doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.ensemble.randomtreesembedding#sklearn.ensemble.RandomTreesEmbedding.set_params |
transform(X) [source]
Transform dataset. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Input data to be transformed. Use dtype=np.float32 for maximum efficiency. Sparse matrices are also supported, use sparse csr_matrix for maximum efficiency. Returns
X_transformedsparse matrix ... | sklearn.modules.generated.sklearn.ensemble.randomtreesembedding#sklearn.ensemble.RandomTreesEmbedding.transform |
class sklearn.ensemble.StackingClassifier(estimators, final_estimator=None, *, cv=None, stack_method='auto', n_jobs=None, passthrough=False, verbose=0) [source]
Stack of estimators with a final classifier. Stacked generalization consists in stacking the output of individual estimator and use a classifier to compute t... | sklearn.modules.generated.sklearn.ensemble.stackingclassifier#sklearn.ensemble.StackingClassifier |
sklearn.ensemble.StackingClassifier
class sklearn.ensemble.StackingClassifier(estimators, final_estimator=None, *, cv=None, stack_method='auto', n_jobs=None, passthrough=False, verbose=0) [source]
Stack of estimators with a final classifier. Stacked generalization consists in stacking the output of individual estim... | sklearn.modules.generated.sklearn.ensemble.stackingclassifier |
decision_function(X) [source]
Predict decision function for samples in X using final_estimator_.decision_function. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features. Returns
decisions... | sklearn.modules.generated.sklearn.ensemble.stackingclassifier#sklearn.ensemble.StackingClassifier.decision_function |
fit(X, y, sample_weight=None) [source]
Fit the estimators. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target values.
sample_weightarray-like ... | sklearn.modules.generated.sklearn.ensemble.stackingclassifier#sklearn.ensemble.StackingClassifier.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.ensemble.stackingclassifier#sklearn.ensemble.StackingClassifier.fit_transform |
get_params(deep=True) [source]
Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter. Parameters
deepbool, default=True
Setting it to True gets the various estimators and the parameters of the est... | sklearn.modules.generated.sklearn.ensemble.stackingclassifier#sklearn.ensemble.StackingClassifier.get_params |
property n_features_in_
Number of features seen during fit. | sklearn.modules.generated.sklearn.ensemble.stackingclassifier#sklearn.ensemble.StackingClassifier.n_features_in_ |
predict(X, **predict_params) [source]
Predict target for X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features.
**predict_paramsdict of str -> obj
Parameters to the predict called by the... | sklearn.modules.generated.sklearn.ensemble.stackingclassifier#sklearn.ensemble.StackingClassifier.predict |
predict_proba(X) [source]
Predict class probabilities for X using final_estimator_.predict_proba. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features. Returns
probabilitiesndarray of sh... | sklearn.modules.generated.sklearn.ensemble.stackingclassifier#sklearn.ensemble.StackingClassifier.predict_proba |
score(X, y, sample_weight=None) [source]
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_featur... | sklearn.modules.generated.sklearn.ensemble.stackingclassifier#sklearn.ensemble.StackingClassifier.score |
set_params(**params) [source]
Set the parameters of an estimator from the ensemble. Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators. Parameters
**paramskeyword arguments
Specific parameters using e.g. set_params(parame... | sklearn.modules.generated.sklearn.ensemble.stackingclassifier#sklearn.ensemble.StackingClassifier.set_params |
transform(X) [source]
Return class labels or probabilities for X for each estimator. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features. Returns
y_predsndarray of shape (n_samples, n_e... | sklearn.modules.generated.sklearn.ensemble.stackingclassifier#sklearn.ensemble.StackingClassifier.transform |
class sklearn.ensemble.StackingRegressor(estimators, final_estimator=None, *, cv=None, n_jobs=None, passthrough=False, verbose=0) [source]
Stack of estimators with a final regressor. Stacked generalization consists in stacking the output of individual estimator and use a regressor to compute the final prediction. Sta... | sklearn.modules.generated.sklearn.ensemble.stackingregressor#sklearn.ensemble.StackingRegressor |
sklearn.ensemble.StackingRegressor
class sklearn.ensemble.StackingRegressor(estimators, final_estimator=None, *, cv=None, n_jobs=None, passthrough=False, verbose=0) [source]
Stack of estimators with a final regressor. Stacked generalization consists in stacking the output of individual estimator and use a regressor... | sklearn.modules.generated.sklearn.ensemble.stackingregressor |
fit(X, y, sample_weight=None) [source]
Fit the estimators. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target values.
sample_weightarray-like ... | sklearn.modules.generated.sklearn.ensemble.stackingregressor#sklearn.ensemble.StackingRegressor.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.ensemble.stackingregressor#sklearn.ensemble.StackingRegressor.fit_transform |
get_params(deep=True) [source]
Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter. Parameters
deepbool, default=True
Setting it to True gets the various estimators and the parameters of the est... | sklearn.modules.generated.sklearn.ensemble.stackingregressor#sklearn.ensemble.StackingRegressor.get_params |
property n_features_in_
Number of features seen during fit. | sklearn.modules.generated.sklearn.ensemble.stackingregressor#sklearn.ensemble.StackingRegressor.n_features_in_ |
predict(X, **predict_params) [source]
Predict target for X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features.
**predict_paramsdict of str -> obj
Parameters to the predict called by the... | sklearn.modules.generated.sklearn.ensemble.stackingregressor#sklearn.ensemble.StackingRegressor.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.ensemble.stackingregressor#sklearn.ensemble.StackingRegressor.score |
set_params(**params) [source]
Set the parameters of an estimator from the ensemble. Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators. Parameters
**paramskeyword arguments
Specific parameters using e.g. set_params(parame... | sklearn.modules.generated.sklearn.ensemble.stackingregressor#sklearn.ensemble.StackingRegressor.set_params |
transform(X) [source]
Return the predictions for X for each estimator. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features. Returns
y_predsndarray of shape (n_samples, n_estimators)
P... | sklearn.modules.generated.sklearn.ensemble.stackingregressor#sklearn.ensemble.StackingRegressor.transform |
class sklearn.ensemble.VotingClassifier(estimators, *, voting='hard', weights=None, n_jobs=None, flatten_transform=True, verbose=False) [source]
Soft Voting/Majority Rule classifier for unfitted estimators. Read more in the User Guide. New in version 0.17. Parameters
estimatorslist of (str, estimator) tuples
... | sklearn.modules.generated.sklearn.ensemble.votingclassifier#sklearn.ensemble.VotingClassifier |
sklearn.ensemble.VotingClassifier
class sklearn.ensemble.VotingClassifier(estimators, *, voting='hard', weights=None, n_jobs=None, flatten_transform=True, verbose=False) [source]
Soft Voting/Majority Rule classifier for unfitted estimators. Read more in the User Guide. New in version 0.17. Parameters
estimato... | sklearn.modules.generated.sklearn.ensemble.votingclassifier |
fit(X, y, sample_weight=None) [source]
Fit the estimators. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target values.
sample_weightarray-like ... | sklearn.modules.generated.sklearn.ensemble.votingclassifier#sklearn.ensemble.VotingClassifier.fit |
fit_transform(X, y=None, **fit_params) [source]
Return class labels or probabilities for each estimator. Return predictions for X for each estimator. Parameters
X{array-like, sparse matrix, dataframe} of shape (n_samples, n_features)
Input samples
yndarray of shape (n_samples,), default=None
Target values (... | sklearn.modules.generated.sklearn.ensemble.votingclassifier#sklearn.ensemble.VotingClassifier.fit_transform |
get_params(deep=True) [source]
Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter. Parameters
deepbool, default=True
Setting it to True gets the various estimators and the parameters of the est... | sklearn.modules.generated.sklearn.ensemble.votingclassifier#sklearn.ensemble.VotingClassifier.get_params |
predict(X) [source]
Predict class labels for X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Returns
majarray-like of shape (n_samples,)
Predicted class labels. | sklearn.modules.generated.sklearn.ensemble.votingclassifier#sklearn.ensemble.VotingClassifier.predict |
property predict_proba
Compute probabilities of possible outcomes for samples in X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Returns
avgarray-like of shape (n_samples, n_classes)
Weighted average probability for each class per sample. | sklearn.modules.generated.sklearn.ensemble.votingclassifier#sklearn.ensemble.VotingClassifier.predict_proba |
score(X, y, sample_weight=None) [source]
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_featur... | sklearn.modules.generated.sklearn.ensemble.votingclassifier#sklearn.ensemble.VotingClassifier.score |
set_params(**params) [source]
Set the parameters of an estimator from the ensemble. Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators. Parameters
**paramskeyword arguments
Specific parameters using e.g. set_params(parame... | sklearn.modules.generated.sklearn.ensemble.votingclassifier#sklearn.ensemble.VotingClassifier.set_params |
transform(X) [source]
Return class labels or probabilities for X for each estimator. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features. Returns
probabilities_or_labels
If voting='so... | sklearn.modules.generated.sklearn.ensemble.votingclassifier#sklearn.ensemble.VotingClassifier.transform |
class sklearn.ensemble.VotingRegressor(estimators, *, weights=None, n_jobs=None, verbose=False) [source]
Prediction voting regressor for unfitted estimators. A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to fo... | sklearn.modules.generated.sklearn.ensemble.votingregressor#sklearn.ensemble.VotingRegressor |
sklearn.ensemble.VotingRegressor
class sklearn.ensemble.VotingRegressor(estimators, *, weights=None, n_jobs=None, verbose=False) [source]
Prediction voting regressor for unfitted estimators. A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averag... | sklearn.modules.generated.sklearn.ensemble.votingregressor |
fit(X, y, sample_weight=None) [source]
Fit the estimators. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target values.
sample_weightarray-like ... | sklearn.modules.generated.sklearn.ensemble.votingregressor#sklearn.ensemble.VotingRegressor.fit |
fit_transform(X, y=None, **fit_params) [source]
Return class labels or probabilities for each estimator. Return predictions for X for each estimator. Parameters
X{array-like, sparse matrix, dataframe} of shape (n_samples, n_features)
Input samples
yndarray of shape (n_samples,), default=None
Target values (... | sklearn.modules.generated.sklearn.ensemble.votingregressor#sklearn.ensemble.VotingRegressor.fit_transform |
get_params(deep=True) [source]
Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter. Parameters
deepbool, default=True
Setting it to True gets the various estimators and the parameters of the est... | sklearn.modules.generated.sklearn.ensemble.votingregressor#sklearn.ensemble.VotingRegressor.get_params |
predict(X) [source]
Predict regression target for X. The predicted regression target of an input sample is computed as the mean predicted regression targets of the estimators in the ensemble. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Returns
yndarray of sh... | sklearn.modules.generated.sklearn.ensemble.votingregressor#sklearn.ensemble.VotingRegressor.predict |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum()... | sklearn.modules.generated.sklearn.ensemble.votingregressor#sklearn.ensemble.VotingRegressor.score |
set_params(**params) [source]
Set the parameters of an estimator from the ensemble. Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators. Parameters
**paramskeyword arguments
Specific parameters using e.g. set_params(parame... | sklearn.modules.generated.sklearn.ensemble.votingregressor#sklearn.ensemble.VotingRegressor.set_params |
transform(X) [source]
Return predictions for X for each estimator. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Returns
predictions: ndarray of shape (n_samples, n_classifiers)
Values predicted by each regressor. | sklearn.modules.generated.sklearn.ensemble.votingregressor#sklearn.ensemble.VotingRegressor.transform |
class sklearn.exceptions.ConvergenceWarning [source]
Custom warning to capture convergence problems Changed in version 0.18: Moved from sklearn.utils. Attributes
args
Methods
with_traceback Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
with_traceback()
Exception.with_tra... | sklearn.modules.generated.sklearn.exceptions.convergencewarning#sklearn.exceptions.ConvergenceWarning |
sklearn.exceptions.ConvergenceWarning
class sklearn.exceptions.ConvergenceWarning [source]
Custom warning to capture convergence problems Changed in version 0.18: Moved from sklearn.utils. Attributes
args
Methods
with_traceback Exception.with_traceback(tb) – set self.__traceback__ to tb and return self. ... | sklearn.modules.generated.sklearn.exceptions.convergencewarning |
with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self. | sklearn.modules.generated.sklearn.exceptions.convergencewarning#sklearn.exceptions.ConvergenceWarning.with_traceback |
sklearn.exceptions.DataConversionWarning
class sklearn.exceptions.DataConversionWarning [source]
Warning used to notify implicit data conversions happening in the code. This warning occurs when some input data needs to be converted or interpreted in a way that may not match the user’s expectations. For example, th... | sklearn.modules.generated.sklearn.exceptions.dataconversionwarning |
class sklearn.exceptions.DataConversionWarning [source]
Warning used to notify implicit data conversions happening in the code. This warning occurs when some input data needs to be converted or interpreted in a way that may not match the user’s expectations. For example, this warning may occur when the user
passes... | sklearn.modules.generated.sklearn.exceptions.dataconversionwarning#sklearn.exceptions.DataConversionWarning |
with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self. | sklearn.modules.generated.sklearn.exceptions.dataconversionwarning#sklearn.exceptions.DataConversionWarning.with_traceback |
sklearn.exceptions.DataDimensionalityWarning
class sklearn.exceptions.DataDimensionalityWarning [source]
Custom warning to notify potential issues with data dimensionality. For example, in random projection, this warning is raised when the number of components, which quantifies the dimensionality of the target proj... | sklearn.modules.generated.sklearn.exceptions.datadimensionalitywarning |
class sklearn.exceptions.DataDimensionalityWarning [source]
Custom warning to notify potential issues with data dimensionality. For example, in random projection, this warning is raised when the number of components, which quantifies the dimensionality of the target projection space, is higher than the number of feat... | sklearn.modules.generated.sklearn.exceptions.datadimensionalitywarning#sklearn.exceptions.DataDimensionalityWarning |
with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self. | sklearn.modules.generated.sklearn.exceptions.datadimensionalitywarning#sklearn.exceptions.DataDimensionalityWarning.with_traceback |
class sklearn.exceptions.EfficiencyWarning [source]
Warning used to notify the user of inefficient computation. This warning notifies the user that the efficiency may not be optimal due to some reason which may be included as a part of the warning message. This may be subclassed into a more specific Warning class. N... | sklearn.modules.generated.sklearn.exceptions.efficiencywarning#sklearn.exceptions.EfficiencyWarning |
sklearn.exceptions.EfficiencyWarning
class sklearn.exceptions.EfficiencyWarning [source]
Warning used to notify the user of inefficient computation. This warning notifies the user that the efficiency may not be optimal due to some reason which may be included as a part of the warning message. This may be subclassed... | sklearn.modules.generated.sklearn.exceptions.efficiencywarning |
with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self. | sklearn.modules.generated.sklearn.exceptions.efficiencywarning#sklearn.exceptions.EfficiencyWarning.with_traceback |
class sklearn.exceptions.FitFailedWarning [source]
Warning class used if there is an error while fitting the estimator. This Warning is used in meta estimators GridSearchCV and RandomizedSearchCV and the cross-validation helper function cross_val_score to warn when there is an error while fitting the estimator. Chan... | sklearn.modules.generated.sklearn.exceptions.fitfailedwarning#sklearn.exceptions.FitFailedWarning |
sklearn.exceptions.FitFailedWarning
class sklearn.exceptions.FitFailedWarning [source]
Warning class used if there is an error while fitting the estimator. This Warning is used in meta estimators GridSearchCV and RandomizedSearchCV and the cross-validation helper function cross_val_score to warn when there is an er... | sklearn.modules.generated.sklearn.exceptions.fitfailedwarning |
with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self. | sklearn.modules.generated.sklearn.exceptions.fitfailedwarning#sklearn.exceptions.FitFailedWarning.with_traceback |
sklearn.exceptions.NotFittedError
class sklearn.exceptions.NotFittedError [source]
Exception class to raise if estimator is used before fitting. This class inherits from both ValueError and AttributeError to help with exception handling and backward compatibility. Attributes
args
Examples >>> from sklearn.svm... | sklearn.modules.generated.sklearn.exceptions.notfittederror |
class sklearn.exceptions.NotFittedError [source]
Exception class to raise if estimator is used before fitting. This class inherits from both ValueError and AttributeError to help with exception handling and backward compatibility. Attributes
args
Examples >>> from sklearn.svm import LinearSVC
>>> from sklearn.e... | sklearn.modules.generated.sklearn.exceptions.notfittederror#sklearn.exceptions.NotFittedError |
with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self. | sklearn.modules.generated.sklearn.exceptions.notfittederror#sklearn.exceptions.NotFittedError.with_traceback |
class sklearn.exceptions.UndefinedMetricWarning [source]
Warning used when the metric is invalid Changed in version 0.18: Moved from sklearn.base. Attributes
args
Methods
with_traceback Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
with_traceback()
Exception.with_traceba... | sklearn.modules.generated.sklearn.exceptions.undefinedmetricwarning#sklearn.exceptions.UndefinedMetricWarning |
sklearn.exceptions.UndefinedMetricWarning
class sklearn.exceptions.UndefinedMetricWarning [source]
Warning used when the metric is invalid Changed in version 0.18: Moved from sklearn.base. Attributes
args
Methods
with_traceback Exception.with_traceback(tb) – set self.__traceback__ to tb and return self. ... | sklearn.modules.generated.sklearn.exceptions.undefinedmetricwarning |
with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self. | sklearn.modules.generated.sklearn.exceptions.undefinedmetricwarning#sklearn.exceptions.UndefinedMetricWarning.with_traceback |
class sklearn.feature_extraction.DictVectorizer(*, dtype=<class 'numpy.float64'>, separator='=', sparse=True, sort=True) [source]
Transforms lists of feature-value mappings to vectors. This transformer turns lists of mappings (dict-like objects) of feature names to feature values into Numpy arrays or scipy.sparse mat... | sklearn.modules.generated.sklearn.feature_extraction.dictvectorizer#sklearn.feature_extraction.DictVectorizer |
sklearn.feature_extraction.DictVectorizer
class sklearn.feature_extraction.DictVectorizer(*, dtype=<class 'numpy.float64'>, separator='=', sparse=True, sort=True) [source]
Transforms lists of feature-value mappings to vectors. This transformer turns lists of mappings (dict-like objects) of feature names to feature ... | sklearn.modules.generated.sklearn.feature_extraction.dictvectorizer |
fit(X, y=None) [source]
Learn a list of feature name -> indices mappings. Parameters
XMapping or iterable over Mappings
Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature values (strings or convertible to dtype). Changed in version 0.24: Accepts multiple string values for one catego... | sklearn.modules.generated.sklearn.feature_extraction.dictvectorizer#sklearn.feature_extraction.DictVectorizer.fit |
fit_transform(X, y=None) [source]
Learn a list of feature name -> indices mappings and transform X. Like fit(X) followed by transform(X), but does not require materializing X in memory. Parameters
XMapping or iterable over Mappings
Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature v... | sklearn.modules.generated.sklearn.feature_extraction.dictvectorizer#sklearn.feature_extraction.DictVectorizer.fit_transform |
get_feature_names() [source]
Returns a list of feature names, ordered by their indices. If one-of-K coding is applied to categorical features, this will include the constructed feature names but not the original ones. | sklearn.modules.generated.sklearn.feature_extraction.dictvectorizer#sklearn.feature_extraction.DictVectorizer.get_feature_names |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.feature_extraction.dictvectorizer#sklearn.feature_extraction.DictVectorizer.get_params |
inverse_transform(X, dict_type=<class 'dict'>) [source]
Transform array or sparse matrix X back to feature mappings. X must have been produced by this DictVectorizer’s transform or fit_transform method; it may only have passed through transformers that preserve the number of features and their order. In the case of o... | sklearn.modules.generated.sklearn.feature_extraction.dictvectorizer#sklearn.feature_extraction.DictVectorizer.inverse_transform |
restrict(support, indices=False) [source]
Restrict the features to those in support using feature selection. This function modifies the estimator in-place. Parameters
supportarray-like
Boolean mask or list of indices (as returned by the get_support member of feature selectors).
indicesbool, default=False
Wh... | sklearn.modules.generated.sklearn.feature_extraction.dictvectorizer#sklearn.feature_extraction.DictVectorizer.restrict |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.feature_extraction.dictvectorizer#sklearn.feature_extraction.DictVectorizer.set_params |
transform(X) [source]
Transform feature->value dicts to array or sparse matrix. Named features not encountered during fit or fit_transform will be silently ignored. Parameters
XMapping or iterable over Mappings of shape (n_samples,)
Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature ... | sklearn.modules.generated.sklearn.feature_extraction.dictvectorizer#sklearn.feature_extraction.DictVectorizer.transform |
class sklearn.feature_extraction.FeatureHasher(n_features=1048576, *, input_type='dict', dtype=<class 'numpy.float64'>, alternate_sign=True) [source]
Implements feature hashing, aka the hashing trick. This class turns sequences of symbolic feature names (strings) into scipy.sparse matrices, using a hash function to c... | sklearn.modules.generated.sklearn.feature_extraction.featurehasher#sklearn.feature_extraction.FeatureHasher |
sklearn.feature_extraction.FeatureHasher
class sklearn.feature_extraction.FeatureHasher(n_features=1048576, *, input_type='dict', dtype=<class 'numpy.float64'>, alternate_sign=True) [source]
Implements feature hashing, aka the hashing trick. This class turns sequences of symbolic feature names (strings) into scipy.... | sklearn.modules.generated.sklearn.feature_extraction.featurehasher |
fit(X=None, y=None) [source]
No-op. This method doesn’t do anything. It exists purely for compatibility with the scikit-learn transformer API. Parameters
Xndarray
Returns
selfFeatureHasher | sklearn.modules.generated.sklearn.feature_extraction.featurehasher#sklearn.feature_extraction.FeatureHasher.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.feature_extraction.featurehasher#sklearn.feature_extraction.FeatureHasher.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.feature_extraction.featurehasher#sklearn.feature_extraction.FeatureHasher.get_params |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.feature_extraction.featurehasher#sklearn.feature_extraction.FeatureHasher.set_params |
transform(raw_X) [source]
Transform a sequence of instances to a scipy.sparse matrix. Parameters
raw_Xiterable over iterable over raw features, length = n_samples
Samples. Each sample must be iterable an (e.g., a list or tuple) containing/generating feature names (and optionally values, see the input_type const... | sklearn.modules.generated.sklearn.feature_extraction.featurehasher#sklearn.feature_extraction.FeatureHasher.transform |
sklearn.feature_extraction.image.extract_patches_2d(image, patch_size, *, max_patches=None, random_state=None) [source]
Reshape a 2D image into a collection of patches The resulting patches are allocated in a dedicated array. Read more in the User Guide. Parameters
imagendarray of shape (image_height, image_width... | sklearn.modules.generated.sklearn.feature_extraction.image.extract_patches_2d#sklearn.feature_extraction.image.extract_patches_2d |
sklearn.feature_extraction.image.grid_to_graph(n_x, n_y, n_z=1, *, mask=None, return_as=<class 'scipy.sparse.coo.coo_matrix'>, dtype=<class 'int'>) [source]
Graph of the pixel-to-pixel connections Edges exist if 2 voxels are connected. Parameters
n_xint
Dimension in x axis
n_yint
Dimension in y axis
n_zin... | sklearn.modules.generated.sklearn.feature_extraction.image.grid_to_graph#sklearn.feature_extraction.image.grid_to_graph |
sklearn.feature_extraction.image.img_to_graph(img, *, mask=None, return_as=<class 'scipy.sparse.coo.coo_matrix'>, dtype=None) [source]
Graph of the pixel-to-pixel gradient connections Edges are weighted with the gradient values. Read more in the User Guide. Parameters
imgndarray of shape (height, width) or (heigh... | sklearn.modules.generated.sklearn.feature_extraction.image.img_to_graph#sklearn.feature_extraction.image.img_to_graph |
class sklearn.feature_extraction.image.PatchExtractor(*, patch_size=None, max_patches=None, random_state=None) [source]
Extracts patches from a collection of images Read more in the User Guide. New in version 0.9. Parameters
patch_sizetuple of int (patch_height, patch_width), default=None
The dimensions of on... | sklearn.modules.generated.sklearn.feature_extraction.image.patchextractor#sklearn.feature_extraction.image.PatchExtractor |
sklearn.feature_extraction.image.PatchExtractor
class sklearn.feature_extraction.image.PatchExtractor(*, patch_size=None, max_patches=None, random_state=None) [source]
Extracts patches from a collection of images Read more in the User Guide. New in version 0.9. Parameters
patch_sizetuple of int (patch_height,... | sklearn.modules.generated.sklearn.feature_extraction.image.patchextractor |
fit(X, y=None) [source]
Do nothing and return the estimator unchanged. This method is just there to implement the usual API and hence work in pipelines. Parameters
Xarray-like of shape (n_samples, n_features)
Training data. | sklearn.modules.generated.sklearn.feature_extraction.image.patchextractor#sklearn.feature_extraction.image.PatchExtractor.fit |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.feature_extraction.image.patchextractor#sklearn.feature_extraction.image.PatchExtractor.get_params |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.feature_extraction.image.patchextractor#sklearn.feature_extraction.image.PatchExtractor.set_params |
transform(X) [source]
Transforms the image samples in X into a matrix of patch data. Parameters
Xndarray of shape (n_samples, image_height, image_width) or (n_samples, image_height, image_width, n_channels)
Array of images from which to extract patches. For color images, the last dimension specifies the channel... | sklearn.modules.generated.sklearn.feature_extraction.image.patchextractor#sklearn.feature_extraction.image.PatchExtractor.transform |
sklearn.feature_extraction.image.reconstruct_from_patches_2d(patches, image_size) [source]
Reconstruct the image from all of its patches. Patches are assumed to overlap and the image is constructed by filling in the patches from left to right, top to bottom, averaging the overlapping regions. Read more in the User Gu... | sklearn.modules.generated.sklearn.feature_extraction.image.reconstruct_from_patches_2d#sklearn.feature_extraction.image.reconstruct_from_patches_2d |
class sklearn.feature_extraction.text.CountVectorizer(*, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1), analyzer='word', max_df=1.0, min_df=1, max_features=None, voca... | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer |
sklearn.feature_extraction.text.CountVectorizer
class sklearn.feature_extraction.text.CountVectorizer(*, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1), analyzer='wo... | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer |
build_analyzer() [source]
Return a callable that handles preprocessing, tokenization and n-grams generation. Returns
analyzer: callable
A function to handle preprocessing, tokenization and n-grams generation. | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.build_analyzer |
build_preprocessor() [source]
Return a function to preprocess the text before tokenization. Returns
preprocessor: callable
A function to preprocess the text before tokenization. | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.build_preprocessor |
build_tokenizer() [source]
Return a function that splits a string into a sequence of tokens. Returns
tokenizer: callable
A function to split a string into a sequence of tokens. | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.build_tokenizer |
decode(doc) [source]
Decode the input into a string of unicode symbols. The decoding strategy depends on the vectorizer parameters. Parameters
docstr
The string to decode. Returns
doc: str
A string of unicode symbols. | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.decode |
fit(raw_documents, y=None) [source]
Learn a vocabulary dictionary of all tokens in the raw documents. Parameters
raw_documentsiterable
An iterable which yields either str, unicode or file objects. Returns
self | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.fit |
fit_transform(raw_documents, y=None) [source]
Learn the vocabulary dictionary and return document-term matrix. This is equivalent to fit followed by transform, but more efficiently implemented. Parameters
raw_documentsiterable
An iterable which yields either str, unicode or file objects. Returns
Xarray of... | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.fit_transform |
get_feature_names() [source]
Array mapping from feature integer indices to feature name. Returns
feature_nameslist
A list of feature names. | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.get_feature_names |
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