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znacer commited on Jun 7, 2025

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aafd0ea

1 Parent(s): c00a51c

anomaly detection: iforest

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Files changed (4) hide show

app/analyzer.py +75 -0
app/service.py +52 -18
pyproject.toml +1 -0
uv.lock +86 -0

app/analyzer.py CHANGED Viewed

@@ -8,6 +8,7 @@ from typing import Any
 import numpy as np
 import polars as pl
 from loguru import logger
 from sqlalchemy import Engine, create_engine, text
 from data.get_mock import get_df
@@ -232,3 +233,77 @@ class TimeSeriesAnalyzer:
                 "end": timestamps[-1] if len(timestamps) > 0 else None,
             },
         }

 import numpy as np
 import polars as pl
 from loguru import logger
+from sklearn.ensemble import IsolationForest
 from sqlalchemy import Engine, create_engine, text
 from data.get_mock import get_df
                 "end": timestamps[-1] if len(timestamps) > 0 else None,
             },
         }
+    def detect_anomalies_isolation_forest(
+        self, data: pl.DataFrame, contamination: float = 0.1
+    ) -> dict[str, Any]:
+        """Detect anomalies in the time series data using Isolation Forest algorithm.
+        Args:
+            data: expect only 1 timeserie with columns datetime and value
+            contamination: expected proportion of anomalies in the data (default: 0.1)
+        Returns:
+            {
+            "anomalies_found": int,
+            "anomalies": list[dict[str, int]],
+            "statistics": {
+                "mean": float,
+                "std": float,
+                "min": float,
+                "max": float
+            }
+        """
+        values = data["value"].to_numpy().reshape(-1, 1)
+        iso_forest = IsolationForest(
+            contamination=contamination, random_state=42, n_estimators=100
+        )
+        # Predict anomalies (-1 for anomalies, 1 for normal)
+        predictions = iso_forest.fit_predict(values)
+        anomaly_scores = -iso_forest.score_samples(values)
+        anomaly_mask = predictions == -1
+        mean_val = data["value"].mean()
+        std_val = data["value"].std()
+        logger.debug(f"anaomaly_mask: {anomaly_mask}")
+        logger.debug(f"anomaly_scores: {anomaly_scores}")
+        logger.debug(
+            pl.Series(anomaly_scores)
+            .filter(anomaly_mask)
+            .alias("anomaly_score"),
+        )
+        # Prepare anomalies data
+        anomalies = (
+            data.select(
+                data["timestamp"].cast(pl.Utf8).alias("timestamp"),
+                data["value"].cast(pl.Float64),
+                pl.Series(anomaly_scores).alias("anomaly_score"),
+                pl.Series(anomaly_scores > np.percentile(anomaly_scores, 90))
+                .cast(pl.Utf8)
+                .alias("severity")
+                .map_elements(
+                    lambda x: "high" if x else "medium",
+                    return_dtype=pl.String,
+                ),
+            )
+            .filter(anomaly_mask)
+            .to_dicts()
+        )
+        logger.debug(f"anomalies: {anomalies}")
+        return {
+            "anomalies_found": len(anomalies),
+            "anomalies": anomalies,
+            "statistics": {
+                "mean": mean_val,
+                "std": std_val,
+                "min": data["value"].min(),
+                "max": data["value"].max(),
+            },
+        }

app/service.py CHANGED Viewed

@@ -58,14 +58,41 @@ def query_timeseries(sensor_id: str, start_time: str, end_time: str) -> str:
 def detect_anomalies(
-    sensor_id: str, start_time: str, end_time: str, threshold: float = 2.0
 ) -> str:
-    """Detect anomalies in the time series data for a specific sensor."""
-    logger.info("detect anomalies")
     if not ensure_analyzer_connected():
         return json.dumps({"error": "Database not connected"})
     data = analyzer.query_metrics(sensor_id, start_time, end_time)
-    anomalies = analyzer.detect_anomalies(data, threshold)
     return json.dumps(anomalies, indent=2)
@@ -103,25 +130,25 @@ def generate_analysis_report(
     include_anomalies: bool = True,
     include_trends: bool = True,
     user_question: str | None = None,
 ) -> str:
     """Generate a comprehensive analysis report for a specific sensor.
-    This function creates a detailed report containing information about the sensor data,
-    including optional trend analysis and anomaly detection sections based on the parameters.
     Args:
-        sensor_id (str): The identifier of the sensor to analyze.
-        start_time (str): The start time of the analysis period in ISO format.
-        end_time (str): The end time of the analysis period in ISO format.
-        include_anomalies (bool, optional): Whether to include anomaly detection in the report. Defaults to True.
-        include_trends (bool, optional): Whether to include trend analysis in the report. Defaults to True.
-        user_question (str | None, optional): A specific question from the user to be included in the report. Defaults to None.
     Returns:
-        str: A formatted string containing the comprehensive analysis report with requested sections.
-    Note:
-        Returns an error message if the database is not connected.
     """
     logger.info("generate report")
@@ -148,8 +175,15 @@ def generate_analysis_report(
         )
         report_sections.append(f"- **End Value:** {trends['end_value']:.2f}")
     if include_anomalies:
-        anomalies = analyzer.detect_anomalies(data)
         report_sections.append("\n### Anomaly Detection")
         report_sections.append(
             f"- **Anomalies Found:** {anomalies['anomalies_found']}"
         )

 def detect_anomalies(
+    sensor_id: str,
+    start_time: str,
+    end_time: str,
+    threshold: float = 2.0,
+    algorithm: str = "zscore",
+    contamination: float = 0.1,
 ) -> str:
+    """Detect anomalies in the time series data for a specific sensor.
+    Args:
+        sensor_id: The identifier of the sensor to analyze
+        start_time: The start time of the analysis period in ISO format
+        end_time: The end time of the analysis period in ISO format
+        threshold: Threshold for z-score based detection (default: 2.0)
+        algorithm: Algorithm to use for detection ("zscore" or "isolation_forest")
+        contamination: Expected proportion of anomalies for isolation forest (default: 0.1)
+    Returns:
+        str: JSON string containing anomaly detection results
+    """
+    logger.info(f"detect anomalies using {algorithm}")
     if not ensure_analyzer_connected():
         return json.dumps({"error": "Database not connected"})
     data = analyzer.query_metrics(sensor_id, start_time, end_time)
+    if algorithm == "zscore":
+        anomalies = analyzer.detect_anomalies(data, threshold)
+    elif algorithm == "isolation_forest":
+        anomalies = analyzer.detect_anomalies_isolation_forest(
+            data, contamination
+        )
+    else:
+        return json.dumps({"error": f"Unknown algorithm: {algorithm}"})
     return json.dumps(anomalies, indent=2)
     include_anomalies: bool = True,
     include_trends: bool = True,
     user_question: str | None = None,
+    anomaly_algorithm: str = "zscore",
+    anomaly_threshold: float = 2.0,
+    anomaly_contamination: float = 0.1,
 ) -> str:
     """Generate a comprehensive analysis report for a specific sensor.
     Args:
+        sensor_id: The identifier of the sensor to analyze
+        start_time: The start time of the analysis period in ISO format
+        end_time: The end time of the analysis period in ISO format
+        include_anomalies: Whether to include anomaly detection in the report
+        include_trends: Whether to include trend analysis in the report
+        user_question: A specific question from the user to be included in the report
+        anomaly_algorithm: Algorithm to use for anomaly detection ("zscore" or "isolation_forest")
+        anomaly_threshold: Threshold for z-score based detection
+        anomaly_contamination: Expected proportion of anomalies for isolation forest
     Returns:
+        str: A formatted string containing the comprehensive analysis report
     """
     logger.info("generate report")
         )
         report_sections.append(f"- **End Value:** {trends['end_value']:.2f}")
     if include_anomalies:
+        if anomaly_algorithm == "zscore":
+            anomalies = analyzer.detect_anomalies(data, anomaly_threshold)
+        else:
+            anomalies = analyzer.detect_anomalies_isolation_forest(
+                data, anomaly_contamination
+            )
         report_sections.append("\n### Anomaly Detection")
+        report_sections.append(f"- **Algorithm:** {anomaly_algorithm}")
         report_sections.append(
             f"- **Anomalies Found:** {anomalies['anomalies_found']}"
         )

pyproject.toml CHANGED Viewed

@@ -13,6 +13,7 @@ dependencies = [
   "polars>=1.30.0",
   "psycopg2-binary>=2.9.10",
   "pyarrow>=20.0.0",
   "sqlalchemy>=2.0.41",
 ]

   "polars>=1.30.0",
   "psycopg2-binary>=2.9.10",
   "pyarrow>=20.0.0",
+  "scikit-learn>=1.7.0",
   "sqlalchemy>=2.0.41",
 ]

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