Python_Note20

Python_scikit_learn

http://scikit-learn.org/stable/

User Guide

http://scikit-learn.org/stable/user_guide.html

Outline：

1. Supervised learning

2. Unsupervised learning

3. Model selection and evaluation

4. Dataset transformations

5. Dataset loading utilities

6. Strategies to scale computationally: bigger data

7. Computational Performance

1.1. Generalized Linear Models

http://scikit-learn.org/stable/modules/linear_model.html

1.1.11. Logistic regression

http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

sklearn.linear_model.LogisticRegression

class sklearn.linear_model.LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True,intercept_scaling=1, class_weight=None, random_state=None, solver='liblinear', max_iter=100, multi_class='ovr',verbose=0, warm_start=False, n_jobs=1)

decision_function(X)	Predict confidence scores for samples.
densify()	Convert coefficient matrix to dense array format.
fit(X, y[, sample_weight])	Fit the model according to the given training data.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict class labels for samples in X.
predict_log_proba(X)	Log of probability estimates.
predict_proba(X)	Probability estimates.
score(X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.
sparsify()	Convert coefficient matrix to sparse format.
transform(*args, **kwargs)	DEPRECATED: Support to use estimators as feature selectors will be removed in version 0.19.

Attributes:

coef_ : array, shape (n_classes, n_features) Coefficient of the features in the decision function. intercept_ : array, shape (n_classes,) Intercept (a.k.a. bias) added to the decision function. If fit_intercept is set to False, the intercept is set to zero. n_iter_ : array, shape (n_classes,) or (1, ) Actual number of iterations for all classes. If binary or multinomial, it returns only 1 element. For liblinear solver, only the maximum number of iteration across all classes is given.

sklearn.linear_model.Perceptron

class sklearn.linear_model.Perceptron(penalty=None, alpha=0.0001, fit_intercept=True, n_iter=5, shuffle=True,verbose=0, eta0=1.0, n_jobs=1, random_state=0, class_weight=None, warm_start=False)

decision_function(X)	Predict confidence scores for samples.
densify()	Convert coefficient matrix to dense array format.
fit(X, y[, coef_init, intercept_init, ...])	Fit linear model with Stochastic Gradient Descent.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
partial_fit(X, y[, classes, sample_weight])	Fit linear model with Stochastic Gradient Descent.
predict(X)	Predict class labels for samples in X.
score(X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
set_params(*args, **kwargs)
sparsify()	Convert coefficient matrix to sparse format.
transform(*args, **kwargs)	DEPRECATED: Support to use estimators as feature selectors will be removed in version 0.19.

1.6. Nearest Neighbors

http://scikit-learn.org/stable/modules/neighbors.html

neighbors.NearestNeighbors([n_neighbors, ...])	Unsupervised learner for implementing neighbor searches.
neighbors.KNeighborsClassifier([...])	Classifier implementing the k-nearest neighbors vote.
neighbors.RadiusNeighborsClassifier([...])	Classifier implementing a vote among neighbors within a given radius
neighbors.KNeighborsRegressor([n_neighbors, ...])	Regression based on k-nearest neighbors.
neighbors.RadiusNeighborsRegressor([radius, ...])	Regression based on neighbors within a fixed radius.
neighbors.NearestCentroid([metric, ...])	Nearest centroid classifier.
neighbors.BallTree	BallTree for fast generalized N-point problems
neighbors.KDTree	KDTree for fast generalized N-point problems
neighbors.LSHForest([n_estimators, radius, ...])	Performs approximate nearest neighbor search using LSH forest.
neighbors.DistanceMetric	DistanceMetric class
neighbors.KernelDensity([bandwidth, ...])	Kernel Density Estimation

neighbors.kneighbors_graph(X, n_neighbors[, ...])	Computes the (weighted) graph of k-Neighbors for points in X
neighbors.radius_neighbors_graph(X, radius)	Computes the (weighted) graph of Neighbors for points in X

sklearn.neighbors.KNeighborsClassifier

class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=1, **kwargs)

1.13. Feature selection

http://scikit-learn.org/stable/modules/feature_selection.html
The sklearn.feature_selection module implements feature selection algorithms. It currently includes univariate filter selection methods and the recursive feature elimination algorithm.

feature_selection.GenericUnivariateSelect([...])	Univariate feature selector with configurable strategy.
feature_selection.SelectPercentile([...])	Select features according to a percentile of the highest scores.
feature_selection.SelectKBest([score_func, k])	Select features according to the k highest scores.
feature_selection.SelectFpr([score_func, alpha])	Filter: Select the pvalues below alpha based on a FPR test.
feature_selection.SelectFdr([score_func, alpha])	Filter: Select the p-values for an estimated false discovery rate
feature_selection.SelectFromModel(estimator)	Meta-transformer for selecting features based on importance weights.
feature_selection.SelectFwe([score_func, alpha])	Filter: Select the p-values corresponding to Family-wise error rate
feature_selection.RFE(estimator[, ...])	Feature ranking with recursive feature elimination.
feature_selection.RFECV(estimator[, step, ...])	Feature ranking with recursive feature elimination and cross-validated selection of the best number of features.
feature_selection.VarianceThreshold([threshold])	Feature selector that removes all low-variance features.

feature_selection.chi2(X, y)	Compute chi-squared stats between each non-negative feature and class.
feature_selection.f_classif(X, y)	Compute the ANOVA F-value for the provided sample.
feature_selection.f_regression(X, y[, center])	Univariate linear regression tests.
feature_selection.mutual_info_classif(X, y)	Estimate mutual information for a discrete target variable.
feature_selection.mutual_info_regression(X, y)	Estimate mutual information for a continuous target variable.

3.1. Cross-validation: evaluating estimator performance

http://scikit-learn.org/stable/modules/cross_validation.html

sklearn.model_selection.train_test_split(*arrays, **options)

Split arrays or matrices into random train and test subsets

Quick utility that wraps input validation and next(ShuffleSplit().split(X, y)) and application to input data into a single call for splitting (and optionally subsampling) data in a oneliner.

4.8.2. Label encoding

http://scikit-learn.org/stable/modules/preprocessing_targets.html#label-encoding

sklearn.preprocessing.LabelEncode

class sklearn.preprocessing.LabelEncoder

Encode labels with value between 0 and n_classes-1.

fit(y)	Fit label encoder
fit_transform(y)	Fit label encoder and return encoded labels
get_params([deep])	Get parameters for this estimator.
inverse_transform(y)	Transform labels back to original encoding.
set_params(**params)	Set the parameters of this estimator.
transform(y)	Transform labels to normalized encoding.

4.3.4. Encoding categorical features

http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing-categorical-features

sklearn.preprocessing.OneHotEncoder

class sklearn.preprocessing.OneHotEncoder(n_values='auto', categorical_features='all', dtype=<type 'numpy.float64'>, sparse=True, handle_unknown='error')

Encode categorical integer features using a one-hot aka one-of-K scheme.

4.3.1.1. Scaling features to a range

http://scikit-learn.org/stable/modules/preprocessing.html#scaling-features-to-a-range

sklearn.preprocessing.MinMaxScaler

class sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True)

Transforms features by scaling each feature to a given range.

This estimator scales and translates each feature individually such that it is in the given range on the training set, i.e. between zero and one.

The transformation is given by:

X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min

sklearn.base: Base classes and utility functions

Base classes for all estimators.

Base classes

base.BaseEstimator	Base class for all estimators in scikit-learn
base.ClassifierMixin	Mixin class for all classifiers in scikit-learn.
base.ClusterMixin	Mixin class for all cluster estimators in scikit-learn.
base.RegressorMixin	Mixin class for all regression estimators in scikit-learn.
base.TransformerMixin	Mixin class for all transformers in scikit-learn.

Functions

base.clone(estimator[, safe])

Constructs a new estimator with the same parameters.