"""
It includes utilities to search the parameter and model space.
Extracted from the no longer maintained ``research-learn`` library.
"""
# License: BSD 3 clause
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
from sklearn.utils.metaestimators import _BaseComposition
from sklearn.utils.validation import check_is_fitted
from sklearn.model_selection import GridSearchCV
from ..utils._check_pipelines import check_estimator_type, check_param_grids
class MultiEstimatorMixin(_BaseComposition):
"""Mixin class for multi estimator."""
def __init__(self, estimators, est_name=None):
self.estimators = estimators
self.est_name = est_name
def _validate_estimators(self):
error_msg = "Invalid `estimators` attribute, `estimators` should"
" be a list of (string, estimator) tuples."
try:
if len(self.estimators) == 0:
raise TypeError(error_msg)
for name, est in self.estimators:
is_str = isinstance(name, str)
is_est = isinstance(est, BaseEstimator)
if not (is_str and is_est):
raise TypeError(error_msg)
except TypeError:
raise TypeError(error_msg)
self.est_names_ = [est_name for est_name, _ in self.estimators]
super(MultiEstimatorMixin, self)._validate_names(self.est_names_)
if self.est_name not in self.est_names_:
raise ValueError(
f'Attribute `est_name` should be one of {", ".join(self.est_names_)}. '
f"Got `{self.est_name}` instead."
)
def set_params(self, **params):
"""Set the parameters.
Valid parameter keys can be listed with get_params().
Parameters
----------
params : keyword arguments
Specific parameters using e.g. set_params(parameter_name=new_value)
In addition, to setting the parameters of the ``MultiEstimatorMixin``,
the individual estimators of the ``MultiEstimatorMixin`` can also be
set or replaced by setting them to None.
"""
super(MultiEstimatorMixin, self)._set_params("estimators", **params)
return self
def get_params(self, deep=True):
"""Get the parameters.
Parameters
----------
deep: bool
Setting it to True gets the various estimators and the parameters
of the estimators as well
"""
return super(MultiEstimatorMixin, self)._get_params("estimators", deep=deep)
def fit(self, X, y, **fit_params):
"""Fit the selected estimator."""
# Validate estimators
self._validate_estimators()
# Copy one of the estimators
estimator = clone(dict(self.estimators)[self.est_name])
# Fit estimator
self.estimator_ = estimator.fit(X, y, **fit_params)
if hasattr(estimator, "classes_"):
self.classes_ = estimator.classes_
return self
def predict(self, X):
"""Predict with the selected estimator."""
check_is_fitted(self, "estimator_")
return self.estimator_.predict(X)
class MultiClassifier(MultiEstimatorMixin, ClassifierMixin):
"""The functionality of a collection of classifiers is provided as
a single metaclassifier. The classifier to be fitted is selected using a
parameter."""
def predict_proba(self, X):
"""Predict the probability with the selected estimator."""
check_is_fitted(self, "estimator_")
return self.estimator_.predict_proba(X)
class MultiRegressor(MultiEstimatorMixin, RegressorMixin):
"""The functionality of a collection of regressors is provided as
a single metaregressor. The regressor to be fitted is selected using a
parameter."""
pass
[docs]
class ModelSearchCV(GridSearchCV):
"""Exhaustive search over specified parameter values for a collection of estimators.
Important members are fit, predict.
ModelSearchCV 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
estimators used.
The parameters of the estimators used to apply these methods are optimized
by cross-validated grid-search over their parameter grids.
Read more in the :ref:`User Guide <user_guide>`.
Parameters
----------
estimators : list of (string, estimator) tuples
Each estimator is assumed to implement the scikit-learn estimator interface.
Either estimator needs to provide a ``score`` function,
or ``scoring`` must be passed.
param_grids : 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 score method is used.
n_jobs : int, default=None
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
pre_dispatch : int or string, default=None
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.
- An int, giving the exact number of total jobs that are spawned.
- A string, as a function of n_jobs i.e. ``'2*n_jobs'``.
cv : int, cross-validation generator or an iterable, default=5
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 as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used. These splitters are instantiated
with `shuffle=False` so the splits will be the same across calls.
refit : boolean, string, 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 string 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 ``cv_results_``. In that
case, the ``best_estimator_`` and ``best_parameters_`` will be set
according to the returned ``best_index_`` while the ``best_score_``
attribute will not be availble.
The refitted estimator is made available at the ``best_estimator_``
attribute and permits using ``predict`` directly on this
``ModelSearchCV`` 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.
verbose : integer, default=0
Controls the verbosity: the higher, the more messages.
error_score : 'raise' or numeric, default=np.nan
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. Default is ``np.nan``.
return_train_score : boolean, 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.
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_dtc_criterion|param_gamma|param_degree|split0_test_score|...|rank_t...|
+===================+===========+============+=================+===+=========+
| 'entropy' | -- | 2 | 0.80 |...| 2 |
+-------------------+-----------+------------+-----------------+---+---------+
| 'entropy' | -- | 3 | 0.70 |...| 4 |
+-------------------+-----------+------------+-----------------+---+---------+
| 'entropy' | 0.1 | -- | 0.80 |...| 3 |
+-------------------+-----------+------------+-----------------+---+---------+
| 'entropy' | 0.2 | -- | 0.93 |...| 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' : [0.80, 0.70, 0.80, 0.93],
'split1_test_score' : [0.82, 0.50, 0.70, 0.78],
'mean_test_score' : [0.81, 0.60, 0.75, 0.85],
'std_test_score' : [0.01, 0.10, 0.05, 0.08],
'rank_test_score' : [2, 4, 3, 1],
'split0_train_score' : [0.80, 0.92, 0.70, 0.93],
'split1_train_score' : [0.82, 0.55, 0.70, 0.87],
'mean_train_score' : [0.81, 0.74, 0.70, 0.90],
'std_train_score' : [0.01, 0.19, 0.00, 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' : [0.01, 0.06, 0.04, 0.04],
'std_score_time' : [0.00, 0.00, 0.00, 0.01],
'params' : [{'kernel': 'poly', 'degree': 2}, ...],
}
NOTE
The key ``'params'`` is used to store a list of parameter
settings dicts for all the parameter candidates.
The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
``std_score_time`` are all in seconds.
For multi-metric evaluation, the scores for all the scorers are
available in the ``cv_results_`` dict at the keys ending with that
scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
above. ('split0_test_precision', 'mean_train_precision' etc.)
best_estimator_ : estimator or dict
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``.
See ``refit`` parameter for more information on allowed values.
best_score_ : float
Mean cross-validated score of the best_estimator
For multi-metric evaluation, this is present only if ``refit`` is
specified.
best_params_ : dict
Parameter setting that gave the best results on the hold out data.
For multi-metric evaluation, this is present only if ``refit`` is
specified.
best_index_ : int
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_``).
For multi-metric evaluation, this is present only if ``refit`` is
specified.
scorer_ : function or a dict
Scorer function used on the held out data to choose the best
parameters for the model.
For multi-metric evaluation, this attribute holds the validated
``scoring`` dict which maps the scorer key to the scorer callable.
n_splits_ : int
The number of cross-validation splits (folds/iterations).
refit_time_ : float
Seconds used for refitting the best model on the whole dataset.
This is present only if ``refit`` is not False.
Notes
-----
The parameters selected are those that maximize the score of the held out
data, unless an explicit score is passed in which case it is used instead.
If `n_jobs` was set to a value higher than one, the data is copied for each
point in the grid (and not `n_jobs` times). This is done for efficiency
reasons if individual jobs take very little time, but may raise errors if
the dataset is large and not enough memory is available. A workaround in
this case is to set `pre_dispatch`. Then, the memory is copied only
`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
n_jobs`.
Examples
--------
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.tree import DecisionTreeClassifier
>>> from sklearn.neighbors import KNeighborsClassifier
>>> from mlresearch.model_selection import ModelSearchCV
>>> X, y, *_ = load_breast_cancer().values()
>>> param_grids = [{'dt__max_depth': [3, 6]}, {'kn__n_neighbors': [3, 5]}]
>>> estimators = [('dt', DecisionTreeClassifier()), ('kn', KNeighborsClassifier())]
>>> model_search_cv = ModelSearchCV(estimators, param_grids)
>>> model_search_cv.fit(X, y)
ModelSearchCV(...)
>>> sorted(model_search_cv.cv_results_.keys())
['mean_fit_time', 'mean_score_time', 'mean_test_score',...]
"""
def __init__(
self,
estimators,
param_grids,
scoring=None,
n_jobs=None,
refit=True,
cv=5,
verbose=0,
pre_dispatch="2*n_jobs",
error_score="raise",
return_train_score=False,
):
estimator = (
MultiClassifier(estimators)
if check_estimator_type(estimators) == "classifier"
else MultiRegressor(estimators)
)
est_names = [est_name for est_name, _ in estimators]
param_grid = check_param_grids(param_grids, est_names)
super(ModelSearchCV, self).__init__(
estimator=estimator,
param_grid=param_grid,
scoring=scoring,
n_jobs=n_jobs,
refit=refit,
cv=cv,
verbose=verbose,
pre_dispatch=pre_dispatch,
error_score=error_score,
return_train_score=return_train_score,
)
self.estimators = estimators
self.param_grids = param_grids
[docs]
def fit(self, X, y=None, groups=None, **fit_params):
# Call superclass fit method
super(ModelSearchCV, self).fit(X, y, groups=groups, **fit_params)
# Recreate best estimator attribute
if hasattr(self, "best_estimator_"):
self.best_estimator_ = self.best_estimator_.estimator_
return self