{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prepare the Data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import addutils.toc ; addutils.toc.js(ipy_notebook=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data in scikit-learn, with very few exceptions, is assumed to be stored as a\n", "**two-dimensional array**, of size `[n_samples, n_features]`.\n", "\n", "Most machine learning algorithms implemented in scikit-learn expect data to be stored in a\n", "**two-dimensional array or matrix**. The arrays can be\n", "either ``numpy`` arrays, pandas ``DataFrame``, or in some cases ``scipy.sparse`` matrices.\n", "The size of the array is expected to be `[n_samples, n_features]`\n", "\n", "The number of features must be fixed in advance. However it can be very high dimensional\n", "(e.g. millions of features) with most of them being zeros for a given sample. This is a case\n", "where `scipy.sparse` matrices can be useful, in that they are\n", "much more memory-efficient than numpy arrays." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import scipy.io\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn import datasets\n", "from addutils import css_notebook\n", "css_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 Datasets available in scikit-learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Typical *scikit-learn* dataset are dictionary-like object that holds all the data and metadata.\n", "\n", "* **Features** are usually stored in the `.data` field in the form of a 2D array `[n_samples, n_features]`.\n", "* **Explanatory variables (targets)** are usually stored in the `.target` field in the form of a 1D array.\n", "\n", "Scikit-learn makes available a host of datasets for testing learning algorithms:\n", "\n", "- **Packaged Data:** these small datasets can be downloaded with ``sklearn.datasets.load_*``\n", "- **Downloadable Data:** larger datasets that can be fetched from the web with ``sklearn.datasets.fetch_*``\n", "- **Generated Data:** can be created with `sklearn.datasets.make_*`\n", "\n", "Try by yourself:\n", "\n", "* `datasets.load_`\n", "* `datasets.fetch_`\n", "* `datasets.make_`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#datasets.make_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1 Example: the \"Iris\" Packaged Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Features in the Iris dataset:\n", "\n", " 1. sepal length in cm\n", " 2. sepal width in cm\n", " 3. petal length in cm\n", " 4. petal width in cm\n", "\n", "- Target classes to predict:\n", "\n", " 1. Iris Setosa\n", " 2. Iris Versicolour\n", " 3. Iris Virginica" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d = datasets.load_iris()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try by yourself one of the following commands where *'d'* is the variable containing the dataset:\n", "\n", " print d.keys() # Structure of the contained data\n", " print d.DESCR # A complete description of the dataset\n", " print d.data.shape # [n_samples, n_features]\n", " print d.target.shape # [n_samples,]\n", " print d.feature_names\n", " datasets.get_data_home() # This is where the datasets are stored" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['target_names', 'DESCR', 'data', 'feature_names', 'target'])\n", "['setosa' 'versicolor' 'virginica']\n", "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n" ] } ], "source": [ "print(d.keys())\n", "print(d.target_names)\n", "print(d.feature_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Example: the \"Digits\" Packaged Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *Digits* contains 1797 samples made of 64 features: each feature represents the grey-scale value of a 8x8 digit image:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ " \n", "\n", "\n", " \n", "\n", "
\n", " \n", " BokehJS successfully loaded.\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import bokeh.plotting as bk\n", "bk.output_notebook()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bokeh.palettes import Greys9\n", "from bokeh.models.ranges import Range1d\n", "import addutils.palette as pal\n", "import addutils.imagegrid as ig\n", "\n", "digits = datasets.load_digits()\n", "\n", "# plot the digits: each image is 8x8 pixels\n", "images = [ digits.images[i][::-1, :] for i in range(40) ]\n", "txt = [ str(i) for i in range(10) ] * 4\n", "\n", "fig = ig.imagegrid_figure(figure_plot_width=760, figure_plot_height=100,\n", " figure_title=None,\n", " images=images, grid_size=(20, 2), \n", " text=txt, text_font_size='9pt', text_color='red',\n", " palette=Greys9[::-1], padding=0.2)\n", "bk.show(fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3 Example: the \"Blob\" Generated Dataset" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import seaborn as sns\n", "cat_colors = list(map(pal.to_hex, sns.color_palette('Paired', 7)))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data, color_indices = datasets.make_blobs(n_samples=2000, n_features=2, centers=7,\n", " center_box=(-4.0, 6.0), cluster_std=0.5)\n", "\n", "fig = bk.figure(title=None)\n", "fig.circle(data[:,0], data[:,1],\n", " line_color='black', line_alpha=0.5, size=8,\n", " fill_color=pal.linear_map(color_indices, cat_colors,\n", " low=0, high=6))\n", "bk.show(fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 Using pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although it is not required, in many cases it can be easier to manage the data pre-processing with **pandas**:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)y
05.13.51.40.20
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" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) y\n", "0 5.1 3.5 1.4 0.2 0\n", "1 4.9 3.0 1.4 0.2 0\n", "2 4.7 3.2 1.3 0.2 0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.options.display.notebook_repr_html = True\n", "df = pd.DataFrame(d.data, columns=d.feature_names)\n", "df['y'] = d.target\n", "df.head(3)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)
mean5.843.053.761.20
std0.830.431.760.76
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" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", "mean 5.84 3.05 3.76 1.20\n", "std 0.83 0.43 1.76 0.76\n", "min 4.30 2.00 1.00 0.10\n", "max 7.90 4.40 6.90 2.50" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.set_option('precision',3)\n", "df[df.columns[:4]].describe().ix[[1,2,3,7]]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_feat, y_feat = 2, 3 # Choose the features to plot (0-3)\n", "fig = bk.figure(title=None)\n", "colors = ['#006CD1', '#26D100', '#D10000']\n", "color_series = [ colors[i] for i in df['y'] ]\n", "fig.scatter(df[df.columns[x_feat]], df[df.columns[y_feat]], \n", " line_color='black', fill_color=color_series,\n", " radius=0.1)\n", "fig.xaxis.axis_label = df.columns[x_feat]\n", "fig.yaxis.axis_label = df.columns[y_feat]\n", "\n", "bk.show(fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas contains some utility functions and plotting functions that can help in previewing the data. In this case we use a `scatter_matrix` pandas plot to plot the four features one versus the other:\n", "\n", "**TODO -** Check if it's possible to add class colors to the following scatter matrix with the property `group_col`." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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i4oiLC2V+aWpqpK6uFo/HE547NRoNVVVnOX++MXxOkiSams5HbK1HY7jX2xlW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\n8M6YddGladru6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "pd.tools.plotting.scatter_matrix(df[df.columns[:4]], figsize=(10, 10),\n", " c=df['y'], diagonal='hist', marker='o')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3 Working with MATLAB files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "v4 (Level 1.0), v6 and v7 to 7.2 matfiles are supported.\n", "To read matlab 7.3 format mat files an HDF5 python library is required. Please check the scipy documentation for more information." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data can be generated with the following MATLAB code:\n", "\n", " % Generate Regression Test Data\n", " X = [1 2 3\n", " 4 5 6\n", " 7 8 9\n", " 0 1 2] + 0.1;\n", " y = sum(X,2);\n", " feat_names = strvcat('Feature One', 'Feature Two', 'Feature Three');\n", " save ('matlab_test_data_01', 'X','y', 'feat_names')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mat_data = scipy.io.loadmat('example_data/matlab_test_data_01.mat')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Variables names included in the `.mat` file are keys of the `mat_data` dictionary. Moreover the key `'__header__'` contains the mat-file information.\n", "Here we load the two variables in Pandas varialbles:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dict_keys(['__globals__', '__header__', 'X', 'y', '__version__', 'feat_names'])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat_data.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following code the `.strip()` method is used to remove the trailing white spaces used by MATLAB to make all the variable names of the same lenght:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Feature One Feature Two Feature Three\n", "0 1.1 2.1 3.1\n", "1 4.1 5.1 6.1\n", "2 7.1 8.1 9.1\n", "3 0.1 1.1 2.1 \n", "\n", " measured\n", "0 6.3\n", "1 15.3\n", "2 24.3\n", "3 3.3\n" ] } ], "source": [ "X = pd.DataFrame(mat_data['X'], columns=[s.strip() for s in list(mat_data['feat_names'])])\n", "y = pd.DataFrame(mat_data['y'], columns=['measured'])\n", "print(X, '\\n\\n', y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4 Preprocessing Data" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "Standardization of datasets is a common requirement for many machine learning estimators: they might behave badly if the individual feature do not more or less look like *standard normally distributed data*: Gaussian with zero mean and unit variance. In practice we often ignore the shape of the distribution and just transform the data to center and scale by dividing by the standard deviation.\n", "\n", "*If the input variables are combined via a distance function* (such as Euclidean distance), standardizing inputs can be crucial. If\n", "one input has a range of 0 to 1, while another input has a range of 0 to 1,000,000, then the contribution of the first input to the distance will be swamped by the second input.\n", "\n", "It is sometimes not enough to center and scale the features independently, since a downstream model can further make some assumption on the linear independence of the features. To address this issue you can use `sklearn.decomposition.PCA` or `sklearn.decomposition.RandomizedPCA` with `whiten=True` to further remove the linear correlation across features.\n", "\n", "`scale` and `StandardScaler` work out-of-the-box with 1d arrays." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 Standardizing = Mean Removal + Variance Scaling:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`preprocessing.scale` scales each column of the features matric to **mean=0 and std=1**. This is also called **\"STANDARDIZATION\"**." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn import preprocessing\n", "X = np.array([[ 10., 1., 0.],\n", " [ 20., 0., 2.]])" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 10. 1. 0.]\n", " [ 20. 0. 2.]]\n", "\n", "Scaled Values: \n", "[[-1. 1. -1.]\n", " [ 1. -1. 1.]]\n" ] } ], "source": [ "X_sc_1 = preprocessing.scale(X)\n", "print(X)\n", "print('\\nScaled Values: ')\n", "print(X_sc_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`preprocessing.StandardScaler` keeps the values of `.mean_` and `.std_` to allow lately `transform` and `inverse_transform` **mean** and **std** scaling can be controlled independently:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scaled Values: \n", "[[-1. 1. -1.]\n", " [ 1. -1. 1.]]\n", "\n", "Standard Scaler Mean: [ 15. 0.5 1. ]\n", "Standard Scaler Std: [ 5. 0.5 1. ]\n", "\n", "Inverse Transform: \n", "[[ 10. 1. 0.]\n", " [ 20. 0. 2.]]\n" ] } ], "source": [ "scaler = preprocessing.StandardScaler(copy=True, with_mean=True, with_std=True)\n", "scaler.fit(X)\n", "X_sc_2 = scaler.transform(X)\n", "print('\\nScaled Values: ')\n", "print(X_sc_2)\n", "print('\\nStandard Scaler Mean: ', scaler.mean_)\n", "print('Standard Scaler Std: ', scaler.std_)\n", "print('\\nInverse Transform: ')\n", "print(scaler.inverse_transform(X_sc_2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Using the preprocessing.StandardScaler with pandas:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ " 0 1 2\n", "0 -1 1 -1\n", "1 1 -1 1" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaler = preprocessing.StandardScaler(copy=False, with_mean=True, with_std=True).fit(df)\n", "print('\\nStandard Scaler Mean: ', scaler.mean_)\n", "print('Standard Scaler Std: ', scaler.std_)\n", "df_sc_2 = pd.DataFrame(scaler.transform(df))\n", "df_sc_2" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ " 0 1 2\n", "0 10 1 0\n", "1 20 0 2" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_inv = pd.DataFrame(scaler.inverse_transform(df_sc_2))\n", "df_inv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 Normalizing = Dividing by a Norm of the Vector:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`preprocessing.normalize` sets thenorm of the vector =1.\n", "\n", "By default `(axis=1)`, so if it's necessary to normalize the columns and not the rows, `axis` must be set to 0." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Normalized Values: \n", "[[ 0.33333333 1. 0. ]\n", " [ 0.66666667 0. 1. ]\n", " [ 0.66666667 0. 0. ]]\n" ] } ], "source": [ "X = np.array([[ 10., 1., 0.],\n", " [ 20., 0., 2.],\n", " [ 20., 0., 0.]])\n", "X_nrm = preprocessing.normalize(X, norm='l2', axis=0)\n", "print('\\nNormalized Values: ')\n", "print(X_nrm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5 Features Extraction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Features extraction (module `sklearn.feature_extraction`) consists in transforming arbitrary data, such as text or images, into numerical features usable for machine learning." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.1 Derived Features:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are obtained by pre-processing the data to generate features that are somehow more informative. Derived\n", "features may be linear or nonlinear combinations of features (such as in Polynomial regression), or may be some more sophisticated transform of the features. The latter is often used in image processing.\n", "\n", "For example, [scikit-image](http://scikit-image.org/) provides a variety of feature\n", "extractors designed for image data: see the ``skimage.feature`` submodule." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.2 DictVectorizer uses \"one-hot\" encoder for categorical features:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`feature_extraction.DictVectorizer` implements what is called one-of-K or “one-hot” coding for categorical (aka nominal, discrete) features.\n", "\n", "In the following code the method `vec.fit_transform(temp)` returns a sparse matrix that is tranformed to dense by `.toarray()`." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
city=Dubaicity=Londoncity=San Fransiscotemperature
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" ], "text/plain": [ " city=Dubai city=London city=San Fransisco temperature\n", "0 1 0 0 33\n", "1 0 1 0 12\n", "2 0 0 1 18" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import feature_extraction\n", "t = [{'city': 'Dubai', 'temperature': 33.},\n", " {'city': 'London', 'temperature': 12.},\n", " {'city': 'San Fransisco', 'temperature': 18.},]\n", "\n", "vec = feature_extraction.DictVectorizer()\n", "t_vec = vec.fit_transform(t)\n", "df = pd.DataFrame(t_vec.toarray(), columns=vec.get_feature_names())\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.3 The Bag of Words representation:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We call **vectorization** the general process of turning a collection of text documents into numerical feature vectors. *This specific strategy (tokenization, counting and normalization) is called the Bag of Words or “Bag of n-grams” representation*. Documents are described by word occurrences while completely ignoring the relative position information of the words in the document.\n", "\n", "`CountVectorizer` implements both tokenization and occurrence counting in a single class:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feature_extraction.text.CountVectorizer?" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
anddocumentfirstisonesecondthethirdthis
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" ], "text/plain": [ " and document first is one second the third this\n", "0 0 1 1 1 0 0 1 0 1\n", "1 0 1 0 1 0 2 1 0 1\n", "2 1 0 0 0 1 0 1 3 0\n", "3 0 1 1 1 0 0 1 0 1" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corpus = ['This is the x-first document.', 'This is the second second document.',\n", " 'And the third third third one.', 'Is this the first document?']\n", "vec = feature_extraction.text.CountVectorizer()\n", "X = vec.fit_transform(corpus)\n", "df = pd.DataFrame(X.toarray(), columns=vec.get_feature_names())\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default configuration tokenizes the string by extracting words of at least 2 letters. The specific function that does this step can be requested explicitly:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['this', 'is', 'the', 'first', 'document']" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analyzer = vec.build_analyzer()\n", "analyzer(\"This is the x-first document.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Words that were not seen in the training corpus will be completely ignored in future calls to the transform method:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0 0 0 0 0 0 0 0 0]]\n" ] } ], "source": [ "print(vec.transform(['Something completely new.']).toarray())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that in the previous corpus we lose the information that the last document is an interrogative form because each word is encoded individually. To preserve the local ordering information we can extract 2-grams of words in addition to the 1-grams (the word themselves):" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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andand thedocumentfirstfirst documentisis theis thisonesecondsecond documentsecond secondthethe firstthe secondthe thirdthirdthird onethird thirdthisthis isthis the
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" ], "text/plain": [ " and and the document first first document is is the is this one \\\n", "0 0 0 1 1 1 1 1 0 0 \n", "1 0 0 1 0 0 1 1 0 0 \n", "2 1 1 0 0 0 0 0 0 1 \n", "3 0 0 1 1 1 1 0 1 0 \n", "\n", " second second document second second the the first the second \\\n", "0 0 0 0 1 1 0 \n", "1 2 1 1 1 0 1 \n", "2 0 0 0 1 0 0 \n", "3 0 0 0 1 1 0 \n", "\n", " the third third third one third third this this is this the \n", "0 0 0 0 0 1 1 0 \n", "1 0 0 0 0 1 1 0 \n", "2 1 3 1 2 0 0 0 \n", "3 0 0 0 0 1 0 1 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vec_bi = feature_extraction.text.CountVectorizer(min_df=1, ngram_range=(1, 2))\n", "X_bi = vec_bi.fit_transform(corpus)\n", "pd.set_option('display.max_columns', None)\n", "df_bi = pd.DataFrame(X_bi.toarray(), columns=vec_bi.get_feature_names())\n", "df_bi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a large text corpus, some words will be very present (e.g. “the”, “a”, “is” in English) hence carrying very little meaningful information. For this reason it is very common to use the `tf–idf` (term-frequency / inverse document-frequency) transform: each row is normalized to have unit euclidean norm. The weights of each feature computed by the fit method call are stored in a model attribute:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.66052121 0. 0.40395613 0.63287533 0. ]\n", " [ 0.63295194 0. 0.77419109 0. 0. ]\n", " [ 0.31878155 0.61087812 0.38991559 0. 0.61087812]\n", " [ 1. 0. 0. 0. 0. ]]\n" ] } ], "source": [ "corpus = [ 'aa aa cc dd', 'aa cc',\n", " 'aa bb cc ff', \"aa aa\"]\n", "tfidf = feature_extraction.text.TfidfVectorizer()\n", "X = tfidf.fit_transform(corpus)\n", "print(X.toarray())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check scikit-learn documentation for additional information on feature extraction." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "Visit [www.add-for.com]() for more tutorials and updates.\n", "\n", "This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License." ] } ], "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": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }