piml.data.outlier_detection.IsolationForest

class piml.data.outlier_detection.IsolationForest(n_estimators=100, max_samples='auto', max_features=1.0, bootstrap=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, standardization=True)

A wrapper of sklearn’s Isolation Forest for outlier detection.

Return the anomaly score of each sample using the IsolationForest algorithm

The IsolationForest ‘isolates’ observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node.

This path length, averaged over a forest of such random trees, is a measure of normality and our decision function.

Random partitioning produces noticeably shorter paths for anomalies. Hence, when a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies.

Parameters:

n_estimatorsint, default=100

The number of base estimators in the ensemble.

max_samples“auto”, int or float, default=”auto”

The number of samples to draw from X to train each base estimator.

• If int, then draw max_samples samples.

• If float, then draw max_samples * X.shape[0] samples.

• If “auto”, then max_samples=min(256, n_samples).

If max_samples is larger than the number of samples provided, all samples will be used for all trees (no sampling).

max_featuresint or float, default=1.0

The number of features to draw from X to train each base estimator.

• If int, then draw max_features features.

• If float, then draw max(1, int(max_features * n_features_in_)) features.

Note: using a float number less than 1.0 or integer less than number of features will enable feature subsampling and leads to a longer runtime.

bootstrapbool, default=False

If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed.

n_jobsint, default=None

The number of jobs to run in parallel for both fit and predict. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

random_stateint, RandomState instance or None, default=None

Controls the pseudo-randomness of the selection of the feature and split values for each branching step and each tree in the forest.

Pass an int for reproducible results across multiple function calls.

verboseint, default=0

Controls the verbosity of the tree building process.

warm_startbool, default=False

When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.

standardizationbool, default=True

Whether to standardize covariates before running the algorithm.

Attributes:

estimator_~sklearn.tree.ExtraTreeRegressor instance

The child estimator template used to create the collection of fitted sub-estimators.

base_estimator_ExtraTreeRegressor instance

The child estimator template used to create the collection of fitted sub-estimators.

estimators_list of ExtraTreeRegressor instances

The collection of fitted sub-estimators.

estimators_features_list of ndarray

The subset of drawn features for each base estimator.

estimators_samples_list of ndarray

The subset of drawn samples (i.e., the in-bag samples) for each base estimator.

max_samples_int

The actual number of samples.

offset_float

Offset used to define the decision function from the raw scores. We have the relation: decision_function = score_samples - offset_. offset_ is defined as follows. When the contamination parameter is set to “auto”, the offset is equal to -0.5 as the scores of inliers are close to 0 and the scores of outliers are close to -1. When a contamination parameter different than “auto” is provided, the offset is defined in such a way we obtain the expected number of outliers (samples with decision function < 0) in training.

n_features_in_int

Number of features seen during fit.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

Methods

decision_function(X[, scale])	Predict raw outliers score of X using the fitted detector.
fit(X[, y, sample_weight])	Fit the model.
predict([X, scale, threshold])	Predict raw outlier indicator.

decision_function(X, scale=True)

Predict raw outliers score of X using the fitted detector.

For consistency, outliers are assigned with larger anomaly scores.

Parameters:

Xnumpy array of shape (n_samples, n_features)

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

scalebool, default=True

If True, scale X before calculating the outlier score.

Returns:

outlier_scoresnumpy array of shape (n_samples,)

The anomaly score of the input samples.

fit(X, y=None, sample_weight=None)

Fit the model.

Parameters:

Xnp.ndarray of shape (n_samples, n_features)

Data features.

ynp.ndarray of shape (n_samples,), default=None

Data response.

sample_weightnp.ndarray of shape (n_samples, ), default=None

Sample weight.

predict(X=None, scale=True, threshold=0.9)

Predict raw outlier indicator.

Normal samples are classified as 1 and outliers are classified as -1.

Parameters:

Xnumpy array of shape (n_samples, n_features)

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

scalebool, default=True

If True, scale X before calculating the outlier score.

thresholdfloat, default=0.9

The quantile threshold of outliers. For example, the samples with outlier scores greater than 90% quantile of the whole sample will be classified as outliers.

Returns:

outlier_indicatornumpy array of shape (n_samples,)

The binary array indicating whether each sample is outlier.

Examples using piml.data.outlier_detection.IsolationForest

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Data Quality Check