econml.iv.sieve.HermiteFeatures

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

Bases: sklearn.base.TransformerMixin

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.

fit(X)

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

fit_transform(X[, y])

Fit to data, then transform it.

transform(X)

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