Feat: Add Public Methods for DB (#674)

* feat: Add ActiveProject for some handy commands

* Update active_project.py

Author: Gorkem-Encord

src/encord_active/public/__init__.py

@@ -0,0 +1,88 @@
+import pandas as pd
+from encord.objects import OntologyStructure
+from sqlalchemy import MetaData, Table, create_engine, select, text
+from sqlalchemy.engine import Engine
+
+
+def get_active_engine(path_to_db: str) -> Engine:
+    return create_engine(f"sqlite:///{path_to_db}")
+
+
+class ActiveProject:
+    def __init__(self, engine: Engine, project_name: str):
+        self._engine = engine
+        self._metadata = MetaData(bind=self._engine)
+        self._project_name = project_name
+
+        active_project = Table("active_project", self._metadata, autoload_with=self._engine)
+        stmt = select(active_project.c.project_hash).where(active_project.c.project_name == f"{self._project_name}")
+
+        with self._engine.connect() as connection:
+            result = connection.execute(stmt).fetchone()
+
+            if result is not None:
+                self._project_hash = result[0]
+            else:
+                self._project_hash = None
+
+    def get_ontology(self) -> OntologyStructure:
+        active_project = Table("active_project", self._metadata, autoload_with=self._engine)
+
+        stmt = select(active_project.c.project_ontology).where(active_project.c.project_hash == f"{self._project_hash}")
+
+        with self._engine.connect() as connection:
+            result = connection.execute(stmt).fetchone()
+
+            if result is not None:
+                ontology = result[0]
+            else:
+                ontology = None
+
+        return OntologyStructure.from_dict(ontology)
+
+    def get_prediction_metrics(self) -> pd.DataFrame:
+        active_project_prediction = Table("active_project_prediction", self._metadata, autoload_with=self._engine)
+        stmt = select(active_project_prediction.c.prediction_hash).where(
+            active_project_prediction.c.project_hash == f"{self._project_hash}"
+        )
+
+        with self._engine.connect() as connection:
+            result = connection.execute(stmt).fetchone()
+
+            if result is not None:
+                prediction_hash = result[0]
+            else:
+                prediction_hash = None
+
+        active_project_prediction_analytics = Table(
+            "active_project_prediction_analytics", self._metadata, autoload_with=self._engine
+        )
+
+        stmt = select(
+            [
+                active_project_prediction_analytics.c.feature_hash,
+                active_project_prediction_analytics.c.metric_area,
+                active_project_prediction_analytics.c.metric_area_relative,
+                active_project_prediction_analytics.c.metric_aspect_ratio,
+                active_project_prediction_analytics.c.metric_brightness,
+                active_project_prediction_analytics.c.metric_contrast,
+                active_project_prediction_analytics.c.metric_sharpness,
+                active_project_prediction_analytics.c.metric_red,
+                active_project_prediction_analytics.c.metric_green,
+                active_project_prediction_analytics.c.metric_blue,
+                active_project_prediction_analytics.c.metric_label_border_closeness,
+                active_project_prediction_analytics.c.metric_label_confidence,
+                text(
+                    """CASE
+                        WHEN feature_hash == match_feature_hash THEN 1
+                        ELSE 0
+                    END AS true_positive
+                """
+                ),
+            ]
+        ).where(active_project_prediction_analytics.c.prediction_hash == prediction_hash)
+
+        with self._engine.begin() as conn:
+            df = pd.read_sql(stmt, conn)
+
+        return df