oddt.scoring.models package

Submodules

oddt.scoring.models.classifiers module

oddt.scoring.models.classifiers.randomforest

alias of RandomForestClassifier

class oddt.scoring.models.classifiers.svm(*args, **kwargs)[source]

Bases: sklearn.base.ClassifierMixin

Assemble a proper SVM classifier

Methods

fit(descs, target_values, **kwargs)
get_params([deep])
predict(descs)
predict_log_proba(descs)
predict_proba(descs)
score(descs, target_values)
set_params(**kwargs)
fit(descs, target_values, **kwargs)[source]
get_params(deep=True)[source]
predict(descs)[source]
predict_log_proba(descs)[source]
predict_proba(descs)[source]
score(descs, target_values)[source]
set_params(**kwargs)[source]
class oddt.scoring.models.classifiers.neuralnetwork(*args, **kwargs)[source]

Bases: sklearn.base.ClassifierMixin

Assemble Neural network using sklearn pipeline

Methods

fit(descs, target_values, **kwargs)
get_params([deep])
predict(descs)
predict_log_proba(descs)
predict_proba(descs)
score(descs, target_values)
set_params(**kwargs)
fit(descs, target_values, **kwargs)[source]
get_params(deep=True)[source]
predict(descs)[source]
predict_log_proba(descs)[source]
predict_proba(descs)[source]
score(descs, target_values)[source]
set_params(**kwargs)[source]

oddt.scoring.models.regressors module

Collection of regressors models

oddt.scoring.models.regressors.randomforest

alias of RandomForestRegressor

class oddt.scoring.models.regressors.svm(*args, **kwargs)[source]

Bases: sklearn.base.RegressorMixin

Assemble a proper SVM using sklearn tools regressor

Methods

fit(descs, target_values, **kwargs)
get_params([deep])
predict(descs)
score(descs, target_values)
set_params(**kwargs)
fit(descs, target_values, **kwargs)[source]
get_params(deep=True)[source]
predict(descs)[source]
score(descs, target_values)[source]
set_params(**kwargs)[source]
oddt.scoring.models.regressors.pls

alias of PLSRegression

class oddt.scoring.models.regressors.neuralnetwork(*args, **kwargs)[source]

Bases: sklearn.base.RegressorMixin

Assemble Neural network using sklearn pipeline

Methods

fit(descs, target_values, **kwargs)
get_params([deep])
predict(descs)
score(descs, target_values)
set_params(**kwargs)
fit(descs, target_values, **kwargs)[source]
get_params(deep=True)[source]
predict(descs)[source]
score(descs, target_values)[source]
set_params(**kwargs)[source]
oddt.scoring.models.regressors.mlr

alias of LinearRegression

Module contents