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‎Assignment 1.ipynb

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‎Assignment 2.ipynb

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‎Assignment 3.ipynb

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‎Assignment 4.ipynb

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‎Classifier Visualization.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"\n",
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"_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
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"\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"source": [
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"# Classifier Visualization Playground\n",
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"\n",
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"The purpose of this notebook is to let you visualize various classsifiers' decision boundaries.\n",
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"\n",
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"The data used in this notebook is based on the [UCI Mushroom Data Set](http://archive.ics.uci.edu/ml/datasets/Mushroom?ref=datanews.io) stored in `mushrooms.csv`. \n",
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"\n",
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"In order to better vizualize the decision boundaries, we'll perform Principal Component Analysis (PCA) on the data to reduce the dimensionality to 2 dimensions. Dimensionality reduction will be covered in a later module of this course.\n",
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"\n",
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"Play around with different models and parameters to see how they affect the classifier's decision boundary and accuracy!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"%matplotlib notebook\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.decomposition import PCA\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"df = pd.read_csv('readonly/mushrooms.csv')\n",
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"df2 = pd.get_dummies(df)\n",
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"\n",
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"df3 = df2.sample(frac=0.08)\n",
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"\n",
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"X = df3.iloc[:,2:]\n",
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"y = df3.iloc[:,1]\n",
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"\n",
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"\n",
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"pca = PCA(n_components=2).fit_transform(X)\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(pca, y, random_state=0)\n",
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"\n",
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"\n",
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"plt.figure(dpi=120)\n",
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"plt.scatter(pca[y.values==0,0], pca[y.values==0,1], alpha=0.5, label='Edible', s=2)\n",
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"plt.scatter(pca[y.values==1,0], pca[y.values==1,1], alpha=0.5, label='Poisonous', s=2)\n",
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"plt.legend()\n",
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"plt.title('Mushroom Data Set\\nFirst Two Principal Components')\n",
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"plt.xlabel('PC1')\n",
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"plt.ylabel('PC2')\n",
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"plt.gca().set_aspect('equal')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def plot_mushroom_boundary(X, y, fitted_model):\n",
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"\n",
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" plt.figure(figsize=(9.8,5), dpi=100)\n",
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" \n",
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" for i, plot_type in enumerate(['Decision Boundary', 'Decision Probabilities']):\n",
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" plt.subplot(1,2,i+1)\n",
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"\n",
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" mesh_step_size = 0.01 # step size in the mesh\n",
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" x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1\n",
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" y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1\n",
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" xx, yy = np.meshgrid(np.arange(x_min, x_max, mesh_step_size), np.arange(y_min, y_max, mesh_step_size))\n",
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" if i == 0:\n",
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" Z = fitted_model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
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" else:\n",
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" try:\n",
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" Z = fitted_model.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:,1]\n",
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" except:\n",
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" plt.text(0.4, 0.5, 'Probabilities Unavailable', horizontalalignment='center',\n",
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" verticalalignment='center', transform = plt.gca().transAxes, fontsize=12)\n",
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" plt.axis('off')\n",
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" break\n",
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" Z = Z.reshape(xx.shape)\n",
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" plt.scatter(X[y.values==0,0], X[y.values==0,1], alpha=0.4, label='Edible', s=5)\n",
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" plt.scatter(X[y.values==1,0], X[y.values==1,1], alpha=0.4, label='Posionous', s=5)\n",
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" plt.imshow(Z, interpolation='nearest', cmap='RdYlBu_r', alpha=0.15, \n",
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" extent=(x_min, x_max, y_min, y_max), origin='lower')\n",
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" plt.title(plot_type + '\\n' + \n",
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" str(fitted_model).split('(')[0]+ ' Test Accuracy: ' + str(np.round(fitted_model.score(X, y), 5)))\n",
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" plt.gca().set_aspect('equal');\n",
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" \n",
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" plt.tight_layout()\n",
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" plt.subplots_adjust(top=0.9, bottom=0.08, wspace=0.02)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true,
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"from sklearn.linear_model import LogisticRegression\n",
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"\n",
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"model = LogisticRegression()\n",
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"model.fit(X_train,y_train)\n",
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"\n",
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"plot_mushroom_boundary(X_test, y_test, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"\n",
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"model = KNeighborsClassifier(n_neighbors=20)\n",
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"model.fit(X_train,y_train)\n",
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"\n",
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"plot_mushroom_boundary(X_test, y_test, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier\n",
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"\n",
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"model = DecisionTreeClassifier(max_depth=3)\n",
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"model.fit(X_train,y_train)\n",
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"\n",
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"plot_mushroom_boundary(X_test, y_test, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier\n",
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"\n",
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"model = DecisionTreeClassifier()\n",
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"model.fit(X_train,y_train)\n",
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"\n",
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"plot_mushroom_boundary(X_test, y_test, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.ensemble import RandomForestClassifier\n",
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"\n",
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"model = RandomForestClassifier()\n",
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"model.fit(X_train,y_train)\n",
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"\n",
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"plot_mushroom_boundary(X_test, y_test, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.svm import SVC\n",
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"\n",
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"model = SVC(kernel='linear')\n",
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"model.fit(X_train,y_train)\n",
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"\n",
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"plot_mushroom_boundary(X_test, y_test, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.svm import SVC\n",
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"\n",
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"model = SVC(kernel='rbf', C=1)\n",
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"model.fit(X_train,y_train)\n",
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"\n",
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"plot_mushroom_boundary(X_test, y_test, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.svm import SVC\n",
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"\n",
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"model = SVC(kernel='rbf', C=10)\n",
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"model.fit(X_train,y_train)\n",
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"\n",
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"plot_mushroom_boundary(X_test, y_test, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.naive_bayes import GaussianNB\n",
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"\n",
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"model = GaussianNB()\n",
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"model.fit(X_train,y_train)\n",
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"\n",
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"plot_mushroom_boundary(X_test, y_test, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"from sklearn.neural_network import MLPClassifier\n",
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"\n",
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"model = MLPClassifier()\n",
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"model.fit(X_train,y_train)\n",
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"\n",
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"plot_mushroom_boundary(X_test, y_test, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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