From a1670a70cb1ebc2bef7799e280a7cb2656c5a1cb Mon Sep 17 00:00:00 2001 From: ChathukiKet Date: Tue, 28 Sep 2021 23:01:37 +0530 Subject: [PATCH 1/3] decision tree regressor model for average dataset --- Chapter9/DecisionTree/Regression/README.md | 23 +++- .../Regression/decision_tree_regression.py | 124 +++++++++++++++++- 2 files changed, 143 insertions(+), 4 deletions(-) diff --git a/Chapter9/DecisionTree/Regression/README.md b/Chapter9/DecisionTree/Regression/README.md index 5c4a0ff..9cddf2e 100644 --- a/Chapter9/DecisionTree/Regression/README.md +++ b/Chapter9/DecisionTree/Regression/README.md @@ -1 +1,22 @@ -This readme should describe 'what is in the python code'. \ No newline at end of file +What it does : + + 1. Reads a csv file from the google drive, then create a decision tree regressor model on the average dataset. Visualize the tree. Calculate RMSE and MAE. + +Dependancies : + + 1. sklearn module is needed to be installed in the local machine. + + 2. pandas module is needed to be installed in the local machine. + + 3. matplotlib module is needed to be installed in the local machine. + + 4. gdown module is needed to be installed in the local machine, to download the CSV file from the google drive. + + +Things to check before running : + + 1. Check whether you have given the correct file location of the csv file. + + 2. Check whether you have access to the file. + + 3. Check whether the file format is correct. \ No newline at end of file diff --git a/Chapter9/DecisionTree/Regression/decision_tree_regression.py b/Chapter9/DecisionTree/Regression/decision_tree_regression.py index 765fac9..0bc32f1 100644 --- a/Chapter9/DecisionTree/Regression/decision_tree_regression.py +++ b/Chapter9/DecisionTree/Regression/decision_tree_regression.py @@ -1,3 +1,121 @@ -# TODO: Create a decision tree model (using scikit-learn) on the averages dataset (regression). -# Visualize the tree. Calculate RMSE and MAE -# TODO: Code should be well commented. \ No newline at end of file +'''Copyright (c) 2021 AIClub + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated +documentation files (the "Software"), to deal in the Software without restriction, including without +limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of +the Software, and to permit persons to whom the Software is furnished to do so, subject to the following +conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial +portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT +LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO +EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN +AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE +OR OTHER DEALINGS IN THE SOFTWARE.''' + + +# Python program to create a decision tree model using scikit-learn on the averages dataset (regression). +# Visualize the tree. Calculate RMSE and MAE. + + +# Import pandas module to read the CSV file and to process the tabular data +import pandas as pd + +# Import train_test_split to split data as test and train +from sklearn.model_selection import train_test_split + +# Import Decision Tree Regressor +from sklearn.tree import DecisionTreeRegressor + +# Import mean_absolute_error and mean_squared_error functions for error calculations +from sklearn.metrics import mean_absolute_error, mean_squared_error + +# Import sqrt function to get the square root of the mean_squared_error +from math import sqrt + +# Import plot_tree function to visualize the decision tree +from sklearn.tree import plot_tree + +# Import pyplot function to visualize the decision tree +from matplotlib import pyplot as plt + +# Import gdown module to download files from google drive +import gdown + + +# ------------------------------------ Get the file from the google drive. ------------------------------------------ + +# Please change the url as needed (make sure you have the access to the file) +url = 'https://drive.google.com/file/d/1Hxksp6KSjoex0wdER032QypLUYmdLNeg/view?usp=sharing' + +# Derive the file id from the url +file_id = url.split('/')[-2] + +# Derive the download url of the file +download_url = 'https://drive.google.com/uc?id=' + file_id + +# Give the location you want to save it in your local machine +file_location = r'average.csv' + +# Download the file from drive to your local machine +gdown.download(download_url, file_location) + + +# -------------------------------- Create the Decision Tree Regressor model -------------------------------------- + +# Read the CSV file +data = pd.read_csv(file_location) + +# Print a sample from the csv dataset +print('\n---------- First 5 rows of the Dataset ----------\n', data.head()) + +# Please change the target variable according to the dataset +# You can refer to the dataset details printed in the above step +target_column = 'AVERAGE' + +# Seperate the training data by removing the target column +X = data.drop(columns=[target_column]) + +# Separate the target values +y = data[target_column].values + +# Split the dataset into train and test data +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +# Create decision tree regressor +# You can change the max_depth in order to reduce the error +regressor = DecisionTreeRegressor(max_depth=3) + +# Train the model +regressor.fit(X_train, y_train) + +# Predict using test values +y_pred = regressor.predict(X_test) + +# Get actual values and predicted values into a table +predicted_results = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred}) +print('\n---------- Predicted Results ----------\n', predicted_results) + + +# --------------------------------------------- Visualize the Tree ------------------------------------------------- + +# Get the feature names +feature_names = list(X.columns) + +# Plot the decision tree +fig = plt.figure(figsize=(15,10)) +plot_tree(regressor, feature_names=feature_names, filled=True) +plt.show() + + +# ---------------------------------------- Calculate the MAE and RMSE ----------------------------------------------- + +# Calculate the MAE +MAE = mean_absolute_error(y_test, y_pred) +print ('\nMean Absolute Error (MAE): ', MAE) + +# Calculate the RMSE +RMSE = sqrt(mean_squared_error(y_test, y_pred)) +print ('\nRoot Mean Squared Error (RMSE): ', RMSE) \ No newline at end of file From 55816bc2687c565313e88e6b1afe7e3180e67cef Mon Sep 17 00:00:00 2001 From: ChathukiKet Date: Mon, 11 Oct 2021 16:01:27 +0530 Subject: [PATCH 2/3] notebook added --- .../Regression/decision_tree_regression.ipynb | 329 ++++++++++++++++++ 1 file changed, 329 insertions(+) create mode 100644 Chapter9/DecisionTree/Regression/decision_tree_regression.ipynb diff --git a/Chapter9/DecisionTree/Regression/decision_tree_regression.ipynb b/Chapter9/DecisionTree/Regression/decision_tree_regression.ipynb new file mode 100644 index 0000000..eb7b924 --- /dev/null +++ b/Chapter9/DecisionTree/Regression/decision_tree_regression.ipynb @@ -0,0 +1,329 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Decision_tree_regressor.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "aTs3aqspLFbI" + }, + "source": [ + "# **Problem: Decision Tree Regressor Model**\n", + "\n", + "The python program reads a csv file (average dataset) from the google drive, and creates a decision tree regression model on that dataset. Visualize the tree, calculate RMSE and MAE. \n", + "\n", + "**Examples:**\n", + "\n", + "Input -->\n", + "\n", + "> url = 'https://drive.google.com/file/d/1Hxksp6KSjoex0wdER032QypLUYmdLNeg/view?usp=sharing'\n", + "\n", + "Output -->\n", + "\n", + "\n", + "> Decision tree\n", + 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)\n", + "\n", + "\n", + "\n", + "> RMSE = 10.929085509617472\n", + " \n", + "\n", + "> MAE = 8.549767857142857\n", + "\n", + "**Notes:**\n", + "\n", + "Following things are needed to be checked before running the program.\n", + "* Check whether you have given the correct location of your csv file.\n", + "* Check whether you file format is correct.\n", + "* Check whether you have access to the file." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6BIs56Kl5GHB" + }, + "source": [ + "#@title MIT License\n", + "\n", + "# Copyright (c) 2021 AIClub\n", + "\n", + "# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated \n", + "# documentation files (the \"Software\"), to deal in the Software without restriction, including without \n", + "# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of \n", + "# the Software, and to permit persons to whom the Software is furnished to do so, subject to the following \n", + "# conditions:\n", + "\n", + "# The above copyright notice and this permission notice shall be included in all copies or substantial\n", + "# portions of the Software.\n", + "\n", + "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT \n", + "# LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO \n", + "# EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN \n", + "# AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE \n", + "# OR OTHER DEALINGS IN THE SOFTWARE." + ], + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VBqjhcUbc-72" + }, + "source": [ + "# Import modules" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9S-OD3v0cTxA" + }, + "source": [ + "# Import pandas module to read the CSV file and to process the tabular data\n", + "import pandas as pd\n", + "\n", + "# Import train_test_split to split data as test and train\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Import Decision Tree Regressor\n", + "from sklearn.tree import DecisionTreeRegressor \n", + "\n", + "# Import mean_absolute_error and mean_squared_error functions for error calculations\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error \n", + "\n", + "# Import sqrt function to get the square root of the mean_squared_error\n", + "from math import sqrt\n", + "\n", + "# Import plot_tree function to visualize the decision tree\n", + "from sklearn.tree import plot_tree\n", + "\n", + "# Import pyplot function to visualize the decision tree\n", + "from matplotlib import pyplot as plt\n", + "\n", + "# Import gdown module to download files from google drive\n", + "import gdown" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NC5yAkbHdCZm" + }, + "source": [ + "# Get the file location from the google drive" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hR2Ar8XHdoav" + }, + "source": [ + "# Please change the url as needed (make sure you have the access to the file)\n", + "url = 'https://drive.google.com/file/d/1Hxksp6KSjoex0wdER032QypLUYmdLNeg/view?usp=sharing'\n", + "\n", + "# Derive the file id from the url\n", + "file_id = url.split('/')[-2]\n", + "\n", + "# Derive the download url of the file\n", + "download_url = 'https://drive.google.com/uc?id=' + file_id\n", + "\n", + "# Give the file name you want to save it \n", + "file_name = \"average.csv\" \n", + "\n", + "# Derive the file location\n", + "file_location = \"/content/\" + file_name" + ], + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gd1WH9wQdou7" + }, + "source": [ + "# Downloading, creating of decision tree regression model and calculating of metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMe4N40Uh5fe" + }, + "source": [ + "Download and read the CSV file" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ouYfE4areWFk" + }, + "source": [ + "# Download the file from drive\n", + "gdown.download(download_url, file_location, quiet=False)\n", + "\n", + "# Read the CSV file\n", + "data = pd.read_csv(file_location)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GHfLu6-aggWJ" + }, + "source": [ + "Display a sample from the dataset" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ipsfzY3BgqAE" + }, + "source": [ + "# Print a sample from the csv dataset\n", + "print('---------- First 5 rows of the Dataset ----------\\n', data.head())" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T7pn823DhbAi" + }, + "source": [ + "Create the decision tree regression model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "I-LutOhihk1-" + }, + "source": [ + "# Please change the target variable according to the dataset\n", + "# You can refer to the dataset details printed in the above step \n", + "target_column = 'AVERAGE'\n", + "\n", + "# Seperate the training data by removing the target column\n", + "X = data.drop(columns=[target_column])\n", + "\n", + "# Separate the target values\n", + "y = data[target_column].values\n", + "\n", + "# Split the dataset into train and test data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n", + "\n", + "# Create decision tree regressor\n", + "# You can change the max_depth in order to reduce the error\n", + "regressor = DecisionTreeRegressor(max_depth=3)\n", + "\n", + "# Train the model\n", + "regressor.fit(X_train, y_train)\n", + "\n", + "# Predict using test values\n", + "y_pred = regressor.predict(X_test)\n", + "\n", + "# Get actual values and predicted values into a table\n", + "predicted_results = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n", + "print('---------- Predicted Results ----------\\n', predicted_results)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qcEI6lkLEbvt" + }, + "source": [ + "Visualize the tree" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YiTuGi1hEcAF" + }, + "source": [ + "# Get the feature names\n", + "feature_names = list(X.columns)\n", + "\n", + "# Plot the decision tree\n", + "fig = plt.figure(figsize=(15,10))\n", + "plot_tree(regressor, feature_names=feature_names, filled=True)\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RxAogMqQhlcW" + }, + "source": [ + "MAE" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "qSMrcrFYhuPX" + }, + "source": [ + "# Calculate the MAE\n", + "MAE = mean_absolute_error(y_test, y_pred)\n", + "print ('\\nMean Absolute Error (MAE): ', MAE)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eEpwTG1oAtls" + }, + "source": [ + "RMSE" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EEfpac8-AveP" + }, + "source": [ + "# Calculate the RMSE\n", + "RMSE = sqrt(mean_squared_error(y_test, y_pred))\n", + "print ('\\nRoot Mean Squared Error (RMSE): ', RMSE)" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 37be1b8d54a3f31fdd50891d7fd624053040bd31 Mon Sep 17 00:00:00 2001 From: ChathukiKet Date: Fri, 26 Nov 2021 22:59:40 +0530 Subject: [PATCH 3/3] remove print statement --- Chapter9/DecisionTree/Regression/decision_tree_regression.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/Chapter9/DecisionTree/Regression/decision_tree_regression.py b/Chapter9/DecisionTree/Regression/decision_tree_regression.py index 0bc32f1..0b10e70 100644 --- a/Chapter9/DecisionTree/Regression/decision_tree_regression.py +++ b/Chapter9/DecisionTree/Regression/decision_tree_regression.py @@ -94,10 +94,6 @@ # Predict using test values y_pred = regressor.predict(X_test) -# Get actual values and predicted values into a table -predicted_results = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred}) -print('\n---------- Predicted Results ----------\n', predicted_results) - # --------------------------------------------- Visualize the Tree -------------------------------------------------