models
class Penalty(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)
Bases: StrEnum, Enum
elasticnet = ‘elasticnet’
l1 = ‘l1’
l2 = ‘l2’
class ScoringMethod(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)
Bases: StrEnum, Enum
accuracy = ‘accuracy’
adjusted_mutual_info_score = ‘adjusted_mutual_info_score’
adjusted_rand_score = ‘adjusted_rand_score’
average_precision = ‘average_precision’
balanced_accuracy = ‘balanced_accuracy’
completeness_score = ‘completeness_score’
d2_absolute_error_score = ‘d2_absolute_error_score’
d2_pinball_score = ‘d2_pinball_score’
d2_tweedie_score = ‘d2_tweedie_score’
explained_variance = ‘explained_variance’
f1 = ‘f1’
f1_macro = ‘f1_macro’
f1_micro = ‘f1_micro’
f1_samples = ‘f1_samples’
f1_weighted = ‘f1_weighted’
fowlkes_mallows_score = ‘fowlkes_mallows_score’
homogeneity_score = ‘homogeneity_score’
jaccard = ‘jaccard’
max_error = ‘max_error’
mutual_info_score = ‘mutual_info_score’
neg_brier_score = ‘neg_brier_score’
neg_log_loss = ‘neg_log_loss’
neg_mean_absolute_error = ‘neg_mean_absolute_error’
neg_mean_absolute_percentage_error = ‘neg_mean_absolute_percentage_error’
neg_mean_gamma_deviance = ‘neg_mean_gamma_deviance’
neg_mean_poisson_deviance = ‘neg_mean_poisson_deviance’
neg_mean_squared_error = ‘neg_mean_squared_error’
neg_mean_squared_log_error = ‘neg_mean_squared_log_error’
neg_median_absolute_error = ‘neg_median_absolute_error’
neg_root_mean_squared_error = ‘neg_root_mean_squared_error’
normalized_mutual_info_score = ‘normalized_mutual_info_score’
precision = ‘precision’
r2 = ‘r2’
rand_score = ‘rand_score’
recall = ‘recall’
roc_auc = ‘roc_auc’
roc_auc_ovo = ‘roc_auc_ovo’
roc_auc_ovo_weighted = ‘roc_auc_ovo_weighted’
roc_auc_ovr = ‘roc_auc_ovr’
roc_auc_ovr_weighted = ‘roc_auc_ovr_weighted’
top_k_accuracy = ‘top_k_accuracy’
v_measure_score = ‘v_measure_score’
class Solver(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)
Bases: StrEnum, Enum
lbfgs = ‘lbfgs’
liblinear = ‘liblinear’
newton_cg = ‘newton-cg’
newton_cholesky = ‘newton-cholesky’
sag = ‘sag’
saga = ‘saga’
permutation_testing(estimator, X, Y, binary=False, nb_permutations=1000, scoring=‘r2’, splits=10, processes=1, verbose=False)
Function to perform permutation testing on a model.
Parameters
estimator : Model to use.
X : Dataframe containing the predictor variables.
Y : Dataframe containing the dependent variables.
binary : If the dependent variable is binary. Defaults to False.
nb_permutations : Number of iterations to perform. Defaults to 1000.
scoring : Scoring method to use. Defaults to ‘r2’.
splits : Number of fold to use in cross-validation. Defaults to 10.
processes : Number of cpus to use during processing. Defaults to 1.
verbose : Verbose mode. Defaults to False.
Returns
mod : Model.
score : Score for the model.
coef : Coefficients for the model.
perm_score : Scores for the permutation testing.
score_pvalue : P-value for the model.
perm_coef : Coefficients for the permutation testing.
coef_pvalue : P-value for the coefficients.
plsr_cv(X, Y, nb_comp, max_iter=1000, splits=10, processes=1, verbose=False)
Function to perform a PLSR model with cross-validation between a set of predictor and dependent variables.
Parameters
X : Dataframe containing the predictor variables.
Y : Dataframe containing the dependent variables.
nb_comp : Number of components to use.
max_iter : Maximum number of iterations. Defaults to 1000.
splits : Number of fold to use in cross-validation. Defaults to 10.
processes : Number of cpus to use during processing. Defaults to 1.
verbose : Verbose mode. Defaults to False.
Returns
plsr : PLSR model.
mse : List of mean squared errors.
score_c : R2 score for the model.
score_cv : R2 score for the cross-validation.
rscore : Square root of the R2 score.
mse_c : Mean squared error for the model.
mse_cv : Mean squared error for the cross-validation.