# -*- encoding: utf-8 -*-
from __future__ import annotations
from typing import (
Any,
Dict,
Iterable,
List,
Mapping,
Optional,
Sequence,
Tuple,
Type,
Union,
)
import warnings
import dask.distributed
import joblib
import numpy as np
import pandas as pd
from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
from scipy.sparse import spmatrix
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
from sklearn.ensemble import VotingClassifier, VotingRegressor
from sklearn.utils.multiclass import type_of_target
from smac.runhistory.runhistory import RunInfo, RunValue
from typing_extensions import Literal
from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor
from autosklearn.data.validation import (
SUPPORTED_FEAT_TYPES,
SUPPORTED_TARGET_TYPES,
convert_if_sparse,
)
from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
from autosklearn.ensembles.ensemble_selection import EnsembleSelection
from autosklearn.ensembles.multiobjective_dummy_ensemble import (
MultiObjectiveDummyEnsemble,
)
from autosklearn.metrics import Scorer
from autosklearn.pipeline.base import BasePipeline
from autosklearn.util.smac_wrap import SMACCallback
class AutoSklearnEstimator(BaseEstimator):
def __init__(
self,
time_left_for_this_task=3600,
per_run_time_limit=None,
initial_configurations_via_metalearning=25,
ensemble_size: int | None = None,
ensemble_class: Type[AbstractEnsemble] | Literal["default"] | None = "default",
ensemble_kwargs: Dict[str, Any] | None = None,
ensemble_nbest=50,
max_models_on_disc=50,
seed=1,
memory_limit=3072,
include: Optional[Dict[str, List[str]]] = None,
exclude: Optional[Dict[str, List[str]]] = None,
resampling_strategy="holdout",
resampling_strategy_arguments=None,
tmp_folder=None,
delete_tmp_folder_after_terminate=True,
n_jobs: Optional[int] = None,
dask_client: Optional[dask.distributed.Client] = None,
disable_evaluator_output=False,
get_smac_object_callback=None,
smac_scenario_args=None,
logging_config=None,
metadata_directory=None,
metric: Scorer | Sequence[Scorer] | None = None,
scoring_functions: Optional[List[Scorer]] = None,
load_models: bool = True,
get_trials_callback: SMACCallback | None = None,
dataset_compression: Union[bool, Mapping[str, Any]] = True,
allow_string_features: bool = True,
):
"""
Parameters
----------
time_left_for_this_task : int, optional (default=3600)
Time limit in seconds for the search of appropriate
models. By increasing this value, *auto-sklearn* has a higher
chance of finding better models.
per_run_time_limit : int, optional (default=1/10 of time_left_for_this_task)
Time limit for a single call to the machine learning model.
Model fitting will be terminated if the machine learning
algorithm runs over the time limit. Set this value high enough so
that typical machine learning algorithms can be fit on the
training data.
initial_configurations_via_metalearning : int, optional (default=25)
Initialize the hyperparameter optimization algorithm with this
many configurations which worked well on previously seen
datasets. Disable if the hyperparameter optimization algorithm
should start from scratch.
ensemble_size : int, optional
Number of models added to the ensemble built by *Ensemble
selection from libraries of models*. Models are drawn with
replacement. If set to ``0`` no ensemble is fit.
Deprecated - will be removed in Auto-sklearn 0.16. Please pass
this argument via ``ensemble_kwargs={"ensemble_size": int}``
if you want to change the ensemble size for ensemble selection.
ensemble_class : Type[AbstractEnsemble] | "default", optional (default="default")
Class implementing the post-hoc ensemble algorithm. Set to
``None`` to disable ensemble building or use :class:`SingleBest`
to obtain only use the single best model instead of an
ensemble.
If set to "default" it will use :class:`EnsembleSelection` for
single-objective problems and :class:`MultiObjectiveDummyEnsemble`
for multi-objective problems.
ensemble_kwargs : Dict, optional
Keyword arguments that are passed to the ensemble class upon
initialization.
ensemble_nbest : int, optional (default=50)
Only consider the ``ensemble_nbest`` models when building an
ensemble. This is inspired by a concept called library pruning
introduced in `Getting Most out of Ensemble Selection`. This
is independent of the ``ensemble_class`` argument and this
pruning step is done prior to constructing an ensemble.
max_models_on_disc: int, optional (default=50),
Defines the maximum number of models that are kept in the disc.
The additional number of models are permanently deleted. Due to the
nature of this variable, it sets the upper limit on how many models
can be used for an ensemble.
It must be an integer greater or equal than 1.
If set to None, all models are kept on the disc.
seed : int, optional (default=1)
Used to seed SMAC. Will determine the output file names.
memory_limit : int, optional (3072)
Memory limit in MB for the machine learning algorithm.
`auto-sklearn` will stop fitting the machine learning algorithm if
it tries to allocate more than ``memory_limit`` MB.
**Important notes:**
* If ``None`` is provided, no memory limit is set.
* In case of multi-processing, ``memory_limit`` will be *per job*, so the total usage is
``n_jobs x memory_limit``.
* The memory limit also applies to the ensemble creation process.
include : Optional[Dict[str, List[str]]] = None
If None, all possible algorithms are used.
Otherwise, specifies a step and the components that are included in search.
See ``/pipeline/components/<step>/*`` for available components.
Incompatible with parameter ``exclude``.
**Possible Steps**:
* ``"data_preprocessor"``
* ``"balancing"``
* ``"feature_preprocessor"``
* ``"classifier"`` - Only for when when using ``AutoSklearnClasssifier``
* ``"regressor"`` - Only for when when using ``AutoSklearnRegressor``
**Example**:
.. code-block:: python
include = {
'classifier': ["random_forest"],
'feature_preprocessor': ["no_preprocessing"]
}
exclude : Optional[Dict[str, List[str]]] = None
If None, all possible algorithms are used.
Otherwise, specifies a step and the components that are excluded from search.
See ``/pipeline/components/<step>/*`` for available components.
Incompatible with parameter ``include``.
**Possible Steps**:
* ``"data_preprocessor"``
* ``"balancing"``
* ``"feature_preprocessor"``
* ``"classifier"`` - Only for when when using ``AutoSklearnClasssifier``
* ``"regressor"`` - Only for when when using ``AutoSklearnRegressor``
**Example**:
.. code-block:: python
exclude = {
'classifier': ["random_forest"],
'feature_preprocessor': ["no_preprocessing"]
}
resampling_strategy : str | BaseCrossValidator | _RepeatedSplits | BaseShuffleSplit = "holdout"
How to to handle overfitting, might need to use ``resampling_strategy_arguments``
if using ``"cv"`` based method or a Splitter object.
* **Options**
* ``"holdout"`` - Use a 67:33 (train:test) split
* ``"cv"``: perform cross validation, requires "folds" in ``resampling_strategy_arguments``
* ``"holdout-iterative-fit"`` - Same as "holdout" but iterative fit where possible
* ``"cv-iterative-fit"``: Same as "cv" but iterative fit where possible
* ``"partial-cv"``: Same as "cv" but uses intensification.
* ``BaseCrossValidator`` - any BaseCrossValidator subclass (found in scikit-learn model_selection module)
* ``_RepeatedSplits`` - any _RepeatedSplits subclass (found in scikit-learn model_selection module)
* ``BaseShuffleSplit`` - any BaseShuffleSplit subclass (found in scikit-learn model_selection module)
If using a Splitter object that relies on the dataset retaining it's current
size and order, you will need to look at the ``dataset_compression`` argument
and ensure that ``"subsample"`` is not included in the applied compression
``"methods"`` or disable it entirely with ``False``.
resampling_strategy_arguments : Optional[Dict] = None
Additional arguments for ``resampling_strategy``, this is required if
using a ``cv`` based strategy. The default arguments if left as ``None``
are:
.. code-block:: python
{
"train_size": 0.67, # The size of the training set
"shuffle": True, # Whether to shuffle before splitting data
"folds": 5 # Used in 'cv' based resampling strategies
}
If using a custom splitter class, which takes ``n_splits`` such as
`PredefinedSplit <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn-model-selection-kfold>`_,
the value of ``"folds"`` will be used.
tmp_folder : string, optional (None)
folder to store configuration output and log files, if ``None``
automatically use ``/tmp/autosklearn_tmp_$pid_$random_number``
delete_tmp_folder_after_terminate: bool, optional (True)
remove tmp_folder, when finished. If tmp_folder is None
tmp_dir will always be deleted
n_jobs : int, optional, experimental
The number of jobs to run in parallel for ``fit()``. ``-1`` means
using all processors.
**Important notes**:
* By default, Auto-sklearn uses one core.
* Ensemble building is not affected by ``n_jobs`` but can be controlled by the number
of models in the ensemble.
* ``predict()`` is not affected by ``n_jobs`` (in contrast to most scikit-learn models)
* If ``dask_client`` is ``None``, a new dask client is created.
dask_client : dask.distributed.Client, optional
User-created dask client, can be used to start a dask cluster and then
attach auto-sklearn to it.
disable_evaluator_output: bool or list, optional (False)
If True, disable model and prediction output. Cannot be used
together with ensemble building. ``predict()`` cannot be used when
setting this True. Can also be used as a list to pass more
fine-grained information on what to save. Allowed elements in the
list are:
* ``'y_optimization'`` : do not save the predictions for the
optimization set, which would later on be used to build an ensemble.
* ``model`` : do not save any model files
smac_scenario_args : dict, optional (None)
Additional arguments inserted into the scenario of SMAC. See the
`SMAC documentation <https://automl.github.io/SMAC3/main/api/smac.scenario.scenario.html#module-smac.scenario.scenario>`_
for a list of available arguments.
get_smac_object_callback : callable
Callback function to create an object of class
`smac.optimizer.smbo.SMBO <https://automl.github.io/SMAC3/main/api/smac.optimizer.smbo.html>`_.
The function must accept the arguments ``scenario_dict``,
``instances``, ``num_params``, ``runhistory``, ``seed`` and ``ta``.
This is an advanced feature. Use only if you are familiar with
`SMAC <https://automl.github.io/SMAC3/main/index.html>`_.
logging_config : dict, optional (None)
dictionary object specifying the logger configuration. If None,
the default logging.yaml file is used, which can be found in
the directory ``util/logging.yaml`` relative to the installation.
metadata_directory : str, optional (None)
path to the metadata directory. If None, the default directory
(autosklearn.metalearning.files) is used.
metric : Scorer, optional (None)
An instance of :class:`autosklearn.metrics.Scorer` as created by
:meth:`autosklearn.metrics.make_scorer`. These are the `Built-in
Metrics`_.
If None is provided, a default metric is selected depending on the task.
scoring_functions : List[Scorer], optional (None)
List of scorers which will be calculated for each pipeline and results will be
available via ``cv_results``
load_models : bool, optional (True)
Whether to load the models after fitting Auto-sklearn.
get_trials_callback: callable
A callable with the following definition.
* (smac.SMBO, smac.RunInfo, smac.RunValue, time_left: float) -> bool | None
This will be called after SMAC, the underlying optimizer for autosklearn,
finishes training each run.
You can use this to record your own information about the optimization
process. You can also use this to enable a early stopping based on some
critera.
See the example:
:ref:`Early Stopping And Callbacks <sphx_glr_examples_40_advanced_example_early_stopping_and_callbacks.py>`.
dataset_compression: Union[bool, Mapping[str, Any]] = True
We compress datasets so that they fit into some predefined amount of memory.
Currently this does not apply to dataframes or sparse arrays, only to raw
numpy arrays.
**NOTE** - If using a custom ``resampling_strategy`` that relies on specific
size or ordering of data, this must be disabled to preserve these properties.
You can disable this entirely by passing ``False`` or leave as the default
``True`` for configuration below.
.. code-block:: python
{
"memory_allocation": 0.1,
"methods": ["precision", "subsample"]
}
You can also pass your own configuration with the same keys and choosing
from the available ``"methods"``.
The available options are described here:
* **memory_allocation**
By default, we attempt to fit the dataset into ``0.1 * memory_limit``.
This float value can be set with ``"memory_allocation": 0.1``.
We also allow for specifying absolute memory in MB, e.g. 10MB is
``"memory_allocation": 10``.
The memory used by the dataset is checked after each reduction method is
performed. If the dataset fits into the allocated memory, any further
methods listed in ``"methods"`` will not be performed.
For example, if ``methods: ["precision", "subsample"]`` and the
``"precision"`` reduction step was enough to make the dataset fit into
memory, then the ``"subsample"`` reduction step will not be performed.
* **methods**
We provide the following methods for reducing the dataset size.
These can be provided in a list and are performed in the order as given.
* ``"precision"`` - We reduce floating point precision as follows:
* ``np.float128 -> np.float64``
* ``np.float96 -> np.float64``
* ``np.float64 -> np.float32``
* ``subsample`` - We subsample data such that it **fits directly into
the memory allocation** ``memory_allocation * memory_limit``.
Therefore, this should likely be the last method listed in
``"methods"``.
Subsampling takes into account classification labels and stratifies
accordingly. We guarantee that at least one occurrence of each
label is included in the sampled set.
allow_string_features: bool = True
Whether autosklearn should process string features. By default the
textpreprocessing is enabled.
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``.
Not all keys returned by scikit-learn are supported yet.
performance_over_time_ : pandas.core.frame.DataFrame
A ``DataFrame`` containing the models performance over time data. Can be
used for plotting directly. Please refer to the example
:ref:`Train and Test Inputs <sphx_glr_examples_40_advanced_example_pandas_train_test.py>`.
""" # noqa (links are too long)
# Raise error if the given total time budget is less than 30 seconds.
if time_left_for_this_task < 30:
raise ValueError("Time left for this task must be at least " "30 seconds. ")
self.time_left_for_this_task = time_left_for_this_task
self.per_run_time_limit = per_run_time_limit
self.initial_configurations_via_metalearning = (
initial_configurations_via_metalearning
)
# Need to resolve the ensemble class here so we can act on it below.
if ensemble_class == "default":
ensemble_class = self._resolve_ensemble_class(metric)
self.ensemble_class = ensemble_class
# User specified `ensemble_size` explicitly, warn them about deprecation
if ensemble_size is not None:
# Keep consistent behaviour
message = (
"`ensemble_size` has been deprecated, please use `ensemble_kwargs = "
"{'ensemble_size': %d}`. Inserting `ensemble_size` into "
"`ensemble_kwargs` for now. `ensemble_size` will be removed in "
"auto-sklearn 0.16."
) % ensemble_size
if ensemble_class == EnsembleSelection:
if ensemble_kwargs is None:
ensemble_kwargs = {"ensemble_size": ensemble_size}
warnings.warn(message, DeprecationWarning, stacklevel=2)
elif "ensemble_size" not in ensemble_kwargs:
ensemble_kwargs["ensemble_size"] = ensemble_size
warnings.warn(message, DeprecationWarning, stacklevel=2)
else:
warnings.warn(
"Deprecated argument `ensemble_size` is both provided "
"as an argument to the constructor and passed inside "
"`ensemble_kwargs`. Will ignore the argument and use "
"the value given in `ensemble_kwargs` (%d). `ensemble_size` "
"will be removed in auto-sklearn 0.16."
% ensemble_kwargs["ensemble_size"],
DeprecationWarning,
stacklevel=2,
)
else:
warnings.warn(
"`ensemble_size` has been deprecated, please use "
"`ensemble_kwargs = {'ensemble_size': %d} if this "
"was intended. Ignoring `ensemble_size` because "
"`ensemble_class` != EnsembleSelection. "
"`ensemble_size` will be removed in auto-sklearn 0.16."
% ensemble_size,
DeprecationWarning,
stacklevel=2,
)
self.ensemble_size = (
ensemble_size # Otherwise sklean.base.get_params() will complain
)
self.ensemble_kwargs = ensemble_kwargs
self.ensemble_nbest = ensemble_nbest
self.max_models_on_disc = max_models_on_disc
self.seed = seed
self.memory_limit = memory_limit
self.include = include
self.exclude = exclude
self.resampling_strategy = resampling_strategy
self.resampling_strategy_arguments = resampling_strategy_arguments
self.tmp_folder = tmp_folder
self.delete_tmp_folder_after_terminate = delete_tmp_folder_after_terminate
self.n_jobs = n_jobs
self.dask_client = dask_client
self.disable_evaluator_output = disable_evaluator_output
self.get_smac_object_callback = get_smac_object_callback
self.smac_scenario_args = smac_scenario_args
self.logging_config = logging_config
self.metadata_directory = metadata_directory
self.metric = metric
self.scoring_functions = scoring_functions
self.load_models = load_models
self.get_trials_callback = get_trials_callback
self.dataset_compression = dataset_compression
self.allow_string_features = allow_string_features
self.automl_ = None # type: Optional[AutoML]
# Handle the number of jobs and the time for them
self._n_jobs = None
if self.n_jobs is None or self.n_jobs == 1:
self._n_jobs = 1
elif self.n_jobs == -1:
self._n_jobs = joblib.cpu_count()
else:
self._n_jobs = self.n_jobs
super().__init__()
def __getstate__(self):
# Cannot serialize a client!
self.dask_client = None
return self.__dict__
def build_automl(self):
initial_configs = self.initial_configurations_via_metalearning
automl = self._get_automl_class()(
temporary_directory=self.tmp_folder,
delete_tmp_folder_after_terminate=self.delete_tmp_folder_after_terminate,
time_left_for_this_task=self.time_left_for_this_task,
per_run_time_limit=self.per_run_time_limit,
initial_configurations_via_metalearning=initial_configs,
ensemble_class=self.ensemble_class,
ensemble_kwargs=self.ensemble_kwargs,
ensemble_nbest=self.ensemble_nbest,
max_models_on_disc=self.max_models_on_disc,
seed=self.seed,
memory_limit=self.memory_limit,
include=self.include,
exclude=self.exclude,
resampling_strategy=self.resampling_strategy,
resampling_strategy_arguments=self.resampling_strategy_arguments,
n_jobs=self._n_jobs,
dask_client=self.dask_client,
get_smac_object_callback=self.get_smac_object_callback,
disable_evaluator_output=self.disable_evaluator_output,
smac_scenario_args=self.smac_scenario_args,
logging_config=self.logging_config,
metadata_directory=self.metadata_directory,
metrics=[self.metric] if isinstance(self.metric, Scorer) else self.metric,
scoring_functions=self.scoring_functions,
get_trials_callback=self.get_trials_callback,
dataset_compression=self.dataset_compression,
allow_string_features=self.allow_string_features,
)
return automl
def fit(self, **kwargs):
# Automatically set the cutoff time per task
if self.per_run_time_limit is None:
self.per_run_time_limit = self._n_jobs * self.time_left_for_this_task // 10
if self.automl_ is None:
self.automl_ = self.build_automl()
self.automl_.fit(load_models=self.load_models, **kwargs)
return self
def fit_pipeline(
self,
X: SUPPORTED_FEAT_TYPES,
y: Union[SUPPORTED_TARGET_TYPES, spmatrix],
config: Union[Configuration, Dict[str, Union[str, float, int]]],
dataset_name: Optional[str] = None,
X_test: Optional[SUPPORTED_FEAT_TYPES] = None,
y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None,
feat_type: Optional[List[str]] = None,
*args,
**kwargs: Dict,
) -> Tuple[Optional[BasePipeline], RunInfo, RunValue]:
"""Fits and individual pipeline configuration and returns
the result to the user.
The Estimator constraints are honored, for example the resampling
strategy, or memory constraints, unless directly provided to the method.
By default, this method supports the same signature as fit(), and any extra
arguments are redirected to the TAE evaluation function, which allows for
further customization while building a pipeline.
Any additional argument provided is directly passed to the
worker exercising the run.
Parameters
----------
X: array-like, shape = (n_samples, n_features)
The features used for training
y: array-like
The labels used for training
X_test: Optionalarray-like, shape = (n_samples, n_features)
If provided, the testing performance will be tracked on this features.
y_test: array-like
If provided, the testing performance will be tracked on this labels
config: Union[Configuration, Dict[str, Union[str, float, int]]]
A configuration object used to define the pipeline steps.
If a dict is passed, a configuration is created based on this dict.
dataset_name: Optional[str]
Name that will be used to tag the Auto-Sklearn run and identify the
Auto-Sklearn run
feat_type : list, optional (default=None)
List of str of `len(X.shape[1])` describing the attribute type.
Possible types are `Categorical` and `Numerical`. `Categorical`
attributes will be automatically One-Hot encoded. The values
used for a categorical attribute must be integers, obtained for
example by `sklearn.preprocessing.LabelEncoder
<https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
Returns
-------
pipeline: Optional[BasePipeline]
The fitted pipeline. In case of failure while fitting the pipeline,
a None is returned.
run_info: RunInFo
A named tuple that contains the configuration launched
run_value: RunValue
A named tuple that contains the result of the run
"""
if self.automl_ is None:
self.automl_ = self.build_automl()
return self.automl_.fit_pipeline(
X=X,
y=y,
dataset_name=dataset_name,
config=config,
feat_type=feat_type,
X_test=X_test,
y_test=y_test,
*args,
**kwargs,
)
def fit_ensemble(
self,
y,
task: int = None,
precision: Literal[16, 21, 64] = 32,
dataset_name: Optional[str] = None,
ensemble_size: int | None = None,
ensemble_kwargs: Optional[Dict[str, Any]] = None,
ensemble_nbest: Optional[int] = None,
ensemble_class: Type[AbstractEnsemble] | Literal["default"] | None = "default",
metric: Scorer | Sequence[Scorer] | None = None,
):
"""Fit an ensemble to models trained during an optimization process.
All parameters are ``None`` by default. If no other value is given,
the default values which were set in a call to ``fit()`` are used.
Parameters
----------
y : array-like
Target values.
task : int
A constant from the module ``autosklearn.constants``. Determines
the task type (binary classification, multiclass classification,
multilabel classification or regression).
precision : int
Numeric precision used when loading ensemble data. Can be either
``16``, ``32`` or ``64``.
dataset_name : str
Name of the current data set.
ensemble_size : int, optional
Number of models added to the ensemble built by *Ensemble
selection from libraries of models*. Models are drawn with
replacement. If set to ``0`` no ensemble is fit.
Deprecated - will be removed in Auto-sklearn 0.16. Please pass
this argument via ``ensemble_kwargs={"ensemble_size": int}``
if you want to change the ensemble size for ensemble selection.
ensemble_kwargs : Dict, optional
Keyword arguments that are passed to the ensemble class upon
initialization.
ensemble_nbest : int
Only consider the ``ensemble_nbest`` models when building an
ensemble. This is inspired by a concept called library pruning
introduced in `Getting Most out of Ensemble Selection`. This
is independent of the ``ensemble_class`` argument and this
pruning step is done prior to constructing an ensemble.
ensemble_class : Type[AbstractEnsemble] | "default", optional (default="default")
Class implementing the post-hoc ensemble algorithm. Set to
``None`` to disable ensemble building or use class:`SingleBest`
to obtain only use the single best model instead of an
ensemble.
If set to "default" it will use :class:`EnsembleSelection` for
single-objective problems and :class:`MultiObjectiveDummyEnsemble`
for multi-objective problems.
metric: Scorer | Sequence[Scorer] | None = None
A metric or list of metrics to score the ensemble with
Returns
-------
self
""" # noqa: E501
if ensemble_class == "default":
# Things are actually a little more nuanced here:
# * If they passed `metric=None` at init, we would infer this in automl
# during `fit` and store it in the automl instance.
# * If they passed a `metric` in init and left it `None` here, this would
# also be in the automl instance
# => We can use self.automl_ as ground truth for metric if no metrics passed
# and we have one created
if metric is None and self.automl_ is not None and self.automl_._metrics:
metric = self.automl_._metrics
ensemble_class = self._resolve_ensemble_class(metric)
self.ensemble_class = ensemble_class
# User specified `ensemble_size` explicitly, warn them about deprecation
if ensemble_size is not None:
# Keep consistent behaviour
message = (
"`ensemble_size` has been deprecated, please use `ensemble_kwargs = "
"{'ensemble_size': %d}`. Inserting `ensemble_size` into "
"`ensemble_kwargs` for now. `ensemble_size` will be removed in "
"auto-sklearn 0.16."
) % ensemble_size
if ensemble_class == EnsembleSelection:
if ensemble_kwargs is None:
ensemble_kwargs = {"ensemble_size": ensemble_size}
warnings.warn(message, DeprecationWarning, stacklevel=2)
elif "ensemble_size" not in ensemble_kwargs:
ensemble_kwargs["ensemble_size"] = ensemble_size
warnings.warn(message, DeprecationWarning, stacklevel=2)
else:
warnings.warn(
"Deprecated argument `ensemble_size` is both provided "
"as an argument to the constructor and passed inside "
"`ensemble_kwargs`. Will ignore the argument and use "
"the value given in `ensemble_kwargs` (%d). `ensemble_size` "
"will be removed in auto-sklearn 0.16."
% ensemble_kwargs["ensemble_size"],
DeprecationWarning,
stacklevel=2,
)
else:
warnings.warn(
"`ensemble_size` has been deprecated, please use "
"`ensemble_kwargs = {'ensemble_size': %d} if this "
"was intended. Ignoring `ensemble_size` because "
"`ensemble_class` != EnsembleSelection. "
"`ensemble_size` will be removed in auto-sklearn 0.16."
% ensemble_size,
DeprecationWarning,
stacklevel=2,
)
if self.automl_ is None:
# Build a dummy automl object to call fit_ensemble
# The ensemble size is honored in the .automl_.fit_ensemble
# call
self.automl_ = self.build_automl()
self.automl_.fit_ensemble(
y=y,
task=task,
precision=precision,
dataset_name=dataset_name,
ensemble_nbest=ensemble_nbest,
ensemble_class=ensemble_class,
ensemble_kwargs=ensemble_kwargs,
metrics=metric,
)
return self
def _resolve_ensemble_class(
self,
metric: Scorer | Sequence[Scorer] | None,
) -> type[AbstractEnsemble]:
return (
EnsembleSelection
if metric is None or isinstance(metric, Scorer) or len(metric) == 1
else MultiObjectiveDummyEnsemble
)
def refit(self, X, y):
"""Refit all models found with fit to new data.
Necessary when using cross-validation. During training, auto-sklearn
fits each model k times on the dataset, but does not keep any trained
model and can therefore not be used to predict for new data points.
This methods fits all models found during a call to fit on the data
given. This method may also be used together with holdout to avoid
only using 66% of the training data to fit the final model.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The targets.
Returns
-------
self
"""
self.automl_.refit(X, y)
return self
def predict(self, X, batch_size=None, n_jobs=1):
return self.automl_.predict(X, batch_size=batch_size, n_jobs=n_jobs)
def predict_proba(self, X, batch_size=None, n_jobs=1):
return self.automl_.predict_proba(X, batch_size=batch_size, n_jobs=n_jobs)
def score(self, X, y):
return self.automl_.score(X, y)
def show_models(self):
"""Returns a dictionary containing dictionaries of ensemble models.
Each model in the ensemble can be accessed by giving its ``model_id`` as key.
A model dictionary contains the following:
* ``"model_id"`` - The id given to a model by ``autosklearn``.
* ``"rank"`` - The rank of the model based on it's ``"cost"``.
* ``"cost"`` - The loss of the model on the validation set.
* ``"ensemble_weight"`` - The weight given to the model in the ensemble.
* ``"voting_model"`` - The ``cv_voting_ensemble`` model (for 'cv' resampling).
* ``"estimators"`` - List of models (dicts) in ``cv_voting_ensemble``
('cv' resampling).
* ``"data_preprocessor"`` - The preprocessor used on the data.
* ``"balancing"`` - The balancing used on the data (for classification).
* ``"feature_preprocessor"`` - The preprocessor for features types.
* ``"classifier"`` / ``"regressor"``
- The autosklearn wrapped classifier or regressor.
* ``"sklearn_classifier"`` or ``"sklearn_regressor"``
- The sklearn classifier or regressor.
**Example**
.. code-block:: python
import sklearn.datasets
import sklearn.metrics
import autosklearn.regression
X, y = sklearn.datasets.load_diabetes(return_X_y=True)
automl = autosklearn.regression.AutoSklearnRegressor(
time_left_for_this_task=120
)
automl.fit(X_train, y_train, dataset_name='diabetes')
ensemble_dict = automl.show_models()
print(ensemble_dict)
Output:
.. code-block:: text
{
25: {'model_id': 25.0,
'rank': 1,
'cost': 0.43667876507897496,
'ensemble_weight': 0.38,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing....>,
'feature_preprocessor': <autosklearn.pipeline.components....>,
'regressor': <autosklearn.pipeline.components.regression....>,
'sklearn_regressor': SGDRegressor(alpha=0.0006517033225329654,...)
},
6: {'model_id': 6.0,
'rank': 2,
'cost': 0.4550418898836528,
'ensemble_weight': 0.3,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing....>,
'feature_preprocessor': <autosklearn.pipeline.components....>,
'regressor': <autosklearn.pipeline.components.regression....>,
'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788,...)
}...
}
Returns
-------
Dict(int, Any) : dictionary of length = number of models in the ensemble
A dictionary of models in the ensemble, where ``model_id`` is the key.
""" # noqa: E501
return self.automl_.show_models()
def get_models_with_weights(self):
"""Return a list of the final ensemble found by auto-sklearn.
Returns
-------
[(weight_1, model_1), ..., (weight_n, model_n)]
"""
return self.automl_.get_models_with_weights()
@property
def performance_over_time_(self):
return self.automl_.performance_over_time_
@property
def cv_results_(self):
return self.automl_.cv_results_
@property
def trajectory_(self):
return self.automl_.trajectory_
@property
def fANOVA_input_(self):
return self.automl_.fANOVA_input_
def sprint_statistics(self):
"""Return the following statistics of the training result:
- dataset name
- metric used
- best validation score
- number of target algorithm runs
- number of successful target algorithm runs
- number of crashed target algorithm runs
- number of target algorithm runs that exceeded the memory limit
- number of target algorithm runs that exceeded the time limit
Returns
-------
str
"""
return self.automl_.sprint_statistics()
def leaderboard(
self,
detailed: bool = False,
ensemble_only: bool = True,
top_k: Union[int, Literal["all"]] = "all",
sort_by: str = "cost",
sort_order: Literal["auto", "ascending", "descending"] = "auto",
include: Optional[Union[str, Iterable[str]]] = None,
) -> pd.DataFrame:
"""Returns a pandas table of results for all evaluated models.
Gives an overview of all models trained during the search process along
with various statistics about their training.
The available statistics are:
**Simple**:
* ``"model_id"`` - The id given to a model by ``autosklearn``.
* ``"rank"`` - The rank of the model based on it's ``"cost"``.
* ``"ensemble_weight"`` - The weight given to the model in the ensemble.
* ``"type"`` - The type of classifier/regressor used.
* ``"cost"`` - The loss of the model on the validation set.
* ``"duration"`` - Length of time the model was optimized for.
**Detailed**:
The detailed view includes all of the simple statistics along with the
following.
* ``"config_id"`` - The id used by SMAC for optimization.
* ``"budget"`` - How much budget was allocated to this model.
* ``"status"`` - The return status of training the model with SMAC.
* ``"train_loss"`` - The loss of the model on the training set.
* ``"balancing_strategy"`` - The balancing strategy used for data preprocessing.
* ``"start_time"`` - Time the model began being optimized
* ``"end_time"`` - Time the model ended being optimized
* ``"data_preprocessors"`` - The preprocessors used on the data
* ``"feature_preprocessors"`` - The preprocessors for features types
Parameters
----------
detailed: bool = False
Whether to give detailed information or just a simple overview.
ensemble_only: bool = True
Whether to view only models included in the ensemble or all models
trained.
top_k: int or "all" = "all"
How many models to display.
sort_by: str = 'cost'
What column to sort by. If that column is not present, the
sorting defaults to the ``"model_id"`` index column.
Defaults to the metric optimized. Sort by the first objective
in case of a multi-objective optimization problem
sort_order: "auto" or "ascending" or "descending" = "auto"
Which sort order to apply to the ``sort_by`` column. If left
as ``"auto"``, it will sort by a sensible default where "better" is
on top, otherwise defaulting to the pandas default for
`DataFrame.sort_values`_ if there is no obvious "better".
.. _DataFrame.sort_values: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html
include: Optional[str or Iterable[str]]
Items to include, other items not specified will be excluded.
The exception is the ``"model_id"`` index column which is always included.
If left as ``None``, it will resort back to using the ``detailed``
param to decide the columns to include.
Returns
-------
pd.DataFrame
A dataframe of statistics for the models, ordered by ``sort_by``.
""" # noqa (links are too long)
# TODO validate that `self` is fitted. This is required for
# self.ensemble_ to get the identifiers of models it will generate
# weights for.
num_metrics = (
1
if self.metric is None or isinstance(self.metric, Scorer)
else len(self.metric)
)
column_types = AutoSklearnEstimator._leaderboard_columns(num_metrics)
if num_metrics == 1:
multi_objective_cost_names = []
else:
multi_objective_cost_names = [f"cost_{i}" for i in range(num_metrics)]
# Validation of top_k
if (
not (isinstance(top_k, str) or isinstance(top_k, int))
or (isinstance(top_k, str) and top_k != "all")
or (isinstance(top_k, int) and top_k <= 0)
):
raise ValueError(
f"top_k={top_k} must be a positive integer or pass"
" `top_k`='all' to view results for all models"
)
# Validate columns to include
if isinstance(include, str):
include = [include]
if include == ["model_id"]:
raise ValueError("Must provide more than just `model_id`")
if include is not None:
columns = [*include]
# 'model_id' should always be present as it is the unique index
# used for pandas
if "model_id" not in columns:
columns.append("model_id")
invalid_include_items = set(columns) - set(column_types["all"])
if len(invalid_include_items) != 0:
raise ValueError(
f"Values {invalid_include_items} are not known"
f" columns to include, must be contained in "
f"{column_types['all']}"
)
elif detailed:
columns = column_types["all"]
else:
columns = column_types["simple"]
# Validation of sorting
if sort_by == "cost":
sort_by_cost = True
if num_metrics == 1:
sort_by = ["cost", "model_id"]
else:
sort_by = multi_objective_cost_names + ["model_id"]
else:
sort_by_cost = False
if isinstance(sort_by, str):
if sort_by not in column_types["all"]:
raise ValueError(
f"sort_by='{sort_by}' must be one of included "
f"columns {set(column_types['all'])}"
)
elif len(set(sort_by) - set(column_types["all"])) > 0:
too_much = set(sort_by) - set(column_types["all"])
raise ValueError(
f"sort_by='{too_much}' must be in the included columns "
f"{set(column_types['all'])}"
)
valid_sort_orders = ["auto", "ascending", "descending"]
if not (isinstance(sort_order, str) and sort_order in valid_sort_orders):
raise ValueError(
f"`sort_order` = {sort_order} must be a str in " f"{valid_sort_orders}"
)
# To get all the models that were optmized, we collect what we can from
# runhistory first.
def additional_info_has_key(rv, key):
return rv.additional_info and key in rv.additional_info
model_runs = {}
for run_key, run_val in self.automl_.runhistory_.data.items():
if not additional_info_has_key(run_val, "num_run"):
continue
else:
model_key = run_val.additional_info["num_run"]
model_run = {
"model_id": run_val.additional_info["num_run"],
"seed": run_key.seed,
"budget": run_key.budget,
"duration": run_val.time,
"config_id": run_key.config_id,
"start_time": run_val.starttime,
"end_time": run_val.endtime,
"status": str(run_val.status),
"train_loss": run_val.additional_info["train_loss"]
if additional_info_has_key(run_val, "train_loss")
else None,
"config_origin": run_val.additional_info["configuration_origin"]
if additional_info_has_key(run_val, "configuration_origin")
else None,
}
if num_metrics == 1:
model_run["cost"] = run_val.cost
else:
for cost_idx, cost in enumerate(run_val.cost):
model_run[f"cost_{cost_idx}"] = cost
model_runs[model_key] = model_run
# Next we get some info about the model itself
model_class_strings = {
AutoMLClassifier: "classifier",
AutoMLRegressor: "regressor",
}
model_type = model_class_strings.get(self._get_automl_class(), None)
if model_type is None:
raise RuntimeError(f"Unknown `automl_class` {self._get_automl_class()}")
# A dict mapping model ids to their configurations
configurations = self.automl_.runhistory_.ids_config
for model_id, run_info in model_runs.items():
config_id = run_info["config_id"]
run_config = configurations[config_id]._values
run_info.update(
{
"balancing_strategy": run_config.get("balancing:strategy", None),
"type": run_config[f"{model_type}:__choice__"],
"data_preprocessors": [
value
for key, value in run_config.items()
if "data_preprocessing" in key and "__choice__" in key
],
"feature_preprocessors": [
value
for key, value in run_config.items()
if "feature_preprocessor" in key and "__choice__" in key
],
}
)
# Get the models ensemble weight if it has one
for (
_,
model_id,
_,
), weight in self.automl_.ensemble_.get_identifiers_with_weights():
# We had issues where the model's in the ensembles are not in the runhistory
# collected. I have no clue why this is but to prevent failures, we fill
# the values with NaN
if model_id not in model_runs:
model_run = {
"model_id": model_id,
"seed": pd.NA,
"budget": pd.NA,
"duration": pd.NA,
"config_id": pd.NA,
"start_time": pd.NA,
"end_time": pd.NA,
"status": pd.NA,
"train_loss": pd.NA,
"config_origin": pd.NA,
"type": pd.NA,
}
if num_metrics == 1:
model_run["cost"] = pd.NA
else:
for cost_idx in range(num_metrics):
model_run[f"cost_{cost_idx}"] = pd.NA
model_runs[model_id] = model_run
model_runs[model_id]["ensemble_weight"] = weight
# Filter out non-ensemble members if needed, else fill in a default
# value of 0 if it's missing
if ensemble_only:
model_runs = {
model_id: info
for model_id, info in model_runs.items()
if ("ensemble_weight" in info and info["ensemble_weight"] > 0)
}
else:
for model_id, info in model_runs.items():
if "ensemble_weight" not in info:
info["ensemble_weight"] = 0
# `rank` relies on `cost` so we include `cost`
# We drop it later if it's not requested
if "rank" in columns:
if num_metrics == 1 and "cost" not in columns:
columns = [*columns, "cost"]
elif num_metrics > 1 and any(
cost_name not in columns for cost_name in multi_objective_cost_names
):
columns = columns + list(multi_objective_cost_names)
# Finally, convert into a tabular format by converting the dict into
# column wise orientation.
dataframe = pd.DataFrame(
{
col: [run_info[col] for run_info in model_runs.values()]
for col in columns
if col != "rank"
}
)
# Give it an index, even if not in the `include`
dataframe.set_index("model_id", inplace=True)
# Add the `rank` column if needed
# requested by the user
if "rank" in columns:
if num_metrics == 1:
dataframe.sort_values(by="cost", ascending=True, inplace=True)
else:
dataframe.sort_values(by="cost_0", ascending=True, inplace=True)
dataframe.insert(
column="rank",
value=range(1, len(dataframe) + 1),
loc=list(columns).index("rank") - 1,
) # account for `model_id`
# Decide on the sort order depending on what it gets sorted by
descending_columns = ["ensemble_weight", "duration"]
if sort_order == "auto":
ascending_param = [
False if sby in descending_columns else True for sby in sort_by
]
else:
ascending_param = False if sort_order == "descending" else True
# Sort by the given column name, defaulting to 'model_id' if not present
if (
(not sort_by_cost and len(set(sort_by) - set(dataframe.columns)) > 0)
or (sort_by_cost and "cost" not in dataframe.columns)
or (
sort_by_cost
and any(
cost_name not in dataframe.columns
for cost_name in multi_objective_cost_names
)
)
):
self.automl_._logger.warning(
f"sort_by = '{sort_by}' was not present"
", defaulting to sort on the index "
"'model_id'"
)
sort_by = "model_id"
sort_by_cost = False
ascending_param = True
# Single objective
if sort_by_cost:
dataframe.sort_values(
by=sort_by, ascending=[True] * len(sort_by), inplace=True
)
else:
dataframe.sort_values(by=sort_by, ascending=ascending_param, inplace=True)
if num_metrics == 1:
if "cost" not in columns and "cost" in dataframe.columns:
dataframe.drop("cost", inplace=True)
else:
for cost_name in multi_objective_cost_names:
if cost_name not in columns and cost_name in dataframe.columns:
dataframe.drop(cost_name, inplace=True)
# Lastly, just grab the top_k
if top_k == "all" or top_k >= len(dataframe):
top_k = len(dataframe)
dataframe = dataframe.head(top_k)
return dataframe
@staticmethod
def _leaderboard_columns(
num_metrics: int,
) -> Dict[Literal["all", "simple", "detailed"], List[str]]:
if num_metrics == 1:
cost_list = ["cost"]
else:
cost_list = [f"cost_{i}" for i in range(num_metrics)]
all = (
[
"model_id",
"rank",
"ensemble_weight",
"type",
]
+ cost_list
+ [
"duration",
"config_id",
"train_loss",
"seed",
"start_time",
"end_time",
"budget",
"status",
"data_preprocessors",
"feature_preprocessors",
"balancing_strategy",
"config_origin",
]
)
simple = (
["model_id", "rank", "ensemble_weight", "type"] + cost_list + ["duration"]
)
detailed = all
return {"all": all, "detailed": detailed, "simple": simple}
def _get_automl_class(self):
raise NotImplementedError()
def get_configuration_space(
self,
X: SUPPORTED_FEAT_TYPES,
y: Union[SUPPORTED_TARGET_TYPES, spmatrix],
X_test: Optional[SUPPORTED_FEAT_TYPES] = None,
y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None,
dataset_name: Optional[str] = None,
feat_type: Optional[List[str]] = None,
) -> ConfigurationSpace:
"""
Returns the Configuration Space object, from which Auto-Sklearn
will sample configurations and build pipelines.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
Array with the training features, used to get characteristics like
data sparsity
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
Array with the problem labels
X_test : array-like or sparse matrix of shape = [n_samples, n_features]
Array with features used for performance estimation
y_test : array-like, shape = [n_samples] or [n_samples, n_outputs]
Array with the problem labels for the testing split
dataset_name: Optional[str]
A string to tag the Auto-Sklearn run
"""
if self.automl_ is None:
self.automl_ = self.build_automl()
return (
self.automl_.fit(
X,
y,
X_test=X_test,
y_test=y_test,
dataset_name=dataset_name,
feat_type=feat_type,
only_return_configuration_space=True,
)
if self.automl_.configuration_space is None
else self.automl_.configuration_space
)
def get_pareto_set(self) -> Sequence[VotingClassifier | VotingRegressor]:
return self.automl_._load_pareto_set()
[docs]class AutoSklearnClassifier(AutoSklearnEstimator, ClassifierMixin):
"""This class implements the classification task."""
[docs] def fit(self, X, y, X_test=None, y_test=None, feat_type=None, dataset_name=None):
"""Fit *auto-sklearn* to given training set (X, y).
Fit both optimizes the machine learning models and builds an ensemble
out of them.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The target classes.
X_test : array-like or sparse matrix of shape = [n_samples, n_features]
Test data input samples. Will be used to save test predictions for
all models. This allows to evaluate the performance of Auto-sklearn
over time.
y_test : array-like, shape = [n_samples] or [n_samples, n_outputs]
Test data target classes. Will be used to calculate the test error
of all models. This allows to evaluate the performance of
Auto-sklearn over time.
feat_type : list, optional (default=None)
List of str of `len(X.shape[1])` describing the attribute type.
Possible types are `Categorical` and `Numerical`. `Categorical`
attributes will be automatically One-Hot encoded. The values
used for a categorical attribute must be integers, obtained for
example by `sklearn.preprocessing.LabelEncoder
<https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
dataset_name : str, optional (default=None)
Create nicer output. If None, a string will be determined by the
md5 hash of the dataset.
Returns
-------
self
"""
# AutoSklearn does not handle sparse y for now
y = convert_if_sparse(y)
# Before running anything else, first check that the
# type of data is compatible with auto-sklearn. Legal target
# types are: binary, multiclass, multilabel-indicator.
target_type = type_of_target(y)
supported_types = ["binary", "multiclass", "multilabel-indicator"]
if target_type not in supported_types:
raise ValueError(
"Classification with data of type {} is "
"not supported. Supported types are {}. "
"You can find more information about scikit-learn "
"data types in: "
"https://scikit-learn.org/stable/modules/multiclass.html"
"".format(target_type, supported_types)
)
# remember target type for using in predict_proba later.
self.target_type = target_type
super().fit(
X=X,
y=y,
X_test=X_test,
y_test=y_test,
feat_type=feat_type,
dataset_name=dataset_name,
)
# After fit, a classifier is expected to define classes_
# A list of class labels known to the classifier, mapping each label
# to a numerical index used in the model representation our output.
self.classes_ = self.automl_.InputValidator.target_validator.classes_
return self
[docs] def predict(self, X, batch_size=None, n_jobs=1):
"""Predict classes for X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
Returns
-------
y : array of shape = [n_samples] or [n_samples, n_labels]
The predicted classes.
"""
return super().predict(X, batch_size=batch_size, n_jobs=n_jobs)
[docs] def predict_proba(self, X, batch_size=None, n_jobs=1):
"""Predict probabilities of classes for all samples X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
batch_size : int (optional)
Number of data points to predict for (predicts all points at once
if ``None``.
n_jobs : int
Returns
-------
y : array of shape = [n_samples, n_classes] or [n_samples, n_labels]
The predicted class probabilities.
"""
pred_proba = super().predict_proba(X, batch_size=batch_size, n_jobs=n_jobs)
# Check if all probabilities sum up to 1.
# Assert only if target type is not multilabel-indicator.
if self.target_type not in ["multilabel-indicator"]:
assert np.allclose(
np.sum(pred_proba, axis=1), np.ones_like(pred_proba[:, 0])
), "prediction probability does not sum up to 1!"
# Check that all probability values lie between 0 and 1.
assert (pred_proba >= 0).all() and (
pred_proba <= 1
).all(), "found prediction probability value outside of [0, 1]!"
return pred_proba
def _get_automl_class(self):
return AutoMLClassifier
[docs]class AutoSklearnRegressor(AutoSklearnEstimator, RegressorMixin):
"""
This class implements the regression task.
"""
[docs] def fit(self, X, y, X_test=None, y_test=None, feat_type=None, dataset_name=None):
"""Fit *Auto-sklearn* to given training set (X, y).
Fit both optimizes the machine learning models and builds an ensemble
out of them.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples] or [n_samples, n_targets]
The regression target.
X_test : array-like or sparse matrix of shape = [n_samples, n_features]
Test data input samples. Will be used to save test predictions for
all models. This allows to evaluate the performance of Auto-sklearn
over time.
y_test : array-like, shape = [n_samples] or [n_samples, n_targets]
The regression target. Will be used to calculate the test error
of all models. This allows to evaluate the performance of
Auto-sklearn over time.
feat_type : list, optional (default=None)
List of str of `len(X.shape[1])` describing the attribute type.
Possible types are `Categorical` and `Numerical`. `Categorical`
attributes will be automatically One-Hot encoded.
dataset_name : str, optional (default=None)
Create nicer output. If None, a string will be determined by the
md5 hash of the dataset.
Returns
-------
self
"""
# Before running anything else, first check that the
# type of data is compatible with auto-sklearn. Legal target
# types are: continuous, continuous-multioutput, and the special cases:
# multiclass : because [3.0, 1.0, 5.0] is considered as multiclass
# binary: because [1.0, 0.0] is considered multiclass
# AutoSklearn does not handle sparse y for now
y = convert_if_sparse(y)
target_type = type_of_target(y)
supported_types = [
"continuous",
"binary",
"multiclass",
"continuous-multioutput",
]
if target_type not in supported_types:
raise ValueError(
"Regression with data of type {} is "
"not supported. Supported types are {}. "
"You can find more information about scikit-learn "
"data types in: "
"https://scikit-learn.org/stable/modules/multiclass.html"
"".format(target_type, supported_types)
)
# Fit is supposed to be idempotent!
# But not if we use share_mode.
super().fit(
X=X,
y=y,
X_test=X_test,
y_test=y_test,
feat_type=feat_type,
dataset_name=dataset_name,
)
return self
[docs] def predict(self, X, batch_size=None, n_jobs=1):
"""Predict regression target for X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
Returns
-------
y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted values.
"""
return super().predict(X, batch_size=batch_size, n_jobs=n_jobs)
def _get_automl_class(self):
return AutoMLRegressor
