ruckus.embedding.OneHotRKHS class

class ruckus.embedding.OneHotRKHS(axis=0, *, take=None, copy_X=True)[source]

Bases: ruckus.base.RKHS

OneHotRKHS is for processing categorical data. If \(X\) is a discrete set, this generates an embedding map \(\phi:X\rightarrow H\) into a Hilbert space \(H\) whose dimension is the cardinality of \(X\), such that \(\phi(x)\) maps the element \(x\) to a one-hot vector with the 1-valued component in the dimension which uniquely corresponds to \(x\).

This is particularly advantageous when working with kernel embeddings of distributions, as the embedded distribution vector is itself a probability vector (positive components and sums to 1).

Parameters

  • axis (int or tuple of ints) – Default = 0. Specifies the axis or axes along which unique entries will be determined. The alphabet will be taken as the unique subarrays indexed by the given axes, and the transformed vector will have the shape of the given axes + an additional axis indexing the alphabet. The 0 axis (that is, the sample axis) will always be included, even if not given.

  • take (numpy.ndarray of dtype int or bool, or tuple of numpy.ndarray instances of type int, or None) – Default = None. Specifies which values to take from the datapoint for transformation. If None, the entire datapoint will be taken in its original shape. If bool array, acts as a mask setting values marked False to 0 and leaving values marked True unchanged. If int array, the integers specify the indices (along the first feature dimension) which are to be taken, in the order/shape of the desired input. If tuple of int arrays, allows for drawing indices across multiple dimensions, similar to passing a tuple to a numpy array.

  • copy_X (bool) – Default = True. If True, input X is copied and stored by the model in the X_fit_ attribute. If no further changes will be done to X, setting copy_X=False saves memory by storing a reference.

Parameters

  • alphabet_ (numpy.ndarray of objects) – The unique elements from self.X_fit_.

  • shape_in_ (tuple) – The required shape of the input datapoints, aka the shape of the domain space \(X\).

  • shape_out_ (tuple) – The final shape of the transformed datapoints, aka the shape of the Hilbert space \(H\).

  • X_fit_ (numpy.ndarray of shape (n_samples,)+self.shape_in_) – The data which was used to fit the model.

fit(X, y=None)[source]

Fit the model from data in X.

Parameters

  • X (numpy.ndarray of shape (n_samples, n_features_1,...,n_features_d)) – Training vector, where n_samples is the number of samples and (n_features_1,...,n_features_d) is the shape of the input data. Must be consistent with preprocessing instructions in self.take.

  • y (Ignored) – Not used, present for API consistency by convention.

Returns

The instance itself

Return type

RKHS

transform(X)[source]

Transform X.

Parameters

X (numpy.ndarray of shape (n_samples, n_features_1,...,n_features_d)) – Data vector, where n_samples is the number of samples and (n_features_1,...,n_features_d) is the shape of the input data. These must match self.shape_in_.

Returns

The transformed data

Return type

numpy.ndarray of shape (n_samples,)+self.shape_out_