wip: lab_4 до дашборда в Prometheus

services/ml_service/app/predictor.py

@@ -1,7 +1,9 @@
 from dataclasses import dataclass
 from enum import Enum
+from itertools import chain
 from pandas import DataFrame
 from pickle import load
+from prometheus_client import Counter, Histogram
 
 
 def open_model_file(file, *, buffering=-1, opener=None, **kwargs_extra):
@@ -61,6 +63,27 @@ class PricePredictionFeatures:
     transmission_type: TransmissionType
 
 
+metric_prediction_latency = Histogram(
+    'model_prediction_seconds', 'Время вычислений в модели',
+    buckets=(
+        list(chain.from_iterable((v * (10 ** p) for v in (1, 2, 5)) for p in range(-4, (1 + 1))))
+        + [float('+inf')]
+    ),
+)
+
+metric_prediction_errors = Counter(
+    'model_prediction_errors_total', 'Ошибки вычислений в модели по типу', ('error_type',),
+)
+
+metric_prediction_value = Histogram(
+    'model_prediction_value', 'Предсказанное значение цены',
+    buckets=(
+        list(chain.from_iterable((v * (10 ** p) for v in (1, 2, 5)) for p in range(-1, (2 + 1))))
+        + [float('+inf')]
+    ),
+)
+
+
 class PricePredictor:
 
     def __init__(self, model_path):
@@ -76,6 +99,13 @@ class PricePredictor:
             'transmission': features.transmission_type.value,
             'age':  features.age,
         }])
-        predictions = self._model.predict(features_df)
+        try:
+            with metric_prediction_latency.time():
+                predictions = self._model.predict(features_df)
+        except Exception as err:
+            metric_prediction_errors.labels(error_type=type(err).__name__).inc()
+            raise
         assert len(predictions) == 1
-        return float(predictions[0])
+        value = float(predictions[0])
+        metric_prediction_value.observe(value)
+        return value