fix: save preproc + thresh model with conf keys (#107)

Signed-off-by: Avik Basu <[email protected]>

numaprom/udf/preprocess.py

@@ -5,7 +5,7 @@
 
 from numaprom import get_logger
 from numaprom.entities import Status, StreamPayload, Header
-from numaprom.tools import msg_forward, load_model
+from numaprom.tools import msg_forward, load_model, get_metric_config
 
 _LOGGER = get_logger(__name__)
 
@@ -18,10 +18,16 @@ def preprocess(_: str, datum: Datum) -> bytes:
     payload = StreamPayload(**orjson.loads(_in_msg))
     _LOGGER.info("%s - Received Payload: %r ", payload.uuid, payload)
 
+    # Load config
+    metric_config = get_metric_config(
+        metric=payload.composite_keys["name"], namespace=payload.composite_keys["namespace"]
+    )
+    preprocess_cfgs = metric_config.numalogic_conf.preprocess
+
     # Load preprocess artifact
     preproc_artifact = load_model(
         skeys=[payload.composite_keys["namespace"], payload.composite_keys["name"]],
-        dkeys=["preproc"],
+        dkeys=[_conf.name for _conf in preprocess_cfgs],
         artifact_type="sklearn",
     )
     if not preproc_artifact:

numaprom/udf/threshold.py

@@ -47,6 +47,7 @@ def threshold(_: str, datum: Datum) -> list[tuple[str, bytes]]:
     metric_config = get_metric_config(
         metric=payload.composite_keys["name"], namespace=payload.composite_keys["namespace"]
     )
+    thresh_cfg = metric_config.numalogic_conf.threshold
 
     # Check if payload needs static inference
     if payload.header == Header.STATIC_INFERENCE:
@@ -63,7 +64,7 @@ def threshold(_: str, datum: Datum) -> list[tuple[str, bytes]]:
     # load threshold artifact
     thresh_artifact = load_model(
         skeys=[payload.composite_keys["namespace"], payload.composite_keys["name"]],
-        dkeys=["thresh"],
+        dkeys=[thresh_cfg.name],
         artifact_type="sklearn",
     )
     if not thresh_artifact:

numaprom/udsink/train_rollout.py

@@ -68,10 +68,10 @@ def _train_model(uuid, x, model_cfg, trainer_cfg):
     return train_reconerr.numpy(), model, trainer
 
 
-def _preprocess(x_raw, preproc_cfg: List[ModelInfo]):
+def _preprocess(x_raw, preproc_cfgs: List[ModelInfo]):
     preproc_factory = PreprocessFactory()
     preproc_clfs = []
-    for _cfg in preproc_cfg:
+    for _cfg in preproc_cfgs:
         _clf = preproc_factory.get_instance(_cfg)
         preproc_clfs.append(_clf)
     preproc_pl = make_pipeline(*preproc_clfs)
@@ -154,8 +154,8 @@ def train_rollout(datums: List[Datum]) -> Responses:
             responses.append(Response.as_success(_datum.id))
             continue
 
-        preproc_cfg = metric_config.numalogic_conf.preprocess
-        x_train, preproc_clf = _preprocess(train_df.to_numpy(), preproc_cfg)
+        preproc_cfgs = metric_config.numalogic_conf.preprocess
+        x_train, preproc_clf = _preprocess(train_df.to_numpy(), preproc_cfgs)
 
         trainer_cfg = metric_config.numalogic_conf.trainer
         x_reconerr, anomaly_model, trainer = _train_model(
@@ -173,7 +173,11 @@ def train_rollout(datums: List[Datum]) -> Responses:
 
         # Save main model
         version = save_model(
-            skeys=skeys, dkeys=[model_cfg.name], model=anomaly_model, uuid=payload.uuid
+            skeys=skeys,
+            dkeys=[model_cfg.name],
+            model=anomaly_model,
+            uuid=payload.uuid,
+            train_size=train_df.shape[0],
         )
         if version:
             _LOGGER.info(
@@ -185,7 +189,7 @@ def train_rollout(datums: List[Datum]) -> Responses:
         # Save preproc model
         version = save_model(
             skeys=skeys,
-            dkeys=["preproc"],
+            dkeys=[_conf.name for _conf in preproc_cfgs],
             model=preproc_clf,
             artifact_type="sklearn",
             uuid=payload.uuid,
@@ -205,7 +209,7 @@ def train_rollout(datums: List[Datum]) -> Responses:
         # Save threshold model
         version = save_model(
             skeys=skeys,
-            dkeys=["thresh"],
+            dkeys=[thresh_cfg.name],
             model=thresh_clf,
             artifact_type="sklearn",
             uuid=payload.uuid,

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "numalogic-prometheus"
-version = "0.2.3"
+version = "0.2.4"
 description = "ML inference on numaflow using numalogic on Prometheus metrics"
 authors = ["Numalogic developers"]
 packages = [{ include = "numaprom" }]