diff --git a/README.md b/README.md index 77738e1..9f9fbf6 100644 --- a/README.md +++ b/README.md @@ -21,14 +21,12 @@ In order to prepare data, train, evaluate, and visualize a decision tree, we wil ```python import numpy as np import pandas as pd +import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score -from sklearn.tree import export_graphviz from sklearn.preprocessing import OneHotEncoder -from IPython.display import Image -from sklearn.tree import export_graphviz -from pydotplus import graph_from_dot_data +from sklearn import tree ``` 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. @@ -71,7 +69,7 @@ df.head() - 0 + 0 sunny hot high @@ -79,7 +77,7 @@ df.head() no - 1 + 1 sunny hot high @@ -87,7 +85,7 @@ df.head() no - 2 + 2 overcast hot high @@ -95,7 +93,7 @@ df.head() yes - 3 + 3 rainy mild high @@ -103,7 +101,7 @@ df.head() yes - 4 + 4 rainy cool normal @@ -122,8 +120,8 @@ Before we do anything we'll want to split our data into **_training_** and **_te ```python -X = df.loc[:, ['outlook', 'temp', 'humidity', 'windy']] -y = df.loc[:, 'play'] +X = df[['outlook', 'temp', 'humidity', 'windy']] +y = df[['play']] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42) ``` @@ -181,7 +179,7 @@ ohe_df.head() - 0 + 0 0.0 0.0 1.0 @@ -194,7 +192,7 @@ ohe_df.head() 0.0 - 1 + 1 1.0 0.0 0.0 @@ -207,7 +205,7 @@ ohe_df.head() 0.0 - 2 + 2 0.0 0.0 1.0 @@ -220,7 +218,7 @@ ohe_df.head() 1.0 - 3 + 3 0.0 1.0 0.0 @@ -233,7 +231,7 @@ ohe_df.head() 1.0 - 4 + 4 0.0 1.0 0.0 @@ -268,43 +266,33 @@ clf.fit(X_train_ohe, y_train) - DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None, - max_features=None, max_leaf_nodes=None, + DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy', + max_depth=None, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, - min_weight_fraction_leaf=0.0, presort=False, + min_weight_fraction_leaf=0.0, presort='deprecated', random_state=None, splitter='best') ## Plot the decision tree -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`. - -> **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`. +You can see what rules the tree learned by plotting this decision tree, using matplotlib and sklearn's `plot_tree` function. ```python -# Create DOT data -dot_data = export_graphviz(clf, out_file=None, - feature_names=ohe_df.columns, - class_names=np.unique(y).astype('str'), - filled=True, rounded=True, special_characters=True) - -# Draw graph -graph = graph_from_dot_data(dot_data) - -# Show graph -Image(graph.create_png()) +fig, axes = plt.subplots(nrows = 1,ncols = 1, figsize = (3,3), dpi=300) +tree.plot_tree(clf, + feature_names = ohe_df.columns, + class_names=np.unique(y).astype('str'), + filled = True) +plt.show() ``` - - ![png](index_files/index_11_0.png) - ## Evaluate the predictive performance 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, so it doesn't matter which classifier you are dealing with. diff --git a/index.ipynb b/index.ipynb index ef8f71b..f57f4c5 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 split the data so that 70% of it is in the training set, and 30% of it is in the testing set."]}, {"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 we have, 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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\n", "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, so it 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": "3.7.4"}}, "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", "import matplotlib.pyplot as plt\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.preprocessing import OneHotEncoder\n", "from sklearn import tree"]}, {"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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outlooktemphumiditywindyplay
0sunnyhothighFalseno
1sunnyhothighTrueno
2overcasthothighFalseyes
3rainymildhighFalseyes
4rainycoolnormalFalseyes
\n", "
"], "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 split the data so that 70% of it is in the training set, and 30% of it is in the testing set."]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": ["X = df[['outlook', 'temp', 'humidity', 'windy']]\n", "y = df[['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": 7, "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": 7, "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 we have, and then we'll fit our data to the model using `.fit()`. "]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"data": {"text/plain": ["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n", " max_depth=None, 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='deprecated',\n", " random_state=None, splitter='best')"]}, "execution_count": 8, "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, using matplotlib and sklearn's `plot_tree` function."]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", 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"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, axes = plt.subplots(nrows = 1,ncols = 1, figsize = (3,3), dpi=300)\n", "tree.plot_tree(clf,\n", " feature_names = ohe_df.columns, \n", " class_names=np.unique(y).astype('str'),\n", " filled = True)\n", "plt.show()"]}, {"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, so it doesn't matter which classifier you are dealing with. "]}, {"cell_type": "code", "execution_count": 13, "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": "3.6.9"}}, "nbformat": 4, "nbformat_minor": 2} \ No newline at end of file diff --git a/index_files/index_11_0.png b/index_files/index_11_0.png index a048e3a..189cb8d 100644 Binary files a/index_files/index_11_0.png and b/index_files/index_11_0.png differ