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snowflake.ml.modeling.model_selection.RandomizedSearchCVΒΆ

class snowflake.ml.modeling.model_selection.RandomizedSearchCV(*, estimator, param_distributions, n_iter=10, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score=nan, return_train_score=False, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)ΒΆ

Bases: BaseTransformer

Randomized search on hyper parameters For more details on this class, see sklearn.model_selection.RandomizedSearchCV

estimator: estimator object

An object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.

param_distributions: dict or list of dicts

Dictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.

n_iter: int, default=10

Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.

scoring: str, callable, list, tuple or dict, default=None

Strategy to evaluate the performance of the cross-validated model on the test set.

If scoring represents a single score, one can use:

  • a single string (see scoring_parameter);

  • a callable (see scoring) that returns a single value.

If scoring represents multiple scores, one can use:

  • a list or tuple of unique strings;

  • a callable returning a dictionary where the keys are the metric names and the values are the metric scores;

  • a dictionary with metric names as keys and callables a values.

See multimetric_grid_search for an example.

If None, the estimator’s score method is used.

n_jobs: int, default=None

Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

refit: bool, str, or callable, default=True

Refit an estimator using the best found parameters on the whole dataset.

For multiple metric evaluation, this needs to be a str denoting the scorer that would be used to find the best parameters for refitting the estimator at the end.

Where there are considerations other than maximum score in choosing a best estimator, refit can be set to a function which returns the selected best_index_ given the cv_results. In that case, the best_estimator_ and best_params_ will be set according to the returned best_index_ while the best_score_ attribute will not be available.

The refitted estimator is made available at the best_estimator_ attribute and permits using predict directly on this RandomizedSearchCV instance.

Also for multiple metric evaluation, the attributes best_index_, best_score_ and best_params_ will only be available if refit is set and all of them will be determined w.r.t this specific scorer.

See scoring parameter to know more about multiple metric evaluation.

cv: int, cross-validation generator or an iterable, default=None

Determines the cross-validation splitting strategy. Possible inputs for cv are:

  • None, to use the default 5-fold cross validation,

  • integer, to specify the number of folds in a (Stratified)KFold,

  • CV splitter,

  • An iterable yielding (train, test) splits as arrays of indices.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. These splitters are instantiated with shuffle=False so the splits will be the same across calls.

Refer User Guide for the various cross-validation strategies that can be used here.

verbose: int

Controls the verbosity: the higher, the more messages.

  • >1: the computation time for each fold and parameter candidate is displayed;

  • >2: the score is also displayed;

  • >3: the fold and candidate parameter indexes are also displayed together with the starting time of the computation.

pre_dispatch: int, or str, default=’2*n_jobs’

Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:

  • None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs

  • An int, giving the exact number of total jobs that are spawned

  • A str, giving an expression as a function of n_jobs, as in β€˜2*n_jobs’

random_state: int, RandomState instance or None, default=None

Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See Glossary.

error_score: β€˜raise’ or numeric, default=np.nan

Value to assign to the score if an error occurs in estimator fitting. If set to β€˜raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error.

return_train_score: bool, default=False

If False, the cv_results_ attribute will not include training scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance.

input_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.

label_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).

output_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.

sample_weight_col: Optional[str]

A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

Methods

decision_function(dataset[, output_cols_prefix])

Call decision_function on the estimator with the best found parameters For more details on this function, see sklearn.model_selection.RandomizedSearchCV.decision_function

fit(dataset)

Run fit with all sets of parameters For more details on this function, see sklearn.model_selection.RandomizedSearchCV.fit

predict(dataset)

Call predict on the estimator with the best found parameters For more details on this function, see sklearn.model_selection.RandomizedSearchCV.predict

predict_log_proba(dataset[, output_cols_prefix])

Call predict_proba on the estimator with the best found parameters For more details on this function, see sklearn.model_selection.RandomizedSearchCV.predict_proba

predict_proba(dataset[, output_cols_prefix])

Call predict_proba on the estimator with the best found parameters For more details on this function, see sklearn.model_selection.RandomizedSearchCV.predict_proba

score(dataset)

Return the score on the given data, if the estimator has been refit For more details on this function, see sklearn.model_selection.RandomizedSearchCV.score

set_input_cols(input_cols)

Input columns setter.

to_sklearn()

Get sklearn.model_selection.RandomizedSearchCV object.

transform(dataset)

Call transform on the estimator with the best found parameters For more details on this function, see sklearn.model_selection.RandomizedSearchCV.transform

Attributes

model_signatures

Returns model signature of current class.
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