nemos.basis.IdentityEval#

class nemos.basis.IdentityEval(bounds=None, label='IdentityEval')[source]#

Bases: EvalBasisMixin, IdentityBasis

Identity basis function.

This function includes the samples themselves as predictor reshaped as a 2D array. It is intended to be used for including a task variable directly as a predictor.

Parameters:

bounds (Optional[Tuple[float, float]]) – The bounds for the basis domain. The default bounds[0] and bounds[1] are the minimum and the maximum of the samples provided when evaluating the basis. If a sample is outside the bounds, the basis will return NaN.
label (Optional[str]) – The label of the basis, intended to be descriptive of the task variable being processed. For example: velocity, position, spike_counts.

Attributes

`bounds`	Range of values covered by the basis.
`input_shape`
`label`	Label for the basis.
`mode`	Mode of operation, either `"conv"` or `"eval"`.
`n_basis_funcs`	Number of basis functions (read-only).
`n_output_features`	Number of features returned by the basis.

__init__(bounds=None, label='IdentityEval')[source]#

Parameters:

bounds (Tuple[float, float] | None)
label (str | None)

Methods

`__init__`([bounds, label])
`compute_features`(xi)	Evaluate basis at sample points.
`evaluate_on_grid`(n_samples)	Evaluate the basis set on a grid of equi-spaced sample points.
`get_params`([deep])	From scikit-learn, get parameters by inspecting init.
`set_input_shape`(xi)	Set the expected input shape for the basis object.
`set_params`(**params)	Set the parameters of this estimator.
`setup_basis`(*xi)	Set all basis states.
`split_by_feature`(x[, axis])	Decompose an array along a specified axis into sub-arrays based on the number of expected inputs.
`to_transformer`()	Turn the Basis into a TransformerBasis for use with scikit-learn.

__add__(other)#

Add two Basis objects together.

Parameters:: other (Basis) – The other Basis object to add.
Returns:: The resulting Basis object.
Return type:: AdditiveBasis

__iter__()#: Makes basis iterable. Re-implemented for additive.

__mul__(other)#

Multiply two Basis objects together.

Parameters:: other (Basis) – The other Basis object to multiply.
Return type:: MultiplicativeBasis
Returns:: The resulting Basis object.

__pow__(exponent)#

Exponentiation of a Basis object.

Define the power of a basis by repeatedly applying the method __multiply__. The exponent must be a positive integer.

Parameters:

exponent (int) – Positive integer exponent

Return type:

MultiplicativeBasis

Returns:

The product of the basis with itself “exponent” times. Equivalent to self * self * ... * self.

Raises:

TypeError – If the provided exponent is not an integer.
ValueError – If the integer is zero or negative.

__sklearn_clone__()#

Clone the basis while preserving attributes related to input shapes.

This method ensures that input shape attributes (e.g., _input_shape_product, _input_shape_) are preserved during cloning. Reinitializing the class as in the regular sklearn clone would drop these attributes, rendering cross-validation unusable.

Return type:: Basis

property bounds#: Range of values covered by the basis.

compute_features(xi)[source]#

Evaluate basis at sample points.

The basis is evaluated at the locations specified in the inputs. For example, compute_features(np.array([0, .5])) would return the array:

b_1(0) ... b_n(0)
b_1(.5) ... b_n(.5)

where b_i is the i-th basis.

Parameters:: *xi (ArrayLike) – The input samples over which to apply the basis transformation. The samples can be passed as multiple arguments, each representing a different dimension for multivariate inputs.
Return type:: TsdFrame | ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]
Returns:: A matrix with the transformed features.

Examples

>>> import numpy as np
>>> from nemos.basis import IdentityEval

>>> # Generate data
>>> num_samples = 1000
>>> X = np.random.normal(size=(num_samples, ))  # raw time series
>>> basis = IdentityEval()
>>> features = basis.compute_features(X)  # basis transformed time series
>>> features.shape
(1000, 1)

evaluate_on_grid(n_samples)[source]#

Evaluate the basis set on a grid of equi-spaced sample points.

Parameters:

n_samples (int) – The number of points in the uniformly spaced grid. A higher number of samples will result in a more detailed visualization of the basis functions.

Return type:

Tuple[NDArray, NDArray]

Returns:

X – Array of shape (n_samples,) containing the equi-spaced sample points where we’ve evaluated the basis.
basis_funcs – OrthExponential basis functions, shape (n_samples, n_basis_funcs).

Examples

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from nemos.basis import IdentityEval
>>> basis = IdentityEval()
>>> sample_points, basis_values = basis.evaluate_on_grid(100)

get_params(deep=True)#

From scikit-learn, get parameters by inspecting init.

Parameters:

deep

Return type:

dict

Returns:

out:: A dictionary containing the parameters. Key is the parameter name, value is the parameter value.

property input_shape: NDArray#

property label: str#: Label for the basis.

property mode#: Mode of operation, either "conv" or "eval".

property n_basis_funcs: tuple | None#: Number of basis functions (read-only).

property n_output_features: int | None#

Number of features returned by the basis.

Notes

The number of output features can be determined only when the number of inputs provided to the basis is known. Therefore, before the first call to compute_features, this property will return None. After that call, or after setting the input shape with set_input_shape, n_output_features will be available.

set_input_shape(xi)[source]#

Set the expected input shape for the basis object.

This method configures the shape of the input data that the basis object expects. xi can be specified as an integer, a tuple of integers, or derived from an array. The method also calculates the total number of input features and output features based on the number of basis functions.

Parameters:

xi (int | tuple[int, …] | NDArray) –

The input shape specification. - An integer: Represents the dimensionality of the input. A value of 1 is treated as scalar input. - A tuple: Represents the exact input shape excluding the first axis (sample axis).

All elements must be integers.

An array: The shape is extracted, excluding the first axis (assumed to be the sample axis).

Raises:

ValueError – If a tuple is provided and it contains non-integer elements.

Returns:

Returns the instance itself to allow method chaining.

Return type:

self

Notes

All state attributes that depends on the input must be set in this method in order for the API of basis to work correctly. In particular, this method is called by setup_basis, which is equivalent to fit for a transformer. If any input dependent state is not set in this method, then compute_features (equivalent to fit_transform) will break.

Examples

>>> import nemos as nmo
>>> import numpy as np
>>> basis = nmo.basis.IdentityEval()
>>> # Configure with an integer input:
>>> _ = basis.set_input_shape(3)
>>> basis.n_output_features
3
>>> # Configure with a tuple:
>>> _ = basis.set_input_shape((4, 5))
>>> basis.n_output_features
20
>>> # Configure with an array:
>>> x = np.ones((10, 4, 5))
>>> _ = basis.set_input_shape(x)
>>> basis.n_output_features
20

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance

setup_basis(*xi)#

Set all basis states.

This method corresponds sklearn transformer fit. As fit, it must receive the input and it must set all basis states, i.e. kernel_ and all the states relative to the input shape. The difference between this method and the transformer fit is in the expected input structure, where the transformer fit method requires the inputs to be concatenated in a 2D array, while here each input is provided as a separate time series for each basis element.

Parameters:: xi (NDArray) – Input arrays.
Return type:: Basis
Returns:: The basis with ready for evaluation.

split_by_feature(x, axis=1)[source]#

Decompose an array along a specified axis into sub-arrays based on the number of expected inputs.

This function takes an array (e.g., a design matrix or model coefficients) and splits it along a designated axis.

How it works:

If the basis expects an input shape (n_samples, n_inputs), then the feature axis length will be total_n_features = n_inputs * n_basis_funcs. This axis is reshaped into dimensions (n_inputs, n_basis_funcs).
If the basis expects an input of shape (n_samples,), then the feature axis length will be total_n_features = n_basis_funcs. This axis is reshaped into (1, n_basis_funcs).

For example, if the input array x has shape (1, 2, total_n_features, 4, 5), then after applying this method, it will be reshaped into (1, 2, n_inputs, n_basis_funcs, 4, 5).

The specified axis (axis) determines where the split occurs, and all other dimensions remain unchanged. See the example section below for the most common use cases.

Parameters:

x (NDArray) –
The input array to be split, representing concatenated features, coefficients, or other data. The shape of x along the specified axis must match the total number of features generated by the basis, i.e., self.n_output_features.

Examples:
- For a design matrix: (n_samples, total_n_features)
- For model coefficients: (total_n_features,) or (total_n_features, n_neurons).
axis (int, optional) – The axis along which to split the features. Defaults to 1. Use axis=1 for design matrices (features along columns) and axis=0 for coefficient arrays (features along rows). All other dimensions are preserved.

Raises:

ValueError – If the shape of x along the specified axis does not match self.n_output_features.

Returns:

A dictionary where:

Key: Label of the basis.
Value: the array reshaped to: (..., n_inputs, n_basis_funcs, ...)

Return type:

dict

Examples

>>> import numpy as np
>>> from nemos.basis import IdentityEval
>>> from nemos.glm import GLM
>>> # Define an additive basis
>>> basis = IdentityEval(label="feature")
>>> # Generate a sample input array and compute features
>>> inp = np.random.randn(20)
>>> X = basis.compute_features(inp)
>>> # Split the feature matrix along axis 1
>>> split_features = basis.split_by_feature(X, axis=1)
>>> for feature, arr in split_features.items():
...     print(f"{feature}: shape {arr.shape}")
feature: shape (20, 1)

to_transformer()#

Turn the Basis into a TransformerBasis for use with scikit-learn.

Return type:: TransformerBasis

Examples

Jointly cross-validating basis and GLM parameters with scikit-learn.

>>> import nemos as nmo
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.model_selection import GridSearchCV
>>> # load some data
>>> X, y = np.random.normal(size=(30, 1)), np.random.poisson(size=30)
>>> basis = nmo.basis.RaisedCosineLinearEval(10).set_input_shape(1).to_transformer()
>>> glm = nmo.glm.GLM(regularizer="Ridge", regularizer_strength=1.)
>>> pipeline = Pipeline([("basis", basis), ("glm", glm)])
>>> param_grid = dict(
...     glm__regularizer_strength=(0.1, 0.01, 0.001, 1e-6),
...     basis__n_basis_funcs=(3, 5, 10, 20, 100),
... )
>>> gridsearch = GridSearchCV(
...     pipeline,
...     param_grid=param_grid,
...     cv=5,
... )
>>> gridsearch = gridsearch.fit(X, y)

nemos.basis.IdentityEval#

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