Version 1.1.1

abacusai/__init__.py

@@ -4,4 +4,4 @@
 from .streaming_client import StreamingClient
 
 
-__version__ = "1.1.0"
+__version__ = "1.1.1"

abacusai/api_class/abstract.py

@@ -3,6 +3,7 @@
 import inspect
 import re
 from abc import ABC
+from copy import deepcopy
 from typing import Any
 
 from .enums import ApiEnum
@@ -137,7 +138,21 @@ def to_dict_helper(api_class_obj):
     def from_dict(cls, input_dict: dict):
         if input_dict:
             if builder := cls._get_builder():
-                return builder.from_dict(input_dict)
+                config_class_key = None
+                value = next((key for key, val in builder.config_class_map.items() if val.__name__ == cls.__name__), None)
+                input_dict_with_config_key = input_dict
+                if value is not None:
+                    input_dict_with_config_key = deepcopy(input_dict)
+                    if builder.config_abstract_class._upper_snake_case_keys:
+                        config_class_key = upper_snake_case(builder.config_class_key)
+                        if config_class_key not in input_dict_with_config_key:
+                            input_dict_with_config_key[config_class_key] = value
+                    else:
+                        config_class_key = builder.config_class_key
+                        if config_class_key not in input_dict_with_config_key and camel_case(config_class_key) not in input_dict_with_config_key:
+                            input_dict_with_config_key[config_class_key] = value
+
+                return builder.from_dict(input_dict_with_config_key)
             if not cls._upper_snake_case_keys:
                 input_dict = {snake_case(k): v for k, v in input_dict.items()}
             if not cls._support_kwargs:
@@ -156,24 +171,29 @@ class _ApiClassFactory(ABC):
     @classmethod
     def from_dict(cls, config: dict) -> ApiClass:
         support_kwargs = cls.config_abstract_class and cls.config_abstract_class._support_kwargs
-        not_upper_snake_case_keys = cls.config_abstract_class and not cls.config_abstract_class._upper_snake_case_keys
-        config_class_key = cls.config_class_key if (not_upper_snake_case_keys) else camel_case(cls.config_class_key)
-        if not_upper_snake_case_keys and config_class_key not in config and camel_case(config_class_key) in config:
+        is_upper_snake_case_keys = cls.config_abstract_class and cls.config_abstract_class._upper_snake_case_keys
+        config_class_key = upper_snake_case(cls.config_class_key) if is_upper_snake_case_keys else cls.config_class_key
+        # Logic here is that the we keep the config_class_key in snake_case if _upper_snake_case_keys is False else we convert it to upper_snake_case
+        # if _upper_snake_case_keys is False then we check in both casing: 1. snake_case and 2. camel_case
+        if not is_upper_snake_case_keys and config_class_key not in config and camel_case(config_class_key) in config:
             config_class_key = camel_case(config_class_key)
+
         if not support_kwargs and config_class_key not in (config or {}):
             raise KeyError(f'Could not find {config_class_key} in {config}')
         config_class_type = config.get(config_class_key, None)
+
         if isinstance(config_class_type, str):
             config_class_type = config_class_type.upper()
         config_class = cls.config_class_map.get(config_class_type)
+
         if support_kwargs:
             if config_class:
                 field_names = set((field.name) for field in dataclasses.fields(config_class))
                 trimmed_config = {}
                 kwargs = {}
                 for k, v in config.items():
                     if snake_case(k) in field_names:
-                        trimmed_config[k] = v
+                        trimmed_config[snake_case(k)] = v
                     else:
                         kwargs[k] = v
                 if len(kwargs):

abacusai/api_class/dataset_application_connector.py

@@ -142,6 +142,16 @@ def __post_init__(self):
         self.application_connector_type = enums.ApplicationConnectorType.ZENDESK
 
 
+@dataclasses.dataclass
+class AbacusUsageMetricsDatasetConfig(DatasetConfig):
+    """
+    Dataset config for Abacus Usage Metrics Application Connector
+    """
+
+    def __post_init__(self):
+        self.application_connector_type = enums.ApplicationConnectorType.ABACUSUSAGEMETRICS
+
+
 @dataclasses.dataclass
 class _DatasetConfigFactory(_ApiClassFactory):
     config_abstract_class = DatasetConfig
@@ -154,4 +164,5 @@ class _DatasetConfigFactory(_ApiClassFactory):
         enums.ApplicationConnectorType.ONEDRIVE: OneDriveDatasetConfig,
         enums.ApplicationConnectorType.SHAREPOINT: SharepointDatasetConfig,
         enums.ApplicationConnectorType.ZENDESK: ZendeskDatasetConfig,
+        enums.ApplicationConnectorType.ABACUSUSAGEMETRICS: AbacusUsageMetricsDatasetConfig,
     }

abacusai/api_class/enums.py

@@ -282,6 +282,17 @@ class ForecastingQuanitlesExtensionMethod(ApiEnum):
     ANCESTRAL_SIMULATION = 'simulation'
 
 
+# Timeseries Anomaly Detection
+class TimeseriesAnomalyDataSplitType(ApiEnum):
+    AUTO = 'Automatic Time Based'
+    TIMESTAMP = 'Fixed Timestamp Based'
+
+
+class TimeseriesAnomalyTypeOfAnomaly(ApiEnum):
+    HIGH_PEAK = 'high_peak'
+    LOW_PEAK = 'low_peak'
+
+
 # Named Entity Recognition
 class NERObjective(ApiEnum):
     LOG_LOSS = 'log_loss'
@@ -338,6 +349,7 @@ class ApplicationConnectorType(ApiEnum):
     SLACK = 'SLACK'
     SHAREPOINT = 'SHAREPOINT'
     TEAMS = 'TEAMS'
+    ABACUSUSAGEMETRICS = 'ABACUSUSAGEMETRICS'
 
 
 class PythonFunctionArgumentType(ApiEnum):

abacusai/api_class/model.py

@@ -620,14 +620,23 @@ class TimeseriesAnomalyTrainingConfig(TrainingConfig):
     Training config for the TS_ANOMALY problem type
 
     Args:
-    type_of_split: Type of data splitting into train/test.
+    type_of_split (TimeseriesAnomalyDataSplitType): Type of data splitting into train/test.
     test_start (str): Limit training data to dates before the given test start.
     test_split (int): 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.
-
+    fill_missing_values (List[dict]): strategies to fill missing values and missing timestamps
+    handle_zeros_as_missing_values (bool): If True, handle zero values in numeric columns as missing data
+    timeseries_frequency (str): set this to control frequency of filling missing values
+    min_samples_in_normal_region (int): Adjust this to fine-tune the number of anomalies to be identified.
+    anomaly_type (TimeseriesAnomalyTypeOfAnomaly): select what kind of peaks to detect as anomalies
     """
-    type_of_split: str = dataclasses.field(default=None)
+    type_of_split: enums.TimeseriesAnomalyDataSplitType = dataclasses.field(default=None)
     test_start: str = dataclasses.field(default=None)
     test_split: int = dataclasses.field(default=None)
+    fill_missing_values: List[dict] = dataclasses.field(default=None)
+    handle_zeros_as_missing_values: bool = dataclasses.field(default=None)
+    timeseries_frequency: str = dataclasses.field(default=None)
+    min_samples_in_normal_region: int = dataclasses.field(default=None)
+    anomaly_type: enums.TimeseriesAnomalyTypeOfAnomaly = dataclasses.field(default=None)
 
     def __post_init__(self):
         self.problem_type = enums.ProblemType.TS_ANOMALY

abacusai/api_class/refresh.py

@@ -32,7 +32,6 @@ def to_dict(self):
 
 @dataclasses.dataclass
 class DatabaseConnectorExportConfig(FeatureGroupExportConfig):
-    connector_type: enums.ConnectorType = dataclasses.field(default=enums.ConnectorType.DATABASE, repr=False)
     database_connector_id: str = dataclasses.field(default=None)
     mode: str = dataclasses.field(default=None)
     object_name: str = dataclasses.field(default=None)

abacusai/application_connector.py

@@ -43,7 +43,7 @@ def to_dict(self):
                 'name': self.name, 'created_at': self.created_at, 'status': self.status, 'auth': self.auth}
         return {key: value for key, value in resp.items() if value is not None}
 
-    def rename(self, name: str):
+    def rename(self, name: str = None):
         """
         Renames a Application Connector
 

abacusai/batch_prediction.py

@@ -24,7 +24,7 @@ class BatchPrediction(AbstractApiClass):
             fileOutputFormat (str): The format of the batch prediction output (CSV or JSON).
             connectorType (str): Null if writing to internal console, else FEATURE_GROUP | FILE_CONNECTOR | DATABASE_CONNECTOR.
             legacyInputLocation (str): The location of the input data.
-            featureGroupTableName (str): The table name of the Batch Prediction feature group.
+            featureGroupTableName (str): The table name of the Batch Prediction output feature group.
             summaryFeatureGroupTableName (str): The table name of the metrics summary feature group output by Batch Prediction.
             csvInputPrefix (str): A prefix to prepend to the input columns, only applies when output format is CSV.
             csvPredictionPrefix (str): A prefix to prepend to the prediction columns, only applies when output format is CSV.
@@ -183,7 +183,7 @@ def set_database_connector_output(self, database_connector_id: str = None, datab
         """
         return self.client.set_batch_prediction_database_connector_output(self.batch_prediction_id, database_connector_id, database_output_config)
 
-    def set_feature_group_output(self, table_name: str):
+    def set_feature_group_output(self, table_name: str = None):
         """
         Creates a feature group and sets it as the batch prediction output.
 
@@ -207,7 +207,7 @@ def set_output_to_console(self):
         """
         return self.client.set_batch_prediction_output_to_console(self.batch_prediction_id)
 
-    def set_feature_group(self, feature_group_type: str, feature_group_id: str = None):
+    def set_feature_group(self, feature_group_type: str = None, feature_group_id: str = None):
         """
         Sets the batch prediction input feature group.
 
@@ -220,7 +220,7 @@ def set_feature_group(self, feature_group_type: str, feature_group_id: str = Non
         """
         return self.client.set_batch_prediction_feature_group(self.batch_prediction_id, feature_group_type, feature_group_id)
 
-    def set_dataset_remap(self, dataset_id_remap: dict):
+    def set_dataset_remap(self, dataset_id_remap: dict = None):
         """
         For the purpose of this batch prediction, will swap out datasets in the training feature groups
 

abacusai/chat_session.py

@@ -66,7 +66,7 @@ def get(self):
         """
         return self.client.get_chat_session(self.chat_session_id)
 
-    def delete_chat_message(self, message_index: int):
+    def delete_chat_message(self, message_index: int = None):
         """
         Deletes a message in a chat session and its associated response.
 
@@ -84,7 +84,7 @@ def export(self):
         """
         return self.client.export_chat_session(self.chat_session_id)
 
-    def rename(self, name: str):
+    def rename(self, name: str = None):
         """
         Renames a chat session with Data Science Co-pilot.