mirror of
https://github.com/rasbt/python_reference.git
synced 2024-11-27 22:11:13 +00:00
994 lines
238 KiB
Plaintext
994 lines
238 KiB
Plaintext
{
|
|
"metadata": {
|
|
"name": "",
|
|
"signature": "sha256:ac00f18b1255fe2fd96fea78f54cf237c8ebc58fc5f36c9d734b1056494a81a8"
|
|
},
|
|
"nbformat": 3,
|
|
"nbformat_minor": 0,
|
|
"worksheets": [
|
|
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[Sebastian Raschka](http://sebastianraschka.com) \n",
|
|
"\n",
|
|
"- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/python_howtos/python_data_entry_point.ipynbc?reate=1) \n",
|
|
"\n",
|
|
"- [Link to this IPython notebook on Github](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/python_howtos/python_data_entry_point.ipynb) \n",
|
|
"\n",
|
|
"- [Link to the GitHub Repository pattern_classification](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/python_howtos/)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"import time\n",
|
|
"import platform\n",
|
|
"print('Last updated: %s' %time.strftime('%d/%m/%Y'))\n",
|
|
"print('Created using Python', platform.python_version())"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"Last updated: 25/06/2014\n",
|
|
"Created using Python 3.4.1\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 4
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<hr>\n",
|
|
"I would be happy to hear your comments and suggestions. \n",
|
|
"Please feel free to drop me a note via\n",
|
|
"[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
|
|
"<hr>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 1,
|
|
"metadata": {},
|
|
"source": [
|
|
"Entry point: Data "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 3,
|
|
"metadata": {},
|
|
"source": [
|
|
"- Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"In this short tutorial I want to provide a short overview of some of my favorite Python tools for common procedures as entry points for general pattern classification and machine learning tasks, and various other data analyses. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 1,
|
|
"metadata": {},
|
|
"source": [
|
|
"Sections"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"- [Installing Python packages](#Installing-Python-packages)\n",
|
|
"\n",
|
|
"- [About the dataset](#About-the-dataset)\n",
|
|
"\n",
|
|
"- [Downloading and saving CSV data files from the web](#Downloading-and-savin-CSV-data-files-from-the-web)\n",
|
|
"\n",
|
|
"- [Reading in a dataset from a CSV file](#Reading-in-a-dataset-from-a-CSV-file)\n",
|
|
"\n",
|
|
"- [Visualizating of a data](#Visualizating-of-a-data)\n",
|
|
"\n",
|
|
" - [Histograms](#Histograms)\n",
|
|
"\n",
|
|
" - [Scatterplots](#Scatterplots)\n",
|
|
"\n",
|
|
"- [Splitting into training and test dataset](#Splitting-into-training-and-test-dataset)\n",
|
|
"\n",
|
|
"- [Feature Scaling](#Feature-Scaling)\n",
|
|
"\n",
|
|
"- [Saving the processed datasets](#Saving-the-processed-datasets)\n",
|
|
"\n",
|
|
" - [Pickle](#Pickle)\n",
|
|
"\n",
|
|
" - [Comma Separated Values (CSV)](#Comma-Separated-Values)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 2,
|
|
"metadata": {},
|
|
"source": [
|
|
"Installing Python packages"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n",
|
|
"**In this section want to recommend a way for installing the required Python-packages packages if you have not done so, yet. Otherwise you can skip this part.**\n",
|
|
"\n",
|
|
"The packages we will be using in this tutorial are:\n",
|
|
"\n",
|
|
"- [NumPy](http://www.numpy.org)\n",
|
|
"- [SciPy](http://www.scipy.org)\n",
|
|
"- [matplotlib](http://matplotlib.org)\n",
|
|
"- [scikit-learn](http://scikit-learn.org/stable/)\n",
|
|
"\n",
|
|
"Although they can be installed step-by-step \"manually\", but I highly recommend you to take a look at the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution for scientific computing.\n",
|
|
"\n",
|
|
"Anaconda is distributed by Continuum Analytics, but it is completely free and includes more than 195+ packages for science and data analysis as of today.\n",
|
|
"The installation procedure is nicely summarized here: http://docs.continuum.io/anaconda/install.html\n",
|
|
"\n",
|
|
"If this is too much, the [Miniconda](http://conda.pydata.org/miniconda.html) might be right for you. Miniconda is basically just a Python distribution with the Conda package manager, which let's us install a list of Python packages into a specified `conda` environment from the Shell terminal, e.g.,\n",
|
|
"\n",
|
|
"<pre>$[bash]> conda create -n myenv python=3\n",
|
|
"$[bash]> source activate myenv\n",
|
|
"$[bash]> conda install -n myenv numpy scipy matplotlib scikit-learn</pre>\n",
|
|
"\n",
|
|
"When we start \"python\" in your current shell session now, it will use the Python distribution in the virtual environment \"myenv\" that we have just created. To un-attach the virtual environment, you can just use\n",
|
|
"<pre>$[bash]> source deactivate myenv</pre>\n",
|
|
"\n",
|
|
"**Note:** environments will be created in ROOT_DIR/envs by default, you can use the `-p` instead of the `-n` flag in the conda commands above in order to specify a custom path.\n",
|
|
"\n",
|
|
"**I find this procedure very convenient, especially if you are working with different distributions and versions of Python with different modules and packages installed and it is extremely useful for testing your own modules.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 2,
|
|
"metadata": {},
|
|
"source": [
|
|
"About the dataset"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"For the following tutorial, we will be working with the free \"Wine\" Dataset that is deposited on the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Wine).\n",
|
|
"\n",
|
|
"<br>\n",
|
|
"\n",
|
|
"<font size=\"1\">\n",
|
|
"**Reference:**\n",
|
|
"Forina, M. et al, PARVUS - An Extendible Package for Data\n",
|
|
"Exploration, Classification and Correlation. Institute of Pharmaceutical\n",
|
|
"and Food Analysis and Technologies, Via Brigata Salerno, \n",
|
|
"16147 Genoa, Italy.</font>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The Wine dataset consists of 3 different classes where each row correspond to a particular wine sample.\n",
|
|
"\n",
|
|
"The class labels (1, 2, 3) are listed in the first column, and the columns 2-14 correspond to the following 13 attributes (features):\n",
|
|
"\n",
|
|
"1) Alcohol \n",
|
|
"2) Malic acid \n",
|
|
"3) Ash \n",
|
|
"4) Alcalinity of ash \n",
|
|
"5) Magnesium \n",
|
|
"6) Total phenols \n",
|
|
"7) Flavanoids \n",
|
|
"8) Nonflavanoid phenols \n",
|
|
"9) Proanthocyanins \n",
|
|
"10) Color intensity \n",
|
|
"11) Hue \n",
|
|
"12) OD280/OD315 of diluted wines \n",
|
|
"13) Proline \n",
|
|
"\n",
|
|
"An excerpt from the wine_data.csv dataset:\n",
|
|
" \n",
|
|
"<pre>1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065\n",
|
|
"1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050\n",
|
|
"[...]\n",
|
|
"2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520\n",
|
|
"2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680\n",
|
|
"[...]\n",
|
|
"3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630\n",
|
|
"3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530</pre>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 2,
|
|
"metadata": {},
|
|
"source": [
|
|
"Downloading and saving CSV data files from the web"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Usually, we have our data stored locally on our disk in as a common text (or CSV) file with comma-, tab-, or whitespace-separated rows. Below is just an example for how you can CSV datafile from a HTML website directly into Python and optionally save it locally."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"import csv\n",
|
|
"import urllib\n",
|
|
"\n",
|
|
"url = 'https://raw.githubusercontent.com/rasbt/pattern_classification/master/data/wine_data.csv'\n",
|
|
"csv_cont = urllib.request.urlopen(url)\n",
|
|
"csv_cont = csv_cont.read() #.decode('utf-8')\n",
|
|
"\n",
|
|
"# Optional: saving the data to your local drive\n",
|
|
"with open('./wine_data.csv', 'wb') as out:\n",
|
|
" out.write(csv_cont)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 100
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Note:** If you'd rather like to work with the data directly in `str`ing format, you could just apply the `.decode('utf-8')` method to the data that was read in byte-format by default.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 2,
|
|
"metadata": {},
|
|
"source": [
|
|
"Reading in a dataset from a CSV file"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Since it is quite typical to have the input data stored locally, as mentioned above, we will use the [`numpy.loadtxt`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html) function now to read in the data from the CSV file. \n",
|
|
"(alternatively [`np.genfromtxt()`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html) could be used in similar way, it provides some additional options)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"# reading in all data into a NumPy array\n",
|
|
"all_data = np.loadtxt(open(\"./wine_data.csv\",\"r\"),\n",
|
|
" delimiter=\",\", \n",
|
|
" skiprows=0, \n",
|
|
" dtype=np.float64\n",
|
|
" )\n",
|
|
"\n",
|
|
"# load class labels from column 1\n",
|
|
"y_wine = all_data[:,0]\n",
|
|
"\n",
|
|
"# conversion of the class labels to integer-type array\n",
|
|
"y_wine = y_wine.astype(np.int64, copy=False)\n",
|
|
"\n",
|
|
"# load the 14 features\n",
|
|
"X_wine = all_data[:,1:]\n",
|
|
"\n",
|
|
"# printing some general information about the data\n",
|
|
"print('\\ntotal number of samples (rows):', X_wine.shape[0])\n",
|
|
"print('total number of features (columns):', X_wine.shape[1])\n",
|
|
"\n",
|
|
"# printing the 1st wine sample\n",
|
|
"float_formatter = lambda x: '{:.2f}'.format(x)\n",
|
|
"np.set_printoptions(formatter={'float_kind':float_formatter})\n",
|
|
"print('\\n1st sample (i.e., 1st row):\\nClass label: {:d}\\n{:}\\n'\n",
|
|
" .format(int(y_wine[0]), X_wine[0]))\n",
|
|
"\n",
|
|
"# printing the rel.frequency of the class labels\n",
|
|
"print('Class label frequencies')\n",
|
|
"print('Class 1 samples: {:.2%}'.format(list(y_wine).count(1)/y_wine.shape[0]))\n",
|
|
"print('Class 2 samples: {:.2%}'.format(list(y_wine).count(2)/y_wine.shape[0]))\n",
|
|
"print('Class 3 samples: {:.2%}'.format(list(y_wine).count(3)/y_wine.shape[0]))"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
"total number of samples (rows): 178\n",
|
|
"total number of features (columns): 13\n",
|
|
"\n",
|
|
"1st sample (i.e., 1st row):\n",
|
|
"Class label: 1\n",
|
|
"[14.23 1.71 2.43 15.60 127.00 2.80 3.06 0.28 2.29 5.64 1.04 3.92 1065.00]\n",
|
|
"\n",
|
|
"Class label frequencies\n",
|
|
"Class 1 samples: 33.15%\n",
|
|
"Class 2 samples: 39.89%\n",
|
|
"Class 3 samples: 26.97%\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 97
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 2,
|
|
"metadata": {},
|
|
"source": [
|
|
"Visualizating of a data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"There are endless way to visualize datasets for get an initial idea of how the data looks like. The most common ones are probably histograms and scatter plots."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 3,
|
|
"metadata": {},
|
|
"source": [
|
|
"Histograms"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Histograms are a useful data to explore the distribution of each feature across the different classes. This could provide us with intuitive insights which features have a good and not-so-good inter-class separation. Below, we will plot a sample histogram for the \"Alcohol content\" feature for the three wine classes."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"%matplotlib inline"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 53
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"from matplotlib import pyplot as plt\n",
|
|
"from math import floor, ceil # for rounding up and down\n",
|
|
"\n",
|
|
"plt.figure(figsize=(10,8))\n",
|
|
"\n",
|
|
"# bin width of the histogram in steps of 0.1\n",
|
|
"bins = np.arange(floor(min(X_wine[:,0])), ceil(max(X_wine[:,0])), 0.1)\n",
|
|
"\n",
|
|
"# get the max count for a particular bin for all classes combined\n",
|
|
"max_bin = max(np.histogram(X_wine[:,0], bins=bins)[0])\n",
|
|
"\n",
|
|
"# the order of the colors for each histogram\n",
|
|
"colors = ('blue', 'red', 'green')\n",
|
|
"\n",
|
|
"for label,color in zip(\n",
|
|
" range(1,4), colors):\n",
|
|
"\n",
|
|
" mean = np.mean(X_wine[:,0][y_wine == label]) # class sample mean\n",
|
|
" stdev = np.std(X_wine[:,0][y_wine == label]) # class standard deviation\n",
|
|
" plt.hist(X_wine[:,0][y_wine == label], \n",
|
|
" bins=bins, \n",
|
|
" alpha=0.3, # opacity level\n",
|
|
" label='class {} ($\\mu={:.2f}$, $\\sigma={:.2f}$)'.format(label, mean, stdev), \n",
|
|
" color=color)\n",
|
|
"\n",
|
|
"plt.ylim([0, max_bin*1.3])\n",
|
|
"plt.title('Wine data set - Distribution of alocohol contents')\n",
|
|
"plt.xlabel('alcohol by volume', fontsize=14)\n",
|
|
"plt.ylabel('count', fontsize=14)\n",
|
|
"plt.legend(loc='upper right')\n",
|
|
"\n",
|
|
"plt.show()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH8CAYAAABl8FOBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxAAAAABJRU5ErkJggg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x142bdd240>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 95
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 3,
|
|
"metadata": {},
|
|
"source": [
|
|
"Scatterplots"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Scatter plots are useful for visualizing features in more than just one dimension, for example to get a feeling for the correlation between particular features. \n",
|
|
"Unfortunately, we can't plot all 13 features here at once, since the visual cortex of us humans is limited to a maximum of three dimensions."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Below, we will create an example 2D-Scatter plot from the features \"Alcohol content\" and \"Malic acid content\". \n",
|
|
"Additionally, we will use the [`scipy.stats.pearsonr`](http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html) function to calculate a Pearson correlation coefficient between these two features.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"from scipy.stats import pearsonr\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.figure(figsize=(10,8))\n",
|
|
"\n",
|
|
"for label,marker,color in zip(\n",
|
|
" range(1,4),('x', 'o', '^'),('blue', 'red', 'green')):\n",
|
|
"\n",
|
|
" # Calculate Pearson correlation coefficient\n",
|
|
" R = pearsonr(X_wine[:,0][y_wine == label], X_wine[:,1][y_wine == label])\n",
|
|
" plt.scatter(x=X_wine[:,0][y_wine == label], # x-axis: feat. from col. 1\n",
|
|
" y=X_wine[:,1][y_wine == label], # y-axis: feat. from col. 2\n",
|
|
" marker=marker, # data point symbol for the scatter plot\n",
|
|
" color=color,\n",
|
|
" alpha=0.7, \n",
|
|
" label='class {:}, R={:.2f}'.format(label, R[0]) # label for the legend\n",
|
|
" )\n",
|
|
" \n",
|
|
"\n",
|
|
"plt.title('Wine Dataset')\n",
|
|
"plt.xlabel('alcohol by volume in percent')\n",
|
|
"plt.ylabel('malic acid in g/l')\n",
|
|
"plt.legend(loc='upper right')\n",
|
|
"\n",
|
|
"plt.show()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAH4CAYAAACSZ0OSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nah7OJ7wrYQAAAABJRU5ErkJggg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x16dacf6a0>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 98
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"If we want to pack 3 different features into one scatter plot at once, we can also do the same thing in 3D:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"from mpl_toolkits.mplot3d import Axes3D\n",
|
|
"\n",
|
|
"fig = plt.figure(figsize=(8,8))\n",
|
|
"ax = fig.add_subplot(111, projection='3d')\n",
|
|
" \n",
|
|
"for label,marker,color in zip(\n",
|
|
" range(1,4),('x', 'o', '^'),('blue','red','green')):\n",
|
|
" \n",
|
|
" ax.scatter(X_wine[:,0][y_wine == label], \n",
|
|
" X_wine[:,1][y_wine == label], \n",
|
|
" X_wine[:,2][y_wine == label], \n",
|
|
" marker=marker, \n",
|
|
" color=color, \n",
|
|
" s=40, \n",
|
|
" alpha=0.7,\n",
|
|
" label='class {}'.format(label))\n",
|
|
"\n",
|
|
"ax.set_xlabel('alcohol by volume in percent')\n",
|
|
"ax.set_ylabel('malic acid in g/l')\n",
|
|
"ax.set_zlabel('ash content in g/l')\n",
|
|
"\n",
|
|
"plt.title('Wine dataset')\n",
|
|
" \n",
|
|
"plt.show()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHMCAYAAABY25iGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nU5IamKFEnlyKlaSm5f8A5/W2rh+LSFkAAAAASUVORK5CYII=\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x106d84a20>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 8
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 2,
|
|
"metadata": {},
|
|
"source": [
|
|
"Splitting into training and test dataset "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"It is a typical procedure for machine learning and pattern classification tasks to split one dataset into two: a training dataset and a test dataset. \n",
|
|
"The training dataset is henceforth used to train our algorithms or classifier, and the test dataset is a way to validate the outcome quite objectively before we apply it to \"new, real world data\".\n",
|
|
"\n",
|
|
"Here, we will split the dataset randomly so that 70% of the total dataset will become our training dataset, and 30% will become our test dataset, respectively."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"from sklearn.cross_validation import train_test_split\n",
|
|
"from sklearn import preprocessing\n",
|
|
"\n",
|
|
"X_train, X_test, y_train, y_test = train_test_split(X_wine, y_wine,\n",
|
|
" test_size=0.30, random_state=123)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 102
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Note that since this a random assignment, the original relative frequencies for each class label are not maintained."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"print('Class label frequencies')\n",
|
|
" \n",
|
|
"print('\\nTraining Dataset:') \n",
|
|
"for l in range(1,4):\n",
|
|
" print('Class {:} samples: {:.2%}'.format(l, list(y_train).count(l)/y_train.shape[0]))\n",
|
|
" \n",
|
|
"print('\\nTest Dataset:') \n",
|
|
"for l in range(1,4):\n",
|
|
" print('Class {:} samples: {:.2%}'.format(l, list(y_test).count(l)/y_test.shape[0]))"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"Class label frequencies\n",
|
|
"\n",
|
|
"Training Dataset:\n",
|
|
"Class 1 samples: 36.29%\n",
|
|
"Class 2 samples: 42.74%\n",
|
|
"Class 3 samples: 20.97%\n",
|
|
"\n",
|
|
"Test Dataset:\n",
|
|
"Class 1 samples: 25.93%\n",
|
|
"Class 2 samples: 33.33%\n",
|
|
"Class 3 samples: 40.74%\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 106
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 2,
|
|
"metadata": {},
|
|
"source": [
|
|
"Feature Scaling"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Another popular procedure is to standardize the data prior to fitting the model and other analyses so that the features will have the properties of a standard normal distribution with \n",
|
|
"\n",
|
|
"$\\mu = 0$ and $\\sigma = 1$\n",
|
|
"\n",
|
|
"where $\\mu$ is the mean (average) and $\\sigma$ is the standard deviation from the mean, so that the standard scores of the samples are calculated as follows:\n",
|
|
"\n",
|
|
"\\begin{equation} z = \\frac{x - \\mu}{\\sigma}\\end{equation} "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"std_scale = preprocessing.StandardScaler().fit(X_train)\n",
|
|
"X_train = std_scale.transform(X_train)\n",
|
|
"X_test = std_scale.transform(X_test)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 107
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"\n",
|
|
"f, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(10,5))\n",
|
|
"\n",
|
|
"for a,x_dat, y_lab in zip(ax, (X_train, X_test), (y_train, y_test)):\n",
|
|
"\n",
|
|
" for label,marker,color in zip(\n",
|
|
" range(1,4),('x', 'o', '^'),('blue','red','green')):\n",
|
|
"\n",
|
|
" a.scatter(x=x_dat[:,0][y_lab == label], \n",
|
|
" y=x_dat[:,1][y_lab == label], \n",
|
|
" marker=marker, \n",
|
|
" color=color, \n",
|
|
" alpha=0.7, \n",
|
|
" label='class {}'.format(label)\n",
|
|
" )\n",
|
|
"\n",
|
|
" a.legend(loc='upper right')\n",
|
|
"\n",
|
|
"ax[0].set_title('Training Dataset')\n",
|
|
"ax[1].set_title('Test Dataset')\n",
|
|
"f.text(0.5, 0.04, 'malic acid (standardized)', ha='center', va='center')\n",
|
|
"f.text(0.08, 0.5, 'alcohol (standardized)', ha='center', va='center', rotation='vertical')\n",
|
|
"\n",
|
|
"plt.show()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFXCAYAAAAMF1IiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x132013390>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 108
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>\n",
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 2,
|
|
"metadata": {},
|
|
"source": [
|
|
"Saving the processed datasets"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 3,
|
|
"metadata": {},
|
|
"source": [
|
|
"Pickle"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The in-built [`pickle`](https://docs.python.org/3.4/library/pickle.html) module is a convenient tool in Python's standard library to save Python objects in byte format. This allows us, for example, to save our NumPy arrays and load them in a later or different Python session to continue working with our data, e.g., to train a classifier."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# export objects via pickle\n",
|
|
"\n",
|
|
"import pickle\n",
|
|
"\n",
|
|
"pickle_out = open('standardized_data.pkl', 'wb')\n",
|
|
"pickle.dump([X_train, X_test, y_train, y_test], pickle_out)\n",
|
|
"pickle_out.close()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 113
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# import objects via pickle\n",
|
|
"\n",
|
|
"my_object_file = open('standardized_data.pkl', 'rb')\n",
|
|
"X_train, X_test, y_train, y_test = pickle.load(my_object_file)\n",
|
|
"my_object_file.close()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 114
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<br>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 3,
|
|
"metadata": {},
|
|
"source": [
|
|
"Comma-Separated-Values"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"[[back to top]](#Sections)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"And it is also always a good idea to save your data in common text formats, such as the CSV format that we started with. But first, let us add back the class labels to the front column of the test and training data sets."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"training_data = np.hstack((y_train.reshape(y_train.shape[0], 1), X_train))\n",
|
|
"test_data = np.hstack((y_test.reshape(y_test.shape[0], 1), X_test))"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 137
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now, we can save our test and training datasets as 2 separate CSV files using the [`numpy.savetxt`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.savetxt.html) function."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"np.savetxt('./training_set.csv', training_data, delimiter=',')\n",
|
|
"np.savetxt('./test_set.csv', test_data, delimiter=',')"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 138
|
|
}
|
|
],
|
|
"metadata": {}
|
|
}
|
|
]
|
|
} |