`ranksim`.RSPClassifier¶

class ranksim.RSPClassifier(*, n_filters='auto', max_filters=5000, n_fast_filters=1000, initialize='random', spreading='max', n_iter=5, random_state=None, filter_function='auto', create_distribution=None, **kwargs)[source]¶

Rank Similarity Probabilistic (RSP) Classifier

Read more in the User Guide.

Parameters

n_filters{‘auto’} or int, default=’auto’

Number of filters to use. ‘auto’ will determine this based on max_filters, n_fast_filters and the size of the input data.

max_filtersint, default=5000

Maximum number of filters to allocate.

Only used when n_filters='auto'.

n_fast_filters: int, default=1000

Minimum number of filters to allocate, unless the input data has fewer samples than this number.

Only used when n_filters='auto'.

initialize{‘random’,’weighted_avg’,’plusplus’}, default=’random’

Type of filter initialization.

‘random’, filters are initialized with a random data point.
‘weighted_avg’, creates filters from similar data, used when
there are more filters than input data.
‘plusplus’, filters are initialized with dissimilar data as k-means++

spreading{‘max’, ‘weighted_avg’} or None, default=’max’

Determines how data is spread between filters during training

‘max’, the data point is allocated to the maximum responding
filter.
‘weighted_avg’, the weighted average of a fixed number of data
points are allocated to the maximum responding filter, used when there are more filters than data.

n_iterint, default=5

Number of iterations/sweeps over the training dataset to perform during training.

random_stateint, RandomState instance, default=None

Determines random number generation for filter initialization. Pass an int for reproducible results across multiple function calls.

filter_function{‘auto’} or callable, default=’auto’

Function which determines the weights from subsections of the input data. ‘auto’ performs a mean and rank, optionally drawn from a distribution.

create_distribution{‘confusion’}, callable or None, default=None

Creates a distribution to draw ranks from.

‘confusion’ is a distribution based on the confusibility of
features in the input data.

Note: the ‘confusion’ option is extremely slow.

Attributes

classes_ndarray or list of ndarray of shape (n_classes,): Class labels for each output.
filters_ndarray of shape (n_filters_, n_features): Weights of the calculated filters.
filter_labels_list of ndarray of shape (n_classes,): Label of the datapoints used to make the filter.
n_filters_int: Number of filters.
n_iter_int: The number of iterations run by the spreading function.
n_outputs_int: Number of outputs.
filterFactory_class: Class used to create the filters.

Examples

>>> from multifilter import RSPClassifier
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_classification(n_samples=1000, random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
>>> clf = RSPClassifier().fit(X_train,y_train)
>>> clf.predict_proba(X_test[:1,:])
array([[0.43370805, 0.56629195]])
>>> clf.predict(X_test[:5, :])
array([1, 0, 1, 0, 1])
>>> clf.score(X_test, y_test)
0.888

Methods

`fit`(X, y)	Fit RSP classifier from the training dataset.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict using the RSP classifier
`predict_proba`(X[, n_best])	Probability estimates for RSP classifier
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_params`(**params)	Set the parameters of this estimator.

ranksim.RSPClassifier¶

`ranksim`.RSPClassifier¶