dislib.classification.RandomForestClassifier

class dislib.classification.rf.forest.RandomForestClassifier(n_estimators=10, try_features='sqrt', max_depth=inf, distr_depth='auto', sklearn_max=100000000.0, hard_vote=False, random_state=None)

Bases: sklearn.base.BaseEstimator

A distributed random forest classifier.

Parameters:

• n_estimators (int, optional (default=10)) – Number of trees to fit.

• try_features (int, str or None, optional (default=’sqrt’)) – The number of features to consider when looking for the best split:

  • If “sqrt”, then try_features=sqrt(n_features).
  • If “third”, then try_features=n_features // 3.
  • If None, then try_features=n_features.

  Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than try_features features.

• max_depth (int or np.inf, optional (default=np.inf)) – The maximum depth of the tree. If np.inf, then nodes are expanded until all leaves are pure.

• distr_depth (int or str, optional (default=’auto’)) – Number of levels of the tree in which the nodes are split in a distributed way.

• sklearn_max (int or float, optional (default=1e8)) – Maximum size (len(subsample)*n_features) of the arrays passed to sklearn’s DecisionTreeClassifier.fit(), which is called to fit subtrees (subsamples) of our DecisionTreeClassifier. sklearn fit() is used because it’s faster, but requires loading the data to memory, which can cause memory problems for large datasets. This parameter can be adjusted to fit the hardware capabilities.

• hard_vote (bool, optional (default=False)) – If True, it uses majority voting over the predict() result of the decision tree predictions. If False, it takes the class with the higher probability given by predict_proba(), which is an average of the probabilities given by the decision trees.

• random_state (int, RandomState instance or None, optional (default=None)) – If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Variables:

• classes (None or ndarray) – Array of distinct classes, set at fit().
• trees (list of DecisionTreeClassifier) – List of the tree classifiers of this forest, populated at fit().

fit(x, y)

Fits the RandomForestClassifier.

Parameters:

• x (ds-array, shape=(n_samples, n_features)) – The training input samples. Internally, its dtype will be converted to dtype=np.float32.
• y (ds-array, shape=(n_samples, 1)) – The target values.

Returns:

self

Return type:

RandomForestClassifier

predict(x)

Predicts classes using a fitted forest.

Parameters:x (ds-array, shape=(n_samples, n_features)) – The input samples. Returns:y_pred – Predicted class labels for x. Return type:ds-array, shape=(n_samples, 1)

predict_proba(x)

Predicts class probabilities using a fitted forest.

The probabilities are obtained as an average of the probabilities of each decision tree.

Parameters:x (ds-array, shape=(n_samples, n_features)) – The input samples. Returns:probabilities – Predicted probabilities for the samples to belong to each class. The columns of the array correspond to the classes given at self.classes. Return type:ds-array, shape=(n_samples, n_classes)

score(x, y)

Accuracy classification score.

Returns the mean accuracy on the given test data.

Parameters:

• x (ds-array, shape=(n_samples, n_features)) – The training input samples.
• y (ds-array, shape (n_samples, 1)) – The true labels.

Returns:

score – Fraction of correctly classified samples.

Return type:

float (as future object)