An introduction to machine learning with scikit-learn¶
In this section, we introduce the machine learning vocabulary that we use throughout scikit-learn and give a simple learning example.
Machine learning: the problem setting¶
In general, a learning problem considers a set of n samples of data and then tries 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.
We can separate learning problems in a few large categories:
- 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 handwritten digit recognition example, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class.
- 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.
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 three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).
Training set and testing set
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 into two sets, one that we call the training set on which we learn data properties and one that we call the testing set on which we test these properties.
Loading an example dataset¶
In the following, we start a Python interpreter from our shell and then
digits datasets. Our notational convention is that
$ denotes the shell prompt while
>>> denotes the Python
$ python >>> from sklearn import datasets >>> iris = datasets.load_iris() >>> digits = datasets.load_digits()
A dataset is a dictionary-like object that holds all the data and some
metadata about the data. This data is stored in the
which is a
n_samples, n_features array. In the case of supervised
problem, one or more response variables are stored in the
.target member. More
details on the different datasets can be found in the dedicated
For instance, in the case of the digits dataset,
access to the features that can be used to classify the digits samples:
>>> print(digits.data) [[ 0. 0. 5. ..., 0. 0. 0.] [ 0. 0. 0. ..., 10. 0. 0.] [ 0. 0. 0. ..., 16. 9. 0.] ..., [ 0. 0. 1. ..., 6. 0. 0.] [ 0. 0. 2. ..., 12. 0. 0.] [ 0. 0. 10. ..., 12. 1. 0.]]
digits.target gives the ground truth for the digit dataset, that
is the number corresponding to each digit image that we are trying to
>>> digits.target array([0, 1, 2, ..., 8, 9, 8])
Shape of the data arrays
The data is always a 2D array, shape
(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
>>> digits.images array([[ 0., 0., 5., 13., 9., 1., 0., 0.], [ 0., 0., 13., 15., 10., 15., 5., 0.], [ 0., 3., 15., 2., 0., 11., 8., 0.], [ 0., 4., 12., 0., 0., 8., 8., 0.], [ 0., 5., 8., 0., 0., 9., 8., 0.], [ 0., 4., 11., 0., 1., 12., 7., 0.], [ 0., 2., 14., 5., 10., 12., 0., 0.], [ 0., 0., 6., 13., 10., 0., 0., 0.]])
The simple example on this dataset illustrates how starting from the original problem one can shape the data for consumption in scikit-learn.
Learning and predicting¶
In the case of the digits dataset, the task is to predict, given an image, which digit it represents. We are given samples of each of the 10 possible classes (the digits zero through nine) on which we fit an estimator to be able to predict the classes to which unseen samples belong.
In scikit-learn, an estimator for classification is a Python object that
implements the methods
fit(X, y) and
An example of an estimator is the class
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
>>> from sklearn import svm >>> clf = svm.SVC(gamma=0.001, C=100.)
Choosing the parameters of the model
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. We select this training set with the
[:-1] Python syntax,
which produces a new array that contains all but
the last entry of
>>> clf.fit(digits.data[:-1], digits.target[:-1]) SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)
Now you can predict new values, in particular, we can ask to the
classifier what is the digit of our last image in the
which we have not used to train the classifier:
>>> clf.predict(digits.data[-1]) array()
The corresponding image is the following:
As you can see, it is a challenging task: the images are of poor resolution. Do you agree with the classifier?
A complete example of this classification problem is available as an example that you can run and study: Recognizing hand-written digits.
It is possible to save a model in the scikit by using Python’s built-in persistence model, namely pickle:
>>> 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) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) >>> import pickle >>> s = pickle.dumps(clf) >>> clf2 = pickle.loads(s) >>> clf2.predict(X) array() >>> y 0
In the specific case of the scikit, it may be more interesting to use
joblib’s replacement of pickle (
which is more efficient on big data, but can only pickle to the disk
and not to a string:
>>> from sklearn.externals import joblib >>> joblib.dump(clf, 'filename.pkl')
Later you can load back the pickled model (possibly in another Python process) with:
>>> clf = joblib.load('filename.pkl')
joblib.dump returns a list of filenames. Each individual numpy array
contained in the
clf object is serialized as a separate file on the
filesystem. All files are required in the same folder when reloading the
model with joblib.load.
Note that pickle has some security and maintainability issues. Please refer to section Model persistence for more detailed information about model persistence with scikit-learn.
scikit-learn estimators follow certain rules to make their behavior more predictive.
Unless otherwise specified, input will be cast to
>>> import numpy as np >>> from sklearn import random_projection >>> rng = np.random.RandomState(0) >>> X = rng.rand(10, 2000) >>> X = np.array(X, dtype='float32') >>> X.dtype dtype('float32') >>> transformer = random_projection.GaussianRandomProjection() >>> X_new = transformer.fit_transform(X) >>> X_new.dtype dtype('float64')
In this example,
float32, which is cast to
Regression targets are cast to
float64, classification targets are
>>> from sklearn import datasets >>> from sklearn.svm import SVC >>> iris = datasets.load_iris() >>> clf = SVC() >>> clf.fit(iris.data, iris.target) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) >>> list(clf.predict(iris.data[:3])) [0, 0, 0] >>> clf.fit(iris.data, iris.target_names[iris.target]) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) >>> list(clf.predict(iris.data[:3])) ['setosa', 'setosa', 'setosa']
Here, the first
predict() returns an integer array, since
(an integer array) was used in
fit. The second
predict returns a string
iris.target_names was for fitting.
Refitting and updating parameters¶
Hyper-parameters of an estimator can be updated after it has been constructed
sklearn.pipeline.Pipeline.set_params method. Calling
more than once will overwrite what was learned by any previous
>>> import numpy as np >>> from sklearn.svm import SVC >>> rng = np.random.RandomState(0) >>> X = rng.rand(100, 10) >>> y = rng.binomial(1, 0.5, 100) >>> X_test = rng.rand(5, 10) >>> clf = SVC() >>> clf.set_params(kernel='linear').fit(X, y) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, kernel='linear', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) >>> clf.predict(X_test) array([1, 0, 1, 1, 0]) >>> clf.set_params(kernel='rbf').fit(X, y) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) >>> clf.predict(X_test) array([0, 0, 0, 1, 0])
Here, the default kernel
rbf is first changed to
linear after the
estimator has been constructed via
SVC(), and changed back to
refit the estimator and to make a second prediction.