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July 27, 2014 07:12
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iris data analysis and logistic regression
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{ | |
"metadata": { | |
"name": "", | |
"signature": "sha256:d530423c61cdfb9dba46a494e6710aa60deffd78587afe39ac57f61f17e2405c" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"%pylab inline\n", | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"import pandas as pd\n", | |
"pylab.rcParams['figure.figsize'] = 10, 8" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
"Populating the interactive namespace from numpy and matplotlib\n" | |
] | |
} | |
], | |
"prompt_number": 4 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"In the above lines, we have set inline plots and imported the required libraries." | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 1, | |
"metadata": {}, | |
"source": [ | |
"Dataset" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"We will use [IRIS dataset](http://archive.ics.uci.edu/ml/machine-learning-databases/iris/). It has 150 instances with 4 features:\n", | |
"1. Sepal Length\n", | |
"2. Sepal Width\n", | |
"3. Petal Length\n", | |
"4. Petal Width\n", | |
"\n", | |
"from 3 classes:\n", | |
"* Iris-setosa\n", | |
"* Iris-versicolor\n", | |
"* Iris-virginica" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"iris = pd.read_csv('iris.data', names = ['sepal_length', 'sepal_width',\\\n", | |
" 'petal_length', 'petal_width', 'label'], skipfooter = 1, engine = 'python')" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 5 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"iris.head()" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"html": [ | |
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>sepal_length</th>\n", | |
" <th>sepal_width</th>\n", | |
" <th>petal_length</th>\n", | |
" <th>petal_width</th>\n", | |
" <th>label</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td> 5.1</td>\n", | |
" <td> 3.5</td>\n", | |
" <td> 1.4</td>\n", | |
" <td> 0.2</td>\n", | |
" <td> Iris-setosa</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td> 4.9</td>\n", | |
" <td> 3.0</td>\n", | |
" <td> 1.4</td>\n", | |
" <td> 0.2</td>\n", | |
" <td> Iris-setosa</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td> 4.7</td>\n", | |
" <td> 3.2</td>\n", | |
" <td> 1.3</td>\n", | |
" <td> 0.2</td>\n", | |
" <td> Iris-setosa</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td> 4.6</td>\n", | |
" <td> 3.1</td>\n", | |
" <td> 1.5</td>\n", | |
" <td> 0.2</td>\n", | |
" <td> Iris-setosa</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td> 5.0</td>\n", | |
" <td> 3.6</td>\n", | |
" <td> 1.4</td>\n", | |
" <td> 0.2</td>\n", | |
" <td> Iris-setosa</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 6, | |
"text": [ | |
" sepal_length sepal_width petal_length petal_width label\n", | |
"0 5.1 3.5 1.4 0.2 Iris-setosa\n", | |
"1 4.9 3.0 1.4 0.2 Iris-setosa\n", | |
"2 4.7 3.2 1.3 0.2 Iris-setosa\n", | |
"3 4.6 3.1 1.5 0.2 Iris-setosa\n", | |
"4 5.0 3.6 1.4 0.2 Iris-setosa" | |
] | |
} | |
], | |
"prompt_number": 6 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"number of instances and vairables in dataset:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"iris.shape" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 7, | |
"text": [ | |
"(150, 5)" | |
] | |
} | |
], | |
"prompt_number": 7 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 1, | |
"metadata": {}, | |
"source": [ | |
"Exploratory Data Analysis" | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 3, | |
"metadata": {}, | |
"source": [ | |
"Distribution of each class in dataset[Pie Chart]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"iris.groupby(by = 'label').size().plot(kind = 'pie', figsize = (3.5, 3.5))" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 8, | |
"text": [ | |
"<matplotlib.axes.AxesSubplot at 0x106d0f110>" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
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CCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYS69v8BM3K5\nvxzKIQ0AAAAASUVORK5CYII=\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x103eb88d0>" | |
] | |
} | |
], | |
"prompt_number": 8 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 3, | |
"metadata": {}, | |
"source": [ | |
"Distribution of each feature variable[Density Plot]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"iris.plot(kind = 'kde', figsize = (7, 4))" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 9, | |
"text": [ | |
"<matplotlib.axes.AxesSubplot at 0x106cb9750>" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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cYH/odDqmTp3KyJEj6dKlC127duWll15i4MCBrF69mtmzZ+Pj40PXrl1Zt25d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\nD6RYurIhqiuQAAAAAElFTkSuQmCC\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x106d695d0>" | |
] | |
} | |
], | |
"prompt_number": 9 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 3, | |
"metadata": {}, | |
"source": [ | |
"Aggregrates and summary Statistics:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"iris.groupby('label').aggregate([numpy.mean, np.median, np.std])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"html": [ | |
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <th></th>\n", | |
" <th colspan=\"3\" halign=\"left\">sepal_length</th>\n", | |
" <th colspan=\"3\" halign=\"left\">sepal_width</th>\n", | |
" <th colspan=\"3\" halign=\"left\">petal_length</th>\n", | |
" <th colspan=\"3\" halign=\"left\">petal_width</th>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th></th>\n", | |
" <th>mean</th>\n", | |
" <th>median</th>\n", | |
" <th>std</th>\n", | |
" <th>mean</th>\n", | |
" <th>median</th>\n", | |
" <th>std</th>\n", | |
" <th>mean</th>\n", | |
" <th>median</th>\n", | |
" <th>std</th>\n", | |
" <th>mean</th>\n", | |
" <th>median</th>\n", | |
" <th>std</th>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>label</th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>Iris-setosa</th>\n", | |
" <td> 5.006</td>\n", | |
" <td> 5.0</td>\n", | |
" <td> 0.352490</td>\n", | |
" <td> 3.418</td>\n", | |
" <td> 3.4</td>\n", | |
" <td> 0.381024</td>\n", | |
" <td> 1.464</td>\n", | |
" <td> 1.50</td>\n", | |
" <td> 0.173511</td>\n", | |
" <td> 0.244</td>\n", | |
" <td> 0.2</td>\n", | |
" <td> 0.107210</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>Iris-versicolor</th>\n", | |
" <td> 5.936</td>\n", | |
" <td> 5.9</td>\n", | |
" <td> 0.516171</td>\n", | |
" <td> 2.770</td>\n", | |
" <td> 2.8</td>\n", | |
" <td> 0.313798</td>\n", | |
" <td> 4.260</td>\n", | |
" <td> 4.35</td>\n", | |
" <td> 0.469911</td>\n", | |
" <td> 1.326</td>\n", | |
" <td> 1.3</td>\n", | |
" <td> 0.197753</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>Iris-virginica</th>\n", | |
" <td> 6.588</td>\n", | |
" <td> 6.5</td>\n", | |
" <td> 0.635880</td>\n", | |
" <td> 2.974</td>\n", | |
" <td> 3.0</td>\n", | |
" <td> 0.322497</td>\n", | |
" <td> 5.552</td>\n", | |
" <td> 5.55</td>\n", | |
" <td> 0.551895</td>\n", | |
" <td> 2.026</td>\n", | |
" <td> 2.0</td>\n", | |
" <td> 0.274650</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 21, | |
"text": [ | |
" sepal_length sepal_width \\\n", | |
" mean median std mean median \n", | |
"label \n", | |
"Iris-setosa 5.006 5.0 0.352490 3.418 3.4 \n", | |
"Iris-versicolor 5.936 5.9 0.516171 2.770 2.8 \n", | |
"Iris-virginica 6.588 6.5 0.635880 2.974 3.0 \n", | |
"\n", | |
" petal_length petal_width \\\n", | |
" std mean median std mean \n", | |
"label \n", | |
"Iris-setosa 0.381024 1.464 1.50 0.173511 0.244 \n", | |
"Iris-versicolor 0.313798 4.260 4.35 0.469911 1.326 \n", | |
"Iris-virginica 0.322497 5.552 5.55 0.551895 2.026 \n", | |
"\n", | |
" \n", | |
" median std \n", | |
"label \n", | |
"Iris-setosa 0.2 0.107210 \n", | |
"Iris-versicolor 1.3 0.197753 \n", | |
"Iris-virginica 2.0 0.274650 " | |
] | |
} | |
], | |
"prompt_number": 21 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"iris.groupby('label').describe() displays all aggregration summary." | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 3, | |
"metadata": {}, | |
"source": [ | |
"Barplots of aggregration" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"groups = iris.groupby(by = 'label')\n", | |
"means, sds = groups.mean(), groups.std()\n", | |
"means.plot(yerr = sds, kind = 'bar', figsize = (9, 5), table = True)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 11, | |
"text": [ | |
"<matplotlib.axes.AxesSubplot at 0x106e14650>" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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aWopTP/C5/t4SBQQEs2zZ+26L1263e837pXbFts/La9vL2Mbt8So/fa+dy0HZ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\nxhhjjDHGGGOMMcYYY4wxxszn/wMwvGYRHhjAVwAAAABJRU5ErkJggg==\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x108037b50>" | |
] | |
} | |
], | |
"prompt_number": 11 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 3, | |
"metadata": {}, | |
"source": [ | |
"Boxplots" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"iris.boxplot(by = \"label\", figsize = (10, 6))" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 12, | |
"text": [ | |
"array([[<matplotlib.axes.AxesSubplot object at 0x106e14810>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x1083f5b10>],\n", | |
" [<matplotlib.axes.AxesSubplot object at 0x1084767d0>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x1084da550>]], dtype=object)" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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7wknd0lGtfIfpy4QR+L4nsXtC+wJ75lzdDjw6V/4gYSRPFeS0YH1dQWgINhEa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\nSZIkSZIkSen5/5sb9lryuLEcAAAAAElFTkSuQmCC\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x108089f90>" | |
] | |
} | |
], | |
"prompt_number": 12 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 3, | |
"metadata": {}, | |
"source": [ | |
"Feature Plot:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"col_map = {'Iris-setosa': 'olive', 'Iris-versicolor': 'navy', 'Iris-virginica': 'magenta'}\n", | |
"pd.tools.plotting.scatter_matrix(iris.loc[:, 'sepal_length':'petal_width']\n", | |
", diagonal = 'kde', color = [col_map[lb] for lb in iris.label], s = 75)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 13, | |
"text": [ | |
"array([[<matplotlib.axes.AxesSubplot object at 0x108483a90>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x10898c890>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x108a0f750>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x108a78a50>],\n", | |
" [<matplotlib.axes.AxesSubplot object at 0x108af8b50>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x108b5d610>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x108bdf650>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x108c60410>],\n", | |
" [<matplotlib.axes.AxesSubplot object at 0x108ccc1d0>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x108d40f50>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x108db3110>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x108e31f90>],\n", | |
" [<matplotlib.axes.AxesSubplot object at 0x108e89ad0>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x108f14d90>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x108f4ef10>,\n", | |
" <matplotlib.axes.AxesSubplot object at 0x109005790>]], dtype=object)" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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KOIxpICLEE7sqiMZrErJRMEffjwXbhZFxnIQs0JMH7/drCJs21fLWW5vx+UKc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A5UNUZGRjGKEsXpLCEzcxpms5PKynv6FXBOM4ooCENsKDVk+qC/uaWlZQePPHIxqqog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qF2aqqZYFAEZQaDDEhJ88NoVKQMSUk5Nl2QLEvY7YZOXRmdiZ9VLBZtNmyokZFhIRIRckqR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\n+Uvf133//ZCbC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CSi+P2NLFjwHdLSynpVr0bfCGqDwTaWL/8WRqMDo9GOx1NDfv45nHPO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\nGoPBjKrGaG3d26Pr6Uv68z3R2tz/9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A6JB0WWmqDy9wH+hlY6GeVFudUZwzDKm3oqT3+u\ny2EeMSPgZuSe7o1GRU+XtDSGYRiGD8xzVOZUTbBbF6IRODeWtphMTTDDiNKI1RnDMPwzD9gllnYh\n4YpMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzAMwzCM4vM/YqCjrq6C4AYAAAAASUVO\nRK5CYII=\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x108543fd0>" | |
] | |
} | |
], | |
"prompt_number": 13 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 1, | |
"metadata": {}, | |
"source": [ | |
"Machine Learning" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Let us divide the dataset into training (70%) and test set(30%). We will use trainset to build our model, and test set to evaluate the perfromance." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from random import sample\n", | |
"idx = sample(iris.index, int(iris.shape[0] * 0.7))\n", | |
"iris_train, iris_test = iris.ix[idx], iris.drop(idx)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 14 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 3, | |
"metadata": {}, | |
"source": [ | |
"Notations:" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"We will denote the $j^{th}$ predictor variables or features of $i^{th}$ instance with $X_j^{(i)} \\in \\mathbb R$, number of data instances as $m$, instance of all predictor variables with $X^{(i)} \\in \\mathbb R^n \\ for\\ i = 1..m$, where $n$ is number of dimensions.\n", | |
"and $X \\in \\mathbb R^{m * n}$ denotes whole training set of all instances and features. The target variable is denoted by $y$." | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Preprocessing (Normalize)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Normalize the dataset so that it has zero mean and unit variance.\n", | |
"$$ X_j^{(i)} = \\frac{ X_j^{(i)} - \\mu_j }{\\sigma_j} $$\n", | |
"This is available in **sklearn.preprocessing**." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"X_train = np.array(iris_train.as_matrix()[:, :4], dtype = np.float16)\n", | |
"X_test = np.array(iris_test.as_matrix()[:, :4], dtype = np.float16)\n", | |
"from sklearn.preprocessing import StandardScaler\n", | |
"scaler = StandardScaler()\n", | |
"X_train = scaler.fit_transform(X_train)\n", | |
"np.mean(X_train), np.std(X_train)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 15, | |
"text": [ | |
"(0.0016661, 1.0)" | |
] | |
} | |
], | |
"prompt_number": 15 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Dimensionality Reduction (PCA)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"We will perform PCA to reduce dimensions to 2, so that it will assist visualization. PCA is available in **sklearn.decomposition**." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from sklearn.decomposition import PCA\n", | |
"pca = PCA()\n", | |
"X_train = pca.fit_transform(X_train)\n", | |
"ax = plt.figure(figsize = (5, 3))\n", | |
"plt.plot([1, 2, 3, 4], np.cumsum(pca.explained_variance_ratio_), '-rd')\n", | |
"plt.xlabel(\"No. of Principal Components\")\n", | |
"plt.ylabel(\"Cumulative variance\")" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 16, | |
"text": [ | |
"<matplotlib.text.Text at 0x10a057d90>" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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| |
"text": [ | |
"<matplotlib.figure.Figure at 0x10a028990>" | |
] | |
} | |
], | |
"prompt_number": 16 | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"From the plot, it is clear that just two principal components contains more than $95\\%$ information. So, let us just drop the other components safely and see how it looks." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"plt.figure(figsize = (5, 4))\n", | |
"plt.scatter(X_train[:, 0], X_train[:, 1], c = [col_map[lb] for lb in iris.label.ix[idx]], s = 75, alpha = 0.4)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "pyout", | |
"prompt_number": 17, | |
"text": [ | |
"<matplotlib.collections.PathCollection at 0x10a1d8910>" | |
] | |
}, | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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W1khnZx96vUQ4DFNT4PXGSE9vISvLhlKpYHT0FK+9dpKiok0MD4fIyZEXvI+c\nzim2bl3+dJ/7IsAlEgm6TnXxQtoLC27XqXUUJgrp7Ohkzdo1t3y9ktISzpvOM+QZItM2t6E/EA7Q\nFG1iz8b5Yy0lSeKRlx7hw3/7kPHBcQp0BZiNZnp8PVxyX6LsqbLZebaKiovQfV/H+WPnOdF2Ao2k\nIaKNUPxwMU9sfmLOakaJRALXgAtpWMIqWWnta6X7bDebH9s8r2fsWtRqJcHg3MddjUZHUVEhbW1t\n6HTZc9pk4vEYo6OXef75baIGt4zi8TgNDfWcO3ccSQoxPR3B6Rynr68HjSaGw2EmMzMbvT6ZjIxS\n1q6tRqPRcOjQb6iqss6p1SUSMmNjnbz++v9ArbYyOtrLzp3rsFotn25P0N4+hEaTO5vcfS2hUIju\n7m4ikQgWi4X8/PwlTS+5LwJcNBpFCkuLLu4CYFVa8fuWZjogrVbLg998kD/8/A8U9RdRaCtEpVDR\nP9XP5cRl1j63lvT09AWPDYVC6Kw6zl8+z6mxU4QSITLXZ7L/O/spLCycs292djbZ/y6bUChELBbD\nYDDMuzkGBwc5+uOj7NDtIDktmUgsgkFjYGR8hCM/PsKeP91z3R0Na9eW8cEHrZjNc9NKysqqCIfr\nuHjxJGlpq5mYGGJ62ks8Ps6+fWupqlp7Az89YSklEgnef/+3jI2dY/XqDGRZR0PDcRKJFuz2BDk5\nueTm6nE6+z9NtE7Q1WUiL68IozFEcvLVHv2enmHOnDlNWppEdbUKSTIzPDzNj3/8HuvWVZKc7MDr\nhby81Tz88FeumTwvyzInT57g7NkjmM0xNBoIBCCRsPDII88tWS7kfRHg1Go1sk5edAZcAE/Mg8W8\ndLPVZmZm8sz/8gytza2cvHiSRCxBamUqj1Q9sujjmtPppOanNWzVbOWp9U+hUCiIxqO0jLRw6ren\nSP5e8mzj/eddaw3UugN1lEfLqR04Q+uwE0lSo5CibCyoYH3Seuo/qOeZ7z1zXe9p9epV1NScxe0e\nw27/fCrLzNTWVmsQj6cFr/cK69aV8fTT+0XS7jLr6upicPAcmzblA3DqVAPgIi9Pi8lk4MKFcXJz\nHVRWJnH58hAaTTK9vY14PG5SPpdD6fUGOHPmDKtWGTEaNUSjcQYGvDz33COMjrqorR1g9+4nKS4u\nxmazLVyYz6mrq6Wp6UM2bcpBo7kaCD0ePwcOvMJzz33/hnv4F3JfBDiFQkHRliIu11xmQ878WUJC\nkRDdqm7klbuhAAAgAElEQVSeK3luSa9rNBqp3lxN9eb50xn9MVmWqX2nlgdND5Jhvdo+p1aqWZO1\nBmlIouFoA/u+uu+6rz82Nsb45XGaewfRKgvItm9HISmIxiPUd3dzZaSHpICOiYmJ62ojMxqNfPvb\nz/DLX/6O3l4nen0y8XiMpqbjeL0xtm9/loyMXGKxCOPjfbz66kG++93nyMiY394o3BlNTQ3k5pqR\nJAmPx0047CaR8GOx6FEqJdLSFAwMuLHbM1mxwkpLSycrV1bgdA5itapIJBLE4wna2/tJSQGj8bMJ\nGq6mlaSlOaioCBIOh64ruE1PT3P69B/mBTcAm83EihXT1NYe4YUXvnHL7/++aRhZu2kt7fZ2moea\niX9uOiBP0MOhgUOsfHTlnEHsd9rg4CD6cT0Z1gwisQjto+1cHLhIz0QP8UScirQKRs6P3NCCzn6/\nn6a2TszaclLM2SikmY9brdSQYy/DEzTT7xyal8R5Lenp6fz1X3+HnTvzkKQuxsZOodFoefrpb5GV\nNdPZodHoyMoqRaMp5rXX3iORSNzwz0NYGpOTo1itM6lAPp8fnW5mgtLP2kpNJiWBwMxSmQaDGlkO\nI8syarWOmpqLvPnmQd5++yAff1xDLBb8dOlA8HgCpKRcrWGlplro7V14Dd8/1tvbi9kcnzN55udl\nZiYxNNROIBC46ff9mfuiBgdgMpl44rtPUHuolgvNF0iRUpiWp/Fb/az52hoq1y60nMSd4/V6SVYk\nc773PBcvXSQrloVJMuGUnZzUn2Truq1YsOD3+xcMxNFolCutV2g/1c6YcwyPz4M75uayc4AVFUpk\nZKQ/mnghw1LIsYHjN9So63K5+PWvf8voaAyVKpeurknicaip+ZDNm3fOaZ+z21Pp6+ult7dXPKou\nE4PBTCg0hdGo+3QQPWi1OiKRKFqtmnA4MSfQJBIyg4MuxsdHUConKS01YTRq8XgGmJhwEo0GKSjI\nYWoqQWnp1cliZVm+7o6kcDjMtea2mJn8QiISidxyVsN9E+AALBYLj73wGN5HvbjdbtRqNenp6XdF\nD59Wq6VpoImM4QyetT2LUXX1g52YnuBQ7SHcRW52a3fPOzYcDvPBqx9gbDeS7konpS8FU8JEo7uR\nMxN9NF9qwp6WREVuBUrpajDzRwIkTKrrrrmGw2F+/vO3mJ5OIy8vh0gkglLZS0pKMX7/GHV1x9i9\n+zE0mqvTlEuShdHRMRHglsnq1Zs4ffo3JCdbcTgctLaCw5GCy9WHVqtmdDRGefnMl5LLFUSrNXPx\nYhfPPbcdyKSm5gPicS+RSITR0QCBgBeXK87evV/B/Lk26+HhKSoqrm9ZSavVSjB4rZETUWIx5ZKk\nbC3/b/YysFgs5OXlkZmZeVcEN4CMjAw6+jvYYdwxJ7gBJOuSqUxUMu4bX7CToe5IHeld6VSoKjD1\nm9iavJV1aevYn7WfB407kENTMA6dQ52zx0yFpmgNXiGzKO26J/dsa2vD5VKSkjIzMFuhUCBJMrIs\nYzKlEgoZGRrqmXOMLMdRq++r79G7Snl5OUplFu3tg+h0OlJT8wgGZZRKC+fOTaBUmklJMRIOx7h0\nyY3LpaSwMBujUUN7+3lWrcpizZoSyssziET0mEw6gkEfJtPVe3Ry0ovXq2PlysWXqfy83NxcJMnB\nxMTUgtu7u0dYtWrzkkw6e3f8dgsMDw9TlV9Ff7CfcGzuuE1f2EdIESLDnMHU1NybIhQK0d/Qz7qM\ndQy2DlJkKUIlzQQUh8HBKmsxJp2EUitx2XmZ5olmzrnO065uJ2mlmerq0uteGvH8+StYLFfz5lQq\nFWlpDgKBmfGuen0aTmf/7PaZ8Y2uu2KF8/uVRqPhhRe+hVpdQm2tk0TCzuCgjsZGiUgkF6XSyCef\n9HPgwCA220b27n2OwsJ0Wlpmxpo6HDM1v9LSYvbs2YDPZ8PtDlJbe4rRUTcXL/bT1hbm6ae/dd01\nLoVCweOPP09rq4/+/tHZdr3p6QgtLf1MT6ewZcsDS/L+xVfrXWJ6eprCjEKS05JpvNyINWpFh44A\nAYL6IMUPFDMSGpk3aH1ycpJkOZloJIp6Wo3RfvUmUyvVVGYWUdd+Bl2OnhWhFSjLlORm5SJJcaam\nLvPgg9ffcxyNRlEq5zaelJQUcPz4eXQ6IwqFanapRVmW6e+/xJo1eWIUwzIzGo08++xLTE5OMjo6\nytat0qejGJz4/VPodGbKyyvIzs6mubmZY8eOEYv5MJnmDgNMSTGyY0cZhw7109ERIi/PwerVe6mo\nWHnNVKWF5OTk8LWv/Tl1dTXU1l5GpYJEQkVl5Q42b962ZCt1iQB3l7BYLHTIHWzP305mdiaTk5NE\nY1Gs2plv0LgcxxvyzqttKRQK4nIcOSGjWKBCXpBcQLE3jUuhZnxhCUPIisfjx2qV+c53nryhXKP8\n/ExOnBjGYrl64zscDjZtqqCxsYWpKTe5uRoGBzuJxcYoL0/nySfFdEh3i6SkJJKSkmb/v9BIg/z8\nfMbGoiQnL9xG5vWGqagowWbL48EHv3JLKUDp6ek8/fTXCIVCRCIRDAbDks+sLQLcXSI7O5valNrZ\n4V1/vPDzpaFLZKzLmPdNmZqaikfvIa6ME1KFCMfCaFVXh2mF42HUyWr+bvf/xo+v/JiNL1aTm5tL\nXl7eDbc/VlWt5tixC0xP56LR6FAoZnplMzMzsFrNNDcfYvfuMjIz0ygr274kiZrCnWU2mykv30Jt\nbRNZWfbZdJJAIEJX1wRtbVOsW5fK2NgUfX199Pf3odPpKSwsvOl1hPV6/Q3XAK+XWHTmLvLZSIYt\n6i2sSF4xZyRDs7mZ/d/bv+DSfWdOnWH8vXGKwkXIV2RKkkqQkEjICY64jmAoN2C2mOkr7uOJl564\n6fJ1d3fzy1/+hsOHL6JSZZGfX0hxcT6SFMHv72X//k1s2bL5Vn4Ewm2SSCQYHx8nFotht9uv2XMe\niUT4j//xeyiVg2RmahgcdDEwMIbJpKCoKJvR0QAtLR42bVrLqlXFRCLg8ShZv34PDzyw844t3HQ9\ni86IAHeXGRkZ4eyRs7haXRgkA36Fn+z12WzctXHB4AYz7V01H9UwdHwIdbsam8uGUWWkV9mLJkND\nUkYSoxmjPPGdJ677WzYej9PR0cGVuiv4x/0MTgzSPjFB/oqtqFRqOjuv0NPTTjjs4aGH1vHNb74w\nOxHA58ViMXw+H2q1+qa/4YVbc/FiE3V1fyAe96BUSoRCMiUl69m166FF27ouX77EgQM/IxAYY2Sk\ng7Vrk0hLs+F0ejh9uoNdu8oIBmNkZq6kuLiUaDRGY2M/a9c+zrZtS9NB8EVEgPsSCwQChMNhjEbj\nnJlBrmV0dJTWC610XupkzDmG2WAmLTON4i3FVKyquO7zxGIxDr11CMVFBautq1HFVfz+aC0TqjC9\njkFKtmxFpzMgyzLRaJiRkTP89V+/REpKyuw5ZtZMPcXJkxeJRCRkOU5+fgoPPrhN9KreQQ0N9TQ0\nvMuqVWlYrTPBLBaL09k5TDicxte//p1FHw+bmy/yj//4N5SWRrBatUQiMs3No1RVpZCTk0wsFqe3\n18/OnY+gUqkJh6OcPj3B97/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PfJoHFx/DttLGVx77yqLrQSxErVZTUVFxU7P22u12bDY1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\nTkSuQmCC\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x108955cd0>" | |
] | |
} | |
], | |
"prompt_number": 17 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 2, | |
"metadata": {}, | |
"source": [ | |
"Model #1: Logistic Regression" | |
] | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 3, | |
"metadata": {}, | |
"source": [ | |
"Learning" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Logistic regression and many others models are available in **scikit-learn**. The below example is brought from [here](http://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html)." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from sklearn import linear_model\n", | |
"h = .02 # step size in the mesh\n", | |
"\n", | |
"logreg = linear_model.LogisticRegression(C=1e5)\n", | |
"\n", | |
"# we create an instance of Neighbours Classifier and fit the data.\n", | |
"logreg.fit(X_train[:, :2], np.array(iris.ix[idx].label, dtype = np.str))\n", | |
"\n", | |
"# Plot the decision boundary. For that, we will assign a color to each\n", | |
"# point in the mesh [x_min, m_max]x[y_min, y_max].\n", | |
"x_min, x_max = X_train[:, 0].min() - .5, X_train[:, 0].max() + .5\n", | |
"y_min, y_max = X_train[:, 1].min() - .5, X_train[:, 1].max() + .5\n", | |
"xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", | |
"Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n", | |
"\n", | |
"# Put the result into a color plot\n", | |
"encoder = {'Iris-setosa': 1, 'Iris-versicolor': 2, 'Iris-virginica': 3}\n", | |
"Z = np.array([encoder[z] for z in Z]).reshape(xx.shape)\n", | |
"plt.figure(figsize=(4, 3))\n", | |
"plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n", | |
"\n", | |
"# Plot also the training points\n", | |
"plt.scatter(X_train[:, 0], X_train[:, 1], c= [col_map[lb] for lb in iris.label.ix[idx]], edgecolors='k', cmap=plt.cm.Paired)\n", | |
"plt.xlabel('comp 1')\n", | |
"plt.ylabel('comp 2')\n", | |
"\n", | |
"plt.xlim(xx.min(), xx.max())\n", | |
"plt.ylim(yy.min(), yy.max())\n", | |
"plt.xticks(())\n", | |
"plt.yticks(())\n", | |
"\n", | |
"plt.show()" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"metadata": {}, | |
"output_type": "display_data", | |
"png": 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scjRwCMjAQwtW+V7gTKm0hkfaJTOhR83baaamJvzlKszLIS87i5AGkRg8So+k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\n", | |
"text": [ | |
"<matplotlib.figure.Figure at 0x10a1e6f90>" | |
] | |
} | |
], | |
"prompt_number": 18 | |
}, | |
{ | |
"cell_type": "heading", | |
"level": 3, | |
"metadata": {}, | |
"source": [ | |
"Evaluation" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"We have fitted our model with training set, now let us check how well it does for test-set. First, let us scale and perform PCA with our fitted model for them with training set. We **should not** scale or perform PCA with test set, but rather use their models from train set to scale and perform PCA." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"X_test = pca.transform(scaler.transform(X_test))\n", | |
"y_pred = logreg.predict(X_test[:, :2])" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [], | |
"prompt_number": 19 | |
}, | |
{ | |
"cell_type": "code", | |
"collapsed": false, | |
"input": [ | |
"from sklearn import metrics\n", | |
"print metrics.classification_report(\\\n", | |
" np.array(iris.label.drop(idx).as_matrix(), dtype = np.str), y_pred)" | |
], | |
"language": "python", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"stream": "stdout", | |
"text": [ | |
" precision recall f1-score support\n", | |
"\n", | |
"Iris-setosa 1.00 1.00 1.00 13\n", | |
"Iris-versicolor 0.92 0.69 0.79 16\n", | |
"Iris-virginica 0.75 0.94 0.83 16\n", | |
"\n", | |
"avg / total 0.88 0.87 0.86 45\n", | |
"\n" | |
] | |
} | |
], | |
"prompt_number": 20 | |
} | |
], | |
"metadata": {} | |
} | |
] | |
} |
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