dask-searchcv¶

Note

Dask-SearchCV is now included in Dask-ML <http://dask-ml.readthedocs.io/>_.

Further development to Dask-SearchCV is occuring in the Dask-ML <https://github.com/dask/dask-ml>_ repository. Please post issues and make pull requests there.

Tools for performing hyperparameter optimization of Scikit-Learn models using Dask.

Introduction¶

This library provides implementations of Scikit-Learn’s GridSearchCV and RandomizedSearchCV. They implement many (but not all) of the same parameters, and should be a drop-in replacement for the subset that they do implement. For certain problems, these implementations can be more efficient than those in Scikit-Learn, as they can avoid expensive repeated computations.

For more information, see this blogpost.

Highlights¶

Drop-in replacement for Scikit-Learn’s GridSearchCV and RandomizedSearchCV.
Flexible Backends. Hyperparameter optimization can be done in parallel using threads, processes, or distributed across a cluster.
Works well with Dask collections. Dask arrays, dataframes, and delayed can be passed to fit.
Avoid repeated work. Candidate estimators with identical parameters and inputs will only be fit once. For composite-estimators such as Pipeline this can be significantly more efficient as it can avoid expensive repeated computations.

Install¶

Dask-searchcv is available via conda or pip:

# Install with conda
$ conda install dask-searchcv -c conda-forge

# Install with pip
$ pip install dask-searchcv

Walkthrough¶

Drop-In Replacement¶

Dask-searchcv provides (almost) drop-in replacements for Scikit-Learn’s GridSearchCV and RandomizedSearchCV. With the exception of a few keyword arguments, the api’s are exactly the same, and often only an import change is necessary:

from sklearn.datasets import load_digits
from sklearn.svm import SVC

# Fit with dask-searchcv
from dask_searchcv import GridSearchCV

param_space = {'C': [1e-4, 1, 1e4],
               'gamma': [1e-3, 1, 1e3],
               'class_weight': [None, 'balanced']}

model = SVC(kernel='rbf')

digits = load_digits()

search = GridSearchCV(model, param_space, cv=3)
search.fit(digits.data, digits.target)

Flexible Backends¶

Dask-searchcv can use any of the dask schedulers. By default the threaded scheduler is used, but this can easily be swapped out for the multiprocessing or distributed scheduler:

# Distribute grid-search across a cluster
from dask.distributed import Client
scheduler_address = '127.0.0.1:8786'
client = Client(scheduler_address)

search.fit(digits.data, digits.target)

Works Well With Dask Collections¶

Dask collections such as dask.array, dask.dataframe and dask.delayed can be passed to fit. This means you can use dask to do your data loading and preprocessing as well, allowing for a clean workflow. This also allows you to work with remote data on a cluster without ever having to pull it locally to your computer:

import dask.dataframe as dd

# Load data from s3
df = dd.read_csv('s3://bucket-name/my-data-*.csv')

# Do some preprocessing steps
df['x2'] = df.x - df.x.mean()
# ...

# Pass to fit without ever leaving the cluster
search.fit(df[['x', 'x2']], df['y'])

Avoid Repeated Work¶

When searching over composite estimators like sklearn.pipeline.Pipeline or sklearn.pipeline.FeatureUnion, dask-searchcv will avoid fitting the same estimator + parameter + data combination more than once. For pipelines with expensive early steps this can be faster, as repeated work is avoided.

For example, given the following 3-stage pipeline and grid (modified from this scikit-learn example).

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.linear_model import SGDClassifier
from sklearn.pipeline import Pipeline

pipeline = Pipeline([('vect', CountVectorizer()),
                     ('tfidf', TfidfTransformer()),
                     ('clf', SGDClassifier())])

grid = {'vect__ngram_range': [(1, 1)],
        'tfidf__norm': ['l1', 'l2'],
        'clf__alpha': [1e-3, 1e-4, 1e-5]}

the Scikit-Learn grid-search implementation looks something like (simplified):

scores = []
for ngram_range in parameters['vect__ngram_range']:
        for norm in parameters['tfidf__norm']:
                for alpha in parameters['clf__alpha']:
                        vect = CountVectorizer(ngram_range=ngram_range)
                        X2 = vect.fit_transform(X, y)
                        tfidf = TfidfTransformer(norm=norm)
                        X3 = tfidf.fit_transform(X2, y)
                        clf = SGDClassifier(alpha=alpha)
                        clf.fit(X3, y)
                        scores.append(clf.score(X3, y))
best = choose_best_parameters(scores, parameters)

As a directed acyclic graph, this might look like:

In contrast, the dask version looks more like:

scores = []
for ngram_range in parameters['vect__ngram_range']:
        vect = CountVectorizer(ngram_range=ngram_range)
        X2 = vect.fit_transform(X, y)
        for norm in parameters['tfidf__norm']:
                tfidf = TfidfTransformer(norm=norm)
                X3 = tfidf.fit_transform(X2, y)
                for alpha in parameters['clf__alpha']:
                        clf = SGDClassifier(alpha=alpha)
                        clf.fit(X3, y)
                        scores.append(clf.score(X3, y))
best = choose_best_parameters(scores, parameters)

With a corresponding directed acyclic graph:

Looking closely, you can see that the Scikit-Learn version ends up fitting earlier steps in the pipeline multiple times with the same parameters and data. Due to the increased flexibility of Dask over Joblib, we’re able to merge these tasks in the graph and only perform the fit step once for any parameter/data/estimator combination. For pipelines that have relatively expensive early steps, this can be a big win when performing a grid search.

Index¶

API¶

`GridSearchCV`(estimator, param_grid[, …])	Exhaustive search over specified parameter values for an estimator.
`RandomizedSearchCV`(estimator, …[, n_iter, …])	Randomized search on hyper parameters.

class dask_searchcv.GridSearchCV(estimator, param_grid, scoring=None, iid=True, refit=True, cv=None, error_score='raise', return_train_score='warn', scheduler=None, n_jobs=-1, cache_cv=True)¶

Exhaustive search over specified parameter values for an estimator.

GridSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.

The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.

Parameters:

estimator : estimator object.

This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.

param_grid : dict or list of dictionaries

Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings.

scoring : string, callable, list/tuple, dict or None, default: None

A single string or a callable to evaluate the predictions on the test set.

For evaluating multiple metrics, either give a list of (unique) strings or a dict with names as keys and callables as values.

NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each.

If None, the estimator’s default scorer (if available) is used.

iid : boolean, default=True

If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.

cv : int, cross-validation generator or an iterable, optional

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

None, to use the default 3-fold cross validation,

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

An object to be used as a cross-validation generator.

An iterable yielding train, test splits.

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.

refit : boolean, or string, default=True

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

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

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

Also for multiple metric evaluation, the attributes best_index_, best_score_ and best_parameters_ 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.

error_score : ‘raise’ (default) or numeric

Value to assign to the score if an error occurs in estimator fitting. If set to ‘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 : boolean, default=True

If 'False', the cv_results_ attribute will not include training scores.

Note that for scikit-learn >= 0.19.1, the default of True is deprecated, and a warning will be raised when accessing train score results without explicitly asking for train scores.

scheduler : string, callable, Client, or None, default=None

The dask scheduler to use. Default is to use the global scheduler if set, and fallback to the threaded scheduler otherwise. To use a different scheduler either specify it by name (either “threading”, “multiprocessing”, or “synchronous”), pass in a dask.distributed.Client, or provide a scheduler get function.

n_jobs : int, default=-1

Number of jobs to run in parallel. Ignored for the synchronous and distributed schedulers. If n_jobs == -1 [default] all cpus are used. For n_jobs < -1, (n_cpus + 1 + n_jobs) are used.

cache_cv : bool, default=True

Whether to extract each train/test subset at most once in each worker process, or every time that subset is needed. Caching the splits can speedup computation at the cost of increased memory usage per worker process.

If True, worst case memory usage is (n_splits + 1) * (X.nbytes + y.nbytes) per worker. If False, worst case memory usage is (n_threads_per_worker + 1) * (X.nbytes + y.nbytes) per worker.

Attributes:

cv_results_ : dict of numpy (masked) ndarrays

A dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame.

For instance the below given table

param_kernel	param_gamma	param_degree	split0_test_score	…	rank…..
‘poly’	–	2	0.8	…	2
‘poly’	–	3	0.7	…	4
‘rbf’	0.1	–	0.8	…	3
‘rbf’	0.2	–	0.9	…	1

will be represented by a cv_results_ dict of:

{
'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],
                                mask = [False False False False]...)
'param_gamma': masked_array(data = [-- -- 0.1 0.2],
                            mask = [ True  True False False]...),
'param_degree': masked_array(data = [2.0 3.0 -- --],
                                mask = [False False  True  True]...),
'split0_test_score'  : [0.8, 0.7, 0.8, 0.9],
'split1_test_score'  : [0.82, 0.5, 0.7, 0.78],
'mean_test_score'    : [0.81, 0.60, 0.75, 0.82],
'std_test_score'     : [0.02, 0.01, 0.03, 0.03],
'rank_test_score'    : [2, 4, 3, 1],
'split0_train_score' : [0.8, 0.7, 0.8, 0.9],
'split1_train_score' : [0.82, 0.7, 0.82, 0.5],
'mean_train_score'   : [0.81, 0.7, 0.81, 0.7],
'std_train_score'    : [0.03, 0.04, 0.03, 0.03],
'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],
'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],
'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],
'std_score_time'     : [0.001, 0.002, 0.003, 0.005],
'params'             : [{'kernel': 'poly', 'degree': 2}, ...],
}

NOTE that the key 'params' is used to store a list of parameter settings dict for all the parameter candidates.

The mean_fit_time, std_fit_time, mean_score_time and std_score_time are all in seconds.

best_estimator_ : estimator

Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.

best_score_ : float or dict of floats

Score of best_estimator on the left out data. When using multiple metrics, best_score_ will be a dictionary where the keys are the names of the scorers, and the values are the mean test score for that scorer.

best_params_ : dict

Parameter setting that gave the best results on the hold out data.

best_index_ : int or dict of ints

The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting.

The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_).

When using multiple metrics, best_index_ will be a dictionary where the keys are the names of the scorers, and the values are the index with the best mean score for that scorer, as described above.

scorer_ : function or dict of functions

Scorer function used on the held out data to choose the best parameters for the model. A dictionary of {scorer_name: scorer} when multiple metrics are used.

n_splits_ : int

The number of cross-validation splits (folds/iterations).

Notes

The parameters selected are those that maximize the score of the left out data, unless an explicit score is passed in which case it is used instead.

Examples

>>> import dask_searchcv as dcv
>>> from sklearn import svm, datasets
>>> iris = datasets.load_iris()
>>> parameters = {'kernel': ['linear', 'rbf'], 'C': [1, 10]}
>>> svc = svm.SVC()
>>> clf = dcv.GridSearchCV(svc, parameters)
>>> clf.fit(iris.data, iris.target)  
GridSearchCV(cache_cv=..., cv=..., error_score=...,
        estimator=SVC(C=..., cache_size=..., class_weight=..., coef0=...,
                      decision_function_shape=..., degree=..., gamma=...,
                      kernel=..., max_iter=-1, probability=False,
                      random_state=..., shrinking=..., tol=...,
                      verbose=...),
        iid=..., n_jobs=..., param_grid=..., refit=..., return_train_score=...,
        scheduler=..., scoring=...)
>>> sorted(clf.cv_results_.keys())  
['mean_fit_time', 'mean_score_time', 'mean_test_score',...
 'mean_train_score', 'param_C', 'param_kernel', 'params',...
 'rank_test_score', 'split0_test_score',...
 'split0_train_score', 'split1_test_score', 'split1_train_score',...
 'split2_test_score', 'split2_train_score',...
 'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...]

Methods

`decision_function`(X)	Call decision_function on the estimator with the best found parameters.
`fit`(X[, y, groups])	Run fit with all sets of parameters.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(Xt)	Call inverse_transform on the estimator with the best found params.
`predict`(X)	Call predict on the estimator with the best found parameters.
`predict_log_proba`(X)	Call predict_log_proba on the estimator with the best found parameters.
`predict_proba`(X)	Call predict_proba on the estimator with the best found parameters.
`score`(X[, y])	Returns the score on the given data, if the estimator has been refit.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Call transform on the estimator with the best found parameters.
`visualize`([filename, format])	Render the task graph for this parameter search using `graphviz`.

decision_function(X)¶

Call decision_function on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports decision_function.

Parameters:

X : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

fit(X, y=None, groups=None, **fit_params)¶

Run fit with all sets of parameters.

Parameters:

X : array-like, shape = [n_samples, n_features]

Training vector, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples] or [n_samples, n_output], optional

Target relative to X for classification or regression; None for unsupervised learning.

groups : array-like, shape = [n_samples], optional

Group labels for the samples used while splitting the dataset into train/test set.

**fit_params :

Parameters passed to the fit method of the estimator

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

inverse_transform(Xt)¶

Call inverse_transform on the estimator with the best found params.

Only available if the underlying estimator implements inverse_transform and refit=True.

Parameters:

Xt : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict(X)¶

Call predict on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict.

Parameters:

X : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict_log_proba(X)¶

Call predict_log_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_log_proba.

Parameters:

X : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict_proba(X)¶

Call predict_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_proba.

Parameters:

X : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

score(X, y=None)¶

Returns the score on the given data, if the estimator has been refit.

This uses the score defined by scoring where provided, and the best_estimator_.score method otherwise.

Parameters:

X : array-like, shape = [n_samples, n_features]

Input data, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples] or [n_samples, n_output], optional

Target relative to X for classification or regression; None for unsupervised learning.

Returns:

score : float

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self :

transform(X)¶

Call transform on the estimator with the best found parameters.

Only available if the underlying estimator supports transform and refit=True.

Parameters:

X : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

visualize(filename='mydask', format=None, **kwargs)¶

Render the task graph for this parameter search using graphviz.

Requires graphviz to be installed.

Parameters:

filename : str or None, optional

The name (without an extension) of the file to write to disk. If filename is None, no file will be written, and we communicate with dot using only pipes.

format : {‘png’, ‘pdf’, ‘dot’, ‘svg’, ‘jpeg’, ‘jpg’}, optional

Format in which to write output file. Default is ‘png’.

**kwargs :

Additional keyword arguments to forward to dask.dot.to_graphviz.

Returns:

result : IPython.diplay.Image, IPython.display.SVG, or None

See dask.dot.dot_graph for more information.

class dask_searchcv.RandomizedSearchCV(estimator, param_distributions, n_iter=10, random_state=None, scoring=None, iid=True, refit=True, cv=None, error_score='raise', return_train_score='warn', scheduler=None, n_jobs=-1, cache_cv=True)¶

Randomized search on hyper parameters.

RandomizedSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.

In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter.

If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.

Parameters:

estimator : estimator object.

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

Dictionary with parameters names (string) 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.

n_iter : int, default=10

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

random_state : int or RandomState

Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions.

scoring : string, callable, list/tuple, dict or None, default: None

A single string or a callable to evaluate the predictions on the test set.

For evaluating multiple metrics, either give a list of (unique) strings or a dict with names as keys and callables as values.

NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each.

If None, the estimator’s default scorer (if available) is used.

iid : boolean, default=True

If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.

cv : int, cross-validation generator or an iterable, optional

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

None, to use the default 3-fold cross validation,

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

An object to be used as a cross-validation generator.

An iterable yielding train, test splits.

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.

refit : boolean, or string, default=True

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

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

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

Also for multiple metric evaluation, the attributes best_index_, best_score_ and best_parameters_ 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.

error_score : ‘raise’ (default) or numeric

Value to assign to the score if an error occurs in estimator fitting. If set to ‘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 : boolean, default=True

If 'False', the cv_results_ attribute will not include training scores.

Note that for scikit-learn >= 0.19.1, the default of True is deprecated, and a warning will be raised when accessing train score results without explicitly asking for train scores.

scheduler : string, callable, Client, or None, default=None

The dask scheduler to use. Default is to use the global scheduler if set, and fallback to the threaded scheduler otherwise. To use a different scheduler either specify it by name (either “threading”, “multiprocessing”, or “synchronous”), pass in a dask.distributed.Client, or provide a scheduler get function.

n_jobs : int, default=-1

Number of jobs to run in parallel. Ignored for the synchronous and distributed schedulers. If n_jobs == -1 [default] all cpus are used. For n_jobs < -1, (n_cpus + 1 + n_jobs) are used.

cache_cv : bool, default=True

Whether to extract each train/test subset at most once in each worker process, or every time that subset is needed. Caching the splits can speedup computation at the cost of increased memory usage per worker process.

If True, worst case memory usage is (n_splits + 1) * (X.nbytes + y.nbytes) per worker. If False, worst case memory usage is (n_threads_per_worker + 1) * (X.nbytes + y.nbytes) per worker.

Attributes:

cv_results_ : dict of numpy (masked) ndarrays

A dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame.

For instance the below given table

param_kernel	param_gamma	param_degree	split0_test_score	…	rank…..
‘poly’	–	2	0.8	…	2
‘poly’	–	3	0.7	…	4
‘rbf’	0.1	–	0.8	…	3
‘rbf’	0.2	–	0.9	…	1

will be represented by a cv_results_ dict of:

{
'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],
                                mask = [False False False False]...)
'param_gamma': masked_array(data = [-- -- 0.1 0.2],
                            mask = [ True  True False False]...),
'param_degree': masked_array(data = [2.0 3.0 -- --],
                                mask = [False False  True  True]...),
'split0_test_score'  : [0.8, 0.7, 0.8, 0.9],
'split1_test_score'  : [0.82, 0.5, 0.7, 0.78],
'mean_test_score'    : [0.81, 0.60, 0.75, 0.82],
'std_test_score'     : [0.02, 0.01, 0.03, 0.03],
'rank_test_score'    : [2, 4, 3, 1],
'split0_train_score' : [0.8, 0.7, 0.8, 0.9],
'split1_train_score' : [0.82, 0.7, 0.82, 0.5],
'mean_train_score'   : [0.81, 0.7, 0.81, 0.7],
'std_train_score'    : [0.03, 0.04, 0.03, 0.03],
'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],
'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],
'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],
'std_score_time'     : [0.001, 0.002, 0.003, 0.005],
'params'             : [{'kernel': 'poly', 'degree': 2}, ...],
}

NOTE that the key 'params' is used to store a list of parameter settings dict for all the parameter candidates.

The mean_fit_time, std_fit_time, mean_score_time and std_score_time are all in seconds.

best_estimator_ : estimator

Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.

best_score_ : float or dict of floats

Score of best_estimator on the left out data. When using multiple metrics, best_score_ will be a dictionary where the keys are the names of the scorers, and the values are the mean test score for that scorer.

best_params_ : dict

Parameter setting that gave the best results on the hold out data.

best_index_ : int or dict of ints

The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting.

The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_).

When using multiple metrics, best_index_ will be a dictionary where the keys are the names of the scorers, and the values are the index with the best mean score for that scorer, as described above.

scorer_ : function or dict of functions

Scorer function used on the held out data to choose the best parameters for the model. A dictionary of {scorer_name: scorer} when multiple metrics are used.

n_splits_ : int

The number of cross-validation splits (folds/iterations).

Notes

The parameters selected are those that maximize the score of the left out data, unless an explicit score is passed in which case it is used instead.

Examples

>>> import dask_searchcv as dcv
>>> from scipy.stats import expon
>>> from sklearn import svm, datasets
>>> iris = datasets.load_iris()
>>> parameters = {'C': expon(scale=100), 'kernel': ['linear', 'rbf']}
>>> svc = svm.SVC()
>>> clf = dcv.RandomizedSearchCV(svc, parameters, n_iter=100)
>>> clf.fit(iris.data, iris.target)  
RandomizedSearchCV(cache_cv=..., cv=..., error_score=...,
        estimator=SVC(C=..., cache_size=..., class_weight=..., coef0=...,
                      decision_function_shape=..., degree=..., gamma=...,
                      kernel=..., max_iter=..., probability=...,
                      random_state=..., shrinking=..., tol=...,
                      verbose=...),
        iid=..., n_iter=..., n_jobs=..., param_distributions=...,
        random_state=..., refit=..., return_train_score=...,
        scheduler=..., scoring=...)
>>> sorted(clf.cv_results_.keys())  
['mean_fit_time', 'mean_score_time', 'mean_test_score',...
 'mean_train_score', 'param_C', 'param_kernel', 'params',...
 'rank_test_score', 'split0_test_score',...
 'split0_train_score', 'split1_test_score', 'split1_train_score',...
 'split2_test_score', 'split2_train_score',...
 'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...]

Methods

`decision_function`(X)	Call decision_function on the estimator with the best found parameters.
`fit`(X[, y, groups])	Run fit with all sets of parameters.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(Xt)	Call inverse_transform on the estimator with the best found params.
`predict`(X)	Call predict on the estimator with the best found parameters.
`predict_log_proba`(X)	Call predict_log_proba on the estimator with the best found parameters.
`predict_proba`(X)	Call predict_proba on the estimator with the best found parameters.
`score`(X[, y])	Returns the score on the given data, if the estimator has been refit.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Call transform on the estimator with the best found parameters.
`visualize`([filename, format])	Render the task graph for this parameter search using `graphviz`.

decision_function(X)¶

Call decision_function on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports decision_function.

Parameters:

X : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

fit(X, y=None, groups=None, **fit_params)¶

Run fit with all sets of parameters.

Parameters:

X : array-like, shape = [n_samples, n_features]

Training vector, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples] or [n_samples, n_output], optional

Target relative to X for classification or regression; None for unsupervised learning.

groups : array-like, shape = [n_samples], optional

Group labels for the samples used while splitting the dataset into train/test set.

**fit_params :

Parameters passed to the fit method of the estimator

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

inverse_transform(Xt)¶

Call inverse_transform on the estimator with the best found params.

Only available if the underlying estimator implements inverse_transform and refit=True.

Parameters:

Xt : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict(X)¶

Call predict on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict.

Parameters:

X : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict_log_proba(X)¶

Call predict_log_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_log_proba.

Parameters:

X : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict_proba(X)¶

Call predict_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_proba.

Parameters:

X : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

score(X, y=None)¶

Returns the score on the given data, if the estimator has been refit.

This uses the score defined by scoring where provided, and the best_estimator_.score method otherwise.

Parameters:

X : array-like, shape = [n_samples, n_features]

Input data, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples] or [n_samples, n_output], optional

Target relative to X for classification or regression; None for unsupervised learning.

Returns:

score : float

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self :

transform(X)¶

Call transform on the estimator with the best found parameters.

Only available if the underlying estimator supports transform and refit=True.

Parameters:

X : indexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

visualize(filename='mydask', format=None, **kwargs)¶

Render the task graph for this parameter search using graphviz.

Requires graphviz to be installed.

Parameters:

filename : str or None, optional

The name (without an extension) of the file to write to disk. If filename is None, no file will be written, and we communicate with dot using only pipes.

format : {‘png’, ‘pdf’, ‘dot’, ‘svg’, ‘jpeg’, ‘jpg’}, optional

Format in which to write output file. Default is ‘png’.

**kwargs :

Additional keyword arguments to forward to dask.dot.to_graphviz.

Returns:

result : IPython.diplay.Image, IPython.display.SVG, or None

See dask.dot.dot_graph for more information.