abacusai.api_class.model

Module Contents

Classes

TrainingConfig

Helper class that provides a standard way to create an ABC using

PersonalizationTrainingConfig

Training config for the PERSONALIZATION problem type

ForecastingTrainingConfig

Training config for the FORECASTING problem type

NamedEntityExtractionTrainingConfig

Training config for the NAMED_ENTITY_EXTRACTION problem type

NaturalLanguageSearchTrainingConfig

Training config for the NATURAL_LANGUAGE_SEARCH problem type

SentenceBoundaryDetectionTrainingConfig

Training config for the SENTENCE_BOUNDARY_DETECTION problem type

SentimentDetectionTrainingConfig

Training config for the SENTIMENT_DETECTION problem type

DocumentClassificationTrainingConfig

Training config for the DOCUMENT_CLASSIFICATION problem type

DocumentSummarizationTrainingConfig

Training config for the DOCUMENT_SUMMARIZATION problem type

DocumentVisualizationTrainingConfig

Training config for the DOCUMENT_VISUALIZATION problem type

ClusteringTrainingConfig

Training config for the CLUSTERING problem type

ClusteringTimeseriesTrainingConfig

Training config for the CLUSTERING_TIMESERIES problem type

CumulativeForecastingTrainingConfig

Training config for the CUMULATIVE_FORECASTING problem type

AnomalyDetectionTrainingConfig

Training config for the ANOMALY_DETECTION problem type

ThemeAnalysisTrainingConfig

Training config for the THEME ANALYSIS problem type

AIAgentTrainingConfig

Training config for the AI_AGENT problem type

CustomTrainedModelTrainingConfig

Training config for the CUSTOM_TRAINED_MODEL problem type

_TrainingConfigFactory

Helper class that provides a standard way to create an ABC using

class abacusai.api_class.model.TrainingConfig

Bases: abacusai.api_class.abstract.ApiClass

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
kwargs: dict
problem_type: abacusai.api_class.enums.ProblemType
class abacusai.api_class.model.PersonalizationTrainingConfig

Bases: TrainingConfig

Training config for the PERSONALIZATION problem type :param problem_type: PERSONALIZATION :type problem_type: ProblemType :param objective: Ranking scheme used to select final best model. :type objective: PersonalizationObjective :param sort_objective: Ranking scheme used to sort models on the metrics page. :type sort_objective: PersonalizationObjective :param training_mode: whether to train in production or experimental mode. :type training_mode: PersonalizationTrainingMode :param target_action_types: List of action types to use as targets for training. :type target_action_types: List[str] :param target_action_weights: Dictionary of action types to weights for training. :type target_action_weights: Dict[str, float] :param session_event_types: List of event types to treat as occurrences of sessions. :type session_event_types: List[str] :param test_split: Percent of dataset to use for test data. We support using a range between 6% to 20% of your dataset to use as test data. :type test_split: int :param recent_days_for_training: Limit training data to a certain latest number of days. :type recent_days_for_training: int :param training_start_date: Only consider training interaction data after this date. Specified in the timezone of the dataset. :type training_start_date: datetime :param test_on_user_split: Use user splits instead of using time splits, when validating and testing the model. :type test_on_user_split: bool :param test_split_on_last_k_items: Use last k items instead of global timestamp splits, when validating and testing the model. :type test_split_on_last_k_items: bool :param test_last_items_length: Number of items to leave out for each user when using leave k out folds. :type test_last_items_length: int :param test_window_length_hours: Duration (in hours) of most recent time window to use when validating and testing the model. :type test_window_length_hours: int :param explicit_time_split: Sets an explicit time-based test boundary. :type explicit_time_split: bool :param test_row_indicator: Column indicating which rows to use for training (TRAIN), validation (VAL) and testing (TEST). :type test_row_indicator: str :param full_data_retraining: Train models separately with all the data. :type full_data_retraining: bool :param sequential_training: Train a mode sequentially through time. :type sequential_training: 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 dropout_rate: Dropout rate for neural network. :type dropout_rate: int :param batch_size: Batch size for neural network. :type batch_size: BatchSize :param disable_transformer: Disable training the transformer algorithm. :type disable_transformer: bool :param disable_gpu: Disable training on GPU. :type disable_gpu: boo :param filter_history: Do not recommend items the user has already interacted with. :type filter_history: bool :param explore_lookback_hours: Number of hours since creation time that an item is eligible for explore fraction. :type explore_lookback_hours: int :param max_history_length: Maximum length of user-item history to include user in training examples. :type max_history_length: int :param compute_rerank_metrics: Compute metrics based on rerank results. :type compute_rerank_metrics: bool :param item_id_dropout: Fraction of item_id values to randomly dropout during training. :type item_id_dropout: float :param add_time_features: Include interaction time as a feature. :type add_time_features: bool :param disable_timestamp_scalar_features: Exclude timestamp scalar features. :type disable_timestamp_scalar_features: bool :param compute_session_metrics: Evaluate models based on how well they are able to predict the next session of interactions. :type compute_session_metrics: bool :param max_user_history_len_percentile: Filter out users with history length above this percentile. :type max_user_history_len_percentile: int :param downsample_item_popularity_percentile: Downsample items more popular than this percentile. :type downsample_item_popularity_percentile: float

problem_type: abacusai.api_class.enums.ProblemType
objective: abacusai.api_class.enums.PersonalizationObjective
sort_objective: abacusai.api_class.enums.PersonalizationObjective
training_mode: abacusai.api_class.enums.PersonalizationTrainingMode
target_action_types: List[str]
target_action_weights: Dict[str, float]
session_event_types: List[str]
test_split: int
recent_days_for_training: int
training_start_date: datetime.datetime
test_on_user_split: bool
test_split_on_last_k_items: bool
test_last_items_length: int
test_window_length_hours: int
explicit_time_split: bool
test_row_indicator: str
full_data_retraining: bool
sequential_training: bool
data_split_feature_group_table_name: str
dropout_rate: int
batch_size: abacusai.api_class.enums.BatchSize
disable_transformer: bool
disable_gpu: bool
filter_history: bool
explore_lookback_hours: int
max_history_length: int
compute_rerank_metrics: bool
item_id_dropout: float
add_time_features: bool
disable_timestamp_scalar_features: bool
compute_session_metrics: bool
max_user_history_len_percentile: int
downsample_item_popularity_percentile: float
class abacusai.api_class.model.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 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_feature_selection: Enable feature selection. :type enable_feature_selection: 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 num_backtesting_windows: Total backtesting windows to use for the training. :type num_backtesting_windows: int :param backtesting_window_step_size: Use this step size to shift backtesting windows for model training. :type backtesting_window_step_size: int :param full_data_retraining: Train models separately with all the data. :type full_data_retraining: bool :param additional_forecast_keys: List[str]: List of categoricals in timeseries that can act as multi-identifier. :param experimentation_mode: Selecting Thorough Experimentation will take longer to train. :type experimentation_mode: ExperimentationMode :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 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_values: If True, handle zero values in demand as missing data. :type handle_zeros_as_missing_values: 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
prediction_length: int
objective: abacusai.api_class.enums.ForecastingObjective
sort_objective: abacusai.api_class.enums.ForecastingObjective
forecast_frequency: abacusai.api_class.enums.ForecastingFrequency
probability_quantiles: List[float]
force_prediction_length: bool
filter_items: bool
enable_feature_selection: bool
enable_cold_start: bool
enable_multiple_backtests: bool
num_backtesting_windows: int
backtesting_window_step_size: int
full_data_retraining: bool
additional_forecast_keys: List[str]
experimentation_mode: abacusai.api_class.enums.ExperimentationMode
type_of_split: abacusai.api_class.enums.ForecastingDataSplitType
test_by_item: bool
test_start: datetime.datetime
test_split: int
loss_function: abacusai.api_class.enums.ForecastingLossFunction
underprediction_weight: float
disable_networks_without_analytic_quantiles: bool
initial_learning_rate: float
l2_regularization_factor: float
dropout_rate: int
recurrent_layers: int
recurrent_units: int
convolutional_layers: int
convolution_filters: int
local_scaling_mode: abacusai.api_class.enums.ForecastingLocalScaling
zero_predictor: bool
skip_missing: bool
batch_size: abacusai.api_class.enums.BatchSize
batch_renormalization: bool
history_length: int
prediction_step_size: int
training_point_overlap: float
max_scale_context: int
quantiles_extension_method: abacusai.api_class.enums.ForecastingQuanitlesExtensionMethod
number_of_samples: int
symmetrize_quantiles: bool
use_log_transforms: bool
smooth_history: float
skip_local_scale_target: bool
timeseries_weight_column: str
item_attributes_weight_column: str
use_timeseries_weights_in_objective: bool
use_item_weights_in_objective: bool
skip_timeseries_weight_scaling: bool
timeseries_loss_weight_column: str
use_item_id: bool
use_all_item_totals: bool
handle_zeros_as_missing_values: bool
datetime_holiday_calendars: List[abacusai.api_class.enums.HolidayCalendars]
fill_missing_values: List[dict]
enable_clustering: bool
data_split_feature_group_table_name: str
custom_loss_functions: List[str]
custom_metrics: List[str]
class abacusai.api_class.model.NamedEntityExtractionTrainingConfig

Bases: TrainingConfig

Training config for the NAMED_ENTITY_EXTRACTION problem type :param problem_type: NAMED_ENTITY_EXTRACTION :type problem_type: ProblemType :param objective: Ranking scheme used to select final best model. :type objective: NERObjective :param sort_objective: Ranking scheme used to sort models on the metrics page. :type sort_objective: NERObjective :param ner_model_type: Type of NER model to use. :type ner_model_type: NERModelType :param test_split: Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset. :type test_split: int :param test_row_indicator: Column indicating which rows to use for training (TRAIN) and testing (TEST). :type test_row_indicator: str :param dropout_rate: Dropout rate for neural network. :type dropout_rate: float :param batch_size: Batch size for neural network. :type batch_size: BatchSize :param active_labels_column: Entities that have been marked in a particular text :type active_labels_column: str :param document_format: Format of the input documents. :type document_format: NLPDocumentFormat :param include_longformer: Whether to include the longformer model. :type include_longformer: bool

problem_type: abacusai.api_class.enums.ProblemType
objective: abacusai.api_class.enums.NERObjective
sort_objective: abacusai.api_class.enums.NERObjective
ner_model_type: abacusai.api_class.enums.NERModelType
test_split: int
test_row_indicator: str
dropout_rate: float
batch_size: abacusai.api_class.enums.BatchSize
active_labels_column: str
document_format: abacusai.api_class.enums.NLPDocumentFormat
include_longformer: bool
class abacusai.api_class.model.NaturalLanguageSearchTrainingConfig

Bases: TrainingConfig

Training config for the NATURAL_LANGUAGE_SEARCH problem type :param problem_type: NATURAL_LANGUAGE_SEARCH :type problem_type: ProblemType :param abacus_internal_model: Use a Abacus.AI LLM to answer questions about your data without using any external APIs :type abacus_internal_model: bool :param num_completion_tokens: Default for maximum number of tokens for chat answers. Reducing this will get faster responses which are more succinct :type num_completion_tokens: int :param larger_embeddings: Use a higher dimension embedding model. :type larger_embeddings: bool :param search_chunk_size: Chunk size for indexing the documents. :type search_chunk_size: int :param chunk_overlap_fraction: Overlap in chunks while indexing the documents. :type chunk_overlap_fraction: float :param test_split: Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset. :type test_split: int

problem_type: abacusai.api_class.enums.ProblemType
abacus_internal_model: bool
num_completion_tokens: int
larger_embeddings: bool
search_chunk_size: int
index_fraction: float
chunk_overlap_fraction: float
class abacusai.api_class.model.SentenceBoundaryDetectionTrainingConfig

Bases: TrainingConfig

Training config for the SENTENCE_BOUNDARY_DETECTION problem type :param problem_type: SENTENCE_BOUNDARY_DETECTION :type problem_type: ProblemType :param test_split: Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset. :type test_split: int :param dropout_rate: Dropout rate for neural network. :type dropout_rate: float :param batch_size: Batch size for neural network. :type batch_size: BatchSize

problem_type: abacusai.api_class.enums.ProblemType
test_split: int
dropout_rate: float
batch_size: abacusai.api_class.enums.BatchSize
class abacusai.api_class.model.SentimentDetectionTrainingConfig

Bases: TrainingConfig

Training config for the SENTIMENT_DETECTION problem type :param problem_type: SENTIMENT_DETECTION :type problem_type: ProblemType :param sentiment_type: Type of sentiment to detect. :type sentiment_type: SentimentType :param test_split: Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset. :type test_split: int :param dropout_rate: Dropout rate for neural network. :type dropout_rate: float :param batch_size: Batch size for neural network. :type batch_size: BatchSize :param compute_metrics: Whether to compute metrics. :type compute_metrics: bool

problem_type: abacusai.api_class.enums.ProblemType
sentiment_type: abacusai.api_class.enums.SentimentType
test_split: int
dropout_rate: float
batch_size: abacusai.api_class.enums.BatchSize
compute_metrics: bool
class abacusai.api_class.model.DocumentClassificationTrainingConfig

Bases: TrainingConfig

Training config for the DOCUMENT_CLASSIFICATION problem type :param problem_type: DOCUMENT_CLASSIFICATION :type problem_type: ProblemType :param zero_shot_hypotheses: Zero shot hypotheses. Example text: ‘This text is about pricing’. :type zero_shot_hypotheses: List[str] :param test_split: Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset. :type test_split: int :param dropout_rate: Dropout rate for neural network. :type dropout_rate: float :param batch_size: Batch size for neural network. :type batch_size: BatchSize

problem_type: abacusai.api_class.enums.ProblemType
zero_shot_hypotheses: List[str]
test_split: int
dropout_rate: float
batch_size: abacusai.api_class.enums.BatchSize
class abacusai.api_class.model.DocumentSummarizationTrainingConfig

Bases: TrainingConfig

Training config for the DOCUMENT_SUMMARIZATION problem type :param problem_type: DOCUMENT_SUMMARIZATION :type problem_type: ProblemType :param test_split: Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset. :type test_split: int :param dropout_rate: Dropout rate for neural network. :type dropout_rate: float :param batch_size: Batch size for neural network. :type batch_size: BatchSize

problem_type: abacusai.api_class.enums.ProblemType
test_split: int
dropout_rate: float
batch_size: abacusai.api_class.enums.BatchSize
class abacusai.api_class.model.DocumentVisualizationTrainingConfig

Bases: TrainingConfig

Training config for the DOCUMENT_VISUALIZATION problem type :param problem_type: DOCUMENT_VISUALIZATION :type problem_type: ProblemType :param test_split: Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset. :type test_split: int :param dropout_rate: Dropout rate for neural network. :type dropout_rate: float :param batch_size: Batch size for neural network. :type batch_size: BatchSize

problem_type: abacusai.api_class.enums.ProblemType
test_split: int
dropout_rate: float
batch_size: abacusai.api_class.enums.BatchSize
class abacusai.api_class.model.ClusteringTrainingConfig

Bases: TrainingConfig

Training config for the CLUSTERING problem type :param problem_type: CLUSTERING :type problem_type: ProblemType :param num_clusters_selection: Number of clusters. If None, will be selected automatically. :type num_clusters_selection: int

problem_type: abacusai.api_class.enums.ProblemType
num_clusters_selection: int
class abacusai.api_class.model.ClusteringTimeseriesTrainingConfig

Bases: TrainingConfig

Training config for the CLUSTERING_TIMESERIES problem type :param problem_type: CLUSTERING_TIMESERIES :type problem_type: ProblemType :param num_clusters_selection: Number of clusters. If None, will be selected automatically. :type num_clusters_selection: int :param imputation: Imputation method for missing values. :type imputation: ClusteringImputationMethod

problem_type: abacusai.api_class.enums.ProblemType
num_clusters_selection: int
imputation: abacusai.api_class.enums.ClusteringImputationMethod
class abacusai.api_class.model.CumulativeForecastingTrainingConfig

Bases: TrainingConfig

Training config for the CUMULATIVE_FORECASTING problem type :param problem_type: CUMULATIVE_FORECASTING :type problem_type: ProblemType :param test_split: Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset. :type test_split: int :param historical_frequency: Forecast frequency :type historical_frequency: str :param cumulative_prediction_lengths: List of Cumulative Prediction Frequencies. Each prediction length must be between 1 and 365. :type cumulative_prediction_lengths: List[int] :param skip_input_transform: Avoid doing numeric scaling transformations on the input. :type skip_input_transform: bool :param skip_target_transform: Avoid doing numeric scaling transformations on the target. :type skip_target_transform: bool :param predict_residuals: Predict residuals instead of totals at each prediction step. :type predict_residuals: bool

problem_type: abacusai.api_class.enums.ProblemType
test_split: int
historical_frequency: str
cumulative_prediction_lengths: List[int]
skip_input_transform: bool
skip_target_transform: bool
predict_residuals: bool
class abacusai.api_class.model.AnomalyDetectionTrainingConfig

Bases: TrainingConfig

Training config for the ANOMALY_DETECTION problem type :param problem_type: ANOMALY_DETECTION :type problem_type: ProblemType :param test_split: Percent of dataset to use for test data. We support using a range between 5 (i.e. 5%) to 20 (i.e. 20%) of your dataset to use as test data. :type test_split: int :param value_high: Detect unusually high values. :type value_high: bool :param mixture_of_gaussians: Detect unusual combinations of values using mixture of Gaussians. :type mixture_of_gaussians: bool :param variational_autoencoder: Use variational autoencoder for anomaly detection. :type variational_autoencoder: bool :param spike_up: Detect outliers with a high value. :type spike_up: bool :param spike_down: Detect outliers with a low value. :type spike_down: bool :param trend_change: Detect changes to the trend. :type trend_change: bool

problem_type: abacusai.api_class.enums.ProblemType
test_split: int
value_high: bool
mixture_of_gaussians: bool
variational_autoencoder: bool
spike_up: bool
spike_down: bool
trend_change: bool
class abacusai.api_class.model.ThemeAnalysisTrainingConfig

Bases: TrainingConfig

Training config for the THEME ANALYSIS problem type :param problem_type: THEME_ANALYSIS :type problem_type: ProblemType

problem_type: abacusai.api_class.enums.ProblemType
class abacusai.api_class.model.AIAgentTrainingConfig

Bases: TrainingConfig

Training config for the AI_AGENT problem type :param problem_type: AI_AGENT :type problem_type: ProblemType :param description: Description of the agent function. :type description: str

problem_type: abacusai.api_class.enums.ProblemType
description: str
class abacusai.api_class.model.CustomTrainedModelTrainingConfig

Bases: TrainingConfig

Training config for the CUSTOM_TRAINED_MODEL problem type :param problem_type: CUSTOM_TRAINED_MODEL :type problem_type: ProblemType :param max_catalog_size: Maximum expected catalog size. :type max_catalog_size: int :param max_dimension: Maximum expected dimension of the catalog. :type max_dimension: int :param index_output_path: Fully qualified cloud location (GCS, S3, etc) to export snapshots of the embedding to. :type index_output_path: str :param docker_image_uri: Docker image URI. :type docker_image_uri: str :param service_port: Service port. :type service_port: int

problem_type: abacusai.api_class.enums.ProblemType
max_catalog_size: int
max_dimension: int
index_output_path: str
docker_image_uri: str
service_port: int
class abacusai.api_class.model._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