scikit-learn is a general-purpose open-source library for data analysis written in python. It is based on other python libraries: NumPy, SciPy, and matplotlib
scikit-learncontains a number of implementation for different popular algorithms of machine learning.
The current stable version of scikit-learn requires:
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__)'
Windows and Mac OSX Installation:
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)
Different operations with data are done using special classes.
Most of the classes belong to one of the following groups:
sklearn.base.ClassifierMixin) to solve classification problems
sklearn.base.RegressorMixin) to solve problem of reconstructing continuous variables (regression problem)
sklearn.base.TransformerMixin) that preprocess the data
Data is stored in
numpy.arrays (but other array-like objects like
pandas.DataFrames are accepted if those are convertible to
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]]
sklearn conventions dataset above contains 5 objects each described by 2 features.
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
target fields. By convention those are denoted as
X, y = iris_dataset['data'], iris_dataset['target'] X.shape, y.shape ((150, 4), (150,))
numpy.unique(y) array([0, 1, 2])
y say that there are 150 samples with 4 features. Each sample belongs to one of following classes: 0, 1 or 2.
y can now be used in training a classifier, by calling the classifier's
Here is the full list of datasets provided by the
sklearn.datasets module with their size and intended use:
|Boston house-prices dataset||506||regression|
|Breast cancer Wisconsin dataset||569||classification (binary)|
|Iris dataset||150||classification (multi-class)|
|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_mlcompfor 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_peoplefor 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.
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
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.
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)