APIlink
Some simple/useful examples are available at Usage, using the following API:
Methods covered:
Residualize
Augment
DummyDeconfounding
Library exceptions:
ConfoundsException
- code
class
base.Residualize(model='linear')[source]link Bases:
base.BaseDeconfoundDeconfounding estimator class that residualizes the input features by subtracting the contributions from the confound variables
Example methods: Linear, Kernel Ridge, Gaussian Process Regression etc
- codemethod
fit(self, X, y=None)[source]link Fits the residualizing model (estimates the contributions of confounding variables (y) to the given [training] feature set X. Variable names X, y had to be used to pass sklearn conventions. y here refers to the confound variables, and NOT the target. See examples in docs!
- Parameters
- X{array-like, sparse matrix}, shape (n_samples, n_features)
The training input samples.
- yndarray
Array of covariates, shape (n_samples, n_covariates) This does not refer to target as is typical in scikit-learn.
- Returns
- selfobject
Returns self
- codemethod
transform(self, X, y=None)[source]link Transforms the given feature set by residualizing the [test] features by subtracting the contributions of their confounding variables.
Variable names X, y had to be used to pass scikit-learn conventions. y here refers to the confound variables for the [test] to be transformed, and NOT their target values. See examples in docs!
- Parameters
- X{array-like, sparse matrix}, shape (n_samples, n_features)
The training input samples.
- yndarray
Array of covariates, shape (n_samples, n_covariates) This does not refer to target as is typical in scikit-learn.
- Returns
- selfobject
Returns self
- codemethod
- code
class
base.Augment[source]link Bases:
base.BaseDeconfoundDeconfounding estimator class that simply augments/concatenates the confounding variables to input features prior to prediction.
- codemethod
fit(self, X, y=None)[source]link Learns the dimensionality of confounding variables to be augmented.
Variable names X, y had to be used to pass sklearn conventions. y here refers to the confound variables, and NOT the target. See examples in docs!
- Parameters
- X{array-like, sparse matrix}, shape (n_samples, n_features)
The training input samples.
- yndarray
Array of covariates, shape (n_samples, n_covariates) This does not refer to target as is typical in scikit-learn.
- Returns
- selfobject
Returns self
- codemethod
transform(self, X, y=None)[source]link Transforms the given feature set by augmenting the confounding variables.
Variable names X, y had to be used to pass sklearn conventions. y here refers to the confound variables for the [test] to be transformed, and NOT their target values. See examples in docs!
- Parameters
- X{array-like, sparse matrix}, shape (n_samples, n_features)
The training input samples.
- yndarray
Array of covariates, shape (n_samples, n_covariates) This does not refer to target as is typical in scikit-learn.
- Returns
- selfobject
Returns self
- codemethod
- code
class
base.DummyDeconfounding[source]link Bases:
base.BaseDeconfoundA do-nothing dummy method, to serve as a reference for methodological comparisons
- codemethod
fit(self, X, y=None)[source]link A do-nothing fit method.
- Parameters
- X{array-like, sparse matrix}, shape (n_samples, n_features)
The training input samples.
- yndarray
Array of covariates, shape (n_samples, n_covariates)
- Returns
- selfobject
Returns self.
- codemethod
transform(self, X, y=None)[source]link A do-nothing transform method.
- Parameters
- X{array-like, sparse-matrix}, shape (n_samples, n_features)
The input samples.
- yndarray
Array of covariates, shape (n_samples, n_covariates)
- Returns
- X_transformedarray, shape (n_samples, n_features)
Same as the input
X.
- codemethod