From 27e2064c7bc5acede06711d5a9639ab3cf016f4b 2012-03-21 14:11:17
From: slojo404 <slojo404@yahoo.com>
Date: 2012-03-21 14:11:17
Subject: [PATCH] adding a basic test and moving tutorial.rst test file to tests directory
---
diff --git a/tests/test_rst2ipynb.py b/tests/test_rst2ipynb.py
new file mode 100644
index 0000000..d30cb26
--- /dev/null
+++ b/tests/test_rst2ipynb.py
@@ -0,0 +1,25 @@
+import os
+import errno
+import subprocess
+import nose.tools as nt
+
+test_rst_fname = 'tests/tutorial.rst.ref'
+ref_ipynb_fname = 'tests/tutorial.ipynb.ref'
+test_generate_ipynb_fname = 'tests/tutorial.ipynb'
+
+
+def clean_dir():
+ "Remove generated ipynb file created during conversion"
+ try:
+ os.unlink(test_generate_ipynb_fname)
+ except OSError, e:
+ if e.errno != errno.ENOENT:
+ raise
+
+
+@nt.with_setup(clean_dir, clean_dir)
+def test_command_line():
+ with open(ref_ipynb_fname, 'rb') as f:
+ ref_output = f.read()
+ output = subprocess.check_output(['./rst2ipynb.py', test_rst_fname])
+ nt.assert_equal(ref_output, output)
diff --git a/tests/tutorial.ipynb.ref b/tests/tutorial.ipynb.ref
new file mode 100644
index 0000000..22548bb
--- /dev/null
+++ b/tests/tutorial.ipynb.ref
@@ -0,0 +1,396 @@
+{
+ "metadata": {},
+ "nbformat": 3,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "source": [
+ "An Introduction to machine learning with scikit-learn"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "source": [
+ "Section contents"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "In this section, we introduce the machine learning",
+ "vocabulary that we use through-out scikit-learn and give a",
+ "simple learning example."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "source": [
+ "Machine learning: the problem setting"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "In general, a learning problem considers a set of n",
+ "samples of",
+ "data and try to predict properties of unknown data. If each sample is",
+ "more than a single number, and for instance a multi-dimensional entry",
+ "(aka multivariate",
+ "data), is it said to have several attributes,",
+ "or features."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "We can separate learning problems in a few large categories:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "supervised learning,",
+ "in which the data comes with additional attributes that we want to predict",
+ "(:ref:`Click here <supervised-learning>`",
+ "to go to the Scikit-Learn supervised learning page).This problem",
+ "can be either:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "classification:",
+ "samples belong to two or more classes and we",
+ "want to learn from already labeled data how to predict the class",
+ "of unlabeled data. An example of classification problem would",
+ "be the digit recognition example, in which the aim is to assign",
+ "each input vector to one of a finite number of discrete",
+ "categories."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "regression:",
+ "if the desired output consists of one or more",
+ "continuous variables, then the task is called regression. An",
+ "example of a regression problem would be the prediction of the",
+ "length of a salmon as a function of its age and weight."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "unsupervised learning,",
+ "in which the training data consists of a set of input vectors x",
+ "without any corresponding target values. The goal in such problems",
+ "may be to discover groups of similar examples within the data, where",
+ "it is called clustering,",
+ "or to determine the distribution of data within the input space, known as",
+ "density estimation, or",
+ "to project the data from a high-dimensional space down to two or thee",
+ "dimensions for the purpose of visualization",
+ "(:ref:`Click here <unsupervised-learning>`",
+ "to go to the Scikit-Learn unsupervised learning page)."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "source": [
+ "Training set and testing set"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Machine learning is about learning some properties of a data set",
+ "and applying them to new data. This is why a common practice in",
+ "machine learning to evaluate an algorithm is to split the data",
+ "at hand in two sets, one that we call a training set on which",
+ "we learn data properties, and one that we call a testing set,",
+ "on which we test these properties."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "source": [
+ "Loading an example dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "scikit-learn comes with a few standard datasets, for instance the",
+ "iris and digits",
+ "datasets for classification and the boston house prices dataset for regression.:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from sklearn import datasets",
+ "iris = datasets.load_iris()",
+ "digits = datasets.load_digits()"
+ ],
+ "language": "python",
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "A dataset is a dictionary-like object that holds all the data and some",
+ "metadata about the data. This data is stored in the .data member,",
+ "which is a n_samples, n_features array. In the case of supervised",
+ "problem, explanatory variables are stored in the .target member. More",
+ "details on the different datasets can be found in the :ref:`dedicated",
+ "section <datasets>`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "For instance, in the case of the digits dataset, digits.data gives",
+ "access to the features that can be used to classify the digits samples:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "print digits.data # doctest: +NORMALIZE_WHITESPACE"
+ ],
+ "language": "python",
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "and digits.target gives the ground truth for the digit dataset, that",
+ "is the number corresponding to each digit image that we are trying to",
+ "learn:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "digits.target"
+ ],
+ "language": "python",
+ "outputs": []
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "source": [
+ "Shape of the data arrays"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The data is always a 2D array, n_samples, n_features, although",
+ "the original data may have had a different shape. In the case of the",
+ "digits, each original sample is an image of shape 8, 8 and can be",
+ "accessed using:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "digits.images[0]"
+ ],
+ "language": "python",
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The :ref:`simple example on this dataset",
+ "<example_plot_digits_classification.py>` illustrates how starting",
+ "from the original problem one can shape the data for consumption in",
+ "the scikit-learn."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "source": [
+ "Learning and Predicting"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "In the case of the digits dataset, the task is to predict the value of a",
+ "hand-written digit from an image. We are given samples of each of the 10",
+ "possible classes on which we fit an",
+ "estimator to be able to predict",
+ "the labels corresponding to new data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "In scikit-learn, an estimator is just a plain Python class that",
+ "implements the methods fit(X, Y) and predict(T)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "An example of estimator is the class sklearn.svm.SVC that",
+ "implements Support Vector Classification. The",
+ "constructor of an estimator takes as arguments the parameters of the",
+ "model, but for the time being, we will consider the estimator as a black",
+ "box:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from sklearn import svm",
+ "clf = svm.SVC(gamma=0.001, C=100.)"
+ ],
+ "language": "python",
+ "outputs": []
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "source": [
+ "Choosing the parameters of the model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "In this example we set the value of gamma manually. It is possible",
+ "to automatically find good values for the parameters by using tools",
+ "such as :ref:`grid search <grid_search>` and :ref:`cross validation",
+ "<cross_validation>`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "We call our estimator instance clf as it is a classifier. It now must",
+ "be fitted to the model, that is, it must learn from the model. This is",
+ "done by passing our training set to the fit method. As a training",
+ "set, let us use all the images of our dataset apart from the last",
+ "one:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "clf.fit(digits.data[:-1], digits.target[:-1])"
+ ],
+ "language": "python",
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Now you can predict new values, in particular, we can ask to the",
+ "classifier what is the digit of our last image in the digits dataset,",
+ "which we have not used to train the classifier:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "clf.predict(digits.data[-1])"
+ ],
+ "language": "python",
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The corresponding image is the following:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "As you can see, it is a challenging task: the images are of poor",
+ "resolution. Do you agree with the classifier?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "A complete example of this classification problem is available as an",
+ "example that you can run and study:",
+ ":ref:`example_plot_digits_classification.py`."
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 2,
+ "source": [
+ "Model persistence"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "It is possible to save a model in the scikit by using Python's built-in",
+ "persistence model, namely pickle:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from sklearn import svm",
+ "from sklearn import datasets",
+ "clf = svm.SVC()",
+ "iris = datasets.load_iris()",
+ "X, y = iris.data, iris.target",
+ "clf.fit(X, y)",
+ "import pickle",
+ "s = pickle.dumps(clf)",
+ "clf2 = pickle.loads(s)",
+ "clf2.predict(X[0])",
+ "y[0]"
+ ],
+ "language": "python",
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "In the specific case of the scikit, it may be more interesting to use",
+ "joblib's replacement of pickle (joblib.dump & joblib.load),",
+ "which is more efficient on big data, but can only pickle to the disk",
+ "and not to a string:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "from sklearn.externals import joblib",
+ "joblib.dump(clf, 'filename.pkl') # doctest: +SKIP"
+ ],
+ "language": "python",
+ "outputs": []
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/tutorial.rst b/tests/tutorial.rst.ref
similarity index 100%
rename from tutorial.rst
rename to tests/tutorial.rst.ref