scikit-learn Getting started with scikit-learn

Remarks

Installation of scikit-learn

The current stable version of scikit-learn requires:

• Python (>= 2.6 or >= 3.3),
• NumPy (>= 1.6.1),
• SciPy (>= 0.9).

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For most installation pip python package manager can install python and all of its dependencies:

pip install scikit-learn
 

However for linux systems it is recommended to use conda package manager to avoid possible build processes

conda install scikit-learn
 

To check that you have scikit-learn , execute in shell:

python -c 'import sklearn; print(sklearn.__version__)'
 

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Windows and Mac OSX Installation:

Canopy and Anaconda both ship a recent version of scikit-learn, in addition to a large set of scientific python library for Windows, Mac OSX (also relevant for Linux).

Creating pipelines

Finding patterns in data often proceeds in a chain of data-processing steps, e.g., feature selection, normalization, and classification. In sklearn , a pipeline of stages is used for this.

For example, the following code shows a pipeline consisting of two stages. The first scales the features, and the second trains a classifier on the resulting augmented dataset:

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier

pipeline = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=4))
 

Once the pipeline is created, you can use it like a regular stage (depending on its specific steps). Here, for example, the pipeline behaves like a classifier. Consequently, we can use it as follows:

# fitting a classifier
pipeline.fit(X_train, y_train)
# getting predictions for the new data sample
pipeline.predict_proba(X_test)
 

Interfaces and conventions:

Different operations with data are done using special classes.

Most of the classes belong to one of the following groups:

• classification algorithms (derived from sklearn.base.ClassifierMixin ) to solve classification problems
• regression algorithms (derived from sklearn.base.RegressorMixin ) to solve problem of reconstructing continuous variables (regression problem)
• data transformations (derived from sklearn.base.TransformerMixin ) that preprocess the data

Data is stored in numpy.array s (but other array-like objects like pandas.DataFrame s are accepted if those are convertible to numpy.array s)

Each object in the data is described by set of features the general convention is that data sample is represented with array, where first dimension is data sample id, second dimension is feature id.

import numpy
data = numpy.arange(10).reshape(5, 2)
print(data)

Output:
[[0 1]
 [2 3]
 [4 5]
 [6 7]
 [8 9]]
 

In sklearn conventions dataset above contains 5 objects each described by 2 features.

Sample datasets

For ease of testing, sklearn provides some built-in datasets in sklearn.datasets module. For example, let's load Fisher's iris dataset:

import sklearn.datasets
iris_dataset = sklearn.datasets.load_iris()
iris_dataset.keys()
['target_names', 'data', 'target', 'DESCR', 'feature_names']
 

You can read full description, names of features and names of classes (target_names ). Those are stored as strings.

We are interested in the data and classes, which stored in data and target fields. By convention those are denoted as X and y

X, y = iris_dataset['data'], iris_dataset['target']
X.shape, y.shape
((150, 4), (150,))
 

numpy.unique(y)
array([0, 1, 2])
 

Shapes of X and y say that there are 150 samples with 4 features. Each sample belongs to one of following classes: 0, 1 or 2.

X and y can now be used in training a classifier, by calling the classifier's fit() method.

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Here is the full list of datasets provided by the sklearn.datasets module with their size and intended use:

Load with	Description	Size	Usage
load_boston()	Boston house-prices dataset	506	regression
load_breast_cancer()	Breast cancer Wisconsin dataset	569	classification (binary)
load_diabetes()	Diabetes dataset	442	regression
load_digits(n_class)	Digits dataset	1797	classification
load_iris()	Iris dataset	150	classification (multi-class)
load_linnerud()	Linnerud dataset	20	multivariate regression

Note that (source: http://scikit-learn.org/stable/datasets/):

These datasets are useful to quickly illustrate the behavior of the various algorithms implemented in the scikit. They are however often too small to be representative of real world machine learning tasks.

In addition to these built-in toy sample datasets, sklearn.datasets also provides utility functions for loading external datasets:

• load_mlcomp for loading sample datasets from the mlcomp.org repository (note that the datasets need to be downloaded before). Here is an example of usage.
• fetch_lfw_pairs and fetch_lfw_people for loading Labeled Faces in the Wild (LFW) pairs dataset from http://vis-www.cs.umass.edu/lfw/, used for face verification (resp. face recognition). This dataset is larger than 200 MB. Here is an example of usage.

Train a classifier with cross-validation

Using iris dataset:

import sklearn.datasets
iris_dataset = sklearn.datasets.load_iris()
X, y = iris_dataset['data'], iris_dataset['target']
 

Data is split into train and test sets. To do this we use the train_test_split utility function to split both X and y (data and target vectors) randomly with the option train_size=0.75 (training sets contain 75% of the data).

Training datasets are fed into a k-nearest neighbors classifier. The method fit of the classifier will fit the model to the data.

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75) 
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier(n_neighbors=3)
clf.fit(X_train, y_train)
 

Finally predicting quality on test sample:

clf.score(X_test, y_test) # Output: 0.94736842105263153
 

By using one pair of train and test sets we might get a biased estimation of the quality of the classifier due to the arbitrary choice the data split. By using cross-validation we can fit of the classifier on different train/test subsets of the data and make an average over all accuracy results. The function cross_val_score fits a classifier to the input data using cross-validation. It can take as input the number of different splits (folds) to be used (5 in the example below).

from sklearn.cross_validation import cross_val_score
scores = cross_val_score(clf, X, y, cv=5)
print(scores)
# Output: array([ 0.96666667,  0.96666667,  0.93333333,  0.96666667,  1.        ])
print "Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2)
# Output: Accuracy: 0.97 (+/- 0.03)