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Time taken in inference: %.4f sec", payload.uuid, time.perf_counter() - _start_time) return messages diff --git a/udf/anomaly-detection/src/udf/preprocess.py b/udf/anomaly-detection/src/udf/preprocess.py index e9064f29..4024053d 100644 --- a/udf/anomaly-detection/src/udf/preprocess.py +++ b/udf/anomaly-detection/src/udf/preprocess.py @@ -22,27 +22,14 @@ class Preprocess: @classmethod def get_df(cls, data_payload: dict, features: List[str]) -> (pd.DataFrame, List[int]): - timestamps = range( - int(data_payload["start_time"]), int(data_payload["end_time"]) - 1, 60 * 1000 - ) - given_timestamps = [int(data["timestamp"]) for data in data_payload["data"]] - - rows = [] - for data in data_payload["data"]: - feature_values = [float(data.get(feature)) for feature in features] - rows.append(pd.Series(feature_values + [int(data["timestamp"])])) - - for timestamp in timestamps: - if timestamp not in given_timestamps: - rows.append(pd.Series([0] * len(features) + [timestamp])) + start_time = int(data_payload["start_time"]) + end_time = int(data_payload["end_time"]) - df = pd.concat(rows, axis=1).T - df.columns = features + ["timestamp"] - df.sort_values("timestamp", inplace=True) - df.reset_index(drop=True, inplace=True) - df = df.fillna(0) - df = df.drop("timestamp", axis=1) - return df, [*timestamps] + df = pd.DataFrame(data_payload["data"], columns=["timestamp", *features]) + df.index = df.timestamp.astype(int) + timestamps = np.arange(start_time+6e4, end_time+6e4, 6e4, dtype=int) + df = df.reindex(timestamps, fill_value=0) + return df, timestamps def preprocess(self, keys: List[str], payload: StreamPayload) -> (np.ndarray, Status): preprocess_cfgs = ConfigManager.get_preprocess_config(config_id=keys[0]) @@ -152,4 +139,5 @@ def run(self, keys: List[str], datum: Datum) -> Messages: messages.append(Message(keys=keys, value=payload.to_json())) _LOGGER.info("%s - Sending Msg: { Keys: %s, Payload: %r }", payload.uuid, keys, payload) + _LOGGER.debug("%s - Time taken in preprocess: %.4f sec", payload.uuid, time.perf_counter() - _start_time) return messages diff --git a/udf/anomaly-detection/src/udf/threshold.py b/udf/anomaly-detection/src/udf/threshold.py index 6785ce0a..f3d5f16d 100644 --- a/udf/anomaly-detection/src/udf/threshold.py +++ b/udf/anomaly-detection/src/udf/threshold.py @@ -142,4 +142,5 @@ def run(self, keys: List[str], datum: Datum) -> Messages: [POSTPROC_VTX_KEY], payload, ) + _LOGGER.debug("%s - Time taken in threshold: %.4f sec", payload.uuid, time.perf_counter() - _start_time) return messages diff --git a/udf/anomaly-detection/src/udf/trainer.py b/udf/anomaly-detection/src/udf/trainer.py index a2b00f4b..1af78aea 100644 --- a/udf/anomaly-detection/src/udf/trainer.py +++ b/udf/anomaly-detection/src/udf/trainer.py @@ -252,6 +252,7 @@ def _train_and_save( ) def run(self, keys: List[str], datum: Datum) -> Messages: + _start_time = time.perf_counter() messages = Messages() redis_client = get_redis_client_from_conf() payload = TrainerPayload(**orjson.loads(datum.value)) @@ -290,7 +291,7 @@ def run(self, keys: List[str], datum: Datum) -> Messages: train_df = get_feature_df(df, payload.metrics) self._train_and_save(numalogic_config, payload, redis_client, train_df) - + _LOGGER.debug("%s - Time taken in training: %.4f sec", payload.uuid, time.perf_counter() - _start_time) messages.append(Message(keys=keys, value=payload.to_json())) return messages