8VjxnUJCJyDvAlkAZ43c334YwT1Ib3vqz6j6MS778FEmOMMQGxri1jjDEBsUBijDEmIBZIjDHGBMQCiTHGmIBYIDHGGBMQCyTGBIGIFPmslLo6mKtFi0hb35V5jaluosJdAGNqiFxV7RvuQhgTDtYiMSaE3Lwu/xKRpe5PR3d7GxFZ6C6Kt1BEkt3tzUTkHRFZ4/4Mdi8VKSLPujkj5otIvbBVypgSLJAYExz1SnRtXeOzz6OqA4EncFZKwH38kqr2BmYCj7nbHwMWqWofIAVY527vBDypqj2Ag8DPQ1obYyrBZrYbEwQiclhVG5ayfTtwvqpmuIvj7VHVM0RkP05CoQJ3+25VbSIi2UArVT3mc422wAJV7eQ+vweoo6p/r4KqGVMha5EYE3paxuOyjinNMZ/HRdj4pqlGLJAYE3rX+Pz+1n38Dc6K0gDXAl+5jxcCt4KT7llE4qqqkMacKvtWY0xw1BOR1T7P56lq8S3AdUVkCc4Xt3HutjuA6SLyOyAbmOhunwJME5GbcFoet+IkFjKm2rIxEmNCyB0jSVXV/eEuizGhYl1bxhhjAmItEmOMMQGxFokxxpiAWCAxxhgTEAskxhhjAmKBxBhjTEAskBhjjAnI/wPtWmMFLWhP7wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot( history.history['accuracy'], label=\"Accuracy\")\n", + "#plt.plot( history.history['val_accuracy'], label=\"val_acc\")\n", + "plt.title('Model Accuracy')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "#plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "87cacc97", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot( history.history['loss'], label=\"loss\")\n", + "plt.plot( history.history['val_loss'], label=\"val_loss\")\n", + "plt.title('Model Loss')\n", + "plt.ylabel('Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "900420d6", + "metadata": {}, + "outputs": [], + "source": [ + "# serialize weights to HDF5\n", + "bnn.save_weights(\"bnn.h5\")" + ] + }, + { + "cell_type": "markdown", + "id": "ae0a0f29", + "metadata": {}, + "source": [ + "### Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "91eefec2", + "metadata": {}, + "outputs": [], + "source": [ + "nClasses = 3\n", + "bnn.load_weights(\"bnn.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "af858d85", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def getPredictions( model, data, T ):\n", + "\n", + " n = data.shape[0]\n", + " preds = np.zeros( shape=(n,nClasses,T) )\n", + " \n", + " for t in range(T):\n", + " if ( t == 10 ): print(\"Iteration 10...\")\n", + " if ( t == 30 ): print(\"Iteration 30...\")\n", + " if ( t == 50 ): print(\"Iteration 50...\")\n", + " if ( t == 70 ): print(\"Iteration 70...\")\n", + " if ( t == 90 ): print(\"Iteration 90...\")\n", + " preds[:,:,t] = model(data)\n", + " \n", + " return preds" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9bf48df9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 10...\n", + "Iteration 30...\n", + "Iteration 50...\n", + "Iteration 70...\n", + "Iteration 90...\n" + ] + }, + { + "data": { + "text/plain": [ + "(215629, 3, 100)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "T = 100\n", + "preds = getPredictions( bnn, xTest.values, T )\n", + "preds.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "f6054b90", + "metadata": {}, + "outputs": [], + "source": [ + "def getUncertainty( preds, T ):\n", + " \n", + " n = preds.shape[0]\n", + " errors = np.zeros( shape=(n, nClasses) )\n", + " means = np.zeros( shape=(n, nClasses) )\n", + " \n", + " for ix in range(n):\n", + " for j in range(nClasses):\n", + " \n", + " means[ix,j] = np.mean( preds[ix,j,:] )\n", + " #errors[ix,j] = (1./T) * np.sum( (preds[ix,j,:] - means[ix,j])**2 )\n", + " errors[ix,j] = 2. * np.std(preds[ix,j,:])\n", + " \n", + " return means, errors" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "9a7053ea", + "metadata": {}, + "outputs": [], + "source": [ + "means, errors = getUncertainty( preds, T )" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ea15ee5b", + "metadata": {}, + "outputs": [], + "source": [ + "bnnPreds = []\n", + "for ix in range(len(means)):\n", + " ix = np.where( means[ix] == np.max(means[ix]) )\n", + " bnnPreds.append( ix[0][0] )\n", + "\n", + "bnnPreds = np.array(bnnPreds)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9d999949", + "metadata": {}, + "outputs": [], + "source": [ + "font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 22}\n", + "plt.rc('font', **font)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0723dfea", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "cm = confusion_matrix( yTest, bnnPreds, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a836b4a7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1,2]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "1302335f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.6742\n", + "Macro F1: 0.5585\n", + "Macro Precision: 0.5179\n", + "Macro Recall: 0.6742\n" + ] + } + ], + "source": [ + "from sklearn.metrics import f1_score\n", + "from sklearn.metrics import recall_score\n", + "from sklearn.metrics import precision_score\n", + "from sklearn.metrics import balanced_accuracy_score\n", + "\n", + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(yTest.values, bnnPreds), 4 ))\n", + "print( \"Macro F1:\", np.round( f1_score(yTest.values, bnnPreds, average='macro'), 4 ))\n", + "print( \"Macro Precision:\", np.round( precision_score(yTest.values, bnnPreds, average='macro'), 4 ))\n", + "print( \"Macro Recall:\", np.round( recall_score(yTest.values, bnnPreds, average='macro'), 4 ))" + ] + }, + { + "cell_type": "markdown", + "id": "a5f7d01f", + "metadata": {}, + "source": [ + "#### Hypothesis Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e40860ea", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import stats\n", + "\n", + "def tTest( sample1, sample2 ):\n", + " \n", + " t_stat, p_value = stats.ttest_ind( sample1, sample2 )\n", + "\n", + " # True = significant difference, False = not significant\n", + " alpha = 0.05\n", + " if p_value < alpha:\n", + " return True\n", + " else:\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f21b1255", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total buildins: 215629\n", + "Predications made: 141152\n", + "% of buildings with prediction: 65.46\n" + ] + } + ], + "source": [ + "ixs = []\n", + "bnnPreds = []\n", + "predsMade = 0\n", + "n = preds.shape[0]\n", + "\n", + "noPrediction = []\n", + "\n", + "for i in range(n):\n", + " \n", + " ix = np.argsort( -means[i,:] ) # sort in descending order\n", + " \n", + " sample1 = preds[i, ix[0], :]\n", + " sample2 = preds[i, ix[1], :]\n", + " significant = tTest( sample1, sample2 )\n", + " \n", + " mx = np.max( means[i,:] )\n", + " ix = np.where( means[i,:] == mx )\n", + " \n", + " if ( significant and (mx >= 0.65) ):\n", + " predsMade += 1\n", + " bnnPreds.append(ix[0][0])\n", + " ixs.append(i)\n", + " else:\n", + " noPrediction.append(i)\n", + " \n", + "print(\"Total buildins:\", n)\n", + "print(\"Predications made:\", predsMade)\n", + "print(\"% of buildings with prediction:\", np.round( (predsMade/n)*100., 2) )" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "671e44b4", + "metadata": {}, + "outputs": [], + "source": [ + "cm = confusion_matrix( yTest.values[ixs], bnnPreds, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "cd48acd5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1,2]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "8e5dfab2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.7705\n", + "Macro F1: 0.7391\n", + "Macro Precision: 0.7156\n", + "Macro Recall: 0.7705\n" + ] + } + ], + "source": [ + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(yTest.values[ixs], bnnPreds), 4 ))\n", + "print( \"Macro F1:\", np.round( f1_score(yTest.values[ixs], bnnPreds, average='macro'), 4 ))\n", + "print( \"Macro Precision:\", np.round( precision_score(yTest.values[ixs], bnnPreds, average='macro'), 4 ))\n", + "print( \"Macro Recall:\", np.round( recall_score(yTest.values[ixs], bnnPreds, average='macro'), 4 ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27e1d3e6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5006cb2c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "538cf18c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Probability')" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter( [1,2,3], means[2,:]*100 ) \n", + "plt.errorbar( [1,2,3], means[2,:]*100, yerr=errors[2,:]*100, fmt=\"o\")\n", + "plt.ylim(0,100)\n", + "plt.title('Probabilistic Building Type Prediction')\n", + "plt.ylabel('Probability')" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "23e0faeb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 [0.05852261 0.10924447 0.83223292] [0.00330635 0.00526524 0.00806944]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Probability')" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range( means.shape[0] ):\n", + "\n", + " mx = np.max( means[i,:] )\n", + " ex = np.max( errors[i,:] )\n", + " if ( mx > 0.7 and yTest.iloc[i] == 2 and ex < 0.1 ):\n", + " print( yTest.iloc[i], means[i,:], errors[i,:] )\n", + " x = [1,2,3]\n", + " y = means[i,:]*100.\n", + " e = errors[i,:]*100.\n", + " break \n", + " \n", + "plt.scatter( x, y ) \n", + "plt.errorbar( x, y, yerr=e, fmt=\"o\")\n", + "plt.ylim(0,100)\n", + "plt.title('Probabilistic Building Type Prediction')\n", + "plt.ylabel('Probability')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36740e36", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57530152", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/EDS_paper/BayesianClassification_Alabama.ipynb b/book/EDS_paper/BayesianClassification_Alabama.ipynb new file mode 100644 index 0000000..b68e356 --- /dev/null +++ b/book/EDS_paper/BayesianClassification_Alabama.ipynb @@ -0,0 +1,1130 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d1bb37a6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-21 14:03:26.848130: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-11-21 14:03:27.416360: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import tensorflow_probability as tfp\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "from tensorflow.keras.optimizers import Adam\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "d9e19465", + "metadata": {}, + "source": [ + "### Setup and Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "68eb4db6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadBuildingType
0-86.45236932.4544462168.99750962660.024170.02.4095577710.0602.03.06.0119.0residentialEducation
1-86.45170132.4544453918.40007562660.024170.02.4095579410.0602.03.06.0119.0residentialEducation
2-86.45165232.453549501.13839762660.024170.02.4095574710.0602.03.06.0119.0residentialEducation
3-86.45614832.454743487.16257062660.024170.02.4095575610.0602.03.06.0119.0residentialEducation
4-86.45148332.45482716.44424462660.024170.02.4095578310.0602.03.06.0119.0residentialEducation
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" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty HousingUnitsCounty \\\n", + "0 -86.452369 32.454446 2168.997509 62660.0 24170.0 \n", + "1 -86.451701 32.454445 3918.400075 62660.0 24170.0 \n", + "2 -86.451652 32.453549 501.138397 62660.0 24170.0 \n", + "3 -86.456148 32.454743 487.162570 62660.0 24170.0 \n", + "4 -86.451483 32.454827 16.444244 62660.0 24170.0 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount EdCount \\\n", + "0 2.409557 77 10.0 602.0 3.0 6.0 \n", + "1 2.409557 94 10.0 602.0 3.0 6.0 \n", + "2 2.409557 47 10.0 602.0 3.0 6.0 \n", + "3 2.409557 56 10.0 602.0 3.0 6.0 \n", + "4 2.409557 83 10.0 602.0 3.0 6.0 \n", + "\n", + " InCount OsmNearestRoad BuildingType \n", + "0 119.0 residential Education \n", + "1 119.0 residential Education \n", + "2 119.0 residential Education \n", + "3 119.0 residential Education \n", + "4 119.0 residential Education " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Alabama data\n", + "file = \"./ML_Training_01.csv\"\n", + "\n", + "# read data into a Pandas dataframe\n", + "df = pd.read_csv(file)\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "df = df.iloc[:, 3:] \n", + "\n", + "df = df.rename( columns={\"OrnlType\":\"BuildingType\"} )\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "999bfa70", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Residential 2060502\n", + "Commercial 136922\n", + "Other 110849\n", + "Name: BuildingType, dtype: int64\n", + "\n", + "Residential 1.000000\n", + "Commercial 15.048728\n", + "Other 18.588368\n", + "Name: BuildingType, dtype: float64\n", + "\n" + ] + } + ], + "source": [ + "# classify a building as \"Residential\", \"Commercial\", or \"Other\"\n", + "df.loc[df[\"BuildingType\"] == \"Industrial\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Assembly\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Education\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Government\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Agriculture\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Utility and Misc\", \"BuildingType\"] = 'Other'\n", + "\n", + "# building type distributions\n", + "x = df['BuildingType'].value_counts()\n", + "print()\n", + "print( x )\n", + "print()\n", + "print( x[0]/df['BuildingType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1e299cf1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_201838/1358776107.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " df.iloc[:, nCols-1] = le.transform( df.iloc[:, nCols-1] )\n", + "/tmp/ipykernel_201838/1358776107.py:23: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " df.iloc[:, nCols-2] = le2.transform( df.iloc[:, nCols-2] )\n" + ] + } + ], + "source": [ + "from sklearn import preprocessing\n", + "\n", + "df = df.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "# preprocess the data - scaling\n", + "scaler = preprocessing.StandardScaler()\n", + " \n", + "columns = ['X', 'Y', 'Area', 'MedianIncomeCounty', \n", + " 'HousingUnitsCounty', 'HousingDensityCounty',\n", + " 'Impervious', 'AgCount', 'CmCount', 'GvCount',\n", + " 'EdCount', 'InCount']\n", + "df[columns] = scaler.fit_transform(df[columns])\n", + "\n", + "df = df.dropna()\n", + "\n", + "nCols = df.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( df.iloc[:, nCols-1] ) # ornl type\n", + "df.iloc[:, nCols-1] = le.transform( df.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( df.iloc[:, nCols-2] ) # nearest road type\n", + "df.iloc[:, nCols-2] = le2.transform( df.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "958d6b5e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1885326-0.2740811.127910-0.1010510.166470-0.4265740.7324980.789931-0.691770-0.417104-0.975065-0.644434-0.18174242
\n", + "
" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty HousingUnitsCounty \\\n", + "949447 -0.188732 0.294984 -0.167009 0.374683 2.322184 \n", + "2018575 -0.091686 0.210615 -0.322905 2.668814 -0.044329 \n", + "1644613 -1.647388 -1.832330 -0.299032 -0.302436 0.989616 \n", + "1596416 -1.691669 -1.987812 -0.373215 -0.302436 0.989616 \n", + "1885326 -0.274081 1.127910 -0.101051 0.166470 -0.426574 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount \\\n", + "949447 -0.024318 0.789931 -0.507780 2.387892 1.377641 \n", + "2018575 1.872737 0.661468 0.136188 0.103582 -1.105771 \n", + "1644613 0.370678 -1.051383 2.988046 0.781750 1.116229 \n", + "1596416 0.370678 -0.023673 2.988046 0.781750 1.116229 \n", + "1885326 0.732498 0.789931 -0.691770 -0.417104 -0.975065 \n", + "\n", + " EdCount InCount OsmNearestRoad BuildingType \n", + "949447 2.080504 2.245948 4 2 \n", + "2018575 0.247364 0.414567 4 2 \n", + "1644613 0.577660 1.041752 4 2 \n", + "1596416 0.577660 1.041752 4 2 \n", + "1885326 -0.644434 -0.181742 4 2 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "61b9c6ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of classes: 3\n" + ] + } + ], + "source": [ + "nClasses = len(df['BuildingType'].unique())\n", + "print(\"Number of classes:\", nClasses)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "bb4e5f39", + "metadata": {}, + "outputs": [], + "source": [ + "buildingTypes = np.array(df['BuildingType'])\n", + "df = df.drop( columns=['BuildingType'] )" + ] + }, + { + "cell_type": "markdown", + "id": "6167606a", + "metadata": {}, + "source": [ + "### Bayesian Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "63f8937e", + "metadata": {}, + "outputs": [], + "source": [ + "from keras import backend as K \n", + "\n", + "# Keras keeps models hanging around in memory. If we retrain a model, Keras will\n", + "# start from the previously concluded weight values. This resets everything.\n", + "K.clear_session()\n", + "\n", + "# KL divergence weighted by the number of training samples, using\n", + "# lambda function to pass as input to the kernel_divergence_fn on\n", + "# flipout layers.\n", + "kl_divergence_function = (lambda q, p, _: tfd.kl_divergence(q, p) / \n", + " tf.cast(df.shape[0], dtype=tf.float32))\n", + "\n", + "tfd = tfp.distributions\n", + "\n", + "# Define a logistic regression model as a Bernoulli distribution\n", + "# parameterized by logits from a single linear layer. We use the Flipout\n", + "# Monte Carlo estimator for the layer: this enables lower variance\n", + "# stochastic gradients than naive reparameterization.\n", + "input_layer = tf.keras.layers.Input(shape=df.shape[1])\n", + "\n", + "#dense_layer = tfp.layers.DenseFlipout(\n", + "# units=1,\n", + "# activation='sigmoid',\n", + "# kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + "# bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + "# kernel_divergence_fn=kl_divergence_function)(input_layer)\n", + "\n", + "layer1 = tfp.layers.DenseFlipout(\n", + " units=26,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(input_layer)\n", + "\n", + "layer2 = tfp.layers.DenseFlipout(\n", + " units=13,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer1)\n", + "\n", + "layer3 = tfp.layers.DenseFlipout(\n", + " units=8,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer2)\n", + "\n", + "layer4 = tfp.layers.DenseFlipout(\n", + " units=4,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer3)\n", + "\n", + "out = tfp.layers.DenseFlipout(\n", + " units=3,\n", + " activation='softmax',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer4)\n", + "\n", + "# Model compilation\n", + "#bnn = tf.keras.Model(inputs=input_layer, outputs=dense_layer)\n", + "bnn = tf.keras.Model(inputs=input_layer, outputs=out)\n", + "optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\n", + " \n", + "# We use the binary_crossentropy loss since this toy example contains\n", + "# two labels. The Keras API will then automatically add the\n", + "# Kullback-Leibler divergence (contained on the individual layers of\n", + "# the model), to the cross entropy loss, effectively\n", + "# calcuating the (negated) Evidence Lower Bound Loss (ELBO)\n", + "bnn.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "6c70029b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 13)] 0 \n", + " \n", + " dense_flipout (DenseFlipout (None, 26) 728 \n", + " ) \n", + " \n", + " dense_flipout_1 (DenseFlipo (None, 13) 702 \n", + " ut) \n", + " \n", + " dense_flipout_2 (DenseFlipo (None, 8) 224 \n", + " ut) \n", + " \n", + " dense_flipout_3 (DenseFlipo (None, 4) 72 \n", + " ut) \n", + " \n", + " dense_flipout_4 (DenseFlipo (None, 3) 30 \n", + " ut) \n", + " \n", + "=================================================================\n", + "Total params: 1,756\n", + "Trainable params: 1,756\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "bnn.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "91eefec2", + "metadata": {}, + "outputs": [], + "source": [ + "bnn.load_weights(\"bnn.h5\")" + ] + }, + { + "cell_type": "markdown", + "id": "ae0a0f29", + "metadata": {}, + "source": [ + "### Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "af858d85", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def getPredictions( model, data, T ):\n", + "\n", + " n = data.shape[0]\n", + " preds = np.zeros( shape=(n,nClasses,T) )\n", + " \n", + " for t in range(T):\n", + " if ( t == 10 ): print(\"Iteration 10...\")\n", + " if ( t == 30 ): print(\"Iteration 30...\")\n", + " if ( t == 50 ): print(\"Iteration 50...\")\n", + " if ( t == 70 ): print(\"Iteration 70...\")\n", + " if ( t == 90 ): print(\"Iteration 90...\")\n", + " preds[:,:,t] = model(data)\n", + " \n", + " return preds" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "9bf48df9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 10...\n", + "Iteration 30...\n", + "Iteration 50...\n", + "Iteration 70...\n", + "Iteration 90...\n" + ] + }, + { + "data": { + "text/plain": [ + "(1140006, 3, 100)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "T = 100\n", + "preds = getPredictions( bnn, df.values, T )\n", + "preds.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f6054b90", + "metadata": {}, + "outputs": [], + "source": [ + "def getUncertainty( preds, T ):\n", + " \n", + " n = preds.shape[0]\n", + " errors = np.zeros( shape=(n, nClasses) )\n", + " means = np.zeros( shape=(n, nClasses) )\n", + " \n", + " for ix in range(n):\n", + " for j in range(nClasses):\n", + " \n", + " means[ix,j] = np.mean( preds[ix,j,:] )\n", + " #errors[ix,j] = (1./T) * np.sum( (preds[ix,j,:] - means[ix,j])**2 )\n", + " errors[ix,j] = 2. * np.std(preds[ix,j,:])\n", + " \n", + " return means, errors" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "9a7053ea", + "metadata": {}, + "outputs": [], + "source": [ + "means, errors = getUncertainty( preds, T )" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ea15ee5b", + "metadata": {}, + "outputs": [], + "source": [ + "bnnPreds = []\n", + "for ix in range(len(means)):\n", + " ix = np.where( means[ix] == np.max(means[ix]) )\n", + " bnnPreds.append( ix[0][0] )\n", + "\n", + "bnnPreds = np.array(bnnPreds)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "dd94d995", + "metadata": {}, + "outputs": [], + "source": [ + "font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 22}\n", + "plt.rc('font', **font)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "0723dfea", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "cm = confusion_matrix( buildingTypes, bnnPreds, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "493725d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1,2]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "1302335f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.4834\n", + "Micro F1: 0.5432\n" + ] + } + ], + "source": [ + "from sklearn.metrics import f1_score\n", + "from sklearn.metrics import balanced_accuracy_score\n", + "\n", + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(buildingTypes, bnnPreds), 4 ))\n", + "print( \"Micro F1:\", np.round( f1_score(buildingTypes, bnnPreds, average='micro'), 4 ))" + ] + }, + { + "cell_type": "markdown", + "id": "a5f7d01f", + "metadata": {}, + "source": [ + "#### Hypothesis Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "e40860ea", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import stats\n", + "\n", + "def tTest( sample1, sample2 ):\n", + " \n", + " t_stat, p_value = stats.ttest_ind( sample1, sample2 )\n", + "\n", + " # True = significant difference, False = not significant\n", + " alpha = 0.05\n", + " if p_value < alpha:\n", + " return True\n", + " else:\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "f21b1255", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total buildins: 1140006\n", + "Predications made: 409507\n", + "% of buildings with prediction: 35.92\n" + ] + } + ], + "source": [ + "ixs = []\n", + "bnnPreds = []\n", + "predsMade = 0\n", + "n = preds.shape[0]\n", + "\n", + "noPrediction = []\n", + "\n", + "for i in range(n):\n", + " \n", + " ix = np.argsort( -means[i,:] ) # sort in descending order\n", + " \n", + " sample1 = preds[i, ix[0], :]\n", + " sample2 = preds[i, ix[1], :]\n", + " significant = tTest( sample1, sample2 )\n", + " \n", + " mx = np.max( means[i,:] )\n", + " ix = np.where( means[i,:] == mx )\n", + " \n", + " if ( significant and (mx >= 0.65) ):\n", + " predsMade += 1\n", + " bnnPreds.append(ix[0][0])\n", + " ixs.append(i)\n", + " else:\n", + " noPrediction.append(i)\n", + " \n", + "print(\"Total buildins:\", n)\n", + "print(\"Predications made:\", predsMade)\n", + "print(\"% of buildings with prediction:\", np.round( (predsMade/n)*100., 2) )" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "671e44b4", + "metadata": {}, + "outputs": [], + "source": [ + "cm = confusion_matrix( buildingTypes[ixs], bnnPreds, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "8f1c3494", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1,2]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "8e5dfab2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.5817\n", + "Micro F1: 0.7346\n" + ] + } + ], + "source": [ + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(buildingTypes[ixs], bnnPreds), 4 ))\n", + "print( \"Micro F1:\", np.round( f1_score(buildingTypes[ixs], bnnPreds, average='micro'), 4 ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27e1d3e6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5006cb2c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "538cf18c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Probability')" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter( [1,2,3], means[2,:]*100 ) \n", + "plt.errorbar( [1,2,3], means[2,:]*100, yerr=errors[2,:]*100, fmt=\"o\")\n", + "plt.ylim(0,100)\n", + "plt.title('Probabilistic Building Type Prediction')\n", + "plt.ylabel('Probability')" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "23e0faeb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 [0.05852261 0.10924447 0.83223292] [0.00330635 0.00526524 0.00806944]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Probability')" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range( means.shape[0] ):\n", + "\n", + " mx = np.max( means[i,:] )\n", + " ex = np.max( errors[i,:] )\n", + " if ( mx > 0.7 and yTest.iloc[i] == 2 and ex < 0.1 ):\n", + " print( yTest.iloc[i], means[i,:], errors[i,:] )\n", + " x = [1,2,3]\n", + " y = means[i,:]*100.\n", + " e = errors[i,:]*100.\n", + " break \n", + " \n", + "plt.scatter( x, y ) \n", + "plt.errorbar( x, y, yerr=e, fmt=\"o\")\n", + "plt.ylim(0,100)\n", + "plt.title('Probabilistic Building Type Prediction')\n", + "plt.ylabel('Probability')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36740e36", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57530152", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/EDS_paper/BayesianClassification_Binary.ipynb b/book/EDS_paper/BayesianClassification_Binary.ipynb new file mode 100644 index 0000000..2886c2f --- /dev/null +++ b/book/EDS_paper/BayesianClassification_Binary.ipynb @@ -0,0 +1,1250 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d1bb37a6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-27 13:59:58.732426: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-11-27 13:59:59.287559: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import tensorflow_probability as tfp\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "from tensorflow.keras.optimizers import Adam\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "d9e19465", + "metadata": {}, + "source": [ + "### Setup and Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "68eb4db6", + "metadata": {}, + "outputs": [], + "source": [ + "# North Carolina data\n", + "url = \"https://github.com/geo-smart/flood-risk-ml-tutorial/blob/main/North_Carolina_Buildings.zip?raw=true\"\n", + "\n", + "# read data into a Pandas dataframe\n", + "rawDf = pd.read_csv(url, compression=\"zip\")\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "rawDf = rawDf.iloc[:, 3:] \n", + "df = rawDf.copy( deep=True )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "999bfa70", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Residential 976690\n", + "Other 101454\n", + "Name: BuildingType, dtype: int64\n", + "\n", + "Residential 1.000000\n", + "Other 9.626925\n", + "Name: BuildingType, dtype: float64\n", + "\n" + ] + } + ], + "source": [ + "# classify a building as \"Residential\" or \"Other\"\n", + "df.loc[df[\"BuildingType\"] == \"Commercial\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Industrial\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Assembly\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Education\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Government\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Agriculture\", \"BuildingType\"] = 'Other'\n", + "df.loc[df[\"BuildingType\"] == \"Utility and Misc\", \"BuildingType\"] = 'Other'\n", + "\n", + "# building type distributions\n", + "print()\n", + "print( df['BuildingType'].value_counts() )\n", + "print()\n", + "print( 976690/df['BuildingType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1e299cf1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_59499/1358776107.py:19: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " df.iloc[:, nCols-1] = le.transform( df.iloc[:, nCols-1] )\n", + "/tmp/ipykernel_59499/1358776107.py:23: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " df.iloc[:, nCols-2] = le2.transform( df.iloc[:, nCols-2] )\n" + ] + } + ], + "source": [ + "from sklearn import preprocessing\n", + "\n", + "df = df.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "# preprocess the data - scaling\n", + "scaler = preprocessing.StandardScaler()\n", + " \n", + "columns = ['X', 'Y', 'Area', 'MedianIncomeCounty', \n", + " 'HousingUnitsCounty', 'HousingDensityCounty',\n", + " 'Impervious', 'AgCount', 'CmCount', 'GvCount',\n", + " 'EdCount', 'InCount']\n", + "df[columns] = scaler.fit_transform(df[columns])\n", + "\n", + "df = df.dropna()\n", + "\n", + "nCols = df.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( df.iloc[:, nCols-1] ) # ornl type\n", + "df.iloc[:, nCols-1] = le.transform( df.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( df.iloc[:, nCols-2] ) # nearest road type\n", + "df.iloc[:, nCols-2] = le2.transform( df.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "958d6b5e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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4832571.390741-3.345183-0.025571-0.865273-1.0471914.091188-0.609319-0.189389-1.180076-1.260106-1.217655-1.22694141
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" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty HousingUnitsCounty \\\n", + "109191 -0.969762 -1.080139 -0.098919 0.139001 -1.071597 \n", + "249110 0.937555 0.297244 -0.029512 -0.803459 -1.068631 \n", + "632766 -0.989369 -0.520914 -0.257804 0.337253 1.051850 \n", + "320545 -0.250256 1.009261 -0.132568 -0.937660 -0.273707 \n", + "483257 1.390741 -3.345183 -0.025571 -0.865273 -1.047191 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount \\\n", + "109191 -1.424272 0.851887 -0.189389 -1.054971 -1.048588 \n", + "249110 -0.697542 -0.982076 0.033225 -1.138290 -1.189600 \n", + "632766 0.031186 0.479131 -0.337799 1.140185 1.278110 \n", + "320545 0.026131 -1.384653 -0.758292 -0.383523 -0.202516 \n", + "483257 4.091188 -0.609319 -0.189389 -1.180076 -1.260106 \n", + "\n", + " EdCount InCount OsmNearestRoad BuildingType \n", + "109191 -1.045623 -0.770605 4 1 \n", + "249110 -1.155967 -0.976654 4 1 \n", + "632766 0.949244 0.979654 4 0 \n", + "320545 -0.520841 -0.321399 4 1 \n", + "483257 -1.217655 -1.226941 4 1 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "61b9c6ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of classes: 2\n" + ] + } + ], + "source": [ + "nClasses = len(df['BuildingType'].unique())\n", + "print(\"Number of classes:\", nClasses)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f2ae35fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Size: (862513, 13)\n", + "Testing Size: (215629, 13)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "train, test = train_test_split(df, test_size=0.2, random_state=42, stratify=df[['BuildingType']])\n", + "\n", + "nCols = test.shape[1]\n", + "xTest = test.iloc[:, :nCols-1]\n", + "yTest = test.iloc[:, nCols-1]\n", + "\n", + "xTrain = train.iloc[:, :nCols-1]\n", + "yTrain = train.iloc[:, nCols-1]\n", + "\n", + "print(\"Training Size:\", xTrain.shape)\n", + "print(\"Testing Size:\", xTest.shape)\n", + "\n", + "# save test/train split\n", + "xTrain.to_pickle(\"./xTrain_binary.pkl\") \n", + "yTrain.to_pickle(\"./yTrain_binary.pkl\") \n", + "xTest.to_pickle(\"./xTest_binary.pkl\") \n", + "yTest.to_pickle(\"./yTest_binary.pkl\") " + ] + }, + { + "cell_type": "markdown", + "id": "6167606a", + "metadata": {}, + "source": [ + "### Bayesian Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "78574d2f", + "metadata": {}, + "outputs": [], + "source": [ + "xTrain = pd.read_pickle(\"./xTrain_binary.pkl\") \n", + "yTrain = pd.read_pickle(\"./yTrain_binary.pkl\") \n", + "xTest = pd.read_pickle(\"./xTest_binary.pkl\") \n", + "yTest = pd.read_pickle(\"./yTest_binary.pkl\") " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b9fb60d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weight for class 0: 5.31\n", + "Weight for class 1: 0.55\n" + ] + } + ], + "source": [ + "# Scaling by total/2 helps keep the loss to a similar magnitude.\n", + "total = xTrain.shape[0]\n", + "ix = np.where( yTrain == 0 )\n", + "zero = len(ix[0])\n", + "ix = np.where( yTrain == 1 )\n", + "one = len(ix[0])\n", + "\n", + "weight_for_0 = (1 / zero) * (total / 2.0)\n", + "weight_for_1 = (1 / one) * (total / 2.0)\n", + "\n", + "class_weight = {0: weight_for_0, 1: weight_for_1}\n", + "\n", + "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n", + "print('Weight for class 1: {:.2f}'.format(weight_for_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "63f8937e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jupyter-narock/.local/lib/python3.9/site-packages/tensorflow_probability/python/layers/util.py:98: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use the `layer.add_weight()` method instead.\n", + " loc = add_variable_fn(\n", + "2023-11-26 13:07:42.166429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5337 MB memory: -> device: 0, name: NVIDIA GeForce GTX TITAN Black, pci bus id: 0000:65:00.0, compute capability: 3.5\n", + "/home/jupyter-narock/.local/lib/python3.9/site-packages/tensorflow_probability/python/layers/util.py:108: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use the `layer.add_weight()` method instead.\n", + " untransformed_scale = add_variable_fn(\n" + ] + } + ], + "source": [ + "from keras import backend as K \n", + "\n", + "# Keras keeps models hanging around in memory. If we retrain a model, Keras will\n", + "# start from the previously concluded weight values. This resets everything.\n", + "K.clear_session()\n", + "\n", + "# KL divergence weighted by the number of training samples, using\n", + "# lambda function to pass as input to the kernel_divergence_fn on\n", + "# flipout layers.\n", + "kl_divergence_function = (lambda q, p, _: tfd.kl_divergence(q, p) / \n", + " tf.cast(xTrain.shape[0], dtype=tf.float32))\n", + "\n", + "tfd = tfp.distributions\n", + "\n", + "# Define a logistic regression model as a Bernoulli distribution\n", + "# parameterized by logits from a single linear layer. We use the Flipout\n", + "# Monte Carlo estimator for the layer: this enables lower variance\n", + "# stochastic gradients than naive reparameterization.\n", + "input_layer = tf.keras.layers.Input(shape=xTrain.shape[1])\n", + "\n", + "#dense_layer = tfp.layers.DenseFlipout(\n", + "# units=1,\n", + "# activation='sigmoid',\n", + "# kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + "# bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + "# kernel_divergence_fn=kl_divergence_function)(input_layer)\n", + "\n", + "layer1 = tfp.layers.DenseFlipout(\n", + " units=26,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(input_layer)\n", + "\n", + "layer2 = tfp.layers.DenseFlipout(\n", + " units=13,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer1)\n", + "\n", + "layer3 = tfp.layers.DenseFlipout(\n", + " units=8,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer2)\n", + "\n", + "layer4 = tfp.layers.DenseFlipout(\n", + " units=4,\n", + " activation='sigmoid',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer3)\n", + "\n", + "out = tfp.layers.DenseFlipout(\n", + " units=2,\n", + " activation='softmax',\n", + " kernel_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " bias_posterior_fn=tfp.layers.default_mean_field_normal_fn(),\n", + " kernel_divergence_fn=kl_divergence_function)(layer4)\n", + "\n", + "# Model compilation\n", + "#bnn = tf.keras.Model(inputs=input_layer, outputs=dense_layer)\n", + "bnn = tf.keras.Model(inputs=input_layer, outputs=out)\n", + "optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\n", + " \n", + "# We use the binary_crossentropy loss since this toy example contains\n", + "# two labels. The Keras API will then automatically add the\n", + "# Kullback-Leibler divergence (contained on the individual layers of\n", + "# the model), to the cross entropy loss, effectively\n", + "# calcuating the (negated) Evidence Lower Bound Loss (ELBO)\n", + "bnn.compile(optimizer, loss='binary_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6c70029b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 13)] 0 \n", + " \n", + " dense_flipout (DenseFlipout (None, 26) 728 \n", + " ) \n", + " \n", + " dense_flipout_1 (DenseFlipo (None, 13) 702 \n", + " ut) \n", + " \n", + " dense_flipout_2 (DenseFlipo (None, 8) 224 \n", + " ut) \n", + " \n", + " dense_flipout_3 (DenseFlipo (None, 4) 72 \n", + " ut) \n", + " \n", + " dense_flipout_4 (DenseFlipo (None, 2) 20 \n", + " ut) \n", + " \n", + "=================================================================\n", + "Total params: 1,746\n", + "Trainable params: 1,746\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "bnn.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "494e0dfa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/25\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-26 13:07:56.898836: I tensorflow/compiler/xla/service/service.cc:169] XLA service 0x7fdd9003b330 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2023-11-26 13:07:56.898892: I tensorflow/compiler/xla/service/service.cc:177] StreamExecutor device (0): NVIDIA GeForce GTX TITAN Black, Compute Capability 3.5\n", + "2023-11-26 13:07:56.905451: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2023-11-26 13:07:57.036695: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8600\n", + "2023-11-26 13:07:57.089561: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n", + "2023-11-26 13:07:57.150242: I ./tensorflow/compiler/jit/device_compiler.h:180] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5391/5391 [==============================] - 89s 16ms/step - loss: 0.3501 - accuracy: 0.8879 - val_loss: 0.2917 - val_accuracy: 0.9057\n", + "Epoch 2/25\n", + "5391/5391 [==============================] - 84s 16ms/step - loss: 0.3174 - accuracy: 0.9008 - val_loss: 0.2579 - val_accuracy: 0.9143\n", + "Epoch 3/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.3116 - accuracy: 0.9024 - val_loss: 0.2867 - val_accuracy: 0.9055\n", + "Epoch 4/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.3066 - accuracy: 0.9030 - val_loss: 0.2982 - val_accuracy: 0.8998\n", + "Epoch 5/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.3027 - accuracy: 0.9020 - val_loss: 0.2564 - val_accuracy: 0.9139\n", + "Epoch 6/25\n", + "5391/5391 [==============================] - 84s 16ms/step - loss: 0.3004 - accuracy: 0.9031 - val_loss: 0.3280 - val_accuracy: 0.8774\n", + "Epoch 7/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2992 - accuracy: 0.9022 - val_loss: 0.3256 - val_accuracy: 0.8859\n", + "Epoch 8/25\n", + "5391/5391 [==============================] - 84s 16ms/step - loss: 0.2977 - accuracy: 0.9029 - val_loss: 0.2975 - val_accuracy: 0.8986\n", + "Epoch 9/25\n", + "5391/5391 [==============================] - 84s 16ms/step - loss: 0.2964 - accuracy: 0.9026 - val_loss: 0.2636 - val_accuracy: 0.9078\n", + "Epoch 10/25\n", + "5391/5391 [==============================] - 84s 16ms/step - loss: 0.2959 - accuracy: 0.9027 - val_loss: 0.2604 - val_accuracy: 0.9177\n", + "Epoch 11/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2954 - accuracy: 0.9024 - val_loss: 0.2950 - val_accuracy: 0.8920\n", + "Epoch 12/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2952 - accuracy: 0.9013 - val_loss: 0.2766 - val_accuracy: 0.9034\n", + "Epoch 13/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2943 - accuracy: 0.9019 - val_loss: 0.2516 - val_accuracy: 0.9150\n", + "Epoch 14/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2940 - accuracy: 0.9026 - val_loss: 0.2630 - val_accuracy: 0.9085\n", + "Epoch 15/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2931 - accuracy: 0.9033 - val_loss: 0.3247 - val_accuracy: 0.8708\n", + "Epoch 16/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2930 - accuracy: 0.9025 - val_loss: 0.2610 - val_accuracy: 0.9102\n", + "Epoch 17/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2920 - accuracy: 0.9020 - val_loss: 0.2604 - val_accuracy: 0.9107\n", + "Epoch 18/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2920 - accuracy: 0.9042 - val_loss: 0.2988 - val_accuracy: 0.8934\n", + "Epoch 19/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2917 - accuracy: 0.9039 - val_loss: 0.2908 - val_accuracy: 0.8993\n", + "Epoch 20/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2914 - accuracy: 0.9046 - val_loss: 0.2670 - val_accuracy: 0.9141\n", + "Epoch 21/25\n", + "5391/5391 [==============================] - 84s 16ms/step - loss: 0.2918 - accuracy: 0.9055 - val_loss: 0.2854 - val_accuracy: 0.9040\n", + "Epoch 22/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2914 - accuracy: 0.9049 - val_loss: 0.3036 - val_accuracy: 0.9025\n", + "Epoch 23/25\n", + "5391/5391 [==============================] - 84s 16ms/step - loss: 0.2916 - accuracy: 0.9050 - val_loss: 0.2720 - val_accuracy: 0.9068\n", + "Epoch 24/25\n", + "5391/5391 [==============================] - 85s 16ms/step - loss: 0.2911 - accuracy: 0.9058 - val_loss: 0.2587 - val_accuracy: 0.9089\n", + "Epoch 25/25\n", + "5391/5391 [==============================] - 84s 16ms/step - loss: 0.2914 - accuracy: 0.9054 - val_loss: 0.2579 - val_accuracy: 0.9148\n" + ] + } + ], + "source": [ + "history = bnn.fit( xTrain, tf.one_hot(yTrain,nClasses), \n", + " epochs=25, verbose=1, batch_size=128, class_weight=class_weight,\n", + " use_multiprocessing=True, validation_split=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "119724ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Epoch')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot( history.history['accuracy'], label=\"Accuracy\")\n", + "#plt.plot( history.history['val_accuracy'], label=\"val_acc\")\n", + "plt.title('Model Accuracy')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "#plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "87cacc97", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot( history.history['loss'], label=\"loss\")\n", + "plt.plot( history.history['val_loss'], label=\"val_loss\")\n", + "plt.title('Model Loss')\n", + "plt.ylabel('Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "900420d6", + "metadata": {}, + "outputs": [], + "source": [ + "# serialize weights to HDF5\n", + "bnn.save_weights(\"bnn_binary.h5\")" + ] + }, + { + "cell_type": "markdown", + "id": "ae0a0f29", + "metadata": {}, + "source": [ + "### Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "91eefec2", + "metadata": {}, + "outputs": [], + "source": [ + "nClasses = 2\n", + "bnn.load_weights(\"bnn_binary.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "af858d85", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def getPredictions( model, data, T ):\n", + "\n", + " n = data.shape[0]\n", + " preds = np.zeros( shape=(n,nClasses,T) )\n", + " \n", + " for t in range(T):\n", + " if ( t == 10 ): print(\"Iteration 10...\")\n", + " if ( t == 30 ): print(\"Iteration 30...\")\n", + " if ( t == 50 ): print(\"Iteration 50...\")\n", + " if ( t == 70 ): print(\"Iteration 70...\")\n", + " if ( t == 90 ): print(\"Iteration 90...\")\n", + " preds[:,:,t] = model(data)\n", + " \n", + " return preds" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "9bf48df9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 10...\n", + "Iteration 30...\n", + "Iteration 50...\n", + "Iteration 70...\n", + "Iteration 90...\n" + ] + }, + { + "data": { + "text/plain": [ + "(215629, 2, 100)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "T = 100\n", + "preds = getPredictions( bnn, xTest.values, T )\n", + "preds.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "f6054b90", + "metadata": {}, + "outputs": [], + "source": [ + "def getUncertainty( preds, T ):\n", + " \n", + " n = preds.shape[0]\n", + " errors = np.zeros( shape=(n, nClasses) )\n", + " means = np.zeros( shape=(n, nClasses) )\n", + " \n", + " for ix in range(n):\n", + " for j in range(nClasses):\n", + " \n", + " means[ix,j] = np.mean( preds[ix,j,:] )\n", + " #errors[ix,j] = (1./T) * np.sum( (preds[ix,j,:] - means[ix,j])**2 )\n", + " errors[ix,j] = 2. * np.std(preds[ix,j,:])\n", + " \n", + " return means, errors" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9a7053ea", + "metadata": {}, + "outputs": [], + "source": [ + "means, errors = getUncertainty( preds, T )" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "ea15ee5b", + "metadata": {}, + "outputs": [], + "source": [ + "bnnPreds = []\n", + "for ix in range(len(means)):\n", + " ix = np.where( means[ix] == np.max(means[ix]) )\n", + " bnnPreds.append( ix[0][0] )\n", + "\n", + "bnnPreds = np.array(bnnPreds)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "9d999949", + "metadata": {}, + "outputs": [], + "source": [ + "font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 22}\n", + "plt.rc('font', **font)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "0723dfea", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "cm = confusion_matrix( yTest, bnnPreds, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "a836b4a7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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77mDmzJmd+7bddlv+8Ic/dHvu/vvvzw033FD3Gt/4xjf4/Oc/39L4pJ5Ublo8JCJ+AxwJbE0RdL8C3AP8jqL8481W+o8inf+vpc3/0+p4DbgldWvIkCGceuqpHHjggTXbl112Wbbbbju22247Dj74YA4++GCmT5/edP+nnXYaBx100CLbV1ppJTbZZBM22WQTJkyYwGc+8xkuu+yylp+HNBjNmzeP4447jvPPP79m+8yZM7nhhhu44YYbOOecczjnnHMYM2ZMN71078tf/jJPPfVUXw1XS4GBUlJSLTOnAFOaPGciMLHOMQm8u+WBlRhwDwIR8SHg48D2wBrAihTzST4KTAXOyMzyFDnNXuMx4B0d7cwceK8q9btvfetbNcH2jBkzuO2221hvvfVYd911Adhkk02YPHkye++9N8Xvp8aMHz9+kWD77rvv5rnnnmOTTTZhtdVWA2C55ZbjlFNO4c9//jNz5szpsq8HH3yQBx98sMt9Dz/8cMNjkgaSE088sSbYXnnlldl888155JFHePTRYoazu+++mwkTJnDJJZewzDKtz4MwatQoXnzxxZbO3WCDDXjnO9/Z5b7111+/y+3S0saAewCLiDWAXwMf7GL3mMpjK+DLEfG/wJeqp72JiHWonXdyambu1LYBa4kyduxYjjrqqM72U089xa677srMmTNZZpllmDx5MrvuWqwDsMUWW3DQQQdx7rnnNtz/9ttvX9P+0Y9+xHe+8x0All9+ea666irWW289oAgGxo0bx5133tllXxdffDE/+MEPmnp+0kD2j3/8g5//fOHMY2uuuSZXXHEFo0ePZv78+UyYMIErr7wSgFtvvZXf//73fOxjH2vqGkcddRRrr702733ve5k3bx5bbVWeWa0x++yzD1/96ldbOleDy0DMcA8WTgs4QEXE6hR31lYH2wuAmyg+OqnOaC9DMW3NhRExpN8GqSXaRz/6UYYMWfjf6eyzz+6s8VywYAGnn356zfGf+MQnmuq/XPd98803d37/+uuvc99999Xsf/XVV5vqXxrMzj33XBYsWHif16GHHsro0aOBotTr2GOPrTn+N7/5TdPXOProo9ljjz1429ve1rvBSqrLDPfA9Stg7ar2I8A+mfn3jg0RsT9wDrBcZdN4irknv9Nfg9SSa5tttqlp33HHHYu0FyxY0Pkx9uabb86wYcN4883G7k+55ppr+MxnPtPZPvroo3nwwQd57rnn2Hbbbdlpp506991555088sgj3fa11VZbcdJJJzFixAheeeUVHnzwQa644gqeeOKJhsYiDTQ33nhjTfu9733vIu2I6Czjuu2225g7dy7Dhw/vryF2uummm/jqV7/KK6+8wogRI3jnO9/Jrrvuyjve8Y76J2tQMcPdOgPuASgitgd2qdo0HzigOtgGyMw/RMRXgdOqNn+lMjdlV+nAHSOiusi2xxKTiNiP4g7dzSn+r9wJfCczL+nm+LcAnwL2AzYGRgKzgXuB84GfZeZrpXPWoVT2AuwDfAP4CPB24L7MfG9341R7lGsvn3vuuZr2nDlzeOmllxg1qlg9d9lll+Xtb397j4Fxteuuu45TTjmFL36xmEb1/e9/P9OmTVvkuBtvvJFjjjmmx7522GEHdthhh5pt//mf/8nZZ5/Nt771LebObWmBMWmxKd97sPrqq9e0l1tuOVZaaSVmzZoFwNy5c3niiScWS830X/7yF/7yl7/UbDvhhBM49NBDOfHEE1l22WW7OVNaelhSMjDtX2pfk5ldF6/CLyimwOkwCtiptwOIiB8BfwB2BEYAywPbABdFxIFdHL8xcBfFhPAfoqgvH0YRdG9PMZXOrRGxbp1LjwT+SjG/5vpA/6drBMBb3/rWmvZrr722yDHlbSuttFJT1/j+97/P0Ucf3WXfAPfffz8nnXQSzzzzTFP9QvGx++GHH84pp5zS9LnS4vbyy7UL2a2wwgqLHFPe9tJLL7V1TM1YsGABv/rVrzrfUGvJMICWdh90DLgHpi1L7eu7OzAz3wBu6+L884E/lbZPr2zveEztYQyfrxx/JfB81fYAvlt9YESMBi4HqoPpv1PUmt9TtW0ccGlE9JTu2BR4D/AicHVljG/0cLz6SVe/BMvbmpmlZNiwYZx66qn87Gc/6wwc7r77bq6++mqeffZZAMaNG8cFF1zA8ccfv8j5jzzyCKeeeir77rsvm266KWPHjmXnnXfm0ksvrTnugAMOYLPNNmt4XNJA1NVrq7ytPwOV9dZbjy984QtceOGF3HHHHfzjH//gyiuvZI899qg57oILLuC228p/oqSljyUlA1N5QtXnujxqoWdL7VUz88AuyjXuzcxFstPduAXYJTNnRcSqwN3AqpV960fEO6pWW/oSxXSFHY7OzEkdjYj4OgvryscB/wL8tIdrXwF8NDNfqpzv55GLwcsvv1wzt+/yyy+/yDHlbeWsXE++8IUv1Myq8JWvfIWzzz4bKMpTfvWrX7HjjjsC8KUvfYmrr76aW2+9tfP4rmZFuOeeezjqqKP405/+xKabbtq5/cMf/jC33357w2OTFre3vvWtNXPbv/7664scU95W/lSqnbqaFWjjjTfm5z//OePHj+euu+7q3H711Vez+eab99vY1B5Lcva5P5jhHpia/R9dPr7xNGP3vpmZswAy83mKGVOqVd/Wvm/p2rtFxHkdDxYtcdmrh+vOpwjYOz8bzcwuJ1+OiE9HxC0RcUv13fzqG+Ua0jXWWKOmvfzyy3fWb0NR093MTYrVc3C/+uqrncF2R1/VbSiC5kYsWLBgkRXwVl111W6Olgamci12x6c+HV577bXO+m2A4cOH8/a3v70/htajIUOGsO2229Zse/7557s5Wlp6GHAPTOXfTmt0edRCq5faL/TBGG4ptcvFgdVZ57FV3wfFzY4HVD12LZ07lu49lpmPNTLAzJyUmVtk5ha9WfBBXSvPklAuyyjPmnDbbbc1PEMJ1AbwXX1cXn4T1TElGlAzXWFX1l577Zr2K6+80s2R0sBUniWo/AlNedagzTffvN9mKClP6Vn25JNP1rRHjBjRzuGoH1nD3TqjlIHp5lL7/d0dGBHLUcwi0tP5TcvMGaVN83vbZ5W39LCv+bvj1Bbnnnsu8+cv/Gc/5JBDauYB/tznPldzfHke4K985Ss899xznY/yohzVN0KOGDGiZh7vZZddlsMOO6zm+Ors+QEHHMCZZ565yOI5AAceeCDjx4+v2dbV7CfSQPbRj360ZuXIX//6150lJvPmzePHP/5xzfHlefBPPvlkVl999c7Hb3/72z4b2/nnn88nP/lJrr/++kXeLP/+97/nT3+qvX1o66237rNrS4OVNdwD04XAl6vaH4yIjTPz7i6O/RTFLCIdZrHwZsi+KC1pxGPAuyrfzwVWycxWU4rWhgwQjz76KD//+c8758pec801ue666xZZ2h0WrnTXjPPPP5+vfOUrne1TTjmFCRMm8MILL/Dud7+7JgP++uuvc/HFF3e2I4LddtuN3XbbjZkzZ3LvvfcyZ84c1ltvPcaOrf0A5c477+Sqq65qamzS4rbuuuty1FFH8bOf/Qwo3qDusMMOiyztDkV2u7pEq1Ff+9rXuPvu4s9KeerMu+++u+YGyEMOOYRDDjkEKD6RuuKKK7jiiisYNWoU7373u1l22WV55JFHeOyxx2r62WSTTdh5552bHpsGpiU1+9wfDLgHoMy8PiKuBjqKVocAF0TEPpnZufxeROwDlO9c+WFmdszBXb7Lpl3LiV3CwoB7OPDjiPhs9ZzbUbxKtwQOppj/+w9tGov60IknnsiYMWM44IADgKKsY5dddqk55u677+bwww9vaoYSgNNOO433vve9NX+Mq2907PDGG2/wxS9+seZj6uprrbzyynzgAx/o8hp33nknhx122CLlKdJg8K1vfYsXXniBCy64AICZM2cu8ubxPe95D2eeeSatlNU9+OCD3c4g8uqrr9bs++AHFy56XB10vfjii1x/fdcTaW2yySacddZZdUvApKWBAffA9UmKZdzXrLTXB+6JiJsparw3oJjxo9qVVE3Zl5nPR8RMYOXKpg0i4g6KVSsT+EVmXtYHY/0BMAFYrWrs+0bE7cDLwGiKqf46Jmm+ow+uqX4wf/58jj32WC688EIOPvhgNt98c0aNGsWrr77KAw88wEUXXcTZZ59dt6azK3PmzOHQQw9l/Pjx7L///myyySasuuqqDB8+nNmzZ/PYY4/xt7/9jcmTJ/P444/XnHv++efzwgsv8KEPfYhNNtmE1VdfnTFjxrDMMsswY8YM7r77bi655BIuvPDCmrIYaTAZOnQoP/nJT9hvv/0455xzuO2223jxxRdZccUV2XDDDdl333059NBDGTZsWL+O64ADDmDMmDFcc8013HXXXTz33HO88MILLFiwgNGjR7Pxxhuz9957s99++zF0qGHGksQMd+ui2ayU+k9ErEmxdPsOdQ5N4GfAv5Zn9IiIk4BF508rfD4zf1w57jGgcx3ezKx5VUXEZODwqk0fzMxrq/ZvQrFQTr2FbQAOy8yzK+etQ2mlyZ5Wv+zOsGHDsvqmOkn94847u1uTS1I77brrrtx55539FgGPHDkyO6ZqbbeLL7741szcol8u1k986zmAZebTFMux7wx8HNiOoizkLRS12o8C1wFnVJealHyDYoaRgymC4eXaNNa7KqtNfpJiafdNWZhZnw48CPwNuCQzb+yyE0mSpCWQAfcgkJlXAS3d9ZWZbwLfrjx6Om6dOvsnUJSN9HTMaxQL2vS0qE35nMdoft5xSZLUzywpaZ3TAkqSJEltZIZbkiRJPVqSF6XpD2a4JUmSpDYywy1JkqS6zHC3zgy3JEmS1EZmuCVJklSXGe7WmeGWJEmS2sgMtyRJkuoyw906M9ySJElSG5nhliRJUo+ch7t3zHBLkiRJbWSGW5IkSXWZ4W6dGW5JkiSpjcxwS5IkqS4z3K0zwy1JkiS1kQG3JEmS1EaWlEiSJKkuS0paZ4ZbkiRJaiMz3JIkSarLDHfrzHBLkiRJbWSGW5IkST1yaffeMcMtSZIktZEZbkmSJNVlhrt1ZrglSZKkNjLDLUmSpLrMcLfODLckSZLURma4JUmSVJcZ7taZ4ZYkSZLayAy3JEmS6jLD3Toz3JIkSVIbmeGWJElSj1xpsnfMcEuSJGlQiog9I+KCiHg6IuZExAsRcW1EHBMRw/qg/5Ui4t8i4i8R8XxEzK18vTsizoiIPRrpxwy3JEmSBpWIGApMBg4p7VoF2LHyODIids/M51u8xi7Ab4DRpV1jKo/3AKsBf6zXlwG3JEmS6hpgJSUnURtszwBuBDYE1q9s2wy4KCK2z8wFzXQeER8ALgWqs+TPAg8A84C3A+9stD8DbkmSJA0aEbEBcFzVpieBzTNzekQMAS4E9qrs2wY4DDizif6HA2ezMNh+E/gMMLk6cI+ItYFxjfRpDbckSZLq6rhxst2PBhxObQw7KTOnA2TmfIrsd7UjmnyqH6fIYHeYmJm/LGfJM/PJzLyykQ7NcEuSJGkw+UCpfXMX7QQ6ovetI2J4Zs5tsP+9S+3fRMS/ANsBKwDPAFcDl2dmNtKhAbckSZLqGkA13OUyjmeqG5n5RkTMAkZVNg0HxlLUXzfifVXfvwlcB6xVOuYrwLSIODAzn6rXoSUlkiRJGkhWiYhbqh6fLu0fWWrP7qKP8rZRXRzTnVWrvh/GosF2h62ByyNi2XodmuGWJElSXf2Y4Z6emVs0cXxXAytva6j0o2J4qf0KRV33VGBT4Fxgzcq+dwETgJ/11KEZbkmSJA0ms0rtFbo4prytfE5PXi61f5GZf8zM2Zn5N+Dk0v6d63VowC1JkqQe9dcMJQ1m0cu12GtWNyJiBWpLSOYCjzbxdB+pc71yu7wwziIMuCVJkjSYXFdqb1Vqb1lqT2tihhKAv5ba5YB65VK77kqWBtySJEmqawBluM8EqufEPioixlTGOBT4Wun4M0rPY2JEZNVjQun4yaX2IRGxYuXcYSw6r/dV9QZswC1JkqRBIzMfAk6t2rQWcF9ETAHuA8ZX7ZsGnNVk/3dRG6S/C3goIv4IPAh8qGrfw4307ywlkiRJqmsAzcMNcDywGnBwpT0a2LN0zB3AvuUVIhv0OWAMsE+lvTq1gTzAQ8CemTmnXmdmuCVJkjSoZOa8zDyEYlXIi4DnKBapmUlR430ssFVm/rPF/t/IzH2BjwJ/pKjTnge8SFHj/SVgs0q2vS4z3JIkSaprgGW4AcjMKcCUJs+ZCExs8NjfA79vemAlZrglSZKkNjLgliRJktrIkhJJkiTVNRBLSgYLM9ySJElSG3Wb4Y6IT/am48z8VW/OlyRJ0sDQxKI06kJPJSWTgexF3wbckiRJWur1FHD/it4F3JIkSVpCmOFuXbcBd2ZO6MdxSJIkSUskZymRJElSXWa4W9dywB0RwynWrZ+TmTP7bkiSJEnSkqPpaQEj4pMRcTMwG3gK+EHVvgMj4pyIGNuHY5QkSdJi1jFTSbsfS6KmAu6ImAz8H/A+4HWg/FN5Evg4cGBfDE6SJEka7BoOuCPicOCTwJ3AFsBK5WMycxrwDDC+rwYoSZKkxc8Md+uaqeE+CngF2Dszn4Zui+cfAdbp9cgkSZKkJUAzAffGwI0dwXYPngG2bH1IkiRJGkiW5Oxzf2imhnsY8GoDx40G3mxtOJIkSdKSpZkM9xPAe3o6ICKGAO+mKCuRJEnSEsIMd+uayXBfDqwfEYf2cMzRwBrApb0alSRJkrSEaCbDfTJwOPDLiHgXcF5l+3IRsRFwEPDvwAzgtD4dpSRJkjRINRxwZ+ZTEbE/cD7wtcojgY9VHgG8DByYmc+3YaySJElaTCwpaV1TC99k5p+Bd1GsLnkvxeI3cylqtk8D3pOZU/t6kJIkSdJg1UxJCQCZ+RwLM9ySJElaCpjhbl1TGW5JkiRJzWk6ww0QEWsCOwBrUdRxPwNcl5lP9eHYJEmSNECY4W5dUwF3RIyhqNU+gEWz4xkRFwCf86ZJSZIkqdBwwB0RKwN/ATYAFgB/Ax6r7F4H2AY4ENg0IrbNzJl9OlJJkiQtFi7t3jvNZLgnAu8ErgY+k5k1q0lGxFjgp8DOwAnAcX00RkmSJGnQauamyf2AF4D9ysE2QGY+CnwEmA7s3yejkyRJ0oDQkeVu92NJ1EzAvSowNTNnd3dAZd9UYExvByZJkiQtCZopKXkaGN7AccMpZi2RJEnSEmJJzT73h2Yy3L8HPhQRq3d3QGXfhyiWf5ckSZKWes0E3P9FsZz7nyNifHlnROxOcUPlvRQ3TUqSJGkJYQ1367otKYmIa7rYPB/YEJgSEbOonRZwZOX7G4ApwIf7aIySJEnSoNVTDfdOPewLYFTlUbYdxeqTkiRJWkIsqdnn/tBTwP3BfhuFJEmStITqNuDOzKn9ORBJkiQNTEtyfXV/aOamSUmSJElNMuCWJEmS2qiZhW8AiIgtgQOBdwJvpbiBsiwz01lKJEmSlhCWlLSuqYA7Ik4BvsDCIDupDbg72s5SIkmSJNFESUlEfAI4DngK+DRwRWXXbsCxwN8ogu3vU6w2KUmSpCWEC9+0rpka7qOAecCHMvMXwLMAmXllZv5vZr4fmAh8CZjd1wOVJEmSBqNmAu5NgRsz85EejjkReBL4Rq9GJUmSpAHFDHfrmgm430JRTtJhDkBEjOjYkJkJ3Eyx2qQkSZK01GvmpsnngdFV7RcqX9cHbq/avhKwYi/HJUmSpAFkSc0+94dmMtwPA2Or2jdT3CT5mY4NEbEhxZLwPZWdSJIkSUuNZgLuK4H1ImKjSvty4GngyIi4KSLOB24EhgNn9e0wJUmStLj0V/32kppFbybg/jXwH8AKAJk5B/goRWnJFsD+FOUkU4BT+naYkiRJUq2I2DMiLoiIpyNiTkS8EBHXRsQxETGsF/1mg49tGumv4RruzHwC+HZp2w0RMRbYAVgZuD8zb+/qfEmSJA1eAyn7HBFDgcnAIaVdqwA7Vh5HRsTumfl8Pw9vEU0v7V6Wma9TlJdIkiRJ/eEkaoPtGRSlzRtSTOgBsBlwUURsn5kLenGt61g4WUjZjEY66HXALUmSpCXfQMlwR8QGFKufd3gS2Dwzp0fEEOBCYK/Kvm2Aw4Aze3HJEzLz2l6c333AHRFv703HlRIUSZIkqS8dTu19iJMyczpAZs6PiJNYGHADHEHvAu5e6ynD/RiQLfabdfqWJEnSIDJQMtzAB0rtm7toJ8X01QBbR8TwzJzb4vUOj4jDKWbimwHcBlyYmbMa7aCnoPgJWg+4JUmSpHYYV2o/U93IzDciYhYwqrJpOMVaMg+0eL0JXWz7cUR8NTP/t5EOug24M3OdFgcl9btNN92UW265ZXEPQ1rqDKCMl6Slx8hSe3YXx8xmYcBN6fu+8BbgJxExLzN/Xu/gZubhliRJ0lKqHxe+WSUibql6fLre0BrY1mzVxvXAl4H3AaMpAvbdgLtKx30/Ipat15l11pIkSRpIpmfmFj3snwWsWtVeoYtjyttmNTOAzCzXiQNcERF3A48Ay1e2jQK2Ba7tqT8z3JIkSerRAFvavVyLvWZprCtQW0IyF3i0dz+BQmY+C9xX2rxGvfMMuCVJkjSYXFdqb1Vqb1lqT2tmhpLKKpbd7RsCrF3a/FK9Pg24JUmSVNcAynCfCVSvHHlURIypjHEo8LXS8WeUnsfEiMiqx4TS8adFxGkR8a7SecsCpwJjqjbPA6bVG7A13JIkSRo0MvOhiDgV+GJl01rAfRFRXtodimD4rCYvsTzF4jqfi4jHgPspYuZNqQ22AU7PzLrLuxtwS5Ikqa4BNg3o8cBqwMGV9mhgz9IxdwD7ZuYCmlM9o8k6lUdXzgK+0kiHBtySJEkaVDJzHnBIRPwGOBLYmiLofgW4B/gd8PPMfLOF7o8DrgF2Bt5FcVPkGOBN4GngBuD/MnNqox0acEuSJKmuAZbhBiAzpwBTmjxnIjCxh/0vU2Svmy1F6VbTN01GxPoRcXJEXB8RD0TESVX7tomIT0fEyL4aoCRJkjSYNZXhjogjgNMp1qSHosZllapDxgD/S5Fy/7++GKAkSZIWv4GY4R4sGs5wR8T2wM+AN4CvUtTKlH/ylwEvA/v01QAlSZKkwayZDPfxFBnt8Zl5Ayz6Ticz34yIB4CN+myEkiRJWqyamCNbXWimhntb4KaOYLsHT9LAEpeSJEnS0qCZDPdKwFMNHDe8yX4lSZI0wJnhbl0zGe7ngbENHLchxRyFkiRJ0lKvmYD7r8DmEbFFdwdExC7AO4FrezkuSZIkaYnQTMB9CsWsJBdExK4RUXNuROwA/BKYB5zWd0OUJEnS4tZx42S7H0uihgPuzJxGMVPJWsCfgBkUs5bsFxH/BP4MrAkcn5l3t2GskiRJ0qDT1EqTmflDYA/gFuCtFBnvkRQL3twD7JeZ/69vhyhJkqTFzQx365qeTSQzLwMui4jRFDdRDgGezMxn+npwkiRJ0mDX8vR9mTmDoqxEkiRJS7glNfvcH5oqKZEkSZLUnIYz3BHxH030m5l5YgvjkSRJ0gCzJNdX94dmSkomUsxK0tVPO6u+j0rbgFuSJElLvWYC7v/sZvsywDuAnYC3U8zF/WTvhiVJkqSBxAx36xoOuDOzu4AbgIhYDvgpsDvwvl6OS5IkSVoi9NlNk5n5BvAZimkCLSeRJElagjgPd+v6dJaSStB9C8XiOJIkSdJSr+V5uOv0uUob+pUkSdJisqRmn/tDn2a4I+KdwAeAp/uyX0mSJGmwamYe7k/2sHtFYBxwGLA88NtejkuSJEkDiBnu1jVTUjKZ2vm2yzr+FabQ/RSCkiRJ0lKlmYD7V3QfcM+lKCO5OjP/2utRSZIkSUuIZubhntDGcUiSJGmAWpKn7OsPDd80GRFfiIgj2zkYSZIkaUnTzCwl/wPs266BSJIkaeBy4ZvWNRNwvwC80q6BSJIkSUuiZm6avB7Ysl0DkSRJ0sC1pGaf+0MzGe7/BNaKiP8Mf+KSJElSQ5rJcG8OnAV8EzgwIi4CHgde7+rgzPxV74cnSZKkgcB8a+u6Dbgj4pfA9Zn5y8qmyRTzcAewEcXKkj0x4JYkSdJSr6cM94TK146Au6eFbyRJkrQEM8PdOhe+kSRJktqomRpuSZIkLYWW5Dmy+0Mzs5RIkiRJapIZbkmSJNVlhrt19QLuAyNipxb6zcxcr4XzJEmSpCVKvYB7xcqjWc5mIkmStAQxw926egH3ZcD3+2MgkiRJ0pKoXsD9XGZO7ZeRSJIkSUsgb5qUJElSXZaUtM5pASVJkqQ2MsMtSZKkusxwt84MtyRJktRG3Wa4M9NgXJIkSS7t3ksG1ZIkSRqUImLPiLggIp6OiDkR8UJEXBsRx0TEsD68zm4RkaXH5EbPt4ZbkiRJdQ2kDHdEDAUmA4eUdq0C7Fh5HBkRu2fm87281krAL3rThxluSZIkDTYnURtszwAuBR6u2rYZcFFE9DbePRVYqzcdGHBLkiSpro467nY/GhjHBsBxVZueBMZl5l7AOGBK1b5tgMN68Zz3Bg6vNB9rtR8DbkmSJA0mh1Mbw07KzOkAmTmfIvtd7YhWLhIRKwOTKs3ngeNb6Qes4ZYkSVIDBlAN9wdK7Zu7aCfQMeCtI2J4Zs5t8jqnA6tXvj8amNXk+Z3McEuSJGkwGVdqP1PdyMw3qA2OhwNjm7lARBwIfLzSPCszL2xuiLXMcEuSJKmuAZThHllqz+7imNnAqKr2qC6O6VJErAr8pNJ8GvhCM4PrigG3JEmSBpJVIuKWqvakzJzU7dELS0d62pZNXP9/gTGV74/IzFlNnNslA25JkiT1qJ9XmpyemVv0sH8WsGpVe4Uujilvm9XIhSNiN+AjlebPMvPyRs6rxxpuSZIkDSYPlNprVjciYgVqS0jmAo822PcaVd9/PCKmdzyAi0rHdux/pF6nBtySJEkaTK4rtbcqtbcstae1MEMJwErA6KrHW0v7l63a1yMDbkmSJNU1UBa+Ac4EFlS1j4qIMZUxDgW+Vjr+jNLzmBgRWfWY0IsfS0MMuCVJkjRoZOZDFMutd1gLuC8ipgD3AeOr9k0Dzmqi78mZGV09gA+WDj+zsm9kvX69aVKSJEl1DaBpAaFY9XE14OBKezSwZ+mYO4B9M3MBi5kZbkmSJA0qmTkvMw8B9qa4mfE54E1gJkWN97HAVpn5z8U3yoXMcEuSJKmuAZbhBiAzpwBTmjxnIjCxhWtdS9dzftdlhluSJElqIzPckiRJ6lE/L3yzxDHDLUmSJLWRGW5JkiTVZYa7dWa4JUmSpDYywy1JkqS6zHC3zgy3JEmS1EZmuCVJklSXGe7WmeGWJEmS2sgMtyRJkuoyw906M9ySJElSGxlwS5IkSW1kSYkkSZJ65NLuvWOGW5IkSWojM9ySJEmqywx368xwS5IkSW1khluSJEl1meFunRluSZIkqY3McEuSJKkuM9ytM8MtSZIktZEZbkmSJNVlhrt1ZrglSZKkNjLDLUmSpB650mTvmOGWJEmS2sgMtyRJkuoyw906M9ySJElSG5nhliRJUl1muFtnhluSJElqIwNuSZIkqY0MuCUBcOmll/KRj3yENddck2WXXZYxY8aw00478ZOf/IQ333yzV33Pnj2b733ve2y99dasvPLKLLfccrzjHe/g0EMP5W9/+1uX50yYMKFzGqpGHhMnTlykj3/+85+ccMIJbLvttqyyyioMGzaM5Zdfnre//e3svffenHXWWcyfP79Xz01qtz322IPzzz+fp556ijfeeIPnn3+eP//5z3z2s59l6NDeVYa+733v45e//CUPP/wwr732Gi+//DJ33XUX3/ve91h11VW7POctb3kLe++9NyeddBJTp07loYce4qWXXuL111/nySef5MILL2T//ffv1bg0MDXzO7k3jyVRZObiHoPUa1tssUXecssti3sYg9K8efOYMGECv/71r7s9ZrPNNuOyyy7r9g9wTx544AF23XVXnnjiiW6P+frXv853vvOdmm0TJkzgzDPPbPg6J5xwQk3QffPNN7Pbbrvx4osv9njejjvuyGWXXcZyyy3X8LW00JL6x3EgGDJkCJMnT+bQQw/t9pjbbruN3XffnRdeeKHp/r/5zW8yceJEhgwZ0uX+WbNmsd9++zF16tSa7QcccADnnXde3f4vvvhiDjroIObOndv02NSYzOy3F+D666+fP/zhD/vlWvvtt9+tmblFv1ysnwyIDHdEZDePeRExKyJuj4gfRcRGi3usABGxU2mck5s8f2Lp/AntGWn7RMTk0nPYqY/6XafU77V90a+6d/zxx9cE26NHj2bPPfdk/fXX79x2++23s++++7JgwYKm+n7llVcWCbY33nhjxo8fz4orrti57bvf/S4/+clPas7dcsstOeCAA7p9lDN7W265ZU37yCOPrAm2V1ppJXbffXe22267miBx6tSpnHrqqU09L6k/nHTSSTXB9vTp05kyZQoPPfRQ57bNN9+ciy66qOk3PocddhgnnnhiZ7D95ptvcu211/K3v/2t83U+cuRILrnkEt7xjnd028+rr77K1KlTueyyy3j++edr9u2zzz7813/9V1Pj0sBmhrt1AyLg7sEQYCXgvcDngbsi4qDFOqIlXEQ8Vh3wLu7xqL0eeuihmmBz7bXX5v7772fKlCncf//97LXXXp37brzxRs4666ym+j/55JNrgu3PfOYz3HXXXfzxj3/k9ttvrwm6/+3f/o1XXnmls33sscdy3nnndfn4/Oc/z7x58zqPHTduHHvssUdne+bMmdx1112d7VGjRnH//ffzpz/9ib/+9a9MmjSpZpx/+ctfmnpeUrutv/76HHfccZ3tJ554gnHjxrH33nszbtw4Lrnkks592267LYcddlhT/ZcD4b333psPfvCDbL/99hx99NGd20eMGMH3vve9Rc6///77+eQnP8no0aPZaaedGD9+PGPHjuXiiy+uOe7II49cYgMoqRkDNeD+E3A+MBWo/ixqKDApIt6yWEa10AsU4+t43Lx4h7NY3Eztz6D5zzO12J155pk1WetPf/rTrLLKKkDxcfbxxx9fc/wZZ5zRVP+//OUva9rf+MY3Or9ff/31+ehHP9rZfuWVV/j973/fUL//8z//U9P+0pe+VPNHfdiwYTX7x44dy+qrr97Z3nrrrWv2jxgxoqHrSv3l8MMPryn1mDRpEjNmzABgwYIFnHTSSTXHH3HEEQ33vf7667POOut0th955BEuv/zyzvYZZ5zB66+/3tn+yEc+wkorrdTZnjp1KhtvvDFnnXVWTbnIa6+9xpe+9KWaa40ePZoxY8Y0PDYNXP2V3V5S36AN1ID7mMw8MDN3ArYCqu/YGglstzgG1SEz762Mr+Nx+uIcz+KQmaeXfgb3Lu4xqXnlzG65LGPLLbes+eU3bdq0husxH330UZ5++unO9uqrr85aa61Vc8xWW23V43i68vDDDzNlypTO9pgxYxbJ7o0YMYLttlv4a+LOO+/k7LPPZvbs2Tz77LN8+9vfrjn+4x//eP0nJPWjD3zgAzXtm2++eZF29ZvlrbfeepE3mt0pB8AzZ86saWcmL730Umd7+PDhNa/V6dOn13zCVO3ZZ59dZNvLL7/c0LikJdlADbg7ZeadwN9Lm0d3dWxEjI6If4+I6yNiRkS8GRHTI+KqiPhURHT52ygito+IcyLikYh4LSLmRsRzEXFHRPxfRBwTEcOrjq9bwx0RK0bEtyt9zomIpyLipxHR0F1nUdg/Ii6IiCci4o2IeDUi7omIkyNizW7OW6QkJCI+FhHXRcTLled3Q0TsXTrv2srx7yhtz3J/le091nBHxKER8YuIuCkinqyMfW5EvBARf4mIr0fESmixuv/++2vab3vb22rayy23HCNHjuxsz507l0cffbRP+gZYY401ejynK6ecckpNoHHsscd2ecPjL3/5S9Zbbz0A5s+fz2GHHcaKK67I2972Nn73u98BsOKKK3Lqqaey77771n9CUj8aN25cTfuZZ56pac+ZM4dZs2Z1tpdddlnGjh3bUN/lAHvs2LE190SMHDlykaB8gw02aKjv8ePH17RvvPFG3njjjYbO1cBnhrt1Az7griiP85nyARHxQeA+4NvA9sDKFCUoo4EPA2cAUyNidOm8g4G/AJ8A1gWWB4YBqwGbAhOA04G3NjrYSiB5HfDvlT6HA2sCRwN3AD3+5qqcfyVwAbA/sDawLPAW4N3AV4C/R8T4bjtZ2NdZwG+BDwAjKs9vG+CiiDiw0efUgn8DjgC2BNaiGPswYBXg/cB3gLsjovu7cdR21X+woZjuq6y8rd6sH+3s+8UXX6yZuWS55ZbjmGOO6fLYDTfckJtuuomdd965y/3LLLMMX/jCF5qufZX6Q/UbXSim1iwrbxs1alRDfT/wwAM8+eSTne1VVlmFk08+mZVXXpnVV1+d008/fZGZS6pLSrqz1lpr8f/+3/+r2eZNk1JhwAfcEbEZUD07yTPAtNIx7wQuBqrfkt8GTAEeqdq2LXBu6RITgY63U/OBGyp93UgXgX2DTgE2q2p39HtDZYwH1zn/XIo3CR2eoahrvx7oSO29FTgvIsbRs0OB6RQBfPUt5AF8t6o9laIW+7XS+eeXHs14HbgduBq4qPJ1etX+tYHTmuxTbdTVNKHlba1mH/qi75/+9Kc1QcYnP/nJbutD//KXv7DRRhtx1VVXAUUwsuuuu7LtttuyzDLLsGDBAr7zne/wvve9j4cffrjZpyP1q65eG+VtzUzz++///u817X/9139lxowZPPvssxx88KJ/oubMmdNjf+uvvz5Tp06tKRv75je/yZ/+9KeGx6SBzwx363o3Y377/CQiXqPIhm7LwnG+BvxLZpZf+ScAHdMdJDA+My+HojQD+Anwmcr+D0XE7pl5WaW9TlU/EzPzv6s7joh1gfEUwWNdEbEGUE6Z7Vk1np2BK1gY5JfP3wXYtWrTWRTPeX5l/3YUgXcAK1C8YeipAPUWYJfMnFUpZ7kb6ChrWT8i3pGZj2fmCZX+H6OqrCQzW82Cfxx4MDNrCn6jKM25huJTCIA9ImJEZr5S7qCeiPg08GmAt7/97S0Oc+k2cuTImqm8Xnut/H5r0W3lzFtPfffUT7N9v/nmm/z4xz/ubEcEX/ziF7s89sUXX2S//fbr/Oj8Xe96F9dff31nBvDKK69kt912IzN59NFH+dznPsdll13WZV/S4jBr1ixWW221zvYKK6ywyDHlbeVPlXpy9tlnM2rUKE4++WSWXXbZRfY/88wzNWVg5Sn/qm2xxRZceumlNfP0/8d//Mci90pIS7OBGnB3VSpxM/DxzPxH9caIWAbYq2rTa8BREXFU1bbVqbUX0PHX9XGgY8LhQyPiZYrSlAeBJyrXa+amyJ2o/ble0xFsA2TmVRFxFbBLN+eXi0nfDvyu9I5vLkWJCRQB6zKZ2d0Eyd/MzFmVaz8fEdOA6vrtt1H8DPraE8BxEbEHMA4YVTXmakMofv63N3uBzJwETIJi4ZvWh7r02nDDDWv+kD799NNsvPHGne3XXnutpsxj+PDhDdeJbrjhhjXt6hsou9tWPqfab3/725o61j333HOROtcOU6ZMqalT/Zd/+Zeaj9t32WUXNtpoI/7+9+L2kKuuuoq5c+cyfPjwRfqSFocHHnigJuBec801ueeeezrbyy+/PCuvvHJne86cOQ3fX9HhtNNO47zzzuNjH/sYG2+8MUOGDOHRRx/liiuu4FOf+hRHHnlk57G33nprl33stttunHfeeZ1TfM6bN4/Pfe5z/OxnP2tqLBocltTsc38YqAF3V7YETomIAzOzetaS0dTWV78FOKBOX9URw0SKLHIAGwLVK2C8FMXCKz/OzKsaHGc51drV7B330H3AXY5mdqxzvREUP4PupuUrL7/4UqndVRDcKxGxOvBXivr1RjRcH6++tcMOO9TMDHLTTTex++67d7bLMyNsvfXWDQel6667LmuuuWZnUP3Pf/6TJ554oubTiJtuummR8XTnlFNOqWl/+ctf7vbYp556qqbd1R+JZZZZWFE3f/58Zs6cWTN1oLQ4XXfddTWvh6222qpm6r7yjELTpk3jzTffpFnPPvvsInXXo0ePZp999ulsP/TQQ9x3332LnHvooYdyxhlndP5OeO211/jEJz6xyFzckgZuDfdYipv7dgGeq9q+D1Cegb+Vt1udd2pl5q8pbiL8JfAPipKUDitRZJyvjMYX3CmPp6vMa09j7tXzKcvMGaVN81vov1nfojbYfpOiDOYCijrwckbdt8yLyeGHH14TeP785z/vXCJ63rx5fP/73685vjzX78SJE2vq7iZPnlyz/1Of+lRNu/oj5gcffLBmeegRI0Zw0EFdv8z+/Oc/c/vtCz8E2Xzzzdlpp526fV5rr712TXvy5Mk105xdddVV3HvvwvfCI0aMcK5gDShnnnkm8+cv/HV91FFH1cyR/7Wvfa3m+PIc+SeccAKZ2fk4/PDDa/aPHj2aD3/4w4vcHLnOOutw3nnn1ZSHlH8PAHzlK1/hzDPP7Ay2p0+fzoc//GGD7SWcNdytG7AZ7sx8A7gqIj4B/Llq1xciYlJmPlBpTwdeocj0QnGD4VrZxN0jmXkTcBNARCxHcTPftsCPq/r9HNDIqhxPlNrv7uKYd/Vwfvkzwd2rS1L6QV+UZry/1N4+MztTpRFxGaXpB7V4bLDBBhx33HGd2eOnnnqKjTbaiG222YYHHnig5mbCrbfeuukZPb761a9y5plndq42OWnSJG644QbWWmstrrvuupobIL/3ve91uwBNeaGbnrLbUJSbjBw5srOm9Z577mHddddlyy235JVXXuHGG2+sucHs4IMPXiTwkBanhx9+mFNPPbVzIZm1116b++67jxtvvJENN9ywZpq+VlaBXX311bnqqqt48cUXeeCBB5g+fTprrbUWG220UU1N9yWXXLJIML/vvvty8skn12x7/PHHu31dnnDCCZ3lW9LSasAG3B0y89qI+CPQsW7zUIqbJA+u7F9Q2f+xyv63Ad+OiBOqS08iYihFIHgYMCkzp1W2fx64E/hrZs6vBPoPRcQTlet0RACNftZ8LUUWueOv94ciYpfMvLJyvQ/RfTkJwCVA9TxnJ0XEPZlZU+waEesBBwLLZmZfzrtUc3NoRKxZvnYDyvOdd0ZVEbEn0PU8bVosTjrpJP75z39yzjnnADBjxgwuvfTSmmPe+973ctFFF9VkwxsxYsQILr/8cnbbbbfOoPvuu+/m7rvvrjnu3/7t37qd3u/BBx+sGc9aa61Vs0JlV0aNGsU555zDQQcd1BnUz5w5s+Yj+Q7vf//7F1m1TxoIjj/+eFZbbTUOOeQQoJi+b6+99qo55vbbb2ffffdtaoaSaqNGjWKbbbbpct8555yzyKdU0PXNze973/t43/ve12U/1Tc7a/BakrPP/WGglpSUlQPKj0VEdZZ4IlVBHfB14KkoFry5uHKj4CyKTPmnKMpVOhxBMSXezIi4KyIui4hLKTLN1WURixawdSEznwF+XbUpgD9GsRjPX4HL6aGEojJ7ytVVmzYBHotisZqLIuLqiHgKeJiivKbROulGlVce6bjueRHRc1pxoWml9o0R8ceIuIliqsbB8v9uqTB06FB+/etfc8kll7Dvvvuy+uqrM2zYMFZeeWV22GEHTj/9dG666aaaG7iaMW7cOO69916+853vsOWWW7LSSisxfPhw1l57bT7xiU/w17/+le9+97vdnn/KKafUBBNf+MIXahbp6M748eN54IEH+MY3vsE222zDyiuvzNChQ1luueVYe+212XvvvTnrrLOYOnUqb32rtxFo4Jk/fz6HHnooe+21FxdeeCHPPvssc+fOZcaMGUydOpVjjjmGrbbaqscZRLrz1FNPceKJJzJ16lSeeuopXn/9dWbPns0jjzzC5MmT2WGHHTjkkEPqTgcoqTHR6rviPh1E1QqGFWMz87HSMZdTO13euZn5sar9HwJ+w8Ip73rygcy8vnLeHRQL3PTkRWCnzLyrcs5O1Ja5nJmZE6rGMooi071JN31dR+1sJP+SmZOrzh9JUb7SSCb4jMzsvJW8i2n9aoL7KFbFrC7m+2BmXlu1fw+gNr250Pkd0wT21E9lKsWbKRYfKruF4s1MdbFu9bnrUFtWMzUzd+pmPJ222GKLvOWW8v2hktrNjJe0+JT/xrfTO9/5zvzRj37UL9caP378rZm5Rb9crJ8MpkxjOct9UES8p6ORmddQTD93PEWw+wIwD3iDoq76Coqb+TbuCLYrvkix6uGfKQK9VyhKQl6iWDznpMo5dzU60Mx8kaJ85XuVPt+kuPlzMvBeitUmezp/FsWbi32A31X6eL3yfGZQBLM/qez/bKPjanDsHeU5fwNebbGPfwBbU4x9BsU0ho9Q/Dx2ZNHFdSRJ0gDnTZOtGxAZbqm3zHBLi8eS+sdRGgz6O8N92mn9szj07rvvvsRluAf8TZOSJEla/AbiG+zKZAxHUHyyvgrwMsUaKOcCPy+t3dJon6tRrGS9BUX1xGiKqaLnUFQs3FHp/7weFh6sYcAtSZKkQaUy+9xk4JDSrlUoyld3BI6MiN0zs9k7izdi0VJmKOLm9SqPAyimr94zM+fW63Aw1XBLkiRpMRlgNdwnURtsz6CY9OHhqm2bARdFRKvx7myKqaP/SHGvX/netp2BzzfSkQG3JEmSBo2I2AA4rmrTk8C4zNyLogRkStW+bSjWYGnGI8DuwMqZ+d7M3DMzPwSsCdxQOnanRjo04JYkSVJdAyjDfTi1MeykzJwOkJnzKbLf1Y5o5nlm5pOZeXm5VCQzX6aYga3a6zTAgFuSJEmDyQdK7Zu7aFdPw7d1RAzv7UUjYkWKVb6r/amRc71pUpIkST0aYHNkjyu1n6luZOYbETELGFXZNBwYCzzQzEUqi/H9gCJBvTLFrCVv6bgM8L/AmY30ZcAtSZKkgWSViKheXGNSZk6qao8sHT+7iz5mszDgpvR9o0ZSzEZSNh+YCJzstICSJEnqM/2Y4Z7e5MI3XQ2svK0vV3ocApwIfKQyLeCz9U6whluSJEmDyaxSe4UujilvK59TV2beUVnNcyiwBvAxihlMOmwGNLT8phluSZIk1TWAargfAFataq8J3N3RiIgVqC0hmQs82urFKjOfPAecGxEPArdX7d43IoZm5rye+jDDLUmSpMHkulJ7q1J7y1J7WiOrQTbo6VJ7KMXS7z0y4JYkSVJdA2ge7jOB6psVj4qIMZUxDgW+Vjr+jNLzmBgRWfWYUNr/o4jYLkqDiYjlge+U+n4JmF5vwJaUSJIkadDIzIci4lTgi5VNawH3RcSNwIbA+lWHTwPOavISn6RYsn1mRNwDvEhRorIpsFLp2J9VSk56ZMAtSZKkugZQDTfA8cBqwMGV9mhgz9IxdwD7Njp1XxdWBnboYf+vgG810pEBtyRJkgaVyk2Kh0TEb4Ajga0pgu5XgHsolmD/eWa+2UL3nwZ2pKgFX6PS71DgZYqbL28Azs7Mmxrt0IBbkiRJg1JmTgGmNHnORIqFa7rbfy5wbq8GVmLALUmSpB4NsKXdBx1nKZEkSZLayAy3JEmS6jLD3Toz3JIkSVIbmeGWJElSXWa4W2eGW5IkSWojM9ySJEmqywx368xwS5IkSW1khluSJEl1meFunRluSZIkqY3McEuSJKlHrjTZO2a4JUmSpDYywy1JkqS6zHC3zgy3JEmS1EZmuCVJklSXGe7WmeGWJEmS2siAW5IkSWojS0okSZJUlyUlrTPDLUmSJLWRGW5JkiTVZYa7dWa4JUmSpDYywy1JkqQeubR775jhliRJktrIDLckSZLqMsPdOjPckiRJUhuZ4ZYkSVJdZrhbZ4ZbkiRJaiMz3JIkSarLDHfrzHBLkiRJbWSGW5IkSXWZ4W6dGW5JkiSpjcxwS5IkqUeuNNk7ZrglSZKkNjLgliRJktrIkhJJkiTVZUlJ68xwS5IkSW1khluSJEl1meFunRluSZIkqY3McEuSJKkuM9ytM8MtSZIktZEZbkmSJNVlhrt1ZrglSZKkNjLDLUmSpB65tHvvmOGWJEmS2sgMtyRJkuoyw906M9ySJElSG5nhliRJUl1muFtnhluSJEmDUkTsGREXRMTTETEnIl6IiGsj4piIGNZin2+LiMMiYlJE3BQRj0fE6xExOyIejIhfRsQWzfRphluSJEl1DaQMd0QMBSYDh5R2rQLsWHkcGRG7Z+bzTXb/78Cx3ezboPI4PCJOyMz/bqRDM9ySJEkabE6iNtieAVwKPFy1bTPgoojoTbz7T+AK4Drg9artywAnRsQejXRiwC1JkqRBIyI2AI6r2vQkMC4z9wLGAVOq9m0DHNbCZa4EPgyskZm7ZeaOwEbAo6XjPt1IZwbckiRJqqtj8Zt2PxpwOLUx7KTMnA6QmfMpst/VjmjyqX4nM3fNzGsyMzs2ZubjwHdKx27YSIcG3JIkSRpMPlBq39xFO6vaW0fE8EY7z8xnetj9bKn9UiN9etOkJEmSejTAlnYfV2rXBMiZ+UZEzAJGVTYNB8YCD/TBtceX2tc2cpIZbkmSJA0mI0vt2V0cU942qotjmhIRuwKfqdo0C/hRI+ea4ZYkSVJd/ZjhXiUibqlqT8rMST0c39XAytuyi2MaFhF7A78DhlQ2zQEOrFN+0smAW5IkSQPJ9MzsaWGZWcCqVe0VujimvG1Wq4OJiCOAn7Ew2H4NOCAzr260D0tKJEmSVNcAmqWkXIu9ZmmcK1BbQjKXRafza/Q5fxP4BQuD7RnAhzPzsmb6MeCWJEnSYHJdqb1Vqb1lqT0tM+c2c4GIWCYiTgdOrNr8GLBdZt7YTF9gwC1JkqQGDKAM95nAgqr2URExpjLGocDXSsefUXoeEyMiqx4TSvuXBc4FjqnafDuwbWY+2NAPq8QabkmSJA0amflQRJwKfLGyaS3gvoi4kWIhmvWrDp8GnNXkJb4PHFDa9jzw467eEGTmgfU6NOCWJElSXQNoHm6A44HVgIMr7dHAnqVj7gD2zcwFNGdkF9t2a7KPGpaUSJIkaVDJzHmZeQiwN3AR8BzwJjCTosb7WGCrzPzn4hvlQma4JUmS1KMBttJkp8ycAkxp8pyJwMQe9k8AJvRiWIswwy1JkiS1kRluSZIk1TUQM9yDhRluSZIkqY0MuCVJkqQ2sqREkiRJdVlS0joz3JIkSVIbmeGWJElSXWa4W2eGW5IkSWojM9ySJEmqywx368xwS5IkSW1khluSJEk9GqhLuw8WZrglSZKkNjLDLUmSpLrMcLfODLckSZLURma4JUmSVJcZ7taZ4ZYkSZLayAy3JEmS6jLD3Toz3JIkSVIbmeGWJElSXWa4W2eGW5IkSWojA25JkiSpjSwp0RLh1ltvnR4Rjy/ucaglqwDTF/cgpKWUr7/B6x39eTGXdu8dA24tETJzzOIeg1oTEbdk5haLexzS0sjXn9Q/DLglSZJUlxnu1lnDLUmSJLWRGW5Ji9ukxT0AaSnm608NM8PdOjPckharzPQPvrSY+PqT+ocZbkmSJNVlhrt1ZrglARARH4qISRFxb0TMjIi5EfF8REyLiJMiYsM+uMZjEZEdj74Yt9SM6v9/pce8iJgVEbdHxI8iYqPFPVaAiNipNM7JTZ4/sXT+hPaMtH0iYnLpOezUR/2uU+r32r7oV+qKAbe0lIuINSLiGuBq4CjgXcAoYBgwBtgK+Crw94j4cUQML53vHy0tCYYAKwHvBT4P3BURBy3WES3hfAM++HTMxd3ux5LIkhJpKRYRqwPTgLWrNi8AbgGeBzYAOjLbywDHAutGxN6ZOb8/xyq1yZ+A1ygWgNkW6HhDORSYFBF/zMzZi2twwAvA+VXtmxfXQBajm4EVq9ovLK6BSK0y4JaWbr+iNth+BNgnM//esSEi9gfOAZarbBoPfA34Tn8NUmqjYzLzMYCI2JQiuBtW2TcS2A64crGMDMjMe4EDF9f1B4LMPB04fXGPY2m3JGef+4MlJdJSKiK2B3ap2jQfOKA62AbIzD9QlJRU+0pEvKXyMfCjpX07NlNiEhH7RcS1EfFyRLwWETdExN49HP+WiPh8RFxdqTGfGxEvRsT1EfHFiFihi3MWKXuJiLdGxPcj4qGImBMRd/Q0Ti35MvNO4O+lzaO7OjYiRkfEv1f+382IiDcjYnpEXBURn4qIYd2ct31EnBMRj1T+v8+NiOci4o6I+L+IOKa6bKuRGu6IWDEivl3pc05EPBURP42IVRt53lHYPyIuiIgnIuKNiHg1Iu6JiJMjYs1uzlukJCQiPhYR1/X0eq68/pLS0uSl55lV23us4Y6IQyPiFxFxU0Q8WRn73Ih4ISL+EhFfj4iVGvlZSO1ihltaeu1fal9TCTi68guKjPaISnsUsFNvBxARP6Kol622DXBRRHw0M88rHb8xcCGwbumckcD2lcenI2LPzPxHD5ceCfwVeE/Lg9eSqpyIeqZ8QER8EPgdxT0O1UYDH648jqyUXs2oOu9g4GygnCZcrfLYFJgAnAtMb2SwlUDyz8BmVZvXBI4G9qnsq3f++ZUxV1sWeHfl8emI+Hhm/qlOX2cBh5Y2d/t67kP/Vhln2SrA+yuPz0bEBzLz8TaNYalghrt1BtzS0mvLUvv67g7MzDci4jZgx9L55wMrUJSZdJgOTK1q39vDGD5fOf52imCjIyMXwHeBzj/QETEauBxYo+r8vwP/ANZhYfA8Drg0It6bmXO6ue6mla8vArdR/C5crptjtZSIiM2A6tlJnqG4x6H6mHcCF1NbU3xb5diNgPUq27alCJyrA9mJLAy25wM3UdQjrwq8HXhbC8M+hdpgu6NfKF6jB9c5vzzGZ4A7Kd5cb0fxBuStwHkR8b7MvL+Hvg6lsdfz1Mpx4yl+f3SorlVv1uvA/cBM4FWKf59NKYJuKErnTqN4EyL1OwNuaelVzs49V+f4Z0vtVTPzwIhYh9qyknszs9Ga01uAXTJzVuXj77tZ+Ed6/Yh4R1VG6kvUBttHVy/aERFfZ2Fd+TjgX4Cf9nDtK4CPZuZLlfOXbXDMWrL8JCKqb5rs+Lv4GvAvXbxpO4GFwXYC4zPzcihKM4CfAJ+p7P9QROyemZdV2utU9TMxM/+7uuOIWJciCH29kYFHxBrAYaXNe1aNZ2eK/+ddpiUjYhdg16pNZ1E85/mV/dtRvBEPisB4IvDxHobU0Os5M0+o9P8YVWUlTfzeKPs48GBmzi09v+HANRSffAHsEREjMvOVFq+z1DPD3ToDbmnp1exvzvLxfTGN1zczcxZAZj4fEdOA6nrPtwEdAfe+pWvvFhHVwcIIau1F9wH3fIqA/aXODrvPhmvJNr6LbTcDHy+XJUXEMhT/rzq8BhwVEUdVbVu91NdeQEfA/TiwfuX7QyPiZeA+4EHgicr1mrk5cCdq/45f0xFsA2TmVRFxFbX3alTbt9R+O/C7UlA1l6K8BIqAdZnMXNBNf828nvvSE8BxEbEHxZvtUVVjrjaE4ud/exvGIPXIgFtaej1P8cepwxrdHVhRDiT6YmquW0rtl0rt6j+aY6u+D+Ajdfoe28O+xzpmppC6sCVwSkQcmJlvVm0fTVFe0eEtwAF1+qr+fziRIoscFNNtnlq176UobjD+cWZe1eA4315qd1W+dQ/dB9zl18iOXR610AiKn0F3r/1mXs99IoqpTf/Kovd1dOet9Q+R+p6zlEhLr/J8vu/v7sCIWA7YvM75Tau+oayiL+f2fksP+xa5EU5LrbHA8hRBaXVZ1T7A90rHtvJ5euf/w8z8NcVNhL+kuPeg+lOilSgyzldG4wvuNPKpU09j7tXzKWvz67k736I22H6TogzmAoqa8HJG3ZqIXggXvmmZAbe09Lqw1P5gZRaQrnyK2pKNWSy8MbK/Voh7rOr7ucBbMzN6eKzTQ1/dfSSupVBmvlHJKn+itOsLEbFhVXs6UF3/+wywTJ3/hzuVrnVTZh6RmetR1EW/Ezi81O/nGhz6E6V2VzN1vKuH88tTeu5e57lEH38y1Be/O8qJgu0z8wOZeUClJrynmzylfmPALS2lMvN6iuXcOwwBLoiI6lkaiIh9gB+UTv9hZr5a+b58g1crMy004pKq74cDP47SnNtR2Coi/l8UC/ZIDcvMa4E/Vm0aSnGTZMf+BaX9bwO+HaU5tyNiaBTzZ58REVtXbf98ROwQEUMq/b2RmQ9RTDFYXaZRLt/qzrXUZpE/VLkRsuN6H6L7chKofU0BnBRdzLkdEetFxNci4j8aHFejan53dHXtBpTnO+9cFTQi9gR2bqFPdaG/sttLaobbGm5p6fZJiinEOv7QrQ/cExE3s3Bp93Glc66kmOIL6Lw5aiawcmXTBlEsIvMIRQbrF1WzNPTGDyjmKF6tauz7RsTtwMsUtaXvofhoHuCOPrimlj7/BexR1f5YRPx31YJQEyluhOworfg6cERE3E1xE+VqFJnmjv1nVfV1BMVUdS9HxOMUGfL5FNP6Vd9DcV8jA83MZyLi1xSvBSjKJf5YuVkxgK3ooYQiMy+LiKtZOC3gJsBjEXELxet/RYpa847fD2c2Mq4m3E/tNIw3VF7PbwI3ZOYPG+hjWqmPGyPieopZZ7ak/z6Bk3pkwC0txSp/sLemWLp9h8rmZYCtuzoc+Bnwrx3ThlU5g9rVKDdl4VzX1/bRWKdXZiX5AwtrNlei+wV45vXFdbV0ycxpEXEFC6fLW4Yiy/2xyv77K5/6/IaFU96tyqILx3To6v/hW4GNK4+yF4FmMsn/CryXIliG4u96xzR4LwLXsehsJNUOBH7PwkzwUIo686709WvqF9QuwLV25dGMEynq7Tve8I9g4cwzt1CUzTRaE686ltTsc3+wpERaymXm05m5I8VHz2dQZNdeovjjOp3i5sgfAu/OzM92M33eN4BvUixE80Ybx3oXRZDyWYpFcJ6jqOeeS5EtvJZiLu5tM/Psdo1DS7z/KrUPiojOVUkz8xqKT36Op/g/9wLF6+UNirrqKyhu5tu4UrrV4YsU/z//TBEIvkKR4X6JYvGckyrn3NXoQDPzRYo65u9V+nyT4nUxmSIQv6PO+bMo3lzsQ1Ha8ihFqcc8YAbF6/8nlf2fbXRcDY79jxRvZP5GsVhNK338gyJB8DuK8c6l+HTtexSzrrzWJ4OVeiky/bRFkiRJ3dt8883zL3/5S79ca8UVV7w1M7fol4v1EzPckiRJUhtZwy1JkqS6rOFunRluSZIkDUoRsWdEXBART0fEnIh4ISKujYhjylN2NtnvLhHxnxHxx0qfWfW4ttn+zHBLkiSproGU4Y6IoRQ3Bx9S2rUKxQ2zOwJHRsTumfl8C5f4OfCOXg2yihluSZIkDTYnURtszwAuBR6u2rYZcFFE9DbendnL881wS5IkqWcDaRXIiNgAOK5q05PA5pX1GoYAF1IsUAXFvPKH0fzCTacCj1FMjTmUYsrMlpnhliRJ0mByOLUx7KTMnA5QWZjtpNLxRzR7gcw8JTP/kJlPtT7MhQy4JS3VIuKx0s0wGRFvRMSjEfGriHjv4h5jh47xdbG94zmssxiG1WsRMbEy/olNnDOhcs7kPhrDtZX+duqL/upca53KtR5r97WkvtSR5W73owEfKLVv7qJd/bty64gY3pvn3lsG3JJUuJziI8czKVYKXI7iY8ibI+Lji3Ng/aWVwFeSFoNxpfYz1Y3MfAOYVbVpODC2zWPqkTXcklT4XmZe29GIiOUp7lI/BJgUEVdkZq9vnGmTDwPDgKcX90AkqR+MLLVnd3HMbGBUVXtUF8f0GzPcktSFzHwd+CzFL+0RwG6Ld0Tdy8xHMvP+zHxzcY9F0pKrH0tKVomIW6oen643tAa2LVKO158MuCWpG5n5CvBgpfkOqK2/jYihEfGViLgzImZHxKzq8yNi64j4bUQ8FRFzK4snXBwR7+/umhGxcUT8ISJmVvq8LSKO7GmcPdVwR+GjEfGniHi+Mo6nI+LqiPhc1XEJnFBpnlCqaZ9Y6vMtEXF8RNwcES9HxOsRcW+lJGXFbsY4rPKz+nsUNfLPRcRZEdFn89xWXeuAiPhlZUyzKtd7OCJOj4i1Gzj/gxFxVUS8GBGvRsT1EbFPD8dHRHw8Iq6IiOlRLL7xRET8vKt/E0l1Tc/MLaoek0r7Z5XaK3TRR3lb+Zx+ZUmJJPXsrZWvc0rbAzgf2B24Dvg78PbOnRFfBk6uNG8DbgDWAvYE9oyIz2Tmz2s6jNgR+BOwPPAAcDuwBvCziHhXswOv3CT0e2AfYD5wI/AEsBrwHuBDwI8rh58JvBfYFLgTuKOqq87vI2Itinr3dwEvVJ7XG8CWFAH7/hGxU2a+WHXOMsAFFNN0vQFcA7xCUQoznmLu3L70u8p1/g5cBSxbeW7HAB+NiO0z88Fuzt0f+BxwL8W/xTuA7Snm8v1yZv5P9cFRrGT3W+AjwOvALcA/KX6+RwIHRMSumXlLnz5DaTGIATItIMXvx1Wr2msCd3c0ImIFaktI5tLLaf16y4BbkroRxQwlHTfa3FHa3RFcvzszqxdaICJ2B35AcSPPRzJzWtW+7YE/AqdHxNSOwC+KmvFfUwTb3wW+kZlZ2bdj5ZxmnUwRbD8I7JuZ91eNYwhF8A9AZk6oZLI3BS7MzInlzqL4a3suRbD9Y+Brmfla1fgnAYcCpwATqk49liLYfhrYqePnFRHLAWcDn2zhufXkYGBKx9gq1xpK8YbgmxTz647v5twvAF/NzB9Unbs3xRuGkyLiqsy8q+r4EymC7euAQ6qnEKt8gnAa8NuIGJeZ8/rk2Um6jtqZSrYCLqtqb1k6flpmzm37qHpgSYkklUTEqEoJwQUUvyfvAKZ2cejXy8F2xX9Wvh5ZHWwDZOZfKYK0YcDRVbsOpMjSPAJ8qyPYrpwzFfhpk89hVYoa9AUUQf/91fszc35mXtxMnxTZ/G0pMuXHVQe0lZr3zwDPA4dERHV26V8rX79Z/fOqzCRwDEVmuM9k5rnVY6tsm5eZ36J4E7RrRIzo5vRbq4PtyrmXAOcAQ4DPd2yPiJUpAvRXgYPK8/Vm5o8psvfr0X2ALw0a0X813PWcSfG7rcNRETGmMsahwNdKx59Reh4dMzJ1PCb04sfSEANuSSr8ORbOcz0TuIgiu30bsF9mLujinD+UN0TEKhTZlZcpphfsSkfwvm3Vth0rX39bWbih7Kz6T6HGhyiC+hsy894mz+3OHpWv53f188jM2RQlFUOpZJgqJSjrUvxxPKeLc56n+59TyyLinRHxhYj4UaWee3IUc3YPpfjbt343p57dzfaOn/9OVds+SPGJxNTK8+hKV//WknohMx+i+KSqw1rAfRExBbiP2je402j+9ycR8ZOIuDEibmTR3/Wbd+yrPHq8zwYsKZGkDpcDz1W+n0ORCf0L8OfqbHOV5ytZ3bKxFPXdbwXm1cnWjKn6fq3K1+7qDB/rqaMudNyMeH+PRzVn3crXkyPi5B6PXPjcOp7XMz18pPtYbwfWoZLd+glF/XRPP/y3drO93s9/raptHT+PPaOLBYlKxtTZLw1oTWSf+8vxFPejHFxpj6aqTK7iDopyuq4SJvW8C9i6m30jSvsu6+a4TgbcklSomYe7Ad2VQQypfH0JuLBOH9ObuN5indKqouO5TaV+kPx4e4fSreOAoyjeMH0J+BvFm6M5ABHxN4psc6uRQ/W/Q8fP4wGKMpueTKuzX1ITKvdEHBIRv6F4g701RdD9CnAPxc3TPx8o06UacEtS33qy8vXNzJzQxHkdi9as083+ZldJ6wh4N2zyvJ50PLffZ+bpDZ7T8bzeFhHDu8lyr9PrkS10UOXr0Zk5pYv93ZWS1BtLx/bqFe06fh53N/lvLQ1KAyzDDUDldd7Va72ncyYCE+scs1PLg+qCNdyS1Icy82mK6alWiYidmji1o9b345UZRMoOaXIo1wBvAttFxEYNntMRDHeXjPlT5etB3exfRGY+SVGmsQzw8fL+yo1OuzTaXwNWrnx9srwjInahfmlHdz/nju3XVm27iuJnvHNEjGx8iJKWNgbcktT3vlX5enZE7FreGRHDI2KfiKi+ke484FmKDOzEqEolRbFQzmebGUDlJr6fUvyePz8i3lkaw5DKdHfVOrLR3QXoFwK3AjtGxE8rs3TUiIh1I+LY0uYfVb7+d0SsW3XsssDpdL1oRas6atY/W5n/u+Na69HYTC9bRsQXqzdExB4U0x3OZ+G85WTmPynGPxK4OCLGlTurzHhzZESs1uwTkQaaATRLyaBjSYkk9bHMvCiKhW9OAi6PiAcp6nznAmtTlHmsRBFE31A557WIOJRiGrlvAgdGRMfCNztQ3JH/xfK16vgqxZR0ewD3RsQNwFMUC0ZsXPla/dftcuA14CMRcR3FFIXzgYsz8+LMXBAR+1HMCX40cHBE3FnpcxWKucnfSbHwS3XJyWnArhQzB9wbEddQTKX3fmA54Ff03Vzc36WYvvBo4IOVn+HKFLPA3EBxY+x2PZz/I+AHlWnC7q08p+0r+47PzDtKxx8PvA34KHBPRNxBkdFfjuLfeiNgeOXrP3v31CQNVma4JakNKisSvo9i/tchFGUTu1GsfjaV4sa+c0vnXANsA1wMrA7sVzn+2Mz8UgtjmAPsDRxGsVDEeyjm+x4H3EWxIE318c9RLFBzLbAJcDhwBLB51TFPUSwy8TmKlTDfDRxQ6fsVigV/PlLqdz6wL/BvFDdb7kwxpd51wBb04QpwmXlDZXyXUryp2ZdiZpFvU/z8691A9YfKcTMoZjzYjOLGy/0zc5GZWTLzzcz8WOU6UyiC730pgvqhFFMh7k/x5kUa1Mxwty66nu1KkiRJKmyxxRZ5yy239Mu1IuLWzNyiXy7WTwy4JUmS1KOIuIyidKw/TM/M3fvpWv3CgFuSJElqI2u4JUmSpDYy4JYkSZLayIBbkiRJaiMDbkmSJKmNDLglSZKkNjLgliRJktrIgFuSJElqIwNuSZIkqY3+P7BQAcSej3MWAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "1302335f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.8841\n", + "Macro F1: 0.8019\n", + "Macro Precision: 0.7568\n", + "Macro Recall: 0.8841\n" + ] + } + ], + "source": [ + "from sklearn.metrics import f1_score\n", + "from sklearn.metrics import recall_score\n", + "from sklearn.metrics import precision_score\n", + "from sklearn.metrics import balanced_accuracy_score\n", + "\n", + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(yTest.values, bnnPreds), 4 ))\n", + "print( \"Macro F1:\", np.round( f1_score(yTest.values, bnnPreds, average='macro'), 4 ))\n", + "print( \"Macro Precision:\", np.round( precision_score(yTest.values, bnnPreds, average='macro'), 4 ))\n", + "print( \"Macro Recall:\", np.round( recall_score(yTest.values, bnnPreds, average='macro'), 4 ))" + ] + }, + { + "cell_type": "markdown", + "id": "a5f7d01f", + "metadata": {}, + "source": [ + "#### Hypothesis Testing" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "e40860ea", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import stats\n", + "\n", + "def tTest( sample1, sample2 ):\n", + " \n", + " t_stat, p_value = stats.ttest_ind( sample1, sample2 )\n", + "\n", + " # True = significant difference, False = not significant\n", + " alpha = 0.05\n", + " if p_value < alpha:\n", + " return True\n", + " else:\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "f21b1255", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total buildins: 215629\n", + "Predications made: 207780\n", + "% of buildings with prediction: 96.36\n" + ] + } + ], + "source": [ + "ixs = []\n", + "bnnPreds = []\n", + "predsMade = 0\n", + "n = preds.shape[0]\n", + "\n", + "noPrediction = []\n", + "\n", + "for i in range(n):\n", + " \n", + " ix = np.argsort( -means[i,:] ) # sort in descending order\n", + " \n", + " sample1 = preds[i, ix[0], :]\n", + " sample2 = preds[i, ix[1], :]\n", + " significant = tTest( sample1, sample2 )\n", + " \n", + " mx = np.max( means[i,:] )\n", + " ix = np.where( means[i,:] == mx )\n", + " \n", + " if ( significant and (mx >= 0.65) ):\n", + " predsMade += 1\n", + " bnnPreds.append(ix[0][0])\n", + " ixs.append(i)\n", + " else:\n", + " noPrediction.append(i)\n", + " \n", + "print(\"Total buildins:\", n)\n", + "print(\"Predications made:\", predsMade)\n", + "print(\"% of buildings with prediction:\", np.round( (predsMade/n)*100., 2) )" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "671e44b4", + "metadata": {}, + "outputs": [], + "source": [ + "cm = confusion_matrix( yTest.values[ixs], bnnPreds, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "cd48acd5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "8e5dfab2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.8991\n", + "Macro F1: 0.8253\n", + "Macro Precision: 0.7808\n", + "Macro Recall: 0.8991\n" + ] + } + ], + "source": [ + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(yTest.values[ixs], bnnPreds), 4 ))\n", + "print( \"Macro F1:\", np.round( f1_score(yTest.values[ixs], bnnPreds, average='macro'), 4 ))\n", + "print( \"Macro Precision:\", np.round( precision_score(yTest.values[ixs], bnnPreds, average='macro'), 4 ))\n", + "print( \"Macro Recall:\", np.round( recall_score(yTest.values[ixs], bnnPreds, average='macro'), 4 ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27e1d3e6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5006cb2c", + "metadata": {}, + "outputs": [], + "source": [ + "font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 16}\n", + "\n", + "plt.rc('font', **font)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "538cf18c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15,5))\n", + "\n", + "ax1.scatter( [1,2], [40,60], s=52 )\n", + "ax1.set_ylim( 0, 100 )\n", + "ax1.set_xlim( 0, 3 )\n", + "ax1.set_title( 'Neural Network')\n", + "ax1.set_ylabel( 'Probability (%)' )\n", + "ax1.xaxis.set_tick_params(labelbottom=False)\n", + "\n", + "ax2.errorbar( [1,2], [40, 60], yerr=[5,5], fmt=\"o\" )\n", + "ax2.set_ylim( 0, 100 )\n", + "ax2.set_xlim( 0, 3 )\n", + "ax2.set_title( 'BNN' )\n", + "ax2.xaxis.set_tick_params(labelbottom=False)\n", + "\n", + "ax3.errorbar( [1,2], [45, 55], yerr=[15,15], fmt=\"o\" )\n", + "ax3.set_ylim( 0, 100 )\n", + "ax3.set_xlim( 0, 3 )\n", + "ax3.set_title( 'BNN' )\n", + "ax3.xaxis.set_tick_params(labelbottom=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "23e0faeb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 [0.05852261 0.10924447 0.83223292] [0.00330635 0.00526524 0.00806944]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Probability')" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range( means.shape[0] ):\n", + "\n", + " mx = np.max( means[i,:] )\n", + " ex = np.max( errors[i,:] )\n", + " if ( mx > 0.7 and yTest.iloc[i] == 2 and ex < 0.1 ):\n", + " print( yTest.iloc[i], means[i,:], errors[i,:] )\n", + " x = [1,2,3]\n", + " y = means[i,:]*100.\n", + " e = errors[i,:]*100.\n", + " break \n", + " \n", + "plt.scatter( x, y ) \n", + "plt.errorbar( x, y, yerr=e, fmt=\"o\")\n", + "plt.ylim(0,100)\n", + "plt.title('Probabilistic Building Type Prediction')\n", + "plt.ylabel('Probability')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36740e36", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57530152", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/EDS_paper/Building_Classification.ipynb b/book/EDS_paper/Building_Classification.ipynb new file mode 100644 index 0000000..ee8d52c --- /dev/null +++ b/book/EDS_paper/Building_Classification.ipynb @@ -0,0 +1,64732 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
Flood Risk Machine Learning Tutorial
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Urban flooding causes billions of dollars in damages annually, with severe flood events becoming more frequent and destructive as our climate changes. While extreme weather is a primary driver of flooding, its consequences depend on the interconnectedness of urban systems - referred to as the Urban Multiplex, which includes the power grid, transportation network, natural surface water and groundwater systems, sewerage and drinking water systems, intertwined with the socioeconomic and public health sectors.

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

This tutorial introduces machine learning concepts through an application in one component of the Urban Multiplex - building classification. Accurate and standardized information about buildings (e.g. classification into residential, commercial, industrial) is critical for assessing the number of people affected by flooding, the propagation of shocks throughout the economy, and for forecasting detailed socioeconomic risk from flooding. Yet, a major discrepancy exists in the way data about buildings are collected across the U.S. There is no harmonization in what data are recorded by city, county, or state governments, let alone at the national scale.

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

In this tutorial we explore multiple means of classifying a building (residential/classification/industrial/etc.) based on its features (location, square footage, proximity to other resources, etc.). Moreover, we are interested in how accurate our machine learning models can be using only free and open data. The data used in this tutorial is consistently and openly available for the continental Unites States. We use the state of North Carolina as an expemplar in this tutorial.

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

It is currently unknown which building features (e.g. location, square footage, etc.) have the most predictive power. As a result, part of this tutorial focuses on the notion of \"feature importance\". We've generated a set of features that may be useful for predicting building type. As we work through the examples, we'll also investigate how important each of these features actually is to predicting a building type.

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import required Python libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import joblib\n", + "import pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "#from pretty_confusion_matrix import pp_matrix_from_data\n", + "\n", + "import tensorflow as tf\n", + "from keras.utils import to_categorical\n", + "from tensorflow.keras.layers import Dense\n", + "from keras.callbacks import EarlyStopping\n", + "from tensorflow.keras.models import Sequential\n", + "\n", + "from sklearn import preprocessing\n", + "from sklearn.metrics import f1_score\n", + "from sklearn.metrics import recall_score\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.metrics import precision_score\n", + "from sklearn.metrics import balanced_accuracy_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.inspection import permutation_importance\n", + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setup and Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# North Carolina data\n", + "url = \"https://github.com/geo-smart/flood-risk-ml-tutorial/blob/main/North_Carolina_Buildings.zip?raw=true\"\n", + "\n", + "# read data into a Pandas dataframe\n", + "rawDf = pd.read_csv(url, compression=\"zip\")\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "rawDf = rawDf.iloc[:, 3:] \n", + "df = rawDf.copy( deep=True )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The data\n", + "\n", + "Our goal is to predict a building's type (Residential/Commercial/Industrial) based on known features of the building (area, location, etc.). To accomplish this, we gather as many building features as possible from open datasets. We begin with as large a set as possible and will work towards identifying each feature's actual importance. \n", + "\n", + "Our sources of building features are [Open Street Map (OSM)](https://www.openstreetmap.org), the [Multi-Resolution Land Characteristics Consortium (MRLC)](https://www.mrlc.gov/data/type/urban-imperviousness), [United States Census' County Business Patterns (CBP)](https://www.census.gov/programs-surveys/cbp/data/tables.html), and the [United States Census' American Community Survey (ACS)](https://www.census.gov/programs-surveys/acs/data.html).\n", + "\n", + "We've preprocessed the data to spatially align all the features. For example, we used building longitude and latitude provided by OSM to determine which county the building resides in. We've then gone to the ACS and looked up socio-econimic data such as median income and housing density. The resulting machine learning ready data looks like this:\n", + "\n", + "| Column | Description | Source |\n", + "| :---------: | :---------- |:-------|\n", + "| X | X coordinate of the building in the EPGS:5070 system | OSM |\n", + "| Y | Y coordinate of the building in the EPGS:5070 system | OSM |\n", + "| Area | Area of building in square meters | OSM |\n", + "| MedianIncomeCounty | Median income of the county in which the building resides | ACS |\n", + "| HousingUnitsCounty | Number of housing units in the county in which the building resides | ACS |\n", + "| HousingDensityCounty | Number of housing units in the county divided by the number of people residing in the county where the building resides | ACS |\n", + "| Impervious | Percentage of the area surrounding the building that is comprised of impervious surfaces such as roads and other paved surfaces. Value provided is the mean area weighted average of imperviousness underneath the building footprint | MRLC |\n", + "| AgCount | Number of agricultural businesses in the county in which the building resides | CBP |\n", + "| CmCount | Number of commercial businesses in the county in which the building resides | CBP |\n", + "| GvCount | Number of government buildings in the county in which the building resides | CBP |\n", + "| EdCount | Number of educational buildings in the county in which the building resides | CBP |\n", + "| InCount | Number of industrial buildings in the county in which the building resides | CBP |\n", + "| OsmNearestRoad | Type of nearest road to the building | OSM |\n", + "| BuildingType | Building classification | FEMA |\n", + "\n", + "The last column, 'BuildingType', is what we are trying to predict. [USA Structures](https://gis-fema.hub.arcgis.com/pages/usa-structures) is an open source building inventory provided by the Department of Homeland Security, FEMA, Oak Ridge National Laboratory, and the U.S. Geological Survey. The building inventory contains a Building Occupany Type, which was determined through census and U.S. mail data. Currently, Building Occupancy Type values are availble for nine states. However, we note that not every building in those nine states has a manually identified occupany type. We are using North Carolina Building Occupany Type data as our \"ground-truth\". If we can predict known building types well, the machine learning models can be reliably deployed on unknown buildings and in states where Building Occupany Type is unavailable." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadBuildingType
0-83.26115735.48449121.95105522429.026649.00.6181774.000000222860351804residentialResidential
1-83.26097235.484583255.15555122429.026649.00.6181775.666667222860351804residentialResidential
2-83.26041735.484768184.62537622429.026649.00.6181777.000000222860351804residentialResidential
3-83.25680635.492268434.20542822429.026649.00.61817722.500000222860351804residentialResidential
4-83.15643535.493657138.36151522429.026649.00.6181774.000000222860351804secondaryResidential
\n", + "
" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty HousingUnitsCounty \\\n", + "0 -83.261157 35.484491 21.951055 22429.0 26649.0 \n", + "1 -83.260972 35.484583 255.155551 22429.0 26649.0 \n", + "2 -83.260417 35.484768 184.625376 22429.0 26649.0 \n", + "3 -83.256806 35.492268 434.205428 22429.0 26649.0 \n", + "4 -83.156435 35.493657 138.361515 22429.0 26649.0 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount EdCount \\\n", + "0 0.618177 4.000000 22 2860 3 51 \n", + "1 0.618177 5.666667 22 2860 3 51 \n", + "2 0.618177 7.000000 22 2860 3 51 \n", + "3 0.618177 22.500000 22 2860 3 51 \n", + "4 0.618177 4.000000 22 2860 3 51 \n", + "\n", + " InCount OsmNearestRoad BuildingType \n", + "0 804 residential Residential \n", + "1 804 residential Residential \n", + "2 804 residential Residential \n", + "3 804 residential Residential \n", + "4 804 secondary Residential " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rawDf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building types and their counts\n", + "\n", + "Residential 976690\n", + "Commercial 64029\n", + "Industrial 16722\n", + "Assembly 7323\n", + "Education 6457\n", + "Government 4910\n", + "Agriculture 1651\n", + "Utility and Misc 362\n", + "Name: BuildingType, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "# building type distributions\n", + "print( \"Building types and their counts\" )\n", + "print()\n", + "print( df['BuildingType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building type counts as compared to the largest class\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "Residential 1.000000\n", + "Commercial 15.253869\n", + "Industrial 58.407487\n", + "Assembly 133.372935\n", + "Education 151.260647\n", + "Government 198.918534\n", + "Agriculture 591.574803\n", + "Utility and Misc 2698.038674\n", + "Name: BuildingType, dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print( \"Building type counts as compared to the largest class\" )\n", + "print()\n", + "976690/df['BuildingType'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

We have significant class imbalance. For example, the residential buildings outnumber the commercial buildings 15 to 1. The residentail buildings outnumber the agricultural buildings nearly 600 to 1. This can be a challenge for machine learning classification algorithms. The algorithm may not \"learn\", but rather obtain high accuracy simply by picking the majority class.

\n", + "\n", + "

We will look at techniques for dealing with such imbalance

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use case driven model selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The type of classification model needed depends on the application. For example, during a flood emergency, first responders may only be interested in identifying residential buildings. In this use case, binary classification (Residential vs. non-Residential) is sufficient. \n", + "\n", + "However, for ecomomic applications, multiclass classification is needed, i.e. Residential vs. Commercial vs. Industrial vs. ...\n", + "\n", + "We provide examples of both scenarios." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Binary classification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we need to preprocess the data and prepare it for machine learning. For this binary classification example, all non-Residential building types are changed to \"Other\".\n", + "\n", + "

The OsmNearestRoad and OrnlType are currently text. We need to encode these as integers before proceeding with machine learning. We also want to scale our columns to a common axis so that columns with large values don't bias the learning.

\n", + "\n", + "

We'll use a standard scaler, which scales based on the standard scoreas: z = (x - u) / s

\n", + "

Where u is the mean of the training samples and s is the standard deviation of the training samples

" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_38438/2345889075.py:21: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " df.iloc[:, nCols-1] = le.transform( df.iloc[:, nCols-1] )\n", + "/tmp/ipykernel_38438/2345889075.py:26: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " df.iloc[:, nCols-2] = le2.transform( df.iloc[:, nCols-2] )\n" + ] + } + ], + "source": [ + "df.loc[df[\"BuildingType\"] != \"Residential\", \"BuildingType\"] = 'Other'\n", + "\n", + "df = df.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "# preprocess the data - scaling\n", + "scaler = preprocessing.StandardScaler()\n", + " \n", + "columns = ['X', 'Y', 'Area', 'MedianIncomeCounty', \n", + " 'HousingUnitsCounty', 'HousingDensityCounty',\n", + " 'Impervious', 'AgCount', 'CmCount', 'GvCount',\n", + " 'EdCount', 'InCount']\n", + "df[columns] = scaler.fit_transform(df[columns])\n", + "\n", + "# preprossing - drop rows with NaN values\n", + "df = df.dropna()\n", + " \n", + "# preprocessing - change output labels from text ('Agriculture') to integers\n", + "nCols = df.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( df.iloc[:, nCols-1] )\n", + "df.iloc[:, nCols-1] = le.transform( df.iloc[:, nCols-1] )\n", + " \n", + "# preprocessing - change output labels of nearest road type from text to integers\n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( df.iloc[:, nCols-2] ) \n", + "df.iloc[:, nCols-2] = le2.transform( df.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Some machine learning algorithms, e.g. Random Forest, don't require scaling. We'll create a second dataframe with OsmNearestRoad and BuildingType encoded, but without the scaling

" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_8062/3183393460.py:10: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + "/tmp/ipykernel_8062/3183393460.py:14: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )\n" + ] + } + ], + "source": [ + "rawDf.loc[rawDf[\"BuildingType\"] != \"Residential\", \"BuildingType\"] = 'Other'\n", + "\n", + "rawDf = rawDf.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "rawDf = rawDf.dropna()\n", + "\n", + "nCols = rawDf.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( rawDf.iloc[:, nCols-1] ) # ornl type\n", + "rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( rawDf.iloc[:, nCols-2] ) # nearest road type\n", + "rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Our \"BuildingType\" values are now encoded as 0 or 1. We can use the inverse_transform to see which one is which.

" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadBuildingType
288174-1.270415-0.523746-0.177327-1.327048-0.9945000.052691-0.400575-1.277726-1.082940-1.048588-1.134246-1.06832941
77140-0.929758-0.9868400.0192600.139001-1.071597-1.424272-0.601864-0.189389-1.054971-1.048588-1.045623-0.77060541
9161960.8870790.456834-0.1311291.3022840.963812-0.3396211.0755421.6657310.8805030.6435561.0978171.04193441
10300920.6149310.955246-0.1394540.061124-0.7450510.324158-0.147101-1.277726-0.785143-0.625552-0.540825-1.00226541
3941912.0859931.4164511.711974-3.202818-1.4738071.321234-0.5347680.503189-1.337897-1.471624-1.308883-1.53616141
\n", + "
" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty HousingUnitsCounty \\\n", + "288174 -1.270415 -0.523746 -0.177327 -1.327048 -0.994500 \n", + "77140 -0.929758 -0.986840 0.019260 0.139001 -1.071597 \n", + "916196 0.887079 0.456834 -0.131129 1.302284 0.963812 \n", + "1030092 0.614931 0.955246 -0.139454 0.061124 -0.745051 \n", + "394191 2.085993 1.416451 1.711974 -3.202818 -1.473807 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount \\\n", + "288174 0.052691 -0.400575 -1.277726 -1.082940 -1.048588 \n", + "77140 -1.424272 -0.601864 -0.189389 -1.054971 -1.048588 \n", + "916196 -0.339621 1.075542 1.665731 0.880503 0.643556 \n", + "1030092 0.324158 -0.147101 -1.277726 -0.785143 -0.625552 \n", + "394191 1.321234 -0.534768 0.503189 -1.337897 -1.471624 \n", + "\n", + " EdCount InCount OsmNearestRoad BuildingType \n", + "288174 -1.134246 -1.068329 4 1 \n", + "77140 -1.045623 -0.770605 4 1 \n", + "916196 1.097817 1.041934 4 1 \n", + "1030092 -0.540825 -1.002265 4 1 \n", + "394191 -1.308883 -1.536161 4 1 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Other', 'Residential'], dtype=object)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "le.inverse_transform([0,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['secondary', 'residential', 'tertiary', 'motorway_link', 'primary',\n", + " 'trunk', 'motorway', 'trunk_link', 'tertiary_link', 'primary_link',\n", + " 'secondary_link'], dtype=object)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "le2.inverse_transform( rawDf.iloc[:, nCols-2].unique() )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 976689\n", + "0 101453\n", + "Name: BuildingType, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['BuildingType'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 1.00000\n", + "0 9.62701\n", + "Name: BuildingType, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "976689/df['BuildingType'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

We still have nearly 10 times more residential buildings as \"other\" buildings. Let's proceed with various classification techniques and see how we do.

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Random Forest does not require data values to be scaled. Let's apply Random Forest to our unscaled dataframe.\n", + "\n", + "The Random Forest model is a means of combining multiple decision trees to create a “forest.” A decision tree is like a flowchart that draws a clear pathway to a decision (prediction) based the training data. The algorithm determines tree branching based on feature values with each branch of the decision tree offering a different possible outcome." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadBuildingType
399823-77.10800936.44199187.04535322486.09803.00.45485350.0412660812050
792689-78.90393535.740231258.59120041189.0462582.00.40957835.01191057233027781778041
786739-78.82865735.766806191.90600041189.0462582.00.40957847.01191057233027781778041
140081-80.63041734.967917188.16218635468.083788.00.35165622.044139666311532441
479781-78.06088033.902268254.05243430529.088330.00.64619350.54480353113218841
\n", + "
" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty \\\n", + "399823 -77.108009 36.441991 87.045353 22486.0 \n", + "792689 -78.903935 35.740231 258.591200 41189.0 \n", + "786739 -78.828657 35.766806 191.906000 41189.0 \n", + "140081 -80.630417 34.967917 188.162186 35468.0 \n", + "479781 -78.060880 33.902268 254.052434 30529.0 \n", + "\n", + " HousingUnitsCounty HousingDensityCounty Impervious AgCount \\\n", + "399823 9803.0 0.454853 50.0 4 \n", + "792689 462582.0 0.409578 35.0 119 \n", + "786739 462582.0 0.409578 47.0 119 \n", + "140081 83788.0 0.351656 22.0 44 \n", + "479781 88330.0 0.646193 50.5 44 \n", + "\n", + " CmCount GvCount EdCount InCount OsmNearestRoad BuildingType \n", + "399823 1266 0 8 120 5 0 \n", + "792689 105723 30 2778 17780 4 1 \n", + "786739 105723 30 2778 17780 4 1 \n", + "140081 13966 6 311 5324 4 1 \n", + "479781 8035 3 113 2188 4 1 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rawDf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample.\n", + "\n", + "The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation. In this tutorial, we split our training data into 5 groups, thus k=5 and our approach is refered to as 5-fold cross-validation.\n", + "\n", + "In machine learning applications, we need to split out data into a training set (what is used to train the machine learning model) and a test set (a portion of data not used in training and reserved for evaluating how well the model is working). Cross-validation allows every data point to be used for both training and evaluation. It also helps us identify if any random splitting of the data results is drastically different performance.\n", + "\n", + "The general procedure is as follows:\n", + "\n", + "1. Shuffle the dataset randomly.\n", + "2. Split the dataset into k groups\n", + "3. For each unique group:\n", + " Take one group as the test data set\n", + " Take the remaining groups as a training data set\n", + " Fit a model on the training set and evaluate it on the test set\n", + " Retain the evaluation score and discard the model\n", + "4. Summarize the skill of the model using the sample of model evaluation scores" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold: 1\n", + " Balanced Accuracy: 0.8539\n", + " Weighted F1: 0.881\n", + " Weighted Precision: 0.9141\n", + " Weighted Recall: 0.8539\n", + "\n", + "Fold: 2\n", + " Balanced Accuracy: 0.8539\n", + " Weighted F1: 0.8795\n", + " Weighted Precision: 0.9105\n", + " Weighted Recall: 0.8539\n", + "\n", + "Fold: 3\n", + " Balanced Accuracy: 0.8558\n", + " Weighted F1: 0.8814\n", + " Weighted Precision: 0.9122\n", + " Weighted Recall: 0.8558\n", + "\n", + "Fold: 4\n", + " Balanced Accuracy: 0.853\n", + " Weighted F1: 0.8798\n", + " Weighted Precision: 0.9125\n", + " Weighted Recall: 0.853\n", + "\n", + "Fold: 5\n", + " Balanced Accuracy: 0.8548\n", + " Weighted F1: 0.8802\n", + " Weighted Precision: 0.9107\n", + " Weighted Recall: 0.8548\n", + "\n", + "Average Balanced Accuracy: 0.8543\n" + ] + } + ], + "source": [ + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "accs = []\n", + "fold = 1\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " predictions = rf.predict(X_test)\n", + " ba = np.round( balanced_accuracy_score(y_test, predictions), 4 )\n", + " accs.append( ba )\n", + " print( \"Fold:\", fold )\n", + " print( \" Balanced Accuracy:\", ba)\n", + " print( \" Macro F1:\", np.round( f1_score(y_test, predictions, average='macro'), 4 ))\n", + " print( \" Macro Precision:\", np.round( precision_score(y_test, predictions, average='macro'), 4 ))\n", + " print( \" Macro Recall:\", np.round( recall_score(y_test, predictions, average='macro'), 4 ))\n", + " print()\n", + " fold += 1\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We get very similar performance across all folds, which is good. Overall, the random forest approach can predict \"Residential\" or \"Other\" correctly with ~86% accuracy.\n", + "\n", + "We are using balanced accuracy to deal with our imbalanced dataset. It is defined as the average of recall obtained on each class. Similarly, we use micro F1, which is the normal F1 formula but calculated using the total number of True Positives (TP), False Positives (FP) and False Negatives (FN), instead of individually for each class." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Machine Learning models can be saved as [Pickle files](https://scikit-learn.org/stable/model_persistence.html). Although, the Joblib approach, is often preferred as it offers compression." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "rf.fit( X_train, y_train )\n", + "\n", + "with open('./rf_model.pkl', 'wb') as f:\n", + " pickle.dump(rf, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions: [1 1 1 0 1]\n", + "Actual: [1 1 1 0 1]\n" + ] + } + ], + "source": [ + "# read the model from file \n", + "with open('./rf_model.pkl', 'rb') as f:\n", + " rf = pickle.load(f)\n", + "\n", + "predictions = rf.predict(X_test)\n", + "print( \"Predictions:\", predictions[0:5] )\n", + "print( \"Actual:\", y_test[0:5] )" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# save the model to a file\n", + "joblib.dump(rf, './rf_model.sav', compress=3)\n", + "\n", + "# read the model from file \n", + "rf = joblib.load('./rf_model.sav')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A confustion matrix is a visual display of true positives, true negatives, false positives, and false negatives. Here, by setting normalize='true', we are displaying proportions rather than counts." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "cm = confusion_matrix( y_test, predictions, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 24}\n", + "plt.rc('font', **font)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-22 18:44:30.862285: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5472 MB memory: -> device: 0, name: NVIDIA GeForce GTX TITAN Black, pci bus id: 0000:65:00.0, compute capability: 3.5\n" + ] + } + ], + "source": [ + "# This is required for my GPU: GeForce RTX 2060\n", + "# Without this config, tensorflow cannot properly allocate GPU memory\n", + "\n", + "# This is a GPU specific configuration and may not be needed on your GPU\n", + "\n", + "config = tf.compat.v1.ConfigProto()\n", + "config.gpu_options.allow_growth = True\n", + "config.gpu_options.per_process_gpu_memory_fraction = 0.90\n", + "session = tf.compat.v1.Session(config=config)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating test/train split...\n" + ] + } + ], + "source": [ + "# columns: StateFips, CountyFips, StateCountyFips,\n", + "# X, Y, Area, MedianIncomeCounty, HousingUnitsCounty,\n", + "# HousingDensityCounty, Impervious, AgCount, CmCount,\n", + "# GvCount, EdCount, InCount, OsmNearestRoad, OrnlType\n", + "\n", + "# test/train split\n", + "print(\"Creating test/train split...\")\n", + "train, test = train_test_split(df, test_size=0.2, random_state=42, stratify=df[['BuildingType']])\n", + "\n", + "nCols = test.shape[1]\n", + "xTest = test.iloc[:, :nCols-1]\n", + "yTest = test.iloc[:, nCols-1]\n", + "\n", + "xTrain = train.iloc[:, :nCols-1]\n", + "yTrain = train.iloc[:, nCols-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Size: (862513, 13)\n", + "Testing Size: (215629, 13)\n" + ] + } + ], + "source": [ + "print(\"Training Size:\", xTrain.shape)\n", + "print(\"Testing Size:\", xTest.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['X', 'Y', 'Area', 'MedianIncomeCounty', 'HousingUnitsCounty',\n", + " 'HousingDensityCounty', 'Impervious', 'AgCount', 'CmCount', 'GvCount',\n", + " 'EdCount', 'InCount', 'OsmNearestRoad'],\n", + " dtype='object')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xTrain.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 1)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xTrain['OsmNearestRoad'][0], yTrain[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

We want the neural network to weight the fewer \"other\" examples that are available. You can do this by passing Keras weights for each class through a parameter. These will cause the model to \"pay more attention\" to examples from an under-represented class.

" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weight for class 0: 5.31\n", + "Weight for class 1: 0.55\n" + ] + } + ], + "source": [ + "## A common weighting technique that involves the dataset size\n", + "## and the number of examples in each class.\n", + "\n", + "# Scaling by total/2 helps keep the loss to a similar magnitude.\n", + "total = xTrain.shape[0]\n", + "ix = np.where( yTrain == 0 )\n", + "zero = len(ix[0])\n", + "ix = np.where( yTrain == 1 )\n", + "one = len(ix[0])\n", + "\n", + "weight_for_0 = (1 / zero) * (total / 2.0)\n", + "weight_for_1 = (1 / one) * (total / 2.0)\n", + "\n", + "class_weight = {0: weight_for_0, 1: weight_for_1}\n", + "\n", + "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n", + "print('Weight for class 1: {:.2f}'.format(weight_for_1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Define a fairly simple three layer fully connected neural network

\n", + "

We will set the network to train for 150 epochs; however, we stop the training early if accuracy hasn't improved over the previous 7 epochs

" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-25 10:12:37.532438: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5337 MB memory: -> device: 0, name: NVIDIA GeForce GTX TITAN Black, pci bus id: 0000:65:00.0, compute capability: 3.5\n" + ] + } + ], + "source": [ + "# convert output from scalar to vector format, i.e. building type=2 to [0,0,1,0,0,0,0,0,0,0,0]\n", + "y = to_categorical(yTrain)\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(30, input_shape=(xTrain.shape[1],), activation='relu'))\n", + "model.add(Dense(15, activation='relu'))\n", + "model.add(Dense(2, activation='softmax'))\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "\n", + "# stop training if we see 7 epochs over which accuracy doesn't improve\n", + "es = EarlyStopping(monitor='accuracy', patience=7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Output a summary of our neural network to see how many trainable parameters there are

" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense (Dense) (None, 30) 420 \n", + " \n", + " dense_1 (Dense) (None, 15) 465 \n", + " \n", + " dense_2 (Dense) (None, 2) 32 \n", + " \n", + "=================================================================\n", + "Total params: 917\n", + "Trainable params: 917\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Train the neural network

" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/150\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-25 10:12:51.974104: I tensorflow/compiler/xla/service/service.cc:169] XLA service 0x7f875f79caf0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2023-11-25 10:12:51.974133: I tensorflow/compiler/xla/service/service.cc:177] StreamExecutor device (0): NVIDIA GeForce GTX TITAN Black, Compute Capability 3.5\n", + "2023-11-25 10:12:51.978607: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2023-11-25 10:12:52.113008: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8600\n", + "2023-11-25 10:12:52.166415: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n", + "2023-11-25 10:12:52.225798: I ./tensorflow/compiler/jit/device_compiler.h:180] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3370/3370 [==============================] - 20s 6ms/step - loss: 0.3440 - accuracy: 0.8862\n", + "Epoch 2/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.3161 - accuracy: 0.9009\n", + "Epoch 3/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.3119 - accuracy: 0.9019\n", + "Epoch 4/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.3090 - accuracy: 0.9015\n", + "Epoch 5/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.3056 - accuracy: 0.9025\n", + "Epoch 6/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.3020 - accuracy: 0.9025\n", + "Epoch 7/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.2982 - accuracy: 0.9027\n", + "Epoch 8/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.2959 - accuracy: 0.9026\n", + "Epoch 9/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.2940 - accuracy: 0.9025\n", + "Epoch 10/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.2928 - accuracy: 0.9022\n", + "Epoch 11/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.2920 - accuracy: 0.9024\n", + "Epoch 12/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.2911 - accuracy: 0.9023\n", + "Epoch 13/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.2903 - accuracy: 0.9030\n", + "Epoch 14/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.2896 - accuracy: 0.9025\n", + "Epoch 15/150\n", + "3370/3370 [==============================] - 18s 5ms/step - loss: 0.2893 - accuracy: 0.9035\n", + "Epoch 16/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.2884 - accuracy: 0.9037\n", + "Epoch 17/150\n", + "3370/3370 [==============================] - 18s 5ms/step - loss: 0.2881 - accuracy: 0.9041\n", + "Epoch 18/150\n", + "3370/3370 [==============================] - 18s 5ms/step - loss: 0.2874 - accuracy: 0.9044\n", + "Epoch 19/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.2870 - accuracy: 0.9043\n", + "Epoch 20/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.2864 - accuracy: 0.9047\n", + "Epoch 21/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.2861 - accuracy: 0.9043\n", + "Epoch 22/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.2860 - accuracy: 0.9044\n", + "Epoch 23/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.2857 - accuracy: 0.9048\n", + "Epoch 24/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.2857 - accuracy: 0.9049\n", + "Epoch 25/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.2853 - accuracy: 0.9051\n", + "Epoch 26/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.2850 - accuracy: 0.9051\n", + "Epoch 27/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.2849 - accuracy: 0.9051\n", + "Epoch 28/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.2845 - accuracy: 0.9054\n", + "Epoch 29/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.2846 - accuracy: 0.9053\n", + "Epoch 30/150\n", + "3370/3370 [==============================] - 18s 5ms/step - loss: 0.2843 - accuracy: 0.9058\n", + "Epoch 31/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.2842 - accuracy: 0.9058\n", + "Epoch 32/150\n", + "3370/3370 [==============================] - 18s 5ms/step - loss: 0.2842 - accuracy: 0.9058\n", + "Epoch 33/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.2838 - accuracy: 0.9059\n", + "Epoch 34/150\n", + "3370/3370 [==============================] - 18s 5ms/step - loss: 0.2839 - accuracy: 0.9058\n", + "Epoch 35/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.2836 - accuracy: 0.9061\n", + "Epoch 36/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.2834 - accuracy: 0.9061\n", + "Epoch 37/150\n", + "3370/3370 [==============================] - 15s 5ms/step - loss: 0.2836 - accuracy: 0.9060\n", + "Epoch 38/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.2833 - accuracy: 0.9059\n", + "Epoch 39/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.2831 - accuracy: 0.9058\n", + "Epoch 40/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.2831 - accuracy: 0.9059\n", + "Epoch 41/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.2831 - accuracy: 0.9059\n", + "Epoch 42/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.2831 - accuracy: 0.9061\n" + ] + } + ], + "source": [ + "# fit the model\n", + "h = model.fit(xTrain, y, epochs=150, batch_size=256, class_weight=class_weight, callbacks=[es])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Epoch')" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot( h.history['accuracy'] )\n", + "plt.title('Training Accuracy')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "model.save_weights(\"./nn_model.h5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Evaluate the neural network on the test set" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6739/6739 [==============================] - 23s 3ms/step - loss: 0.2459 - accuracy: 0.9163\n", + "Accuracy on the test set: 0.9163\n" + ] + } + ], + "source": [ + "y = to_categorical(yTest)\n", + "loss, acc = model.evaluate( xTest, y )\n", + "print(\"Accuracy on the test set:\", np.round(acc,4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use Balanced Accuracy and Micro F1 to get a better sense of how we did" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6739/6739 [==============================] - 15s 2ms/step\n" + ] + }, + { + "data": { + "text/plain": [ + "215629" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ps = model.predict(xTest)\n", + "predictions = []\n", + "for p in ps:\n", + " predictions.append( np.argmax( p ) )\n", + "len(predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.8871\n", + "Weighted F1: 0.8046\n", + "Weighted Precision: 0.7592\n", + "Weighted Recall: 0.8871\n" + ] + } + ], + "source": [ + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(yTest.values, predictions), 4 ))\n", + "print( \"Macro F1:\", np.round( f1_score(yTest.values, predictions, average='macro'), 4 ))\n", + "print( \"Macro Precision:\", np.round( precision_score(yTest.values, predictions, average='macro'), 4 ))\n", + "print( \"Macro Recall:\", np.round( recall_score(yTest.values, predictions, average='macro'), 4 ))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "cm = confusion_matrix( yTest.values, predictions, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature Importance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Feature importance is a technique for assessing how important each of our input features are to making accurate predictions. Features with low importance do not contribute much (are not used/do not have much weight) to prediction accuracy. Low importance features can be ignored creating simpler more scalable machine learning modesl.

\n", + "\n", + "We use a technique called [Permutation Importance](https://scikit-learn.org/stable/modules/permutation_importance.html) in which features are randomly shuffled. If a column's values can be randomly shuffled and prediction accuracy does not decrease, then that feature has low importance.\n", + "\n", + "

We'll use our Random Forest model to demonstrate feature importance

" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time to compute the importances: 502.000 seconds\n" + ] + } + ], + "source": [ + "start_time = time.time()\n", + "result = permutation_importance(rf, X_test, y_test, n_repeats=10, random_state=42, n_jobs=1)\n", + "elapsed_time = time.time() - start_time\n", + "print(f\"Elapsed time to compute the importances: {elapsed_time:.3f} seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "font = {'family' : 'normal',\n", + " 'weight' : 'normal',\n", + " 'size' : 16}\n", + "plt.rc('font', **font)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_idx = result.importances_mean.argsort()\n", + "forest_importances = pd.Series(result.importances_mean[sorted_idx], index=rawDf.columns[sorted_idx])\n", + "\n", + "fig, ax = plt.subplots( figsize=(10, 10) )\n", + "forest_importances.plot.bar(yerr=result.importances_std[sorted_idx], ax=ax)\n", + "ax.set_title(\"Feature importances using permutation method\")\n", + "ax.set_ylabel(\"Mean accuracy decrease\")\n", + "ax.set_yscale('log')\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The features 'HousingUnitsCounty', 'HousingDensityCounty', 'AgCount', 'CmCount', 'GvCount', 'EdCount', and 'InCount' have low importance. Let's retrain a smaller Random Forest model without those features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove features that are not important/helpful to simplify the model" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.8482\n", + "Micro F1: 0.9603\n", + "\n", + "Balanced Accuracy: 0.8477\n", + "Micro F1: 0.9599\n", + "\n", + "Balanced Accuracy: 0.8498\n", + "Micro F1: 0.9605\n", + "\n", + "Balanced Accuracy: 0.8435\n", + "Micro F1: 0.9593\n", + "\n", + "Balanced Accuracy: 0.8464\n", + "Micro F1: 0.9601\n", + "\n", + "Average Balanced Accuracy: 0.8471\n" + ] + } + ], + "source": [ + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "X = rawDf.drop(['BuildingType', 'HousingUnitsCounty', 'HousingDensityCounty', 'AgCount', 'CmCount',\n", + " 'GvCount', 'EdCount', 'InCount'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "accs = []\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " ba = np.round( balanced_accuracy_score(y_test, rf.predict(X_test)), 4 )\n", + " accs.append( ba )\n", + " print( \"Balanced Accuracy:\", ba)\n", + " print( \"Macro F1:\", np.round( f1_score(y_test, rf.predict(X_test), average='macro'), 4 ))\n", + " print()\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our average Balanced Accuracy from the previous Random Forest model using all the features was 85.48%. Our reduced model has accuracy of 84.71%. We can create a smaller model without much loss in accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "with open('./rf_reduced_model.pkl', 'wb') as f:\n", + " pickle.dump(rf, f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Strategies to deal with unbalanced classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Undersampling refers to a group of techniques designed to balance the class distribution. Undersampling techniques remove examples from the training dataset that belong to the majority class in order to better balance the class distribution. \n", + "\n", + "This is in contrast to oversampling, which involves adding examples to the minority class in an effort to reduce the class distribution imbalance.\n", + "\n", + "Near Miss refers to a collection of undersampling methods that select examples based on the distance of majority class examples to minority class examples.\n", + "\n", + "The approaches were proposed by Jianping Zhang and Inderjeet Mani in their 2003 paper [KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction](https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf)\n", + "\n", + "There are three versions of the technique, named NearMiss-1, NearMiss-2, and NearMiss-3.\n", + "\n", + "* NearMiss-1 selects examples from the majority class that have the smallest average distance to the k closest examples from the minority class. \n", + "* NearMiss-2 selects examples from the majority class that have the smallest average distance to the k furthest examples from the minority class. \n", + "* NearMiss-3 involves selecting a given number of majority class examples for each example in the minority class that are closest.\n", + "\n", + "Distance is determined in feature space using Euclidean distance.\n", + "\n", + "We'll use NearMiss-3, which keeps majority class examples that are on the decision boundary." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "from imblearn.under_sampling import NearMiss\n", + "\n", + "# define the undersampling method\n", + "undersample = NearMiss( version=3, n_neighbors_ver3=5 )\n", + "\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "# transform the dataset\n", + "X, y = undersample.fit_resample( X, y )" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Residential: 101453\n", + "Other: 101453\n" + ] + } + ], + "source": [ + "ix = np.where( y == 1 )\n", + "print( \"Residential:\", len(ix[0]) )\n", + "\n", + "ix = np.where( y == 0 )\n", + "print( \"Other:\", len(ix[0]) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest example of Near-Miss" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.8446\n", + "Weighted F1: 0.8446\n", + "Weighted Precision: 0.84\n", + "Weighted Recall: 0.84\n", + "\n", + "Balanced Accuracy: 0.8433\n", + "Weighted F1: 0.8433\n", + "Weighted Precision: 0.84\n", + "Weighted Recall: 0.84\n", + "\n", + "Balanced Accuracy: 0.8412\n", + "Weighted F1: 0.8412\n", + "Weighted Precision: 0.84\n", + "Weighted Recall: 0.84\n", + "\n", + "Balanced Accuracy: 0.8418\n", + "Weighted F1: 0.8418\n", + "Weighted Precision: 0.84\n", + "Weighted Recall: 0.84\n", + "\n", + "Balanced Accuracy: 0.8361\n", + "Weighted F1: 0.8361\n", + "Weighted Precision: 0.84\n", + "Weighted Recall: 0.84\n", + "\n", + "Average Balanced Accuracy: 0.8414\n" + ] + } + ], + "source": [ + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "accs = []\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " predictions = rf.predict(X_test)\n", + " ba = np.round( balanced_accuracy_score(y_test, predictions), 4 )\n", + " accs.append( ba )\n", + " print( \"Balanced Accuracy:\", ba)\n", + " print( \"Macro F1:\", np.round( f1_score(y_test, predictions, average='macro'), 4 ))\n", + " print( \"Macro Precision:\", np.round( precision_score(y_test, predictions, average='macro'), 2 ) )\n", + " print( \"Macro Recall:\", np.round( recall_score(y_test, predictions, average='macro'), 2 ) )\n", + " print()\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our average Balanced Accuracy from the original Random Forest model was 85.48%. The Balanced Accuracy using Near-Miss-3 and balanced classed results in an accuracy of 84%. This technique was not particularly helpful for our application." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "with open('./rf_balanced_model.pkl', 'wb') as f:\n", + " pickle.dump(rf, f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Threshold Moving" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perhaps the simplest approach to handle a severe class imbalance is to change the decision threshold. Many classification algorithms will return a probability of class membership where all values equal or greater than a threshold are mapped to one class and all other values are mapped to the other class. The default of many algorithms is to set the threshold at 0.5. Threshold moving simply moves the threshold attempting to achieve higher accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting the model...\n", + "Making predictions...\n", + "Generating precision-recall curve...\n" + ] + } + ], + "source": [ + "from matplotlib import pyplot\n", + "from sklearn.metrics import precision_recall_curve\n", + "\n", + "# generate dataset\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "# split into train/test sets\n", + "trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.2, random_state=2, stratify=y)\n", + "\n", + "# fit a model\n", + "print(\"Fitting the model...\")\n", + "model = RandomForestClassifier(class_weight='balanced')\n", + "model.fit(trainX, trainy)\n", + "\n", + "# predict probabilities\n", + "print(\"Making predictions...\")\n", + "yhat = model.predict_proba(testX)\n", + "\n", + "# keep probabilities for the positive outcome only\n", + "yhat = yhat[:, 1]\n", + "\n", + "# calculate precision-recall curve\n", + "print(\"Generating precision-recall curve...\")\n", + "precision, recall, thresholds = precision_recall_curve(testy, yhat)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Threshold=0.550000, F-Score=0.979\n" + ] + } + ], + "source": [ + "# convert to f score\n", + "fscore = (2 * precision * recall) / (precision + recall)\n", + "\n", + "# locate the index of the largest f score\n", + "ix = np.argmax(fscore)\n", + "print('Best Threshold=%f, F-Score=%.3f' % (thresholds[ix], fscore[ix]))" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LgTHAZQTjNXnh82XAseFztcwsH3gZ6AucTDBVejfgFTNrX0veLsBsgpUJzgSOCz96xcz6JRxbCMwDfgD8AjgEmAiU1qmWIiKtQZrWO6tv19mJwJXuPjku7VNgctiaOZUgGFXndKAP8EN3/xjAzN4GPiIIHjfUkPdsYAdgSFzel4FPCFpWY+KOvYNgKvZB7r4lTHu1TjUUEWktmmLXGdANqO7W0X+Fn9dkFDA3FigA3H0J8Dowupa8g4GPEvKuB2YBR5pZGwAz2wUYCdwSF2RERCRD6htovgT2r+az/cLPa7I7kGzSwLtA/1rylgGbk6RvIujC2yV8HyvfRjP7Zzg+s9rM7gu730REBNLWdVbfQPMgMMHMLjWzPmaWFw7QXwJMAGpbbLMzsDpJ+iqgUy15PwB2iw8WZpYF/Cju3LC1VXUP8CFwGHARcATwfJinCjM7w8wWmNmCkpKSWooiItICVLpvJrr7aOobaCYCjxKMiXwEfAd8DEyOS6+NJ0mzOuS7g6C895nZLma2E3AzW28iLQ+fY3Wa6e7nuvvL7j4VOAfYh6BbrWqh3Ke6e5G7FxUUFNShOCIizVyl+2aiu4+mXoHG3Uvd/QRgD+A8gllm5wED3H2su5fVcorVbG15xOtE8pZO/LU/AcYSBIuPCbrpioEbw0OWh8+xkPzPhFO8ED5Ht42ciEhzkqYWTYNu2HT3dwnGVerrXYJxmkT9gffqcN3pZvYEwbTlze6+2MxuBz5z92Vx14DkLSfY2vIREWndmkqLxsx6mllO3OsaH7WcbgYw2Mz6xJ2/kGAAf0ZdCuzuZe6+KAwy3Qju37k97pC5wFfAoQlZY+/n1+U6IiItXpqmN9elRbOEoItqHrCU6lsKMTWtDnAXQVfbk2b2h/BcVwKfAXfGDjKzXsBi4Ap3vyJMywH+SHA/zFqCltElBC2Y62N53b3UzC4GppnZHcBjwK4E40gzCW4YFRGRRBu/iuS0dQk0/0vwpR97XVugqZa7rzezYQTjKvcTTAJ4CbjQ3b+LO9QIAlZ8i8sJVhE4AegIfE4ws+xqd6807dnd/2Zm5QSzzU4lmNX2AHCJuze4/CIiLUrvcfDxXQR3jwDLnw122SwoTullTN+7VRUVFfmCBQsyXQwRkejNGgOfPRK+yYJBV8HulzToVGb2hrtXWX+yvtObk524v5kdE46XiIhIc9I5fiJueSQTAuq7evOt4bhH7P1PCVZyfgR4z8z2TXH5REQkSqUb4t5EM8W5vi2awwjWNIuZBDwNDCKYLFDTgpoiItLU5O0U9yaaKc71DTQ7Esw8w8x6EMz8muLu/yW4S18tGhGR5mTzmrg3TaNFsxHoEL4eQjDNODZq/h2wTYrKJSIi6ZCG7ZzruzLAm8C5ZrYMOBf4p7vH7rTvzdZlYEREpDlIwzI09Q00E4DnCCYArAHOivvsKIJxGhERaS7SsAxNvQKNu88Pl5npS7AJ2dq4j6cSrOgsIiLNRRNs0cR2tXwjSfozKSmRiIikT1No0ZjZOOAZd/8mfF0jd78vJSUTEZHoNZEWzTRgMME+L9NqOdYBBRoRkeaiKbRoqDybrHdNB4qISDNTqQWTlZkWjbt/muy1iIi0AF2HEtxSWQ5ZOeH71KrvWmeDzWxMNZ/93Mx+nJpiiYhI+kWzmn99VwaYQvKtmAH6hZ+LiEhzsWImFTvcl5eG71OrvoFmEMFWycnMAwY2rjgiIpJWlQb/m8A2AUC7GvJkA+0bVxwREUmrNEwGqG+gWQSMquazUcAHjSuOiIikVdehYNnB64gmA9R3ZYA7gDvNbC1wF/A50B04AxgPnJPa4omISOS8youUqu9aZ3eZ2Q+BXwK/iv8IuNHdp6aycCIiErFkkwEKilN6iYasdfYbM7sdOBjoDKwEXnT3T1JaMhERiV5uF7a2ZKKZDFDvQAPg7ouBxSkui4iIpFsTnAyAmbU3s1+Y2aNm9rKZ7RamH2dmfVNeQhERiU7XoWBhmyOrTeYnA5jZzsBMoAfwPjCArds3HwSMAE5LYflERCRdopkLUO8WzfXAJmA3YB/A4j57FTgwReUSEZF0WDETPJwM4GWRrAxQ3zGag4Ez3H2ZWWzidYUvCKY6i4hIc9F1aHD/TPmmoOnQBFYGyAXWVfPZdsCWxhVHRETSqqAYfnBu8NrL4c0LoWROSi9R30DzNnBMNZ8dRpItnkVEpIkrXR++cCjfnPLus/p2nV0HPGpmAP8vTOtvZqMJVgaobnkaERFpqrrsCx/fCVgwAy3FM8/q1aJx98cIlpn5OfBimHwfcCFwnrs/V9s5zGzncGr0t2a21sweM7Oedbm+mfUO864xs/Vm9oqZFdWS53gzczP7vC7XEBFpdSy+zZH6qWf13fhsO+BegkH/kcCJBF1mPeqy/IyZ5QMvA32Bk4GTCGawvWJmNa78bGZdgNkEU6rPBI4LP3rFzPpVk6cjcCPwVW1lExFptVb9J3zhkcw8q3PXmZm1Ab4Bjnb3p9jaoqmP04E+wA/d/ePwvG8DHxEEjxtqyHs2sAMwJC7vy8AnwCQg2c6ffwQWAssJ7vEREZFEBcXw4Z/JeNeZu5cCXwNljbjeKGBuLFCE510CvA6MriXvYOCjhLzrgVnAkWEgrGBm+xO0uM5tRHlFRFq+irtVnIx3nQEP0Lg7/3cH3kmS/i7Qv5a8ZcDmJOmbgDxgl1iCmeUAU4Hr4gOTiIgksWrB1teZ7DoLLQXGmtl84EmCLqlK4c/d76khf2dgdZL0VUCnWq79AXCwmXVx928AzCwL+FHcuWMuAtoCU2o5ZwUzO4NgXx169qzT3AQRkZah4ADg2uB1BF1n9Q00fwmfuxEsQZPIgZoCTeyYRJYkLdEdwC+A+8zsF8AGYALQO/y8HMDMdg3Tj3b37+tw3qBQwWSGqQBFRUURrfgjItIEZUU766y+gebHBCsDbGzg9VZTueUR04nkLZ0K7v6JmY0lCHax7rA3CWaV/YagdQVwM8HMtrnhrDMIVjSw8P0md29o+UVEWp6Vc7e+jnWdpXDzs1rHaMws28wmmtkaYC7B6gA3AN+6+6eJj1pO9y7BOE2i/sB7tZXF3acTTK3uD+zq7vsAHYDP3H1Z3LkOJwhcscfxBK2w1dSjO01EpFXoetDW1xnqOjsLuIxge4D5BNOTjwbWAqfW83ozgD+ZWZ/YjpxmVgjsD1xclxO4exmwKMzbDTiWYMWCmOOAdgnZLibo6vs5oBs3RUTiNYGus9OBu9z9zFiCmZ0J3GpmZ7p7splg1bkLOA940sz+QFCjK4HPgDvjzt+LYAfPK9z9ijAth+C+mFcJgtzuwCUEraTrY3ndPa4NWHG+Uwi6zGbWo6wiIq3Dile3vs5E1xlBC+aRhLS/A9lAr/pcLLzvZRjwIXA/8CCwBBjm7t/FHWrh+ePL5wSrCNwJ/INg2Zt7gJH1DHYiIhKvoqvMICs3I11nHQhaEPFiWwVsQz2FYynVrQAdO2YpCTPRwhtGj6zv9cK8pzQkn4hIq1BQHASYvJ2g/+9T2pqBus86625mfeLeZ8elr4k/MDb2IiIizUTJnGB7gPXLgv1oOu6R0mBT10DzaDXpTyRJS9x5U0REmrKKlQDi9qNJc6Cp78wyERFpTjI9RuPuf0vpFUVEpGkpKIY220Gb9rDH5Skfo6nvopoiItLSlMyB0rXw/ZfBGE3JnJSeXoFGRKS1WzGTihs1Y2M0KVTftc5ERKSl6TqUijtKMnQfjYiItGQFxdBuJ/BSGHilxmhERCTFSubA98th0wqN0YiISAQiHqNRoBERae26DqUiHEQwRqNAIyLS2hUUQ/tCyO0Me9+kMRoREUmxkjmwfilsXqUxGhERicCKmUB58FpjNCIiknIRj9HoPpoGWrt2LStWrGDLli2ZLopIveTk5NC1a1e23XbbTBdFmoqCYui8D3z/Nez/UMb2o5E4a9eu5euvv6Z79+7k5eVhZrVnEmkC3J2NGzfyxRdfACjYyFaWRUX3WYqp66wBVqxYQffu3cnPz1eQkWbFzMjPz6d79+6sWLEi08WRpqJkDnwzHzZ8Di8P12SApmDLli3k5eVluhgiDZaXl6duX9lKkwGaJrVkpDnT769U0nVo2HWGbtgUEZEIFBTD9j+BdjvAsJd0w6aIiETAssA9klMr0AjTpk3DzCoeubm57LLLLvz+97/n+++/z0iZCgsLOeWUUzJy7URDhw6t9O8Te/To0SPTRatkzZo1TJw4kTfffDPTRZHmpmQOlMwKVm+OYDKApjdLhUceeYQePXqwbt06Hn/8caZMmcK6deu45ZZbMl20jBs4cCB33nlnpbS2bdtmqDTJrVmzhkmTJtGjRw/23nvvTBdHmpMVM8HLgtexyQAp7D5ToJEKe+65J7vuuisABx98MB999BF33303f/7zn8nKat2N32222YbBgwen/LybNm1qcgFLWqGuQ8Gyg2CjyQCSTnvvvTcbN25k5cqVFWkvvPAChx9+ODvttBP5+fkMGDCA66+/nrKyskp5CwsLOfHEE3nooYfo168f7du3p6ioiNmzZ1e5zp///GcKCwtp164dRUVFzJo1K2l55s2bx4gRI+jQoQPt27dn+PDhzJs3r9Ixp5xyCj169GDBggXst99+5OXl8cMf/pBnnnkGgBtuuIHCwkK23XZbRo8eTUlJSWP/mQD44IMPOProo+nYsSN5eXkMHjyY5557rtIxEydOxMx45513GDlyJB06dGDMmDEAbNiwgYsuuojevXuTm5tL7969mTx5MuXlW2+g++677zj//PPp2bMnbdu2ZYcddmDEiBG8//77LF26lN69ewNw+umnV3TvTZs2LSX1kxauoBh2GA65nSKZDKAWTVNRMidornYdmvIfckMtXbqU7bbbji5dulSkffLJJwwfPpzzzz+fdu3asWDBAiZOnEhJSQnXXHNNpfyzZs3igw8+4Morr6Rdu3ZceumlHHnkkSxdupSOHTsCcPfdd3PhhRdyyimncOyxx/Lxxx9z/PHHs27dukrnevvttxkyZAj9+/evGFO65pprGDJkCHPnzmXQoEEVx65du5Zx48bxm9/8hm7dujF58mSOOeYYzj33XD788EP+8pe/8PXXX3PhhRdy7rnn8vDDD9fp36O0tLTS++zsbMyML7/8kp/85Cdss8023HrrrWy33Xb85S9/4YgjjuDpp5/msMMOq5Rv9OjRjB8/nosuuoisrCxKS0sZOXIk7733Hpdeeil77LEHc+fO5corr2TVqlVcf/31APzyl79kxowZXH311ey222588803vP7666xZs4a99tqLxx57jJ/+9KdccskljBo1CoBddtmlTnUTgegmAyjQpMobF8LqtxqWd8u3sPptghumsqDTQMjZrv7n6bQn7HNTw8oAlJWVUVpaWjFGM336dG666Says7MrjjnrrLMqXrs7BxxwAJs3b+ZPf/oTV199daUutrVr1/LWW2/RqVMnAHbccUf23Xdfnn32WU444QTKy8uZOHEiI0eO5N57763IV1BQwHHHHVepbFdccQVt27blpZdeqghSBx98MIWFhUyaNInHHnus4th169Zxxx13cOCBBwLQrVs3Bg0axNNPP817771XUZ933nmHW265hbKyskp1TOb1118nJyenUtpdd93Faaedxg033MDq1auZM2dORdfj4YcfTv/+/ZkwYUKVQPOLX/yCCy64oOL9/fffz+zZs3n11Vcryjx8+HAAJk2axEUXXUTXrl2ZM2cOY8eOZfz48RV5jz766IrXe+21FwB9+vSJpJtPWrCSOfD1P4Ous5eHp7xVo66zpmDzt2xdY6g8fJ9+ffv2JScnh86dOzN+/HjOPPNMzjvvvErHLF++nDPPPJNevXqRm5tLTk4Of/jDH1izZk2VJU2Ki4srggzAHnvsAcCyZcsA+Pzzz/n8888ruo9ijjnmGNq0qfw30GuvvcaRRx5ZEWQgWKdr1KhRvPrqq5WObd++fcUXdqxeACNGjKgUUPr27UtpaSnLly+v9d9m0KBBzJ8/v9LjqKOOqijb4MGDK4IMBK2d448/nrfeeou1a9dWOld8cAB47rnn6NWrF/vttx+lpaUVj0MOOYQtW7Ywd+5cAPbdd1+mTZvG1VdfzYIFC6p0V4o02IqZ4NGtDJD2Fo2Z7QzcCBwMGPAicKG7L6tD3t7AdcAIIAeYB/zW3RfEHfMD4FzgIKAPsA6YD1zq7gtTW5s4jWhJUDIn+CuifHMwELffgxnpPnv88cfp0aMHJSUl3HDDDdx22238+Mc/Zty4cQCUl5czatQovvzySyZOnEjfvn3Jy8vjiSeeYPLkyVWmQnfu3LnS+9igd+y42Bf8DjvsUOm4Nm3aVOquA1i1ahU77bRTlTLvuOOOrF69ulJafDACyM3NBagU9OLT6zKFu0OHDhQVFSX9bNWqVRWticSyuTurV6+utHhlYj1WrFjBp59+WqXFFPPNN98AcMstt7Djjjtyzz33MGHCBDp37sy4ceOYPHky+fn5tdZBpFqxlQEimgyQ1kBjZvnAy8Am4GTAgauAV8xsoLuvryFvF2A2QeA4E9gA/CrM+yN3XxQeeghBkPkb8CbQEfgd8G8z29/d34iibo1SUBw0VTM8RjNgwICKv8qHDRvGwIED+e1vf8sxxxxD+/btWbx4MQsWLOD+++/nxBNPrMj31FNPNeh6sS/cr7/+ulJ6aWlpxZdrTOfOnfnqq6+qnOOrr76qEtDSraaymVmV8iUu/9KlSxd69+5d7VhRYWEhEAS7KVOmMGXKFD799FMeffRRLr74YnJzc7n22mtTUxlpnQqKodvh8PVMOOj5Zr8ywOkErYyj3P0Jd38SGAX0IggeNTkb2AE40t3/7u5PAUcSBJxJccc9BAxy9+vd/RV3fxw4FNgIXJB40iajoBh2v6TJTARo27Yt1113HStWrOC2224DgplRQKW/vLds2cKDDz7YoGv06NGDnXfeucoX7PTp06sMvA8ZMoRnnnmm0iSBdevW8dRTTzFkyJAGXT9VYhMSli5dWpFWVlbG3//+d/baay+22WabGvMfeuihfPbZZxWtpsTH9ttvXyVPr169+PWvf80ee+zBO++8A2xtMW7cuDF1lZPWI28naNM+ku+gdHedjQLmuvvHsQR3X2JmrwOjgRtqyDsY+Cgh73ozmwUcaWZt3L3U3VcmZnT3b83sQ6B7ymrSCowaNYp9992XP/3pT5x33nn069ePXr16MWHCBLKzs8nJyeHGG29s8PmzsrK4/PLLOe200zj11FM57rjj+Pjjj5kyZUqVfVIuvfRSnn76aYYPH85FF12EmXHttdeyYcMGLrvsssZWtVF++ctfMm3aNA4++GAmTZrEtttuy2233caHH35YMa26JmPHjuXee+9l+PDh/PrXv2bQoEFs3ryZxYsXM2PGDJ544gny8/MpLi5m1KhR7LHHHnTo0IFXX32VhQsXcvLJJwNBF2SXLl146KGHGDhwIO3bt6d3795VuiFFktr4NZSuC7rym3mLZnfgnSTp7wL9a8lbBmxOkr4JyAOqncdpZp2BAcCi6o6R5K666ipWrFjBHXfcQW5uLk888QQ77rgj48aN49xzz+XAAw/k4osvbvD5x48fz0033cTLL7/M6NGjuffee3nooYeqjKcMHDiQmTNnsu2223LyySdz0kknVXzZxk9tzoRu3boxe/Zsdt99d84++2x+9rOfsWrVKp555hkOPfTQWvPn5OTw/PPPc/rppzN16lQOP/xwxo4dy9/+9jf222+/irGkAw88kIcffpixY8dyxBFH8Oijj3LjjTdWzGDLysrir3/9K6tXr2bEiBHsu+++De7WlFamZA58+QyUro9kCRrziOZNJ72Y2WbgBne/OCH9KuBid6+2hWVmfwTOA3Z292/CtCzgA2BXYD93T/qvY2YPAkcDA+NbRAnHnAGcAdCzZ899Pv3002rrsWjRIvr161ft5yLNgX6PpcK7U2DhBMCDFQIGXhl05deTmb3h7lVmzWRienOyyFaXzTHuICjvfWa2i5ntBNwM9A4/T7oHqZldApwAnFddkAFw96nuXuTuRQUFBXUojohICxFbggZaxBI0q4FkU4Q6hZ9Vy90/AcYC+wAfA18CxQRTpQGq3AxhZmcBVwN/cPd7Gl5sEZEWrKAYehwFWe1axH407xKM0yTqD7xXW2Z3n04woN8f2NXd9wE6AJ8l3odjZicBtwHXu/vkxhZcRKRFy+sG2W0jmXWW7kAzAxhsZn1iCWZWCOwfflYrdy9z90XuvtjMugHHArfHH2NmRwP3An9199+kqvAiIi3W919B2fcpnwgA6Q80dwFLgSfNbLSZjQKeBD4DKjb7MLNeZlZqZpfFpeWY2Y1mdpSZDTOz84EFBK2k6+OOOxD4P+BtYJqZDY57VL19W0SktSuZA589BuWbmv/GZ+F9L8MIxlXuJ5gE8BLBEjTfxR1qQDaVA6EDuxEM7HcEPgfuAa529/hpz8OAtsBewOsJRfgUKExRdUREWoaWtvFZOJZyTC3HLCVhJpq7lxKsBFDb+ScCExtcQBGR1qZi47PSFjHrTEREmpqCYug5BqxNi5h1JiIirYwCjYhIa1cyB5Y9HHSdRTAZQIFGACq2R449srOz6d69O2PGjOGDDz5I+fVmzpzJxIkTKS9PuqCDiKRTsskAKaRAI5U88sgjzJkzh9dee40pU6bwn//8h+HDh/Ptt6nd9XPmzJlMmjRJgUakKYh4CZq0zzqTpm3PPfes2Pxs//33p1u3bhx88MH861//4rDDDstw6UQkEgXF0Ot4WPqgJgNI+sX2hdmyZUtF2sKFCxk1ahSdOnUiLy+P/fffn1mzZlXKN3/+fA4++GC6dOlCfn4+ffr04ZxzzgFg4sSJTJoU7FWXk5NT0V0nIhnUfmcwaxFL0EgSDz74IIWFhWRlZVFYWNjgHStToaysjNLSUjZt2sSiRYv4/e9/T9euXRk6dCgAb775Jvvttx+rVq3irrvuYvr06XTp0oURI0bwxhvBLtnfffcdI0eOJDs7m2nTpvHss89y2WWXVeyaedpppzF+/HgAZs+ezZw5c5gzJ/XLXohIfWSRfHH9FHB3PRIe++yzj9fkvffeq/Hz+njggQc8Pz/fw5+wA56fn+8PPPBAyq5RF/fee2+lMsQe3bp183nz5lUcN2zYMO/bt69v2rSpIq20tNT79u3ro0ePdnf3+fPnO+ALFy6s9nqXX365A75ly5bI6iQ1S+XvsbQAc051fxD3Ff9q8CmABZ7kO1UtmgybMGECGzZsqJS2YcMGJkyYkJHyPP7448yfP5958+bxxBNP0L9/fw4//HAWLVrExo0befXVV/n5z39OVlYWpaWllJaW4u6MGDGC1157DYDddtuNjh07cuaZZ/LAAw/w2WefZaQuIlJHJXNgyf3Ba01vbnmWLVtWr/SoDRgwgKKiIvbdd19Gjx7NjBkzcHcmTpzIqlWrKCsr48orryQnJ6fS49Zbb2X16tWUl5ez3Xbb8corr9CtWzfOOeccevbsyYABA5g+fXpG6iQitYh4erNmnWVYz549SbZtdM+ePTNQmqry8vLo06cPb7/9Nh07diQrK4tzzz2XcePGJT0+Kyv422XPPfdk+vTplJaWsmDBAqZMmcKYMWNYuHAhAwYMSGcVRKQ2WuusZZs8eTL5+fmV0vLz85k8uWns1bZhwwYWL15MQUEB7du354ADDmDhwoXsvffeFBUVVXkkatOmDYMHD+bKK6+kvLycRYsWAdC2bVsANm7cmNb6iEgSBcXQ++Tg9bB/pnzmmVo0GTZ27FggGKtZtmwZPXv2ZPLkyRXp6fbWW2+xcuVK3J3ly5dz6623smrVKs4//3wAbrjhBg488EBGjhzJ+PHj2WmnnVi5ciVvvvkmZWVlXHPNNTz99NNMnTqVo446it69e7N+/XpuvvlmttlmG4qLg1/g/v37A3D99ddz2GGHkZ2dnTRQiUiatA97UboMTv25k80QaO2PdM46ayqSzTorKCjwgw46yJ977rlKx7733nt+7LHHekFBgefm5nr37t39f/7nf/yZZ55xd/f333/fx4wZ44WFhd62bVvffvvt/bDDDvO5c+dWnKO0tNTPOeccLygocDPz4FdR0qkl/h5LI8w5LZh19vVrDT4F1cw6s+AziVdUVOQLFiyo9vNFixbRr1+/NJZIJPX0eywVSubAiwcGYzTZeQ1eHcDM3nD3Kl0TGqMREWnttKimiIhEquvQYNMz0KwzERGJQEEx7PK/weuhz2lRTRERiUBs1tn2P075qRVoREQE1odLRa2cm/JTK9A0kGbrSXOm31+ppGQOLL47eD3zMK111hTk5OTojnZp1jZu3EhOTk6miyFNhWadNT1du3bliy++YMOGDfrLUJoVd2fDhg188cUXdO3aNdPFkaai61DIbhusd6atnJuG2K6TX375ZaWdJ0Wag5ycHHbYYYeK32MRCoqDmzRXzAyCjNY6axq23XZb/UcVkZajoDiSbZxBXWciIhIxBRoREYmUAo2IiERKgUZERCKlQCMiIpFSoBERkUhp47MkzKwE+LSB2bcHVqawOM2B6tw6tLY6t7b6QuPr3MvdCxITFWhSzMwWJNthriVTnVuH1lbn1lZfiK7O6joTEZFIKdCIiEikFGhSb2qmC5ABqnPr0Nrq3NrqCxHVWWM0IiISKbVoREQkUgo0IiISKQWaOjCznc3sUTP71szWmtljZtazjnnbmdl1ZrbczDaa2RwzOzDqMjdWQ+tsZkVmNtXM3jezDWa2zMweNLPe6Sh3YzTm55xwnkvMzM1sdhTlTKXG1tnM+pnZI2a2Mvz9/sDMLoiyzI3VyP/PPc3sb+Hv9QYz+9DMrjKz9lGXu6HMrIeZ3RJ+92wIfzcL65g3Jd9fCjS1MLN84GWgL3AycBKwG/BKHX+57gZOBy4DjgSWA8+b2Z6RFDgFGlnn44DdgZuBw4CLgb2BBWa2c2SFbqQU/Jxj5+kDTABWRFHOVGpsnc2sCPg30BY4DTgcuB7IjqrMjdWYOoefvwgcCFwKHAH8Ffg1cE+ExW6sXYExwGpgVj3zpub7y931qOEBXACUAbvGpfUGSoFf1ZJ3EODAqXFpbYAPgBmZrltEdS5IktYLKAeuyHTdoqhzwnmeB+4EZgKzM12vCH/OWcC7wOOZrkca63xI+P/5kIT0a8L8+ZmuX3U/q7jXp4V1KKxDvpR9f6lFU7tRwFx3/ziW4O5LgNeB0XXIuwX4e1zeUuAhYKSZtU19cVOiwXV295IkaZ8CJUD3FJczlRrzcwbAzE4gaL1dEkkJU68xdR4K9AduiKx00WhMnXPD57UJ6WsIAq+lqIwp5e7lDcyasu8vBZra7Q68kyT9XYL/aLXlXeLuG5LkzSVo0jZFjalzFWbWD+gKLGpkuaLUqDqbWSfgRuB37r4qxWWLSmPq/JPwuZ2ZzTWzLWa2wsxuNrO8lJYytRpT5xeBj4Brzay/mXUws2EEraQ73H19aouacSn7/lKgqV1ngr7NRKuATo3IG/u8KWpMnSsxszbAHQQtmrsbX7TINLbO1wEfAtNSWKaoNabO3cLnvwMvAAcDfyTomvl/qSpgBBpcZ3f/niDAxroN1wEvAU8D56W2mE1Cyr6/2qSkOC1fsrta69JMtkbkzbRUlftWYD/gCHdP9kvblDSozmZ2ADAO2NvDjuxmpKE/59gfqQ+4+2Xh65lmlg1cY2b93f29lJQw9Rr6c25HEFi7EkwiWAb8iGCgvBQ4O4VlbApS9v2lQFO71SSP3J1IHu3jrQKSTZvsFPd5U9SYOlcwsynAGcDJ7v5CisoWlcbU+U6C1trnZtYxTGsDZIfvN7r7phSVM5UaU+dvwud/JqS/QDA4vifQFANNY+o8nmBsald3XxymvWZm3wJTzewOd1+YspJmXsq+v9R1Vrt3CfoqE/Wn9v9I7wK9wymViXk3Ax9XzdIkNKbOAJjZBIKpzRe4+/0pLFtUGlPnfsBZBF9Uscf+wODwdVP9S7exv9tQ9S/e2F+7DR2Ajlpj6rwHsDouyMTMC5/7NbJsTU3Kvr8UaGo3Axgc3h8BQHiz0/7hZ7XlzQF+Hpe3DXAs8EIT/SsXGldnzOwXwFXABHe/JapCplhj6nxQksdCgkHng4BHIyhvKjSmzv8ANgGHJqSPDJ8XpKiMqdaYOn8FdDKzxEHwH4fPX6SqkE1E6r6/Mj3Hu6k/gPYEkfu/BNMfRxF8iXwCdIg7rhdBP+1lCfkfIvir9jRgOMGXzvcE/fkZr1+q60xww2Y5wRfR4IRH/0zXLaqfc5LzzaTp30fT2N/ty8P0q4ERBC3YjcC0TNctijoDhQRTmz8kuNnzIOC3YdoC4u5XaWoP4Gfh43aCVujZ4fshtfyMU/L9lfF/gObwIOinnB7+Qq0DniDhhqfwl9CBiQnpeQT3GnwV/oD+DQzNdJ2iqjPBrCuv5jEz0/WK6uec5FxNPtA0ts4E3WS/Cr+4NxNsf34FkJPpekVY5/7Aw8BnBEH1Q+BPQKdM16uWOtf4fzLq7y9tEyAiIpHSGI2IiERKgUZERCKlQCMiIpFSoBERkUgp0IiISKQUaEREJFIKNCIRMLNTwi1zY4/NZrbYzK4OF2fMVLmmmdnSuPeFYflOyVSZpOXTopoi0fo58DmwDXA0waZo2wDnZ7JQIumkQCMSrbd8626O/zSz3YDxZnaBN3znQ5FmRV1nIun1JsGyHtsDmFm+mV1rZkvC7rUlZjbBzCr93zSzAjO7zcw+M7NN4fP9se10zWzX8P0SM9toZp+Y2e3hzp8iGaUWjUh6FQLfAt+EK+E+T7B+1pUECz0OBi4l2DPl11CxTfS/wrSrgLcJNt8aTbCl7iaCHS8/By4kWASxD/B74FmgOB0VE6mOAo1ItLLDgBIbozkGuNDdy8zsJIKtgYe4+2vh8S+ZGcDlZnatu68AfkkQOIrc/T9x5/6/2Iswf+wcmNm/CBa7nGVmeyXkE0krdZ2JROt9YAvBboR3A3e6+63hZ4cSrHj8LzNrE3sQ7FKZQ9C6ATgEmF9TsDCzXDP7vZm9b2Ybw2vOCj/+YcprJVIPatGIROtogi6tAoIl9c8xs3+7+30E3V+9CIJCMl3inmvbIngKwUy2Kwi62dYBPYDHgIxNpxYBBRqRqL0Tm3VmZi8TjK9cZ2bTgW+AJcCYavIuDZ9XAt1ruc5xwH3uflUswcw6NKLcIimjQCOSJu6+ycx+CzwJnAM8RzBm8527v19D1heAP5jZIHevrmWTT9WW0amNLbNIKijQiKSRu88ws/nAb4BdCYLBS2Z2PUH3WC6wC8EWw0e5+wbgRuAE4EUzu4pgdtr2BLPOznL3dQRB62Qz+y/BJICfAvultXIi1VCgEUm/PxBMaz4NGAlcDJwB9AbWA4uBZwi2R8bd15jZ/gRTmy8mGLP5Gng5dgzB+IwBk8P3zwLHA/Oir45IzbSVs4iIRErTm0VEJFIKNCIiEikFGhERiZQCjYiIREqBRkREIqVAIyIikVKgERGRSCnQiIhIpP4/b12I5OkMXFEAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the curve for the model\n", + "no_skill = len(testy[testy==1]) / len(testy)\n", + "pyplot.plot(recall, precision, marker='.', label='Random Forest', color='orange')\n", + "pyplot.scatter(recall[ix], precision[ix], marker='o', color='black', label='Best')\n", + "\n", + "# axis labels\n", + "pyplot.xlabel('Recall')\n", + "pyplot.ylabel('Precision')\n", + "pyplot.legend()\n", + "\n", + "# show the plot\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've found the best threshold, what accuracy can we achieve with it?" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.8664\n", + "Weighted F1: 0.8844\n", + "Weighted Precision: 0.9\n", + "Weighted Recall: 0.87\n" + ] + } + ], + "source": [ + "threshold = 0.55\n", + "\n", + "predicted_proba = model.predict_proba(testX)\n", + "predicted = (predicted_proba [:,1] >= threshold).astype('int')\n", + "\n", + "ba = np.round( balanced_accuracy_score(testy, predicted), 4 )\n", + "\n", + "print( \"Balanced Accuracy:\", ba)\n", + "print( \"Macro F1:\", np.round( f1_score(testy, predicted, average='macro'), 4 ))\n", + "print( \"Macro Precision:\", np.round( precision_score(testy, predicted, average='macro'), 2 ) )\n", + "print( \"Macro Recall:\", np.round( recall_score(testy, predicted, average='macro'), 2 ) )" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "with open('./rf_model_threshold_moving.pkl', 'wb') as f:\n", + " pickle.dump(model, f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiclass Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All classes" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_8062/1900062145.py:14: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + "/tmp/ipykernel_8062/1900062145.py:18: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.3642\n", + "Weighted F1: 0.4375\n", + "Weighted Precision: 0.72\n", + "Weighted Recall: 0.36\n", + "\n", + "Balanced Accuracy: 0.3709\n", + "Weighted F1: 0.4502\n", + "Weighted Precision: 0.77\n", + "Weighted Recall: 0.37\n", + "\n", + "Balanced Accuracy: 0.3617\n", + "Weighted F1: 0.4374\n", + "Weighted Precision: 0.75\n", + "Weighted Recall: 0.36\n", + "\n", + "Balanced Accuracy: 0.3687\n", + "Weighted F1: 0.4467\n", + "Weighted Precision: 0.76\n", + "Weighted Recall: 0.37\n", + "\n", + "Balanced Accuracy: 0.3612\n", + "Weighted F1: 0.4369\n", + "Weighted Precision: 0.76\n", + "Weighted Recall: 0.36\n", + "\n", + "Average Balanced Accuracy: 0.3653\n" + ] + } + ], + "source": [ + "# read into a Pandas dataframe\n", + "rawDf = pd.read_csv( url, compression=\"zip\" )\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "rawDf = rawDf.iloc[:, 3:] \n", + "\n", + "rawDf = rawDf.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "rawDf = rawDf.dropna()\n", + "\n", + "nCols = rawDf.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( rawDf.iloc[:, nCols-1] ) # ornl type\n", + "rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( rawDf.iloc[:, nCols-2] ) # nearest road type\n", + "rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )\n", + "\n", + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "accs = []\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " predictions = rf.predict(X_test)\n", + " ba = np.round( balanced_accuracy_score(y_test, predictions), 4 )\n", + " accs.append( ba )\n", + " print( \"Balanced Accuracy:\", ba)\n", + " print( \"Macro F1:\", np.round( f1_score(y_test, predictions, average='macro'), 4 ))\n", + " print( \"Macro Precision:\", np.round( precision_score(y_test, predictions, average='macro'), 2 ) )\n", + " print( \"Macro Recall:\", np.round( recall_score(y_test, predictions, average='macro'), 2 ) )\n", + " print()\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What does all of this look like if we want to do multi-class classification instead of binary classification? Let's look at another use case where we want to classify a building as \"Residential\", \"Commercial\", \"Industrial\", or \"Other\"." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Residential 976690\n", + "Commercial 64029\n", + "Other 20703\n", + "Industrial 16722\n", + "Name: BuildingType, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "# read into a Pandas dataframe\n", + "rawDf = pd.read_csv( url, compression=\"zip\" )\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "rawDf = rawDf.iloc[:, 3:] \n", + "\n", + "# convert to 'other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Assembly\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Education\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Government\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Agriculture\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Utility and Misc\", \"BuildingType\"] = 'Other'\n", + "\n", + "# building type distributions\n", + "print()\n", + "print( rawDf['BuildingType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.5633\n", + "Weighted F1: 0.9402\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.5721\n", + "Weighted F1: 0.9415\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.5666\n", + "Weighted F1: 0.9407\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.5687\n", + "Weighted F1: 0.9413\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.5711\n", + "Weighted F1: 0.9412\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Average Balanced Accuracy: 0.5684\n" + ] + } + ], + "source": [ + "rawDf = rawDf.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "rawDf = rawDf.dropna()\n", + "\n", + "nCols = rawDf.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( rawDf.iloc[:, nCols-1] ) # ornl type\n", + "rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( rawDf.iloc[:, nCols-2] ) # nearest road type\n", + "rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )\n", + "\n", + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "accs = []\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " predictions = rf.predict(X_test)\n", + " ba = np.round( balanced_accuracy_score(y_test, predictions), 4 )\n", + " accs.append( ba )\n", + " print( \"Balanced Accuracy:\", ba)\n", + " print( \"Macro F1:\", np.round( f1_score(y_test, predictions, average='macro'), 4 ))\n", + " print( \"Macro Precision:\", np.round( precision_score(y_test, predictions, average='macro'), 2 ) )\n", + " print( \"Macro Recall:\", np.round( recall_score(y_test, predictions, average='macro'), 2 ) )\n", + " print()\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try \"Residential\", \"Commercial\", or \"Other\"" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Residential 976690\n", + "Commercial 64029\n", + "Other 37425\n", + "Name: BuildingType, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "# read into a Pandas dataframe\n", + "rawDf = pd.read_csv( url, compression=\"zip\" )\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "rawDf = rawDf.iloc[:, 3:] \n", + "\n", + "# drop the rows we don't need\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Industrial\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Assembly\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Education\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Government\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Agriculture\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Utility and Misc\", \"BuildingType\"] = 'Other'\n", + "\n", + "# building type distributions\n", + "print()\n", + "print( rawDf['BuildingType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Residential 1.000000\n", + "Commercial 15.253869\n", + "Other 26.097261\n", + "Name: BuildingType, dtype: float64\n", + "\n" + ] + } + ], + "source": [ + "# building type distributions\n", + "print()\n", + "print( 976690/rawDf['BuildingType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocess" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_38438/2590111546.py:8: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + "/tmp/ipykernel_38438/2590111546.py:12: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )\n" + ] + } + ], + "source": [ + "rawDf = rawDf.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "rawDf = rawDf.dropna()\n", + "\n", + "nCols = rawDf.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( rawDf.iloc[:, nCols-1] ) # ornl type\n", + "rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( rawDf.iloc[:, nCols-2] ) # nearest road type\n", + "rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadBuildingType
160055-77.64560236.499769308.43911924578.010570.00.60500376.0275030611652
777985-78.90671335.713843256.92590041189.0462582.00.40957845.81191057233027781778042
815298-78.51810235.914213290.11917241189.0462582.00.40957850.01191057233027781778042
721153-80.71393535.287639120.53600036443.0478966.00.42938016.0381180343926071735242
509097-80.89217635.396898143.35000036443.0478966.00.4293809.0381180343926071735242
\n", + "
" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty \\\n", + "160055 -77.645602 36.499769 308.439119 24578.0 \n", + "777985 -78.906713 35.713843 256.925900 41189.0 \n", + "815298 -78.518102 35.914213 290.119172 41189.0 \n", + "721153 -80.713935 35.287639 120.536000 36443.0 \n", + "509097 -80.892176 35.396898 143.350000 36443.0 \n", + "\n", + " HousingUnitsCounty HousingDensityCounty Impervious AgCount \\\n", + "160055 10570.0 0.605003 76.0 27 \n", + "777985 462582.0 0.409578 45.8 119 \n", + "815298 462582.0 0.409578 50.0 119 \n", + "721153 478966.0 0.429380 16.0 38 \n", + "509097 478966.0 0.429380 9.0 38 \n", + "\n", + " CmCount GvCount EdCount InCount OsmNearestRoad BuildingType \n", + "160055 503 0 6 116 5 2 \n", + "777985 105723 30 2778 17780 4 2 \n", + "815298 105723 30 2778 17780 4 2 \n", + "721153 118034 39 2607 17352 4 2 \n", + "509097 118034 39 2607 17352 4 2 " + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rawDf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random Forest Multiclass Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.6723\n", + "Weighted F1: 0.7151\n", + "Weighted Precision: 0.78\n", + "Weighted Recall: 0.67\n", + "\n", + "Balanced Accuracy: 0.6714\n", + "Weighted F1: 0.7142\n", + "Weighted Precision: 0.78\n", + "Weighted Recall: 0.67\n", + "\n", + "Balanced Accuracy: 0.6761\n", + "Weighted F1: 0.7177\n", + "Weighted Precision: 0.78\n", + "Weighted Recall: 0.68\n", + "\n", + "Balanced Accuracy: 0.6704\n", + "Weighted F1: 0.7137\n", + "Weighted Precision: 0.78\n", + "Weighted Recall: 0.67\n", + "\n", + "Balanced Accuracy: 0.672\n", + "Weighted F1: 0.7141\n", + "Weighted Precision: 0.78\n", + "Weighted Recall: 0.67\n", + "\n", + "Average Balanced Accuracy: 0.6724\n" + ] + } + ], + "source": [ + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "accs = []\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " predictions = rf.predict(X_test)\n", + " ba = np.round( balanced_accuracy_score(y_test, predictions), 4 )\n", + " accs.append( ba )\n", + " print( \"Balanced Accuracy:\", ba)\n", + " print( \"Macro F1:\", np.round( f1_score(y_test, predictions, average='macro'), 4 ))\n", + " print( \"Macro Precision:\", np.round( precision_score(y_test, predictions, average='macro'), 2 ) )\n", + " print( \"Macro Recall:\", np.round( recall_score(y_test, predictions, average='macro'), 2 ) )\n", + " print()\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "with open('./rf_model_multi.pkl', 'wb') as f:\n", + " pickle.dump(rf, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = rf.predict(X_test)\n", + "cm = confusion_matrix( y_test, predictions, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 22}\n", + "plt.rc('font', **font)\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1,2]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Commercial', 'Other', 'Residential'], dtype=object)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "le.inverse_transform( np.arange(0,3,1) )" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time to compute the importances: 523.175 seconds\n" + ] + } + ], + "source": [ + "start_time = time.time()\n", + "result = permutation_importance(rf, X_test, y_test, n_repeats=10, random_state=42, n_jobs=1)\n", + "elapsed_time = time.time() - start_time\n", + "print(f\"Elapsed time to compute the importances: {elapsed_time:.3f} seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_idx = result.importances_mean.argsort()\n", + "forest_importances = pd.Series(result.importances_mean[sorted_idx], index=df.columns[sorted_idx])\n", + "\n", + "fig, ax = plt.subplots( figsize=(10, 10) )\n", + "forest_importances.plot.bar(yerr=result.importances_std[sorted_idx], ax=ax)\n", + "ax.set_title(\"Feature importances using permutation method\")\n", + "ax.set_ylabel(\"Mean accuracy decrease\")\n", + "ax.set_yscale('log')\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## One-vs-One Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One-vs-One is a multi-class classification strategy for dealing with unbalanced classes. The One-vs-One strategy splits a multi-class classification into one binary classification problem per each pair of classes, e.g. Residentail vs. Commercial, Residential vs. Industrial, Commercial vs. Industrial. The final class assignment is determined by aggregating the results of the binary classifiers." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadBuildingType
160055-77.64560236.499769308.43911924578.010570.00.60500376.0275030611652
777985-78.90671335.713843256.92590041189.0462582.00.40957845.81191057233027781778042
815298-78.51810235.914213290.11917241189.0462582.00.40957850.01191057233027781778042
721153-80.71393535.287639120.53600036443.0478966.00.42938016.0381180343926071735242
509097-80.89217635.396898143.35000036443.0478966.00.4293809.0381180343926071735242
\n", + "
" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty \\\n", + "160055 -77.645602 36.499769 308.439119 24578.0 \n", + "777985 -78.906713 35.713843 256.925900 41189.0 \n", + "815298 -78.518102 35.914213 290.119172 41189.0 \n", + "721153 -80.713935 35.287639 120.536000 36443.0 \n", + "509097 -80.892176 35.396898 143.350000 36443.0 \n", + "\n", + " HousingUnitsCounty HousingDensityCounty Impervious AgCount \\\n", + "160055 10570.0 0.605003 76.0 27 \n", + "777985 462582.0 0.409578 45.8 119 \n", + "815298 462582.0 0.409578 50.0 119 \n", + "721153 478966.0 0.429380 16.0 38 \n", + "509097 478966.0 0.429380 9.0 38 \n", + "\n", + " CmCount GvCount EdCount InCount OsmNearestRoad BuildingType \n", + "160055 503 0 6 116 5 2 \n", + "777985 105723 30 2778 17780 4 2 \n", + "815298 105723 30 2778 17780 4 2 \n", + "721153 118034 39 2607 17352 4 2 \n", + "509097 118034 39 2607 17352 4 2 " + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rawDf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Size: (862513, 13)\n", + "Testing Size: (215629, 13)\n" + ] + } + ], + "source": [ + "train, test = train_test_split(rawDf, test_size=0.2, random_state=42, stratify=rawDf[['BuildingType']])\n", + "\n", + "nCols = test.shape[1]\n", + "xTest = test.iloc[:, :nCols-1]\n", + "yTest = test.iloc[:, nCols-1]\n", + "\n", + "xTrain = train.iloc[:, :nCols-1]\n", + "yTrain = train.iloc[:, nCols-1]\n", + "\n", + "print(\"Training Size:\", xTrain.shape)\n", + "print(\"Testing Size:\", xTest.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoad
729403-80.65134335.183935239.17490036443.0478966.00.42938019.75000038118034392607173527
412292-77.62152836.431713103.11440020777.024735.00.50872045.5000004028430313354
461335-76.89171334.860509138.97410428939.046800.00.46465461.666667896859412211034
68893-80.37023134.994120183.20703435468.083788.00.35165616.5000004413966631153245
256890-78.38032435.661713342.91760030833.084340.00.39046520.0000005310016418439084
\n", + "
" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty \\\n", + "729403 -80.651343 35.183935 239.174900 36443.0 \n", + "412292 -77.621528 36.431713 103.114400 20777.0 \n", + "461335 -76.891713 34.860509 138.974104 28939.0 \n", + "68893 -80.370231 34.994120 183.207034 35468.0 \n", + "256890 -78.380324 35.661713 342.917600 30833.0 \n", + "\n", + " HousingUnitsCounty HousingDensityCounty Impervious AgCount \\\n", + "729403 478966.0 0.429380 19.750000 38 \n", + "412292 24735.0 0.508720 45.500000 40 \n", + "461335 46800.0 0.464654 61.666667 89 \n", + "68893 83788.0 0.351656 16.500000 44 \n", + "256890 84340.0 0.390465 20.000000 53 \n", + "\n", + " CmCount GvCount EdCount InCount OsmNearestRoad \n", + "729403 118034 39 2607 17352 7 \n", + "412292 2843 0 31 335 4 \n", + "461335 6859 4 122 1103 4 \n", + "68893 13966 6 311 5324 5 \n", + "256890 10016 4 184 3908 4 " + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xTrain.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.6681\n", + "Weighted F1: 0.7104\n", + "Weighted Precision: 0.78\n", + "Weighted Recall: 0.67\n" + ] + } + ], + "source": [ + "from sklearn.multiclass import OneVsOneClassifier\n", + "\n", + "# define model\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "# define ovo strategy\n", + "ovo = OneVsOneClassifier(rf)\n", + "ovo.fit( xTrain, yTrain )\n", + " \n", + "# make predictions\n", + "yhat = ovo.predict(xTest)\n", + "\n", + "ba = np.round( balanced_accuracy_score(yTest, yhat), 4 )\n", + "print( \"Balanced Accuracy:\", ba)\n", + "print( \"Macro F1:\", np.round( f1_score(yTest, yhat, average='macro'), 4 ))\n", + "print( \"Macro Precision:\", np.round( precision_score(yTest, yhat, average='macro'), 2 ) )\n", + "print( \"Macro Recall:\", np.round( recall_score(yTest, yhat, average='macro'), 2 ) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## One-vs-Rest Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The One-vs-Rest strategy is a related approach for dealing with multi-class imbalance. Here, fewer models are created. There is one binary classification problem per class, e.g. Residentail vs. Rest, Commercial vs. Rest, Industrial vs. Rest." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.6706\n", + "Weighted F1: 0.7146\n", + "Weighted Precision: 0.79\n", + "Weighted Recall: 0.67\n" + ] + } + ], + "source": [ + "from sklearn.multiclass import OneVsRestClassifier\n", + "\n", + "# define model\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "# define ovr strategy\n", + "ovr = OneVsRestClassifier(rf)\n", + "ovr.fit( xTrain, yTrain )\n", + " \n", + "# make predictions\n", + "yhat = ovr.predict(xTest)\n", + "\n", + "ba = np.round( balanced_accuracy_score(yTest, yhat), 4 )\n", + "print( \"Balanced Accuracy:\", ba)\n", + "print( \"Macro F1:\", np.round( f1_score(yTest, yhat, average='macro'), 4 ))\n", + "print( \"Macro Precision:\", np.round( precision_score(yTest, yhat, average='macro'), 2 ) )\n", + "print( \"Macro Recall:\", np.round( recall_score(yTest, yhat, average='macro'), 2 ) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Network for multiclass classification" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_38438/3238574568.py:29: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + "/tmp/ipykernel_38438/3238574568.py:33: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n", + " rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Size: (862515, 13)\n", + "Testing Size: (215629, 13)\n" + ] + } + ], + "source": [ + "# read into a Pandas dataframe\n", + "rawDf = pd.read_csv( url, compression=\"zip\" )\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "rawDf = rawDf.iloc[:, 3:] \n", + "\n", + "# drop the rows we don't need\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Industrial\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Assembly\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Education\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Government\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Agriculture\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Utility and Misc\", \"BuildingType\"] = 'Other'\n", + "\n", + "rawDf = rawDf.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "# preprocess the data - scaling\n", + "scaler = preprocessing.StandardScaler()\n", + " \n", + "columns = ['X', 'Y', 'Area', 'MedianIncomeCounty', \n", + " 'HousingUnitsCounty', 'HousingDensityCounty',\n", + " 'Impervious', 'AgCount', 'CmCount', 'GvCount',\n", + " 'EdCount', 'InCount']\n", + "rawDf[columns] = scaler.fit_transform(rawDf[columns])\n", + "\n", + "nCols = rawDf.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( rawDf.iloc[:, nCols-1] ) # ornl type\n", + "rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( rawDf.iloc[:, nCols-2] ) # nearest road type\n", + "rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )\n", + "\n", + "train, test = train_test_split(rawDf, test_size=0.2, random_state=42, stratify=rawDf[['BuildingType']])\n", + "\n", + "nCols = test.shape[1]\n", + "xTest = test.iloc[:, :nCols-1]\n", + "yTest = test.iloc[:, nCols-1]\n", + "\n", + "xTrain = train.iloc[:, :nCols-1]\n", + "yTrain = train.iloc[:, nCols-1]\n", + "\n", + "print(\"Training Size:\", xTrain.shape)\n", + "print(\"Testing Size:\", xTest.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoad
8076340.9629901.023992-0.0961541.3022840.963812-0.339621-0.3558451.6657310.8805030.6435561.0978171.0419344
77835-0.929522-0.8920311.4710290.139001-1.071597-1.4242722.104350-0.189389-1.054971-1.048588-1.045623-0.7706055
2487450.9159790.262305-0.182595-0.803459-1.068631-0.697542-1.2951920.033225-1.138290-1.189600-1.155967-0.9766544
8073390.9165301.061387-0.0369881.3022840.963812-0.339621-1.2169131.6657310.8805030.6435561.0978171.0419347
371189-0.1185921.267249-0.133292-0.937660-0.2737070.0261310.583503-0.758292-0.383523-0.202516-0.520841-0.3213994
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" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty HousingUnitsCounty \\\n", + "807634 0.962990 1.023992 -0.096154 1.302284 0.963812 \n", + "77835 -0.929522 -0.892031 1.471029 0.139001 -1.071597 \n", + "248745 0.915979 0.262305 -0.182595 -0.803459 -1.068631 \n", + "807339 0.916530 1.061387 -0.036988 1.302284 0.963812 \n", + "371189 -0.118592 1.267249 -0.133292 -0.937660 -0.273707 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount \\\n", + "807634 -0.339621 -0.355845 1.665731 0.880503 0.643556 \n", + "77835 -1.424272 2.104350 -0.189389 -1.054971 -1.048588 \n", + "248745 -0.697542 -1.295192 0.033225 -1.138290 -1.189600 \n", + "807339 -0.339621 -1.216913 1.665731 0.880503 0.643556 \n", + "371189 0.026131 0.583503 -0.758292 -0.383523 -0.202516 \n", + "\n", + " EdCount InCount OsmNearestRoad \n", + "807634 1.097817 1.041934 4 \n", + "77835 -1.045623 -0.770605 5 \n", + "248745 -1.155967 -0.976654 4 \n", + "807339 1.097817 1.041934 7 \n", + "371189 -0.520841 -0.321399 4 " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xTrain.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "807634 2\n", + "77835 0\n", + "248745 2\n", + "807339 2\n", + "371189 2\n", + "Name: BuildingType, dtype: int64" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yTrain.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weight for class 0: 8.42\n", + "Weight for class 1: 14.40\n", + "Weight for class 2: 0.55\n" + ] + } + ], + "source": [ + "# Scaling by total/2 helps keep the loss to a similar magnitude.\n", + "total = xTrain.shape[0]\n", + "ix = np.where( yTrain == 0 )\n", + "zero = len(ix[0])\n", + "ix = np.where( yTrain == 1 )\n", + "one = len(ix[0])\n", + "ix = np.where( yTrain == 2 )\n", + "two = len(ix[0])\n", + "\n", + "weight_for_0 = (1 / zero) * (total / 2.0)\n", + "weight_for_1 = (1 / one) * (total / 2.0)\n", + "weight_for_2 = (1 / two) * (total / 2.0)\n", + "\n", + "class_weight = {0: weight_for_0, 1: weight_for_1, 2: weight_for_2}\n", + "\n", + "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n", + "print('Weight for class 1: {:.2f}'.format(weight_for_1))\n", + "print('Weight for class 2: {:.2f}'.format(weight_for_2))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoad
8076340.9629901.023992-0.0961541.3022840.963812-0.339621-0.3558451.6657310.8805030.6435561.0978171.0419344
77835-0.929522-0.8920311.4710290.139001-1.071597-1.4242722.104350-0.189389-1.054971-1.048588-1.045623-0.7706055
2487450.9159790.262305-0.182595-0.803459-1.068631-0.697542-1.2951920.033225-1.138290-1.189600-1.155967-0.9766544
8073390.9165301.061387-0.0369881.3022840.963812-0.339621-1.2169131.6657310.8805030.6435561.0978171.0419347
371189-0.1185921.267249-0.133292-0.937660-0.2737070.0261310.583503-0.758292-0.383523-0.202516-0.520841-0.3213994
\n", + "
" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty HousingUnitsCounty \\\n", + "807634 0.962990 1.023992 -0.096154 1.302284 0.963812 \n", + "77835 -0.929522 -0.892031 1.471029 0.139001 -1.071597 \n", + "248745 0.915979 0.262305 -0.182595 -0.803459 -1.068631 \n", + "807339 0.916530 1.061387 -0.036988 1.302284 0.963812 \n", + "371189 -0.118592 1.267249 -0.133292 -0.937660 -0.273707 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount \\\n", + "807634 -0.339621 -0.355845 1.665731 0.880503 0.643556 \n", + "77835 -1.424272 2.104350 -0.189389 -1.054971 -1.048588 \n", + "248745 -0.697542 -1.295192 0.033225 -1.138290 -1.189600 \n", + "807339 -0.339621 -1.216913 1.665731 0.880503 0.643556 \n", + "371189 0.026131 0.583503 -0.758292 -0.383523 -0.202516 \n", + "\n", + " EdCount InCount OsmNearestRoad \n", + "807634 1.097817 1.041934 4 \n", + "77835 -1.045623 -0.770605 5 \n", + "248745 -1.155967 -0.976654 4 \n", + "807339 1.097817 1.041934 7 \n", + "371189 -0.520841 -0.321399 4 " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xTrain.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/150\n", + "2696/2696 [==============================] - 17s 6ms/step - loss: nan - accuracy: 0.2175 - val_loss: nan - val_accuracy: 0.0593\n", + "Epoch 2/150\n", + "2696/2696 [==============================] - 16s 6ms/step - loss: nan - accuracy: 0.0594 - val_loss: nan - val_accuracy: 0.0593\n", + "Epoch 3/150\n", + "2696/2696 [==============================] - 16s 6ms/step - loss: nan - accuracy: 0.0594 - val_loss: nan - val_accuracy: 0.0593\n", + "Epoch 4/150\n", + "2696/2696 [==============================] - 16s 6ms/step - loss: nan - accuracy: 0.0594 - val_loss: nan - val_accuracy: 0.0593\n", + "Epoch 5/150\n", + "2696/2696 [==============================] - 16s 6ms/step - loss: nan - accuracy: 0.0594 - val_loss: nan - val_accuracy: 0.0593\n", + "Epoch 6/150\n", + "2696/2696 [==============================] - 16s 6ms/step - loss: nan - accuracy: 0.0594 - val_loss: nan - val_accuracy: 0.0593\n", + "Epoch 7/150\n", + "2696/2696 [==============================] - 16s 6ms/step - loss: nan - accuracy: 0.0594 - val_loss: nan - val_accuracy: 0.0593\n", + "Epoch 8/150\n", + "2696/2696 [==============================] - 16s 6ms/step - loss: nan - accuracy: 0.0594 - val_loss: nan - val_accuracy: 0.0593\n" + ] + } + ], + "source": [ + "# convert output from scalar to vector format, i.e. building type=2 to [0,0,1]\n", + "y = to_categorical(yTrain)\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(30, input_shape=(xTrain.shape[1],), activation='relu'))\n", + "model.add(Dense(15, activation='relu'))\n", + "model.add(Dense(3, activation='softmax'))\n", + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "\n", + "# stop training if we see 7 epochs over which val_accuracy doesn't improve\n", + "es = EarlyStopping(monitor='val_accuracy', patience=7, restore_best_weights=True)\n", + "\n", + "# fit the model\n", + "h = model.fit(xTrain, y, epochs=150, batch_size=256, callbacks=[es], \\\n", + " class_weight=class_weight, validation_split=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 1.], dtype=float32)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot( h.history['accuracy'], label='Training' )\n", + "plt.plot( h.history['val_accuracy'], label='Validation' )\n", + "plt.title('Training Accuracy')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6739/6739 [==============================] - 22s 3ms/step - loss: nan - accuracy: 0.0594\n", + "Accuracy on the test set: 0.0594\n" + ] + } + ], + "source": [ + "y = to_categorical(yTest)\n", + "loss, acc = model.evaluate( xTest, y )\n", + "print(\"Accuracy on the test set:\", np.round(acc,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6739/6739 [==============================] - 5s 694us/step\n" + ] + }, + { + "data": { + "text/plain": [ + "215629" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ps = model.predict(xTest)\n", + "predictions = []\n", + "for p in ps:\n", + " predictions.append( np.argmax( p ) )\n", + "len(predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.371\n", + "Weighted F1: 0.866\n", + "Weighted Precision: 0.85\n", + "Weighted Recall: 0.89\n" + ] + } + ], + "source": [ + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(yTest.values, predictions), 4 ))\n", + "print( \"Macro F1:\", np.round( f1_score(yTest.values, predictions, average='macro'), 4 ))\n", + "print( \"Macro Precision:\", np.round( precision_score(yTest.values, predictions, average='macro'), 2 ) )\n", + "print( \"Macro Recall:\", np.round( recall_score(yTest.values, predictions, average='macro'), 2 ) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summary of Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

\n", + "

Binary Classification
\n", + "\n", + "| Approach | Accuracy | Precision | Recall | F1 \n", + "-----------|----------|-----------|--------|---\n", + "| Random Forest | 85.5% | 96% | 96% | 96% \n", + "| Random Forest w/ balanced classes | 84% | 84% | 84% | 84%\n", + "| Random Forest w/ threshold moving | 87% | 96% | 96% | 96%\n", + "| Neural Network w/ class weighting | 87% | 94% | 90% | 91% \n", + "

\n", + "\n", + "
\n", + "\n", + "

\n", + "

Multiclass Classification
\n", + " \n", + "| Approach | Accuracy | Precision | Recall | F1 \n", + "-----------|----------|-----------|--------|---\n", + "| Random Forest | 67% | 94% | 95% | 94%\n", + "| Random Forest w/ one-vs-one | 65% | 94% | 94% | 94%\n", + "| Random Forest w/ one-vs-rest | 65% | 94% | 94% | 94%\n", + "| Neural Network w/ class weighting | 37% | 85% | 89% | 87%\n", + "

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explainability" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models. The various machine learning algorithms have different levels of inherent explainability. For example, random forest models, which are based on decision trees, are more easily able to explain why they made the predictions they did. Neural networks, on the other hand, are more of a black box.\n", + "\n", + "Because of this, we may actually favor the random forest model with threshold moving over the neural network model in the binary classification scenario. Even though the random forest model has slightly worse accuracy, the rationale behind its predictions are much more explainable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classify Unknown Buildings in North Carolina" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now apply our trained model to a dataset of buildings with unknown type." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " StateFips CountyFips StateCountyFips X Y Area \\\n", + "0 37 99 37099 -83.260602 35.484954 20.716411 \n", + "1 37 99 37099 -83.155694 35.493472 68.605588 \n", + "2 37 99 37099 -83.155694 35.493287 246.025647 \n", + "3 37 99 37099 -83.154768 35.491620 142.232825 \n", + "4 37 99 37099 -83.158009 35.486898 170.658810 \n", + "\n", + " MedianIncomeCounty HousingUnitsCounty HousingDensityCounty Impervious \\\n", + "0 22429.0 26649.0 0.618177 2.0 \n", + "1 22429.0 26649.0 0.618177 6.0 \n", + "2 22429.0 26649.0 0.618177 8.0 \n", + "3 22429.0 26649.0 0.618177 0.0 \n", + "4 22429.0 26649.0 0.618177 0.5 \n", + "\n", + " AgCount CmCount GvCount EdCount InCount OsmNearestRoad FEMA_100yr \n", + "0 22 2860 3 51 804 tertiary 0.0 \n", + "1 22 2860 3 51 804 secondary 0.0 \n", + "2 22 2860 3 51 804 secondary 0.0 \n", + "3 22 2860 3 51 804 secondary 0.0 \n", + "4 22 2860 3 51 804 residential 0.0 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# unknown buildings in North Carolina\n", + "url = \"https://github.com/geo-smart/flood-risk-ml-tutorial/blob/main/unknownBuildings.zip?raw=true\"\n", + "\n", + "unknown = pd.read_csv( url, compression=\"zip\" )\n", + "unknown.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# read the models from file \n", + "with open('./models/rf_model_multi.pkl', 'rb') as f:\n", + " rf = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# shuffle the dataframe (technically, we randomly resample the entire df)\n", + "unknown = unknown.sample(frac=1) \n", + "\n", + "unknown = unknown.dropna()\n", + "\n", + "nCols = unknown.shape[1] \n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( unknown.iloc[:, nCols-2] ) # nearest road type\n", + "unknown.iloc[:, nCols-2] = le.transform( unknown.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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StateFipsCountyFipsStateCountyFipsXYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadFEMA_100yr
216003376337063-78.89097236.098843252.33260035085.0144559.00.4450260.25000002675812892373240.0
537093717937179-80.58745435.11476869.91563835468.083788.00.3516560.00000044139666311532450.0
105460371937019-78.02717633.94282458.45440930529.088330.00.64619352.0000004480353113218840.0
1654993718337183-78.44328735.774306165.87730041189.0462582.00.40957831.6666671191057233027781778040.0
1012143711537115-82.78912035.76421389.80333826782.011045.00.5211630.000000080801729340.0
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" + ], + "text/plain": [ + " StateFips CountyFips StateCountyFips X Y \\\n", + "216003 37 63 37063 -78.890972 36.098843 \n", + "53709 37 179 37179 -80.587454 35.114768 \n", + "105460 37 19 37019 -78.027176 33.942824 \n", + "165499 37 183 37183 -78.443287 35.774306 \n", + "101214 37 115 37115 -82.789120 35.764213 \n", + "\n", + " Area MedianIncomeCounty HousingUnitsCounty \\\n", + "216003 252.332600 35085.0 144559.0 \n", + "53709 69.915638 35468.0 83788.0 \n", + "105460 58.454409 30529.0 88330.0 \n", + "165499 165.877300 41189.0 462582.0 \n", + "101214 89.803338 26782.0 11045.0 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount EdCount \\\n", + "216003 0.445026 0.250000 0 26758 12 892 \n", + "53709 0.351656 0.000000 44 13966 6 311 \n", + "105460 0.646193 52.000000 44 8035 3 113 \n", + "165499 0.409578 31.666667 119 105723 30 2778 \n", + "101214 0.521163 0.000000 0 808 0 17 \n", + "\n", + " InCount OsmNearestRoad FEMA_100yr \n", + "216003 3732 4 0.0 \n", + "53709 5324 5 0.0 \n", + "105460 2188 4 0.0 \n", + "165499 17780 4 0.0 \n", + "101214 293 4 0.0 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unknown.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "219054 unknown buildings in North Carolina\n" + ] + } + ], + "source": [ + "print( unknown.shape[0], 'unknown buildings in North Carolina')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make predictions for all unknown buildings in North Carolina" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# generate dataset\n", + "X = unknown.drop(['StateFips','CountyFips','StateCountyFips','FEMA_100yr'], axis=1).values\n", + "\n", + "# make predictions\n", + "predictions = rf.predict(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "residential 212954\n", + "commercial 5324\n", + "other 776\n", + "Name: predictions, dtype: int64\n" + ] + } + ], + "source": [ + "# ['Commercial', 'Other', 'Residential']\n", + "pString = []\n", + "\n", + "# update prediction array\n", + "for p in predictions:\n", + " if ( p == 0 ):\n", + " pString.append('commercial')\n", + " elif ( p == 1 ):\n", + " pString.append('other')\n", + " elif ( p == 2 ):\n", + " pString.append('residential')\n", + "\n", + "# add a column to the dataframe\n", + "unknown['predictions'] = pString\n", + "\n", + "print( unknown['predictions'].value_counts() )" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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StateFipsCountyFipsStateCountyFipsXYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadFEMA_100yrpredictions
216003376337063-78.89097236.098843252.33260035085.0144559.00.4450260.25000002675812892373240.0residential
537093717937179-80.58745435.11476869.91563835468.083788.00.3516560.00000044139666311532450.0residential
105460371937019-78.02717633.94282458.45440930529.088330.00.64619352.0000004480353113218840.0residential
1654993718337183-78.44328735.774306165.87730041189.0462582.00.40957831.6666671191057233027781778040.0residential
1012143711537115-82.78912035.76421389.80333826782.011045.00.5211630.000000080801729340.0residential
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" + ], + "text/plain": [ + " StateFips CountyFips StateCountyFips X Y \\\n", + "216003 37 63 37063 -78.890972 36.098843 \n", + "53709 37 179 37179 -80.587454 35.114768 \n", + "105460 37 19 37019 -78.027176 33.942824 \n", + "165499 37 183 37183 -78.443287 35.774306 \n", + "101214 37 115 37115 -82.789120 35.764213 \n", + "\n", + " Area MedianIncomeCounty HousingUnitsCounty \\\n", + "216003 252.332600 35085.0 144559.0 \n", + "53709 69.915638 35468.0 83788.0 \n", + "105460 58.454409 30529.0 88330.0 \n", + "165499 165.877300 41189.0 462582.0 \n", + "101214 89.803338 26782.0 11045.0 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount EdCount \\\n", + "216003 0.445026 0.250000 0 26758 12 892 \n", + "53709 0.351656 0.000000 44 13966 6 311 \n", + "105460 0.646193 52.000000 44 8035 3 113 \n", + "165499 0.409578 31.666667 119 105723 30 2778 \n", + "101214 0.521163 0.000000 0 808 0 17 \n", + "\n", + " InCount OsmNearestRoad FEMA_100yr predictions \n", + "216003 3732 4 0.0 residential \n", + "53709 5324 5 0.0 residential \n", + "105460 2188 4 0.0 residential \n", + "165499 17780 4 0.0 residential \n", + "101214 293 4 0.0 residential " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unknown.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect predictions with socio-economic data and FEMA 100-year flood predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We obtained the 100-year flood map from the FEMA National Flood Hazards Layer. The phrase “100-year flood” is used to describe the extent of a flood that statistically has a 1-percent chance of occurring in any given year [1]. Here, it is used for illustrative purposes of an overall disaster response application. Rows with a 1 in the \"FEMA_100yr\" column were found be flooded at a significant depth in the FEMA 100-year flood maps.\n", + "\n", + "[1] Maidment, David 2009. “FEMA Flood Map Accuracy.”World Envi-ronmental and Water Resources Congress 2009: Great Rivers1–10.Microsoft. 2018.US Building Footprints.Noy, Natasha, Yuqing Gao, Anshu Ja" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5838 buildings impacted by the 100-year flood\n" + ] + } + ], + "source": [ + "flood = unknown[ unknown['FEMA_100yr'] == 1 ]\n", + "print(flood.shape[0], \"buildings impacted by the 100-year flood\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Residential buildings impacted by 100-year flood: 5452\n", + "Commercial buildings impacted by 100-year flood: 349\n", + "'Other' buildings impacted by 100-year flood: 37\n" + ] + } + ], + "source": [ + "residential = flood[ flood['predictions'] == 'residential' ]\n", + "commercial = flood[ flood['predictions'] == 'commercial' ]\n", + "other = flood[ flood['predictions'] == 'other' ]\n", + "\n", + "print(\"Residential buildings impacted by 100-year flood:\", residential.shape[0])\n", + "print(\"Commercial buildings impacted by 100-year flood:\", commercial.shape[0])\n", + "print(\"'Other' buildings impacted by 100-year flood:\", other.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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StateFipsCountyFipsStateCountyFipsXYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadFEMA_100yrpredictions
59439375537055-75.46495435.57541731.35686732538.034237.00.92745527.0406497042142641.0residential
762023712737127-77.79504636.004213105.64608326177.043154.00.45439632.0716492064117641.0residential
1103283711937119-80.90847235.46810217.75679536443.0478966.00.4293800.0381180343926071735241.0residential
6445376537065-77.75671335.95976927.81258424298.023059.00.4715547.00198601936341.0residential
732443712737127-77.81143535.949676203.12194826177.043154.00.45439623.0716492064117641.0residential
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" + ], + "text/plain": [ + " StateFips CountyFips StateCountyFips X Y \\\n", + "59439 37 55 37055 -75.464954 35.575417 \n", + "76202 37 127 37127 -77.795046 36.004213 \n", + "110328 37 119 37119 -80.908472 35.468102 \n", + "6445 37 65 37065 -77.756713 35.959769 \n", + "73244 37 127 37127 -77.811435 35.949676 \n", + "\n", + " Area MedianIncomeCounty HousingUnitsCounty \\\n", + "59439 31.356867 32538.0 34237.0 \n", + "76202 105.646083 26177.0 43154.0 \n", + "110328 17.756795 36443.0 478966.0 \n", + "6445 27.812584 24298.0 23059.0 \n", + "73244 203.121948 26177.0 43154.0 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount EdCount \\\n", + "59439 0.927455 27.0 40 6497 0 42 \n", + "76202 0.454396 32.0 71 6492 0 64 \n", + "110328 0.429380 0.0 38 118034 39 2607 \n", + "6445 0.471554 7.0 0 1986 0 19 \n", + "73244 0.454396 23.0 71 6492 0 64 \n", + "\n", + " InCount OsmNearestRoad FEMA_100yr predictions \n", + "59439 1426 4 1.0 residential \n", + "76202 1176 4 1.0 residential \n", + "110328 17352 4 1.0 residential \n", + "6445 363 4 1.0 residential \n", + "73244 1176 4 1.0 residential " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flood.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "fips = []\n", + "counts = []\n", + "fip = flood['StateCountyFips'].unique()\n", + "for f in fip:\n", + " county = flood[ (flood['StateCountyFips'] == f) & (flood['predictions'] == 'residential') ]\n", + " count = county.shape[0]\n", + " counts.append(count) \n", + " fips.append( str(f) )" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame( list(zip(fips, counts)), columns =['fips', 'count'] )" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import geojson_rewind\n", + "import geopandas as gpd\n", + "\n", + "f = open('./north_carolina.geojson')\n", + "counties = json.load(f)\n", + "f.close()\n", + "\n", + "counties = geojson_rewind.rewind(counties, rfc7946=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "coloraxis": "coloraxis", + "featureidkey": "properties.fip_code", + "geo": "geo", + "geojson": { + "crs": { + "properties": { + "name": "urn:ogc:def:crs:OGC:1.3:CRS84" + }, + "type": "name" + }, + "features": [ + { + "geometry": { + "coordinates": [ + [ + [ + [ + -79.542428, + 35.843303 + ], + [ + -79.541728, + 35.899851 + ], + [ + -79.541084, + 35.928579 + ], + [ + -79.539774, + 36.000136 + ], + [ + -79.539153, + 36.082569 + ], + [ + -79.537963, + 36.113538 + ], + [ + -79.536417, + 36.160978 + ], + [ + -79.53478, + 36.188227 + ], + [ + -79.533795, + 36.21503 + ], + [ + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.express as px\n", + "\n", + "fig = px.choropleth( df, geojson=counties, color='count', locations='fips', \\\n", + " color_continuous_scale='Viridis', featureidkey='properties.fip_code') \n", + "fig.update_layout( margin={\"r\":0, \"t\":0, \"l\":0, \"b\":0} )\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Median Individual Income in County')" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m = 30106 # median individal income in NC in 2020\n", + "\n", + "plt.hist( residential['MedianIncomeCounty'] )\n", + "plt.plot( np.zeros(1600)+m, np.arange(0,1600,1), c='red' )\n", + "plt.ylabel('# of impacted residential homes')\n", + "plt.xlabel('Median Individual Income in County')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "33.27 % residential buildings impacted are in counties below median income\n" + ] + } + ], + "source": [ + "ix = np.where( residential['MedianIncomeCounty'] < m )\n", + "p = (len(ix[0])/residential['MedianIncomeCounty'].shape[0])*100.\n", + "print( np.round(p,2), '% residential buildings impacted are in counties below median income')" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Median Individual Income in County')" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m = 30106 # median individal income in NC in 2020\n", + "\n", + "plt.hist( commercial['MedianIncomeCounty'] )\n", + "plt.plot( np.zeros(150)+m, np.arange(0,150,1), c='red' )\n", + "plt.ylabel('# of impacted commercial buildings')\n", + "plt.xlabel('Median Individual Income in County')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "62.46 % commercial buildings impacted are in counties below median income\n" + ] + } + ], + "source": [ + "ix = np.where( commercial['MedianIncomeCounty'] < m )\n", + "p = (len(ix[0])/commercial['MedianIncomeCounty'].shape[0])*100.\n", + "print( np.round(p,2), '% commercial buildings impacted are in counties below median income')" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Median Individual Income in County')" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m = 30106 # median individal income in NC in 2020\n", + "\n", + "plt.hist( other['MedianIncomeCounty'] )\n", + "plt.plot( np.zeros(15)+m, np.arange(0,15,1), c='red' )\n", + "plt.ylabel('# of impacted \"other\" buildings')\n", + "plt.xlabel('Median Individual Income in County')" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40.54 % other buildings impacted are in counties below median income\n" + ] + } + ], + "source": [ + "ix = np.where( other['MedianIncomeCounty'] < m )\n", + "p = (len(ix[0])/other['MedianIncomeCounty'].shape[0])*100.\n", + "print( np.round(p,2), '% other buildings impacted are in counties below median income')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our machine learning models enable us to predict the building type for 219,054 buildings in North Carolina. Combining these predictions with socio-economic data and flood forecast models, we are able to make risk assessments that were not possible without the machine learning.\n", + "\n", + "In terms of impacts to residential homes, nearly a third of the previously unknown building impacted by the 100-year flood are in counties where the median income is below the state median. Sixty three percent of the commercial buildings predicted to flood during a 100-year flood are in economically disadvantaged counties.\n", + "\n", + "Although buildings are just one small part of the urban multiplex, the application of machine learning can help us better understand this interconnected system leading to enhanced risk assessment and forecasting. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/EDS_paper/Create_ML_Models_Focal_Loss.ipynb b/book/EDS_paper/Create_ML_Models_Focal_Loss.ipynb new file mode 100644 index 0000000..fa337d6 --- /dev/null +++ b/book/EDS_paper/Create_ML_Models_Focal_Loss.ipynb @@ -0,0 +1,1552 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import pickle\n", + "import itertools\n", + "import numpy as np\n", + "import pandas as pd\n", + "import utilities as utils\n", + "import configuration as config\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import tensorflow as tf\n", + "#from keras.utils import to_categorical\n", + "from tensorflow.keras.layers import Dense\n", + "from keras.callbacks import EarlyStopping\n", + "from tensorflow.keras.models import Sequential\n", + "\n", + "#from focal_loss import SparseCategoricalFocalLoss\n", + "\n", + "from sklearn import preprocessing\n", + "from sklearn.metrics import f1_score\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.metrics import balanced_accuracy_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.inspection import permutation_importance\n", + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setup and Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# North Carolina\n", + "state = 37\n", + "\n", + "# input file\n", + "inFile = config.outDir + 'ML_Training_' + str(state) + '.csv'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Residential 976690\n", + "Commercial 64029\n", + "Industrial 16722\n", + "Assembly 7323\n", + "Education 6457\n", + "Government 4910\n", + "Agriculture 1651\n", + "Utility and Misc 362\n", + "Name: OrnlType, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "# read into a Pandas dataframe\n", + "df = pd.read_csv( inFile )\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "df = df.iloc[:, 3:] \n", + "\n", + "# building type distributions\n", + "print()\n", + "print( df['OrnlType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Residential 1.000000\n", + "Commercial 15.253869\n", + "Industrial 58.407487\n", + "Assembly 133.372935\n", + "Education 151.260647\n", + "Government 198.918534\n", + "Agriculture 591.574803\n", + "Utility and Misc 2698.038674\n", + "Name: OrnlType, dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "976690/df['OrnlType'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df.loc[df[\"OrnlType\"] != \"Residential\", \"OrnlType\"] = 'Other'\n", + "\n", + "df = df.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "# preprocess the data - scaling\n", + "scaler = preprocessing.StandardScaler()\n", + " \n", + "# columns: StateFips, CountyFips, StateCountyFips,\n", + "# X, Y, Area, MedianIncomeCounty, HousingUnitsCounty,\n", + "# HousingDensityCounty, Impervious, AgCount, CmCount,\n", + "# GvCount, EdCount, InCount, OsmNearestRoad, OrnlType\n", + "columns = ['X', 'Y', 'Area', 'MedianIncomeCounty', \n", + " 'HousingUnitsCounty', 'HousingDensityCounty',\n", + " 'Impervious', 'AgCount', 'CmCount', 'GvCount',\n", + " 'EdCount', 'InCount']\n", + "df[columns] = scaler.fit_transform(df[columns])\n", + "\n", + "\n", + "# preprossing - drop rows with NaN values\n", + "df = df.dropna()\n", + " \n", + "# preprocessing - change output labels from text ('Agriculture') to integers\n", + "nCols = df.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( df.iloc[:, nCols-1] )\n", + "df.iloc[:, nCols-1] = le.transform( df.iloc[:, nCols-1] )\n", + " \n", + "# preprocessing - change output labels of nearest road type from text to integers\n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( df.iloc[:, nCols-2] ) \n", + "df.iloc[:, nCols-2] = le2.transform( df.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Other', 'Residential'], dtype=object)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "le.inverse_transform([0,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadOrnlType
487030-1.075439-0.348103-0.0266050.3372531.0518500.031186-0.788243-0.3377991.1401851.2781100.9492440.97965441
10036040.7405320.260605-0.1051581.3022840.963812-0.339621-1.3548331.6657310.8805030.6435561.0978171.04193441
8390010.9067660.687815-0.1731681.3022840.963812-0.3396210.6282331.6657310.8805030.6435561.0978171.04193441
330798-0.1592251.268571-0.155860-0.937660-0.2737070.026131-0.311114-0.758292-0.383523-0.202516-0.520841-0.32139951
348352-0.1112681.278203-0.126456-0.937660-0.2737070.026131-0.400575-0.758292-0.383523-0.202516-0.520841-0.32139941
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" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty HousingUnitsCounty \\\n", + "487030 -1.075439 -0.348103 -0.026605 0.337253 1.051850 \n", + "1003604 0.740532 0.260605 -0.105158 1.302284 0.963812 \n", + "839001 0.906766 0.687815 -0.173168 1.302284 0.963812 \n", + "330798 -0.159225 1.268571 -0.155860 -0.937660 -0.273707 \n", + "348352 -0.111268 1.278203 -0.126456 -0.937660 -0.273707 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount \\\n", + "487030 0.031186 -0.788243 -0.337799 1.140185 1.278110 \n", + "1003604 -0.339621 -1.354833 1.665731 0.880503 0.643556 \n", + "839001 -0.339621 0.628233 1.665731 0.880503 0.643556 \n", + "330798 0.026131 -0.311114 -0.758292 -0.383523 -0.202516 \n", + "348352 0.026131 -0.400575 -0.758292 -0.383523 -0.202516 \n", + "\n", + " EdCount InCount OsmNearestRoad OrnlType \n", + "487030 0.949244 0.979654 4 1 \n", + "1003604 1.097817 1.041934 4 1 \n", + "839001 1.097817 1.041934 4 1 \n", + "330798 -0.520841 -0.321399 5 1 \n", + "348352 -0.520841 -0.321399 4 1 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network with focal loss (binary)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-30 10:10:07.760433: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-11-30 10:10:07.831288: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:10:08.056318: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:10:08.056567: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:10:09.253354: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:10:09.253535: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:10:09.253658: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:10:09.254682: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5331 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5\n" + ] + } + ], + "source": [ + "# This is required for my GPU: GeForce RTX 2060\n", + "# Without this config, tensorflow cannot properly allocate GPU memory\n", + "\n", + "# This is a GPU specific configuration and may not be needed on your GPU\n", + "\n", + "config = tf.compat.v1.ConfigProto()\n", + "config.gpu_options.allow_growth = True\n", + "config.gpu_options.per_process_gpu_memory_fraction = 0.90\n", + "session = tf.compat.v1.Session(config=config)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating test/train split...\n", + "shape: (215629, 14)\n" + ] + } + ], + "source": [ + "# columns: StateFips, CountyFips, StateCountyFips,\n", + "# X, Y, Area, MedianIncomeCounty, HousingUnitsCounty,\n", + "# HousingDensityCounty, Impervious, AgCount, CmCount,\n", + "# GvCount, EdCount, InCount, OsmNearestRoad, OrnlType\n", + "\n", + "# test/train split\n", + "print(\"Creating test/train split...\")\n", + "train, test = train_test_split(df, test_size=0.2, random_state=42, stratify=df[['OrnlType']])\n", + "\n", + "print(\"shape:\", test.shape)\n", + "\n", + "nCols = test.shape[1]\n", + "xTest = test.iloc[:, :nCols-1]\n", + "yTest = test.iloc[:, nCols-1]\n", + "\n", + "xTrain = train.iloc[:, :nCols-1]\n", + "yTrain = train.iloc[:, nCols-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Size: (862513, 13)\n", + "Testing Size: (215629, 13)\n" + ] + } + ], + "source": [ + "print(\"Training Size:\", xTrain.shape)\n", + "print(\"Testing Size:\", xTest.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0. 1.]\n", + " [0. 1.]\n", + " [1. 0.]\n", + " ...\n", + " [0. 1.]\n", + " [0. 1.]\n", + " [0. 1.]]\n" + ] + } + ], + "source": [ + "binary_matrix = np.eye(np.max(yTrain) + 1)[yTrain]\n", + "\n", + "print(binary_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yTrain[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-11-30 10:11:12.342661: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:11:12.342847: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:11:12.342952: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:11:12.343198: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:11:12.343309: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:11:12.343411: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:11:12.343547: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:11:12.343654: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-11-30 10:11:12.343730: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5331 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/150\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/narock/.local/lib/python3.10/site-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: \"`binary_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n", + " return dispatch_target(*args, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3370/3370 [==============================] - 17s 4ms/step - loss: 0.0413 - accuracy: 0.9459\n", + "Epoch 2/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0382 - accuracy: 0.9492\n", + "Epoch 3/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0377 - accuracy: 0.9499\n", + "Epoch 4/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0374 - accuracy: 0.9503\n", + "Epoch 5/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0372 - accuracy: 0.9505\n", + "Epoch 6/150\n", + "3370/3370 [==============================] - 8s 2ms/step - loss: 0.0370 - accuracy: 0.9506\n", + "Epoch 7/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0368 - accuracy: 0.9507\n", + "Epoch 8/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0366 - accuracy: 0.9507\n", + "Epoch 9/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0365 - accuracy: 0.9508\n", + "Epoch 10/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0363 - accuracy: 0.9509\n", + "Epoch 11/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0362 - accuracy: 0.9508\n", + "Epoch 12/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0361 - accuracy: 0.9509\n", + "Epoch 13/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0360 - accuracy: 0.9511\n", + "Epoch 14/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0359 - accuracy: 0.9511\n", + "Epoch 15/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0358 - accuracy: 0.9512\n", + "Epoch 16/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0357 - accuracy: 0.9513\n", + "Epoch 17/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0357 - accuracy: 0.9512\n", + "Epoch 18/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0356 - accuracy: 0.9513\n", + "Epoch 19/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0355 - accuracy: 0.9514\n", + "Epoch 20/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0355 - accuracy: 0.9514\n", + "Epoch 21/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0355 - accuracy: 0.9515\n", + "Epoch 22/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0354 - accuracy: 0.9515\n", + "Epoch 23/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0354 - accuracy: 0.9515\n", + "Epoch 24/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0354 - accuracy: 0.9515\n", + "Epoch 25/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0353 - accuracy: 0.9515\n", + "Epoch 26/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0353 - accuracy: 0.9517\n", + "Epoch 27/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0352 - accuracy: 0.9516\n", + "Epoch 28/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0352 - accuracy: 0.9517\n", + "Epoch 29/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0352 - accuracy: 0.9517\n", + "Epoch 30/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0352 - accuracy: 0.9519\n", + "Epoch 31/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0352 - accuracy: 0.9518\n", + "Epoch 32/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0352 - accuracy: 0.9517\n", + "Epoch 33/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0351 - accuracy: 0.9519\n", + "Epoch 34/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0351 - accuracy: 0.9517\n", + "Epoch 35/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0351 - accuracy: 0.9519\n", + "Epoch 36/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0351 - accuracy: 0.9519\n", + "Epoch 37/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0351 - accuracy: 0.9520\n", + "Epoch 38/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0350 - accuracy: 0.9520\n", + "Epoch 39/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0351 - accuracy: 0.9519\n", + "Epoch 40/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0350 - accuracy: 0.9519\n", + "Epoch 41/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0350 - accuracy: 0.9519\n", + "Epoch 42/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0350 - accuracy: 0.9520\n", + "Epoch 43/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0350 - accuracy: 0.9521\n", + "Epoch 44/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0350 - accuracy: 0.9520\n", + "Epoch 45/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0350 - accuracy: 0.9521\n", + "Epoch 46/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0349 - accuracy: 0.9521\n", + "Epoch 47/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0349 - accuracy: 0.9521\n", + "Epoch 48/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0349 - accuracy: 0.9521\n", + "Epoch 49/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0349 - accuracy: 0.9521\n", + "Epoch 50/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0349 - accuracy: 0.9519\n", + "Epoch 51/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0349 - accuracy: 0.9521\n", + "Epoch 52/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0349 - accuracy: 0.9521\n", + "Epoch 53/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0348 - accuracy: 0.9520\n", + "Epoch 54/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0348 - accuracy: 0.9522\n", + "Epoch 55/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0348 - accuracy: 0.9522\n", + "Epoch 56/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0348 - accuracy: 0.9522\n", + "Epoch 57/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0348 - accuracy: 0.9523\n", + "Epoch 58/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0348 - accuracy: 0.9522\n", + "Epoch 59/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0348 - accuracy: 0.9523\n", + "Epoch 60/150\n", + "3370/3370 [==============================] - 9s 3ms/step - loss: 0.0348 - accuracy: 0.9522\n", + "Epoch 61/150\n", + "3370/3370 [==============================] - 9s 3ms/step - loss: 0.0347 - accuracy: 0.9523\n", + "Epoch 62/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0348 - accuracy: 0.9521\n", + "Epoch 63/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0347 - accuracy: 0.9522\n", + "Epoch 64/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0347 - accuracy: 0.9523\n", + "Epoch 65/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0347 - accuracy: 0.9523\n", + "Epoch 66/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0347 - accuracy: 0.9522\n", + "Epoch 67/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0347 - accuracy: 0.9523\n", + "Epoch 68/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0347 - accuracy: 0.9523\n", + "Epoch 69/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0347 - accuracy: 0.9523\n", + "Epoch 70/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0347 - accuracy: 0.9523\n", + "Epoch 71/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0347 - accuracy: 0.9524\n", + "Epoch 72/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0347 - accuracy: 0.9523\n", + "Epoch 73/150\n", + "3370/3370 [==============================] - 12s 3ms/step - loss: 0.0346 - accuracy: 0.9523\n", + "Epoch 74/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0346 - accuracy: 0.9523\n", + "Epoch 75/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0346 - accuracy: 0.9524\n", + "Epoch 76/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0346 - accuracy: 0.9523\n", + "Epoch 77/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0346 - accuracy: 0.9523\n", + "Epoch 78/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0346 - accuracy: 0.9524\n", + "Epoch 79/150\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0346 - accuracy: 0.9525\n", + "Epoch 80/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.0346 - accuracy: 0.9525\n", + "Epoch 81/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0346 - accuracy: 0.9525\n", + "Epoch 82/150\n", + "3370/3370 [==============================] - 15s 5ms/step - loss: 0.0346 - accuracy: 0.9525\n", + "Epoch 83/150\n", + "3370/3370 [==============================] - 15s 5ms/step - loss: 0.0346 - accuracy: 0.9524\n", + "Epoch 84/150\n", + "3370/3370 [==============================] - 15s 5ms/step - loss: 0.0346 - accuracy: 0.9524\n", + "Epoch 85/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0346 - accuracy: 0.9524\n", + "Epoch 86/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0346 - accuracy: 0.9525\n", + "Epoch 87/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0345 - accuracy: 0.9525\n", + "Epoch 88/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0345 - accuracy: 0.9525\n", + "Epoch 89/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0345 - accuracy: 0.9525\n", + "Epoch 90/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0345 - accuracy: 0.9525\n", + "Epoch 91/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0345 - accuracy: 0.9525\n", + "Epoch 92/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0345 - accuracy: 0.9525\n", + "Epoch 93/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0345 - accuracy: 0.9525\n" + ] + } + ], + "source": [ + "# convert output from scalar to vector format, i.e. building type=2 to [0,0,1,0,0,0,0,0,0,0,0]\n", + "#y = to_categorical(yTrain)\n", + "y = binary_matrix\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(30, input_shape=(xTrain.shape[1],), activation='relu'))\n", + "model.add(Dense(15, activation='relu'))\n", + "model.add(Dense(2, activation='softmax'))\n", + "model.compile(loss=tf.keras.losses.BinaryFocalCrossentropy(gamma=2.0, from_logits=True), \n", + " optimizer='adam', metrics=['accuracy'])\n", + "\n", + "# stop training if we see 7 epochs over which accuracy doesn't improve\n", + "es = EarlyStopping(monitor='accuracy', patience=7)\n", + "\n", + "# fit the model\n", + "h = model.fit(xTrain, y, epochs=150, batch_size=256, callbacks=[es])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense (Dense) (None, 30) 420 \n", + " \n", + " dense_1 (Dense) (None, 15) 465 \n", + " \n", + " dense_2 (Dense) (None, 2) 32 \n", + " \n", + "=================================================================\n", + "Total params: 917\n", + "Trainable params: 917\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Epoch')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot( h.history['accuracy'] )\n", + "plt.title('Training Accuracy')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "model.save_weights(\"./focal_loss_binary_model.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 97/6739 [..............................] - ETA: 6s - loss: 0.0377 - accuracy: 0.9491 " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/narock/.local/lib/python3.10/site-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: \"`binary_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n", + " return dispatch_target(*args, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6739/6739 [==============================] - 7s 974us/step - loss: 0.0352 - accuracy: 0.9524\n", + "Accuracy on the test set: 0.9524\n" + ] + } + ], + "source": [ + "#y = to_categorical(yTest)\n", + "y = np.eye(np.max(yTest) + 1)[yTest]\n", + "\n", + "loss, acc = model.evaluate( xTest, y )\n", + "print(\"Accuracy on the test set:\", np.round(acc,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "215629" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ps = model.predict(xTest)\n", + "predictions = []\n", + "for p in ps:\n", + " predictions.append( np.argmax( p ) )\n", + "len(predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.8086\n", + "Macro F1: 0.8439\n", + "Macro Precision: 0.89\n", + "Macro Recall: 0.81\n" + ] + } + ], + "source": [ + "from sklearn.metrics import precision_score, recall_score\n", + "\n", + "testy = yTest.values\n", + "\n", + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(testy, predictions), 4 ) )\n", + "print( \"Macro F1:\", np.round( f1_score(testy, predictions, average='macro'), 4 ))\n", + "print( \"Macro Precision:\", np.round( precision_score(testy, predictions, average='macro'), 2 ) )\n", + "print( \"Macro Recall:\", np.round( recall_score(testy, predictions, average='macro'), 2 ) )" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "cm = confusion_matrix( testy, predictions, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 22}\n", + "plt.rc('font', **font)\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network for multiclass classification - 3 classes" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Residential 976690\n", + "Commercial 64029\n", + "Industrial 16722\n", + "Assembly 7323\n", + "Education 6457\n", + "Government 4910\n", + "Agriculture 1651\n", + "Utility and Misc 362\n", + "Name: OrnlType, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "# read into a Pandas dataframe\n", + "df = pd.read_csv( inFile )\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "df = df.iloc[:, 3:] \n", + "\n", + "# building type distributions\n", + "print()\n", + "print( df['OrnlType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Residential 976690\n", + "Commercial 64029\n", + "Other 37425\n", + "Name: OrnlType, dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "# change the rows we don't need\n", + "df.loc[df[\"OrnlType\"] == \"Industrial\", \"OrnlType\"] = 'Other'\n", + "df.loc[df[\"OrnlType\"] == \"Assembly\", \"OrnlType\"] = 'Other'\n", + "df.loc[df[\"OrnlType\"] == \"Education\", \"OrnlType\"] = 'Other'\n", + "df.loc[df[\"OrnlType\"] == \"Government\", \"OrnlType\"] = 'Other'\n", + "df.loc[df[\"OrnlType\"] == \"Agriculture\", \"OrnlType\"] = 'Other'\n", + "df.loc[df[\"OrnlType\"] == \"Utility and Misc\", \"OrnlType\"] = 'Other'\n", + "\n", + "df['OrnlType'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# preprocess the data - scaling\n", + "scaler = preprocessing.StandardScaler()\n", + " \n", + "# columns: StateFips, CountyFips, StateCountyFips,\n", + "# X, Y, Area, MedianIncomeCounty, HousingUnitsCounty,\n", + "# HousingDensityCounty, Impervious, AgCount, CmCount,\n", + "# GvCount, EdCount, InCount, OsmNearestRoad, OrnlType\n", + "columns = ['X', 'Y', 'Area', 'MedianIncomeCounty', \n", + " 'HousingUnitsCounty', 'HousingDensityCounty',\n", + " 'Impervious', 'AgCount', 'CmCount', 'GvCount',\n", + " 'EdCount', 'InCount']\n", + "df[columns] = scaler.fit_transform(df[columns])\n", + "\n", + "\n", + "# preprossing - drop rows with NaN values\n", + "df = df.dropna()\n", + " \n", + "# preprocessing - change output labels from text ('Agriculture') to integers\n", + "nCols = df.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( df.iloc[:, nCols-1] )\n", + "df.iloc[:, nCols-1] = le.transform( df.iloc[:, nCols-1] )\n", + " \n", + "# preprocessing - change output labels of nearest road type from text to integers\n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( df.iloc[:, nCols-2] ) \n", + "df.iloc[:, nCols-2] = le2.transform( df.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Commercial', 'Other', 'Residential'], dtype=object)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "le.inverse_transform([0,1,2])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating test/train split...\n", + "Training Size: (862513, 13)\n", + "Testing Size: (215629, 13)\n", + "[0 1 2]\n" + ] + } + ], + "source": [ + "# columns: StateFips, CountyFips, StateCountyFips,\n", + "# X, Y, Area, MedianIncomeCounty, HousingUnitsCounty,\n", + "# HousingDensityCounty, Impervious, AgCount, CmCount,\n", + "# GvCount, EdCount, InCount, OsmNearestRoad, OrnlType\n", + "\n", + "# test/train split\n", + "print(\"Creating test/train split...\")\n", + "train, test = train_test_split(df, test_size=0.2, random_state=42, stratify=df[['OrnlType']])\n", + "\n", + "nCols = test.shape[1]\n", + "xTest = test.iloc[:, :nCols-1]\n", + "yTest = test.iloc[:, nCols-1]\n", + "\n", + "xTrain = train.iloc[:, :nCols-1]\n", + "yTrain = train.iloc[:, nCols-1]\n", + "\n", + "print(\"Training Size:\", xTrain.shape)\n", + "print(\"Testing Size:\", xTest.shape)\n", + "print( np.unique(df.OrnlType) )" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(df.OrnlType)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".binary_focal_loss_fixed at 0x7f80f9bf5360>\n", + ".categorical_focal_loss_fixed at 0x7f80f8fb7910>\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "Define our custom loss function.\n", + "\"\"\"\n", + "import numpy as np\n", + "from keras import backend as K\n", + "import tensorflow as tf\n", + "\n", + "import dill\n", + "\n", + "\n", + "def binary_focal_loss(gamma=2., alpha=.25):\n", + " \"\"\"\n", + " Binary form of focal loss.\n", + "\n", + " FL(p_t) = -alpha * (1 - p_t)**gamma * log(p_t)\n", + "\n", + " where p = sigmoid(x), p_t = p or 1 - p depending on if the label is 1 or 0, respectively.\n", + "\n", + " References:\n", + " https://arxiv.org/pdf/1708.02002.pdf\n", + " Usage:\n", + " model.compile(loss=[binary_focal_loss(alpha=.25, gamma=2)], metrics=[\"accuracy\"], optimizer=adam)\n", + "\n", + " \"\"\"\n", + "\n", + " def binary_focal_loss_fixed(y_true, y_pred):\n", + " \"\"\"\n", + " :param y_true: A tensor of the same shape as `y_pred`\n", + " :param y_pred: A tensor resulting from a sigmoid\n", + " :return: Output tensor.\n", + " \"\"\"\n", + " y_true = tf.cast(y_true, tf.float32)\n", + " # Define epsilon so that the back-propagation will not result in NaN for 0 divisor case\n", + " epsilon = K.epsilon()\n", + " # Add the epsilon to prediction value\n", + " # y_pred = y_pred + epsilon\n", + " # Clip the prediciton value\n", + " y_pred = K.clip(y_pred, epsilon, 1.0 - epsilon)\n", + " # Calculate p_t\n", + " p_t = tf.where(K.equal(y_true, 1), y_pred, 1 - y_pred)\n", + " # Calculate alpha_t\n", + " alpha_factor = K.ones_like(y_true) * alpha\n", + " alpha_t = tf.where(K.equal(y_true, 1), alpha_factor, 1 - alpha_factor)\n", + " # Calculate cross entropy\n", + " cross_entropy = -K.log(p_t)\n", + " weight = alpha_t * K.pow((1 - p_t), gamma)\n", + " # Calculate focal loss\n", + " loss = weight * cross_entropy\n", + " # Sum the losses in mini_batch\n", + " loss = K.mean(K.sum(loss, axis=1))\n", + " return loss\n", + "\n", + " return binary_focal_loss_fixed\n", + "\n", + "\n", + "def categorical_focal_loss(alpha, gamma=2.):\n", + " \"\"\"\n", + " Softmax version of focal loss.\n", + " When there is a skew between different categories/labels in your data set, you can try to apply this function as a\n", + " loss.\n", + " m\n", + " FL = ∑ -alpha * (1 - p_o,c)^gamma * y_o,c * log(p_o,c)\n", + " c=1\n", + "\n", + " where m = number of classes, c = class and o = observation\n", + "\n", + " Parameters:\n", + " alpha -- the same as weighing factor in balanced cross entropy. Alpha is used to specify the weight of different\n", + " categories/labels, the size of the array needs to be consistent with the number of classes.\n", + " gamma -- focusing parameter for modulating factor (1-p)\n", + "\n", + " Default value:\n", + " gamma -- 2.0 as mentioned in the paper\n", + " alpha -- 0.25 as mentioned in the paper\n", + "\n", + " References:\n", + " Official paper: https://arxiv.org/pdf/1708.02002.pdf\n", + " https://www.tensorflow.org/api_docs/python/tf/keras/backend/categorical_crossentropy\n", + "\n", + " Usage:\n", + " model.compile(loss=[categorical_focal_loss(alpha=[[.25, .25, .25]], gamma=2)], metrics=[\"accuracy\"], optimizer=adam)\n", + " \"\"\"\n", + "\n", + " alpha = np.array(alpha, dtype=np.float32)\n", + "\n", + " def categorical_focal_loss_fixed(y_true, y_pred):\n", + " \"\"\"\n", + " :param y_true: A tensor of the same shape as `y_pred`\n", + " :param y_pred: A tensor resulting from a softmax\n", + " :return: Output tensor.\n", + " \"\"\"\n", + "\n", + " # Clip the prediction value to prevent NaN's and Inf's\n", + " epsilon = K.epsilon()\n", + " y_pred = K.clip(y_pred, epsilon, 1. - epsilon)\n", + "\n", + " # Calculate Cross Entropy\n", + " cross_entropy = -y_true * K.log(y_pred)\n", + "\n", + " # Calculate Focal Loss\n", + " loss = alpha * K.pow(1 - y_pred, gamma) * cross_entropy\n", + "\n", + " # Compute mean loss in mini_batch\n", + " return K.mean(K.sum(loss, axis=-1))\n", + "\n", + " return categorical_focal_loss_fixed\n", + "\n", + "\n", + "if __name__ == '__main__':\n", + "\n", + " # Test serialization of nested functions\n", + " bin_inner = dill.loads(dill.dumps(binary_focal_loss(gamma=2., alpha=.25)))\n", + " print(bin_inner)\n", + "\n", + " cat_inner = dill.loads(dill.dumps(categorical_focal_loss(gamma=2., alpha=.25)))\n", + " print(cat_inner)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0232 - accuracy: 0.9285\n", + "Epoch 2/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0215 - accuracy: 0.9316\n", + "Epoch 3/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0213 - accuracy: 0.9323\n", + "Epoch 4/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0211 - accuracy: 0.9327\n", + "Epoch 5/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0210 - accuracy: 0.9332\n", + "Epoch 6/150\n", + "3370/3370 [==============================] - 12s 3ms/step - loss: 0.0209 - accuracy: 0.9333\n", + "Epoch 7/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0208 - accuracy: 0.9334\n", + "Epoch 8/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0207 - accuracy: 0.9335\n", + "Epoch 9/150\n", + "3370/3370 [==============================] - 10s 3ms/step - loss: 0.0206 - accuracy: 0.9336\n", + "Epoch 10/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0204 - accuracy: 0.9337\n", + "Epoch 11/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0204 - accuracy: 0.9340\n", + "Epoch 12/150\n", + "3370/3370 [==============================] - 17s 5ms/step - loss: 0.0203 - accuracy: 0.9341\n", + "Epoch 13/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0203 - accuracy: 0.9340\n", + "Epoch 14/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0203 - accuracy: 0.9342\n", + "Epoch 15/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0202 - accuracy: 0.9344\n", + "Epoch 16/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0202 - accuracy: 0.9343\n", + "Epoch 17/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0202 - accuracy: 0.9342\n", + "Epoch 18/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0201 - accuracy: 0.9344\n", + "Epoch 19/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0201 - accuracy: 0.9344\n", + "Epoch 20/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0201 - accuracy: 0.9344\n", + "Epoch 21/150\n", + "3370/3370 [==============================] - 12s 3ms/step - loss: 0.0201 - accuracy: 0.9344\n", + "Epoch 22/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0201 - accuracy: 0.9344\n", + "Epoch 23/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0201 - accuracy: 0.9345\n", + "Epoch 24/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0200 - accuracy: 0.9345\n", + "Epoch 25/150\n", + "3370/3370 [==============================] - 14s 4ms/step - loss: 0.0200 - accuracy: 0.9345\n", + "Epoch 26/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0200 - accuracy: 0.9345\n", + "Epoch 27/150\n", + "3370/3370 [==============================] - 15s 5ms/step - loss: 0.0200 - accuracy: 0.9345\n", + "Epoch 28/150\n", + "3370/3370 [==============================] - 11s 3ms/step - loss: 0.0200 - accuracy: 0.9346\n", + "Epoch 29/150\n", + "3370/3370 [==============================] - 12s 4ms/step - loss: 0.0200 - accuracy: 0.9347\n", + "Epoch 30/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0200 - accuracy: 0.9347\n", + "Epoch 31/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0199 - accuracy: 0.9347\n", + "Epoch 32/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0199 - accuracy: 0.9347\n", + "Epoch 33/150\n", + "3370/3370 [==============================] - 9s 3ms/step - loss: 0.0199 - accuracy: 0.9349\n", + "Epoch 34/150\n", + "3370/3370 [==============================] - 13s 4ms/step - loss: 0.0199 - accuracy: 0.9348\n", + "Epoch 35/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0199 - accuracy: 0.9349\n", + "Epoch 36/150\n", + "3370/3370 [==============================] - 20s 6ms/step - loss: 0.0199 - accuracy: 0.9347\n", + "Epoch 37/150\n", + "3370/3370 [==============================] - 12s 3ms/step - loss: 0.0199 - accuracy: 0.9349\n", + "Epoch 38/150\n", + "3370/3370 [==============================] - 19s 6ms/step - loss: 0.0199 - accuracy: 0.9349\n", + "Epoch 39/150\n", + "3370/3370 [==============================] - 15s 4ms/step - loss: 0.0199 - accuracy: 0.9348\n", + "Epoch 40/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0199 - accuracy: 0.9348\n", + "Epoch 41/150\n", + "3370/3370 [==============================] - 20s 6ms/step - loss: 0.0199 - accuracy: 0.9348\n", + "Epoch 42/150\n", + "3370/3370 [==============================] - 16s 5ms/step - loss: 0.0199 - accuracy: 0.9348\n" + ] + } + ], + "source": [ + "# convert output from scalar to vector format, i.e. building type=2 to [0,0,1,0,0,0,0,0,0,0,0]\n", + "#y = to_categorical(yTrain)\n", + "y = np.eye(np.max(yTrain) + 1)[yTrain]\n", + "\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(30, input_shape=(xTrain.shape[1],), activation='relu'))\n", + "model.add(Dense(15, activation='relu'))\n", + "model.add(Dense(3, activation='softmax'))\n", + "model.compile(loss=[categorical_focal_loss(alpha=[[.25, .25, .25]], gamma=2)], \n", + " optimizer='adam', metrics=['accuracy'])\n", + "\n", + "# stop training if we see 7 epochs over which accuracy doesn't improve\n", + "es = EarlyStopping(monitor='accuracy', patience=7)\n", + "\n", + "# fit the model\n", + "h = model.fit(xTrain, y, epochs=150, batch_size=256, callbacks=[es])" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Epoch')" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot( h.history['accuracy'] )\n", + "plt.title('Training Accuracy')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6739/6739 [==============================] - 6s 884us/step - loss: 0.0200 - accuracy: 0.9348\n", + "Accuracy on the test set: 0.9348\n" + ] + } + ], + "source": [ + "#y = to_categorical(yTest)\n", + "y = np.eye(np.max(yTest) + 1)[yTest]\n", + "\n", + "loss, acc = model.evaluate( xTest, y )\n", + "print(\"Accuracy on the test set:\", np.round(acc,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "215629" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ps = model.predict(xTest)\n", + "predictions = []\n", + "for p in ps:\n", + " predictions.append( np.argmax( p ) )\n", + "len(predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.5788\n", + "Macro F1: 0.6193\n", + "Macro Precision: 0.71\n", + "Macro Recall: 0.58\n" + ] + } + ], + "source": [ + "testy = yTest.values\n", + "\n", + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(testy, predictions), 4 ) )\n", + "print( \"Macro F1:\", np.round( f1_score(testy, predictions, average='macro'), 4 ))\n", + "print( \"Macro Precision:\", np.round( precision_score(testy, predictions, average='macro'), 2 ) )\n", + "print( \"Macro Recall:\", np.round( recall_score(testy, predictions, average='macro'), 2 ) )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/EDS_paper/test.file b/book/EDS_paper/test.file new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/book/EDS_paper/test.file @@ -0,0 +1 @@ + diff --git a/book/North_Carolina_Buildings.zip b/book/North_Carolina_Buildings.zip new file mode 100644 index 0000000..afcdb0c Binary files /dev/null and b/book/North_Carolina_Buildings.zip differ diff --git a/book/_toc.yml b/book/_toc.yml index dc5d40a..96c37df 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -10,60 +10,11 @@ parts: - title: Project Pythia Foundations url: https://foundations.projectpythia.org/landing-page.html - title: About Use Case Library - file: chapters/about + file: chapters/Introduction - caption: Chapter One chapters: - - file: chapters/motivation - -- caption: Chapter Two - chapters: - - file: chapters/methods - -- caption: Chapter Three - chapters: - - file: chapters/data - -- caption: Chapter Four - chapters: - - file: chapters/development - -- caption: Chapter Five - chapters: - - file: chapters/training - -- caption: Chapter Six - chapters: - - file: chapters/evaluation - -- caption: Chapter Seven - chapters: - - file: chapters/workflow - -- caption: Chapter Eight - chapters: - - file: chapters/reproducibility - -- caption: Chapter Nine - chapters: - - file: chapters/conclusion - -- caption: Chapter Ten (optional) - chapters: - - file: chapters/todo - -- caption: Chapter Eleven (optional) - chapters: - - file: chapters/questions - -- caption: Chapter Twelve (optional) - chapters: - - file: chapters/troubleshooting - -- caption: Chapter Thirteen - chapters: - - file: chapters/example - + - file: chapters/chapter2 - caption: Reference chapters: - file: reference/glossary diff --git a/book/chapters/Introduction.md b/book/chapters/Introduction.md new file mode 100644 index 0000000..5785fc2 --- /dev/null +++ b/book/chapters/Introduction.md @@ -0,0 +1,25 @@ +# flood-risk-ml-tutorial + +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geo-smart/flood-risk-ml-tutorial/HEAD?labpath=Building_Classification.ipynb) +[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://flood-risk-ml-tutorial.github.io/intro.html) +[![GeoSMART Library Badge](./use_case_badge.svg)](https://geo-smart.github.io/usecases) + +To cite this tutorial use: [![DOI](https://zenodo.org/badge/586902507.svg)](https://zenodo.org/badge/latestdoi/586902507) + +The results of this tutorial are also published in [Enhancing Flood Risk Assessment Through Machine Learning and Open Data](https://doi.org/10.31223/X5XW95) + + +A machine learning tutorial demonstrating building classification for flood risk assessment + +Urban flooding causes billions of dollars in damages annually, with severe flood events becoming more frequent and destructive as our climate changes. While extreme weather is a primary driver of flooding, its consequences depend on the interconnectedness of urban systems - referred to as the Urban Multiplex, which includes the power grid, transportation network, natural surface water and groundwater systems, sewerage and drinking water systems, intertwined with the socioeconomic and public health sectors. + +This tutorial introduces machine learning concepts through an application in one component of the Urban Multiplex - building classification. Accurate and standardized information about buildings (e.g. classification into residential, commercial, industrial) is critical for assessing the number of people affected by flooding, the propagation of shocks throughout the economy, and for forecasting detailed socioeconomic risk from flooding. Yet, a major discrepancy exists in the way data about buildings are collected across the U.S. There is no harmonization in what data are recorded by city, county, or state governments, let alone at the national scale. + +In this tutorial we explore multiple means of classifying a building (residential/classification/industrial/etc.) based on its features (location, square footage, proximity to other resources, etc.). Moreover, we are interested in how accurate or machine learning models can be using only free and open data. The data used in this tutorial is consistently and openly available for the continental Unites States. We use the state of North Carolina as an expemplar in this tutorial. + +

+ +

+ +### Raw Data and Preprocessing +The tutorial notebook Building_Classification.ipynb utilizes a machine learning ready dataset provided in this GitHub repo. The raw data used to create the machine learning ready dataset are too large to include here. Raw data are available here: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7696864.svg)](https://doi.org/10.5281/zenodo.7696864) and our preprocessing scripts can be found in the "preprocessing" folder. diff --git a/book/chapters/about.ipynb b/book/chapters/about.ipynb deleted file mode 100644 index adc78c3..0000000 --- a/book/chapters/about.ipynb +++ /dev/null @@ -1,83 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# About the GeoSMART Use Case Library\n", - "\n", - "### General Overview\n", - "\n", - "The GeoSMART use case library is a collection of books demonstrating various machine learning workflows relevant to the geosciences, with the goal of fostering further adoption and growth in the space. Books in the library can be identified by the badge:\n", - "\n", - "[![GeoSMART Use Case](../img/use_case_badge.svg)](https://geo-smart.github.io/usecases)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Contributing Content\n", - "\n", - "Tutorial content can be integrated into jupyterbooks in one of two ways:\n", - "* Do it yourself (use this template book and add your content)\n", - "* Provide use your content (preferably in a github repo) and we will integrate it\n", - "\n", - "The goal is to provide executable code on some platform. The contributor can choose between:\n", - "* Binder\n", - "* Google Colab\n", - "* Free AWS (smaller cloud-based examples)\n", - "If none of the above options work for you, please contact us directly to discuss further.\n", - "\n", - "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geo-smart/use_case_template/HEAD?urlpath=lab)\n", - "[![Open in Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/geo-smart/use_case_template)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Technical Details\n", - "\n", - "The following section is dedicated to helping anyone who might be looking to use this template to contribute a use case. We recommend that you clone this navigating to use case template repository and clicking the \"Use this template\" button, the follow along locally. Much of the important information for succesufully using the template is in `README.md` files, and familiarizing oneself with the project structure is also important. \n", - "\n", - "The `.github` folder already contains the github actions that will handle CI/CD deployment to github pages. There is no need to create a gh-pages branch, the first run of the github actions should handle that automatically.\n", - "\n", - "The `binder` folder should store symlink(s) to environment configuration files in the `conda` folder. We recommend you use a package manager to both make your work more reproducible and make running your work with Binder as painless as possible. See the `conda/README.md` file for more detailed information.\n", - "\n", - "The `book` folder houses the content of the project. Inside there, you will find two very important files, `_config.yml` and `_toc.yml`. The config file tells jupyter how to compile your notebook to html (for display on github pages). You probably won't need to change anything in here except the title, author and website_url. The table of contents file will require some more changes. We have already laid out a basic project structure, and we recommend you at least look through it, even if you don't end up following it exactly. Chapters can contain more than one file, although if you want to make significant changes you should check [this](https://jupyterbook.org/en/stable/structure/toc.html) page out first." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/chapter2.ipynb b/book/chapters/chapter2.ipynb new file mode 100644 index 0000000..964a909 --- /dev/null +++ b/book/chapters/chapter2.ipynb @@ -0,0 +1,63933 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
Flood Risk Machine Learning Tutorial
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Urban flooding causes billions of dollars in damages annually, with severe flood events becoming more frequent and destructive as our climate changes. While extreme weather is a primary driver of flooding, its consequences depend on the interconnectedness of urban systems - referred to as the Urban Multiplex, which includes the power grid, transportation network, natural surface water and groundwater systems, sewerage and drinking water systems, intertwined with the socioeconomic and public health sectors.

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

This tutorial introduces machine learning concepts through an application in one component of the Urban Multiplex - building classification. Accurate and standardized information about buildings (e.g. classification into residential, commercial, industrial) is critical for assessing the number of people affected by flooding, the propagation of shocks throughout the economy, and for forecasting detailed socioeconomic risk from flooding. Yet, a major discrepancy exists in the way data about buildings are collected across the U.S. There is no harmonization in what data are recorded by city, county, or state governments, let alone at the national scale.

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

In this tutorial we explore multiple means of classifying a building (residential/classification/industrial/etc.) based on its features (location, square footage, proximity to other resources, etc.). Moreover, we are interested in how accurate our machine learning models can be using only free and open data. The data used in this tutorial is consistently and openly available for the continental Unites States. We use the state of North Carolina as an expemplar in this tutorial.

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

It is currently unknown which building features (e.g. location, square footage, etc.) have the most predictive power. As a result, part of this tutorial focuses on the notion of \"feature importance\". We've generated a set of features that may be useful for predicting building type. As we work through the examples, we'll also investigate how important each of these features actually is to predicting a building type.

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import required Python libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import joblib\n", + "import pickle\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from pretty_confusion_matrix import pp_matrix_from_data\n", + "\n", + "import tensorflow as tf\n", + "from keras.utils import to_categorical\n", + "from tensorflow.keras.layers import Dense\n", + "from keras.callbacks import EarlyStopping\n", + "from tensorflow.keras.models import Sequential\n", + "\n", + "from sklearn import preprocessing\n", + "from sklearn.metrics import f1_score\n", + "from sklearn.metrics import recall_score\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.metrics import precision_score\n", + "from sklearn.metrics import balanced_accuracy_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.inspection import permutation_importance\n", + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Setup and Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# North Carolina data\n", + "url = \"https://github.com/geo-smart/flood-risk-ml-tutorial/blob/main/North_Carolina_Buildings.zip?raw=true\"\n", + "\n", + "# read data into a Pandas dataframe\n", + "rawDf = pd.read_csv(url, compression=\"zip\")\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "rawDf = rawDf.iloc[:, 3:] \n", + "df = rawDf.copy( deep=True )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The data\n", + "\n", + "Our goal is to predict a building's type (Residential/Commercial/Industrial) based on known features of the building (area, location, etc.). To accomplish this, we gather as many building features as possible from open datasets. We begin with as large a set as possible and will work towards identifying each feature's actual importance. \n", + "\n", + "Our sources of building features are [Open Street Map (OSM)](https://www.openstreetmap.org), the [Multi-Resolution Land Characteristics Consortium (MRLC)](https://www.mrlc.gov/data/type/urban-imperviousness), [United States Census' County Business Patterns (CBP)](https://www.census.gov/programs-surveys/cbp/data/tables.html), and the [United States Census' American Community Survey (ACS)](https://www.census.gov/programs-surveys/acs/data.html).\n", + "\n", + "We've preprocessed the data to spatially align all the features. For example, we used building longitude and latitude provided by OSM to determine which county the building resides in. We've then gone to the ACS and looked up socio-econimic data such as median income and housing density. The resulting machine learning ready data looks like this:\n", + "\n", + "| Column | Description | Source |\n", + "| :---------: | :---------- |:-------|\n", + "| X | X coordinate of the building in the EPGS:5070 system | OSM |\n", + "| Y | Y coordinate of the building in the EPGS:5070 system | OSM |\n", + "| Area | Area of building in square meters | OSM |\n", + "| MedianIncomeCounty | Median income of the county in which the building resides | ACS |\n", + "| HousingUnitsCounty | Number of housing units in the county in which the building resides | ACS |\n", + "| HousingDensityCounty | Number of housing units in the county divided by the number of people residing in the county where the building resides | ACS |\n", + "| Impervious | Percentage of the area surrounding the building that is comprised of impervious surfaces such as roads and other paved surfaces. Value provided is the mean area weighted average of imperviousness underneath the building footprint | MRLC |\n", + "| AgCount | Number of agricultural businesses in the county in which the building resides | CBP |\n", + "| CmCount | Number of commercial businesses in the county in which the building resides | CBP |\n", + "| GvCount | Number of government buildings in the county in which the building resides | CBP |\n", + "| EdCount | Number of educational buildings in the county in which the building resides | CBP |\n", + "| InCount | Number of industrial buildings in the county in which the building resides | CBP |\n", + "| OsmNearestRoad | Type of nearest road to the building | OSM |\n", + "| BuildingType | Building classification | FEMA |\n", + "\n", + "The last column, 'BuildingType', is what we are trying to predict. [USA Structures](https://gis-fema.hub.arcgis.com/pages/usa-structures) is an open source building inventory provided by the Department of Homeland Security, FEMA, Oak Ridge National Laboratory, and the U.S. Geological Survey. The building inventory contains a Building Occupany Type, which was determined through census and U.S. mail data. Currently, Building Occupancy Type values are availble for nine states. However, we note that not every building in those nine states has a manually identified occupany type. We are using North Carolina Building Occupany Type data as our \"ground-truth\". If we can predict known building types well, the machine learning models can be reliably deployed on unknown buildings and in states where Building Occupany Type is unavailable." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0-83.26115735.48449121.95105522429.026649.00.6181774.000000222860351804residentialResidential
1-83.26097235.484583255.15555122429.026649.00.6181775.666667222860351804residentialResidential
2-83.26041735.484768184.62537622429.026649.00.6181777.000000222860351804residentialResidential
3-83.25680635.492268434.20542822429.026649.00.61817722.500000222860351804residentialResidential
4-83.15643535.493657138.36151522429.026649.00.6181774.000000222860351804secondaryResidential
\n", + "
" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty HousingUnitsCounty \\\n", + "0 -83.261157 35.484491 21.951055 22429.0 26649.0 \n", + "1 -83.260972 35.484583 255.155551 22429.0 26649.0 \n", + "2 -83.260417 35.484768 184.625376 22429.0 26649.0 \n", + "3 -83.256806 35.492268 434.205428 22429.0 26649.0 \n", + "4 -83.156435 35.493657 138.361515 22429.0 26649.0 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount EdCount \\\n", + "0 0.618177 4.000000 22 2860 3 51 \n", + "1 0.618177 5.666667 22 2860 3 51 \n", + "2 0.618177 7.000000 22 2860 3 51 \n", + "3 0.618177 22.500000 22 2860 3 51 \n", + "4 0.618177 4.000000 22 2860 3 51 \n", + "\n", + " InCount OsmNearestRoad BuildingType \n", + "0 804 residential Residential \n", + "1 804 residential Residential \n", + "2 804 residential Residential \n", + "3 804 residential Residential \n", + "4 804 secondary Residential " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rawDf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building types and their counts\n", + "\n", + "Residential 976690\n", + "Commercial 64029\n", + "Industrial 16722\n", + "Assembly 7323\n", + "Education 6457\n", + "Government 4910\n", + "Agriculture 1651\n", + "Utility and Misc 362\n", + "Name: BuildingType, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "# building type distributions\n", + "print( \"Building types and their counts\" )\n", + "print()\n", + "print( df['BuildingType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building type counts as compared to the largest class\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "Residential 1.000000\n", + "Commercial 15.253869\n", + "Industrial 58.407487\n", + "Assembly 133.372935\n", + "Education 151.260647\n", + "Government 198.918534\n", + "Agriculture 591.574803\n", + "Utility and Misc 2698.038674\n", + "Name: BuildingType, dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print( \"Building type counts as compared to the largest class\" )\n", + "print()\n", + "976690/df['BuildingType'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

We have significant class imbalance. For example, the residential buildings outnumber the commercial buildings 15 to 1. The residentail buildings outnumber the agricultural buildings nearly 600 to 1. This can be a challenge for machine learning classification algorithms. The algorithm may not \"learn\", but rather obtain high accuracy simply by picking the majority class.

\n", + "\n", + "

We will look at techniques for dealing with such imbalance

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use case driven model selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The type of classification model needed depends on the application. For example, during a flood emergency, first responders may only be interested in identifying residential buildings. In this use case, binary classification (Residential vs. non-Residential) is sufficient. \n", + "\n", + "However, for ecomomic applications, multiclass classification is needed, i.e. Residential vs. Commercial vs. Industrial vs. ...\n", + "\n", + "We provide examples of both scenarios." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Binary classification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we need to preprocess the data and prepare it for machine learning. For this binary classification example, all non-Residential building types are changed to \"Other\".\n", + "\n", + "

The OsmNearestRoad and OrnlType are currently text. We need to encode these as integers before proceeding with machine learning. We also want to scale our columns to a common axis so that columns with large values don't bias the learning.

\n", + "\n", + "

We'll use a standard scaler, which scales based on the standard scoreas: z = (x - u) / s

\n", + "

Where u is the mean of the training samples and s is the standard deviation of the training samples

" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df.loc[df[\"BuildingType\"] != \"Residential\", \"BuildingType\"] = 'Other'\n", + "\n", + "df = df.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "# preprocess the data - scaling\n", + "scaler = preprocessing.StandardScaler()\n", + " \n", + "columns = ['X', 'Y', 'Area', 'MedianIncomeCounty', \n", + " 'HousingUnitsCounty', 'HousingDensityCounty',\n", + " 'Impervious', 'AgCount', 'CmCount', 'GvCount',\n", + " 'EdCount', 'InCount']\n", + "df[columns] = scaler.fit_transform(df[columns])\n", + "\n", + "# preprossing - drop rows with NaN values\n", + "df = df.dropna()\n", + " \n", + "# preprocessing - change output labels from text ('Agriculture') to integers\n", + "nCols = df.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( df.iloc[:, nCols-1] )\n", + "df.iloc[:, nCols-1] = le.transform( df.iloc[:, nCols-1] )\n", + " \n", + "# preprocessing - change output labels of nearest road type from text to integers\n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( df.iloc[:, nCols-2] ) \n", + "df.iloc[:, nCols-2] = le2.transform( df.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Some machine learning algorithms, e.g. Random Forest, don't require scaling. We'll create a second dataframe with OsmNearestRoad and BuildingType encoded, but without the scaling

" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "rawDf.loc[rawDf[\"BuildingType\"] != \"Residential\", \"BuildingType\"] = 'Other'\n", + "\n", + "rawDf = rawDf.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "rawDf = rawDf.dropna()\n", + "\n", + "nCols = rawDf.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( rawDf.iloc[:, nCols-1] ) # ornl type\n", + "rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( rawDf.iloc[:, nCols-2] ) # nearest road type\n", + "rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Our \"BuildingType\" values are now encoded as 0 or 1. We can use the inverse_transform to see which one is which.

" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty HousingUnitsCounty \\\n", + "963767 0.710923 0.573174 -0.176558 1.302284 0.963812 \n", + "988894 0.825657 0.266649 -0.065683 1.302284 0.963812 \n", + "818879 0.908104 0.862325 -0.207194 1.302284 0.963812 \n", + "962542 0.721475 0.138977 -0.065971 1.302284 0.963812 \n", + "329885 -0.102134 1.262338 0.495512 -0.937660 -0.273707 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount \\\n", + "963767 -0.339621 -0.519858 1.665731 0.880503 0.643556 \n", + "988894 -0.339621 -1.160999 1.665731 0.880503 0.643556 \n", + "818879 -0.339621 1.165003 1.665731 0.880503 0.643556 \n", + "962542 -0.339621 0.594685 1.665731 0.880503 0.643556 \n", + "329885 0.026131 1.936610 -0.758292 -0.383523 -0.202516 \n", + "\n", + " EdCount InCount OsmNearestRoad BuildingType \n", + "963767 1.097817 1.041934 4 1 \n", + "988894 1.097817 1.041934 4 1 \n", + "818879 1.097817 1.041934 4 1 \n", + "962542 1.097817 1.041934 4 1 \n", + "329885 -0.520841 -0.321399 4 0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Other', 'Residential'], dtype=object)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "le.inverse_transform([0,1])" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['residential', 'primary', 'tertiary', 'secondary', 'trunk',\n", + " 'motorway_link', 'motorway', 'trunk_link', 'secondary_link',\n", + " 'tertiary_link', 'primary_link'], dtype=object)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "le2.inverse_transform( rawDf.iloc[:, nCols-2].unique() )" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 976689\n", + "0 101453\n", + "Name: BuildingType, dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['BuildingType'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 1.00000\n", + "0 9.62701\n", + "Name: BuildingType, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "976689/df['BuildingType'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

We still have nearly 10 times more residential buildings as \"other\" buildings. Let's proceed with various classification techniques and see how we do.

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Random Forest does not require data values to be scaled. Let's apply Random Forest to our unscaled dataframe.\n", + "\n", + "The Random Forest model is a means of combining multiple decision trees to create a “forest.” A decision tree is like a flowchart that draws a clear pathway to a decision (prediction) based the training data. The algorithm determines tree branching based on feature values with each branch of the decision tree offering a different possible outcome." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadBuildingType
1029795-78.90847235.948935106.5792035085.0144559.00.44502630.50000002675812892373241
667149-80.80921335.177268368.9036036443.0478966.00.42938041.000000381180343926071735241
265214-77.78310235.9792131713.1599926177.043154.00.45439684.500000716492064117620
1022363-78.92125035.925139151.0238035085.0144559.00.44502626.66666702675812892373241
943591-78.68449135.826343284.7900941189.0462582.00.40957849.5000001191057233027781778041
\n", + "
" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty \\\n", + "1029795 -78.908472 35.948935 106.57920 35085.0 \n", + "667149 -80.809213 35.177268 368.90360 36443.0 \n", + "265214 -77.783102 35.979213 1713.15999 26177.0 \n", + "1022363 -78.921250 35.925139 151.02380 35085.0 \n", + "943591 -78.684491 35.826343 284.79009 41189.0 \n", + "\n", + " HousingUnitsCounty HousingDensityCounty Impervious AgCount \\\n", + "1029795 144559.0 0.445026 30.500000 0 \n", + "667149 478966.0 0.429380 41.000000 38 \n", + "265214 43154.0 0.454396 84.500000 71 \n", + "1022363 144559.0 0.445026 26.666667 0 \n", + "943591 462582.0 0.409578 49.500000 119 \n", + "\n", + " CmCount GvCount EdCount InCount OsmNearestRoad BuildingType \n", + "1029795 26758 12 892 3732 4 1 \n", + "667149 118034 39 2607 17352 4 1 \n", + "265214 6492 0 64 1176 2 0 \n", + "1022363 26758 12 892 3732 4 1 \n", + "943591 105723 30 2778 17780 4 1 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rawDf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample.\n", + "\n", + "The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation. In this tutorial, we split our training data into 5 groups, thus k=5 and our approach is refered to as 5-fold cross-validation.\n", + "\n", + "In machine learning applications, we need to split out data into a training set (what is used to train the machine learning model) and a test set (a portion of data not used in training and reserved for evaluating how well the model is working). Cross-validation allows every data point to be used for both training and evaluation. It also helps us identify if any random splitting of the data results is drastically different performance.\n", + "\n", + "The general procedure is as follows:\n", + "\n", + "1. Shuffle the dataset randomly.\n", + "2. Split the dataset into k groups\n", + "3. For each unique group:\n", + " Take one group as the test data set\n", + " Take the remaining groups as a training data set\n", + " Fit a model on the training set and evaluate it on the test set\n", + " Retain the evaluation score and discard the model\n", + "4. Summarize the skill of the model using the sample of model evaluation scores" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold: 1\n", + " Balanced Accuracy: 0.855\n", + " Weighted F1: 0.9602\n", + " Weighted Precision: 0.9599\n", + " Weighted Recall: 0.9616\n", + "\n", + "Fold: 2\n", + " Balanced Accuracy: 0.8526\n", + " Weighted F1: 0.9605\n", + " Weighted Precision: 0.9603\n", + " Weighted Recall: 0.9621\n", + "\n", + "Fold: 3\n", + " Balanced Accuracy: 0.8545\n", + " Weighted F1: 0.9609\n", + " Weighted Precision: 0.9606\n", + " Weighted Recall: 0.9623\n", + "\n", + "Fold: 4\n", + " Balanced Accuracy: 0.8538\n", + " Weighted F1: 0.9602\n", + " Weighted Precision: 0.9599\n", + " Weighted Recall: 0.9616\n", + "\n", + "Fold: 5\n", + " Balanced Accuracy: 0.8578\n", + " Weighted F1: 0.9615\n", + " Weighted Precision: 0.9612\n", + " Weighted Recall: 0.9628\n", + "\n", + "Average Balanced Accuracy: 0.8547\n" + ] + } + ], + "source": [ + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "accs = []\n", + "fold = 1\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " predictions = rf.predict(X_test)\n", + " ba = np.round( balanced_accuracy_score(y_test, predictions), 4 )\n", + " accs.append( ba )\n", + " print( \"Fold:\", fold )\n", + " print( \" Balanced Accuracy:\", ba)\n", + " print( \" Weighted F1:\", np.round( f1_score(y_test, predictions, average='weighted'), 4 ))\n", + " print( \" Weighted Precision:\", np.round( precision_score(y_test, predictions, average='weighted'), 4 ))\n", + " print( \" Weighted Recall:\", np.round( recall_score(y_test, predictions, average='weighted'), 4 ))\n", + " print()\n", + " fold += 1\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We get very similar performance across all folds, which is good. Overall, the random forest approach can predict \"Residential\" or \"Other\" correctly with ~86% accuracy.\n", + "\n", + "We are using balanced accuracy to deal with our imbalanced dataset. It is defined as the average of recall obtained on each class. Similarly, we use micro F1, which is the normal F1 formula but calculated using the total number of True Positives (TP), False Positives (FP) and False Negatives (FN), instead of individually for each class." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Machine Learning models can be saved as [Pickle files](https://scikit-learn.org/stable/model_persistence.html). Although, the Joblib approach, is often preferred as it offers compression." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "rf.fit( X_train, y_train )\n", + "\n", + "with open('./rf_model.pkl', 'wb') as f:\n", + " pickle.dump(rf, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions: [1 1 1 1 1]\n", + "Actual: [1 1 1 1 1]\n" + ] + } + ], + "source": [ + "# read the model from file \n", + "with open('./rf_model.pkl', 'rb') as f:\n", + " rf = pickle.load(f)\n", + "\n", + "predictions = rf.predict(X_test)\n", + "print( \"Predictions:\", predictions[0:5] )\n", + "print( \"Actual:\", y_test[0:5] )" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# save the model to a file\n", + "joblib.dump(rf, './rf_model.sav', compress=3)\n", + "\n", + "# read the model from file \n", + "rf = joblib.load('./rf_model.sav')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A confustion matrix is a visual display of true positives, true negatives, false positives, and false negatives. Here, by setting normalize='true', we are displaying proportions rather than counts." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "cm = confusion_matrix( y_test, predictions, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 22}\n", + "plt.rc('font', **font)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-04-27 14:31:17.990417: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-04-27 14:31:18.066435: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW\n", + "2023-04-27 14:31:18.066535: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: narock.goucher.edu\n", + "2023-04-27 14:31:18.066558: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: narock.goucher.edu\n", + "2023-04-27 14:31:18.066810: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 525.105.17\n", + "2023-04-27 14:31:18.066885: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 525.85.5\n", + "2023-04-27 14:31:18.066905: E tensorflow/stream_executor/cuda/cuda_diagnostics.cc:313] kernel version 525.85.5 does not match DSO version 525.105.17 -- cannot find working devices in this configuration\n" + ] + } + ], + "source": [ + "# This is required for my GPU: GeForce RTX 2060\n", + "# Without this config, tensorflow cannot properly allocate GPU memory\n", + "\n", + "# This is a GPU specific configuration and may not be needed on your GPU\n", + "\n", + "config = tf.compat.v1.ConfigProto()\n", + "config.gpu_options.allow_growth = True\n", + "config.gpu_options.per_process_gpu_memory_fraction = 0.90\n", + "session = tf.compat.v1.Session(config=config)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating test/train split...\n" + ] + } + ], + "source": [ + "# columns: StateFips, CountyFips, StateCountyFips,\n", + "# X, Y, Area, MedianIncomeCounty, HousingUnitsCounty,\n", + "# HousingDensityCounty, Impervious, AgCount, CmCount,\n", + "# GvCount, EdCount, InCount, OsmNearestRoad, OrnlType\n", + "\n", + "# test/train split\n", + "print(\"Creating test/train split...\")\n", + "train, test = train_test_split(df, test_size=0.2, random_state=42, stratify=df[['BuildingType']])\n", + "\n", + "nCols = test.shape[1]\n", + "xTest = test.iloc[:, :nCols-2]\n", + "yTest = test.iloc[:, nCols-1]\n", + "\n", + "xTrain = train.iloc[:, :nCols-2]\n", + "yTrain = train.iloc[:, nCols-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Size: (862513, 12)\n", + "Testing Size: (215629, 12)\n" + ] + } + ], + "source": [ + "print(\"Training Size:\", xTrain.shape)\n", + "print(\"Testing Size:\", xTest.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

We want the neural network to weight the fewer \"other\" examples that are available. You can do this by passing Keras weights for each class through a parameter. These will cause the model to \"pay more attention\" to examples from an under-represented class.

" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weight for class 0: 5.31\n", + "Weight for class 1: 0.55\n" + ] + } + ], + "source": [ + "## A common weighting technique that involves the dataset size\n", + "## and the number of examples in each class.\n", + "\n", + "# Scaling by total/2 helps keep the loss to a similar magnitude.\n", + "total = xTrain.shape[0]\n", + "ix = np.where( yTrain == 0 )\n", + "zero = len(ix[0])\n", + "ix = np.where( yTrain == 1 )\n", + "one = len(ix[0])\n", + "\n", + "weight_for_0 = (1 / zero) * (total / 2.0)\n", + "weight_for_1 = (1 / one) * (total / 2.0)\n", + "\n", + "class_weight = {0: weight_for_0, 1: weight_for_1}\n", + "\n", + "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n", + "print('Weight for class 1: {:.2f}'.format(weight_for_1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Define a fairly simple three layer fully connected neural network

\n", + "

We will set the network to train for 150 epochs; however, we stop the training early if accuracy hasn't improved over the previous 7 epochs

" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# convert output from scalar to vector format, i.e. building type=2 to [0,0,1,0,0,0,0,0,0,0,0]\n", + "y = to_categorical(yTrain)\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(30, input_shape=(xTrain.shape[1],), activation='relu'))\n", + "model.add(Dense(15, activation='relu'))\n", + "model.add(Dense(2, activation='softmax'))\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "\n", + "# stop training if we see 7 epochs over which accuracy doesn't improve\n", + "es = EarlyStopping(monitor='accuracy', patience=7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Output a summary of our neural network to see how many trainable parameters there are

" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense (Dense) (None, 30) 390 \n", + " \n", + " dense_1 (Dense) (None, 15) 465 \n", + " \n", + " dense_2 (Dense) (None, 2) 32 \n", + " \n", + "=================================================================\n", + "Total params: 887\n", + "Trainable params: 887\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Train the neural network

" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/150\n", + "3370/3370 [==============================] - 6s 2ms/step - loss: 0.3577 - accuracy: 0.8811\n", + "Epoch 2/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3393 - accuracy: 0.8919\n", + "Epoch 3/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3337 - accuracy: 0.8924\n", + "Epoch 4/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3303 - accuracy: 0.8928\n", + "Epoch 5/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3279 - accuracy: 0.8927\n", + "Epoch 6/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3259 - accuracy: 0.8931\n", + "Epoch 7/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3250 - accuracy: 0.8935\n", + "Epoch 8/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3241 - accuracy: 0.8934\n", + "Epoch 9/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3232 - accuracy: 0.8937\n", + "Epoch 10/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3226 - accuracy: 0.8939\n", + "Epoch 11/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3218 - accuracy: 0.8942\n", + "Epoch 12/150\n", + "3370/3370 [==============================] - 3s 991us/step - loss: 0.3211 - accuracy: 0.8940\n", + "Epoch 13/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3205 - accuracy: 0.8944\n", + "Epoch 14/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3202 - accuracy: 0.8942\n", + "Epoch 15/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3195 - accuracy: 0.8943\n", + "Epoch 16/150\n", + "3370/3370 [==============================] - 5s 2ms/step - loss: 0.3190 - accuracy: 0.8947\n", + "Epoch 17/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3186 - accuracy: 0.8949\n", + "Epoch 18/150\n", + "3370/3370 [==============================] - 6s 2ms/step - loss: 0.3181 - accuracy: 0.8950\n", + "Epoch 19/150\n", + "3370/3370 [==============================] - 5s 2ms/step - loss: 0.3178 - accuracy: 0.8951\n", + "Epoch 20/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3175 - accuracy: 0.8949\n", + "Epoch 21/150\n", + "3370/3370 [==============================] - 6s 2ms/step - loss: 0.3173 - accuracy: 0.8951\n", + "Epoch 22/150\n", + "3370/3370 [==============================] - 6s 2ms/step - loss: 0.3167 - accuracy: 0.8956\n", + "Epoch 23/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3166 - accuracy: 0.8954\n", + "Epoch 24/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3162 - accuracy: 0.8960\n", + "Epoch 25/150\n", + "3370/3370 [==============================] - 5s 2ms/step - loss: 0.3160 - accuracy: 0.8961\n", + "Epoch 26/150\n", + "3370/3370 [==============================] - 6s 2ms/step - loss: 0.3158 - accuracy: 0.8960\n", + "Epoch 27/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3152 - accuracy: 0.8959\n", + "Epoch 28/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3149 - accuracy: 0.8961\n", + "Epoch 29/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3148 - accuracy: 0.8961\n", + "Epoch 30/150\n", + "3370/3370 [==============================] - 6s 2ms/step - loss: 0.3145 - accuracy: 0.8960\n", + "Epoch 31/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3144 - accuracy: 0.8961\n", + "Epoch 32/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3141 - accuracy: 0.8961\n", + "Epoch 33/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3139 - accuracy: 0.8963\n", + "Epoch 34/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3137 - accuracy: 0.8963\n", + "Epoch 35/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3135 - accuracy: 0.8964\n", + "Epoch 36/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3133 - accuracy: 0.8964\n", + "Epoch 37/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3130 - accuracy: 0.8964\n", + "Epoch 38/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3130 - accuracy: 0.8963\n", + "Epoch 39/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3128 - accuracy: 0.8966\n", + "Epoch 40/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3125 - accuracy: 0.8962\n", + "Epoch 41/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3125 - accuracy: 0.8967\n", + "Epoch 42/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3122 - accuracy: 0.8963\n", + "Epoch 43/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3121 - accuracy: 0.8967\n", + "Epoch 44/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3121 - accuracy: 0.8964\n", + "Epoch 45/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3119 - accuracy: 0.8967\n", + "Epoch 46/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3119 - accuracy: 0.8966\n", + "Epoch 47/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3116 - accuracy: 0.8967\n", + "Epoch 48/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3115 - accuracy: 0.8969\n", + "Epoch 49/150\n", + "3370/3370 [==============================] - 3s 1ms/step - loss: 0.3113 - accuracy: 0.8968\n", + "Epoch 50/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3115 - accuracy: 0.8966\n", + "Epoch 51/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3110 - accuracy: 0.8970\n", + "Epoch 52/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3110 - accuracy: 0.8966\n", + "Epoch 53/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3106 - accuracy: 0.8966\n", + "Epoch 54/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3110 - accuracy: 0.8968\n", + "Epoch 55/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3107 - accuracy: 0.8969\n", + "Epoch 56/150\n", + "3370/3370 [==============================] - 4s 1ms/step - loss: 0.3105 - accuracy: 0.8967\n", + "Epoch 57/150\n", + "3370/3370 [==============================] - 5s 2ms/step - loss: 0.3104 - accuracy: 0.8965\n", + "Epoch 58/150\n", + "3370/3370 [==============================] - 5s 1ms/step - loss: 0.3103 - accuracy: 0.8968\n" + ] + } + ], + "source": [ + "# fit the model\n", + "h = model.fit(xTrain, y, epochs=150, batch_size=256, class_weight=class_weight, callbacks=[es])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Epoch')" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot( h.history['accuracy'] )\n", + "plt.title('Training Accuracy')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "model.save_weights(\"./nn_model.h5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Evaluate the neural network on the test set" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6739/6739 [==============================] - 14s 2ms/step - loss: 0.3009 - accuracy: 0.8939\n", + "Accuracy on the test set: 0.8939\n" + ] + } + ], + "source": [ + "y = to_categorical(yTest)\n", + "loss, acc = model.evaluate( xTest, y )\n", + "print(\"Accuracy on the test set:\", np.round(acc,4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use Balanced Accuracy and Micro F1 to get a better sense of how we did" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6739/6739 [==============================] - 3s 455us/step\n" + ] + }, + { + "data": { + "text/plain": [ + "215629" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ps = model.predict(xTest)\n", + "predictions = []\n", + "for p in ps:\n", + " predictions.append( np.argmax( p ) )\n", + "len(predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.8728\n", + "Weighted F1: 0.907\n", + "Weighted Precision: 0.9339\n", + "Weighted Recall: 0.8939\n" + ] + } + ], + "source": [ + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(yTest.values, predictions), 4 ))\n", + "print( \"Weighted F1:\", np.round( f1_score(yTest.values, predictions, average='weighted'), 4 ))\n", + "print( \"Weighted Precision:\", np.round( precision_score(yTest.values, predictions, average='weighted'), 4 ))\n", + "print( \"Weighted Recall:\", np.round( recall_score(yTest.values, predictions, average='weighted'), 4 ))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "cm = confusion_matrix( yTest.values, predictions, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature Importance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Feature importance is a technique for assessing how important each of our input features are to making accurate predictions. Features with low importance do not contribute much (are not used/do not have much weight) to prediction accuracy. Low importance features can be ignored creating simpler more scalable machine learning modesl.

\n", + "\n", + "We use a technique called [Permutation Importance](https://scikit-learn.org/stable/modules/permutation_importance.html) in which features are randomly shuffled. If a column's values can be randomly shuffled and prediction accuracy does not decrease, then that feature has low importance.\n", + "\n", + "

We'll use our Random Forest model to demonstrate feature importance

" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time to compute the importances: 419.344 seconds\n" + ] + } + ], + "source": [ + "start_time = time.time()\n", + "result = permutation_importance(rf, X_test, y_test, n_repeats=10, random_state=42, n_jobs=1)\n", + "elapsed_time = time.time() - start_time\n", + "print(f\"Elapsed time to compute the importances: {elapsed_time:.3f} seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "font = {'family' : 'normal',\n", + " 'weight' : 'normal',\n", + " 'size' : 16}\n", + "plt.rc('font', **font)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_idx = result.importances_mean.argsort()\n", + "forest_importances = pd.Series(result.importances_mean[sorted_idx], index=rawDf.columns[:-1])\n", + "\n", + "fig, ax = plt.subplots( figsize=(10, 10) )\n", + "forest_importances.plot.bar(yerr=result.importances_std[sorted_idx], ax=ax)\n", + "ax.set_title(\"Feature importances using permutation method\")\n", + "ax.set_ylabel(\"Mean accuracy decrease\")\n", + "ax.set_yscale('log')\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The features 'HousingUnitsCounty', 'HousingDensityCounty', 'AgCount', 'CmCount', 'GvCount', 'EdCount', and 'InCount' have low importance. Let's retrain a smaller Random Forest model without those features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove features that are not important/helpful to simplify the model" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.8482\n", + "Micro F1: 0.9603\n", + "\n", + "Balanced Accuracy: 0.8477\n", + "Micro F1: 0.9599\n", + "\n", + "Balanced Accuracy: 0.8498\n", + "Micro F1: 0.9605\n", + "\n", + "Balanced Accuracy: 0.8435\n", + "Micro F1: 0.9593\n", + "\n", + "Balanced Accuracy: 0.8464\n", + "Micro F1: 0.9601\n", + "\n", + "Average Balanced Accuracy: 0.8471\n" + ] + } + ], + "source": [ + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "X = rawDf.drop(['BuildingType', 'HousingUnitsCounty', 'HousingDensityCounty', 'AgCount', 'CmCount',\n", + " 'GvCount', 'EdCount', 'InCount'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "accs = []\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " ba = np.round( balanced_accuracy_score(y_test, rf.predict(X_test)), 4 )\n", + " accs.append( ba )\n", + " print( \"Balanced Accuracy:\", ba)\n", + " print( \"Micro F1:\", np.round( f1_score(y_test, rf.predict(X_test), average='micro'), 4 ))\n", + " print()\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our average Balanced Accuracy from the previous Random Forest model using all the features was 85.48%. Our reduced model has accuracy of 84.71%. We can create a smaller model without much loss in accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "with open('./rf_reduced_model.pkl', 'wb') as f:\n", + " pickle.dump(rf, f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Strategies to deal with unbalanced classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Undersampling refers to a group of techniques designed to balance the class distribution. Undersampling techniques remove examples from the training dataset that belong to the majority class in order to better balance the class distribution. \n", + "\n", + "This is in contrast to oversampling, which involves adding examples to the minority class in an effort to reduce the class distribution imbalance.\n", + "\n", + "Near Miss refers to a collection of undersampling methods that select examples based on the distance of majority class examples to minority class examples.\n", + "\n", + "The approaches were proposed by Jianping Zhang and Inderjeet Mani in their 2003 paper [KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction](https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf)\n", + "\n", + "There are three versions of the technique, named NearMiss-1, NearMiss-2, and NearMiss-3.\n", + "\n", + "* NearMiss-1 selects examples from the majority class that have the smallest average distance to the k closest examples from the minority class. \n", + "* NearMiss-2 selects examples from the majority class that have the smallest average distance to the k furthest examples from the minority class. \n", + "* NearMiss-3 involves selecting a given number of majority class examples for each example in the minority class that are closest.\n", + "\n", + "Distance is determined in feature space using Euclidean distance.\n", + "\n", + "We'll use NearMiss-3, which keeps majority class examples that are on the decision boundary." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "from imblearn.under_sampling import NearMiss\n", + "\n", + "# define the undersampling method\n", + "undersample = NearMiss( version=3, n_neighbors_ver3=5 )\n", + "\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "# transform the dataset\n", + "X, y = undersample.fit_resample( X, y )" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Residential: 101453\n", + "Other: 101453\n" + ] + } + ], + "source": [ + "ix = np.where( y == 1 )\n", + "print( \"Residential:\", len(ix[0]) )\n", + "\n", + "ix = np.where( y == 0 )\n", + "print( \"Other:\", len(ix[0]) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest example of Near-Miss" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.842\n", + "Weighted F1: 0.842\n", + "Weighted Precision: 0.84\n", + "Weighted Recall: 0.84\n", + "\n", + "Balanced Accuracy: 0.8419\n", + "Weighted F1: 0.8419\n", + "Weighted Precision: 0.84\n", + "Weighted Recall: 0.84\n", + "\n", + "Balanced Accuracy: 0.8394\n", + "Weighted F1: 0.8394\n", + "Weighted Precision: 0.84\n", + "Weighted Recall: 0.84\n", + "\n", + "Balanced Accuracy: 0.841\n", + "Weighted F1: 0.841\n", + "Weighted Precision: 0.84\n", + "Weighted Recall: 0.84\n", + "\n", + "Balanced Accuracy: 0.8409\n", + "Weighted F1: 0.8409\n", + "Weighted Precision: 0.84\n", + "Weighted Recall: 0.84\n", + "\n", + "Average Balanced Accuracy: 0.841\n" + ] + } + ], + "source": [ + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "accs = []\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " predictions = rf.predict(X_test)\n", + " ba = np.round( balanced_accuracy_score(y_test, predictions), 4 )\n", + " accs.append( ba )\n", + " print( \"Balanced Accuracy:\", ba)\n", + " print( \"Weighted F1:\", np.round( f1_score(y_test, predictions, average='weighted'), 4 ))\n", + " print( \"Weighted Precision:\", np.round( precision_score(y_test, predictions, average='weighted'), 2 ) )\n", + " print( \"Weighted Recall:\", np.round( recall_score(y_test, predictions, average='weighted'), 2 ) )\n", + " print()\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our average Balanced Accuracy from the original Random Forest model was 85.48%. The Balanced Accuracy using Near-Miss-3 and balanced classed results in an accuracy of 84%. This technique was not particularly helpful for our application." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "with open('./rf_balanced_model.pkl', 'wb') as f:\n", + " pickle.dump(rf, f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Threshold Moving" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Perhaps the simplest approach to handle a severe class imbalance is to change the decision threshold. Many classification algorithms will return a probability of class membership where all values equal or greater than a threshold are mapped to one class and all other values are mapped to the other class. The default of many algorithms is to set the threshold at 0.5. Threshold moving simply moves the threshold attempting to achieve higher accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting the model...\n", + "Making predictions...\n", + "Generating precision-recall curve...\n" + ] + } + ], + "source": [ + "from matplotlib import pyplot\n", + "from sklearn.metrics import precision_recall_curve\n", + "\n", + "# generate dataset\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "# split into train/test sets\n", + "trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.2, random_state=2, stratify=y)\n", + "\n", + "# fit a model\n", + "print(\"Fitting the model...\")\n", + "model = RandomForestClassifier(class_weight='balanced')\n", + "model.fit(trainX, trainy)\n", + "\n", + "# predict probabilities\n", + "print(\"Making predictions...\")\n", + "yhat = model.predict_proba(testX)\n", + "\n", + "# keep probabilities for the positive outcome only\n", + "yhat = yhat[:, 1]\n", + "\n", + "# calculate precision-recall curve\n", + "print(\"Generating precision-recall curve...\")\n", + "precision, recall, thresholds = precision_recall_curve(testy, yhat)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Threshold=0.550000, F-Score=0.979\n" + ] + } + ], + "source": [ + "# convert to f score\n", + "fscore = (2 * precision * recall) / (precision + recall)\n", + "\n", + "# locate the index of the largest f score\n", + "ix = np.argmax(fscore)\n", + "print('Best Threshold=%f, F-Score=%.3f' % (thresholds[ix], fscore[ix]))" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the curve for the model\n", + "no_skill = len(testy[testy==1]) / len(testy)\n", + "pyplot.plot(recall, precision, marker='.', label='Random Forest', color='orange')\n", + "pyplot.scatter(recall[ix], precision[ix], marker='o', color='black', label='Best')\n", + "\n", + "# axis labels\n", + "pyplot.xlabel('Recall')\n", + "pyplot.ylabel('Precision')\n", + "pyplot.legend()\n", + "\n", + "# show the plot\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've found the best threshold, what accuracy can we achieve with it?" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.8637\n", + "Weighted F1: 0.9612\n", + "Weighted Precision: 0.96\n", + "Weighted Recall: 0.96\n" + ] + } + ], + "source": [ + "threshold = 0.55\n", + "\n", + "predicted_proba = model.predict_proba(testX)\n", + "predicted = (predicted_proba [:,1] >= threshold).astype('int')\n", + "\n", + "ba = np.round( balanced_accuracy_score(testy, predicted), 4 )\n", + "\n", + "print( \"Balanced Accuracy:\", ba)\n", + "print( \"Weighted F1:\", np.round( f1_score(testy, predicted, average='weighted'), 4 ))\n", + "print( \"Weighted Precision:\", np.round( precision_score(testy, predicted, average='weighted'), 2 ) )\n", + "print( \"Weighted Recall:\", np.round( recall_score(testy, predicted, average='weighted'), 2 ) )" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "with open('./rf_model_threshold_moving.pkl', 'wb') as f:\n", + " pickle.dump(model, f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiclass Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All classes" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.3619\n", + "Weighted F1: 0.9395\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.3579\n", + "Weighted F1: 0.9402\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.3654\n", + "Weighted F1: 0.9397\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.3739\n", + "Weighted F1: 0.9405\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.3645\n", + "Weighted F1: 0.9395\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Average Balanced Accuracy: 0.3647\n" + ] + } + ], + "source": [ + "# read into a Pandas dataframe\n", + "rawDf = pd.read_csv( url, compression=\"zip\" )\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "rawDf = rawDf.iloc[:, 3:] \n", + "\n", + "rawDf = rawDf.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "rawDf = rawDf.dropna()\n", + "\n", + "nCols = rawDf.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( rawDf.iloc[:, nCols-1] ) # ornl type\n", + "rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( rawDf.iloc[:, nCols-2] ) # nearest road type\n", + "rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )\n", + "\n", + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "accs = []\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " predictions = rf.predict(X_test)\n", + " ba = np.round( balanced_accuracy_score(y_test, predictions), 4 )\n", + " accs.append( ba )\n", + " print( \"Balanced Accuracy:\", ba)\n", + " print( \"Weighted F1:\", np.round( f1_score(y_test, predictions, average='weighted'), 4 ))\n", + " print( \"Weighted Precision:\", np.round( precision_score(y_test, predictions, average='weighted'), 2 ) )\n", + " print( \"Weighted Recall:\", np.round( recall_score(y_test, predictions, average='weighted'), 2 ) )\n", + " print()\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What does all of this look like if we want to do multi-class classification instead of binary classification? Let's look at another use case where we want to classify a building as \"Residential\", \"Commercial\", \"Industrial\", or \"Other\"." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Residential 976690\n", + "Commercial 64029\n", + "Other 20703\n", + "Industrial 16722\n", + "Name: BuildingType, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "# read into a Pandas dataframe\n", + "rawDf = pd.read_csv( url, compression=\"zip\" )\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "rawDf = rawDf.iloc[:, 3:] \n", + "\n", + "# convert to 'other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Assembly\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Education\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Government\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Agriculture\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Utility and Misc\", \"BuildingType\"] = 'Other'\n", + "\n", + "# building type distributions\n", + "print()\n", + "print( rawDf['BuildingType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.5633\n", + "Weighted F1: 0.9402\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.5721\n", + "Weighted F1: 0.9415\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.5666\n", + "Weighted F1: 0.9407\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.5687\n", + "Weighted F1: 0.9413\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.5711\n", + "Weighted F1: 0.9412\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Average Balanced Accuracy: 0.5684\n" + ] + } + ], + "source": [ + "rawDf = rawDf.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "rawDf = rawDf.dropna()\n", + "\n", + "nCols = rawDf.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( rawDf.iloc[:, nCols-1] ) # ornl type\n", + "rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( rawDf.iloc[:, nCols-2] ) # nearest road type\n", + "rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )\n", + "\n", + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "accs = []\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " predictions = rf.predict(X_test)\n", + " ba = np.round( balanced_accuracy_score(y_test, predictions), 4 )\n", + " accs.append( ba )\n", + " print( \"Balanced Accuracy:\", ba)\n", + " print( \"Weighted F1:\", np.round( f1_score(y_test, predictions, average='weighted'), 4 ))\n", + " print( \"Weighted Precision:\", np.round( precision_score(y_test, predictions, average='weighted'), 2 ) )\n", + " print( \"Weighted Recall:\", np.round( recall_score(y_test, predictions, average='weighted'), 2 ) )\n", + " print()\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try \"Residential\", \"Commercial\", or \"Other\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Residential 976690\n", + "Commercial 64029\n", + "Other 37425\n", + "Name: BuildingType, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "# read into a Pandas dataframe\n", + "rawDf = pd.read_csv( url, compression=\"zip\" )\n", + "\n", + "# ignore first few columns, which are FIPs codes, not needed for ML\n", + "rawDf = rawDf.iloc[:, 3:] \n", + "\n", + "# drop the rows we don't need\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Industrial\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Assembly\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Education\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Government\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Agriculture\", \"BuildingType\"] = 'Other'\n", + "rawDf.loc[rawDf[\"BuildingType\"] == \"Utility and Misc\", \"BuildingType\"] = 'Other'\n", + "\n", + "# building type distributions\n", + "print()\n", + "print( rawDf['BuildingType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Residential 1.000000\n", + "Commercial 15.253869\n", + "Other 26.097261\n", + "Name: BuildingType, dtype: float64\n", + "\n" + ] + } + ], + "source": [ + "# building type distributions\n", + "print()\n", + "print( 976690/rawDf['BuildingType'].value_counts() )\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocess" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "rawDf = rawDf.sample(frac=1) # shuffle the dataframe (technically, we randomly resample the entire df)\n", + "\n", + "rawDf = rawDf.dropna()\n", + "\n", + "nCols = rawDf.shape[1]\n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( rawDf.iloc[:, nCols-1] ) # ornl type\n", + "rawDf.iloc[:, nCols-1] = le.transform( rawDf.iloc[:, nCols-1] )\n", + " \n", + "le2 = preprocessing.LabelEncoder()\n", + "le2.fit( rawDf.iloc[:, nCols-2] ) # nearest road type\n", + "rawDf.iloc[:, nCols-2] = le2.transform( rawDf.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadBuildingType
348360-79.76356536.125509158.112030173.0232277.00.42911020.000000214579818915841172
242066-78.51291735.509306255.346030833.084340.00.39046513.33333353100164184390842
207083-77.83152834.282824129.686031054.0113368.00.50229112.00000042676110469518142
174978-77.89717634.177824371.581531054.0113368.00.50229140.66666742676110469518142
27419-80.68393535.400139438.876734297.087410.00.38710650.00000019144226289375142
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" + ], + "text/plain": [ + " X Y Area MedianIncomeCounty \\\n", + "348360 -79.763565 36.125509 158.1120 30173.0 \n", + "242066 -78.512917 35.509306 255.3460 30833.0 \n", + "207083 -77.831528 34.282824 129.6860 31054.0 \n", + "174978 -77.897176 34.177824 371.5815 31054.0 \n", + "27419 -80.683935 35.400139 438.8767 34297.0 \n", + "\n", + " HousingUnitsCounty HousingDensityCounty Impervious AgCount \\\n", + "348360 232277.0 0.429110 20.000000 21 \n", + "242066 84340.0 0.390465 13.333333 53 \n", + "207083 113368.0 0.502291 12.000000 4 \n", + "174978 113368.0 0.502291 40.666667 4 \n", + "27419 87410.0 0.387106 50.000000 19 \n", + "\n", + " CmCount GvCount EdCount InCount OsmNearestRoad BuildingType \n", + "348360 45798 18 915 8411 7 2 \n", + "242066 10016 4 184 3908 4 2 \n", + "207083 26761 10 469 5181 4 2 \n", + "174978 26761 10 469 5181 4 2 \n", + "27419 14422 6 289 3751 4 2 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rawDf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random Forest Multiclass Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.6741\n", + "Weighted F1: 0.9441\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.6737\n", + "Weighted F1: 0.944\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.6759\n", + "Weighted F1: 0.9452\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.6754\n", + "Weighted F1: 0.9444\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Balanced Accuracy: 0.6683\n", + "Weighted F1: 0.9432\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.95\n", + "\n", + "Average Balanced Accuracy: 0.6735\n" + ] + } + ], + "source": [ + "kf = KFold( n_splits=5, shuffle=True, random_state=42 )\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "X = rawDf.drop(['BuildingType'], axis=1).values\n", + "y = rawDf['BuildingType'].values\n", + "\n", + "accs = []\n", + "for trainIndex, testIndex in kf.split(X):\n", + " X_train, X_test = X[trainIndex], X[testIndex]\n", + " y_train, y_test = y[trainIndex], y[testIndex]\n", + " rf.fit( X_train, y_train )\n", + " predictions = rf.predict(X_test)\n", + " ba = np.round( balanced_accuracy_score(y_test, predictions), 4 )\n", + " accs.append( ba )\n", + " print( \"Balanced Accuracy:\", ba)\n", + " print( \"Weighted F1:\", np.round( f1_score(y_test, predictions, average='weighted'), 4 ))\n", + " print( \"Weighted Precision:\", np.round( precision_score(y_test, predictions, average='weighted'), 2 ) )\n", + " print( \"Weighted Recall:\", np.round( recall_score(y_test, predictions, average='weighted'), 2 ) )\n", + " print()\n", + "print( \"Average Balanced Accuracy:\", np.round(np.mean(accs),4) )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "with open('./rf_model_multi.pkl', 'wb') as f:\n", + " pickle.dump(rf, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = rf.predict(X_test)\n", + "cm = confusion_matrix( y_test, predictions, normalize='true' )" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 22}\n", + "plt.rc('font', **font)\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=le.inverse_transform([0,1,2]))#rf.classes_)\n", + "disp.plot(ax=ax, cmap='gist_yarg')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Commercial', 'Other', 'Residential'], dtype=object)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "le.inverse_transform( np.arange(0,3,1) )" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time to compute the importances: 422.395 seconds\n" + ] + } + ], + "source": [ + "start_time = time.time()\n", + "result = permutation_importance(rf, X_test, y_test, n_repeats=10, random_state=42, n_jobs=1)\n", + "elapsed_time = time.time() - start_time\n", + "print(f\"Elapsed time to compute the importances: {elapsed_time:.3f} seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_idx = result.importances_mean.argsort()\n", + "forest_importances = pd.Series(result.importances_mean[sorted_idx], index=df.columns[:-1])\n", + "\n", + "fig, ax = plt.subplots( figsize=(10, 10) )\n", + "forest_importances.plot.bar(yerr=result.importances_std[sorted_idx], ax=ax)\n", + "ax.set_title(\"Feature importances using permutation method\")\n", + "ax.set_ylabel(\"Mean accuracy decrease\")\n", + "ax.set_yscale('log')\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## One-vs-One Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One-vs-One is a multi-class classification strategy for dealing with unbalanced classes. The One-vs-One strategy splits a multi-class classification into one binary classification problem per each pair of classes, e.g. Residentail vs. Commercial, Residential vs. Industrial, Commercial vs. Industrial. The final class assignment is determined by aggregating the results of the binary classifiers." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Size: (862513, 12)\n", + "Testing Size: (215629, 12)\n" + ] + } + ], + "source": [ + "train, test = train_test_split(rawDf, test_size=0.2, random_state=42, stratify=rawDf[['BuildingType']])\n", + "\n", + "nCols = test.shape[1]\n", + "xTest = test.iloc[:, :nCols-2]\n", + "yTest = test.iloc[:, nCols-1]\n", + "\n", + "xTrain = train.iloc[:, :nCols-2]\n", + "yTrain = train.iloc[:, nCols-1]\n", + "\n", + "print(\"Training Size:\", xTrain.shape)\n", + "print(\"Testing Size:\", xTest.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.645\n", + "Weighted F1: 0.9384\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.94\n" + ] + } + ], + "source": [ + "from sklearn.multiclass import OneVsOneClassifier\n", + "\n", + "# define model\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "# define ovo strategy\n", + "ovo = OneVsOneClassifier(rf)\n", + "ovo.fit( xTrain, yTrain )\n", + " \n", + "# make predictions\n", + "yhat = ovo.predict(xTest)\n", + "\n", + "ba = np.round( balanced_accuracy_score(yTest, yhat), 4 )\n", + "print( \"Balanced Accuracy:\", ba)\n", + "print( \"Weighted F1:\", np.round( f1_score(yTest, yhat, average='weighted'), 4 ))\n", + "print( \"Weighted Precision:\", np.round( precision_score(yTest, yhat, average='weighted'), 2 ) )\n", + "print( \"Weighted Recall:\", np.round( recall_score(yTest, yhat, average='weighted'), 2 ) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## One-vs-Rest Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The One-vs-Rest strategy is a related approach for dealing with multi-class imbalance. Here, fewer models are created. There is one binary classification problem per class, e.g. Residentail vs. Rest, Commercial vs. Rest, Industrial vs. Rest." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.6473\n", + "Weighted F1: 0.9392\n", + "Weighted Precision: 0.94\n", + "Weighted Recall: 0.94\n" + ] + } + ], + "source": [ + "from sklearn.multiclass import OneVsRestClassifier\n", + "\n", + "# define model\n", + "rf = RandomForestClassifier(class_weight='balanced')\n", + "\n", + "# define ovr strategy\n", + "ovr = OneVsRestClassifier(rf)\n", + "ovr.fit( xTrain, yTrain )\n", + " \n", + "# make predictions\n", + "yhat = ovr.predict(xTest)\n", + "\n", + "ba = np.round( balanced_accuracy_score(yTest, yhat), 4 )\n", + "print( \"Balanced Accuracy:\", ba)\n", + "print( \"Weighted F1:\", np.round( f1_score(yTest, yhat, average='weighted'), 4 ))\n", + "print( \"Weighted Precision:\", np.round( precision_score(yTest, yhat, average='weighted'), 2 ) )\n", + "print( \"Weighted Recall:\", np.round( recall_score(yTest, yhat, average='weighted'), 2 ) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Network for multiclass classification" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weight for class 0: 8.42\n", + "Weight for class 1: 14.40\n", + "Weight for class 2: 0.55\n" + ] + } + ], + "source": [ + "# Scaling by total/2 helps keep the loss to a similar magnitude.\n", + "total = xTrain.shape[0]\n", + "ix = np.where( yTrain == 0 )\n", + "zero = len(ix[0])\n", + "ix = np.where( yTrain == 1 )\n", + "one = len(ix[0])\n", + "ix = np.where( yTrain == 2 )\n", + "two = len(ix[0])\n", + "\n", + "weight_for_0 = (1 / zero) * (total / 2.0)\n", + "weight_for_1 = (1 / one) * (total / 2.0)\n", + "weight_for_2 = (1 / two) * (total / 2.0)\n", + "\n", + "class_weight = {0: weight_for_0, 1: weight_for_1, 2: weight_for_2}\n", + "\n", + "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n", + "print('Weight for class 1: {:.2f}'.format(weight_for_1))\n", + "print('Weight for class 2: {:.2f}'.format(weight_for_2))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-05-11 15:22:19.808603: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-11 15:22:20.054133: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-11 15:22:20.054367: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-11 15:22:20.056497: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-05-11 15:22:20.058965: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-11 15:22:20.059192: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-11 15:22:20.059390: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-11 15:22:21.465633: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-11 15:22:21.465821: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-11 15:22:21.465958: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2023-05-11 15:22:21.466066: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4517 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/150\n", + "2696/2696 [==============================] - 15s 5ms/step - loss: 451.2835 - accuracy: 0.4303 - val_loss: 121.3688 - val_accuracy: 0.0616\n", + "Epoch 2/150\n", + "2696/2696 [==============================] - 12s 4ms/step - loss: 89.3031 - accuracy: 0.4163 - val_loss: 9.5930 - val_accuracy: 0.4323\n", + "Epoch 3/150\n", + "2696/2696 [==============================] - 13s 5ms/step - loss: 42.5724 - accuracy: 0.3823 - val_loss: 1.6108 - val_accuracy: 0.7390\n", + "Epoch 4/150\n", + "2696/2696 [==============================] - 10s 4ms/step - loss: 28.7446 - accuracy: 0.3727 - val_loss: 25.3638 - val_accuracy: 0.0591\n", + "Epoch 5/150\n", + "2696/2696 [==============================] - 14s 5ms/step - loss: 9.0845 - accuracy: 0.2738 - val_loss: 1.3378 - val_accuracy: 0.3368\n", + "Epoch 6/150\n", + "2696/2696 [==============================] - 16s 6ms/step - loss: 1.8896 - accuracy: 0.4200 - val_loss: 1.0525 - val_accuracy: 0.8924\n", + "Epoch 7/150\n", + "2696/2696 [==============================] - 13s 5ms/step - loss: 1.6636 - accuracy: 0.5986 - val_loss: 1.1009 - val_accuracy: 0.0593\n", + "Epoch 8/150\n", + "2696/2696 [==============================] - 11s 4ms/step - loss: 1.6536 - accuracy: 0.2713 - val_loss: 1.0984 - val_accuracy: 0.0348\n", + "Epoch 9/150\n", + "2696/2696 [==============================] - 11s 4ms/step - loss: 1.6481 - accuracy: 0.2286 - val_loss: 1.0995 - val_accuracy: 0.0348\n", + "Epoch 10/150\n", + "2696/2696 [==============================] - 13s 5ms/step - loss: 1.6480 - accuracy: 0.3127 - val_loss: 1.1170 - val_accuracy: 0.0348\n", + "Epoch 11/150\n", + "2696/2696 [==============================] - 15s 6ms/step - loss: 1.6480 - accuracy: 0.1608 - val_loss: 1.1041 - val_accuracy: 0.0348\n", + "Epoch 12/150\n", + "2696/2696 [==============================] - 13s 5ms/step - loss: 1.6481 - accuracy: 0.2954 - val_loss: 1.1076 - val_accuracy: 0.0348\n", + "Epoch 13/150\n", + "2696/2696 [==============================] - 14s 5ms/step - loss: 1.6481 - accuracy: 0.2393 - val_loss: 1.0947 - val_accuracy: 0.0593\n" + ] + } + ], + "source": [ + "# convert output from scalar to vector format, i.e. building type=2 to [0,0,1]\n", + "y = to_categorical(yTrain)\n", + "\n", + "# define the keras model\n", + "model = Sequential()\n", + "model.add(Dense(30, input_shape=(xTrain.shape[1],), activation='relu'))\n", + "model.add(Dense(15, activation='relu'))\n", + "model.add(Dense(3, activation='softmax'))\n", + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "\n", + "# stop training if we see 7 epochs over which val_accuracy doesn't improve\n", + "es = EarlyStopping(monitor='val_accuracy', patience=7, restore_best_weights=True)\n", + "\n", + "# fit the model\n", + "h = model.fit(xTrain, y, epochs=150, batch_size=256, callbacks=[es], \\\n", + " class_weight=class_weight, validation_split=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot( h.history['accuracy'], label='Training' )\n", + "plt.plot( h.history['val_accuracy'], label='Validation' )\n", + "plt.title('Training Accuracy')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6739/6739 [==============================] - 7s 980us/step - loss: 1.0510 - accuracy: 0.8916\n", + "Accuracy on the test set: 0.8916\n" + ] + } + ], + "source": [ + "y = to_categorical(yTest)\n", + "loss, acc = model.evaluate( xTest, y )\n", + "print(\"Accuracy on the test set:\", np.round(acc,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6739/6739 [==============================] - 5s 694us/step\n" + ] + }, + { + "data": { + "text/plain": [ + "215629" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ps = model.predict(xTest)\n", + "predictions = []\n", + "for p in ps:\n", + " predictions.append( np.argmax( p ) )\n", + "len(predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balanced Accuracy: 0.371\n", + "Weighted F1: 0.866\n", + "Weighted Precision: 0.85\n", + "Weighted Recall: 0.89\n" + ] + } + ], + "source": [ + "print( \"Balanced Accuracy:\", np.round( balanced_accuracy_score(yTest.values, predictions), 4 ))\n", + "print( \"Weighted F1:\", np.round( f1_score(yTest.values, predictions, average='weighted'), 4 ))\n", + "print( \"Weighted Precision:\", np.round( precision_score(yTest.values, predictions, average='weighted'), 2 ) )\n", + "print( \"Weighted Recall:\", np.round( recall_score(yTest.values, predictions, average='weighted'), 2 ) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summary of Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

\n", + "

Binary Classification
\n", + "\n", + "| Approach | Accuracy | Precision | Recall | F1 \n", + "-----------|----------|-----------|--------|---\n", + "| Random Forest | 85.5% | 96% | 96% | 96% \n", + "| Random Forest w/ balanced classes | 84% | 84% | 84% | 84%\n", + "| Random Forest w/ threshold moving | 87% | 96% | 96% | 96%\n", + "| Neural Network w/ class weighting | 87% | 94% | 90% | 91% \n", + "

\n", + "\n", + "
\n", + "\n", + "

\n", + "

Multiclass Classification
\n", + " \n", + "| Approach | Accuracy | Precision | Recall | F1 \n", + "-----------|----------|-----------|--------|---\n", + "| Random Forest | 67% | 94% | 95% | 94%\n", + "| Random Forest w/ one-vs-one | 65% | 94% | 94% | 94%\n", + "| Random Forest w/ one-vs-rest | 65% | 94% | 94% | 94%\n", + "| Neural Network w/ class weighting | 37% | 85% | 89% | 87%\n", + "

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explainability" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models. The various machine learning algorithms have different levels of inherent explainability. For example, random forest models, which are based on decision trees, are more easily able to explain why they made the predictions they did. Neural networks, on the other hand, are more of a black box.\n", + "\n", + "Because of this, we may actually favor the random forest model with threshold moving over the neural network model in the binary classification scenario. Even though the random forest model has slightly worse accuracy, the rationale behind its predictions are much more explainable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classify Unknown Buildings in North Carolina" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now apply our trained model to a dataset of buildings with unknown type." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " StateFips CountyFips StateCountyFips X Y Area \\\n", + "0 37 99 37099 -83.260602 35.484954 20.716411 \n", + "1 37 99 37099 -83.155694 35.493472 68.605588 \n", + "2 37 99 37099 -83.155694 35.493287 246.025647 \n", + "3 37 99 37099 -83.154768 35.491620 142.232825 \n", + "4 37 99 37099 -83.158009 35.486898 170.658810 \n", + "\n", + " MedianIncomeCounty HousingUnitsCounty HousingDensityCounty Impervious \\\n", + "0 22429.0 26649.0 0.618177 2.0 \n", + "1 22429.0 26649.0 0.618177 6.0 \n", + "2 22429.0 26649.0 0.618177 8.0 \n", + "3 22429.0 26649.0 0.618177 0.0 \n", + "4 22429.0 26649.0 0.618177 0.5 \n", + "\n", + " AgCount CmCount GvCount EdCount InCount OsmNearestRoad FEMA_100yr \n", + "0 22 2860 3 51 804 tertiary 0.0 \n", + "1 22 2860 3 51 804 secondary 0.0 \n", + "2 22 2860 3 51 804 secondary 0.0 \n", + "3 22 2860 3 51 804 secondary 0.0 \n", + "4 22 2860 3 51 804 residential 0.0 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# unknown buildings in North Carolina\n", + "url = \"https://github.com/geo-smart/flood-risk-ml-tutorial/blob/main/unknownBuildings.zip?raw=true\"\n", + "\n", + "unknown = pd.read_csv( url, compression=\"zip\" )\n", + "unknown.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# read the models from file \n", + "with open('./models/rf_model_multi.pkl', 'rb') as f:\n", + " rf = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# shuffle the dataframe (technically, we randomly resample the entire df)\n", + "unknown = unknown.sample(frac=1) \n", + "\n", + "unknown = unknown.dropna()\n", + "\n", + "nCols = unknown.shape[1] \n", + "le = preprocessing.LabelEncoder()\n", + "le.fit( unknown.iloc[:, nCols-2] ) # nearest road type\n", + "unknown.iloc[:, nCols-2] = le.transform( unknown.iloc[:, nCols-2] )" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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StateFipsCountyFipsStateCountyFipsXYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadFEMA_100yr
216003376337063-78.89097236.098843252.33260035085.0144559.00.4450260.25000002675812892373240.0
537093717937179-80.58745435.11476869.91563835468.083788.00.3516560.00000044139666311532450.0
105460371937019-78.02717633.94282458.45440930529.088330.00.64619352.0000004480353113218840.0
1654993718337183-78.44328735.774306165.87730041189.0462582.00.40957831.6666671191057233027781778040.0
1012143711537115-82.78912035.76421389.80333826782.011045.00.5211630.000000080801729340.0
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" + ], + "text/plain": [ + " StateFips CountyFips StateCountyFips X Y \\\n", + "216003 37 63 37063 -78.890972 36.098843 \n", + "53709 37 179 37179 -80.587454 35.114768 \n", + "105460 37 19 37019 -78.027176 33.942824 \n", + "165499 37 183 37183 -78.443287 35.774306 \n", + "101214 37 115 37115 -82.789120 35.764213 \n", + "\n", + " Area MedianIncomeCounty HousingUnitsCounty \\\n", + "216003 252.332600 35085.0 144559.0 \n", + "53709 69.915638 35468.0 83788.0 \n", + "105460 58.454409 30529.0 88330.0 \n", + "165499 165.877300 41189.0 462582.0 \n", + "101214 89.803338 26782.0 11045.0 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount EdCount \\\n", + "216003 0.445026 0.250000 0 26758 12 892 \n", + "53709 0.351656 0.000000 44 13966 6 311 \n", + "105460 0.646193 52.000000 44 8035 3 113 \n", + "165499 0.409578 31.666667 119 105723 30 2778 \n", + "101214 0.521163 0.000000 0 808 0 17 \n", + "\n", + " InCount OsmNearestRoad FEMA_100yr \n", + "216003 3732 4 0.0 \n", + "53709 5324 5 0.0 \n", + "105460 2188 4 0.0 \n", + "165499 17780 4 0.0 \n", + "101214 293 4 0.0 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unknown.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "219054 unknown buildings in North Carolina\n" + ] + } + ], + "source": [ + "print( unknown.shape[0], 'unknown buildings in North Carolina')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make predictions for all unknown buildings in North Carolina" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# generate dataset\n", + "X = unknown.drop(['StateFips','CountyFips','StateCountyFips','FEMA_100yr'], axis=1).values\n", + "\n", + "# make predictions\n", + "predictions = rf.predict(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "residential 212954\n", + "commercial 5324\n", + "other 776\n", + "Name: predictions, dtype: int64\n" + ] + } + ], + "source": [ + "# ['Commercial', 'Other', 'Residential']\n", + "pString = []\n", + "\n", + "# update prediction array\n", + "for p in predictions:\n", + " if ( p == 0 ):\n", + " pString.append('commercial')\n", + " elif ( p == 1 ):\n", + " pString.append('other')\n", + " elif ( p == 2 ):\n", + " pString.append('residential')\n", + "\n", + "# add a column to the dataframe\n", + "unknown['predictions'] = pString\n", + "\n", + "print( unknown['predictions'].value_counts() )" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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StateFipsCountyFipsStateCountyFipsXYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadFEMA_100yrpredictions
216003376337063-78.89097236.098843252.33260035085.0144559.00.4450260.25000002675812892373240.0residential
537093717937179-80.58745435.11476869.91563835468.083788.00.3516560.00000044139666311532450.0residential
105460371937019-78.02717633.94282458.45440930529.088330.00.64619352.0000004480353113218840.0residential
1654993718337183-78.44328735.774306165.87730041189.0462582.00.40957831.6666671191057233027781778040.0residential
1012143711537115-82.78912035.76421389.80333826782.011045.00.5211630.000000080801729340.0residential
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" + ], + "text/plain": [ + " StateFips CountyFips StateCountyFips X Y \\\n", + "216003 37 63 37063 -78.890972 36.098843 \n", + "53709 37 179 37179 -80.587454 35.114768 \n", + "105460 37 19 37019 -78.027176 33.942824 \n", + "165499 37 183 37183 -78.443287 35.774306 \n", + "101214 37 115 37115 -82.789120 35.764213 \n", + "\n", + " Area MedianIncomeCounty HousingUnitsCounty \\\n", + "216003 252.332600 35085.0 144559.0 \n", + "53709 69.915638 35468.0 83788.0 \n", + "105460 58.454409 30529.0 88330.0 \n", + "165499 165.877300 41189.0 462582.0 \n", + "101214 89.803338 26782.0 11045.0 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount EdCount \\\n", + "216003 0.445026 0.250000 0 26758 12 892 \n", + "53709 0.351656 0.000000 44 13966 6 311 \n", + "105460 0.646193 52.000000 44 8035 3 113 \n", + "165499 0.409578 31.666667 119 105723 30 2778 \n", + "101214 0.521163 0.000000 0 808 0 17 \n", + "\n", + " InCount OsmNearestRoad FEMA_100yr predictions \n", + "216003 3732 4 0.0 residential \n", + "53709 5324 5 0.0 residential \n", + "105460 2188 4 0.0 residential \n", + "165499 17780 4 0.0 residential \n", + "101214 293 4 0.0 residential " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unknown.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect predictions with socio-economic data and FEMA 100-year flood predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We obtained the 100-year flood map from the FEMA National Flood Hazards Layer. The phrase “100-year flood” is used to describe the extent of a flood that statistically has a 1-percent chance of occurring in any given year [1]. Here, it is used for illustrative purposes of an overall disaster response application. Rows with a 1 in the \"FEMA_100yr\" column were found be flooded at a significant depth in the FEMA 100-year flood maps.\n", + "\n", + "[1] Maidment, David 2009. “FEMA Flood Map Accuracy.”World Envi-ronmental and Water Resources Congress 2009: Great Rivers1–10.Microsoft. 2018.US Building Footprints.Noy, Natasha, Yuqing Gao, Anshu Ja" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5838 buildings impacted by the 100-year flood\n" + ] + } + ], + "source": [ + "flood = unknown[ unknown['FEMA_100yr'] == 1 ]\n", + "print(flood.shape[0], \"buildings impacted by the 100-year flood\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Residential buildings impacted by 100-year flood: 5452\n", + "Commercial buildings impacted by 100-year flood: 349\n", + "'Other' buildings impacted by 100-year flood: 37\n" + ] + } + ], + "source": [ + "residential = flood[ flood['predictions'] == 'residential' ]\n", + "commercial = flood[ flood['predictions'] == 'commercial' ]\n", + "other = flood[ flood['predictions'] == 'other' ]\n", + "\n", + "print(\"Residential buildings impacted by 100-year flood:\", residential.shape[0])\n", + "print(\"Commercial buildings impacted by 100-year flood:\", commercial.shape[0])\n", + "print(\"'Other' buildings impacted by 100-year flood:\", other.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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StateFipsCountyFipsStateCountyFipsXYAreaMedianIncomeCountyHousingUnitsCountyHousingDensityCountyImperviousAgCountCmCountGvCountEdCountInCountOsmNearestRoadFEMA_100yrpredictions
59439375537055-75.46495435.57541731.35686732538.034237.00.92745527.0406497042142641.0residential
762023712737127-77.79504636.004213105.64608326177.043154.00.45439632.0716492064117641.0residential
1103283711937119-80.90847235.46810217.75679536443.0478966.00.4293800.0381180343926071735241.0residential
6445376537065-77.75671335.95976927.81258424298.023059.00.4715547.00198601936341.0residential
732443712737127-77.81143535.949676203.12194826177.043154.00.45439623.0716492064117641.0residential
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" + ], + "text/plain": [ + " StateFips CountyFips StateCountyFips X Y \\\n", + "59439 37 55 37055 -75.464954 35.575417 \n", + "76202 37 127 37127 -77.795046 36.004213 \n", + "110328 37 119 37119 -80.908472 35.468102 \n", + "6445 37 65 37065 -77.756713 35.959769 \n", + "73244 37 127 37127 -77.811435 35.949676 \n", + "\n", + " Area MedianIncomeCounty HousingUnitsCounty \\\n", + "59439 31.356867 32538.0 34237.0 \n", + "76202 105.646083 26177.0 43154.0 \n", + "110328 17.756795 36443.0 478966.0 \n", + "6445 27.812584 24298.0 23059.0 \n", + "73244 203.121948 26177.0 43154.0 \n", + "\n", + " HousingDensityCounty Impervious AgCount CmCount GvCount EdCount \\\n", + "59439 0.927455 27.0 40 6497 0 42 \n", + "76202 0.454396 32.0 71 6492 0 64 \n", + "110328 0.429380 0.0 38 118034 39 2607 \n", + "6445 0.471554 7.0 0 1986 0 19 \n", + "73244 0.454396 23.0 71 6492 0 64 \n", + "\n", + " InCount OsmNearestRoad FEMA_100yr predictions \n", + "59439 1426 4 1.0 residential \n", + "76202 1176 4 1.0 residential \n", + "110328 17352 4 1.0 residential \n", + "6445 363 4 1.0 residential \n", + "73244 1176 4 1.0 residential " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flood.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "fips = []\n", + "counts = []\n", + "fip = flood['StateCountyFips'].unique()\n", + "for f in fip:\n", + " county = flood[ (flood['StateCountyFips'] == f) & (flood['predictions'] == 'residential') ]\n", + " count = county.shape[0]\n", + " counts.append(count) \n", + " fips.append( str(f) )" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame( list(zip(fips, counts)), columns =['fips', 'count'] )" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import geojson_rewind\n", + "import geopandas as gpd\n", + "\n", + "f = open('./north_carolina.geojson')\n", + "counties = json.load(f)\n", + "f.close()\n", + "\n", + "counties = geojson_rewind.rewind(counties, rfc7946=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "coloraxis": "coloraxis", + "featureidkey": "properties.fip_code", + "geo": "geo", + "geojson": { + "crs": { + "properties": { + "name": "urn:ogc:def:crs:OGC:1.3:CRS84" + }, + "type": "name" + }, + "features": [ + { + "geometry": { + "coordinates": [ + [ + [ + [ + -79.542428, + 35.843303 + ], + [ + -79.541728, + 35.899851 + ], + [ + -79.541084, + 35.928579 + ], + [ + -79.539774, + 36.000136 + ], + [ + -79.539153, + 36.082569 + ], + [ + -79.537963, + 36.113538 + ], + [ + -79.536417, + 36.160978 + ], + [ + -79.53478, + 36.188227 + ], + [ + -79.533795, + 36.21503 + ], + [ + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.express as px\n", + "\n", + "fig = px.choropleth( df, geojson=counties, color='count', locations='fips', \\\n", + " color_continuous_scale='Viridis', featureidkey='properties.fip_code') \n", + "fig.update_layout( margin={\"r\":0, \"t\":0, \"l\":0, \"b\":0} )\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Median Individual Income in County')" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m = 30106 # median individal income in NC in 2020\n", + "\n", + "plt.hist( residential['MedianIncomeCounty'] )\n", + "plt.plot( np.zeros(1600)+m, np.arange(0,1600,1), c='red' )\n", + "plt.ylabel('# of impacted residential homes')\n", + "plt.xlabel('Median Individual Income in County')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "33.27 % residential buildings impacted are in counties below median income\n" + ] + } + ], + "source": [ + "ix = np.where( residential['MedianIncomeCounty'] < m )\n", + "p = (len(ix[0])/residential['MedianIncomeCounty'].shape[0])*100.\n", + "print( np.round(p,2), '% residential buildings impacted are in counties below median income')" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Median Individual Income in County')" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m = 30106 # median individal income in NC in 2020\n", + "\n", + "plt.hist( commercial['MedianIncomeCounty'] )\n", + "plt.plot( np.zeros(150)+m, np.arange(0,150,1), c='red' )\n", + "plt.ylabel('# of impacted commercial buildings')\n", + "plt.xlabel('Median Individual Income in County')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "62.46 % commercial buildings impacted are in counties below median income\n" + ] + } + ], + "source": [ + "ix = np.where( commercial['MedianIncomeCounty'] < m )\n", + "p = (len(ix[0])/commercial['MedianIncomeCounty'].shape[0])*100.\n", + "print( np.round(p,2), '% commercial buildings impacted are in counties below median income')" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Median Individual Income in County')" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m = 30106 # median individal income in NC in 2020\n", + "\n", + "plt.hist( other['MedianIncomeCounty'] )\n", + "plt.plot( np.zeros(15)+m, np.arange(0,15,1), c='red' )\n", + "plt.ylabel('# of impacted \"other\" buildings')\n", + "plt.xlabel('Median Individual Income in County')" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40.54 % other buildings impacted are in counties below median income\n" + ] + } + ], + "source": [ + "ix = np.where( other['MedianIncomeCounty'] < m )\n", + "p = (len(ix[0])/other['MedianIncomeCounty'].shape[0])*100.\n", + "print( np.round(p,2), '% other buildings impacted are in counties below median income')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our machine learning models enable us to predict the building type for 219,054 buildings in North Carolina. Combining these predictions with socio-economic data and flood forecast models, we are able to make risk assessments that were not possible without the machine learning.\n", + "\n", + "In terms of impacts to residential homes, a 100-year flood will disproportionately impact Mecklenburg, Wake, Dare, and Brunswick counties. Nearly a third of all flooded residential buildings will be in counties where the median income is below the state median. Sixty three percent of commercial buildings predicted to flood during a 100-year flood are in economically disadvantaged counties.\n", + "\n", + "Although buildings are just one small part of the urban multiplex, the application of machine learning can help us better understand this interconnected system leading to enhanced risk assessment and forecasting. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/book/chapters/conclusion.ipynb b/book/chapters/conclusion.ipynb deleted file mode 100644 index 68ccb1f..0000000 --- a/book/chapters/conclusion.ipynb +++ /dev/null @@ -1,56 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Discussion / Conclusion \n", - "\n", - "### Conclusion\n", - "\n", - "Is ML the right tool for the research?/How to identify the best ML tool for my problem? \n", - "Can a simple model be as good as a complex one? ML model discovery \t\t\t\n", - "Do I have enough confidence to extrapolate the results? Is the model transferable?\t\n", - "\n", - "### Discussion\n", - "\n", - "This chapter has two parts: lessons learnt and open questions. \n", - "\n", - "Learns learnt should summerize the new stuff we learn from this use case of AI. What new contribution does AI give to solve this problem? Is it good enough to achieve your expected goal? What part of work is unexpected before you dive in? Do you think the model can work in your production environment? etc.\n", - "\n", - "Open questions should focus on future possibilities like if your team wants to adopt this model, what else you should do to make it fully work? How should we better tackle the data bias problem? How should we address the generalization issue on spatial and temporal extent in practice?\n", - "\n", - "Please elaborate a little bit on these questions with your real thoughts, which will be very helpful for us to tell the final story to students. \n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/data.ipynb b/book/chapters/data.ipynb deleted file mode 100644 index c30086e..0000000 --- a/book/chapters/data.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Data Preparation\n", - "\n", - "Data description include source, size, type, attributes, modality, etc. Data retrieval from community data centers, personal cloud storage, or published datasets. Feature extraction and engineering.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/development.ipynb b/book/chapters/development.ipynb deleted file mode 100644 index b6afd01..0000000 --- a/book/chapters/development.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Model Development and Paramter Tuning\n", - "\n", - "Explain the effects of parameter tuning and how it was performed. If you used a tool to perform automatic tuning , explaing that as well. If it is the case, explain model under or over fitting and the consequences.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/evaluation.ipynb b/book/chapters/evaluation.ipynb deleted file mode 100644 index 09280a1..0000000 --- a/book/chapters/evaluation.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Performance Evaluation\n", - "\n", - "Testing of the model on independent datasets.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/example.ipynb b/book/chapters/example.ipynb deleted file mode 100644 index c0ee06e..0000000 --- a/book/chapters/example.ipynb +++ /dev/null @@ -1,1921 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Sample Jupyter Notebook" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Demo of executability below.\n", - "Loading and plotting sample data from plotly express." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "customdata": [ - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.4 - ], - [ - 0.3 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.1 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.1 - ], - [ - 0.1 - ], - [ - 0.2 - ], - [ - 0.4 - ], - [ - 0.4 - ], - [ - 0.3 - ], - [ - 0.3 - ], - [ - 0.3 - ], - [ - 0.2 - ], - [ - 0.4 - ], - [ - 0.2 - ], - [ - 0.5 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.4 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.4 - ], - [ - 0.1 - ], - [ - 0.2 - ], - [ - 0.1 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.1 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.3 - ], - [ - 0.3 - ], - [ - 0.2 - ], - [ - 0.6 - ], - [ - 0.4 - ], - [ - 0.3 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.2 - ], - [ - 0.2 - ] - ], - "hovertemplate": "species=setosa
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] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "sepal_width" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "sepal_length" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas\n", - "import plotly.express as px\n", - "df = px.data.iris()\n", - "fig = px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", color=\"species\",\n", - " size='petal_length', hover_data=['petal_width'])\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15\n" - ] - } - ], - "source": [ - "import util\n", - "\n", - "x = 10\n", - "y = 20\n", - "print(util.dummy_method(x, y))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "geosmart", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - }, - "vscode": { - "interpreter": { - "hash": "a0d8bf954ea6db8eac7eea84f0a01b1ff10874f08007b8fbfb0c71c8847e6862" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/methods.ipynb b/book/chapters/methods.ipynb deleted file mode 100644 index 63df639..0000000 --- a/book/chapters/methods.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Machine Learning Methods and Tools\n", - "\n", - "Explain the method and why you think it's suitable for your use case. Explain the choice of tools/packages/data and the reason for use.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/motivation.ipynb b/book/chapters/motivation.ipynb deleted file mode 100644 index 8f0ef2a..0000000 --- a/book/chapters/motivation.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Motivation (Science or Utility)\n", - "\n", - "Explain the science motivation, data challenge and any existing attempts. Explain why you need ML to solve the problem as opposed to a conventional approach.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/questions.ipynb b/book/chapters/questions.ipynb deleted file mode 100644 index 92c141b..0000000 --- a/book/chapters/questions.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Open questions\n", - "\n", - "More future oriented tasks that require deeper and long term research, and will have big returns.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/reproducibility.ipynb b/book/chapters/reproducibility.ipynb deleted file mode 100644 index 609f7d8..0000000 --- a/book/chapters/reproducibility.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Reproducibility\n", - "\n", - "How to get credit for the work. Steps for getting a DOI. Discuss workflow adaptation to other datasets and science questions if suitable.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/todo.ipynb b/book/chapters/todo.ipynb deleted file mode 100644 index eb1b021..0000000 --- a/book/chapters/todo.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Try something on your own\n", - "\n", - "Interesting and meaningful tasks that the tutorials didn’t do, but you could try and they should be achievable with reasonably small amount of time and effort\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/training.ipynb b/book/chapters/training.ipynb deleted file mode 100644 index ba800a2..0000000 --- a/book/chapters/training.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Model Training\n", - "\n", - "Explain how you split the data into training, testing and validation sets. Explore feature importance. Save the model.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/chapters/troubleshooting.ipynb b/book/chapters/troubleshooting.ipynb deleted file mode 100644 index c6b8b67..0000000 --- a/book/chapters/troubleshooting.ipynb +++ /dev/null @@ -1,66 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Trouble Shooting\n", - "\n", - "This page lists all the issues we have met when creating or following tutorials.\n", - "\n", - "If you have no issues with running the tutorials, you can skip this chapter. It is very likely that some platform specific issues happen now and then, we would love to collect those knowledge to help students in future to avoid wasting time on them. \n", - "\n", - "In this chapter, each section should address one techincal issue/concern. Please list your running environment in many details as possible. The following is an example:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example Issue: Cannot run the `model.train` in Chapter 2. It omits error: \"xxxxx\".\n", - "\n", - "#### Environment\n", - "Machine: Apple M1 laptop\n", - "Python: 3.10\n", - "Conda: 4.12\n", - "Scikit-learn: 1.0.2\n", - "...\n", - "\n", - "#### Code\n", - "Line 1xxx in Chapter 2 (link)\n", - "```\n", - "clf = RandomForestClassifier(max_depth=2, random_state=0)\n", - "clf.train(X, y)\n", - "```\n", - "\n", - "#### Error\n", - "raised Error xxxx\n", - "\n", - "#### Diagnose\n", - "This might be caused by the incompatibility among xxxx\n", - "\n", - "#### Solution\n", - "Please remove xxx, install xxx, and do xxx to try again. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/book/chapters/util.py b/book/chapters/util.py deleted file mode 100644 index a57942f..0000000 --- a/book/chapters/util.py +++ /dev/null @@ -1,6 +0,0 @@ -''' -This script includes all packages and functions required by book chapters -''' - -def dummy_method(a, b): - return (a + 5) % b \ No newline at end of file diff --git a/book/chapters/workflow.ipynb b/book/chapters/workflow.ipynb deleted file mode 100644 index 3a67d0b..0000000 --- a/book/chapters/workflow.ipynb +++ /dev/null @@ -1,43 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Workflow Management / Cloud Computing\n", - "\n", - "Explain cloud-based workflows and compute requirements.\n", - "If you seek to make the model run in production or achieve higher productivity and scalability, use GeoWeaver or other tools.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:23:07) [MSC v.1927 32 bit (Intel)]" - }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/book/environment.yml 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'/data/ufokn/building_classification/data/census_county_naics_2020.txt' + +# Output directory +outDir = '/data/ufokn/building_classification/data/' + +# output file containing unique OSM key:value +osmTypeFile = '/data/ufokn/building_classification/data/OSM_UniqueKeyValue.csv' diff --git a/book/preprocessing/create_training_data.py b/book/preprocessing/create_training_data.py new file mode 100644 index 0000000..0f83798 --- /dev/null +++ b/book/preprocessing/create_training_data.py @@ -0,0 +1,230 @@ +import json +import time +import numpy as np +import pandas as pd +import geopandas as gpd +import utilities as utils +import configuration as config +import datacommons_pandas as dc + +def add_name_geoId(df): + + # add a new column called name, + # where each value is the name for the place dcid in the index + df['name'] = df.index.map(dc.get_property_values(df.index, 'name')) + df['geoId'] = df.index.map(dc.get_property_values(df.index, 'geoId')) + + # keep just the first name, instead of a list of all names. + df['name'] = df['name'].str[0] + + # geoId is a list of one element. convert to scalar + df['geoId'] = df['geoId'].str[0] + +# start the timer +start = time.perf_counter() + +# data commons ID for United States is country/USA +usa = 'country/USA' + +# get list of all counties in the US +counties = dc.get_places_in([usa], 'County')[usa] + +# get StatVarObservations for counties +df_county = dc.build_multivariate_dataframe(counties, \ + ['Median_Income_Person', 'Count_HousingUnit', 'Count_Person']) + +# add the county name and geoId +add_name_geoId( df_county ) + +# read the naics file +naicsData = pd.read_csv( config.naics, dtype=str ) + +# find all the geopackage files +geoFiles = utils.getListOfFiles( config.dataDir ) +print("Found", len(geoFiles), "geopackage files") + +# counters +ix = 0 +buildingCount = 0 +trianingCount = 0 +percent10 = np.ceil(0.10 * len(geoFiles)) +percent25 = np.ceil(0.25 * len(geoFiles)) +percent50 = np.ceil(0.50 * len(geoFiles)) +percent75 = np.ceil(0.75 * len(geoFiles)) +percent90 = np.ceil(0.90 * len(geoFiles)) + +# open the output files +stateFips = '37' +trainingFile = 'ML_Training_' + stateFips + '.csv' + +# without HAZUS summary +header = "StateFips,CountyFips,StateCountyFips," + \ + "X,Y,Area,MedianIncomeCounty,HousingUnitsCounty," + \ + "HousingDensityCounty,Impervious,AgCount,CmCount,GvCount,EdCount," + \ + "InCount,OsmNearestRoad,OrnlType\n" + +####### +# building height (ORNL) is not available very often, ignored for ML +####### + +outFile = open( config.outDir + trainingFile, 'w' ) +outFile.write(header) +outFile.close() + +# open another file for all unknown buildings +header = "StateFips,CountyFips,StateCountyFips," + \ + "X,Y,Area,MedianIncomeCounty,HousingUnitsCounty," + \ + "HousingDensityCounty,Impervious,AgCount,CmCount,GvCount,EdCount," + \ + "InCount,OsmNearestRoad,FEMA_100yr\n" +unknownFile = open( config.outDir + 'uknownBuildings.csv', 'w' ) +unknownFile.write(header) + +# counters for ORNL types +ornl = { 'Agriculture':0, 'Commercial':0, 'Residential':0, 'Education':0, + 'Government':0, 'Industrial':0, 'Utility and Misc':0, 'Assembly':0, 'Unclassified':0 } + +# iterate over all the geopackage files +usableBuildingsPerCounty = {} +for file in geoFiles: + + if ( ix == percent10 ): + print('10% complete...') + if ( ix == percent25 ): + print('25% complete...') + if ( ix == percent50 ): + print('50% complete...') + if ( ix == percent75 ): + print('75% complete...') + if ( ix == percent90 ): + print('90% complete...') + + # extract all the buildings + data = gpd.read_file(file) + buildings = data[ data['osm_building'] == 'yes' ] + buildingCount += buildings.shape[0] + + # valid buildings are those with known type to be used in supervised learning + validBuildings = buildings.loc[ buildings['ornl_OCC_CLS'].notnull() ] + + # unknown buildings are those without known type + unknownBuildings = buildings.loc[ buildings['ornl_OCC_CLS'].isnull() ] + + n1 = validBuildings.shape[0] + n2 = unknownBuildings.shape[0] + n3 = buildings.shape[0] + assert (n1+n2) == n3, "building shapes not correct" + + # count of valid buildings found + usableBuildingsThisCounty = validBuildings.shape[0] + + # get fips codes for this county + stateValues = validBuildings.loc[ validBuildings['STATEFP'].notnull() ] # values are occasionally None + countyValues = validBuildings.loc[ validBuildings['COUNTYFP'].notnull() ] + state = stateValues['STATEFP'].iloc[0] + county = countyValues['COUNTYFP'].iloc[0] + print("Working on state/county:", state, county) + print(" Usable buildings:", usableBuildingsThisCounty ) + + # get the naics summary for this county + agN, cmN, gvN, edN, idN = utils.getNaicsSummary( naicsData, state, county ) + + # open the state file + outState = open( config.outDir + trainingFile, 'a' ) + + # keep track of usable buildings per county + usableBuildingsPerCounty[ state+county ] = usableBuildingsThisCounty + + # get county data from data commons + medianIncomeCounty, housingUnitsCounty, housingDensityCounty = \ + utils.getCountyData( state+county, df_county ) + + # iterate over all the unknown buildings + for idex, row in unknownBuildings.iterrows(): + + # get values from data file + x = row['x'] + y = row['y'] + s = gpd.GeoSeries( row['geometry'], crs='epsg:5070' ) + area = float(s.area) * 1.22e10 + imp = row['imperv'] + #height = row['ornl_HEIGHT'] + nearestRoad = row['osm_nearest_road_type'] + secondaryType = row['ornl_PRIM_OCC'] + fema = row['fema_100yr'] + + # output line + line = state + ',' + county + ',' + state+county + ',' + \ + str(x) + ',' + str(y) + ',' + str(area) + ',' + str(medianIncomeCounty) + ',' + \ + str(housingUnitsCounty) + ',' + str(housingDensityCounty) + ',' + \ + str(imp) + ',' + str(agN) + ',' + str(cmN) + ',' + \ + str(gvN) + ',' + str(edN) + ',' + str(idN) + ',' + \ + str(nearestRoad) + ',' + str(fema) + '\n' + + unknownFile.write( line ) + + # iterate over all the valid buildings + for idex, row in validBuildings.iterrows(): + + # get ORNL OCC type, which are the same names as HAZUS + ornlType = row['ornl_OCC_CLS'] + cnt = ornl[ ornlType ] + ornl[ ornlType ] = cnt + 1 + + # continue if usable ORNL type + if ( (ornlType != None) and (ornlType != 'Unclassified') ): + + # update the training size counter + trianingCount += 1 + + # get values from data file + x = row['x'] + y = row['y'] + s = gpd.GeoSeries( row['geometry'], crs='epsg:5070' ) + area = float(s.area) * 1.22e10 + imp = row['imperv'] + #height = row['ornl_HEIGHT'] + nearestRoad = row['osm_nearest_road_type'] + secondaryType = row['ornl_PRIM_OCC'] + + # output line + line = state + ',' + county + ',' + state+county + ',' + \ + str(x) + ',' + str(y) + ',' + str(area) + ',' + str(medianIncomeCounty) + ',' + \ + str(housingUnitsCounty) + ',' + str(housingDensityCounty) + ',' + \ + str(imp) + ',' + str(agN) + ',' + str(cmN) + ',' + \ + str(gvN) + ',' + str(edN) + ',' + str(idN) + ',' + \ + str(nearestRoad) + ',' + ornlType + '\n' + + outState.write( line ) + +# close the state file +outState.close() + +# close the unknown buildings file +unknownFile.close() + +# output usable buildings per county +outFile = open( config.outDir + 'ML_UsableBuildingsPerCounty.csv', 'w' ) +outFile.write("StateCounty,Buildings\n") +for key, value in usableBuildingsPerCounty.items(): + outFile.write( str(key) + ',' + str(value) + '\n' ) +outFile.close + +# output counts per ORNL type +outFile = open( config.outDir + 'ML_HazusCounts.csv', 'w' ) +outFile.write("Type,Count\n") +for key, value in ornl.items(): + outFile.write( str(key) + ',' + str(value) + '\n' ) +outFile.close + +# output statistics +outFile = open( config.outDir + 'ML_Data_Summary.txt', 'w' ) +outFile.write("Total number of buildings: " + str(buildingCount) + "\n" ) +outFile.write("Buildings in training set: " + str(trianingCount) + "\n" ) + +# stop the timer +stop = time.perf_counter() +duration = stop - start # in seconds +duration /= 60 # in minutes +duration /= 60 # in hours +outFile.write("Code took " + str(np.round(duration,2)) + " hours to run\n") +outFile.close() diff --git a/book/unknownBuildings.zip b/book/unknownBuildings.zip new file mode 100644 index 0000000..a66392e Binary files /dev/null and b/book/unknownBuildings.zip differ diff --git a/use_case_badge.svg b/use_case_badge.svg new file mode 100644 index 0000000..e101ce4 --- /dev/null +++ b/use_case_badge.svg @@ -0,0 +1,33 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +