Ensemble API¶

class msitrees.ensemble.MSIRandomForestClassifier(n_estimators: int = 100, bootstrap: bool = True, feature_sampling: bool = True, n_jobs: int = - 1, random_state: Optional[int] = None, **kwargs)¶

MSI Random Forest Classifier

A collection of MSI based decision tree classifiers fitted on bootstrapped sub-samples of dataset. Final class label is decided by majority voting between all estimators.

Parameters

n_estimators (int, default=100) – Number of tree estimators to fit
bootstrap (bool, default=True) – When true, each estimator in will be fitted with bootstrap sub-sample of original dataset.
feature_sampling (bool, default=True) – When true, number of features considered at each split will be equal to sqrt(n_features). This is equivalent of sklearn max_features param set to ‘auto’.
n_jobs (int, default=-1) – Number of parallel jobs to run. When set to -1 all CPUs are used. 1 means no parallel processing.
random_state (int, default=None) – Sets seed for class instance. Used to control bootstrap. Note that this seeds only generator that sets random state for each estimator, so trees generally should have their own unique seeds. When random_state is set to a number, those seeds will be reproduced at each run. Parameter set to None should result in different estimator seeding each time.

estimators¶

list with all fited MSIDecisionTreeClassifier instances.

Type: list

fitted¶

Boolean variable indicating if tree was previously fitted.

Type: bool

shape¶

Shape of dataset X with (n_samples, n_features) or None if tree was not yet fitted.

Type: tuple

ncls¶

Number of classification categories or None if tree was not yet fitted.

Type: int

ndim¶

Number of dataset X dimensions. 1 if n_features eq 1, 2 if n_features > 1 or None if tree was not yet fitted.

Type: int

importances¶

Array with feature importances or None if tree was not fitted.

Type: np.ndarray

References

Examples

>>> from msitrees.ensemble import MSIRandomForestClassifier
>>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import cross_val_score
>>> data = load_iris()
>>> clf = MSIRandomForestClassifier()
>>> cross_val_score(clf, data['data'], data['target'], cv=10)
...
array([1.        , 1.        , 1.        , 1.        , 0.93333333,
   0.86666667, 0.93333333, 0.86666667, 0.8       , 1.        ])

property feature_importances_¶

Returns feature importances

Each feature importance is calculated as normalized sum of gini based information gain at nodes where split was made on that particular feature. For random forest classifier, final importance is a mean over all estimators.

Returns: importances – Normalized array of feature importances.
Return type: np.ndarray

fit(x: Union[numpy.ndarray, pandas.core.frame.DataFrame, pandas.core.series.Series], y: Union[numpy.ndarray, pandas.core.series.Series]) → msitrees.ensemble.MSIRandomForestClassifier ¶

Fits random forest classifier to training dataset.

Parameters

x (np.ndarray) – Training data of shape (n_samples, n_features) or (n_samples, ). All values have to be numerical, so perform any required encoding before calling this method.
y (np.ndarray) – Ground truth data of shape (n_samples, ). All values have to be numerical, so perform any required encoding before calling this method.

Returns

self – Fitted estimator.

Return type

MSIRandomForestClassifier

get_params(**kwargs) → dict¶

Get parameters for this estimator

Notes

scikit-learn API compatibility.

predict(x: Union[numpy.ndarray, pandas.core.frame.DataFrame]) → numpy.ndarray¶

Predicts class labels for each sample in input data X

Parameters: x (np.ndarray) – Array of samples with shape (n_samples, n_features). Class label is predicted for each sample.
Returns: pred – Array with shape (n_samples, ) Class label prediction for each sample.
Return type: np.ndarray

predict_log_proba(x: Union[numpy.array, pandas.core.frame.DataFrame]) → numpy.ndarray¶

Predicts class log probability for each sample in input data X.

Probability is defined as fraction of class label in a leaf.

Parameters: x (np.ndarray) – Array of samples with shape (n_samples, n_features). Class log probabilities are predicted for each sample.
Returns: logprobas – Array with shape (n_samples, n_targets) Array of log probabilities. Each index corresponds to class label and holds predicted log porbability of this class.
Return type: np.ndarray

predict_proba(x: Union[numpy.array, pandas.core.frame.DataFrame]) → numpy.ndarray¶

Predicts class probability for each sample in input data X.

Probability is defined as fraction of class label in a leaf.

Parameters: x (np.ndarray) – Array of samples with shape (n_samples, n_features). Class probabilities are predicted for each sample.
Returns: probas – Array with shape (n_samples, n_targets) Array of probabilities. Each index corresponds to class label and holds predicted porbability of this class.
Return type: np.ndarray

score(x: Union[numpy.ndarray, pandas.core.frame.DataFrame], y: Union[numpy.ndarray, pandas.core.frame.DataFrame]) → float¶

Predicts class label for each sample in X and computes accuracy score wrt. ground truth.

Parameters

x (np.ndarray) – Array of samples with shape (n_samples, n_features). Class label is predicted for each sample.
y (np.ndarray) – Array of ground truth labels.

Returns

accuracy – Accuracy score for predicted class labels.

Return type

float

set_params(**params) → msitrees.ensemble.MSIRandomForestClassifier ¶

Set the parameters of this estimator

Notes

scikit-learn API compatibility.