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, orscoring
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 usingpredict
directly on thisGridSearchCV
instance.Also for multiple metric evaluation, the attributes
best_index_
,best_score_
andbest_parameters_
will only be available ifrefit
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'
, thecv_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 schedulerget
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. Forn_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
andstd_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 supportsdecision_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
andrefit=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 supportspredict
.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 supportspredict_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 supportspredict_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 thebest_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
andrefit=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, orscoring
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 usingpredict
directly on thisGridSearchCV
instance.Also for multiple metric evaluation, the attributes
best_index_
,best_score_
andbest_parameters_
will only be available ifrefit
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'
, thecv_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 schedulerget
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. Forn_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
andstd_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 supportsdecision_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
andrefit=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 supportspredict
.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 supportspredict_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 supportspredict_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 thebest_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
andrefit=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.