diff --git a/_images/chapter3_17_0.png b/_images/chapter3_17_0.png new file mode 100644 index 0000000..a659e9e Binary files /dev/null and b/_images/chapter3_17_0.png differ diff --git a/_images/chapter4_35_0.png b/_images/chapter4_35_0.png new file mode 100644 index 0000000..7eb4206 Binary files /dev/null and b/_images/chapter4_35_0.png differ diff --git a/_images/chapter4_38_0.png b/_images/chapter4_38_0.png new file mode 100644 index 0000000..ef3f5aa Binary files /dev/null and b/_images/chapter4_38_0.png differ diff --git a/_images/use_case_badge.svg b/_images/use_case_badge.svg deleted file mode 100644 index e101ce4..0000000 --- a/_images/use_case_badge.svg +++ /dev/null @@ -1,33 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/_sources/chapters/about.ipynb b/_sources/chapters/about.ipynb deleted file mode 100644 index adc78c3..0000000 --- a/_sources/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/_sources/chapters/chapter1.md b/_sources/chapters/chapter1.md new file mode 100644 index 0000000..18c19bd --- /dev/null +++ b/_sources/chapters/chapter1.md @@ -0,0 +1,24 @@ +# Chapter 3: AI for sea ice forecasting + +The changing climate patterns caused by Arctic amplification have led to an increase in the frequency and intensity of extreme weather events. The loss of sea ice in the Arctic, as observed through satellite data, is a crucial aspect of this phenomenon. Accurately forecasting Arctic sea ice on subseasonal to seasonal scales presents significant scientific challenges. In addition to physics-based Earth system models, researchers have explored the use of statistical and machine learning models for sea ice forecasting. + +In this chapter, we examine three different approaches for predicting monthly Pan-Arctic sea ice extent up to 3 months in advance: traditional machine learning, deep learning, and ensemble learning. Leveraging monthly satellite-retrieved sea ice data from NSIDC and atmospheric/oceanic variables from the ERA5 reanalysis product spanning the period of 1979-2021, we demonstrate the potential of ensemble methods in achieving promising predictive performance with longer lead times. These advancements will greatly enhance our ability to forecast future changes in Arctic sea ice, enabling us to make more informed predictions regarding transportation routes, resource development, coastal erosion, and the potential impact on Arctic coastal communities and wildlife. + +## Code: + +- [Monthly_Polar_Sea_Ice_Prediction_Attention_MLR+LSTM.ipynb](code/Monthly_Polar_Sea_Ice_Prediction_Attention_MLR+LSTM.ipynb): Jupyter notebook containing the code for monthly polar sea ice prediction using the attention-based Multivariate Linear Regression and Long Short-Term Memory (MLR+LSTM) model. + +- [Multiple_Linear_Regression.ipynb](code/Multiple_Linear_Regression.ipynb): Jupyter notebook with the implementation of the Multiple Linear Regression model for sea ice prediction. + +- [Sea_Ice_Prediction_monthly_LSTM.ipynb](code/Sea_Ice_Prediction_monthly_LSTM.ipynb): Jupyter notebook showcasing the monthly sea ice prediction using the Long Short-Term Memory (LSTM) model. + +## Data: + +- [Arctic_domain_mean_monthly_1979_2021.csv](data/Arctic_domain_mean_monthly_1979_2021.csv): CSV file containing the monthly mean data for the Arctic domain from 1979 to 2021. + +- [monthly_features_1979_Aug2021.npy](data/monthly_features_1979_Aug2021.npy): NumPy array file containing the monthly features data from 1979 to August 2021. + +- [monthly_target_1979_Aug2021.npy](data/monthly_target_1979_Aug2021.npy): NumPy array file containing the monthly target data from 1979 to August 2021. + +- [placeholder.txt](data/placeholder.txt): Placeholder text file. + diff --git a/_sources/chapters/chapter2.ipynb b/_sources/chapters/chapter2.ipynb new file mode 100644 index 0000000..6f0ea55 --- /dev/null +++ b/_sources/chapters/chapter2.ipynb @@ -0,0 +1,792 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Monthly_Polar_Sea_Ice_Prediction_Attention_MLR+LSTM.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "d0W0GAu4wRJ0" + }, + "source": [ + "## Sea Ice Prediction - MLR+LSTM" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XpImo-RyFHE5" + }, + "source": [ + "from numpy.random import seed\n", + "seed(1)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Xj6J2OiifPrz" + }, + "source": [ + "#Install latest attention package\n", + "pip install attention" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jdRdTOsz9raL" + }, + "source": [ + "### Initial Setup" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "U0pzwDXw9p0l" + }, + "source": [ + "import os\n", + "import math\n", + "import glob\n", + "import numpy as np\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from keras.models import Sequential\n", + "from tensorflow.keras.optimizers import Adam\n", + "from attention import Attention\n", + "from keras.layers import Dense, Dropout\n", + "from keras.layers import LSTM,TimeDistributed\n", + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import train_test_split\n", + "from keras.callbacks import EarlyStopping, ModelCheckpoint\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L8VX5KkQ_FX6" + }, + "source": [ + "## Loading Combined Data 1979-2021\n", + "\n", + "Features:\n", + "'wind_10m', 'specific_humidity', 'LW_down', 'SW_down', 'rainfall', 'snowfall' 'sst', 't2m', 'surface_pressure','sea_ice_extent'\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lMlcMP96tmrt" + }, + "source": [ + "df = pd.read_csv('.../Arctic_domain_mean_monthly_1979_2021.csv')\n", + "df = df.drop(['Date'],axis=1)\n", + "data = np.array(df)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Train a Linear Regression Model" + ], + "metadata": { + "id": "ylQxEPC7QzH6" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "y = data[:,-1] #assigning last column to be target variable\n", + "x = data[:,:] #dropping last column from features\n", + "\n", + "model = LinearRegression()\n", + "model.fit(x, y)\n", + "\n", + "lr_data = model.predict(x)\n", + "print(lr_data.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-RJ2Mqy_F1W5", + "outputId": "a5ea4245-f3da-43c3-c51d-4363c20957e2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(512,)\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "H0S-K8n0vOQk", + "outputId": "f7b428c6-2ff6-4425-d93c-f42d5b9443a6" + }, + "source": [ + "lr= lr_data.reshape(len(lr_data),1)\n", + "print(lr.shape)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(512, 1)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uXObfgS9bwox" + }, + "source": [ + "Adding LR predictions as additional feature in LSTM dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pKwmIvYU_nxx" + }, + "source": [ + "#### Adding a Lag to Y values\n", + "Here lag = 1 - 3 months\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8IoHN4-0vAzm", + "outputId": "8be76605-6940-4180-f239-84b6e89927d2" + }, + "source": [ + "data = np.concatenate((data,lr),axis=1)\n", + "print(data.shape)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(512, 11)\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uES68xGKwgSy", + "outputId": "96f51d8d-4543-420e-db15-847be8299014" + }, + "source": [ + "#Adding a lag to monthly targets\n", + "lag = 3\n", + "#test_data = data[-2:,:,:]\n", + "target = data[lag:,-1]\n", + "data = data[:-lag,:]\n", + "\n", + "print(data.shape)\n", + "print(target.shape)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(509, 11)\n", + "(509,)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lWyDrrpAQ74Q" + }, + "source": [ + "### Train Validation Split" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rnbZ9jp0zv4e" + }, + "source": [ + "LSTM network expects the input data to be provided with a specific array structure in the form of: [samples, time steps, features]. We load the csv file and only retain the feature and target columns. The features and target are stored in separate np arrays." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BUVLJ8d7yWlc", + "outputId": "535e7e9c-caa6-42be-d100-c416183afdb4" + }, + "source": [ + "# Sequential split train:val data \n", + "\n", + "LEN_DATA = len(data) #total number of pixels\n", + "\n", + "NUM_TRAIN = LEN_DATA - (24+6) #reserve last 2.5 years for testing\n", + "NUM_VALID = LEN_DATA - NUM_TRAIN\n", + "\n", + "print('LEN_DATA:',LEN_DATA)\n", + "print('NUM_TRAIN:',NUM_TRAIN)\n", + "print('NUM_VALID:',NUM_VALID)\n", + "\n", + "x_train = data[0:NUM_TRAIN]\n", + "x_valid = data[NUM_TRAIN:]\n", + "\n", + "#split features and labels\n", + "y_train=target[:NUM_TRAIN] #target is last column i-e sea-ice\n", + "y_valid=target[NUM_TRAIN:] #target is last column i-e sea-ice" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "LEN_DATA: 509\n", + "NUM_TRAIN: 479\n", + "NUM_VALID: 30\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fPxr5VW3ynVk", + "outputId": "de2511b3-6c70-43a2-a60e-9e241ef3aa12" + }, + "source": [ + "print('x_train.shape:',x_train.shape)\n", + "print('y_train.shape:',y_train.shape)\n", + "print('x_valid.shape:',x_valid.shape)\n", + "print('y_valid.shape:',y_valid.shape)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "x_train.shape: (479, 11)\n", + "y_train.shape: (479,)\n", + "x_valid.shape: (30, 11)\n", + "y_valid.shape: (30,)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8V9PlIs8OUUM" + }, + "source": [ + "### Reshaping Input and Target Features" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "v8qmWF4VHxrR" + }, + "source": [ + "# convert an array of values into a dataset matrix\n", + "def reshape_features(dataset, timesteps=1):\n", + " print(dataset.shape)\n", + " X = dataset.reshape((int(dataset.shape[0]/timesteps)), timesteps, dataset.shape[1])\n", + " return X" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-TOp3WLtJ6xJ" + }, + "source": [ + "### Normalization\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "X9nc-dTGJ8qr" + }, + "source": [ + "# normalize the features\n", + "\n", + "scaler_f = StandardScaler()\n", + "x_train = scaler_f.fit_transform(x_train) \n", + "x_valid = scaler_f.transform(x_valid) \n", + "#test_data = scaler_f.transform(forecast)\n", + "\n", + "scaler_l = StandardScaler()\n", + "y_train = scaler_l.fit_transform(y_train.reshape(-1,1)) #reshaping to 2d for standard scaling\n", + "y_valid = scaler_l.transform(y_valid.reshape(-1,1)) #reshaping to 2d for standard scaling\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iwL_XadANsWH", + "outputId": "5c22b9c4-bc8a-4a43-8730-3db2b5121172" + }, + "source": [ + "#Reshaping data to 3D for modeling\n", + "timesteps = 1\n", + "x_train = reshape_features(x_train, timesteps) # reshaping to 3d for model\n", + "x_valid = reshape_features(x_valid, timesteps) # reshaping to 3d for model" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(479, 11)\n", + "(30, 11)\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lDxL_gE5Onx9", + "outputId": "9fbe99b5-4d87-44a1-d899-203096001c8f" + }, + "source": [ + "print('x_train.shape:',x_train.shape)\n", + "print('y_train.shape:',y_train.shape)\n", + "print('x_valid.shape:',x_valid.shape)\n", + "print('y_valid.shape:',y_valid.shape)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "x_train.shape: (479, 1, 11)\n", + "y_train.shape: (479, 1)\n", + "x_valid.shape: (30, 1, 11)\n", + "y_valid.shape: (30, 1)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HRaNlUDXr7Qt" + }, + "source": [ + "## LSTM Network" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "psOiJCscr8wu", + "outputId": "85f2a69e-1b09-47cf-8235-370f5874ffa0" + }, + "source": [ + "import numpy as np\n", + "from tensorflow.keras import Input\n", + "from tensorflow.keras.layers import Dense, LSTM\n", + "from tensorflow.keras.models import load_model, Model\n", + "\n", + "timestep = timesteps\n", + "features = 11\n", + "\n", + "model_input = Input(shape=(timestep,features))\n", + "x = LSTM(64, return_sequences=True)(model_input)\n", + "x = Dropout(0.2)(x)\n", + "x = LSTM(32, return_sequences=True)(x)\n", + "x = LSTM(16, return_sequences=True)(x)\n", + "x = Attention(32)(x)\n", + "#x = Dropout(0.2)(x)\n", + "x = Dense(32)(x)\n", + "x = Dense(16)(x)\n", + "x = Dense(1)(x)\n", + "model = Model(model_input, x)\n", + "\n", + "print(model.summary())" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 1, 11)] 0 \n", + " \n", + " lstm (LSTM) (None, 1, 64) 19456 \n", + " \n", + " dropout (Dropout) (None, 1, 64) 0 \n", + " \n", + " lstm_1 (LSTM) (None, 1, 32) 12416 \n", + " \n", + " lstm_2 (LSTM) (None, 1, 16) 3136 \n", + " \n", + " attention (Attention) (None, 32) 1280 \n", + " \n", + " dense (Dense) (None, 32) 1056 \n", + " \n", + " dense_1 (Dense) (None, 16) 528 \n", + " \n", + " dense_2 (Dense) (None, 1) 17 \n", + " \n", + "=================================================================\n", + "Total params: 37,889\n", + "Trainable params: 37,889\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qrWXbk9DsTJ1" + }, + "source": [ + "### Compiling the Network and Fitting Model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JAo2H2JssXjN" + }, + "source": [ + "#Compiling the network\n", + "model.compile(loss='mean_squared_error', optimizer='adam')\n", + "checkpoint_path='./testmodel.h5'\n", + "keras_callbacks = [\n", + " EarlyStopping(monitor='val_loss', patience=60, mode='min', min_delta=0.001),\n", + " ModelCheckpoint(checkpoint_path, monitor='val_loss', save_best_only=True, mode='min')\n", + "]\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vxresQUSTYgq" + }, + "source": [ + "history=model.fit(x_train, y_train, epochs=500, batch_size=12, verbose=2, validation_split =0.33, shuffle=True,callbacks=keras_callbacks)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LvmYA01IaEdB" + }, + "source": [ + "### Model Predictions" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CaHjsvNLaD5I" + }, + "source": [ + "trainPred = model.predict(x_train)\n", + "testPred = model.predict(x_valid)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2rbm4HV3PYRs", + "outputId": "a872dded-b341-44ff-e220-b49fc22b9179" + }, + "source": [ + "print(testPred.shape)\n", + "print(trainPred.shape)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(30, 1)\n", + "(479, 1)\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nBgDrWcGPnZj" + }, + "source": [ + "# invert scaling for forecasted values \n", + "\n", + "inv_testPred = scaler_l.inverse_transform(testPred)\n", + "print(inv_testPred[1])\n", + "\n", + "# invert scaling for actual values\n", + "\n", + "inv_y_valid = scaler_l.inverse_transform(y_valid)\n", + "print(inv_y_valid[1])\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "oNZOX9p8Btxw" + }, + "source": [ + "# calculate RMSE\n", + "from sklearn.metrics import mean_squared_error\n", + "from math import sqrt\n", + "\n", + "rmse = sqrt(mean_squared_error(inv_y_valid, inv_testPred))\n", + "print('Test RMSE: %.3f' % rmse)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "23_bcO2qIpdn" + }, + "source": [ + "# calculate Normalized RMSE\n", + "y_max = inv_y_valid.max()\n", + "y_min = inv_y_valid.min()\n", + "nrmse = rmse /(inv_y_valid.mean()) \n", + "print('Test NRMSE:', nrmse)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "DK5wa1PUzvQv" + }, + "source": [ + "# calculate R-square\n", + "from sklearn.metrics import r2_score\n", + "from math import sqrt\n", + "\n", + "r_sq = r2_score(inv_y_valid, inv_testPred)\n", + "print('Test R_Square: %.3f' % r_sq)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-6q16qTmaZ8N" + }, + "source": [ + "### Plotting" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5P2TOQqU7Wry" + }, + "source": [ + "from matplotlib import pyplot\n", + "\n", + "pyplot.plot(history.history['loss'], label='train')\n", + "pyplot.plot(history.history['val_loss'], label='test')\n", + "pyplot.legend()\n", + "pyplot.show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "mdq_2QDSal-d" + }, + "source": [ + "fig, ax = plt.subplots()\n", + "ax.scatter(y_train,trainPred)\n", + "ax.plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', lw=4)\n", + "ax.set_xlabel('observed')\n", + "ax.set_ylabel('predicted')\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "gixcJfYHgvyP" + }, + "source": [ + "fig, ax = plt.subplots()\n", + "ax.scatter(inv_y_valid,inv_testPred) #[:,:,6]\n", + "ax.plot([inv_y_valid.min(), inv_y_valid.max()], [inv_y_valid.min(), inv_y_valid.max()], 'k--', lw=4)\n", + "ax.set_xlabel('observed')\n", + "ax.set_ylabel('predicted')\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zJ9u67hnUCfU" + }, + "source": [ + "trainPred = np.transpose(trainPred.flatten())\n", + "print(trainPred.shape)\n", + "print(y_train.shape)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "gQNoyQLlhIBV" + }, + "source": [ + "from matplotlib import pyplot\n", + "\n", + "pyplot.plot(trainPred)\n", + "pyplot.plot(y_train)\n", + "pyplot.show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Mmgl59KcPYgO" + }, + "source": [ + "from datetime import datetime\n", + "lead_time = lag\n", + "\n", + "time_range=pd.date_range(start=\"2019-01-01\",end=\"2021-07-31\",freq='m')\n", + "fig, ax= plt.subplots(figsize=(12, 4))\n", + "\n", + "plt.plot(inv_y_valid/(10**6), color = 'red', label = 'Observed sea ice')\n", + "plt.plot(inv_testPred/(10**6), color = 'blue', label = 'LR_LSTM predictions')\n", + "#plt.title('Sea ice prediction (Lead time:'+str(lead_time)+' month)',fontsize = 15)\n", + "plt.xlabel('Month',fontsize = 10)\n", + "plt.ylabel('Sea ice extent ($10^6$ $Km^2$)',fontsize = 15)\n", + "#ax.grid(False)\n", + "#ax.set_facecolor('white')\n", + "time_idx=np.arange(0,30,3)\n", + "date_str=np.array(time_range[time_idx].strftime('%Y-%m'))\n", + "ax.set_xticks(time_idx)\n", + "ax.set_xticklabels(date_str)\n", + "plt.legend()\n", + "#plt.show()\n", + "fig.savefig('Time_series_sea_ice_prediction_attention_lead_time_'+str(lead_time)+'.png')" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/_sources/chapters/chapter3.ipynb b/_sources/chapters/chapter3.ipynb new file mode 100644 index 0000000..dad79c4 --- /dev/null +++ b/_sources/chapters/chapter3.ipynb @@ -0,0 +1,707 @@ +{ + "nbformat": 4, + "nbformat_minor": 5, + "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": "3.7.4" + }, + "colab": { + "provenance": [], + "toc_visible": true + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Initial Setup" + ], + "metadata": { + "id": "Viw5lvFTba8f" + }, + "id": "Viw5lvFTba8f" + }, + { + "cell_type": "code", + "metadata": { + "id": "79852be5" + }, + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, preprocessing\n", + "from sklearn.model_selection import train_test_split\n", + "import pandas_profiling\n", + "from matplotlib import pyplot as plt\n", + "import sklearn.gaussian_process as gp\n", + "from sklearn.metrics import r2_score\n", + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "#from sklearn.gaussian_process import GaussianProcessRegressor\n", + "#from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\n" + ], + "id": "79852be5", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Loading the Dataset" + ], + "metadata": { + "id": "b4UQ7xtJbeQt" + }, + "id": "b4UQ7xtJbeQt" + }, + { + "cell_type": "code", + "metadata": { + "id": "ebc9ecce", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 + }, + "outputId": "f50a8030-d0b9-4858-9aea-15dc30fc5303" + }, + "source": [ + "\n", + "df = pd.read_csv(\"/content/Arctic_domain_mean_monthly_1979_2021.csv\")\n", + "# remove date from the set\n", + "df = df.drop('Date', 1)\n", + "df.loc[:, 'sea_ice_extent_2'] = df['sea_ice_extent']\n", + "df.head()\n" + ], + "id": "ebc9ecce", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n", + " df = df.drop('Date', 1)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " wind_10m specific_humidity LW_down SW_down rainfall snowfall \\\n", + "0 5.531398 0.811961 186.687054 3.127880 1.009872 0.892319 \n", + "1 5.328020 0.688896 174.794571 18.541594 0.920831 0.781347 \n", + "2 5.432511 0.916124 190.741933 67.690429 0.983327 0.855266 \n", + "3 4.792836 1.272056 212.937925 156.223673 0.890723 0.705203 \n", + "4 4.819028 2.239776 253.690478 230.950833 1.201308 0.688723 \n", + "\n", + " sst t2m surface_pressure sea_ice_extent sea_ice_extent_2 \n", + "0 273.355237 250.388101 984.633032 15604191 15604191 \n", + "1 273.121885 247.071202 983.980418 16378929 16378929 \n", + "2 273.088099 252.954138 985.140468 16521089 16521089 \n", + "3 273.126062 259.557456 989.314698 15561238 15561238 \n", + "4 273.393551 269.375118 984.483658 14085613 14085613 " + ], + "text/html": [ + "\n", + "
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 2 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "AZDJoxO4-EO6" + }, + "source": [ + "#Creating datasets with lag of 1 month\n", + "df1 = df.assign(sea_ice_extent_2 = df.sea_ice_extent_2.shift(-1)).drop(df.index[-1])" + ], + "id": "AZDJoxO4-EO6", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MzoViPt1EcrJ", + "outputId": "a0bf254b-6a91-49ea-fa78-4d16512e02ee" + }, + "source": [ + "df1.head" + ], + "id": "MzoViPt1EcrJ", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Train Test Split" + ], + "metadata": { + "id": "splUv0p6b-uh" + }, + "id": "splUv0p6b-uh" + }, + { + "cell_type": "code", + "metadata": { + "id": "aqxrj67AwKR1" + }, + "source": [ + "data = np.array(df1)\n", + "target = data[:,-1] #assign last column to be target variable\n", + "data = data[:,:-1] #dropping last column from features\n", + "\n", + "# print(data.shape)\n", + "# print(target.shape)\n", + "LEN_DATA = len(data) #total number of pixels\n", + "NUM_TRAIN = LEN_DATA - (24+6) #reserve last 30 months for testing\n", + "\n", + "\n", + "x_train = data[0:NUM_TRAIN]\n", + "y_train = target[0:NUM_TRAIN]\n", + "\n", + "x_test = data[NUM_TRAIN:]\n", + "y_test=target[NUM_TRAIN:]\n", + "\n" + ], + "id": "aqxrj67AwKR1", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9ijjRAQbIfZ3", + "outputId": "daf883dc-539b-409b-eea1-cbd741ca99ff" + }, + "source": [ + "print(x_train.shape)\n", + "print(y_train.shape)\n", + "print(x_test.shape)\n", + "print(y_test.shape)" + ], + "id": "9ijjRAQbIfZ3", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(481, 10)\n", + "(481,)\n", + "(30, 10)\n", + "(30,)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Data Normalization" + ], + "metadata": { + "id": "maF5-maYcIC8" + }, + "id": "maF5-maYcIC8" + }, + { + "cell_type": "code", + "metadata": { + "id": "xv7_csvuyCqv" + }, + "source": [ + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "\n", + "scaler_x = MinMaxScaler()\n", + "x_train = scaler_x.fit_transform(x_train) \n", + "x_test = scaler_x.transform(x_test) \n", + "\n", + "scaler_y = MinMaxScaler()\n", + "y_train = scaler_y.fit_transform(y_train.reshape(-1,1))\n", + "y_test = scaler_y.transform(y_test.reshape(-1,1)) " + ], + "id": "xv7_csvuyCqv", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Defining Model" + ], + "metadata": { + "id": "rYCJJbcdcCMV" + }, + "id": "rYCJJbcdcCMV" + }, + { + "cell_type": "code", + "metadata": { + "id": "efdeaf3c-5460-466b-a1db-ecb811ac921e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 75 + }, + "outputId": "4f213a7c-1ea4-490e-da44-c3fc82f2f4df" + }, + "source": [ + "model = LinearRegression()\n", + "model.fit(x_train, y_train)" + ], + "id": "efdeaf3c-5460-466b-a1db-ecb811ac921e", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LinearRegression()" + ], + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Model Predictions" + ], + "metadata": { + "id": "qD5UX2zncL_v" + }, + "id": "qD5UX2zncL_v" + }, + { + "cell_type": "code", + "metadata": { + "id": "ef3a1000-a1f6-4a8b-8330-c670c336325c" + }, + "source": [ + "y_pred = model.predict(x_test)\n", + "y_train_pred = model.predict(x_train)" + ], + "id": "ef3a1000-a1f6-4a8b-8330-c670c336325c", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zbXroQaVvDEK" + }, + "source": [ + "#Sample code to inverse transform data\n", + "#Inverse transformation should be performed after getting predictions\n", + "inv_y_train = scaler_y.inverse_transform(y_train)\n", + "inv_y_test = scaler_y.inverse_transform(y_test)\n", + "inv_y_pred = scaler_y.inverse_transform(y_pred)\n" + ], + "id": "zbXroQaVvDEK", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Plotting Results" + ], + "metadata": { + "id": "BKpZlD_QcPkW" + }, + "id": "BKpZlD_QcPkW" + }, + { + "cell_type": "code", + "metadata": { + "id": "e46e0440-fc67-4836-b4ca-60ccaec5770b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 632 + }, + "outputId": "c0f1a7de-1dcc-4bee-b9fa-e4f280683f37" + }, + "source": [ + "%matplotlib inline\n", + "#plot \n", + "fig, ax= plt.subplots(figsize=(15, 6), dpi = 600)\n", + "plt.plot(inv_y_test, color='red')\n", + "plt.plot(inv_y_pred)\n", + "\n", + "plt.legend(['y_test','y_pred'])\n", + "plt.title(\"Sea Ice Extent Observation vs Prediction (2019-21)\")\n", + "ax.set_xlabel(\"Time (Months)\")\n", + "ax.set_ylabel(r\"Sea Ice Extent mil. $km^2$\")\n", + "plt.show()\n", + "fig.savefig('Time_series_sea_ice_extent_trend_1979_2021_lag1.png')" + ], + "id": "e46e0440-fc67-4836-b4ca-60ccaec5770b", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Performance Evaluation" + ], + "metadata": { + "id": "eySnlzTvcX0u" + }, + "id": "eySnlzTvcX0u" + }, + { + "cell_type": "code", + "metadata": { + "id": "2e62173a-e930-4705-92ed-db59d6a4640f", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4ea6703e-a992-499e-fdd1-ffd041faf40e" + }, + "source": [ + "rmse1 = np.sqrt(np.mean((inv_y_pred - inv_y_test) ** 2))\n", + "print('Test RMSE: %0.2f Mil. sq Km' %(rmse1))" + ], + "id": "2e62173a-e930-4705-92ed-db59d6a4640f", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test RMSE: 433143.27 Mil. sq Km\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "nrmse1 = rmse1/(np.mean(inv_y_test))\n", + "nrmse1" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kPhbGoPGljZO", + "outputId": "ec6546f9-ce4c-4407-d181-f735b6a161ae" + }, + "id": "kPhbGoPGljZO", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.04124839984143858" + ] + }, + "metadata": {}, + "execution_count": 35 + } + ] + }, + { + "cell_type": "code", + "source": [ + "r2 = r2_score(inv_y_test, inv_y_pred)\n", + "print('R2 Score: %0.2f' %(r2))" + ], + "metadata": { + "id": "xhN7FJ6NnhtB", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "318240ab-4b3f-4c1a-aaf8-a858a2b796aa" + }, + "id": "xhN7FJ6NnhtB", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "R2 Score: 0.99\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/_sources/chapters/chapter4.ipynb b/_sources/chapters/chapter4.ipynb new file mode 100644 index 0000000..941b6ec --- /dev/null +++ b/_sources/chapters/chapter4.ipynb @@ -0,0 +1,1191 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "rSkGCT8lwmj6" + }, + "source": [ + "## Sea Ice Prediction" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "g3fTrL1TI6u0" + }, + "source": [ + "from numpy.random import seed\n", + "seed(1)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Initial Setup" + ], + "metadata": { + "id": "an4uqQ0JdDkj" + } + }, + { + "cell_type": "code", + "metadata": { + "id": "Xj6J2OiifPrz", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ac90ccf6-351e-487f-c219-267547bccb6d" + }, + "source": [ + "pip install attention" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting attention\n", + " Downloading attention-4.1-py3-none-any.whl (8.6 kB)\n", + "Requirement already satisfied: tensorflow>=2.1 in /usr/local/lib/python3.8/dist-packages (from attention) (2.11.0)\n", + "Requirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.8/dist-packages (from attention) (1.22.4)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (23.0)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (57.4.0)\n", + "Requirement already satisfied: keras<2.12,>=2.11.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (2.11.0)\n", + "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (3.1.0)\n", + "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (2.2.0)\n", + "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (15.0.6.1)\n", + "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (0.4.0)\n", + "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (4.5.0)\n", + "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (3.3.0)\n", + "Requirement already satisfied: tensorboard<2.12,>=2.11 in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (2.11.2)\n", + "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (1.51.3)\n", + "Requirement already satisfied: absl-py>=1.0.0 in 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/usr/local/lib/python3.8/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow>=2.1->attention) (3.2.2)\n", + "Installing collected packages: attention\n", + "Successfully installed attention-4.1\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jdRdTOsz9raL" + }, + "source": [ + "## Initial Setup" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "U0pzwDXw9p0l" + }, + "source": [ + "import os\n", + "import math\n", + "import glob\n", + "import numpy as np\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from keras.models import Sequential\n", + "from tensorflow.keras.optimizers import Adam\n", + "from attention import Attention\n", + "from keras.layers import Dense, Dropout\n", + "from keras.layers import LSTM,TimeDistributed\n", + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import train_test_split\n", + "from keras.callbacks import EarlyStopping, ModelCheckpoint\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L8VX5KkQ_FX6" + }, + "source": [ + "## Loading Combined Data 1979- 2021\n", + "\n", + "Features:\n", + "'wind_10m', 'specific_humidity', 'LW_down', 'SW_down', 'rainfall', 'snowfall', 'sosaline', 'sst', 't2m', 'surface_pressure','sea_ice_extent'\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lMlcMP96tmrt" + }, + "source": [ + "data = np.load('/monthly_features_1979_Aug2021.npy',allow_pickle=True)\n", + "target = np.load('/monthly_target_1979_Aug2021.npy',allow_pickle=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pKwmIvYU_nxx" + }, + "source": [ + "### Adding a Lag to Y values\n", + "Here lag = 1 month\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uES68xGKwgSy", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "36eaaa42-da71-4d2b-f86d-00ad1b0f7543" + }, + "source": [ + "#Adding a lag to monthly targets\n", + "lag = 1\n", + "data = data[:-lag,:,:]\n", + "target = target[lag:]\n", + "\n", + "print(data.shape)\n", + "print(target.shape)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(511, 1, 10)\n", + "(511,)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lWyDrrpAQ74Q" + }, + "source": [ + "## Train Validation Split" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rnbZ9jp0zv4e" + }, + "source": [ + "LSTM network expects the input data to be provided with a specific array structure in the form of: [samples, time steps, features]. We load the csv file and only retain the feature and target columns. The features and target are stored in separate np arrays." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BUVLJ8d7yWlc", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dcdbd642-bfb5-4e87-efd0-f257ccac7e69" + }, + "source": [ + "\n", + "# Sequential split train:val data in 80:20 sequentially \n", + "\n", + "LEN_DATA = len(data) #total number of pixels\n", + "\n", + "NUM_TRAIN = LEN_DATA - (24+6) #reserve last 30 months for testing\n", + "NUM_VALID = LEN_DATA - NUM_TRAIN\n", + "\n", + "print('LEN_DATA:',LEN_DATA)\n", + "print('NUM_TRAIN:',NUM_TRAIN)\n", + "print('NUM_VALID:',NUM_VALID)\n", + "\n", + "x_train = data[0:NUM_TRAIN]\n", + "x_valid = data[NUM_TRAIN:]\n", + "\n", + "#split features and labels\n", + "y_train=target[:NUM_TRAIN] #target is last column i-e sea-ice\n", + "y_valid=target[NUM_TRAIN:] #target is last column i-e sea-ice\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "LEN_DATA: 511\n", + "NUM_TRAIN: 481\n", + "NUM_VALID: 30\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fPxr5VW3ynVk", + "outputId": "3bcbc9d5-f796-4e54-fbc7-3de8c8b56a95" + }, + "source": [ + "print('x_train.shape:',x_train.shape)\n", + "print('y_train.shape:',y_train.shape)\n", + "print('x_valid.shape:',x_valid.shape)\n", + "print('y_valid.shape:',y_valid.shape)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "x_train.shape: (481, 1, 10)\n", + "y_train.shape: (481,)\n", + "x_valid.shape: (30, 1, 10)\n", + "y_valid.shape: (30,)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8V9PlIs8OUUM" + }, + "source": [ + "## Reshaping Input and Target Features" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "v8qmWF4VHxrR" + }, + "source": [ + "# convert an array of values into a dataset matrix\n", + "def reshape_features(dataset, timesteps=1):\n", + " print(dataset.shape)\n", + " X = dataset.reshape((int(dataset.shape[0]/timesteps)), timesteps, dataset.shape[1])\n", + " return X" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-TOp3WLtJ6xJ" + }, + "source": [ + "## Normalization\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "X9nc-dTGJ8qr" + }, + "source": [ + "# normalize the features\n", + "\n", + "scaler_f = StandardScaler()\n", + "x_train = scaler_f.fit_transform(x_train.reshape(-1,10)) #reshaping to 2d for standard scaling\n", + "x_valid = scaler_f.transform(x_valid.reshape(-1,10)) #reshaping to 2d for standard scaling\n", + "\n", + "scaler_l = StandardScaler()\n", + "y_train = scaler_l.fit_transform(y_train.reshape(-1,1)) #reshaping to 2d for standard scaling\n", + "y_valid = scaler_l.transform(y_valid.reshape(-1,1)) #reshaping to 2d for standard scaling\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "iwL_XadANsWH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "acf6a6ac-dff5-4366-f95d-a1b396b888b2" + }, + "source": [ + "#Reshaping data to 3D for modeling\n", + "timesteps = 1\n", + "x_train = reshape_features(x_train, timesteps) # reshaping to 3d for model\n", + "x_valid = reshape_features(x_valid, timesteps) # reshaping to 3d for model\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(481, 10)\n", + "(30, 10)\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lDxL_gE5Onx9", + "outputId": "01a6058e-ed89-4f69-b136-f19425620c12" + }, + "source": [ + "print('x_train.shape:',x_train.shape)\n", + "print('y_train.shape:',y_train.shape)\n", + "print('x_valid.shape:',x_valid.shape)\n", + "print('y_valid.shape:',y_valid.shape)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "x_train.shape: (481, 1, 10)\n", + "y_train.shape: (481, 1)\n", + "x_valid.shape: (30, 1, 10)\n", + "y_valid.shape: (30, 1)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HRaNlUDXr7Qt" + }, + "source": [ + "## LSTM Network" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "psOiJCscr8wu", + "outputId": "da64e69c-66bc-460f-d7a0-53a6b4569787" + }, + "source": [ + "import numpy as np\n", + "from tensorflow.keras import Input\n", + "from tensorflow.keras.layers import Dense, LSTM\n", + "from tensorflow.keras.models import load_model, Model\n", + "\n", + "timestep = timesteps\n", + "features = 10\n", + "\n", + "model_input = Input(shape=(timestep,features))\n", + "x = LSTM(64, return_sequences=True)(model_input)\n", + "x = Dropout(0.2)(x)\n", + "x = LSTM(32, return_sequences=True)(x)\n", + "x = LSTM(16, return_sequences=True)(x)\n", + "x = LSTM(16, return_sequences=True)(x)\n", + "x = Attention(trainable = True)(x)\n", + "x = Dropout(0.2)(x)\n", + "x = Dense(32)(x)\n", + "x = Dense(16)(x)\n", + "x = Dense(1)(x)\n", + "model = Model(model_input, x)\n", + "#model.compile(loss='mae', optimizer='adam')\n", + "print(model.summary())" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 1, 10)] 0 \n", + " \n", + " lstm (LSTM) (None, 1, 64) 19200 \n", + " \n", + " dropout (Dropout) (None, 1, 64) 0 \n", + " \n", + " lstm_1 (LSTM) (None, 1, 32) 12416 \n", + " \n", + " lstm_2 (LSTM) (None, 1, 16) 3136 \n", + " \n", + " lstm_3 (LSTM) (None, 1, 16) 2112 \n", + " \n", + " attention (Attention) (None, 128) 4352 \n", + " \n", + " dropout_1 (Dropout) (None, 128) 0 \n", + " \n", + " dense (Dense) (None, 32) 4128 \n", + " \n", + " dense_1 (Dense) (None, 16) 528 \n", + " \n", + " dense_2 (Dense) (None, 1) 17 \n", + " \n", + "=================================================================\n", + "Total params: 45,889\n", + "Trainable params: 45,889\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PvRxn-_n-Lji", + "outputId": "eec2f15c-0bd8-4937-93e5-c94848a4c2f4" + }, + "source": [ + "extent = target\n", + "print(extent.shape)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(511,)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qrWXbk9DsTJ1" + }, + "source": [ + "## Compiling the Network and Fitting Model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JAo2H2JssXjN" + }, + "source": [ + "#Compiling the network\n", + "model.compile(loss='mean_squared_error', optimizer='adam')\n", + "checkpoint_path='./testmodel.h5'\n", + "keras_callbacks = [\n", + " EarlyStopping(monitor='val_loss', patience=60, mode='min', min_delta=0.001),\n", + " ModelCheckpoint(checkpoint_path, monitor='val_loss', save_best_only=True, mode='min')\n", + "]\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vxresQUSTYgq", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5e60bd14-8b89-49f9-a75b-b2b24727776d" + }, + "source": [ + "history=model.fit(x_train, y_train, epochs=500, batch_size=12, verbose=2, validation_split =0.3, shuffle=True,callbacks=keras_callbacks)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/500\n", + "28/28 - 17s - loss: 0.8364 - val_loss: 0.8320 - 17s/epoch - 622ms/step\n", + "Epoch 2/500\n", + "28/28 - 0s - loss: 0.1656 - val_loss: 0.1492 - 349ms/epoch - 12ms/step\n", + "Epoch 3/500\n", + "28/28 - 0s - loss: 0.0414 - val_loss: 0.0798 - 365ms/epoch - 13ms/step\n", + "Epoch 4/500\n", + "28/28 - 0s - loss: 0.0315 - val_loss: 0.0750 - 369ms/epoch - 13ms/step\n", + "Epoch 5/500\n", + "28/28 - 0s - loss: 0.0327 - val_loss: 0.0807 - 276ms/epoch - 10ms/step\n", + "Epoch 6/500\n", + "28/28 - 0s - loss: 0.0279 - val_loss: 0.0370 - 366ms/epoch - 13ms/step\n", + "Epoch 7/500\n", + "28/28 - 0s - loss: 0.0248 - val_loss: 0.0489 - 283ms/epoch - 10ms/step\n", + "Epoch 8/500\n", + "28/28 - 0s - loss: 0.0230 - val_loss: 0.0697 - 274ms/epoch - 10ms/step\n", + "Epoch 9/500\n", + "28/28 - 0s - loss: 0.0210 - val_loss: 0.0402 - 310ms/epoch - 11ms/step\n", + "Epoch 10/500\n", + "28/28 - 0s - loss: 0.0197 - val_loss: 0.0591 - 272ms/epoch - 10ms/step\n", + "Epoch 11/500\n", + "28/28 - 0s - loss: 0.0223 - val_loss: 0.0479 - 296ms/epoch - 11ms/step\n", + "Epoch 12/500\n", + "28/28 - 0s - loss: 0.0201 - val_loss: 0.0536 - 353ms/epoch - 13ms/step\n", + "Epoch 13/500\n", + "28/28 - 0s - loss: 0.0214 - val_loss: 0.0653 - 291ms/epoch - 10ms/step\n", + "Epoch 14/500\n", + "28/28 - 0s - loss: 0.0206 - val_loss: 0.0369 - 355ms/epoch - 13ms/step\n", + "Epoch 15/500\n", + "28/28 - 0s - loss: 0.0178 - val_loss: 0.0372 - 294ms/epoch - 11ms/step\n", + "Epoch 16/500\n", + "28/28 - 0s - loss: 0.0183 - val_loss: 0.0444 - 373ms/epoch - 13ms/step\n", + "Epoch 17/500\n", + "28/28 - 0s - loss: 0.0240 - val_loss: 0.0564 - 390ms/epoch - 14ms/step\n", + "Epoch 18/500\n", + "28/28 - 0s - loss: 0.0226 - val_loss: 0.0430 - 437ms/epoch - 16ms/step\n", + "Epoch 19/500\n", + "28/28 - 1s - loss: 0.0189 - val_loss: 0.0343 - 531ms/epoch - 19ms/step\n", + "Epoch 20/500\n", + "28/28 - 0s - loss: 0.0225 - val_loss: 0.0545 - 401ms/epoch - 14ms/step\n", + "Epoch 21/500\n", + "28/28 - 0s - loss: 0.0270 - val_loss: 0.0465 - 369ms/epoch - 13ms/step\n", + "Epoch 22/500\n", + "28/28 - 0s - loss: 0.0217 - val_loss: 0.0615 - 278ms/epoch - 10ms/step\n", + "Epoch 23/500\n", + "28/28 - 0s - loss: 0.0208 - val_loss: 0.0445 - 281ms/epoch - 10ms/step\n", + "Epoch 24/500\n", + "28/28 - 0s - loss: 0.0200 - val_loss: 0.0295 - 345ms/epoch - 12ms/step\n", + "Epoch 25/500\n", + "28/28 - 0s - loss: 0.0201 - val_loss: 0.0208 - 352ms/epoch - 13ms/step\n", + "Epoch 26/500\n", + "28/28 - 0s - loss: 0.0209 - val_loss: 0.0644 - 292ms/epoch - 10ms/step\n", + "Epoch 27/500\n", + "28/28 - 0s - loss: 0.0202 - val_loss: 0.0519 - 275ms/epoch - 10ms/step\n", + "Epoch 28/500\n", + "28/28 - 0s - loss: 0.0212 - val_loss: 0.0199 - 350ms/epoch - 13ms/step\n", + "Epoch 29/500\n", + "28/28 - 0s - loss: 0.0187 - val_loss: 0.0457 - 282ms/epoch - 10ms/step\n", + "Epoch 30/500\n", + "28/28 - 0s - loss: 0.0165 - val_loss: 0.0446 - 295ms/epoch - 11ms/step\n", + "Epoch 31/500\n", + "28/28 - 0s - loss: 0.0215 - val_loss: 0.0647 - 290ms/epoch - 10ms/step\n", + "Epoch 32/500\n", + "28/28 - 0s - loss: 0.0193 - val_loss: 0.0237 - 284ms/epoch - 10ms/step\n", + "Epoch 33/500\n", + "28/28 - 0s - loss: 0.0174 - val_loss: 0.0409 - 297ms/epoch - 11ms/step\n", + "Epoch 34/500\n", + "28/28 - 0s - loss: 0.0186 - val_loss: 0.0221 - 293ms/epoch - 10ms/step\n", + "Epoch 35/500\n", + "28/28 - 0s - loss: 0.0230 - val_loss: 0.0481 - 288ms/epoch - 10ms/step\n", + "Epoch 36/500\n", + "28/28 - 0s - loss: 0.0207 - val_loss: 0.0495 - 362ms/epoch - 13ms/step\n", + "Epoch 37/500\n", + "28/28 - 0s - loss: 0.0175 - val_loss: 0.0543 - 275ms/epoch - 10ms/step\n", + "Epoch 38/500\n", + "28/28 - 0s - loss: 0.0170 - val_loss: 0.0340 - 287ms/epoch - 10ms/step\n", + "Epoch 39/500\n", + "28/28 - 0s - loss: 0.0203 - val_loss: 0.0713 - 285ms/epoch - 10ms/step\n", + "Epoch 40/500\n", + "28/28 - 0s - loss: 0.0251 - val_loss: 0.0254 - 278ms/epoch - 10ms/step\n", + "Epoch 41/500\n", + "28/28 - 0s - loss: 0.0176 - val_loss: 0.0657 - 295ms/epoch - 11ms/step\n", + "Epoch 42/500\n", + "28/28 - 0s - loss: 0.0189 - val_loss: 0.0496 - 282ms/epoch - 10ms/step\n", + "Epoch 43/500\n", + "28/28 - 0s - loss: 0.0170 - val_loss: 0.0355 - 276ms/epoch - 10ms/step\n", + "Epoch 44/500\n", + "28/28 - 0s - loss: 0.0156 - val_loss: 0.0405 - 286ms/epoch - 10ms/step\n", + "Epoch 45/500\n", + "28/28 - 0s - loss: 0.0174 - val_loss: 0.0319 - 297ms/epoch - 11ms/step\n", + "Epoch 46/500\n", + "28/28 - 0s - loss: 0.0165 - val_loss: 0.0241 - 394ms/epoch - 14ms/step\n", + "Epoch 47/500\n", + "28/28 - 0s - loss: 0.0181 - val_loss: 0.0513 - 303ms/epoch - 11ms/step\n", + "Epoch 48/500\n", + "28/28 - 0s - loss: 0.0188 - val_loss: 0.0378 - 288ms/epoch - 10ms/step\n", + "Epoch 49/500\n", + "28/28 - 0s - loss: 0.0186 - val_loss: 0.0513 - 283ms/epoch - 10ms/step\n", + "Epoch 50/500\n", + "28/28 - 0s - loss: 0.0171 - val_loss: 0.0472 - 309ms/epoch - 11ms/step\n", + "Epoch 51/500\n", + "28/28 - 0s - loss: 0.0153 - val_loss: 0.0308 - 282ms/epoch - 10ms/step\n", + "Epoch 52/500\n", + "28/28 - 0s - loss: 0.0152 - val_loss: 0.0264 - 289ms/epoch - 10ms/step\n", + "Epoch 53/500\n", + "28/28 - 0s - loss: 0.0155 - val_loss: 0.0356 - 297ms/epoch - 11ms/step\n", + "Epoch 54/500\n", + "28/28 - 0s - loss: 0.0161 - val_loss: 0.0465 - 404ms/epoch - 14ms/step\n", + "Epoch 55/500\n", + "28/28 - 0s - loss: 0.0165 - val_loss: 0.0313 - 436ms/epoch - 16ms/step\n", + "Epoch 56/500\n", + "28/28 - 0s - loss: 0.0179 - val_loss: 0.0375 - 387ms/epoch - 14ms/step\n", + "Epoch 57/500\n", + "28/28 - 0s - loss: 0.0160 - val_loss: 0.0435 - 456ms/epoch - 16ms/step\n", + "Epoch 58/500\n", + "28/28 - 0s - loss: 0.0165 - val_loss: 0.0250 - 450ms/epoch - 16ms/step\n", + "Epoch 59/500\n", + "28/28 - 0s - loss: 0.0155 - val_loss: 0.0296 - 364ms/epoch - 13ms/step\n", + "Epoch 60/500\n", + "28/28 - 0s - loss: 0.0160 - val_loss: 0.0370 - 299ms/epoch - 11ms/step\n", + "Epoch 61/500\n", + "28/28 - 0s - loss: 0.0170 - val_loss: 0.0280 - 287ms/epoch - 10ms/step\n", + "Epoch 62/500\n", + "28/28 - 0s - loss: 0.0161 - val_loss: 0.0245 - 287ms/epoch - 10ms/step\n", + "Epoch 63/500\n", + "28/28 - 0s - loss: 0.0160 - val_loss: 0.0449 - 279ms/epoch - 10ms/step\n", + "Epoch 64/500\n", + "28/28 - 0s - loss: 0.0163 - val_loss: 0.0390 - 281ms/epoch - 10ms/step\n", + "Epoch 65/500\n", + "28/28 - 0s - loss: 0.0169 - val_loss: 0.0415 - 285ms/epoch - 10ms/step\n", + "Epoch 66/500\n", + "28/28 - 0s - loss: 0.0158 - val_loss: 0.0428 - 276ms/epoch - 10ms/step\n", + "Epoch 67/500\n", + "28/28 - 0s - loss: 0.0187 - val_loss: 0.0379 - 300ms/epoch - 11ms/step\n", + "Epoch 68/500\n", + "28/28 - 0s - loss: 0.0170 - val_loss: 0.0428 - 318ms/epoch - 11ms/step\n", + "Epoch 69/500\n", + "28/28 - 0s - loss: 0.0196 - val_loss: 0.0429 - 294ms/epoch - 10ms/step\n", + "Epoch 70/500\n", + "28/28 - 0s - loss: 0.0159 - val_loss: 0.0472 - 329ms/epoch - 12ms/step\n", + "Epoch 71/500\n", + "28/28 - 0s - loss: 0.0153 - val_loss: 0.0270 - 285ms/epoch - 10ms/step\n", + "Epoch 72/500\n", + "28/28 - 0s - loss: 0.0169 - val_loss: 0.0333 - 284ms/epoch - 10ms/step\n", + "Epoch 73/500\n", + "28/28 - 0s - loss: 0.0157 - val_loss: 0.0344 - 288ms/epoch - 10ms/step\n", + "Epoch 74/500\n", + "28/28 - 0s - loss: 0.0161 - val_loss: 0.0678 - 303ms/epoch - 11ms/step\n", + "Epoch 75/500\n", + "28/28 - 0s - loss: 0.0193 - val_loss: 0.0118 - 333ms/epoch - 12ms/step\n", + "Epoch 76/500\n", + "28/28 - 0s - loss: 0.0181 - val_loss: 0.0596 - 281ms/epoch - 10ms/step\n", + "Epoch 77/500\n", + "28/28 - 0s - loss: 0.0142 - val_loss: 0.0312 - 307ms/epoch - 11ms/step\n", + "Epoch 78/500\n", + "28/28 - 0s - loss: 0.0153 - val_loss: 0.0631 - 294ms/epoch - 10ms/step\n", + "Epoch 79/500\n", + "28/28 - 0s - loss: 0.0155 - val_loss: 0.0258 - 285ms/epoch - 10ms/step\n", + "Epoch 80/500\n", + "28/28 - 0s - loss: 0.0173 - val_loss: 0.0295 - 286ms/epoch - 10ms/step\n", + "Epoch 81/500\n", + "28/28 - 0s - loss: 0.0143 - val_loss: 0.0247 - 290ms/epoch - 10ms/step\n", + "Epoch 82/500\n", + "28/28 - 0s - loss: 0.0174 - val_loss: 0.0343 - 298ms/epoch - 11ms/step\n", + "Epoch 83/500\n", + "28/28 - 0s - loss: 0.0144 - val_loss: 0.0259 - 352ms/epoch - 13ms/step\n", + "Epoch 84/500\n", + "28/28 - 0s - loss: 0.0167 - val_loss: 0.0387 - 294ms/epoch - 10ms/step\n", + "Epoch 85/500\n", + "28/28 - 0s - loss: 0.0170 - val_loss: 0.0217 - 294ms/epoch - 10ms/step\n", + "Epoch 86/500\n", + "28/28 - 0s - loss: 0.0141 - val_loss: 0.0358 - 274ms/epoch - 10ms/step\n", + "Epoch 87/500\n", + "28/28 - 0s - loss: 0.0144 - val_loss: 0.0479 - 301ms/epoch - 11ms/step\n", + "Epoch 88/500\n", + "28/28 - 0s - loss: 0.0155 - val_loss: 0.0434 - 276ms/epoch - 10ms/step\n", + "Epoch 89/500\n", + "28/28 - 0s - loss: 0.0155 - val_loss: 0.0219 - 288ms/epoch - 10ms/step\n", + "Epoch 90/500\n", + "28/28 - 0s - loss: 0.0147 - val_loss: 0.0246 - 290ms/epoch - 10ms/step\n", + "Epoch 91/500\n", + "28/28 - 0s - loss: 0.0159 - val_loss: 0.0384 - 277ms/epoch - 10ms/step\n", + "Epoch 92/500\n", + "28/28 - 0s - loss: 0.0145 - val_loss: 0.0142 - 283ms/epoch - 10ms/step\n", + "Epoch 93/500\n", + "28/28 - 0s - loss: 0.0169 - val_loss: 0.0426 - 449ms/epoch - 16ms/step\n", + "Epoch 94/500\n", + "28/28 - 0s - loss: 0.0127 - val_loss: 0.0658 - 374ms/epoch - 13ms/step\n", + "Epoch 95/500\n", + "28/28 - 0s - loss: 0.0176 - val_loss: 0.0296 - 383ms/epoch - 14ms/step\n", + "Epoch 96/500\n", + "28/28 - 0s - loss: 0.0148 - val_loss: 0.0313 - 468ms/epoch - 17ms/step\n", + "Epoch 97/500\n", + "28/28 - 0s - loss: 0.0159 - val_loss: 0.0396 - 446ms/epoch - 16ms/step\n", + "Epoch 98/500\n", + "28/28 - 0s - loss: 0.0174 - val_loss: 0.0330 - 359ms/epoch - 13ms/step\n", + "Epoch 99/500\n", + "28/28 - 0s - loss: 0.0151 - val_loss: 0.0456 - 291ms/epoch - 10ms/step\n", + "Epoch 100/500\n", + "28/28 - 0s - loss: 0.0152 - val_loss: 0.0274 - 309ms/epoch - 11ms/step\n", + "Epoch 101/500\n", + "28/28 - 0s - loss: 0.0140 - val_loss: 0.0384 - 289ms/epoch - 10ms/step\n", + "Epoch 102/500\n", + "28/28 - 0s - loss: 0.0146 - val_loss: 0.0212 - 303ms/epoch - 11ms/step\n", + "Epoch 103/500\n", + "28/28 - 0s - loss: 0.0165 - val_loss: 0.0199 - 291ms/epoch - 10ms/step\n", + "Epoch 104/500\n", + "28/28 - 0s - loss: 0.0140 - val_loss: 0.0263 - 281ms/epoch - 10ms/step\n", + "Epoch 105/500\n", + "28/28 - 0s - loss: 0.0146 - val_loss: 0.0269 - 291ms/epoch - 10ms/step\n", + "Epoch 106/500\n", + "28/28 - 0s - loss: 0.0135 - val_loss: 0.0352 - 282ms/epoch - 10ms/step\n", + "Epoch 107/500\n", + "28/28 - 0s - loss: 0.0133 - val_loss: 0.0262 - 283ms/epoch - 10ms/step\n", + "Epoch 108/500\n", + "28/28 - 0s - loss: 0.0120 - val_loss: 0.0328 - 302ms/epoch - 11ms/step\n", + "Epoch 109/500\n", + "28/28 - 0s - loss: 0.0154 - val_loss: 0.0485 - 301ms/epoch - 11ms/step\n", + "Epoch 110/500\n", + "28/28 - 0s - loss: 0.0147 - val_loss: 0.0377 - 279ms/epoch - 10ms/step\n", + "Epoch 111/500\n", + "28/28 - 0s - loss: 0.0157 - val_loss: 0.0262 - 286ms/epoch - 10ms/step\n", + "Epoch 112/500\n", + "28/28 - 0s - loss: 0.0158 - val_loss: 0.0329 - 287ms/epoch - 10ms/step\n", + "Epoch 113/500\n", + "28/28 - 0s - loss: 0.0151 - val_loss: 0.0169 - 284ms/epoch - 10ms/step\n", + "Epoch 114/500\n", + "28/28 - 0s - loss: 0.0159 - val_loss: 0.0483 - 289ms/epoch - 10ms/step\n", + "Epoch 115/500\n", + "28/28 - 0s - loss: 0.0162 - val_loss: 0.0208 - 316ms/epoch - 11ms/step\n", + "Epoch 116/500\n", + "28/28 - 0s - loss: 0.0169 - val_loss: 0.0513 - 293ms/epoch - 10ms/step\n", + "Epoch 117/500\n", + "28/28 - 0s - loss: 0.0157 - val_loss: 0.0430 - 276ms/epoch - 10ms/step\n", + "Epoch 118/500\n", + "28/28 - 0s - loss: 0.0164 - val_loss: 0.0612 - 290ms/epoch - 10ms/step\n", + "Epoch 119/500\n", + "28/28 - 0s - loss: 0.0156 - val_loss: 0.0233 - 287ms/epoch - 10ms/step\n", + "Epoch 120/500\n", + "28/28 - 0s - loss: 0.0144 - val_loss: 0.0397 - 296ms/epoch - 11ms/step\n", + "Epoch 121/500\n", + "28/28 - 0s - loss: 0.0140 - val_loss: 0.0410 - 272ms/epoch - 10ms/step\n", + "Epoch 122/500\n", + "28/28 - 0s - loss: 0.0182 - val_loss: 0.0306 - 293ms/epoch - 10ms/step\n", + "Epoch 123/500\n", + "28/28 - 0s - loss: 0.0148 - val_loss: 0.0261 - 294ms/epoch - 11ms/step\n", + "Epoch 124/500\n", + "28/28 - 0s - loss: 0.0140 - val_loss: 0.0190 - 269ms/epoch - 10ms/step\n", + "Epoch 125/500\n", + "28/28 - 0s - loss: 0.0135 - val_loss: 0.0238 - 287ms/epoch - 10ms/step\n", + "Epoch 126/500\n", + "28/28 - 0s - loss: 0.0153 - val_loss: 0.0345 - 325ms/epoch - 12ms/step\n", + "Epoch 127/500\n", + "28/28 - 0s - loss: 0.0155 - val_loss: 0.0252 - 295ms/epoch - 11ms/step\n", + "Epoch 128/500\n", + "28/28 - 0s - loss: 0.0139 - val_loss: 0.0406 - 306ms/epoch - 11ms/step\n", + "Epoch 129/500\n", + "28/28 - 0s - loss: 0.0140 - val_loss: 0.0411 - 296ms/epoch - 11ms/step\n", + "Epoch 130/500\n", + "28/28 - 0s - loss: 0.0152 - val_loss: 0.0257 - 284ms/epoch - 10ms/step\n", + "Epoch 131/500\n", + "28/28 - 0s - loss: 0.0130 - val_loss: 0.0371 - 315ms/epoch - 11ms/step\n", + "Epoch 132/500\n", + "28/28 - 0s - loss: 0.0159 - val_loss: 0.0332 - 467ms/epoch - 17ms/step\n", + "Epoch 133/500\n", + "28/28 - 0s - loss: 0.0152 - val_loss: 0.0349 - 447ms/epoch - 16ms/step\n", + "Epoch 134/500\n", + "28/28 - 0s - loss: 0.0151 - val_loss: 0.0466 - 453ms/epoch - 16ms/step\n", + "Epoch 135/500\n", + "28/28 - 0s - loss: 0.0149 - val_loss: 0.0225 - 453ms/epoch - 16ms/step\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LvmYA01IaEdB" + }, + "source": [ + "## Model Predictions" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CaHjsvNLaD5I", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5f783bbf-11bd-452d-b5b9-46245d9ec76a" + }, + "source": [ + "trainPred = model.predict(x_train)\n", + "testPred = model.predict(x_valid)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "16/16 [==============================] - 2s 5ms/step\n", + "1/1 [==============================] - 0s 33ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2rbm4HV3PYRs", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "558111aa-e217-4b95-c18e-b81bf50149cc" + }, + "source": [ + "print(testPred.shape)\n", + "print(trainPred.shape)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(30, 1)\n", + "(481, 1)\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "eAQQknmh8PQA", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "49093205-8d23-4eab-c801-d0465568f283" + }, + "source": [ + "#Reverting data back to 2D from 3D\n", + "x_valid_t = x_valid.reshape((x_valid.shape[0], x_valid.shape[2]))\n", + "print(x_valid_t.shape)\n", + "print(testPred.shape)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(30, 10)\n", + "(30, 1)\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nBgDrWcGPnZj", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "2245e1ba-b466-4af1-e855-cc6c45e13947" + }, + "source": [ + "# invert scaling for forecasted values \n", + "\n", + "inv_testPred = scaler_l.inverse_transform(testPred)\n", + "print(inv_testPred[1])\n", + "\n", + "# invert scaling for actual values\n", + "\n", + "inv_y_valid = scaler_l.inverse_transform(y_valid)\n", + "print(inv_y_valid[1])\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[14369558.]\n", + "[13525194.]\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oNZOX9p8Btxw", + "outputId": "6a8980da-28ac-4c84-fd6d-36eed3f39102" + }, + "source": [ + "# calculate RMSE\n", + "from sklearn.metrics import mean_squared_error\n", + "from math import sqrt\n", + "\n", + "rmse = sqrt(mean_squared_error(inv_y_valid, inv_testPred))\n", + "print('Test RMSE: %.3f' % rmse)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test RMSE: 635049.981\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "23_bcO2qIpdn", + "outputId": "749b7a37-ad44-43d9-f4f8-48852254eb60" + }, + "source": [ + "# calculate Normalized RMSE\n", + "y_max = inv_y_valid.max()\n", + "y_min = inv_y_valid.min()\n", + "nrmse = rmse /(inv_y_valid.mean()) \n", + "print('Test NRMSE:', nrmse)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test NRMSE: 0.06047605320267713\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DK5wa1PUzvQv", + "outputId": "84120066-d677-4d86-8721-283645ff5165" + }, + "source": [ + "# calculate R-square\n", + "from sklearn.metrics import r2_score\n", + "from math import sqrt\n", + "\n", + "r_sq = r2_score(inv_y_valid, inv_testPred)\n", + "print('Test R_Square: %.3f' % r_sq)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test R_Square: 0.968\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-6q16qTmaZ8N" + }, + "source": [ + "## Plotting" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5P2TOQqU7Wry", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "outputId": "811b8b9b-75c8-4d22-85e9-ea34a5612c2c" + }, + "source": [ + "from matplotlib import pyplot\n", + "\n", + "pyplot.plot(history.history['loss'], label='train')\n", + "pyplot.plot(history.history['val_loss'], label='test')\n", + "pyplot.legend()\n", + "pyplot.show()\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mdq_2QDSal-d", + "outputId": "1d956cc2-56c4-4706-8f08-b602617d949b" + }, + "source": [ + "fig, ax = plt.subplots()\n", + "ax.scatter(y_train,trainPred)\n", + "ax.plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', lw=4)\n", + "ax.set_xlabel('observed')\n", + "ax.set_ylabel('predicted')\n", + "plt.show()\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "\"\\nfig, ax = plt.subplots()\\nax.scatter(y_train,trainPred)\\nax.plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', lw=4)\\nax.set_xlabel('observed')\\nax.set_ylabel('predicted')\\nplt.show()\\n\"" + ] + }, + "metadata": {}, + "execution_count": 33 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gixcJfYHgvyP", + "outputId": "e890bc3e-a915-489f-cf4a-b06059efb487" + }, + "source": [ + "fig, ax = plt.subplots()\n", + "ax.scatter(inv_y_valid,inv_testPred) #[:,:,6]\n", + "ax.plot([inv_y_valid.min(), inv_y_valid.max()], [inv_y_valid.min(), inv_y_valid.max()], 'k--', lw=4)\n", + "ax.set_xlabel('observed')\n", + "ax.set_ylabel('predicted')\n", + "#plt.savefig('test_prediction.png',bbox_inches='tight',dpi=1200)\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "\"\\nfig, ax = plt.subplots()\\nax.scatter(inv_y_valid,inv_testPred) #[:,:,6]\\nax.plot([inv_y_valid.min(), inv_y_valid.max()], [inv_y_valid.min(), inv_y_valid.max()], 'k--', lw=4)\\nax.set_xlabel('observed')\\nax.set_ylabel('predicted')\\n#plt.savefig('test_prediction.png',bbox_inches='tight',dpi=1200)\\nplt.show()\\n\"" + ] + }, + "metadata": {}, + "execution_count": 34 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from matplotlib import pyplot\n", + "fig, ax= plt.subplots(figsize=(24, 8),dpi = 600)\n", + "pyplot.plot(inv_testPred)\n", + "pyplot.plot(inv_y_valid)\n", + "plt.legend(['y_pred','y_test'])\n", + "plt.title(\"Sea Ice Extent Observation vs LSTM Prediction (2019-21)\")\n", + "ax.set_xlabel(\"Time (Months)\")\n", + "ax.set_ylabel(r\"Sea Ice Extent mil. $km^2$\")\n", + "pyplot.show()\n", + "fig.savefig('Time_series_sea_ice_extent_trend_1979_2021_lstm.png')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 550 + }, + "id": "eXdrBKBD2fNe", + "outputId": "a36360cb-d9aa-4cc6-93a5-fa4bb52b1de1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": { + "needs_background": "light" + } + } + ] + } + ] +} \ No newline at end of file diff --git a/_sources/chapters/conclusion.ipynb b/_sources/chapters/conclusion.ipynb deleted file mode 100644 index 68ccb1f..0000000 --- a/_sources/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/_sources/chapters/data.ipynb b/_sources/chapters/data.ipynb deleted file mode 100644 index c30086e..0000000 --- a/_sources/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/_sources/chapters/development.ipynb b/_sources/chapters/development.ipynb deleted file mode 100644 index b6afd01..0000000 --- a/_sources/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/_sources/chapters/evaluation.ipynb b/_sources/chapters/evaluation.ipynb deleted file mode 100644 index 09280a1..0000000 --- a/_sources/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/_sources/chapters/example.ipynb b/_sources/chapters/example.ipynb deleted file mode 100644 index c0ee06e..0000000 --- a/_sources/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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"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/_sources/chapters/methods.ipynb b/_sources/chapters/methods.ipynb deleted file mode 100644 index 63df639..0000000 --- a/_sources/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/_sources/chapters/motivation.ipynb b/_sources/chapters/motivation.ipynb deleted file mode 100644 index 8f0ef2a..0000000 --- a/_sources/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/_sources/chapters/questions.ipynb b/_sources/chapters/questions.ipynb deleted file mode 100644 index 92c141b..0000000 --- a/_sources/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/_sources/chapters/reproducibility.ipynb b/_sources/chapters/reproducibility.ipynb deleted file mode 100644 index 609f7d8..0000000 --- a/_sources/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/_sources/chapters/todo.ipynb b/_sources/chapters/todo.ipynb deleted file mode 100644 index eb1b021..0000000 --- a/_sources/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/_sources/chapters/training.ipynb b/_sources/chapters/training.ipynb deleted file mode 100644 index ba800a2..0000000 --- a/_sources/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/_sources/chapters/troubleshooting.ipynb b/_sources/chapters/troubleshooting.ipynb deleted file mode 100644 index c6b8b67..0000000 --- a/_sources/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/_sources/chapters/workflow.ipynb b/_sources/chapters/workflow.ipynb deleted file mode 100644 index 3a67d0b..0000000 --- a/_sources/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/chapters/about.html b/chapters/about.html deleted file mode 100644 index de8bb39..0000000 --- a/chapters/about.html +++ /dev/null @@ -1,684 +0,0 @@ - - - - - - - - - About the GeoSMART Use Case Library — Use Case Template - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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About the GeoSMART Use Case Library

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About the GeoSMART Use Case Library#

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General Overview#

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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:

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GeoSMART Use Case

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Contributing Content#

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Tutorial content can be integrated into jupyterbooks in one of two ways:

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  • Do it yourself (use this template book and add your content)

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  • Provide use your content (preferably in a github repo) and we will integrate it

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The goal is to provide executable code on some platform. The contributor can choose between:

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  • Google Colab

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  • Free AWS (smaller cloud-based examples) -If none of the above options work for you, please contact us directly to discuss further.

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Binder -Open in Collab

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Technical Details#

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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.

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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.

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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.

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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 page out first.

-
-
- - - - -
- -
- -
-
-
-

- - By eScience Institute, University of Washington
- - © Copyright 2022.
-

-
-
- -
- - -
-
- - - - - - - \ No newline at end of file diff --git a/chapters/workflow.html b/chapters/chapter1.html similarity index 70% rename from chapters/workflow.html rename to chapters/chapter1.html index f44b7de..3137522 100644 --- a/chapters/workflow.html +++ b/chapters/chapter1.html @@ -6,7 +6,7 @@ - Workflow Management / Cloud Computing — Use Case Template + Chapter 3: AI for sea ice forecasting — Use Case Template @@ -53,8 +53,8 @@ - - + + @@ -122,165 +122,52 @@

Use Case Template

Project Pythia Foundations -
  • - - About Use Case Library - -

    • Chapter One

    - -

    - - Chapter Two - -

    - -

    - - Chapter Three - -

    - -

    - - Chapter Four - -

    - -

    - - Chapter Five - -

    - -

    - - Chapter Six - -

    - -

    - - Chapter Seven - -

    -

    - - Chapter Eight - -

    -

    - Chapter Nine - -

    - -

    - - Chapter Ten (optional) - -

    - -

    - - Chapter Eleven (optional) + Chapter Two

    - Chapter Twelve (optional) + Chapter Three

    - Chapter Thirteen + Chapter Four

    @@ -351,35 +238,6 @@

    Use Case Template

    -
    - -
    - -
    -
    -
    +
    +
    + Contents +
    +
    -

    Workflow Management / Cloud Computing

    +

    Chapter 3: AI for sea ice forecasting

    +
    +

    Contents

    +
    +
    @@ -525,10 +430,27 @@

    Workflow Management / Cloud Computing

    -
    -

    Workflow Management / Cloud Computing#

    -

    Explain cloud-based workflows and compute requirements. -If you seek to make the model run in production or achieve higher productivity and scalability, use GeoWeaver or other tools.

    +
    +

    Chapter 3: AI for sea ice forecasting#

    +

    The changing climate patterns caused by Arctic amplification have led to an increase in the frequency and intensity of extreme weather events. The loss of sea ice in the Arctic, as observed through satellite data, is a crucial aspect of this phenomenon. Accurately forecasting Arctic sea ice on subseasonal to seasonal scales presents significant scientific challenges. In addition to physics-based Earth system models, researchers have explored the use of statistical and machine learning models for sea ice forecasting.

    +

    In this chapter, we examine three different approaches for predicting monthly Pan-Arctic sea ice extent up to 3 months in advance: traditional machine learning, deep learning, and ensemble learning. Leveraging monthly satellite-retrieved sea ice data from NSIDC and atmospheric/oceanic variables from the ERA5 reanalysis product spanning the period of 1979-2021, we demonstrate the potential of ensemble methods in achieving promising predictive performance with longer lead times. These advancements will greatly enhance our ability to forecast future changes in Arctic sea ice, enabling us to make more informed predictions regarding transportation routes, resource development, coastal erosion, and the potential impact on Arctic coastal communities and wildlife.

    +
    +

    Code:#

    +
      +

    • Monthly_Polar_Sea_Ice_Prediction_Attention_MLR+LSTM.ipynb: Jupyter notebook containing the code for monthly polar sea ice prediction using the attention-based Multivariate Linear Regression and Long Short-Term Memory (MLR+LSTM) model.

      • +

    • Multiple_Linear_Regression.ipynb: Jupyter notebook with the implementation of the Multiple Linear Regression model for sea ice prediction.

      • +

    • Sea_Ice_Prediction_monthly_LSTM.ipynb: Jupyter notebook showcasing the monthly sea ice prediction using the Long Short-Term Memory (LSTM) model.

      • +
    +
    +
    +

    Data:#

    +
      +

    • Arctic_domain_mean_monthly_1979_2021.csv: CSV file containing the monthly mean data for the Arctic domain from 1979 to 2021.

      • +

    • monthly_features_1979_Aug2021.npy: NumPy array file containing the monthly features data from 1979 to August 2021.

      • +

    • monthly_target_1979_Aug2021.npy: NumPy array file containing the monthly target data from 1979 to August 2021.

      • +

    • placeholder.txt: Placeholder text file.

      • +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + + + +
    +
    + + + + + + + + + + +
    + +
    + +
    + + + + +
    +
    + + + + +
    +
    +
    + +
    + +
    +
    + + + +
    + +
    + +
    +
    + +
    + +
    + +
    +
    + + +
    +
    + + +
    +
    + Contents +
    + +
    +
    +
    +
    + +
    +

    Sea Ice Prediction - MLR+LSTM

    + +
    + +
    +
    +
    + +
    + +
    +

    Sea Ice Prediction - MLR+LSTM#

    +
    +
    +
    from numpy.random import seed
+seed(1)
+
    +
    +
    +
    +
    +
    +
    #Install latest attention package
+pip install attention
+
    +
    +
    +
    +
    +

    Initial Setup#

    +
    +
    +
    import os
+import math
+import glob
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from keras.models import Sequential
+from tensorflow.keras.optimizers import Adam
+from attention import Attention
+from keras.layers import Dense, Dropout
+from keras.layers import LSTM,TimeDistributed
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+from keras.callbacks import EarlyStopping, ModelCheckpoint
+
    +
    +
    +
    +
    +
    +
    +

    Loading Combined Data 1979-2021#

    +

    Features: +‘wind_10m’, ‘specific_humidity’, ‘LW_down’, ‘SW_down’, ‘rainfall’, ‘snowfall’ ‘sst’, ‘t2m’, ‘surface_pressure’,’sea_ice_extent’

    +
    +
    +
    df = pd.read_csv('.../Arctic_domain_mean_monthly_1979_2021.csv')
+df = df.drop(['Date'],axis=1)
+data = np.array(df)
+
    +
    +
    +
    +
    +

    Train a Linear Regression Model#

    +
    +
    +
    from sklearn.linear_model import LinearRegression
+
+y = data[:,-1] #assigning last column to be target variable
+x = data[:,:] #dropping last column from features
+
+model = LinearRegression()
+model.fit(x, y)
+
+lr_data = model.predict(x)
+print(lr_data.shape)
+
    +
    +
    +
    +
    (512,)
+
    +
    +
    +
    +
    +
    +
    lr= lr_data.reshape(len(lr_data),1)
+print(lr.shape)
+
    +
    +
    +
    +
    (512, 1)
+
    +
    +
    +
    +

    Adding LR predictions as additional feature in LSTM dataset

    +
    +

    Adding a Lag to Y values#

    +

    Here lag = 1 - 3 months

    +
    +
    +
    data = np.concatenate((data,lr),axis=1)
+print(data.shape)
+
    +
    +
    +
    +
    (512, 11)
+
    +
    +
    +
    +
    +
    +
    #Adding a lag to monthly targets
+lag = 3
+#test_data = data[-2:,:,:]
+target = data[lag:,-1]
+data = data[:-lag,:]
+
+print(data.shape)
+print(target.shape)
+
    +
    +
    +
    +
    (509, 11)
+(509,)
+
    +
    +
    +
    +
    +
    +
    +

    Train Validation Split#

    +

    LSTM network expects the input data to be provided with a specific array structure in the form of: [samples, time steps, features]. We load the csv file and only retain the feature and target columns. The features and target are stored in separate np arrays.

    +
    +
    +
    # Sequential split train:val data 
+
+LEN_DATA = len(data) #total number of pixels
+
+NUM_TRAIN = LEN_DATA - (24+6) #reserve last 2.5 years for testing
+NUM_VALID = LEN_DATA - NUM_TRAIN
+
+print('LEN_DATA:',LEN_DATA)
+print('NUM_TRAIN:',NUM_TRAIN)
+print('NUM_VALID:',NUM_VALID)
+
+x_train = data[0:NUM_TRAIN]
+x_valid = data[NUM_TRAIN:]
+
+#split features and labels
+y_train=target[:NUM_TRAIN] #target is last column i-e sea-ice
+y_valid=target[NUM_TRAIN:] #target is last column i-e sea-ice
+
    +
    +
    +
    +
    LEN_DATA: 509
+NUM_TRAIN: 479
+NUM_VALID: 30
+
    +
    +
    +
    +
    +
    +
    print('x_train.shape:',x_train.shape)
+print('y_train.shape:',y_train.shape)
+print('x_valid.shape:',x_valid.shape)
+print('y_valid.shape:',y_valid.shape)
+
    +
    +
    +
    +
    x_train.shape: (479, 11)
+y_train.shape: (479,)
+x_valid.shape: (30, 11)
+y_valid.shape: (30,)
+
    +
    +
    +
    +
    +
    +

    Reshaping Input and Target Features#

    +
    +
    +
    # convert an array of values into a dataset matrix
+def reshape_features(dataset, timesteps=1):
+    print(dataset.shape)
+    X = dataset.reshape((int(dataset.shape[0]/timesteps)), timesteps, dataset.shape[1])
+    return X
+
    +
    +
    +
    +
    +
    +

    Normalization#

    +
    +
    +
    # normalize the features
+
+scaler_f = StandardScaler()
+x_train = scaler_f.fit_transform(x_train) 
+x_valid = scaler_f.transform(x_valid) 
+#test_data = scaler_f.transform(forecast)
+
+scaler_l = StandardScaler()
+y_train = scaler_l.fit_transform(y_train.reshape(-1,1)) #reshaping to 2d for standard scaling
+y_valid = scaler_l.transform(y_valid.reshape(-1,1)) #reshaping to 2d for standard scaling
+
    +
    +
    +
    +
    +
    +
    #Reshaping data to 3D for modeling
+timesteps = 1
+x_train = reshape_features(x_train, timesteps) # reshaping to 3d for model
+x_valid = reshape_features(x_valid, timesteps) # reshaping to 3d for model
+
    +
    +
    +
    +
    (479, 11)
+(30, 11)
+
    +
    +
    +
    +
    +
    +
    print('x_train.shape:',x_train.shape)
+print('y_train.shape:',y_train.shape)
+print('x_valid.shape:',x_valid.shape)
+print('y_valid.shape:',y_valid.shape)
+
    +
    +
    +
    +
    x_train.shape: (479, 1, 11)
+y_train.shape: (479, 1)
+x_valid.shape: (30, 1, 11)
+y_valid.shape: (30, 1)
+
    +
    +
    +
    +
    +
    +
    +

    LSTM Network#

    +
    +
    +
    import numpy as np
+from tensorflow.keras import Input
+from tensorflow.keras.layers import Dense, LSTM
+from tensorflow.keras.models import load_model, Model
+
+timestep = timesteps
+features = 11
+
+model_input = Input(shape=(timestep,features))
+x = LSTM(64, return_sequences=True)(model_input)
+x = Dropout(0.2)(x)
+x = LSTM(32, return_sequences=True)(x)
+x = LSTM(16, return_sequences=True)(x)
+x = Attention(32)(x)
+#x = Dropout(0.2)(x)
+x = Dense(32)(x)
+x = Dense(16)(x)
+x = Dense(1)(x)
+model = Model(model_input, x)
+
+print(model.summary())
+
    +
    +
    +
    +
    Model: "model"
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ input_1 (InputLayer)        [(None, 1, 11)]           0         
+                                                                 
+ lstm (LSTM)                 (None, 1, 64)             19456     
+                                                                 
+ dropout (Dropout)           (None, 1, 64)             0         
+                                                                 
+ lstm_1 (LSTM)               (None, 1, 32)             12416     
+                                                                 
+ lstm_2 (LSTM)               (None, 1, 16)             3136      
+                                                                 
+ attention (Attention)       (None, 32)                1280      
+                                                                 
+ dense (Dense)               (None, 32)                1056      
+                                                                 
+ dense_1 (Dense)             (None, 16)                528       
+                                                                 
+ dense_2 (Dense)             (None, 1)                 17        
+                                                                 
+=================================================================
+Total params: 37,889
+Trainable params: 37,889
+Non-trainable params: 0
+_________________________________________________________________
+None
+
    +
    +
    +
    +
    +

    Compiling the Network and Fitting Model#

    +
    +
    +
    #Compiling the network
+model.compile(loss='mean_squared_error', optimizer='adam')
+checkpoint_path='./testmodel.h5'
+keras_callbacks   = [
+      EarlyStopping(monitor='val_loss', patience=60, mode='min', min_delta=0.001),
+      ModelCheckpoint(checkpoint_path, monitor='val_loss', save_best_only=True, mode='min')
+]
+
    +
    +
    +
    +
    +
    +
    history=model.fit(x_train, y_train, epochs=500, batch_size=12, verbose=2, validation_split =0.33, shuffle=True,callbacks=keras_callbacks)
+
    +
    +
    +
    +
    +
    +

    Model Predictions#

    +
    +
    +
    trainPred = model.predict(x_train)
+testPred = model.predict(x_valid)
+
    +
    +
    +
    +
    +
    +
    print(testPred.shape)
+print(trainPred.shape)
+
    +
    +
    +
    +
    (30, 1)
+(479, 1)
+
    +
    +
    +
    +
    +
    +
    # invert scaling for forecasted values 
+
+inv_testPred = scaler_l.inverse_transform(testPred)
+print(inv_testPred[1])
+
+# invert scaling for actual values
+
+inv_y_valid = scaler_l.inverse_transform(y_valid)
+print(inv_y_valid[1])
+
    +
    +
    +
    +
    +
    +
    # calculate RMSE
+from sklearn.metrics import mean_squared_error
+from math import sqrt
+
+rmse = sqrt(mean_squared_error(inv_y_valid, inv_testPred))
+print('Test RMSE: %.3f' % rmse)
+
    +
    +
    +
    +
    +
    +
    # calculate Normalized RMSE
+y_max = inv_y_valid.max()
+y_min = inv_y_valid.min()
+nrmse = rmse /(inv_y_valid.mean()) 
+print('Test NRMSE:', nrmse)
+
    +
    +
    +
    +
    +
    +
    # calculate R-square
+from sklearn.metrics import r2_score
+from math import sqrt
+
+r_sq = r2_score(inv_y_valid, inv_testPred)
+print('Test R_Square: %.3f' % r_sq)
+
    +
    +
    +
    +
    +
    +

    Plotting#

    +
    +
    +
    from matplotlib import pyplot
+
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+
    +
    +
    +
    +
    +
    +
    fig, ax = plt.subplots()
+ax.scatter(y_train,trainPred)
+ax.plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', lw=4)
+ax.set_xlabel('observed')
+ax.set_ylabel('predicted')
+plt.show()
+
    +
    +
    +
    +
    +
    +
    fig, ax = plt.subplots()
+ax.scatter(inv_y_valid,inv_testPred) #[:,:,6]
+ax.plot([inv_y_valid.min(), inv_y_valid.max()], [inv_y_valid.min(), inv_y_valid.max()], 'k--', lw=4)
+ax.set_xlabel('observed')
+ax.set_ylabel('predicted')
+plt.show()
+
    +
    +
    +
    +
    +
    +
    trainPred = np.transpose(trainPred.flatten())
+print(trainPred.shape)
+print(y_train.shape)
+
    +
    +
    +
    +
    +
    +
    from matplotlib import pyplot
+
+pyplot.plot(trainPred)
+pyplot.plot(y_train)
+pyplot.show()
+
    +
    +
    +
    +
    +
    +
    from datetime import datetime
+lead_time = lag
+
+time_range=pd.date_range(start="2019-01-01",end="2021-07-31",freq='m')
+fig, ax= plt.subplots(figsize=(12, 4))
+
+plt.plot(inv_y_valid/(10**6), color = 'red', label = 'Observed sea ice')
+plt.plot(inv_testPred/(10**6), color = 'blue', label = 'LR_LSTM predictions')
+#plt.title('Sea ice prediction (Lead time:'+str(lead_time)+' month)',fontsize = 15)
+plt.xlabel('Month',fontsize = 10)
+plt.ylabel('Sea ice extent ($10^6$ $Km^2$)',fontsize = 15)
+#ax.grid(False)
+#ax.set_facecolor('white')
+time_idx=np.arange(0,30,3)
+date_str=np.array(time_range[time_idx].strftime('%Y-%m'))
+ax.set_xticks(time_idx)
+ax.set_xticklabels(date_str)
+plt.legend()
+#plt.show()
+fig.savefig('Time_series_sea_ice_prediction_attention_lead_time_'+str(lead_time)+'.png')
+
    +
    +
    +
    +
    +
    + + + + +
    + +
    + +
    +
    +
    +

    + + By eScience Institute, University of Washington
    + + © Copyright 2022.
    +

    +
    +
    + +
    + + +
    +
    + + + + + + + \ No newline at end of file diff --git a/chapters/chapter3.html b/chapters/chapter3.html new file mode 100644 index 0000000..a3b791f --- /dev/null +++ b/chapters/chapter3.html @@ -0,0 +1,1033 @@ + + + + + + + + + Initial Setup — Use Case Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + + + +
    +
    + + + + + + + + + + +
    + +
    + +
    + + + + +
    +
    + + + + +
    +
    +
    + +
    + +
    +
    + + + +
    + +
    + +
    +
    + +
    + +
    + +
    +
    + + +
    +
    + + +
    +
    + Contents +
    + +
    +
    +
    +
    + +
    +

    Initial Setup

    + +
    + +
    +
    +
    + +
    + +
    +

    Initial Setup#

    +
    +
    +
    import numpy as np
+import pandas as pd
+from sklearn import datasets, preprocessing
+from sklearn.model_selection import train_test_split
+import pandas_profiling
+from matplotlib import pyplot as plt
+import sklearn.gaussian_process as gp
+from sklearn.metrics import r2_score
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+from sklearn.linear_model import LinearRegression
+#from sklearn.gaussian_process import GaussianProcessRegressor
+#from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
+
    +
    +
    +
    +
    +
    +

    Loading the Dataset#

    +
    +
    +
    df = pd.read_csv("/content/Arctic_domain_mean_monthly_1979_2021.csv")
+# remove date from the set
+df = df.drop('Date', 1)
+df.loc[:, 'sea_ice_extent_2'] = df['sea_ice_extent']
+df.head()
+
    +
    +
    +
    +
    <ipython-input-2-a806c5377ae6>:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only
+  df = df.drop('Date', 1)
+
    +
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    wind_10mspecific_humidityLW_downSW_downrainfallsnowfallsstt2msurface_pressuresea_ice_extentsea_ice_extent_2
    05.5313980.811961186.6870543.1278801.0098720.892319273.355237250.388101984.6330321560419115604191
    15.3280200.688896174.79457118.5415940.9208310.781347273.121885247.071202983.9804181637892916378929
    25.4325110.916124190.74193367.6904290.9833270.855266273.088099252.954138985.1404681652108916521089
    34.7928361.272056212.937925156.2236730.8907230.705203273.126062259.557456989.3146981556123815561238
    44.8190282.239776253.690478230.9508331.2013080.688723273.393551269.375118984.4836581408561314085613
    +
    + + + + + +
    +
    +
    +
    +
    +
    +
    #Creating datasets with lag of 1 month
+df1 = df.assign(sea_ice_extent_2 = df.sea_ice_extent_2.shift(-1)).drop(df.index[-1])
+
    +
    +
    +
    +
    +
    +
    df1.head
+
    +
    +
    +
    +
    <bound method NDFrame.head of      wind_10m  specific_humidity     LW_down     SW_down  rainfall  snowfall  \
+0    5.531398           0.811961  186.687054    3.127880  1.009872  0.892319   
+1    5.328020           0.688896  174.794571   18.541594  0.920831  0.781347   
+2    5.432511           0.916124  190.741933   67.690429  0.983327  0.855266   
+3    4.792836           1.272056  212.937925  156.223673  0.890723  0.705203   
+4    4.819028           2.239776  253.690478  230.950833  1.201308  0.688723   
+..        ...                ...         ...         ...       ...       ...   
+506  5.494218           1.006108  194.683072   68.315949  1.135685  0.919100   
+507  5.383687           1.529497  225.171796  156.567743  1.159049  0.929777   
+508  4.777020           2.451088  260.956781  229.604138  1.077705  0.575494   
+509  4.771453           4.176458  294.931709  244.702852  1.585094  0.360146   
+510  4.754014           5.193846  313.311345  196.774631  1.957911  0.248088   
+
+            sst         t2m  surface_pressure  sea_ice_extent  \
+0    273.355237  250.388101        984.633032        15604191   
+1    273.121885  247.071202        983.980418        16378929   
+2    273.088099  252.954138        985.140468        16521089   
+3    273.126062  259.557456        989.314698        15561238   
+4    273.393551  269.375118        984.483658        14085613   
+..          ...         ...               ...             ...   
+506  273.414735  254.391240        977.764826        14640000   
+507  273.470298  263.146395        985.801841        13840000   
+508  273.888627  271.557464        985.606182        12660000   
+509  275.245088  278.360921        978.436682        10710000   
+510  277.253314  280.413695        978.293303         7690000   
+
+     sea_ice_extent_2  
+0          16378929.0  
+1          16521089.0  
+2          15561238.0  
+3          14085613.0  
+4          12653185.0  
+..                ...  
+506        13840000.0  
+507        12660000.0  
+508        10710000.0  
+509         7690000.0  
+510         5750000.0  
+
+[511 rows x 11 columns]>
+
    +
    +
    +
    +
    +
    +

    Train Test Split#

    +
    +
    +
    data = np.array(df1)
+target = data[:,-1] #assign last column to be target variable
+data = data[:,:-1] #dropping last column from features
+
+# print(data.shape)
+# print(target.shape)
+LEN_DATA = len(data) #total number of pixels
+NUM_TRAIN = LEN_DATA - (24+6) #reserve last 30 months for testing
+
+
+x_train = data[0:NUM_TRAIN]
+y_train = target[0:NUM_TRAIN]
+
+x_test = data[NUM_TRAIN:]
+y_test=target[NUM_TRAIN:]
+
    +
    +
    +
    +
    +
    +
    print(x_train.shape)
+print(y_train.shape)
+print(x_test.shape)
+print(y_test.shape)
+
    +
    +
    +
    +
    (481, 10)
+(481,)
+(30, 10)
+(30,)
+
    +
    +
    +
    +
    +
    +

    Data Normalization#

    +
    +
    +
    from sklearn.preprocessing import MinMaxScaler, StandardScaler
+
+scaler_x = MinMaxScaler()
+x_train = scaler_x.fit_transform(x_train) 
+x_test = scaler_x.transform(x_test) 
+
+scaler_y = MinMaxScaler()
+y_train = scaler_y.fit_transform(y_train.reshape(-1,1))
+y_test = scaler_y.transform(y_test.reshape(-1,1)) 
+
    +
    +
    +
    +
    +
    +

    Defining Model#

    +
    +
    +
    model = LinearRegression()
+model.fit(x_train, y_train)
+
    +
    +
    +
    +
    LinearRegression()
    In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
    On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
    +
    +
    +
    +

    Model Predictions#

    +
    +
    +
    y_pred = model.predict(x_test)
+y_train_pred = model.predict(x_train)
+
    +
    +
    +
    +
    +
    +
    #Sample code to inverse transform data
+#Inverse transformation should be performed after getting predictions
+inv_y_train = scaler_y.inverse_transform(y_train)
+inv_y_test = scaler_y.inverse_transform(y_test)
+inv_y_pred = scaler_y.inverse_transform(y_pred)
+
    +
    +
    +
    +
    +
    +

    Plotting Results#

    +
    +
    +
    %matplotlib inline
+#plot 
+fig, ax= plt.subplots(figsize=(15, 6), dpi = 600)
+plt.plot(inv_y_test, color='red')
+plt.plot(inv_y_pred)
+
+plt.legend(['y_test','y_pred'])
+plt.title("Sea Ice Extent Observation vs Prediction (2019-21)")
+ax.set_xlabel("Time (Months)")
+ax.set_ylabel(r"Sea Ice Extent mil. $km^2$")
+plt.show()
+fig.savefig('Time_series_sea_ice_extent_trend_1979_2021_lag1.png')
+
    +
    +
    +
    +../_images/chapter3_17_0.png +
    +
    +
    +
    +

    Performance Evaluation#

    +
    +
    +
    rmse1 = np.sqrt(np.mean((inv_y_pred - inv_y_test) ** 2))
+print('Test RMSE: %0.2f Mil. sq Km' %(rmse1))
+
    +
    +
    +
    +
    Test RMSE: 433143.27 Mil. sq Km
+
    +
    +
    +
    +
    +
    +
    nrmse1 = rmse1/(np.mean(inv_y_test))
+nrmse1
+
    +
    +
    +
    +
    0.04124839984143858
+
    +
    +
    +
    +
    +
    +
    r2 = r2_score(inv_y_test, inv_y_pred)
+print('R2 Score: %0.2f' %(r2))
+
    +
    +
    +
    +
    R2 Score: 0.99
+
    +
    +
    +
    +
    + + + + +
    + +
    + +
    +
    +
    +

    + + By eScience Institute, University of Washington
    + + © Copyright 2022.
    +

    +
    +
    + +
    + + +
    +
    + + + + + + + \ No newline at end of file diff --git a/chapters/chapter4.html b/chapters/chapter4.html new file mode 100644 index 0000000..199cf87 --- /dev/null +++ b/chapters/chapter4.html @@ -0,0 +1,1453 @@ + + + + + + + + + Sea Ice Prediction — Use Case Template + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + + + +
    +
    + + + + + + + + + + +
    + +
    + +
    + + + + +
    +
    + + + + +
    +
    +
    + +
    + +
    +
    + + + +
    + +
    + +
    +
    + +
    + +
    + +
    +
    + + +
    +
    + + +
    +
    + Contents +
    + +
    +
    +
    +
    + +
    +

    Sea Ice Prediction

    + +
    + +
    +
    +
    + +
    + +
    +

    Sea Ice Prediction#

    +
    +
    +
    from numpy.random import seed
+seed(1)
+
    +
    +
    +
    +
    +
    +

    Initial Setup#

    +
    +
    +
    pip install attention
+
    +
    +
    +
    +
    Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
+Collecting attention
+  Downloading attention-4.1-py3-none-any.whl (8.6 kB)
+Requirement already satisfied: tensorflow>=2.1 in /usr/local/lib/python3.8/dist-packages (from attention) (2.11.0)
+Requirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.8/dist-packages (from attention) (1.22.4)
+Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (23.0)
+Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (57.4.0)
+Requirement already satisfied: keras<2.12,>=2.11.0 in /usr/local/lib/python3.8/dist-packages (from tensorflow>=2.1->attention) (2.11.0)
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+Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard<2.12,>=2.11->tensorflow>=2.1->attention) (0.6.1)
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+Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow>=2.1->attention) (0.2.8)
+Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.8/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow>=2.1->attention) (1.3.1)
+Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.8/dist-packages (from markdown>=2.6.8->tensorboard<2.12,>=2.11->tensorflow>=2.1->attention) (6.0.0)
+Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow>=2.1->attention) (2022.12.7)
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+Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.12,>=2.11->tensorflow>=2.1->attention) (3.15.0)
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+Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow>=2.1->attention) (3.2.2)
+Installing collected packages: attention
+Successfully installed attention-4.1
+
    +
    +
    +
    +
    +
    +

    Initial Setup#

    +
    +
    +
    import os
+import math
+import glob
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from keras.models import Sequential
+from tensorflow.keras.optimizers import Adam
+from attention import Attention
+from keras.layers import Dense, Dropout
+from keras.layers import LSTM,TimeDistributed
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+from keras.callbacks import EarlyStopping, ModelCheckpoint
+
    +
    +
    +
    +
    +
    +

    Loading Combined Data 1979- 2021#

    +

    Features: +‘wind_10m’, ‘specific_humidity’, ‘LW_down’, ‘SW_down’, ‘rainfall’, ‘snowfall’, ‘sosaline’, ‘sst’, ‘t2m’, ‘surface_pressure’,’sea_ice_extent’

    +
    +
    +
    data = np.load('/monthly_features_1979_Aug2021.npy',allow_pickle=True)
+target = np.load('/monthly_target_1979_Aug2021.npy',allow_pickle=True)
+
    +
    +
    +
    +
    +

    Adding a Lag to Y values#

    +

    Here lag = 1 month

    +
    +
    +
    #Adding a lag to monthly targets
+lag = 1
+data = data[:-lag,:,:]
+target = target[lag:]
+
+print(data.shape)
+print(target.shape)
+
    +
    +
    +
    +
    (511, 1, 10)
+(511,)
+
    +
    +
    +
    +
    +
    +
    +

    Train Validation Split#

    +

    LSTM network expects the input data to be provided with a specific array structure in the form of: [samples, time steps, features]. We load the csv file and only retain the feature and target columns. The features and target are stored in separate np arrays.

    +
    +
    +
    
+# Sequential split train:val data in 80:20 sequentially 
+
+LEN_DATA = len(data) #total number of pixels
+
+NUM_TRAIN = LEN_DATA - (24+6) #reserve last 30 months for testing
+NUM_VALID = LEN_DATA - NUM_TRAIN
+
+print('LEN_DATA:',LEN_DATA)
+print('NUM_TRAIN:',NUM_TRAIN)
+print('NUM_VALID:',NUM_VALID)
+
+x_train = data[0:NUM_TRAIN]
+x_valid = data[NUM_TRAIN:]
+
+#split features and labels
+y_train=target[:NUM_TRAIN] #target is last column i-e sea-ice
+y_valid=target[NUM_TRAIN:] #target is last column i-e sea-ice
+
    +
    +
    +
    +
    LEN_DATA: 511
+NUM_TRAIN: 481
+NUM_VALID: 30
+
    +
    +
    +
    +
    +
    +
    print('x_train.shape:',x_train.shape)
+print('y_train.shape:',y_train.shape)
+print('x_valid.shape:',x_valid.shape)
+print('y_valid.shape:',y_valid.shape)
+
    +
    +
    +
    +
    x_train.shape: (481, 1, 10)
+y_train.shape: (481,)
+x_valid.shape: (30, 1, 10)
+y_valid.shape: (30,)
+
    +
    +
    +
    +
    +
    +

    Reshaping Input and Target Features#

    +
    +
    +
    # convert an array of values into a dataset matrix
+def reshape_features(dataset, timesteps=1):
+    print(dataset.shape)
+    X = dataset.reshape((int(dataset.shape[0]/timesteps)), timesteps, dataset.shape[1])
+    return X
+
    +
    +
    +
    +
    +
    +

    Normalization#

    +
    +
    +
    # normalize the features
+
+scaler_f = StandardScaler()
+x_train = scaler_f.fit_transform(x_train.reshape(-1,10)) #reshaping to 2d for standard scaling
+x_valid = scaler_f.transform(x_valid.reshape(-1,10)) #reshaping to 2d for standard scaling
+
+scaler_l = StandardScaler()
+y_train = scaler_l.fit_transform(y_train.reshape(-1,1)) #reshaping to 2d for standard scaling
+y_valid = scaler_l.transform(y_valid.reshape(-1,1)) #reshaping to 2d for standard scaling
+
    +
    +
    +
    +
    +
    +
    #Reshaping data to 3D for modeling
+timesteps = 1
+x_train = reshape_features(x_train, timesteps) # reshaping to 3d for model
+x_valid = reshape_features(x_valid, timesteps) # reshaping to 3d for model
+
    +
    +
    +
    +
    (481, 10)
+(30, 10)
+
    +
    +
    +
    +
    +
    +
    print('x_train.shape:',x_train.shape)
+print('y_train.shape:',y_train.shape)
+print('x_valid.shape:',x_valid.shape)
+print('y_valid.shape:',y_valid.shape)
+
    +
    +
    +
    +
    x_train.shape: (481, 1, 10)
+y_train.shape: (481, 1)
+x_valid.shape: (30, 1, 10)
+y_valid.shape: (30, 1)
+
    +
    +
    +
    +
    +
    +

    LSTM Network#

    +
    +
    +
    import numpy as np
+from tensorflow.keras import Input
+from tensorflow.keras.layers import Dense, LSTM
+from tensorflow.keras.models import load_model, Model
+
+timestep = timesteps
+features = 10
+
+model_input = Input(shape=(timestep,features))
+x = LSTM(64, return_sequences=True)(model_input)
+x = Dropout(0.2)(x)
+x = LSTM(32, return_sequences=True)(x)
+x = LSTM(16, return_sequences=True)(x)
+x = LSTM(16, return_sequences=True)(x)
+x = Attention(trainable = True)(x)
+x = Dropout(0.2)(x)
+x = Dense(32)(x)
+x = Dense(16)(x)
+x = Dense(1)(x)
+model = Model(model_input, x)
+#model.compile(loss='mae', optimizer='adam')
+print(model.summary())
+
    +
    +
    +
    +
    Model: "model"
+_________________________________________________________________
+ Layer (type)                Output Shape              Param #   
+=================================================================
+ input_1 (InputLayer)        [(None, 1, 10)]           0         
+                                                                 
+ lstm (LSTM)                 (None, 1, 64)             19200     
+                                                                 
+ dropout (Dropout)           (None, 1, 64)             0         
+                                                                 
+ lstm_1 (LSTM)               (None, 1, 32)             12416     
+                                                                 
+ lstm_2 (LSTM)               (None, 1, 16)             3136      
+                                                                 
+ lstm_3 (LSTM)               (None, 1, 16)             2112      
+                                                                 
+ attention (Attention)       (None, 128)               4352      
+                                                                 
+ dropout_1 (Dropout)         (None, 128)               0         
+                                                                 
+ dense (Dense)               (None, 32)                4128      
+                                                                 
+ dense_1 (Dense)             (None, 16)                528       
+                                                                 
+ dense_2 (Dense)             (None, 1)                 17        
+                                                                 
+=================================================================
+Total params: 45,889
+Trainable params: 45,889
+Non-trainable params: 0
+_________________________________________________________________
+None
+
    +
    +
    +
    +
    +
    +
    extent = target
+print(extent.shape)
+
    +
    +
    +
    +
    (511,)
+
    +
    +
    +
    +
    +
    +

    Compiling the Network and Fitting Model#

    +
    +
    +
    #Compiling the network
+model.compile(loss='mean_squared_error', optimizer='adam')
+checkpoint_path='./testmodel.h5'
+keras_callbacks   = [
+      EarlyStopping(monitor='val_loss', patience=60, mode='min', min_delta=0.001),
+      ModelCheckpoint(checkpoint_path, monitor='val_loss', save_best_only=True, mode='min')
+]
+
    +
    +
    +
    +
    +
    +
    history=model.fit(x_train, y_train, epochs=500, batch_size=12, verbose=2, validation_split =0.3, shuffle=True,callbacks=keras_callbacks)
+
    +
    +
    +
    +
    Epoch 1/500
+28/28 - 17s - loss: 0.8364 - val_loss: 0.8320 - 17s/epoch - 622ms/step
+Epoch 2/500
+28/28 - 0s - loss: 0.1656 - val_loss: 0.1492 - 349ms/epoch - 12ms/step
+Epoch 3/500
+28/28 - 0s - loss: 0.0414 - val_loss: 0.0798 - 365ms/epoch - 13ms/step
+Epoch 4/500
+28/28 - 0s - loss: 0.0315 - val_loss: 0.0750 - 369ms/epoch - 13ms/step
+Epoch 5/500
+28/28 - 0s - loss: 0.0327 - val_loss: 0.0807 - 276ms/epoch - 10ms/step
+Epoch 6/500
+28/28 - 0s - loss: 0.0279 - val_loss: 0.0370 - 366ms/epoch - 13ms/step
+Epoch 7/500
+28/28 - 0s - loss: 0.0248 - val_loss: 0.0489 - 283ms/epoch - 10ms/step
+Epoch 8/500
+28/28 - 0s - loss: 0.0230 - val_loss: 0.0697 - 274ms/epoch - 10ms/step
+Epoch 9/500
+28/28 - 0s - loss: 0.0210 - val_loss: 0.0402 - 310ms/epoch - 11ms/step
+Epoch 10/500
+28/28 - 0s - loss: 0.0197 - val_loss: 0.0591 - 272ms/epoch - 10ms/step
+Epoch 11/500
+28/28 - 0s - loss: 0.0223 - val_loss: 0.0479 - 296ms/epoch - 11ms/step
+Epoch 12/500
+28/28 - 0s - loss: 0.0201 - val_loss: 0.0536 - 353ms/epoch - 13ms/step
+Epoch 13/500
+28/28 - 0s - loss: 0.0214 - val_loss: 0.0653 - 291ms/epoch - 10ms/step
+Epoch 14/500
+28/28 - 0s - loss: 0.0206 - val_loss: 0.0369 - 355ms/epoch - 13ms/step
+Epoch 15/500
+28/28 - 0s - loss: 0.0178 - val_loss: 0.0372 - 294ms/epoch - 11ms/step
+Epoch 16/500
+28/28 - 0s - loss: 0.0183 - val_loss: 0.0444 - 373ms/epoch - 13ms/step
+Epoch 17/500
+28/28 - 0s - loss: 0.0240 - val_loss: 0.0564 - 390ms/epoch - 14ms/step
+Epoch 18/500
+28/28 - 0s - loss: 0.0226 - val_loss: 0.0430 - 437ms/epoch - 16ms/step
+Epoch 19/500
+28/28 - 1s - loss: 0.0189 - val_loss: 0.0343 - 531ms/epoch - 19ms/step
+Epoch 20/500
+28/28 - 0s - loss: 0.0225 - val_loss: 0.0545 - 401ms/epoch - 14ms/step
+Epoch 21/500
+28/28 - 0s - loss: 0.0270 - val_loss: 0.0465 - 369ms/epoch - 13ms/step
+Epoch 22/500
+28/28 - 0s - loss: 0.0217 - val_loss: 0.0615 - 278ms/epoch - 10ms/step
+Epoch 23/500
+28/28 - 0s - loss: 0.0208 - val_loss: 0.0445 - 281ms/epoch - 10ms/step
+Epoch 24/500
+28/28 - 0s - loss: 0.0200 - val_loss: 0.0295 - 345ms/epoch - 12ms/step
+Epoch 25/500
+28/28 - 0s - loss: 0.0201 - val_loss: 0.0208 - 352ms/epoch - 13ms/step
+Epoch 26/500
+28/28 - 0s - loss: 0.0209 - val_loss: 0.0644 - 292ms/epoch - 10ms/step
+Epoch 27/500
+28/28 - 0s - loss: 0.0202 - val_loss: 0.0519 - 275ms/epoch - 10ms/step
+Epoch 28/500
+28/28 - 0s - loss: 0.0212 - val_loss: 0.0199 - 350ms/epoch - 13ms/step
+Epoch 29/500
+28/28 - 0s - loss: 0.0187 - val_loss: 0.0457 - 282ms/epoch - 10ms/step
+Epoch 30/500
+28/28 - 0s - loss: 0.0165 - val_loss: 0.0446 - 295ms/epoch - 11ms/step
+Epoch 31/500
+28/28 - 0s - loss: 0.0215 - val_loss: 0.0647 - 290ms/epoch - 10ms/step
+Epoch 32/500
+28/28 - 0s - loss: 0.0193 - val_loss: 0.0237 - 284ms/epoch - 10ms/step
+Epoch 33/500
+28/28 - 0s - loss: 0.0174 - val_loss: 0.0409 - 297ms/epoch - 11ms/step
+Epoch 34/500
+28/28 - 0s - loss: 0.0186 - val_loss: 0.0221 - 293ms/epoch - 10ms/step
+Epoch 35/500
+28/28 - 0s - loss: 0.0230 - val_loss: 0.0481 - 288ms/epoch - 10ms/step
+Epoch 36/500
+28/28 - 0s - loss: 0.0207 - val_loss: 0.0495 - 362ms/epoch - 13ms/step
+Epoch 37/500
+28/28 - 0s - loss: 0.0175 - val_loss: 0.0543 - 275ms/epoch - 10ms/step
+Epoch 38/500
+28/28 - 0s - loss: 0.0170 - val_loss: 0.0340 - 287ms/epoch - 10ms/step
+Epoch 39/500
+28/28 - 0s - loss: 0.0203 - val_loss: 0.0713 - 285ms/epoch - 10ms/step
+Epoch 40/500
+28/28 - 0s - loss: 0.0251 - val_loss: 0.0254 - 278ms/epoch - 10ms/step
+Epoch 41/500
+28/28 - 0s - loss: 0.0176 - val_loss: 0.0657 - 295ms/epoch - 11ms/step
+Epoch 42/500
+28/28 - 0s - loss: 0.0189 - val_loss: 0.0496 - 282ms/epoch - 10ms/step
+Epoch 43/500
+28/28 - 0s - loss: 0.0170 - val_loss: 0.0355 - 276ms/epoch - 10ms/step
+Epoch 44/500
+28/28 - 0s - loss: 0.0156 - val_loss: 0.0405 - 286ms/epoch - 10ms/step
+Epoch 45/500
+28/28 - 0s - loss: 0.0174 - val_loss: 0.0319 - 297ms/epoch - 11ms/step
+Epoch 46/500
+28/28 - 0s - loss: 0.0165 - val_loss: 0.0241 - 394ms/epoch - 14ms/step
+Epoch 47/500
+28/28 - 0s - loss: 0.0181 - val_loss: 0.0513 - 303ms/epoch - 11ms/step
+Epoch 48/500
+28/28 - 0s - loss: 0.0188 - val_loss: 0.0378 - 288ms/epoch - 10ms/step
+Epoch 49/500
+28/28 - 0s - loss: 0.0186 - val_loss: 0.0513 - 283ms/epoch - 10ms/step
+Epoch 50/500
+28/28 - 0s - loss: 0.0171 - val_loss: 0.0472 - 309ms/epoch - 11ms/step
+Epoch 51/500
+28/28 - 0s - loss: 0.0153 - val_loss: 0.0308 - 282ms/epoch - 10ms/step
+Epoch 52/500
+28/28 - 0s - loss: 0.0152 - val_loss: 0.0264 - 289ms/epoch - 10ms/step
+Epoch 53/500
+28/28 - 0s - loss: 0.0155 - val_loss: 0.0356 - 297ms/epoch - 11ms/step
+Epoch 54/500
+28/28 - 0s - loss: 0.0161 - val_loss: 0.0465 - 404ms/epoch - 14ms/step
+Epoch 55/500
+28/28 - 0s - loss: 0.0165 - val_loss: 0.0313 - 436ms/epoch - 16ms/step
+Epoch 56/500
+28/28 - 0s - loss: 0.0179 - val_loss: 0.0375 - 387ms/epoch - 14ms/step
+Epoch 57/500
+28/28 - 0s - loss: 0.0160 - val_loss: 0.0435 - 456ms/epoch - 16ms/step
+Epoch 58/500
+28/28 - 0s - loss: 0.0165 - val_loss: 0.0250 - 450ms/epoch - 16ms/step
+Epoch 59/500
+28/28 - 0s - loss: 0.0155 - val_loss: 0.0296 - 364ms/epoch - 13ms/step
+Epoch 60/500
+28/28 - 0s - loss: 0.0160 - val_loss: 0.0370 - 299ms/epoch - 11ms/step
+Epoch 61/500
+28/28 - 0s - loss: 0.0170 - val_loss: 0.0280 - 287ms/epoch - 10ms/step
+Epoch 62/500
+28/28 - 0s - loss: 0.0161 - val_loss: 0.0245 - 287ms/epoch - 10ms/step
+Epoch 63/500
+28/28 - 0s - loss: 0.0160 - val_loss: 0.0449 - 279ms/epoch - 10ms/step
+Epoch 64/500
+28/28 - 0s - loss: 0.0163 - val_loss: 0.0390 - 281ms/epoch - 10ms/step
+Epoch 65/500
+28/28 - 0s - loss: 0.0169 - val_loss: 0.0415 - 285ms/epoch - 10ms/step
+Epoch 66/500
+28/28 - 0s - loss: 0.0158 - val_loss: 0.0428 - 276ms/epoch - 10ms/step
+Epoch 67/500
+28/28 - 0s - loss: 0.0187 - val_loss: 0.0379 - 300ms/epoch - 11ms/step
+Epoch 68/500
+28/28 - 0s - loss: 0.0170 - val_loss: 0.0428 - 318ms/epoch - 11ms/step
+Epoch 69/500
+28/28 - 0s - loss: 0.0196 - val_loss: 0.0429 - 294ms/epoch - 10ms/step
+Epoch 70/500
+28/28 - 0s - loss: 0.0159 - val_loss: 0.0472 - 329ms/epoch - 12ms/step
+Epoch 71/500
+28/28 - 0s - loss: 0.0153 - val_loss: 0.0270 - 285ms/epoch - 10ms/step
+Epoch 72/500
+28/28 - 0s - loss: 0.0169 - val_loss: 0.0333 - 284ms/epoch - 10ms/step
+Epoch 73/500
+28/28 - 0s - loss: 0.0157 - val_loss: 0.0344 - 288ms/epoch - 10ms/step
+Epoch 74/500
+28/28 - 0s - loss: 0.0161 - val_loss: 0.0678 - 303ms/epoch - 11ms/step
+Epoch 75/500
+28/28 - 0s - loss: 0.0193 - val_loss: 0.0118 - 333ms/epoch - 12ms/step
+Epoch 76/500
+28/28 - 0s - loss: 0.0181 - val_loss: 0.0596 - 281ms/epoch - 10ms/step
+Epoch 77/500
+28/28 - 0s - loss: 0.0142 - val_loss: 0.0312 - 307ms/epoch - 11ms/step
+Epoch 78/500
+28/28 - 0s - loss: 0.0153 - val_loss: 0.0631 - 294ms/epoch - 10ms/step
+Epoch 79/500
+28/28 - 0s - loss: 0.0155 - val_loss: 0.0258 - 285ms/epoch - 10ms/step
+Epoch 80/500
+28/28 - 0s - loss: 0.0173 - val_loss: 0.0295 - 286ms/epoch - 10ms/step
+Epoch 81/500
+28/28 - 0s - loss: 0.0143 - val_loss: 0.0247 - 290ms/epoch - 10ms/step
+Epoch 82/500
+28/28 - 0s - loss: 0.0174 - val_loss: 0.0343 - 298ms/epoch - 11ms/step
+Epoch 83/500
+28/28 - 0s - loss: 0.0144 - val_loss: 0.0259 - 352ms/epoch - 13ms/step
+Epoch 84/500
+28/28 - 0s - loss: 0.0167 - val_loss: 0.0387 - 294ms/epoch - 10ms/step
+Epoch 85/500
+28/28 - 0s - loss: 0.0170 - val_loss: 0.0217 - 294ms/epoch - 10ms/step
+Epoch 86/500
+28/28 - 0s - loss: 0.0141 - val_loss: 0.0358 - 274ms/epoch - 10ms/step
+Epoch 87/500
+28/28 - 0s - loss: 0.0144 - val_loss: 0.0479 - 301ms/epoch - 11ms/step
+Epoch 88/500
+28/28 - 0s - loss: 0.0155 - val_loss: 0.0434 - 276ms/epoch - 10ms/step
+Epoch 89/500
+28/28 - 0s - loss: 0.0155 - val_loss: 0.0219 - 288ms/epoch - 10ms/step
+Epoch 90/500
+28/28 - 0s - loss: 0.0147 - val_loss: 0.0246 - 290ms/epoch - 10ms/step
+Epoch 91/500
+28/28 - 0s - loss: 0.0159 - val_loss: 0.0384 - 277ms/epoch - 10ms/step
+Epoch 92/500
+28/28 - 0s - loss: 0.0145 - val_loss: 0.0142 - 283ms/epoch - 10ms/step
+Epoch 93/500
+28/28 - 0s - loss: 0.0169 - val_loss: 0.0426 - 449ms/epoch - 16ms/step
+Epoch 94/500
+28/28 - 0s - loss: 0.0127 - val_loss: 0.0658 - 374ms/epoch - 13ms/step
+Epoch 95/500
+28/28 - 0s - loss: 0.0176 - val_loss: 0.0296 - 383ms/epoch - 14ms/step
+Epoch 96/500
+28/28 - 0s - loss: 0.0148 - val_loss: 0.0313 - 468ms/epoch - 17ms/step
+Epoch 97/500
+28/28 - 0s - loss: 0.0159 - val_loss: 0.0396 - 446ms/epoch - 16ms/step
+Epoch 98/500
+28/28 - 0s - loss: 0.0174 - val_loss: 0.0330 - 359ms/epoch - 13ms/step
+Epoch 99/500
+28/28 - 0s - loss: 0.0151 - val_loss: 0.0456 - 291ms/epoch - 10ms/step
+Epoch 100/500
+28/28 - 0s - loss: 0.0152 - val_loss: 0.0274 - 309ms/epoch - 11ms/step
+Epoch 101/500
+28/28 - 0s - loss: 0.0140 - val_loss: 0.0384 - 289ms/epoch - 10ms/step
+Epoch 102/500
+28/28 - 0s - loss: 0.0146 - val_loss: 0.0212 - 303ms/epoch - 11ms/step
+Epoch 103/500
+28/28 - 0s - loss: 0.0165 - val_loss: 0.0199 - 291ms/epoch - 10ms/step
+Epoch 104/500
+28/28 - 0s - loss: 0.0140 - val_loss: 0.0263 - 281ms/epoch - 10ms/step
+Epoch 105/500
+28/28 - 0s - loss: 0.0146 - val_loss: 0.0269 - 291ms/epoch - 10ms/step
+Epoch 106/500
+28/28 - 0s - loss: 0.0135 - val_loss: 0.0352 - 282ms/epoch - 10ms/step
+Epoch 107/500
+28/28 - 0s - loss: 0.0133 - val_loss: 0.0262 - 283ms/epoch - 10ms/step
+Epoch 108/500
+28/28 - 0s - loss: 0.0120 - val_loss: 0.0328 - 302ms/epoch - 11ms/step
+Epoch 109/500
+28/28 - 0s - loss: 0.0154 - val_loss: 0.0485 - 301ms/epoch - 11ms/step
+Epoch 110/500
+28/28 - 0s - loss: 0.0147 - val_loss: 0.0377 - 279ms/epoch - 10ms/step
+Epoch 111/500
+28/28 - 0s - loss: 0.0157 - val_loss: 0.0262 - 286ms/epoch - 10ms/step
+Epoch 112/500
+28/28 - 0s - loss: 0.0158 - val_loss: 0.0329 - 287ms/epoch - 10ms/step
+Epoch 113/500
+28/28 - 0s - loss: 0.0151 - val_loss: 0.0169 - 284ms/epoch - 10ms/step
+Epoch 114/500
+28/28 - 0s - loss: 0.0159 - val_loss: 0.0483 - 289ms/epoch - 10ms/step
+Epoch 115/500
+28/28 - 0s - loss: 0.0162 - val_loss: 0.0208 - 316ms/epoch - 11ms/step
+Epoch 116/500
+28/28 - 0s - loss: 0.0169 - val_loss: 0.0513 - 293ms/epoch - 10ms/step
+Epoch 117/500
+28/28 - 0s - loss: 0.0157 - val_loss: 0.0430 - 276ms/epoch - 10ms/step
+Epoch 118/500
+28/28 - 0s - loss: 0.0164 - val_loss: 0.0612 - 290ms/epoch - 10ms/step
+Epoch 119/500
+28/28 - 0s - loss: 0.0156 - val_loss: 0.0233 - 287ms/epoch - 10ms/step
+Epoch 120/500
+28/28 - 0s - loss: 0.0144 - val_loss: 0.0397 - 296ms/epoch - 11ms/step
+Epoch 121/500
+28/28 - 0s - loss: 0.0140 - val_loss: 0.0410 - 272ms/epoch - 10ms/step
+Epoch 122/500
+28/28 - 0s - loss: 0.0182 - val_loss: 0.0306 - 293ms/epoch - 10ms/step
+Epoch 123/500
+28/28 - 0s - loss: 0.0148 - val_loss: 0.0261 - 294ms/epoch - 11ms/step
+Epoch 124/500
+28/28 - 0s - loss: 0.0140 - val_loss: 0.0190 - 269ms/epoch - 10ms/step
+Epoch 125/500
+28/28 - 0s - loss: 0.0135 - val_loss: 0.0238 - 287ms/epoch - 10ms/step
+Epoch 126/500
+28/28 - 0s - loss: 0.0153 - val_loss: 0.0345 - 325ms/epoch - 12ms/step
+Epoch 127/500
+28/28 - 0s - loss: 0.0155 - val_loss: 0.0252 - 295ms/epoch - 11ms/step
+Epoch 128/500
+28/28 - 0s - loss: 0.0139 - val_loss: 0.0406 - 306ms/epoch - 11ms/step
+Epoch 129/500
+28/28 - 0s - loss: 0.0140 - val_loss: 0.0411 - 296ms/epoch - 11ms/step
+Epoch 130/500
+28/28 - 0s - loss: 0.0152 - val_loss: 0.0257 - 284ms/epoch - 10ms/step
+Epoch 131/500
+28/28 - 0s - loss: 0.0130 - val_loss: 0.0371 - 315ms/epoch - 11ms/step
+Epoch 132/500
+28/28 - 0s - loss: 0.0159 - val_loss: 0.0332 - 467ms/epoch - 17ms/step
+Epoch 133/500
+28/28 - 0s - loss: 0.0152 - val_loss: 0.0349 - 447ms/epoch - 16ms/step
+Epoch 134/500
+28/28 - 0s - loss: 0.0151 - val_loss: 0.0466 - 453ms/epoch - 16ms/step
+Epoch 135/500
+28/28 - 0s - loss: 0.0149 - val_loss: 0.0225 - 453ms/epoch - 16ms/step
+
    +
    +
    +
    +
    +
    +

    Model Predictions#

    +
    +
    +
    trainPred = model.predict(x_train)
+testPred = model.predict(x_valid)
+
    +
    +
    +
    +
    16/16 [==============================] - 2s 5ms/step
+1/1 [==============================] - 0s 33ms/step
+
    +
    +
    +
    +
    +
    +
    print(testPred.shape)
+print(trainPred.shape)
+
    +
    +
    +
    +
    (30, 1)
+(481, 1)
+
    +
    +
    +
    +
    +
    +
    #Reverting data back to 2D from 3D
+x_valid_t = x_valid.reshape((x_valid.shape[0], x_valid.shape[2]))
+print(x_valid_t.shape)
+print(testPred.shape)
+
    +
    +
    +
    +
    (30, 10)
+(30, 1)
+
    +
    +
    +
    +
    +
    +
    # invert scaling for forecasted values 
+
+inv_testPred = scaler_l.inverse_transform(testPred)
+print(inv_testPred[1])
+
+# invert scaling for actual values
+
+inv_y_valid = scaler_l.inverse_transform(y_valid)
+print(inv_y_valid[1])
+
    +
    +
    +
    +
    [14369558.]
+[13525194.]
+
    +
    +
    +
    +
    +
    +
    # calculate RMSE
+from sklearn.metrics import mean_squared_error
+from math import sqrt
+
+rmse = sqrt(mean_squared_error(inv_y_valid, inv_testPred))
+print('Test RMSE: %.3f' % rmse)
+
    +
    +
    +
    +
    Test RMSE: 635049.981
+
    +
    +
    +
    +
    +
    +
    # calculate Normalized RMSE
+y_max = inv_y_valid.max()
+y_min = inv_y_valid.min()
+nrmse = rmse /(inv_y_valid.mean()) 
+print('Test NRMSE:', nrmse)
+
    +
    +
    +
    +
    Test NRMSE: 0.06047605320267713
+
    +
    +
    +
    +
    +
    +
    # calculate R-square
+from sklearn.metrics import r2_score
+from math import sqrt
+
+r_sq = r2_score(inv_y_valid, inv_testPred)
+print('Test R_Square: %.3f' % r_sq)
+
    +
    +
    +
    +
    Test R_Square: 0.968
+
    +
    +
    +
    +
    +
    +

    Plotting#

    +
    +
    +
    from matplotlib import pyplot
+
+pyplot.plot(history.history['loss'], label='train')
+pyplot.plot(history.history['val_loss'], label='test')
+pyplot.legend()
+pyplot.show()
+
    +
    +
    +
    +../_images/chapter4_35_0.png +
    +
    +
    +
    +
    fig, ax = plt.subplots()
+ax.scatter(y_train,trainPred)
+ax.plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', lw=4)
+ax.set_xlabel('observed')
+ax.set_ylabel('predicted')
+plt.show()
+
    +
    +
    +
    +
    "\nfig, ax = plt.subplots()\nax.scatter(y_train,trainPred)\nax.plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'k--', lw=4)\nax.set_xlabel('observed')\nax.set_ylabel('predicted')\nplt.show()\n"
+
    +
    +
    +
    +
    +
    +
    fig, ax = plt.subplots()
+ax.scatter(inv_y_valid,inv_testPred) #[:,:,6]
+ax.plot([inv_y_valid.min(), inv_y_valid.max()], [inv_y_valid.min(), inv_y_valid.max()], 'k--', lw=4)
+ax.set_xlabel('observed')
+ax.set_ylabel('predicted')
+#plt.savefig('test_prediction.png',bbox_inches='tight',dpi=1200)
+plt.show()
+
    +
    +
    +
    +
    "\nfig, ax = plt.subplots()\nax.scatter(inv_y_valid,inv_testPred) #[:,:,6]\nax.plot([inv_y_valid.min(), inv_y_valid.max()], [inv_y_valid.min(), inv_y_valid.max()], 'k--', lw=4)\nax.set_xlabel('observed')\nax.set_ylabel('predicted')\n#plt.savefig('test_prediction.png',bbox_inches='tight',dpi=1200)\nplt.show()\n"
+
    +
    +
    +
    +
    +
    +
    from matplotlib import pyplot
+fig, ax= plt.subplots(figsize=(24, 8),dpi = 600)
+pyplot.plot(inv_testPred)
+pyplot.plot(inv_y_valid)
+plt.legend(['y_pred','y_test'])
+plt.title("Sea Ice Extent Observation vs LSTM Prediction (2019-21)")
+ax.set_xlabel("Time (Months)")
+ax.set_ylabel(r"Sea Ice Extent mil. $km^2$")
+pyplot.show()
+fig.savefig('Time_series_sea_ice_extent_trend_1979_2021_lstm.png')
+
    +
    +
    +
    +../_images/chapter4_38_0.png +
    +
    +
    + + + + +
    + +
    + +
    +
    +
    +

    + + By eScience Institute, University of Washington
    + + © Copyright 2022.
    +

    +
    +
    + +
    + + +
    +
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    Discussion / Conclusion

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    - -
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    Contents

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    Conclusion#

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    Is ML the right tool for the research?/How to identify the best ML tool for my problem? -Can a simple model be as good as a complex one? ML model discovery -Do I have enough confidence to extrapolate the results? Is the model transferable?

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    Discussion#

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    This chapter has two parts: lessons learnt and open questions.

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    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.

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    Data Preparation#

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    Model Development and Paramter Tuning

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    Model Development and Paramter Tuning#

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    Performance Evaluation#

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    Testing of the model on independent datasets.

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    Sample Jupyter Notebook#

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    Demo of executability below. -Loading and plotting sample data from plotly express.

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    Open questions#

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    More future oriented tasks that require deeper and long term research, and will have big returns.

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