pyccea.projection package

Submodules

pyccea.projection.cipls module

class pyccea.projection.cipls.CIPLS(n_components=10, copy=True)

Bases: BaseEstimator

Covariance-free Partial Least Squares (CIPLS).

Jordao, Artur, et al. “Covariance-free partial least squares: An incremental dimensionality reduction method.” Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (2021). Source: https://github.com/arturjordao/IncrementalDimensionalityReduction

Attributes:

n: int

Number of iterations. It starts at 0 and incrementally goes up to the number of samples (n_samples).

n_features: int

Number of variables.

x_weights_: np.ndarray (n_features, n_components)

Projection matrix.

x_scores_: np.ndarray (n_samples, n_components)

The transformed training samples (latent components).

x_loadings_: np.ndarray (n_features, n_components)

The loadings of X.

y_loadings_: np.ndarray (n_targets, n_components)

The loadings of Y, where n_targets is the number of response variables.

x_rotations_: np.ndarray (n_components, n_features)

Transposed and non-normalized projection matrix.

sum_x: np.ndarray (n_features,)

The sum of each feature individually across all training samples.

sum_y: np.ndarray (1,)

The sum of targets across all training samples.

Methods

fit(X, Y)	Fit model to data
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
normalize(x)	Scale input vectors individually to unit norm (vector length).
set_params(**params)	Set the parameters of this estimator.
transform(X[, Y])	Apply the dimension reduction learned on the training data.

fit(X, Y)

Fit model to data

Parameters:

X: np.ndarray (n_samples, n_features)

Training data.

Y: np.ndarray (n_samples,) or (n_samples, n_targets)

Target data.

normalize(x)

Scale input vectors individually to unit norm (vector length).

transform(X, Y=None)

Apply the dimension reduction learned on the training data.

Parameters:

X: np.ndarray (n_samples, n_features)

Training data.

Y: np.ndarray (n_samples,) or (n_samples, n_targets), default None

Target data.

pyccea.projection.vip module

class pyccea.projection.vip.VIP(model)

Bases: object

Variable Importance in Projection (VIP).

Mehmood, Tahir, et al. “A review of variable selection methods in partial least squares regression.” Chemometrics and intelligent laboratory systems 118 (2012): 62-69. Source: https://github.com/scikit-learn/scikit-learn/issues/7050

Attributes:

n_featuresint

Number of variables.

n_componentsint

Number of components.

x_rotations_np.ndarray (n_features, n_components)

Projection matrix used to transform X.

x_scores_np.ndarray (n_samples, n_components)

The transformed training samples (latent components).

y_loadings_np.ndarray (n_targets, n_components)

The loadings of Y.

importancesnp.ndarray (n_features,)

Importance of each feature based on its contribution to yield the latent space.

Methods

compute()

Calculate feature importances.

compute()

Calculate feature importances.