update reference to v3

tests/tutorial.ipynb.ref

@@ -1,12 +1,16 b''
 {
- "metadata": {},
+ "metadata": {
+  "name": "tutorial"
+ },
  "nbformat": 3,
+ "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
     {
      "cell_type": "heading",
      "level": 1,
+     "metadata": {},
      "source": [
       "An Introduction to machine learning with scikit-learn"
      ]
@@ -14,121 +18,134 b''
     {
      "cell_type": "heading",
      "level": 1,
+     "metadata": {},
      "source": [
       "Section contents"
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "source": [
-      "In this section, we introduce the machine learning",
-      "vocabulary that we use through-out scikit-learn and give a",
+      "In this section, we introduce the machine learning\n",
+      "vocabulary that we use through-out scikit-learn and give a\n",
       "simple learning example."
      ]
     },
     {
      "cell_type": "heading",
      "level": 2,
+     "metadata": {},
      "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,",
+     "metadata": {},
+     "source": [
+      "In general, a learning problem considers a set of n\n",
+      "samples of\n",
+      "data and try to predict properties of unknown data. If each sample is\n",
+      "more than a single number, and for instance a multi-dimensional entry\n",
+      "(aka multivariate\n",
+      "data), is it said to have several attributes,\n",
       "or features."
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "source": [
       "We can separate learning problems in a few large categories:"
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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",
+      "supervised learning,\n",
+      "in which the data comes with additional attributes that we want to predict\n",
+      "(:ref:`Click here <supervised-learning>`\n",
+      "to go to the Scikit-Learn supervised learning page).This problem\n",
       "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",
+     "metadata": {},
+     "source": [
+      "classification:\n",
+      "samples belong to two or more classes and we\n",
+      "want to learn from already labeled data how to predict the class\n",
+      "of unlabeled data. An example of classification problem would\n",
+      "be the digit recognition example, in which the aim is to assign\n",
+      "each input vector to one of a finite number of discrete\n",
       "categories."
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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",
+      "regression:\n",
+      "if the desired output consists of one or more\n",
+      "continuous variables, then the task is called regression. An\n",
+      "example of a regression problem would be the prediction of the\n",
       "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>`",
+     "metadata": {},
+     "source": [
+      "unsupervised learning,\n",
+      "in which the training data consists of a set of input vectors x\n",
+      "without any corresponding target values. The goal in such problems\n",
+      "may be to discover groups of similar examples within the data, where\n",
+      "it is called clustering,\n",
+      "or to determine the distribution of data within the input space, known as\n",
+      "density estimation, or\n",
+      "to project the data from a high-dimensional space down to two or thee\n",
+      "dimensions for the purpose of visualization\n",
+      "(:ref:`Click here <unsupervised-learning>`\n",
       "to go to the Scikit-Learn unsupervised learning page)."
      ]
     },
     {
      "cell_type": "heading",
      "level": 2,
+     "metadata": {},
      "source": [
       "Training set and testing set"
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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,",
+      "Machine learning is about learning some properties of a data set\n",
+      "and applying them to new data. This is why a common practice in\n",
+      "machine learning to evaluate an algorithm is to split the data\n",
+      "at hand in two sets, one that we call a training set on which\n",
+      "we learn data properties, and one that we call a testing set,\n",
       "on which we test these properties."
      ]
     },
     {
      "cell_type": "heading",
      "level": 2,
+     "metadata": {},
      "source": [
       "Loading an example dataset"
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "source": [
-      "scikit-learn comes with a few standard datasets, for instance the",
-      "iris and digits",
+      "scikit-learn comes with a few standard datasets, for instance the\n",
+      "iris and digits\n",
       "datasets for classification and the boston house prices dataset for regression.:"
      ]
     },
@@ -136,28 +153,31 b''
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from sklearn import datasets",
-      "iris = datasets.load_iris()",
+      "from sklearn import datasets\n",
+      "iris = datasets.load_iris()\n",
       "digits = datasets.load_digits()"
      ],
      "language": "python",
+     "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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",
+      "A dataset is a dictionary-like object that holds all the data and some\n",
+      "metadata about the data. This data is stored in the .data member,\n",
+      "which is a n_samples, n_features array. In the case of supervised\n",
+      "problem, explanatory variables are stored in the .target member. More\n",
+      "details on the different datasets can be found in the :ref:`dedicated\n",
       "section <datasets>`."
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "source": [
-      "For instance, in the case of the digits dataset, digits.data gives",
+      "For instance, in the case of the digits dataset, digits.data gives\n",
       "access to the features that can be used to classify the digits samples:"
      ]
     },
@@ -168,13 +188,15 b''
       "print digits.data  # doctest: +NORMALIZE_WHITESPACE"
      ],
      "language": "python",
+     "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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",
+      "and digits.target gives the ground truth for the digit dataset, that\n",
+      "is the number corresponding to each digit image that we are trying to\n",
       "learn:"
      ]
     },
@@ -185,21 +207,24 b''
       "digits.target"
      ],
      "language": "python",
+     "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "heading",
      "level": 2,
+     "metadata": {},
      "source": [
       "Shape of the data arrays"
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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",
+      "The data is always a 2D array, n_samples, n_features, although\n",
+      "the original data may have had a different shape. In the case of the\n",
+      "digits, each original sample is an image of shape 8, 8 and can be\n",
       "accessed using:"
      ]
     },
@@ -210,48 +235,54 b''
       "digits.images[0]"
      ],
      "language": "python",
+     "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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 :ref:`simple example on this dataset\n",
+      "<example_plot_digits_classification.py>` illustrates how starting\n",
+      "from the original problem one can shape the data for consumption in\n",
       "the scikit-learn."
      ]
     },
     {
      "cell_type": "heading",
      "level": 2,
+     "metadata": {},
      "source": [
       "Learning and Predicting"
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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",
+      "In the case of the digits dataset, the task is to predict the value of a\n",
+      "hand-written digit from an image. We are given samples of each of the 10\n",
+      "possible classes on which we fit an\n",
+      "estimator to be able to predict\n",
       "the labels corresponding to new data."
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "source": [
-      "In scikit-learn, an estimator is just a plain Python class that",
+      "In scikit-learn, an estimator is just a plain Python class that\n",
       "implements the methods fit(X, Y) and predict(T)."
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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",
+      "An example of estimator is the class sklearn.svm.SVC that\n",
+      "implements Support Vector Classification. The\n",
+      "constructor of an estimator takes as arguments the parameters of the\n",
+      "model, but for the time being, we will consider the estimator as a black\n",
       "box:"
      ]
     },
@@ -259,35 +290,39 b''
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from sklearn import svm",
+      "from sklearn import svm\n",
       "clf = svm.SVC(gamma=0.001, C=100.)"
      ],
      "language": "python",
+     "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "heading",
      "level": 2,
+     "metadata": {},
      "source": [
       "Choosing the parameters of the model"
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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",
+      "In this example we set the value of gamma manually. It is possible\n",
+      "to automatically find good values for the parameters by using tools\n",
+      "such as :ref:`grid search <grid_search>` and :ref:`cross validation\n",
       "<cross_validation>`."
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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",
+      "We call our estimator instance clf as it is a classifier. It now must\n",
+      "be fitted to the model, that is, it must learn from the model. This is\n",
+      "done by passing our training set to the fit method. As a training\n",
+      "set, let us use all the images of our dataset apart from the last\n",
       "one:"
      ]
     },
@@ -298,13 +333,15 b''
       "clf.fit(digits.data[:-1], digits.target[:-1])"
      ],
      "language": "python",
+     "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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,",
+      "Now you can predict new values, in particular, we can ask to the\n",
+      "classifier what is the digit of our last image in the digits dataset,\n",
       "which we have not used to train the classifier:"
      ]
     },
@@ -315,40 +352,46 b''
       "clf.predict(digits.data[-1])"
      ],
      "language": "python",
+     "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "source": [
       "The corresponding image is the following:"
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "source": [
-      "As you can see, it is a challenging task: the images are of poor",
+      "As you can see, it is a challenging task: the images are of poor\n",
       "resolution. Do you agree with the classifier?"
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "source": [
-      "A complete example of this classification problem is available as an",
-      "example that you can run and study:",
+      "A complete example of this classification problem is available as an\n",
+      "example that you can run and study:\n",
       ":ref:`example_plot_digits_classification.py`."
      ]
     },
     {
      "cell_type": "heading",
      "level": 2,
+     "metadata": {},
      "source": [
       "Model persistence"
      ]
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "source": [
-      "It is possible to save a model in the scikit by using Python's built-in",
+      "It is possible to save a model in the scikit by using Python's built-in\n",
       "persistence model, namely pickle:"
      ]
     },
@@ -356,27 +399,29 b''
      "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])",
+      "from sklearn import svm\n",
+      "from sklearn import datasets\n",
+      "clf = svm.SVC()\n",
+      "iris = datasets.load_iris()\n",
+      "X, y = iris.data, iris.target\n",
+      "clf.fit(X, y)\n",
+      "import pickle\n",
+      "s = pickle.dumps(clf)\n",
+      "clf2 = pickle.loads(s)\n",
+      "clf2.predict(X[0])\n",
       "y[0]"
      ],
      "language": "python",
+     "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
+     "metadata": {},
      "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",
+      "In the specific case of the scikit, it may be more interesting to use\n",
+      "joblib's replacement of pickle (joblib.dump & joblib.load),\n",
+      "which is more efficient on big data, but can only pickle to the disk\n",
       "and not to a string:"
      ]
     },
@@ -384,13 +429,15 b''
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from sklearn.externals import joblib",
+      "from sklearn.externals import joblib\n",
       "joblib.dump(clf, 'filename.pkl') # doctest: +SKIP"
      ],
      "language": "python",
+     "metadata": {},
      "outputs": []
     }
-   ]
+   ],
+   "metadata": {}
   }
  ]
 }
  No newline at end of file