Integrate Concept Drift

src/analytics/drift/pipelines.py

@@ -1,9 +1,8 @@
 import pandas as pd
-from typing import Dict, Union, Any
 import json
 from evidently.report import Report
 from evidently.metric_preset import DataDriftPreset, TargetDriftPreset
-from src.schemas.driftingMetric import DataDriftTable
+from src.schemas.driftingMetric import DataDriftTable, ConceptDriftTable
 
 
 def run_data_drift_pipeline(
@@ -54,15 +53,14 @@ def run_data_drift_pipeline(
     initial_report = json.loads(initial_report)
 
     data_drift_report = {}
-    data_drift_report["timestamp"] = initial_report["timestamp"]
     data_drift_report["drift_summary"] = initial_report["metrics"][1]["result"]
 
     return DataDriftTable(**data_drift_report["drift_summary"])
 
 
 def run_concept_drift_pipeline(
     reference_dataset: pd.DataFrame, current_dataset: pd.DataFrame, target_feature: str
-) -> Dict[str, Union[TargetDriftPreset, str]]:
+) -> ConceptDriftTable:
     """
     To estimate the categorical target drift, we compare the distribution of the target in the two datasets.
     This solution works for both binary and multi-class classification.
@@ -89,10 +87,12 @@ def run_concept_drift_pipeline(
     initial_report = drift_report.json()
     initial_report = json.loads(initial_report)
     concept_drift_report = {}
-    concept_drift_report["timestamp"] = initial_report["timestamp"]
     concept_drift_report["concept_drift_summary"] = initial_report["metrics"][0][
         "result"
     ]
     concept_drift_report["column_correlation"] = initial_report["metrics"][1]["result"]
 
-    return concept_drift_report
+    return ConceptDriftTable(
+        concept_drift_summary=concept_drift_report["concept_drift_summary"],
+        column_correlation=concept_drift_report["column_correlation"],
+    )

src/analytics/tests/test_pipelines.py

@@ -203,36 +203,44 @@ def test_create_data_drift_pipeline(self):
         )
 
     def test_create_concept_drift_pipeline_drift_not_detected(self):
-        concept_drift_report = run_concept_drift_pipeline(
-            reference_concept_drift, current_concept_drift, "y_testing_multi"
+        concept_drift_report = vars(
+            run_concept_drift_pipeline(
+                reference_concept_drift, current_concept_drift, "y_testing_multi"
+            )
         )
         assert list(concept_drift_report.keys()) == [
-            "timestamp",
             "concept_drift_summary",
             "column_correlation",
         ]
         assert (
-            round(concept_drift_report["concept_drift_summary"]["drift_score"], 3)
+            round(vars(concept_drift_report["concept_drift_summary"])["drift_score"], 3)
             == 0.082
         )
-        assert concept_drift_report["concept_drift_summary"]["drift_detected"] == False
+        assert (
+            vars(concept_drift_report["concept_drift_summary"])["drift_detected"]
+            == False
+        )
 
     def test_create_concept_drift_pipeline_drift_detected(self):
-        concept_drift_report = run_concept_drift_pipeline(
-            reference_concept_drift_detected,
-            current_concept_drift_detected,
-            "discount_price__currency",
+        concept_drift_report = vars(
+            run_concept_drift_pipeline(
+                reference_concept_drift_detected,
+                current_concept_drift_detected,
+                "discount_price__currency",
+            )
         )
         assert list(concept_drift_report.keys()) == [
-            "timestamp",
             "concept_drift_summary",
             "column_correlation",
         ]
         assert (
-            round(concept_drift_report["concept_drift_summary"]["drift_score"], 3)
+            round(vars(concept_drift_report["concept_drift_summary"])["drift_score"], 3)
             == 0.008
         )
-        assert concept_drift_report["concept_drift_summary"]["drift_detected"] == True
+        assert (
+            vars(concept_drift_report["concept_drift_summary"])["drift_detected"]
+            == True
+        )
 
     def test_create_binary_classification_training_model_pipeline(self):
         model, eval = create_binary_classification_training_model_pipeline(

src/cron_tasks/monitoring_metrics.py

@@ -5,7 +5,10 @@
 from sqlalchemy.orm import sessionmaker, Session
 
 from src import crud, entities
-from src.analytics.drift.pipelines import run_data_drift_pipeline
+from src.analytics.drift.pipelines import (
+    run_data_drift_pipeline,
+    run_concept_drift_pipeline,
+)
 from src.analytics.metrics.pipelines import (
     create_binary_classification_evaluation_metrics_pipeline,
     create_feature_metrics_pipeline,
@@ -56,10 +59,16 @@ async def run_calculate_drifting_metrics_pipeline(
     data_drift_report = run_data_drift_pipeline(
         processed_training_dropped_target_df, processed_inference_dropped_target_df
     )
+    concept_drift_report = run_concept_drift_pipeline(
+        training_processed_df,
+        inference_processed_df,
+        model.prediction,
+    )
 
     new_drifting_metric = entities.DriftingMetric(
         timestamp=str(datetime.utcnow()),
         model_id=model.id,
+        concept_drift_summary=concept_drift_report,
         data_drift_summary=data_drift_report,
     )
 

src/entities/DriftingMetric.py

@@ -1,4 +1,4 @@
-from sqlalchemy import Column, Float, ForeignKey, String, DateTime, JSON
+from sqlalchemy import Column, ForeignKey, String, DateTime, JSON
 from src.entities.Base import Base
 from src.utils.id_gen import generate_uuid
 
@@ -9,8 +9,7 @@ class DriftingMetric(Base):
     id = Column(String, primary_key=True, unique=True, default=generate_uuid)
     model_id = Column(String, ForeignKey("models.id", ondelete="CASCADE"))
     timestamp = Column(DateTime)
-    # TODO: Fix pipeline to return a DataDriftTable first
-    # concept_drift_summary = Column(JSON)
+    concept_drift_summary = Column(JSON)
     data_drift_summary = Column(JSON)
     created_at = Column(DateTime)
     updated_at = Column(DateTime)

src/schemas/driftingMetric.py

@@ -1,5 +1,5 @@
 from datetime import datetime
-from typing import Dict, Union
+from typing import Dict, List, Union
 from pydantic import BaseModel
 from src.schemas.base import ItemBase
 
@@ -28,15 +28,15 @@ class CramerV(BaseModel):
 
     column_name: str
     kind: str
-    values: Dict[str, Dict[str, str]]
+    values: Dict[str, List[str]]
 
 
 class ColumnConceptDriftCorrelationMetrics(BaseModel):
     """One column concept drift correlation metrics"""
 
     column_name: str
-    current: CramerV
-    reference: CramerV
+    current: Dict[str, CramerV]
+    reference: Dict[str, CramerV]
 
 
 class ColumnConceptDriftMetrics(BaseModel):
@@ -57,12 +57,10 @@ class ConceptDriftTable(BaseModel):
     column_correlation: ColumnConceptDriftCorrelationMetrics
 
 
-# TODO: Need to include the class of the concept drift
 class DriftingMetricBase(ItemBase):
     model_id: str
     timestamp: Union[str, datetime]
-    # TODO: The pipeline needs to return a DataDriftTable. If evidently does not provide it we should create it.
-    # concept_drift_summary: DataDriftTable
+    concept_drift_summary: ConceptDriftTable
     data_drift_summary: DataDriftTable