1 files changed, 348 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/signals.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/signals.py
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+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/signals.py
@@ -0,0 +1,348 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=unused-argument
+
+from marshmallow import fields, post_load, pre_dump, ValidationError
+
+from azure.ai.ml._schema.job.input_output_entry import DataInputSchema, MLTableInputSchema
+from azure.ai.ml.constants._common import AzureMLResourceType
+from azure.ai.ml.constants._monitoring import (
+    MonitorSignalType,
+    ALL_FEATURES,
+    MonitorModelType,
+    MonitorDatasetContext,
+    FADColumnNames,
+)
+from azure.ai.ml._schema.core.schema import PatchedSchemaMeta
+from azure.ai.ml._schema.core.fields import ArmVersionedStr, NestedField, UnionField, StringTransformedEnum
+from azure.ai.ml._schema.monitoring.thresholds import (
+    DataDriftMetricThresholdSchema,
+    DataQualityMetricThresholdSchema,
+    PredictionDriftMetricThresholdSchema,
+    FeatureAttributionDriftMetricThresholdSchema,
+    ModelPerformanceMetricThresholdSchema,
+    CustomMonitoringMetricThresholdSchema,
+    GenerationSafetyQualityMetricThresholdSchema,
+    GenerationTokenStatisticsMonitorMetricThresholdSchema,
+)
+
+
+class DataSegmentSchema(metaclass=PatchedSchemaMeta):
+    feature_name = fields.Str()
+    feature_values = fields.List(fields.Str)
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import DataSegment
+
+        return DataSegment(**data)
+
+
+class MonitorFeatureFilterSchema(metaclass=PatchedSchemaMeta):
+    top_n_feature_importance = fields.Int()
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import MonitorFeatureFilter
+
+        if not isinstance(data, MonitorFeatureFilter):
+            raise ValidationError("Cannot dump non-MonitorFeatureFilter object into MonitorFeatureFilter")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import MonitorFeatureFilter
+
+        return MonitorFeatureFilter(**data)
+
+
+class BaselineDataRangeSchema(metaclass=PatchedSchemaMeta):
+    window_start = fields.Str()
+    window_end = fields.Str()
+    lookback_window_size = fields.Str()
+    lookback_window_offset = fields.Str()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import BaselineDataRange
+
+        return BaselineDataRange(**data)
+
+
+class ProductionDataSchema(metaclass=PatchedSchemaMeta):
+    input_data = UnionField(union_fields=[NestedField(DataInputSchema), NestedField(MLTableInputSchema)])
+    data_context = StringTransformedEnum(allowed_values=[o.value for o in MonitorDatasetContext])
+    pre_processing_component = fields.Str()
+    data_window = NestedField(BaselineDataRangeSchema)
+    data_column_names = fields.Dict(keys=fields.Str(), values=fields.Str())
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import ProductionData
+
+        return ProductionData(**data)
+
+
+class ReferenceDataSchema(metaclass=PatchedSchemaMeta):
+    input_data = UnionField(union_fields=[NestedField(DataInputSchema), NestedField(MLTableInputSchema)])
+    data_context = StringTransformedEnum(allowed_values=[o.value for o in MonitorDatasetContext])
+    pre_processing_component = fields.Str()
+    target_column_name = fields.Str()
+    data_window = NestedField(BaselineDataRangeSchema)
+    data_column_names = fields.Dict(keys=fields.Str(), values=fields.Str())
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import ReferenceData
+
+        return ReferenceData(**data)
+
+
+class MonitoringSignalSchema(metaclass=PatchedSchemaMeta):
+    production_data = NestedField(ProductionDataSchema)
+    reference_data = NestedField(ReferenceDataSchema)
+    properties = fields.Dict()
+    alert_enabled = fields.Bool()
+
+
+class DataSignalSchema(MonitoringSignalSchema):
+    features = UnionField(
+        union_fields=[
+            NestedField(MonitorFeatureFilterSchema),
+            StringTransformedEnum(allowed_values=ALL_FEATURES),
+            fields.List(fields.Str),
+        ]
+    )
+    feature_type_override = fields.Dict()
+
+
+class DataDriftSignalSchema(DataSignalSchema):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.DATA_DRIFT, required=True)
+    metric_thresholds = NestedField(DataDriftMetricThresholdSchema)
+    data_segment = NestedField(DataSegmentSchema)
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import DataDriftSignal
+
+        if not isinstance(data, DataDriftSignal):
+            raise ValidationError("Cannot dump non-DataDriftSignal object into DataDriftSignal")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import DataDriftSignal
+
+        data.pop("type", None)
+        return DataDriftSignal(**data)
+
+
+class DataQualitySignalSchema(DataSignalSchema):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.DATA_QUALITY, required=True)
+    metric_thresholds = NestedField(DataQualityMetricThresholdSchema)
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import DataQualitySignal
+
+        if not isinstance(data, DataQualitySignal):
+            raise ValidationError("Cannot dump non-DataQualitySignal object into DataQualitySignal")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import DataQualitySignal
+
+        data.pop("type", None)
+        return DataQualitySignal(**data)
+
+
+class PredictionDriftSignalSchema(MonitoringSignalSchema):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.PREDICTION_DRIFT, required=True)
+    metric_thresholds = NestedField(PredictionDriftMetricThresholdSchema)
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import PredictionDriftSignal
+
+        if not isinstance(data, PredictionDriftSignal):
+            raise ValidationError("Cannot dump non-PredictionDriftSignal object into PredictionDriftSignal")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import PredictionDriftSignal
+
+        data.pop("type", None)
+        return PredictionDriftSignal(**data)
+
+
+class ModelSignalSchema(MonitoringSignalSchema):
+    model_type = StringTransformedEnum(allowed_values=[MonitorModelType.CLASSIFICATION, MonitorModelType.REGRESSION])
+
+
+class FADProductionDataSchema(metaclass=PatchedSchemaMeta):
+    input_data = UnionField(union_fields=[NestedField(DataInputSchema), NestedField(MLTableInputSchema)])
+    data_context = StringTransformedEnum(allowed_values=[o.value for o in MonitorDatasetContext])
+    data_column_names = fields.Dict(
+        keys=StringTransformedEnum(allowed_values=[o.value for o in FADColumnNames]), values=fields.Str()
+    )
+    pre_processing_component = fields.Str()
+    data_window = NestedField(BaselineDataRangeSchema)
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import FADProductionData
+
+        return FADProductionData(**data)
+
+
+class FeatureAttributionDriftSignalSchema(metaclass=PatchedSchemaMeta):
+    production_data = fields.List(NestedField(FADProductionDataSchema))
+    reference_data = NestedField(ReferenceDataSchema)
+    alert_enabled = fields.Bool()
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.FEATURE_ATTRIBUTION_DRIFT, required=True)
+    metric_thresholds = NestedField(FeatureAttributionDriftMetricThresholdSchema)
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import FeatureAttributionDriftSignal
+
+        if not isinstance(data, FeatureAttributionDriftSignal):
+            raise ValidationError(
+                "Cannot dump non-FeatureAttributionDriftSignal object into FeatureAttributionDriftSignal"
+            )
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import FeatureAttributionDriftSignal
+
+        data.pop("type", None)
+        return FeatureAttributionDriftSignal(**data)
+
+
+class ModelPerformanceSignalSchema(metaclass=PatchedSchemaMeta):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.MODEL_PERFORMANCE, required=True)
+    production_data = NestedField(ProductionDataSchema)
+    reference_data = NestedField(ReferenceDataSchema)
+    data_segment = NestedField(DataSegmentSchema)
+    alert_enabled = fields.Bool()
+    metric_thresholds = NestedField(ModelPerformanceMetricThresholdSchema)
+    properties = fields.Dict()
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import ModelPerformanceSignal
+
+        if not isinstance(data, ModelPerformanceSignal):
+            raise ValidationError("Cannot dump non-ModelPerformanceSignal object into ModelPerformanceSignal")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import ModelPerformanceSignal
+
+        data.pop("type", None)
+        return ModelPerformanceSignal(**data)
+
+
+class ConnectionSchema(metaclass=PatchedSchemaMeta):
+    environment_variables = fields.Dict(keys=fields.Str(), values=fields.Str())
+    secret_config = fields.Dict(keys=fields.Str(), values=fields.Str())
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import Connection
+
+        return Connection(**data)
+
+
+class CustomMonitoringSignalSchema(metaclass=PatchedSchemaMeta):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.CUSTOM, required=True)
+    connection = NestedField(ConnectionSchema)
+    component_id = ArmVersionedStr(azureml_type=AzureMLResourceType.COMPONENT)
+    metric_thresholds = fields.List(NestedField(CustomMonitoringMetricThresholdSchema))
+    input_data = fields.Dict(keys=fields.Str(), values=NestedField(ReferenceDataSchema))
+    alert_enabled = fields.Bool()
+    inputs = fields.Dict(
+        keys=fields.Str, values=UnionField(union_fields=[NestedField(DataInputSchema), NestedField(MLTableInputSchema)])
+    )
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import CustomMonitoringSignal
+
+        if not isinstance(data, CustomMonitoringSignal):
+            raise ValidationError("Cannot dump non-CustomMonitoringSignal object into CustomMonitoringSignal")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import CustomMonitoringSignal
+
+        data.pop("type", None)
+        return CustomMonitoringSignal(**data)
+
+
+class LlmDataSchema(metaclass=PatchedSchemaMeta):
+    input_data = UnionField(union_fields=[NestedField(DataInputSchema), NestedField(MLTableInputSchema)])
+    data_column_names = fields.Dict()
+    data_window = NestedField(BaselineDataRangeSchema)
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import LlmData
+
+        return LlmData(**data)
+
+
+class GenerationSafetyQualitySchema(metaclass=PatchedSchemaMeta):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.GENERATION_SAFETY_QUALITY, required=True)
+    production_data = fields.List(NestedField(LlmDataSchema))
+    connection_id = fields.Str()
+    metric_thresholds = NestedField(GenerationSafetyQualityMetricThresholdSchema)
+    alert_enabled = fields.Bool()
+    properties = fields.Dict()
+    sampling_rate = fields.Float()
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import GenerationSafetyQualitySignal
+
+        if not isinstance(data, GenerationSafetyQualitySignal):
+            raise ValidationError("Cannot dump non-GenerationSafetyQuality object into GenerationSafetyQuality")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import GenerationSafetyQualitySignal
+
+        data.pop("type", None)
+        return GenerationSafetyQualitySignal(**data)
+
+
+class GenerationTokenStatisticsSchema(metaclass=PatchedSchemaMeta):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.GENERATION_TOKEN_STATISTICS, required=True)
+    production_data = NestedField(LlmDataSchema)
+    metric_thresholds = NestedField(GenerationTokenStatisticsMonitorMetricThresholdSchema)
+    alert_enabled = fields.Bool()
+    properties = fields.Dict()
+    sampling_rate = fields.Float()
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import GenerationTokenStatisticsSignal
+
+        if not isinstance(data, GenerationTokenStatisticsSignal):
+            raise ValidationError("Cannot dump non-GenerationSafetyQuality object into GenerationSafetyQuality")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import GenerationTokenStatisticsSignal
+
+        data.pop("type", None)
+        return GenerationTokenStatisticsSignal(**data)