API reference / Analysis and models

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metbit.cross_validation

Perform cross validation.

import metbit.cross_validation

Classes

CrossValidation

Stratified cross validation

Parameters:

estimatorstr

Estimator indicates algorithm for model construction. Values can be "pls" for PLS and "opls" for OPLS. Default is "opls".

kfoldint

k fold cross validation. if k equals to len(X), leave one out cross validation will be performed. Default is 10.

scalerstr

Scaler for scaling data matrix. Valid values are "uv" for zero-mean-unit-variance scaling, "pareto" for Pareto scaling, "minmax" for Min-Max scaling and "mean" for mean centering. Default is "pareto".

Returns

CrossValidation object

Methods

__init__(self, estimator='opls', kfold=10, scaler='pareto')

fit(self, x, y)

Fitting variable matrix X

Parameters

xnp.ndarray

Variable matrix with size n samples by p variables.

ynp.ndarray | list

Dependent matrix with size n samples by 1. The values in this vector must be 0 and 1, otherwise the classification performance will be wrongly concluded.

Returns

CrossValidation object

predict(self, x)

Do prediction using optimal model.

Parameters

xnp.ndarray

Variable matrix with size n samples by p variables.

Returns

np.ndarray Predictions for the x

reset_optimal_num_component(self, k)

Reset the optimal number of components for manual setup.

Parameters

kint

Number of components according to the error plot.

Returns

None

orthogonal_score(self)

Cross validated orthogonal score.

Returns

np.ndarray The first orthogonal scores.

Raises

ValueError If OPLS / OPLS-DA is not used.

predictive_score(self)

Cross validated predictive score.

Returns

np.ndarray The first predictive scores.

Raises

ValueError If OPLS / OPLS-DA is not used.

scores(self)

Returns

np.ndarray The first predictive score, if the method is OPLS/OPLS-DA, otherwise is the scores of X

q2(self)

Returns

q2float

optimal_component_num(self)

Number of components determined by CV.

Returns

int

R2Xcorr(self)

Returns

float Modeled joint X-y covariation of X.

Raises

ValueError If OPLS / OPLS-DA is not used.

R2XYO(self)

Returns

float Modeled structured noise variation of X.

Raises

ValueError If OPLS / OPLS-DA is not used.

R2X(self)

Returns

float Modeled variation of X

R2y(self)

Returns

float Modeled variation of y

correlation(self)

Correlation

Returns

np.ndarray Correlation loading profile

Raises

ValueError If OPLS / OPLS-DA is not used.

References

[1] Wiklund S, et al. Visualization of GC/TOF-MS-Based Metabolomics Data for Identification of Biochemically Interesting Compounds Using OPLS Class Models. Anal Chem. 2008, 80, 115-122.

covariance(self)

Covariance

Returns

np.ndarray Correlation loading profile

Raises

ValueError If OPLS / OPLS-DA is not used.

References

[1] Wiklund S, et al. Visualization of GC/TOF-MS-Based Metabolomics Data for Identification of Biochemically Interesting Compounds Using OPLS Class Models. Anal Chem. 2008, 80, 115-122.

loadings_cv(self)

Loadings from cross validation.

Returns

np.ndarray Correlation loading profile

Raises

ValueError If OPLS / OPLS-DA is not used.

min_nmc(self)

Returns

float Minimal number of mis-classifications

mis_classifications(self)

Returns

list Mis-classifications at different principal components.

Source

metbit/cross_validation.py at v1.0.7