from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
import numpy as np
import pandas as pd
from autoPyTorch.api.base_task import BaseTask
from autoPyTorch.automl_common.common.utils.backend import Backend
from autoPyTorch.constants import (
TABULAR_CLASSIFICATION,
TASK_TYPES_TO_STRING,
)
from autoPyTorch.data.tabular_validator import TabularInputValidator
from autoPyTorch.data.utils import (
DatasetCompressionSpec,
get_dataset_compression_mapping,
)
from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType
from autoPyTorch.datasets.resampling_strategy import (
HoldoutValTypes,
ResamplingStrategies,
)
from autoPyTorch.datasets.tabular_dataset import TabularDataset
from autoPyTorch.evaluation.utils import DisableFileOutputParameters
from autoPyTorch.pipeline.tabular_classification import TabularClassificationPipeline
from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates
[docs]class TabularClassificationTask(BaseTask):
"""
Tabular Classification API to the pipelines.
Args:
seed (int: default=1):
seed to be used for reproducibility.
n_jobs (int: default=1):
number of consecutive processes to spawn.
n_threads (int: default=1):
number of threads to use for each process.
logging_config (Optional[Dict]):
Specifies configuration for logging, if None, it is loaded from the logging.yaml
ensemble_size (int: default=50):
Number of models added to the ensemble built by
Ensemble selection from libraries of models.
Models are drawn with replacement.
ensemble_nbest (int: default=50):
Only consider the ensemble_nbest
models to build the ensemble
max_models_on_disc (int: default=50):
Maximum number of models saved to disc.
Also, controls the size of the ensemble
as any additional models will be deleted.
Must be greater than or equal to 1.
temporary_directory (str):
Folder to store configuration output and log file
output_directory (str):
Folder to store predictions for optional test set
delete_tmp_folder_after_terminate (bool):
Determines whether to delete the temporary directory,
when finished
include_components (Optional[Dict[str, Any]]):
Dictionary containing components to include. Key is the node
name and Value is an Iterable of the names of the components
to include. Only these components will be present in the
search space.
exclude_components (Optional[Dict[str, Any]]):
Dictionary containing components to exclude. Key is the node
name and Value is an Iterable of the names of the components
to exclude. All except these components will be present in
the search space.
resampling_strategy resampling_strategy (RESAMPLING_STRATEGIES),
(default=HoldoutValTypes.holdout_validation):
strategy to split the training data.
resampling_strategy_args (Optional[Dict[str, Any]]): arguments
required for the chosen resampling strategy. If None, uses
the default values provided in DEFAULT_RESAMPLING_PARAMETERS
in ```datasets/resampling_strategy.py```.
search_space_updates (Optional[HyperparameterSearchSpaceUpdates]):
Search space updates that can be used to modify the search
space of particular components or choice modules of the pipeline
"""
def __init__(
self,
seed: int = 1,
n_jobs: int = 1,
n_threads: int = 1,
logging_config: Optional[Dict] = None,
ensemble_size: int = 50,
ensemble_nbest: int = 50,
max_models_on_disc: int = 50,
temporary_directory: Optional[str] = None,
output_directory: Optional[str] = None,
delete_tmp_folder_after_terminate: bool = True,
delete_output_folder_after_terminate: bool = True,
include_components: Optional[Dict[str, Any]] = None,
exclude_components: Optional[Dict[str, Any]] = None,
resampling_strategy: ResamplingStrategies = HoldoutValTypes.holdout_validation,
resampling_strategy_args: Optional[Dict[str, Any]] = None,
backend: Optional[Backend] = None,
search_space_updates: Optional[HyperparameterSearchSpaceUpdates] = None
):
super().__init__(
seed=seed,
n_jobs=n_jobs,
n_threads=n_threads,
logging_config=logging_config,
ensemble_size=ensemble_size,
ensemble_nbest=ensemble_nbest,
max_models_on_disc=max_models_on_disc,
temporary_directory=temporary_directory,
output_directory=output_directory,
delete_tmp_folder_after_terminate=delete_tmp_folder_after_terminate,
delete_output_folder_after_terminate=delete_output_folder_after_terminate,
include_components=include_components,
exclude_components=exclude_components,
backend=backend,
resampling_strategy=resampling_strategy,
resampling_strategy_args=resampling_strategy_args,
search_space_updates=search_space_updates,
task_type=TASK_TYPES_TO_STRING[TABULAR_CLASSIFICATION],
)
[docs] def build_pipeline(
self,
dataset_properties: Dict[str, BaseDatasetPropertiesType],
include_components: Optional[Dict[str, Any]] = None,
exclude_components: Optional[Dict[str, Any]] = None,
search_space_updates: Optional[HyperparameterSearchSpaceUpdates] = None
) -> TabularClassificationPipeline:
"""
Build pipeline according to current task
and for the passed dataset properties
Args:
dataset_properties (Dict[str, Any]):
Characteristics of the dataset to guide the pipeline
choices of components
include_components (Optional[Dict[str, Any]]):
Dictionary containing components to include. Key is the node
name and Value is an Iterable of the names of the components
to include. Only these components will be present in the
search space.
exclude_components (Optional[Dict[str, Any]]):
Dictionary containing components to exclude. Key is the node
name and Value is an Iterable of the names of the components
to exclude. All except these components will be present in
the search space.
search_space_updates (Optional[HyperparameterSearchSpaceUpdates]):
Search space updates that can be used to modify the search
space of particular components or choice modules of the pipeline
Returns:
TabularClassificationPipeline
"""
return TabularClassificationPipeline(dataset_properties=dataset_properties,
include=include_components,
exclude=exclude_components,
search_space_updates=search_space_updates)
def _get_dataset_input_validator(
self,
X_train: Union[List, pd.DataFrame, np.ndarray],
y_train: Union[List, pd.DataFrame, np.ndarray],
X_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
y_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
resampling_strategy: Optional[ResamplingStrategies] = None,
resampling_strategy_args: Optional[Dict[str, Any]] = None,
dataset_name: Optional[str] = None,
dataset_compression: Optional[DatasetCompressionSpec] = None,
**kwargs: Any,
) -> Tuple[TabularDataset, TabularInputValidator]:
"""
Returns an object of `TabularDataset` and an object of
`TabularInputValidator` according to the current task.
Args:
X_train (Union[List, pd.DataFrame, np.ndarray]):
Training feature set.
y_train (Union[List, pd.DataFrame, np.ndarray]):
Training target set.
X_test (Optional[Union[List, pd.DataFrame, np.ndarray]]):
Testing feature set
y_test (Optional[Union[List, pd.DataFrame, np.ndarray]]):
Testing target set
resampling_strategy (Optional[RESAMPLING_STRATEGIES]):
Strategy to split the training data. if None, uses
HoldoutValTypes.holdout_validation.
resampling_strategy_args (Optional[Dict[str, Any]]):
arguments required for the chosen resampling strategy. If None, uses
the default values provided in DEFAULT_RESAMPLING_PARAMETERS
in ```datasets/resampling_strategy.py```.
dataset_name (Optional[str]):
name of the dataset, used as experiment name.
dataset_compression (Optional[DatasetCompressionSpec]):
specifications for dataset compression. For more info check
documentation for `BaseTask.get_dataset`.
kwargs (Any):
Currently for tabular tasks, expect `feat_types: (Optional[List[str]]` which
specifies whether a feature is 'numerical' or 'categorical'.
Returns:
TabularDataset:
the dataset object.
TabularInputValidator:
the input validator fitted on the data.
"""
resampling_strategy = resampling_strategy if resampling_strategy is not None else self.resampling_strategy
resampling_strategy_args = resampling_strategy_args if resampling_strategy_args is not None else \
self.resampling_strategy_args
feat_types = kwargs.pop('feat_types', None)
# Create a validator object to make sure that the data provided by
# the user matches the autopytorch requirements
input_validator = TabularInputValidator(
is_classification=True,
logger_port=self._logger_port,
dataset_compression=dataset_compression,
feat_types=feat_types
)
# Fit a input validator to check the provided data
# Also, an encoder is fit to both train and test data,
# to prevent unseen categories during inference
input_validator.fit(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)
dataset = TabularDataset(
X=X_train, Y=y_train,
X_test=X_test, Y_test=y_test,
validator=input_validator,
resampling_strategy=resampling_strategy,
resampling_strategy_args=resampling_strategy_args,
dataset_name=dataset_name
)
return dataset, input_validator
[docs] def search(
self,
optimize_metric: str,
X_train: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
y_train: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
X_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
y_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None,
dataset_name: Optional[str] = None,
feat_types: Optional[List[str]] = None,
budget_type: str = 'epochs',
min_budget: int = 5,
max_budget: int = 50,
total_walltime_limit: int = 100,
func_eval_time_limit_secs: Optional[int] = None,
enable_traditional_pipeline: bool = True,
memory_limit: int = 4096,
smac_scenario_args: Optional[Dict[str, Any]] = None,
get_smac_object_callback: Optional[Callable] = None,
all_supported_metrics: bool = True,
precision: int = 32,
disable_file_output: Optional[List[Union[str, DisableFileOutputParameters]]] = None,
load_models: bool = True,
portfolio_selection: Optional[str] = None,
dataset_compression: Union[Mapping[str, Any], bool] = False,
) -> 'BaseTask':
"""
Search for the best pipeline configuration for the given dataset.
Fit both optimizes the machine learning models and builds an ensemble out of them.
To disable ensembling, set ensemble_size==0.
using the optimizer.
Args:
X_train, y_train, X_test, y_test: Union[np.ndarray, List, pd.DataFrame]
A pair of features (X_train) and targets (y_train) used to fit a
pipeline. Additionally, a holdout of this pairs (X_test, y_test) can
be provided to track the generalization performance of each stage.
feat_types (Optional[List[str]]):
Description about the feature types of the columns.
Accepts `numerical` for integers, float data and `categorical`
for categories, strings and bool. Defaults to None.
optimize_metric (str):
name of the metric that is used to evaluate a pipeline.
budget_type (str):
Type of budget to be used when fitting the pipeline.
It can be one of:
+ `epochs`: The training of each pipeline will be terminated after
a number of epochs have passed. This number of epochs is determined by the
budget argument of this method.
+ `runtime`: The training of each pipeline will be terminated after
a number of seconds have passed. This number of seconds is determined by the
budget argument of this method. The overall fitting time of a pipeline is
controlled by func_eval_time_limit_secs. 'runtime' only controls the allocated
time to train a pipeline, but it does not consider the overall time it takes
to create a pipeline (data loading and preprocessing, other i/o operations, etc.).
budget_type will determine the units of min_budget/max_budget. If budget_type=='epochs'
is used, min_budget will refer to epochs whereas if budget_type=='runtime' then
min_budget will refer to seconds.
min_budget (int):
Auto-PyTorch uses `Hyperband <https://arxiv.org/abs/1603.06560>`_ to
trade-off resources between running many pipelines at min_budget and
running the top performing pipelines on max_budget.
min_budget states the minimum resource allocation a pipeline should have
so that we can compare and quickly discard bad performing models.
For example, if the budget_type is epochs, and min_budget=5, then we will
run every pipeline to a minimum of 5 epochs before performance comparison.
max_budget (int):
Auto-PyTorch uses `Hyperband <https://arxiv.org/abs/1603.06560>`_ to
trade-off resources between running many pipelines at min_budget and
running the top performing pipelines on max_budget.
max_budget states the maximum resource allocation a pipeline is going to
be ran. For example, if the budget_type is epochs, and max_budget=50,
then the pipeline training will be terminated after 50 epochs.
total_walltime_limit (int: default=100):
Time limit in seconds for the search of appropriate models.
By increasing this value, autopytorch has a higher
chance of finding better models.
func_eval_time_limit_secs (Optional[int]):
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.
When set to None, this time will automatically be set to
total_walltime_limit // 2 to allow enough time to fit
at least 2 individual machine learning algorithms.
Set to np.inf in case no time limit is desired.
enable_traditional_pipeline (bool: default=True):
We fit traditional machine learning algorithms
(LightGBM, CatBoost, RandomForest, ExtraTrees, KNN, SVM)
prior building PyTorch Neural Networks. You can disable this
feature by turning this flag to False. All machine learning
algorithms that are fitted during search() are considered for
ensemble building.
memory_limit (int: default=4096):
Memory limit in MB for the machine learning algorithm.
Autopytorch will stop fitting the machine learning algorithm
if it tries to allocate more than memory_limit MB. If None
is provided, no memory limit is set. In case of multi-processing,
memory_limit will be per job. This memory limit also applies to
the ensemble creation process.
smac_scenario_args (Optional[Dict]):
Additional arguments inserted into the scenario of SMAC. See the
`SMAC documentation <https://automl.github.io/SMAC3/master/options.html?highlight=scenario#scenario>`_
for a list of available arguments.
get_smac_object_callback (Optional[Callable]):
Callback function to create an object of class
`smac.optimizer.smbo.SMBO <https://automl.github.io/SMAC3/master/apidoc/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/master/index.html>`_.
tae_func (Optional[Callable]):
TargetAlgorithm to be optimised. If None, `eval_function`
available in autoPyTorch/evaluation/train_evaluator is used.
Must be child class of AbstractEvaluator.
all_supported_metrics (bool: default=True):
If True, all metrics supporting current task will be calculated
for each pipeline and results will be available via cv_results
precision (int: default=32):
Numeric precision used when loading ensemble data.
Can be either '16', '32' or '64'.
disable_file_output (Optional[List[Union[str, DisableFileOutputParameters]]]):
Used as a list to pass more fine-grained
information on what to save. Must be a member of `DisableFileOutputParameters`.
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. Note that SMAC
optimizes a metric evaluated on the optimization set.
+ `pipeline`:
do not save any individual pipeline files
+ `pipelines`:
In case of cross validation, disables saving the joint model of the
pipelines fit on each fold.
+ `y_test`:
do not save the predictions for the test set.
+ `all`:
do not save any of the above.
For more information check `autoPyTorch.evaluation.utils.DisableFileOutputParameters`.
load_models (bool: default=True):
Whether to load the models after fitting AutoPyTorch.
portfolio_selection (Optional[str]):
This argument controls the initial configurations that
AutoPyTorch uses to warm start SMAC for hyperparameter
optimization. By default, no warm-starting happens.
The user can provide a path to a json file containing
configurations, similar to (...herepathtogreedy...).
Additionally, the keyword 'greedy' is supported,
which would use the default portfolio from
`AutoPyTorch Tabular <https://arxiv.org/abs/2006.13799>`_.
dataset_compression: Union[bool, Mapping[str, Any]] = True
We compress datasets so that they fit into some predefined amount of memory.
**NOTE**
Default configuration when left as ``True``:
.. code-block:: python
{
"memory_allocation": 0.1,
"methods": ["precision"]
}
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.
**methods**
We currently 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``
* pandas dataframes are reduced using the downcast option of `pd.to_numeric`
to the lowest possible precision.
* ``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.
Returns:
self
"""
self._dataset_compression = get_dataset_compression_mapping(memory_limit, dataset_compression)
self.dataset, self.input_validator = self._get_dataset_input_validator(
X_train=X_train,
y_train=y_train,
X_test=X_test,
y_test=y_test,
resampling_strategy=self.resampling_strategy,
resampling_strategy_args=self.resampling_strategy_args,
dataset_name=dataset_name,
dataset_compression=self._dataset_compression,
feat_types=feat_types)
return self._search(
dataset=self.dataset,
optimize_metric=optimize_metric,
budget_type=budget_type,
min_budget=min_budget,
max_budget=max_budget,
total_walltime_limit=total_walltime_limit,
func_eval_time_limit_secs=func_eval_time_limit_secs,
enable_traditional_pipeline=enable_traditional_pipeline,
memory_limit=memory_limit,
smac_scenario_args=smac_scenario_args,
get_smac_object_callback=get_smac_object_callback,
all_supported_metrics=all_supported_metrics,
precision=precision,
disable_file_output=disable_file_output,
load_models=load_models,
portfolio_selection=portfolio_selection,
)
[docs] def predict(
self,
X_test: np.ndarray,
batch_size: Optional[int] = None,
n_jobs: int = 1
) -> np.ndarray:
"""Generate the estimator predictions.
Generate the predictions based on the given examples from the test set.
Args:
X_test (np.ndarray):
The test set examples.
Returns:
Array with estimator predictions.
"""
if self.input_validator is None or not self.input_validator._is_fitted:
raise ValueError("predict() is only supported after calling search. Kindly call first "
"the estimator search() method.")
X_test = self.input_validator.feature_validator.transform(X_test)
predicted_probabilities = super().predict(X_test, batch_size=batch_size,
n_jobs=n_jobs)
if self.input_validator.target_validator.is_single_column_target():
predicted_indexes = np.argmax(predicted_probabilities, axis=1)
else:
predicted_indexes = (predicted_probabilities > 0.5).astype(int)
# Allow to predict in the original domain -- that is, the user is not interested
# in our encoded values
return self.input_validator.target_validator.inverse_transform(predicted_indexes)
def predict_proba(self,
X_test: Union[np.ndarray, pd.DataFrame, List],
batch_size: Optional[int] = None, n_jobs: int = 1) -> np.ndarray:
if self.input_validator is None or not self.input_validator._is_fitted:
raise ValueError("predict() is only supported after calling search. Kindly call first "
"the estimator search() method.")
X_test = self.input_validator.feature_validator.transform(X_test)
return super().predict(X_test, batch_size=batch_size, n_jobs=n_jobs)
