abacusai.api_class
Submodules
Package Contents
Classes
Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
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An abstract class for the sampling config of a feature group |
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The number of distinct values of the key columns to include in the sample, or number of rows if key columns not specified. |
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The fraction of distinct values of the feature group to include in the sample. |
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Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
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Training config for the FORECASTING problem type |
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Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
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Helper class that provides a standard way to create an ABC using |
- class abacusai.api_class.ApiClass
Bases:
abc.ABC
Helper class that provides a standard way to create an ABC using inheritance.
- __post_init__()
- to_dict()
Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.
- class abacusai.api_class.ParsingConfig
Bases:
abacusai.api_class.abstract.ApiClass
Helper class that provides a standard way to create an ABC using inheritance.
- class abacusai.api_class.ApiClass
Bases:
abc.ABC
Helper class that provides a standard way to create an ABC using inheritance.
- __post_init__()
- to_dict()
Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.
- class abacusai.api_class._ApiClassFactory
Bases:
abc.ABC
Helper class that provides a standard way to create an ABC using inheritance.
- config_abstract_class
- config_class_key
- config_class_map
- class abacusai.api_class.SamplingConfig
Bases:
abacusai.api_class.abstract.ApiClass
An abstract class for the sampling config of a feature group
- __post_init__()
- class abacusai.api_class.NSamplingConfig
Bases:
SamplingConfig
The number of distinct values of the key columns to include in the sample, or number of rows if key columns not specified.
- Parameters:
sampling_method (SamplingMethodType) – N_SAMPLING
sample_count (int) – The number of rows to include in the sample
key_columns (list[str]) – The feature(s) to use as the key(s) when sampling
- sampling_method: abacusai.api_class.enums.SamplingMethodType
- class abacusai.api_class.PercentSamplingConfig
Bases:
SamplingConfig
The fraction of distinct values of the feature group to include in the sample.
- Parameters:
sampling_method (SamplingMethodType) – PERCENT_SAMPLING
sample_percent (float) – The percentage of the rows to sample
key_columns (list[str]) – The feature(s) to use as the key(s) when sampling
- sampling_method: abacusai.api_class.enums.SamplingMethodType
- class abacusai.api_class._SamplingConfigFactory
Bases:
abacusai.api_class.abstract._ApiClassFactory
Helper class that provides a standard way to create an ABC using inheritance.
- config_class_key = 'sampling_method'
- config_class_map
- class abacusai.api_class.ApiClass
Bases:
abc.ABC
Helper class that provides a standard way to create an ABC using inheritance.
- __post_init__()
- to_dict()
Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.
- class abacusai.api_class._ApiClassFactory
Bases:
abc.ABC
Helper class that provides a standard way to create an ABC using inheritance.
- config_abstract_class
- config_class_key
- config_class_map
- class abacusai.api_class.TrainingConfig
Bases:
abacusai.api_class.abstract.ApiClass
Helper class that provides a standard way to create an ABC using inheritance.
- problem_type: abacusai.api_class.enums.ProblemType
- class abacusai.api_class.ForecastingTrainingConfig
Bases:
TrainingConfig
Training config for the FORECASTING problem type :param problem_type: FORECASTING :type problem_type: ProblemType :param prediction_length: How many timesteps in the future to predict. :type prediction_length: int :param objective: Ranking scheme used to select final best model. :type objective: ForecastingObjective :param sort_objective: Ranking scheme used to sort models on the metrics page. :type sort_objective: ForecastingObjective :param forecast_frequency: Forecast frequency. :type forecast_frequency: ForecastingFrequency :param probability_quantiles: Prediction quantiles. :type probability_quantiles: list[float] :param no_validation_set: Do not generate validation set, test set will be used instead. :type no_validation_set: bool :param force_prediction_length: Force length of test window to be the same as prediction length. :type force_prediction_length: int :param filter_items: Filter items with small history and volume. :type filter_items: bool :param enable_cold_start: Enable cold start forecasting by training/predicting for zero history items. :type enable_cold_start: bool :param enable_multiple_backtests: Whether to enable multiple backtesting or not. :type enable_multiple_backtests: bool :param total_backtesting_windows: Total backtesting windows to use for the training. :type total_backtesting_windows: int :param backtest_window_step_size: Use this step size to shift backtesting windows for model training. :type backtest_window_step_size: int :param full_data_retraining: Train models separately with all the data. :type full_data_retraining: bool :param type_of_split: Type of data splitting into train/test. :type type_of_split: ForecastingDataSplitType :param test_by_item: Partition train/test data by item rather than time if true. :type test_by_item: bool :param test_start: Limit training data to dates before the given test start. :type test_start: datetime :param test_split: Percent of dataset to use for test data. We support using a range between 5% to 20% of your dataset to use as test data. :type test_split: int :param loss_function: Loss function for training neural network. :type loss_function: ForecastingLossFunction :param underprediction_weight: Weight for underpredictions :type underprediction_weight: float :param disable_networks_without_analytic_quantiles: Disable neural networks, which quantile functions do not have analytic expressions (e.g, mixture models) :type disable_networks_without_analytic_quantiles: bool :param initial_learning_rate: Initial learning rate. :type initial_learning_rate: float :param l2_regularization_factor: L2 regularization factor. :type l2_regularization_factor: float :param dropout_rate: Dropout percentage rate. :type dropout_rate: int :param recurrent_layers: Number of recurrent layers to stack in network. :type recurrent_layers: int :param recurrent_units: Number of units in each recurrent layer. :type recurrent_units: int :param convolutional_layers: Number of convolutional layers to stack on top of recurrent layers in network. :type convolutional_layers: int :param convolution_filters: Number of filters in each convolution. :type convolution_filters: int :param local_scaling_mode: Options to make NN inputs stationary in high dynamic range datasets. :type local_scaling_mode: ForecastingLocalScaling :param zero_predictor: Include subnetwork to classify points where target equals zero. :type zero_predictor: bool :param skip_missing: Make the RNN ignore missing entries rather instead of processing them. :type skip_missing: bool :param batch_size: Batch size. :type batch_size: ForecastingBatchSize :param batch_renormalization: Enable batch renormalization between layers. :type batch_renormalization: bool :param history_length: While training, how much history to consider. :type history_length: int :param prediction_step_size: Number of future periods to include in objective for each training sample. :type prediction_step_size: int :param training_point_overlap: Amount of overlap to allow between training samples. :type training_point_overlap: float :param max_scale_context: Maximum context to use for local scaling. :type max_scale_context: int :param quantiles_extension_method: Quantile extension method :type quantiles_extension_method: ForecastingQuanitlesExtensionMethod :param number_of_samples: Number of samples for ancestral simulation :type number_of_samples: int :param symmetrize_quantiles: Force symmetric quantiles (like in Gaussian distribution) :type symmetrize_quantiles: bool :param use_log_transforms: Apply logarithmic transformations to input data. :type use_log_transforms: bool :param smooth_history: Smooth (low pass filter) the timeseries. :type smooth_history: float :param prediction_offset: Offset for prediction. :type prediction_offset: int :param skip_local_scale_target: Skip using per training/prediction window target scaling. :type skip_local_scale_target: bool :param timeseries_weight_column: If set, we use the values in this column from timeseries data to assign time dependent item weights during training and evaluation. :type timeseries_weight_column: str :param item_attributes_weight_column: If set, we use the values in this column from item attributes data to assign weights to items during training and evaluation. :type item_attributes_weight_column: str :param use_timeseries_weights_in_objective: If True, we include weights from column set as “TIMESERIES WEIGHT COLUMN” in objective functions. :type use_timeseries_weights_in_objective: bool :param use_item_weights_in_objective: If True, we include weights from column set as “ITEM ATTRIBUTES WEIGHT COLUMN” in objective functions. :type use_item_weights_in_objective: bool :param skip_timeseries_weight_scaling: If True, we will avoid normalizing the weights. :type skip_timeseries_weight_scaling: bool :param timeseries_loss_weight_column: Use value in this column to weight the loss while training. :type timeseries_loss_weight_column: str :param use_item_id: Include a feature to indicate the item being forecast. :type use_item_id: bool :param use_all_item_totals: Include as input total target across items. :type use_all_item_totals: bool :param handle_zeros_as_missing: If True, handle zero values in demand as missing data. :type handle_zeros_as_missing: bool :param datetime_holiday_calendars: Holiday calendars to augment training with. :type datetime_holiday_calendars: list[HolidayCalendars] :param fill_missing_values: Strategy for filling in missing values. :type fill_missing_values: list[dict] :param enable_clustering: Enable clustering in forecasting. :type enable_clustering: bool :param data_split_feature_group_table_name: Specify the table name of the feature group to export training data with the fold column. :type data_split_feature_group_table_name: str :param custom_loss_functions: Registered custom losses available for selection. :type custom_loss_functions: list[str] :param custom_metrics: Registered custom metrics available for selection. :type custom_metrics: list[str]
- problem_type: abacusai.api_class.enums.ProblemType
- sort_objective: abacusai.api_class.enums.ForecastingObjective
- forecast_frequency: abacusai.api_class.enums.ForecastingFrequency
- type_of_split: abacusai.api_class.enums.ForecastingDataSplitType
- test_start: datetime.datetime
- loss_function: abacusai.api_class.enums.ForecastingLossFunction
- local_scaling_mode: abacusai.api_class.enums.ForecastingLocalScaling
- batch_size: abacusai.api_class.enums.BatchSize
- quantiles_extension_method: abacusai.api_class.enums.ForecastingQuanitlesExtensionMethod
- datetime_holiday_calendars: List[abacusai.api_class.enums.HolidayCalendars]
- class abacusai.api_class._TrainingConfigFactory
Bases:
abacusai.api_class.abstract._ApiClassFactory
Helper class that provides a standard way to create an ABC using inheritance.
- config_abstract_class
- config_class_key = 'problem_type'
- config_class_map
- class abacusai.api_class.ApiClass
Bases:
abc.ABC
Helper class that provides a standard way to create an ABC using inheritance.
- __post_init__()
- to_dict()
Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.
- class abacusai.api_class._ApiClassFactory
Bases:
abc.ABC
Helper class that provides a standard way to create an ABC using inheritance.
- config_abstract_class
- config_class_key
- config_class_map
- class abacusai.api_class.FeatureGroupExportConfig
Bases:
abacusai.api_class.abstract.ApiClass
Helper class that provides a standard way to create an ABC using inheritance.
- connector_type: abacusai.api_class.enums.ConnectorType
- class abacusai.api_class.FileConnectorExportConfig
Bases:
FeatureGroupExportConfig
Helper class that provides a standard way to create an ABC using inheritance.
- connector_type: abacusai.api_class.enums.ConnectorType
- to_dict()
Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.
- class abacusai.api_class.DatabaseConnectorExportConfig
Bases:
FeatureGroupExportConfig
Helper class that provides a standard way to create an ABC using inheritance.
- connector_type: abacusai.api_class.enums.ConnectorType
- to_dict()
Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.
- class abacusai.api_class._FeatureGroupExportConfigFactory
Bases:
abacusai.api_class.abstract._ApiClassFactory
Helper class that provides a standard way to create an ABC using inheritance.
- config_abstract_class
- config_class_key = 'connectorType'
- config_class_map