From 4e3f75952a6e50786d48006cbe62c4e58162f043 Mon Sep 17 00:00:00 2001 From: Sumedh Date: Fri, 15 Nov 2019 14:50:47 -0500 Subject: [PATCH] update readme --- README.md | 3 ++- index.ipynb | 2 +- index_files/index_11_0.png | Bin 43209 -> 66168 bytes 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f005693..e048a6a 100644 --- a/README.md +++ b/README.md @@ -288,7 +288,8 @@ You can see what rules the tree learned by plotting this decision tree. To do th # Create DOT data dot_data = export_graphviz(clf, out_file=None, feature_names=ohe_df.columns, - class_names=np.unique(y).astype('str')) + class_names=np.unique(y).astype('str'), + filled=True, rounded=True, special_characters=True) # Draw graph graph = graph_from_dot_data(dot_data) diff --git a/index.ipynb b/index.ipynb index 1deadef..16b812e 100644 --- a/index.ipynb +++ b/index.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Building Trees using scikit-learn\n", "\n", "## Introduction\n", "\n", "In this lesson, we will cover decision trees (for classification) in Python, using scikit-learn and pandas. The emphasis will be on the basics and understanding the resulting decision tree. Scikit-learn provides a consistent interface for running different classifiers/regressors. For classification tasks, evaluation is performed using the same measures as we have seen before. Let's look at our example from earlier lessons and grow a tree to find our solution. \n", "\n", "## Objectives \n", "\n", "You will be able to:\n", "\n", "- Use scikit-learn to fit a decision tree classification model \n", "- Plot a decision tree using Python \n", "\n", "\n", "## Import necessary modules and data\n", "\n", "In order to prepare data, train, evaluate, and visualize a decision tree, we will make use of several modules in the scikit-learn package. Run the cell below to import everything we'll need for this lesson: "]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["import numpy as np \n", "import pandas as pd \n", "from sklearn.model_selection import train_test_split\n", "from sklearn.tree import DecisionTreeClassifier \n", "from sklearn.metrics import accuracy_score\n", "from sklearn.tree import export_graphviz\n", "from sklearn.preprocessing import OneHotEncoder\n", "from IPython.display import Image \n", "from sklearn.tree import export_graphviz\n", "from pydotplus import graph_from_dot_data"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The play tennis dataset is available in the repo as `'tennis.csv'`. For this step, we'll start by importing the csv file as a pandas DataFrame."]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [{"data": {"text/html": ["
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"], "text/plain": [" outlook temp humidity windy play\n", "0 sunny hot high False no\n", "1 sunny hot high True no\n", "2 overcast hot high False yes\n", "3 rainy mild high False yes\n", "4 rainy cool normal False yes"]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["# Load the dataset\n", "df = pd.read_csv('tennis.csv')\n", "\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Create training and test sets\n", "\n", "Before we do anything we'll want to split our data into **_training_** and **_test_** sets. We'll accomplish this by first splitting the DataFrame into features (`X`) and target (`y`), then passing `X` and `y` to the `train_test_split()` function to create a 70/30 train test split."]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": ["X = df.loc[:, ['outlook', 'temp', 'humidity', 'windy']]\n", "y = df.loc[:, 'play']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Encode categorical data as numbers\n", "\n", "Since all of our data is currently categorical (recall that each column is in string format), we need to encode them as numbers. For this, we'll use a handy helper object from sklearn's `preprocessing` module called `OneHotEncoder`."]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"data": {"text/html": ["
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"], "text/plain": [" outlook_overcast outlook_rainy outlook_sunny temp_cool temp_hot \\\n", "0 0.0 0.0 1.0 1.0 0.0 \n", "1 1.0 0.0 0.0 0.0 1.0 \n", "2 0.0 0.0 1.0 0.0 1.0 \n", "3 0.0 1.0 0.0 0.0 0.0 \n", "4 0.0 1.0 0.0 1.0 0.0 \n", "\n", " temp_mild humidity_high humidity_normal windy_False windy_True \n", "0 0.0 0.0 1.0 1.0 0.0 \n", "1 0.0 1.0 0.0 1.0 0.0 \n", "2 0.0 1.0 0.0 0.0 1.0 \n", "3 1.0 1.0 0.0 0.0 1.0 \n", "4 0.0 0.0 1.0 1.0 0.0 "]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["# One-hot encode the training data and show the resulting DataFrame with proper column names\n", "ohe = OneHotEncoder()\n", "\n", "ohe.fit(X_train)\n", "X_train_ohe = ohe.transform(X_train).toarray()\n", "\n", "# Creating this DataFrame is not necessary its only to show the result of the ohe\n", "ohe_df = pd.DataFrame(X_train_ohe, columns=ohe.get_feature_names(X_train.columns))\n", "\n", "ohe_df.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Train the decision tree \n", "\n", "One awesome feature of scikit-learn is the uniformity of its interfaces for every classifier -- no matter what classifier we're using, we can expect it to have the same important methods such as `.fit()` and `.predict()`. This means that this next part should feel familiar.\n", "\n", "We'll first create an instance of the classifier with any parameter values, and then we'll fit our data to the model using `.fit()`. "]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"text/plain": ["DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n", " max_features=None, 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, presort=False,\n", " random_state=None, splitter='best')"]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create the classifier, fit it on the training data and make predictions on the test set\n", "clf = DecisionTreeClassifier(criterion='entropy')\n", "\n", "clf.fit(X_train_ohe, y_train)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Plot the decision tree \n", "\n", "You can see what rules the tree learned by plotting this decision tree. To do this, you need to use additional packages such as `pytdotplus`. \n", "\n", "> **Note:** If you are run into errors while generating the plot, you probably need to install `python-graphviz` in your machine using `conda install python-graphviz`. "]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"image/png": 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"text/plain": [""]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create DOT data\n", "dot_data = export_graphviz(clf, out_file=None, \n", " feature_names=ohe_df.columns, \n", " class_names=np.unique(y).astype('str'))\n", "\n", "# Draw graph\n", "graph = graph_from_dot_data(dot_data) \n", "\n", "# Show graph\n", "Image(graph.create_png())"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Evaluate the predictive performance\n", "\n", "Now that we have a trained model, we can generate some predictions, and go on to see how accurate our predictions are. We can use a simple accuracy measure, AUC, a confusion matrix, or all of them. This step is performed in the exactly the same manner, doesn't matter which classifier you are dealing with. "]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Accuracy: 0.6\n"]}], "source": ["X_test_ohe = ohe.transform(X_test)\n", "y_preds = clf.predict(X_test_ohe)\n", "\n", "print('Accuracy: ', accuracy_score(y_test, y_preds))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["##\u00a0Summary \n", "\n", "In this lesson, we looked at how to grow a decision tree using `scikit-learn`. We looked at different stages of data processing, training, and evaluation that you would normally come across while growing a tree or training any other such classifier. We shall now move to a lab, where you will be required to build a tree for a given problem, following the steps shown in this lesson. "]}], "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": "3.7.3"}}, "nbformat": 4, "nbformat_minor": 2} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Building Trees using scikit-learn\n", "\n", "## Introduction\n", "\n", "In this lesson, we will cover decision trees (for classification) in Python, using scikit-learn and pandas. The emphasis will be on the basics and understanding the resulting decision tree. Scikit-learn provides a consistent interface for running different classifiers/regressors. For classification tasks, evaluation is performed using the same measures as we have seen before. Let's look at our example from earlier lessons and grow a tree to find our solution. \n", "\n", "## Objectives \n", "\n", "You will be able to:\n", "\n", "- Use scikit-learn to fit a decision tree classification model \n", "- Plot a decision tree using Python \n", "\n", "\n", "## Import necessary modules and data\n", "\n", "In order to prepare data, train, evaluate, and visualize a decision tree, we will make use of several modules in the scikit-learn package. Run the cell below to import everything we'll need for this lesson: "]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["import numpy as np \n", "import pandas as pd \n", "from sklearn.model_selection import train_test_split\n", "from sklearn.tree import DecisionTreeClassifier \n", "from sklearn.metrics import accuracy_score\n", "from sklearn.tree import export_graphviz\n", "from sklearn.preprocessing import OneHotEncoder\n", "from IPython.display import Image \n", "from sklearn.tree import export_graphviz\n", "from pydotplus import graph_from_dot_data"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The play tennis dataset is available in the repo as `'tennis.csv'`. For this step, we'll start by importing the csv file as a pandas DataFrame."]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [{"data": {"text/html": ["
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"], "text/plain": [" outlook temp humidity windy play\n", "0 sunny hot high False no\n", "1 sunny hot high True no\n", "2 overcast hot high False yes\n", "3 rainy mild high False yes\n", "4 rainy cool normal False yes"]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["# Load the dataset\n", "df = pd.read_csv('tennis.csv')\n", "\n", "df.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Create training and test sets\n", "\n", "Before we do anything we'll want to split our data into **_training_** and **_test_** sets. We'll accomplish this by first splitting the DataFrame into features (`X`) and target (`y`), then passing `X` and `y` to the `train_test_split()` function to create a 70/30 train test split."]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": ["X = df.loc[:, ['outlook', 'temp', 'humidity', 'windy']]\n", "y = df.loc[:, 'play']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Encode categorical data as numbers\n", "\n", "Since all of our data is currently categorical (recall that each column is in string format), we need to encode them as numbers. For this, we'll use a handy helper object from sklearn's `preprocessing` module called `OneHotEncoder`."]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"data": {"text/html": ["
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"], "text/plain": [" outlook_overcast outlook_rainy outlook_sunny temp_cool temp_hot \\\n", "0 0.0 0.0 1.0 1.0 0.0 \n", "1 1.0 0.0 0.0 0.0 1.0 \n", "2 0.0 0.0 1.0 0.0 1.0 \n", "3 0.0 1.0 0.0 0.0 0.0 \n", "4 0.0 1.0 0.0 1.0 0.0 \n", "\n", " temp_mild humidity_high humidity_normal windy_False windy_True \n", "0 0.0 0.0 1.0 1.0 0.0 \n", "1 0.0 1.0 0.0 1.0 0.0 \n", "2 0.0 1.0 0.0 0.0 1.0 \n", "3 1.0 1.0 0.0 0.0 1.0 \n", "4 0.0 0.0 1.0 1.0 0.0 "]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["# One-hot encode the training data and show the resulting DataFrame with proper column names\n", "ohe = OneHotEncoder()\n", "\n", "ohe.fit(X_train)\n", "X_train_ohe = ohe.transform(X_train).toarray()\n", "\n", "# Creating this DataFrame is not necessary its only to show the result of the ohe\n", "ohe_df = pd.DataFrame(X_train_ohe, columns=ohe.get_feature_names(X_train.columns))\n", "\n", "ohe_df.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Train the decision tree \n", "\n", "One awesome feature of scikit-learn is the uniformity of its interfaces for every classifier -- no matter what classifier we're using, we can expect it to have the same important methods such as `.fit()` and `.predict()`. This means that this next part should feel familiar.\n", "\n", "We'll first create an instance of the classifier with any parameter values, and then we'll fit our data to the model using `.fit()`. "]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"text/plain": ["DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n", " max_features=None, 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, presort=False,\n", " random_state=None, splitter='best')"]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create the classifier, fit it on the training data and make predictions on the test set\n", "clf = DecisionTreeClassifier(criterion='entropy')\n", "\n", "clf.fit(X_train_ohe, y_train)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Plot the decision tree \n", "\n", "You can see what rules the tree learned by plotting this decision tree. To do this, you need to use additional packages such as `pytdotplus`. \n", "\n", "> **Note:** If you are run into errors while generating the plot, you probably need to install `python-graphviz` in your machine using `conda install python-graphviz`. "]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"image/png": 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"text/plain": [""]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create DOT data\n", "dot_data = export_graphviz(clf, out_file=None, \n", " feature_names=ohe_df.columns, \n", " class_names=np.unique(y).astype('str'), \n", " filled=True, rounded=True, special_characters=True)\n", "\n", "# Draw graph\n", "graph = graph_from_dot_data(dot_data) \n", "\n", "# Show graph\n", "Image(graph.create_png())"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Evaluate the predictive performance\n", "\n", "Now that we have a trained model, we can generate some predictions, and go on to see how accurate our predictions are. We can use a simple accuracy measure, AUC, a confusion matrix, or all of them. This step is performed in the exactly the same manner, doesn't matter which classifier you are dealing with. "]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Accuracy: 0.6\n"]}], "source": ["X_test_ohe = ohe.transform(X_test)\n", "y_preds = clf.predict(X_test_ohe)\n", "\n", "print('Accuracy: ', accuracy_score(y_test, y_preds))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["##\u00a0Summary \n", "\n", "In this lesson, we looked at how to grow a decision tree using `scikit-learn`. We looked at different stages of data processing, training, and evaluation that you would normally come across while growing a tree or training any other such classifier. We shall now move to a lab, where you will be required to build a tree for a given problem, following the steps shown in this lesson. 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