regression_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/souvikg544/CodeNCollab/blob/main/regression_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8Hh4vdWsXh0p"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from math import *\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression, Lasso\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import make_scorer\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "import os\n",
        "from datetime import datetime\n",
        "from hypopt import GridSearch\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEKQllbUXh0x"
      },
      "source": [
        "### Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uUbsNPIyXh01"
      },
      "outputs": [],
      "source": [
        "def percentage_rmse(y_true, y_pred):\n",
        "    \"\"\"\n",
        "    Return RMSE calculation\n",
        "    Params:\n",
        "    ------\n",
        "    \n",
        "    - y_true: Test vector\n",
        "    - y_pred: Predicted vector\n",
        "    \n",
        "    \"\"\"\n",
        "    return sqrt(np.square(y_true-y_pred).sum(axis=0)/y_true.shape[0])\n",
        "\n",
        "def compute_metrics(y_true,y_pred):\n",
        "    \"\"\"\n",
        "    Return relevant percentage metrics\n",
        "    Params:\n",
        "    ------\n",
        "    \n",
        "    - y_true: Test vector\n",
        "    - y_pred: Predicted vector\n",
        "    \n",
        "    \"\"\"\n",
        "    mse=np.square(y_true-y_pred).sum(axis=0)/y_true.shape[0]\n",
        "    rmse=sqrt(mse)\n",
        "    mae=np.abs(y_true-y_pred).sum(axis=0)/y_true.shape[0]\n",
        "    \n",
        "    return {\"RMSE\":[rmse], \"MSE\":[mse], \"MAE\":[mae]}"
      ]
    },
    {
      "source": [
        "We use a regression model to model the power curve"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "DQdbrizUXh03"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1MgcN4kXh04"
      },
      "source": [
        "### Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F33tg5VkXh05"
      },
      "outputs": [],
      "source": [
        "metrics_filename=\"metrics.csv\"\n",
        "models={}\n",
        "metrics_model={}\n",
        "metric_columns=[\"date\",\"model\",\"params\",\"best_train_score\",\"RMSE\",\"MSE\",\"MAE\",\"WithCrossVal\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocQKX9WOXh06"
      },
      "source": [
        "## Grid search with Cross validation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZls3srjXh07"
      },
      "source": [
        "### Loading Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PJ90vjVCXh08"
      },
      "outputs": [],
      "source": [
        "train_data=pd.read_csv(\"data/train_prep.csv\")\n",
        "test_data=pd.read_csv(\"data/test_prep.csv\")\n",
        "dev_data=pd.read_csv(\"data/dev_prep.csv\")\n",
        "#Check if metrics file exists\n",
        "if os.path.isfile('./'+metrics_filename):\n",
        "    metrics=pd.read_csv(metrics_filename)\n",
        "else:\n",
        "    metrics=pd.DataFrame(columns=metric_columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dx9JF-iWXh09"
      },
      "source": [
        "### Training model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e1oGgXiHXh0-"
      },
      "outputs": [],
      "source": [
        "label_column=\"POWER\"\n",
        "columns=list(train_data.columns)\n",
        "columns.remove(label_column)\n",
        "columns.remove('TIMESTAMP')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G9XtDDOpXh0_"
      },
      "outputs": [],
      "source": [
        "X_train=train_data[columns]\n",
        "y_train=train_data[label_column]\n",
        "\n",
        "X_test=test_data[columns]\n",
        "y_test=test_data[label_column]\n",
        "\n",
        "X_val=dev_data[columns]\n",
        "y_val=dev_data[label_column]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "owkSbno1Xh0_"
      },
      "source": [
        "### Lasso Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b2aydeBlXh1A",
        "outputId": "3df67983-7fc5-4490-8964-955f3c375904"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics\n                     date  model                             params  \\\n37  10/24/2020, 00:11:16  Lasso  {'alpha': 0.01, 'max_iter': 1000}   \n\n   best_train_score      RMSE        MSE       MAE WithCrossVal  \n37         0.265113  0.264296  0.0698523  0.221629          Yes  \n"
          ]
        }
      ],
      "source": [
        "#Parameters\n",
        "model_name=\"Lasso\"\n",
        "alphas=[1e-2, 1e-1,1]\n",
        "max_iters=[100,1000,10000]\n",
        "K=5\n",
        "grid_values={\"alpha\":alphas,\"max_iter\":max_iters}\n",
        "model=Lasso()\n",
        "\n",
        "#Grid Search Cross validation\n",
        "grid_model = GridSearchCV(model, param_grid = grid_values,scoring = make_scorer(percentage_rmse, greater_is_better=False),cv=K)\n",
        "grid_model.fit(X_train,y_train)\n",
        "\n",
        "#Save model and best score\n",
        "models[model_name]=grid_model\n",
        "metrics_model[model_name]=grid_model.best_score_\n",
        "\n",
        "#Prediction on test set \n",
        "y_pred=grid_model.predict(X_test)\n",
        "\n",
        "# Calculate metrics\n",
        "dict_metrics=compute_metrics(y_test,y_pred)\n",
        "metrics_model[model_name]=dict_metrics\n",
        "dict_metrics[\"date\"]=[datetime.now().strftime(\"%m/%d/%Y, %H:%M:%S\")]\n",
        "dict_metrics[\"model\"]=[\"Lasso\"]\n",
        "dict_metrics[\"params\"]=[grid_model.best_params_]\n",
        "dict_metrics[\"best_train_score\"]=[-grid_model.best_score_]\n",
        "dict_metrics[\"WithCrossVal\"]=[\"Yes\"]\n",
        "lr_metrics=pd.DataFrame.from_dict(dict_metrics)\n",
        "lr_metrics=lr_metrics[metric_columns]\n",
        "metrics=metrics.append(lr_metrics,ignore_index=True)\n",
        "print('Metrics\\n',metrics.tail(1))"
      ]
    },
    {
      "source": [
        "### Residuals Plot"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "zamRwLlMXh1C"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "68x-rGeiXh1D",
        "outputId": "afd0a5bd-96e9-44de-bb72-ac6d38b62dea"
      },
      "outputs": [
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(y_pred-y_test)\n",
        "plt.show()"
      ]
    },
    {
      "source": [
        "One of the main hypothesis for our linear regression is the normality of the residuals. \n",
        "However we can see from the plot above that the residuals are clearly not following a normal distribution. It seems that the models tends to overestimate the predictions and there's clearly a bias since the distribution is not symmetrical around 0. Let's try out a more complex model."
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "34yNBPsGXh1D"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ckZ3SjCHXh1E"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g1ZkpzDaXh1E",
        "outputId": "fde55544-dde2-4db3-ea12-2931d8c78bcd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics\n                     date model                                 params  \\\n38  10/24/2020, 00:12:14    RF  {'max_depth': 10, 'n_estimators': 50}   \n\n   best_train_score      RMSE        MSE      MAE WithCrossVal  \n38         0.130644  0.130742  0.0170935  0.09599          Yes  \n"
          ]
        }
      ],
      "source": [
        "model_name=\"RF\"\n",
        "max_depths=[5,10,15]\n",
        "num_trees=[10,50]\n",
        "K=5\n",
        "grid_values={\"n_estimators\":num_trees,\"max_depth\":max_depths}\n",
        "\n",
        "model=RandomForestRegressor(random_state=42)\n",
        "grid_model = GridSearchCV(model, param_grid = grid_values,scoring = make_scorer(percentage_rmse, greater_is_better=False),cv=K)\n",
        "grid_model.fit(X_train,y_train)\n",
        "\n",
        "#Save model and best score\n",
        "models[model_name]=grid_model\n",
        "\n",
        "#Prediction on test set \n",
        "y_pred=grid_model.predict(X_test)\n",
        "\n",
        "# Calculate metrics\n",
        "dict_metrics=compute_metrics(y_test,y_pred)\n",
        "#Save metrics with model\n",
        "metrics_model[model_name]=dict_metrics\n",
        "#Add other metrics\n",
        "dict_metrics[\"date\"]=[datetime.now().strftime(\"%m/%d/%Y, %H:%M:%S\")]\n",
        "dict_metrics[\"model\"]=[model_name]\n",
        "dict_metrics[\"params\"]=[grid_model.best_params_]\n",
        "dict_metrics[\"best_train_score\"]=[-grid_model.best_score_]\n",
        "dict_metrics[\"WithCrossVal\"]=[\"Yes\"]\n",
        "rf_metrics=pd.DataFrame.from_dict(dict_metrics)\n",
        "rf_metrics=rf_metrics[metric_columns]\n",
        "metrics=metrics.append(rf_metrics,ignore_index=True)\n",
        "print('Metrics\\n',metrics.tail(1))"
      ]
    },
    {
      "source": [
        "### Residuals Plot"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "9yuySmMxXh1F"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hmo-QvJhXh1F",
        "outputId": "390797dc-b9d3-4f61-982d-8aaebf5bc177"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.distplot(y_pred-y_test)\n",
        "plt.show()"
      ]
    },
    {
      "source": [
        "The residuals distribution is a lot more similar to a normal distribution that it was with our first model. "
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "ypwfTajhXh1G"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_02OvJFEXh1G"
      },
      "source": [
        "### Pick Best model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gd_ePLZGXh1H",
        "outputId": "88e0aa57-2446-452b-992f-76ad07fc0e36"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "best_model:  GridSearchCV(cv=5, error_score='raise-deprecating',\n       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n           max_features='auto', max_leaf_nodes=None,\n           min_impurity_decrease=0.0, min_impurity_split=None,\n           min_samples_leaf=1, min_samples_split=2,\n           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n           oob_score=False, random_state=42, verbose=0, warm_start=False),\n       fit_params=None, iid='warn', n_jobs=None,\n       param_grid={'n_estimators': [10, 50], 'max_depth': [5, 10, 15]},\n       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n       scoring=make_scorer(percentage_rmse, greater_is_better=False),\n       verbose=0)\n"
          ]
        }
      ],
      "source": [
        "score=\"RMSE\"\n",
        "best_model=models[min(metrics_model, key=lambda x:metrics_model[x][score])]\n",
        "print(\"best_model: \", best_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Pjy4HEgXh1H"
      },
      "source": [
        "### Export metrics file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s8reOMpNXh1I"
      },
      "outputs": [],
      "source": [
        "metrics.to_csv('metrics.csv',mode='a',header=True,index=False)"
      ]
    }
  ],
  "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": "3.6.10-final"
    },
    "colab": {
      "name": "regression_model.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}