# econml.iv.sieve.HermiteFeatures¶

class econml.iv.sieve.HermiteFeatures(degree, shift=0, joint=False)[source]

Featurizer that returns(unscaled) Hermite function evaluations.

The evaluated functions are of degrees 0..degree, differentiated shift times.

If the input has shape(n, x) and joint is False, the output will have shape(n, (degree+ 1)×x) if shift is 0. If the input has shape(n, x) and joint is True, the output will have shape(n, (degree+ 1) ^ x) if shift is 0. In either case, if shift is nonzero there will be shift additional dimensions of size x between the first and last.

__init__(degree, shift=0, joint=False)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__(degree[, shift, joint]) Initialize self. Fits the data(a NOP for this class) and returns self. fit_transform(X[, y]) Fit to data, then transform it. Transform the data by applying the appropriate Hermite functions.
fit(X)[source]

Fits the data(a NOP for this class) and returns self.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters
• X (array-like of shape (n_samples, n_features)) – Input samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).

• **fit_params (dict) – Additional fit parameters.

Returns

X_new – Transformed array.

Return type

ndarray array of shape (n_samples, n_features_new)

transform(X)[source]

Transform the data by applying the appropriate Hermite functions.

Parameters

X (array_like) – 2-dimensional array of input features

Returns

Return type

The transformed data