Source code for ibm_watson_machine_learning.experiment.autoai.autoai

#  -----------------------------------------------------------------------------------------
#  (C) Copyright IBM Corp. 2020-2024.
#  -----------------------------------------------------------------------------------------

import copy
from typing import List, Union
from warnings import warn

from ibm_watson_machine_learning.preprocessing import DataJoinGraph
from ibm_watson_machine_learning.utils.autoai.enums import (
    TShirtSize, ClassificationAlgorithms, RegressionAlgorithms, ForecastingAlgorithms, PredictionType, Metrics, \
    Transformers, DataConnectionTypes, PipelineTypes, PositiveLabelClass, ClassificationAlgorithmsCP4D,
    RegressionAlgorithmsCP4D, ForecastingAlgorithmsCP4D, SamplingTypes, ImputationStrategy, ForecastingPipelineTypes,
    TimeseriesAnomalyPredictionPipelineTypes, TimeseriesAnomalyPredictionAlgorithms)
from ibm_watson_machine_learning.utils.autoai.errors import LocalInstanceButRemoteParameter, MissingPositiveLabel, \
    NonForecastPredictionColumnMissing, ForecastPredictionColumnsMissing, ForecastingCannotBeRunAsLocalScenario, \
    TSNotSupported, TSADNotSupported, ImputationListNotSupported, \
    MissingEstimatorForExistingBatchedEstimator, TimeseriesAnomalyPredictionFeatureColumnsMissing, \
    TimeseriesAnomalyPredictionCannotBeRunAsLocalScenario, TimeseriesAnomalyPredictionUnsupportedMetric
from ibm_watson_machine_learning.utils.autoai.utils import check_dependencies_versions, \
    validate_additional_params_for_optimizer, validate_optimizer_enum_values, \
    translate_imputation_string_strategy_to_enum, translate_estimator_string_to_enum, \
from ibm_watson_machine_learning.workspace import WorkSpace
from ibm_watson_machine_learning.wml_client_error import ForbiddenActionForGitBasedProject, WMLClientError, ParamOutOfRange
from ibm_watson_machine_learning.messages.messages import Messages
from .engines import WMLEngine
from .optimizers import LocalAutoPipelines, RemoteAutoPipelines
from .runs import AutoPipelinesRuns, LocalAutoPipelinesRuns
from ..base_experiment.base_experiment import BaseExperiment

__all__ = [

[docs] class AutoAI(BaseExperiment): """AutoAI class for pipeline models optimization automation. :param wml_credentials: credentials to Watson Machine Learning instance :type wml_credentials: dict :param project_id: ID of the Watson Studio project :type project_id: str, optional :param space_id: ID of the Watson Studio Space :type space_id: str, optional :param verify: user can pass as verify one of following: - the path to a CA_BUNDLE file - the path of directory with certificates of trusted CAs - `True` - default path to truststore will be taken - `False` - no verification will be made :type verify: bool or str, optional **Example** .. code-block:: python from ibm_watson_machine_learning.experiment import AutoAI experiment = AutoAI( wml_credentials={ "apikey": "...", "iam_apikey_description": "...", "iam_apikey_name": "...", "iam_role_crn": "...", "iam_serviceid_crn": "...", "instance_id": "...", "url": "" }, project_id="...", space_id="...") """ # note: initialization of AutoAI enums as class properties # note: Enums with estimators can be overwritten in _init based on environment type (CPD or Cloud) ClassificationAlgorithms = ClassificationAlgorithms RegressionAlgorithms = RegressionAlgorithms ForecastingAlgorithms = ForecastingAlgorithms # end note TShirtSize = TShirtSize PredictionType = PredictionType Metrics = Metrics Transformers = Transformers DataConnectionTypes = DataConnectionTypes PipelineTypes = PipelineTypes SamplingTypes = SamplingTypes def __init__(self, wml_credentials: Union[dict, 'WorkSpace'] = None, project_id: str = None, space_id: str = None, verify=None) -> None: # note: as workspace is not clear enough to understand, there is a possibility to use pure # wml credentials with project and space IDs, but in addition we # leave a possibility to use a previous WorkSpace implementation, it could be passed as a first argument if wml_credentials is None: self._workspace = None self.runs = LocalAutoPipelinesRuns() else: if isinstance(wml_credentials, WorkSpace): self._workspace = wml_credentials else: self._workspace = WorkSpace(wml_credentials=wml_credentials.copy(), project_id=project_id, space_id=space_id, verify=verify) self.project_id = self._workspace.project_id self.space_id = self._workspace.space_id self.runs = AutoPipelinesRuns(engine=WMLEngine(self._workspace)) self.runs._workspace = self._workspace #self._block_autoai_on_git_based_project() self._init_estimator_enums() self._20_class_limit_removal_test = False # --- end note
[docs] def runs(self, *, filter: str) -> Union['AutoPipelinesRuns', 'LocalAutoPipelinesRuns']: """Get the historical runs but with WML Pipeline name filter (for remote scenario). Get the historical runs but with experiment name filter (for local scenario). :param filter: WML Pipeline name to filter the historical runs or experiment name to filter the local historical runs :type filter: str :return: object managing the list of runs :rtype: AutoPipelinesRuns or LocalAutoPipelinesRuns **Example** .. code-block:: python from ibm_watson_machine_learning.experiment import AutoAI experiment = AutoAI(...) experiment.runs(filter='Test').list() """ if self._workspace is None: return LocalAutoPipelinesRuns(filter=filter) else: return AutoPipelinesRuns(engine=WMLEngine(self._workspace.wml_client), filter=filter)
[docs] def optimizer(self, name: str, *, prediction_type: 'PredictionType', prediction_column: str = None, prediction_columns: List[str] = None, timestamp_column_name: str = None, scoring: 'Metrics' = None, desc: str = None, test_size: float = None, # deprecated holdout_size: float = None, max_number_of_estimators: int = None, train_sample_rows_test_size: float = None, include_only_estimators: List[Union['ClassificationAlgorithms', 'RegressionAlgorithms', 'ForecastingAlgorithms', 'TimeseriesAnomalyPredictionAlgorithms']] = None, daub_include_only_estimators: List[Union['ClassificationAlgorithms', 'RegressionAlgorithms']] = None, # deprecated include_batched_ensemble_estimators: List[Union['BatchedClassificationAlgorithms', 'BatchedRegressionAlgorithms']] = None, backtest_num: int = None, lookback_window: int = None, forecast_window: int = None, backtest_gap_length: int = None, feature_columns: List[str] = None, pipeline_types: List[Union['ForecastingPipelineTypes', 'TimeseriesAnomalyPredictionPipelineTypes']] = None, supporting_features_at_forecast: bool = None, cognito_transform_names: List['Transformers'] = None, data_join_graph: 'DataJoinGraph' = None, csv_separator: Union[List[str], str] = ',', excel_sheet: Union[str, int] = None, encoding: str = 'utf-8', positive_label: str = None, data_join_only: bool = False, drop_duplicates: bool = True, outliers_columns: list = None, text_processing: bool = None, word2vec_feature_number: int = None, daub_give_priority_to_runtime: float = None, fairness_info: dict = None, sampling_type: 'SamplingTypes' = None, sample_size_limit: int = None, sample_rows_limit: int = None, sample_percentage_limit: float = None, n_parallel_data_connections: int = None, number_of_batch_rows: int = None, categorical_imputation_strategy: ImputationStrategy = None, numerical_imputation_strategy: ImputationStrategy = None, numerical_imputation_value: float = None, imputation_threshold: float = None, retrain_on_holdout: bool = None, categorical_columns: list = None, numerical_columns: list = None, test_data_csv_separator: Union[List[str], str] = ',', test_data_excel_sheet: str = None, test_data_encoding: str = 'utf-8', confidence_level: float = None, incremental_learning: bool = None, early_stop_enabled: bool = None, early_stop_window_size: int = None, time_ordered_data: bool = None, feature_selector_mode: str = None, **kwargs) -> Union['RemoteAutoPipelines', 'LocalAutoPipelines']: """ Initialize an AutoAi optimizer. :param name: name for the AutoPipelines :type name: str :param prediction_type: type of the prediction :type prediction_type: PredictionType :param prediction_column: name of the target/label column, required for `multiclass`, `binary` and `regression` prediction types :type prediction_column: str, optional :param prediction_columns: names of the target/label columns, required for `forecasting` prediction type :type prediction_columns: list[str], optional :param timestamp_column_name: name of timestamp column for time series forecasting :type timestamp_column_name: str, optional :param scoring: type of the metric to optimize with, not used for forecasting :type scoring: Metrics, optional :param desc: description :type desc: str, optional :param test_size: deprecated, use `holdout_size` instead :param holdout_size: percentage of the entire dataset to leave as a holdout :type holdout_size: float, optional :param max_number_of_estimators: maximum number (top-K ranked by DAUB model selection) of the selected algorithm, or estimator types, for example `LGBMClassifierEstimator`, `XGBoostClassifierEstimator`, or `LogisticRegressionEstimator` to use in pipeline composition, the default is `None` that means the true default value will be determined by the internal different algorithms, where only the highest ranked by model selection algorithm type is used :type max_number_of_estimators: int, optional :param train_sample_rows_test_size: training data sampling percentage :type train_sample_rows_test_size: float, optional :param daub_include_only_estimators: deprecated, use `include_only_estimators` instead :param include_batched_ensemble_estimators: list of batched ensemble estimators to include in computation process, see: AutoAI.BatchedClassificationAlgorithms, AutoAI.BatchedRegressionAlgorithms :type include_batched_ensemble_estimators: list[BatchedClassificationAlgorithms or BatchedRegressionAlgorithms], optional :param include_only_estimators: list of estimators to include in computation process, see: AutoAI.ClassificationAlgorithms, AutoAI.RegressionAlgorithms or AutoAI.ForecastingAlgorithms :type include_only_estimators: List[ClassificationAlgorithms or RegressionAlgorithms or ForecastingAlgorithms]], optional :param backtest_num: number of backtests used for forecasting prediction type, default value: 4, value from range [0, 20] :type backtest_num: int, optional :param lookback_window: length of lookback window used for forecasting prediction type, default value: 10, if set to -1 lookback window will be auto-detected :type lookback_window: int, optional :param forecast_window: length of forecast window used for forecasting prediction type, default value: 1, value from range [1, 60] :type forecast_window: int, optional :param backtest_gap_length: gap between backtests used for forecasting prediction type, default value: 0, value from range [0, data length / 4] :type backtest_gap_length: int, optional :param feature_columns: list of feature columns used for forecasting prediction type, may contain target column and/or supporting feature columns, list of columns to be detected whether there are anomalies for timeseries anomaly prediction type :type feature_columns: list[str], optional :param pipeline_types: list of pipeline types to be used for forecasting or timeseries anomaly prediction type :type pipeline_types: list[ForecastingPipelineTypes or TimeseriesAnomalyPredictionPipelineTypes], optional :param supporting_features_at_forecast: enables usage of future supporting feature values during forecast :type supporting_features_at_forecast: bool, optional :param cognito_transform_names: list of transformers to include in the feature enginnering computation process, see: AutoAI.Transformers :type cognito_transform_names: list[Transformers], optional :param csv_separator: the separator, or list of separators to try for separating columns in a CSV file, not used if the file_name is not a CSV file, default is ',' :type csv_separator: list[str] or str, optional :param excel_sheet: name of the excel sheet to use, only applicable when xlsx file is an input, support for number of the sheet is deprecated, by default first sheet is used :type excel_sheet: str, optional :param encoding: encoding type for CSV training file :type encoding: str, optional :param positive_label: the positive class to report when binary classification, when multiclass or regression, this will be ignored :type positive_label: str, optional :param t_shirt_size: the size of the remote AutoAI POD instance (computing resources), only applicable to a remote scenario, see: AutoAI.TShirtSize :type t_shirt_size: TShirtSize, optional :param data_join_graph: a graph object with definition of join structure for multiple input data sources, data preprocess step for multiple files :type data_join_graph: DataJoinGraph, optional :param data_join_only: if `True` only preprocessing will be executed :type data_join_only: bool, optional :param drop_duplicates: if `True` duplicated rows in data will be removed before further processing :type drop_duplicates: bool, optional :param outliers_columns: replace outliers with NaN using IQR method for specified columns. By default, turned ON for regression learning_type and target column. To turn OFF empty list of columns must be passed :type outliers_columns: list, optional :param text_processing: if `True` text processing will be enabled, applicable only on Cloud :type text_processing: bool, optional :param word2vec_feature_number: number of features which will be generated from text column, will be applied only if `text_processing` is `True`, if `None` the default value will be taken :type word2vec_feature_number: int, optional :param daub_give_priority_to_runtime: the importance of run time over score for pipelines ranking, can take values between 0 and 5, if set to 0.0 only score is used, if set to 1 equally score and runtime are used, if set to value higher than 1 the runtime gets higher importance over score :type daub_give_priority_to_runtime: float, optional :param fairness_info: dictionary that specifies metadata needed for measuring fairness, it contains three key values: `favorable_labels`, `unfavorable_labels` and `protected_attributes`, the `favorable_labels` attribute indicates that when the class column contains one of the value from list, that is considered a positive outcome, the `unfavorable_labels` is oposite to the `favorable_labels` and is obligatory for regression learning type, a protected attribute is a list of features that partition the population into groups whose outcome should have parity, if protected attribute is empty list then automatic detection of protected attributes will be run, if `fairness_info` is passed then fairness metric will be calculated :type fairness_info: fairness_info :param n_parallel_data_connections: number of maximum parallel connection to data source, supported only for IBM Cloud Pak® for Data 4.0.1 and above :type n_parallel_data_connections: int, optional :param categorical_imputation_strategy: missing values imputation strategy for categorical columns Possible values (only non-forecasting scenario): - ImputationStrategy.MEAN - ImputationStrategy.MEDIAN - ImputationStrategy.MOST_FREQUENT (default) :type categorical_imputation_strategy: ImputationStrategy, optional :param numerical_imputation_strategy: missing values imputation strategy for numerical columns Possible values (non-forecasting scenario): - ImputationStrategy.MEAN - ImputationStrategy.MEDIAN (default) - ImputationStrategy.MOST_FREQUENT Possible values (forecasting scenario): - ImputationStrategy.MEAN - ImputationStrategy.MEDIAN - ImputationStrategy.BEST_OF_DEFAULT_IMPUTERS (default) - ImputationStrategy.VALUE - ImputationStrategy.FLATTEN_ITERATIVE - ImputationStrategy.LINEAR - ImputationStrategy.CUBIC - ImputationStrategy.PREVIOUS - ImputationStrategy.NEXT - ImputationStrategy.NO_IMPUTATION :param numerical_imputation_value: value for filling missing values if numerical_imputation_strategy is set to ImputationStrategy.VALUE, for forecasting only :type numerical_imputation_value: float, optional :param imputation_threshold: maximum threshold of missing values imputation, for forecasting only :type imputation_threshold: float, optional :param retrain_on_holdout: if True final pipelines will be train also on holdout data :type retrain_on_holdout: bool, optional :param categorical_columns: list of columns names that must be treated as categorical :type categorical_columns: list, optional :param numerical_columns: list of columns names that must be treated as numerical :type numerical_columns: list, optional :param sampling_type: type of sampling data for training, one of SamplingTypes enum values, default is SamplingTypes.FIRST_N_RECORDS, supported only for IBM Cloud Pak® for Data 4.0.1 and above :type sampling_type: str, optional :param sample_size_limit: the size of sample upper bound (in bytes). The default value is 1GB, supported only for IBM Cloud Pak® for Data 4.5 and above :type sample_size_limit: int, optional :param sample_rows_limit: the size of sample upper bound (in rows), supported only for IBM Cloud Pak® for Data 4.6 and above :type sample_rows_limit: int, optional :param sample_percentage_limit: the size of sample upper bound (as fraction of dataset size), supported only for IBM Cloud Pak® for Data 4.6 and above :type sample_percentage_limit: float, optional :param number_of_batch_rows: number of rows to read in each batch when reading from flight connection :type number_of_batch_rows: int, optional :param test_data_csv_separator: the separator, or list of separators to try for separating columns in a CSV user-defined holdout/test file, not used if the file_name is not a CSV file, default is ',' :type test_data_csv_separator: list[str] or str, optional :param test_data_excel_sheet: name of the excel sheet to use for user-defined holdout/test data, only use when xlsx file is an test, dataset file, by default first sheet is used :type test_data_excel_sheet: str or int, optional :param test_data_encoding: encoding type for CSV user-defined holdout/test file :type test_data_encoding: str, optional :param confidence_level: when the pipeline "PointwiseBoundedHoltWinters" or "PointwiseBoundedBATS" is used, the prediction interval is calculated at a given confidence_level to decide if a data record is an anomaly or not, optional for timeseries anomaly prediction :type confidence_level: float, optional :param incremental_learning: triggers incremental learning process for supported pipelines :type incremental_learning: bool, optional :param early_stop_enabled: enables early stop for incremental learning process :type early_stop_enabled: bool, optional :param early_stop_window_size: the number of iterations without score improvements before training stop :type early_stop_window_size: int, optional :param time_ordered_data: defines user preference about time-based analise. If True, the analysis will consider the data as time-ordered and time-based. Supported only for regression. :type time_ordered_data: bool, optional :param feature_selector_mode: defines if feature selector should be triggered ["on", "off", "auto"]. The "auto" mode analyses the impact of removal of insignificant features. If there is drop in accuracy, the PCA is applied to insignificant features. Principal components describing variance in 30% or higher are selected in place of insignificant features. The model is evaluated again. If there is still drop in accuracy all features are used. The "on" mode removes all insignificant features (0.0. importance). Feature selector is applied during cognito phase (applicable to pipelines with feature engineering stage). :type feature_selector_mode: str, optional :return: RemoteAutoPipelines or LocalAutoPipelines, depends on how you initialize the AutoAI object :rtype: RemoteAutoPipelines or LocalAutoPipelines **Examples** .. code-block:: python from ibm_watson_machine_learning.experiment import AutoAI experiment = AutoAI(...) fairness_info = { "protected_attributes": [ {"feature": "Sex", "reference_group": ['male'], "monitored_group": ['female']}, {"feature": "Age", "reference_group": [[50,60]], "monitored_group": [[18, 49]]} ], "favorable_labels": ["No Risk"], "unfavorable_labels": ["Risk"], } optimizer = experiment.optimizer( name="name of the optimizer.", prediction_type=AutoAI.PredictionType.BINARY, prediction_column="y", scoring=AutoAI.Metrics.ROC_AUC_SCORE, desc="Some description.", holdout_size=0.1, max_number_of_estimators=1, fairness_info= fairness_info, cognito_transform_names=[AutoAI.Transformers.SUM,AutoAI.Transformers.MAX], train_sample_rows_test_size=1, include_only_estimators=[AutoAI.ClassificationAlgorithms.LGBM, AutoAI.ClassificationAlgorithms.XGB], t_shirt_size=AutoAI.TShirtSize.L ) optimizer = experiment.optimizer( name="name of the optimizer.", prediction_type=AutoAI.PredictionType.MULTICLASS, prediction_column="y", scoring=AutoAI.Metrics.ROC_AUC_SCORE, desc="Some description.", ) """ # note: convert `timeseries` type to PredictionType.FORECASTING: if prediction_type == 'timeseries': prediction_type = PredictionType.FORECASTING if prediction_type != PredictionType.FORECASTING and retrain_on_holdout is None: retrain_on_holdout = True # Deprecation of excel_sheet as number: if isinstance(excel_sheet, int): warn( message="Support for excel sheet as number of the sheet (int) is deprecated! Please set excel sheet with name of the sheet.") if data_join_graph is not None: if self._workspace.wml_client.CPD_version >= 4.6: raise WMLClientError(Messages.get_message(message_id="obm_removal_message_cpd")) elif self._workspace.wml_client.ICP: print(Messages.get_message(message_id="obm_deprecation_message_cpd")) else: raise WMLClientError(Messages.get_message(message_id="obm_removal_message_cloud")) if prediction_type == PredictionType.FORECASTING and self._workspace.wml_client.ICP and \ (self._workspace.wml_client.wml_credentials['version'].startswith('2.5') or \ self._workspace.wml_client.wml_credentials['version'].startswith('3.0') or \ self._workspace.wml_client.wml_credentials['version'].startswith('3.5')): raise TSNotSupported() if prediction_type == PredictionType.TIMESERIES_ANOMALY_PREDICTION and self._workspace.wml_client.ICP and \ self._workspace.wml_client.wml_credentials['version'].startswith(('2.5', '3.0', '3.5', '4.0', '4.5', '4.6')): raise TSADNotSupported() if prediction_type in (PredictionType.FORECASTING, 'timeseries'): if not numerical_imputation_strategy and type(numerical_imputation_strategy) is not list: numerical_imputation_strategy = ImputationStrategy.BEST_OF_DEFAULT_IMPUTERS elif not numerical_imputation_strategy and type(numerical_imputation_strategy) is list: numerical_imputation_strategy = ImputationStrategy.NO_IMPUTATION if prediction_column is not None or prediction_columns is None: raise ForecastPredictionColumnsMissing() elif prediction_type == PredictionType.TIMESERIES_ANOMALY_PREDICTION: if feature_columns is None or prediction_column is not None or prediction_columns is not None: raise TimeseriesAnomalyPredictionFeatureColumnsMissing() if scoring is not None and scoring not in ( Metrics.F1_SCORE, Metrics.ROC_AUC_SCORE, Metrics.AVERAGE_PRECISION_SCORE, Metrics.PRECISION_SCORE, Metrics.RECALL_SCORE): raise TimeseriesAnomalyPredictionUnsupportedMetric(scoring) else: if prediction_column is None or prediction_columns is not None: raise NonForecastPredictionColumnMissing(prediction_type) if test_size: print('Note: Using `test_size` is deprecated. Use `holdout_size` instead.') if not holdout_size: holdout_size = test_size test_size = None if daub_include_only_estimators: print('Note: Using `daub_include_only_estimators` is deprecated. Use `include_only_estimators` instead.') if not include_only_estimators: include_only_estimators = daub_include_only_estimators daub_include_only_estimators = None if train_sample_rows_test_size and self._workspace.wml_client.CPD_version >= 4.6: print('Note: Using `train_sample_rows_test_size` is deprecated.' 'Use either `sample_rows_limit` or `sample_percentage_limit` instead.') if not sample_rows_limit and not sample_percentage_limit: if type(train_sample_rows_test_size) is float and train_sample_rows_test_size <= 1: print('Value of `train_sample_rows_test_size` parameter' 'will be passed as `sample_percentage_limit`.') sample_percentage_limit = train_sample_rows_test_size elif int(train_sample_rows_test_size) == train_sample_rows_test_size and train_sample_rows_test_size > 1: print('Value of `train_sample_rows_test_size` parameter' 'will be passed as `sample_rows_limit`.') sample_rows_limit = int(train_sample_rows_test_size) train_sample_rows_test_size = None elif sample_rows_limit or sample_percentage_limit: print('Parameter `train_sample_rows_test_size` will be ignored.') train_sample_rows_test_size = None def translate_str_imputation_param(x): if type(x) is list and prediction_type != PredictionType.FORECASTING: raise ImputationListNotSupported() if type(x) == str or (type(x) == list and type(x[0]) == str): return translate_imputation_string_strategy_to_enum(x, prediction_type) else: return x def translate_str_include_only_estimators_param(x): return [translate_estimator_string_to_enum(estimator) for estimator in x] def translate_str_include_batched_ensemble_estimators_param(x): return [translate_batched_estimator_string_to_enum(estimator) for estimator in x] def translate_str_pipeline_types_param(x): if prediction_type == PredictionType.TIMESERIES_ANOMALY_PREDICTION: return [TimeseriesAnomalyPredictionPipelineTypes(pipeline_type) for pipeline_type in x] else: return [ForecastingPipelineTypes(pipeline_type) for pipeline_type in x] categorical_imputation_strategy = translate_str_imputation_param(categorical_imputation_strategy) numerical_imputation_strategy = translate_str_imputation_param(numerical_imputation_strategy) include_only_estimators = translate_str_include_only_estimators_param(include_only_estimators) if include_only_estimators else None include_batched_ensemble_estimators = translate_str_include_batched_ensemble_estimators_param(include_batched_ensemble_estimators) if include_batched_ensemble_estimators else None pipeline_types = translate_str_pipeline_types_param(pipeline_types) if pipeline_types != None else None if include_batched_ensemble_estimators: for batched_estimator in include_batched_ensemble_estimators: basic_estimator_str = batched_estimator.value.split("(")[1][:-1] basic_estimator = translate_estimator_string_to_enum(basic_estimator_str) if include_only_estimators is not None and basic_estimator not in include_only_estimators: raise MissingEstimatorForExistingBatchedEstimator(batched_estimator, basic_estimator) validate_optimizer_enum_values( prediction_type=prediction_type, daub_include_only_estimators=daub_include_only_estimators, include_only_estimators=include_only_estimators, include_batched_ensemble_estimators=include_batched_ensemble_estimators, cognito_transform_names=cognito_transform_names, imputation_strategies=[x for y in list(filter(None, [categorical_imputation_strategy, numerical_imputation_strategy])) for x in (y if type(y) is list else [y])], scoring=scoring, t_shirt_size=kwargs.get("t_shirt_size", TShirtSize.M), is_cpd=self._workspace.wml_client.ICP ) if daub_give_priority_to_runtime is not None: if daub_give_priority_to_runtime < 0.0 or daub_give_priority_to_runtime > 5.0: raise ParamOutOfRange('daub_give_priority_to_runtime', daub_give_priority_to_runtime, 0.0, 5.0) if data_join_graph: data_join_graph.problem_type = prediction_type data_join_graph.target_column = prediction_column if (prediction_type == PredictionType.BINARY and scoring in vars(PositiveLabelClass).values() and positive_label is None): raise MissingPositiveLabel(scoring, reason=f"\"{scoring}\" needs a \"positive_label\" " f"parameter to be defined when used with binary classification.") if self._workspace is None and kwargs.get('t_shirt_size'): raise LocalInstanceButRemoteParameter( "t_shirt_size", reason="During LocalOptimizer initialization, \"t_shirt_size\" parameter was provided. " "\"t_shirt_size\" parameter is only applicable to the RemoteOptimizer instance." ) elif self._workspace is None: if prediction_type == PredictionType.FORECASTING: raise ForecastingCannotBeRunAsLocalScenario() if prediction_type == PredictionType.TIMESERIES_ANOMALY_PREDICTION: raise TimeseriesAnomalyPredictionCannotBeRunAsLocalScenario() reduced_kwargs = copy.copy(kwargs) for n in ['_force_local_scenario']: if n in reduced_kwargs: del reduced_kwargs[n] validate_additional_params_for_optimizer(reduced_kwargs) return LocalAutoPipelines( name=name, prediction_type='classification' if prediction_type in ['binary', 'multiclass'] else prediction_type, prediction_column=prediction_column, scoring=scoring, desc=desc, holdout_size=holdout_size, max_num_daub_ensembles=max_number_of_estimators, train_sample_rows_test_size=train_sample_rows_test_size, include_only_estimators=include_only_estimators, include_batched_ensemble_estimators=include_batched_ensemble_estimators, cognito_transform_names=cognito_transform_names, positive_label=positive_label, _force_local_scenario=kwargs.get('_force_local_scenario', False), **reduced_kwargs ) else: reduced_kwargs = copy.copy(kwargs) if max_number_of_estimators is None: max_number_of_estimators = reduced_kwargs.get('max_num_daub_ensembles', None) for n in ['t_shirt_size', 'notebooks', 'autoai_pod_version', 'obm_pod_version', 'max_num_daub_ensembles']: if n in reduced_kwargs: del reduced_kwargs[n] validate_additional_params_for_optimizer(reduced_kwargs) engine = WMLEngine(self._workspace) if self._20_class_limit_removal_test: engine._20_class_limit_removal_test = True optimizer = RemoteAutoPipelines( name=name, prediction_type=prediction_type, prediction_column=prediction_column, prediction_columns=prediction_columns, timestamp_column_name=timestamp_column_name, scoring=scoring, desc=desc, holdout_size=holdout_size, max_num_daub_ensembles=max_number_of_estimators, t_shirt_size=self._workspace.restrict_pod_size(t_shirt_size=kwargs.get( 't_shirt_size', TShirtSize.M if self._workspace.wml_client.ICP else TShirtSize.L) ), train_sample_rows_test_size=train_sample_rows_test_size, include_only_estimators=include_only_estimators, include_batched_ensemble_estimators=include_batched_ensemble_estimators, backtest_num=backtest_num, lookback_window=lookback_window, forecast_window=forecast_window, backtest_gap_length=backtest_gap_length, cognito_transform_names=cognito_transform_names, data_join_graph=data_join_graph, drop_duplicates=drop_duplicates, outliers_columns=outliers_columns, text_processing=text_processing, word2vec_feature_number=word2vec_feature_number, csv_separator=csv_separator, excel_sheet=excel_sheet, encoding=encoding, positive_label=positive_label, data_join_only=data_join_only, engine=engine, daub_give_priority_to_runtime=daub_give_priority_to_runtime, notebooks=kwargs.get('notebooks', True), autoai_pod_version=kwargs.get('autoai_pod_version', None), obm_pod_version=kwargs.get('obm_pod_version', None), fairness_info=fairness_info, sampling_type=sampling_type, sample_size_limit=sample_size_limit, sample_rows_limit=sample_rows_limit, sample_percentage_limit=sample_percentage_limit, number_of_batch_rows=number_of_batch_rows, n_parallel_data_connections=n_parallel_data_connections, categorical_imputation_strategy=categorical_imputation_strategy, numerical_imputation_strategy=numerical_imputation_strategy, numerical_imputation_value=numerical_imputation_value, imputation_threshold=imputation_threshold, retrain_on_holdout=retrain_on_holdout, numerical_columns=numerical_columns, categorical_columns=categorical_columns, feature_columns=feature_columns, pipeline_types=pipeline_types, supporting_features_at_forecast=supporting_features_at_forecast, test_data_csv_separator=test_data_csv_separator, test_data_excel_sheet=test_data_excel_sheet, test_data_encoding=test_data_encoding, confidence_level=confidence_level, incremental_learning=incremental_learning, early_stop_enabled=early_stop_enabled, early_stop_window_size=early_stop_window_size, time_ordered_data=time_ordered_data, feature_selector_mode=feature_selector_mode, **reduced_kwargs ) optimizer._workspace = self._workspace return optimizer
def _init_estimator_enums(self): if self._workspace and self._workspace.wml_client.ICP: self.ClassificationAlgorithms = ClassificationAlgorithmsCP4D self.RegressionAlgorithms = RegressionAlgorithmsCP4D self.ForecastingAlgorithms = ForecastingAlgorithmsCP4D else: self.ClassificationAlgorithms = ClassificationAlgorithms self.RegressionAlgorithms = RegressionAlgorithms self.ForecastingAlgorithms = ForecastingAlgorithms def _block_autoai_on_git_based_project(self): """Raises ForbiddenActionForGitBasedProject error for AutoAI experiments on git based project. It can be disabled by setting environment variable ENABLE_AUTOAI to 'true' """ from os import environ if self._workspace: if getattr(self._workspace.wml_client, 'project_type', None) == 'local_git_storage' \ and environ.get('ENABLE_AUTOAI', 'false').lower() == 'false': raise ForbiddenActionForGitBasedProject(reason="Creating AutoAI experiment is not supported for git based project.")