From 071fe13880906310c52148c07b76ce76624d9881 Mon Sep 17 00:00:00 2001 From: Nandita Koppisetty Date: Mon, 17 Jul 2023 11:44:22 -0700 Subject: [PATCH] feat: multi timeseries models Signed-off-by: Nandita Koppisetty --- .../config/default-configs/config.yaml | 48 - .../default-configs/numalogic_config.yaml | 21 - .../default-configs/pipeline_config.yaml | 5 - .../config/user-configs/config.yaml | 13 - udf/anomaly-detection/src/__init__.py | 4 +- udf/anomaly-detection/src/_config.py | 16 +- .../src/connectors/_config.py | 1 + udf/anomaly-detection/src/connectors/druid.py | 39 +- udf/anomaly-detection/src/entities.py | 25 +- udf/anomaly-detection/src/tools.py | 10 +- udf/anomaly-detection/src/udf/inference.py | 52 +- udf/anomaly-detection/src/udf/postprocess.py | 59 +- udf/anomaly-detection/src/udf/preprocess.py | 47 +- udf/anomaly-detection/src/udf/threshold.py | 32 +- udf/anomaly-detection/src/udsink/train.py | 32 +- udf/anomaly-detection/src/watcher.py | 81 +- udf/anomaly-detection/tests/__init__.py | 8 +- ...efault-config.yaml => default_config.yaml} | 20 +- .../resources/configs/pipeline_config.yaml | 8 + .../{users-config.yaml => users_config.yaml} | 18 +- .../tests/resources/data/druid.csv | 16539 ++++++++++++++-- udf/anomaly-detection/tests/test_trainer.py | 4 +- udf/anomaly-detection/tests/test_watcher.py | 65 +- .../tests/udf/test_inference.py | 35 +- .../tests/udf/test_postprocess.py | 6 +- .../tests/udf/test_preprocess.py | 15 +- .../tests/udf/test_threshold.py | 29 +- 27 files changed, 14628 insertions(+), 2604 deletions(-) delete mode 100644 udf/anomaly-detection/config/default-configs/config.yaml delete mode 100644 udf/anomaly-detection/config/default-configs/numalogic_config.yaml delete mode 100644 udf/anomaly-detection/config/default-configs/pipeline_config.yaml delete mode 100644 udf/anomaly-detection/config/user-configs/config.yaml rename udf/anomaly-detection/tests/resources/configs/{default-config.yaml => default_config.yaml} (51%) create mode 100644 udf/anomaly-detection/tests/resources/configs/pipeline_config.yaml rename udf/anomaly-detection/tests/resources/configs/{users-config.yaml => users_config.yaml} (54%) diff --git a/udf/anomaly-detection/config/default-configs/config.yaml b/udf/anomaly-detection/config/default-configs/config.yaml deleted file mode 100644 index 31d379e6..00000000 --- a/udf/anomaly-detection/config/default-configs/config.yaml +++ /dev/null @@ -1,48 +0,0 @@ -configs: - - name: "fciAsset" - source: "druid" - composite_keys: [ "assetId" ] - metrics: [ "failed" , "degraded" ] - druid_fetcher: - dimensions: [ "ciStatus" ] - datasource: "tech-ip-customer-interaction-metrics" - group_by: [ "timestamp", "ciStatus" ] - pivot: - columns: - - "ciStatus" - metric_configs: - - metric: "failed" - retrain_conf: - train_hours: 240 - retrain_freq_hr: 48 - - metric: "degraded" - retrain_conf: - train_hours: 240 - retrain_freq_hr: 48 - - name: "service-mesh" - composite_keys: [ "destination_asset_id", "env" ] - metrics: [ "error_rate" , "error_count" ] - - name: "service-mesh-s2s" - composite_keys: ["source_asset_id, destination_asset_id", "env"] - metrics: [ "error_rate", "error_count" ] - - name: "argo-rollouts" - composite_keys: [ "namespace", "name", "app", "rollouts_pod_template_hash" ] - metrics: [ "namespace_app_rollouts_http_request_error_rate"] - metric_configs: - - metric: "namespace_app_rollouts_http_request_error_rate" - static_threshold: - weight: 0.7 - - name: "argo-cd" - composite_keys: [ "namespace", "name" ] - metrics: [ "namespace_app_http_server_requests_errors", - "namespace_app_http_server_requests_error_rate", - "namespace_app_http_server_requests_latency", - "namespace_app_cpu_utilization", - "namespace_app_memory_utilization" ] - metric_configs: - - metric: "namespace_app_cpu_utilization" - static_threshold: - upper_limit: 80 - - metric: "namespace_app_memory_utilization" - static_threshold: - upper_limit: 80 diff --git a/udf/anomaly-detection/config/default-configs/numalogic_config.yaml b/udf/anomaly-detection/config/default-configs/numalogic_config.yaml deleted file mode 100644 index 2d101406..00000000 --- a/udf/anomaly-detection/config/default-configs/numalogic_config.yaml +++ /dev/null @@ -1,21 +0,0 @@ -model: - name: "SparseVanillaAE" - conf: - seq_len: 12 - n_features: 1 - encoder_layersizes: - - 16 - - 8 - decoder_layersizes: - - 8 - - 16 - dropout_p: 0.25 -trainer: - max_epochs: 30 -preprocess: - - name: "StandardScaler" -threshold: - name: "StdDevThreshold" -postprocess: - name: "TanhNorm" - stateful: false diff --git a/udf/anomaly-detection/config/default-configs/pipeline_config.yaml b/udf/anomaly-detection/config/default-configs/pipeline_config.yaml deleted file mode 100644 index 44d2d781..00000000 --- a/udf/anomaly-detection/config/default-configs/pipeline_config.yaml +++ /dev/null @@ -1,5 +0,0 @@ -redis_conf: - host: "isbsvc-redis-isbs-redis-svc.numalogic-rollouts.svc" - port: 26379 - expiry: 360 - master_name: "mymaster" diff --git a/udf/anomaly-detection/config/user-configs/config.yaml b/udf/anomaly-detection/config/user-configs/config.yaml deleted file mode 100644 index ac3ff822..00000000 --- a/udf/anomaly-detection/config/user-configs/config.yaml +++ /dev/null @@ -1,13 +0,0 @@ -configs: - - name: "dev-devx-o11yfuzzygqlfederation-usw2-qal" - composite_keys: ["namespace", "name", "hash_id"] - metrics: ["namespace_rollout_api_error_rate", "namespace_rollout_api_latency"] - - name: "dev-devx-o11yfuzzygqlfederation-usw2-pprd" - composite_keys: [ "namespace", "name", "hash_id" ] - metrics: ["namespace_rollout_api_error_rate", "namespace_rollout_api_latency"] - - name: "dev-devx-o11yfuzzygqlfederation-usw2-stg" - composite_keys: [ "namespace", "name", "hash_id" ] - metrics: ["namespace_rollout_api_error_rate", "namespace_rollout_api_latency"] - - name: "dev-devx-o11yfuzzygqlfederation-usw2-prd" - composite_keys: [ "namespace", "name", "hash_id" ] - metrics: ["namespace_rollout_api_error_rate", "namespace_rollout_api_latency"] \ No newline at end of file diff --git a/udf/anomaly-detection/src/__init__.py b/udf/anomaly-detection/src/__init__.py index 2fdbf5e6..7404784e 100644 --- a/udf/anomaly-detection/src/__init__.py +++ b/udf/anomaly-detection/src/__init__.py @@ -1,7 +1,7 @@ import logging import os -from src._config import UnifiedConf, MetricConf, DataStreamConf, PipelineConf, Configs +from src._config import UnifiedConf, StreamConf, PipelineConf, Configs def get_logger(name): @@ -25,4 +25,4 @@ def get_logger(name): return logger -__all__ = ["get_logger", "UnifiedConf", "MetricConf", "DataStreamConf", "Configs", "PipelineConf"] +__all__ = ["get_logger", "UnifiedConf", "StreamConf", "Configs", "PipelineConf"] diff --git a/udf/anomaly-detection/src/_config.py b/udf/anomaly-detection/src/_config.py index 46f7791e..bfecf8aa 100644 --- a/udf/anomaly-detection/src/_config.py +++ b/udf/anomaly-detection/src/_config.py @@ -29,34 +29,28 @@ class StaticThreshold: weight: float = 0.0 -@dataclass -class MetricConf: - metric: str - retrain_conf: ReTrainConf = field(default_factory=lambda: ReTrainConf()) - static_threshold: StaticThreshold = field(default_factory=lambda: StaticThreshold()) - numalogic_conf: NumalogicConf = MISSING - - class DataSource(str, Enum): PROMETHEUS = "prometheus" DRUID = "druid" @dataclass -class DataStreamConf: +class StreamConf: name: str = "default" source: str = DataSource.PROMETHEUS.value window_size: int = 12 composite_keys: List[str] = field(default_factory=list) metrics: List[str] = field(default_factory=list) - metric_configs: List[MetricConf] = field(default_factory=list) + retrain_conf: ReTrainConf = field(default_factory=lambda: ReTrainConf()) + static_threshold: StaticThreshold = field(default_factory=lambda: StaticThreshold()) + numalogic_conf: NumalogicConf = MISSING unified_config: UnifiedConf = field(default_factory=lambda: UnifiedConf()) druid_fetcher: DruidFetcherConf = MISSING @dataclass class Configs: - configs: List[DataStreamConf] + configs: List[StreamConf] @dataclass diff --git a/udf/anomaly-detection/src/connectors/_config.py b/udf/anomaly-detection/src/connectors/_config.py index c566172d..7c34fd84 100644 --- a/udf/anomaly-detection/src/connectors/_config.py +++ b/udf/anomaly-detection/src/connectors/_config.py @@ -7,6 +7,7 @@ class PrometheusConf: server: str pushgateway: str + scrape_interval: int = 30 @dataclass diff --git a/udf/anomaly-detection/src/connectors/druid.py b/udf/anomaly-detection/src/connectors/druid.py index 9637aa0f..ebafe88c 100644 --- a/udf/anomaly-detection/src/connectors/druid.py +++ b/udf/anomaly-detection/src/connectors/druid.py @@ -21,16 +21,16 @@ def __init__(self, url: str, endpoint: str): self.client = PyDruid(url, endpoint) def fetch_data( - self, - datasource: str, - filter_keys: list[str], - filter_values: list[str], - dimensions: list[str], - granularity: str = "minute", - aggregations: dict = None, - group_by: list[str] = None, - pivot: Pivot = None, - hours: float = 24, + self, + datasource: str, + filter_keys: list[str], + filter_values: list[str], + dimensions: list[str], + granularity: str = "minute", + aggregations: dict = None, + group_by: list[str] = None, + pivot: Pivot = None, + hours: float = 24, ) -> pd.DataFrame: _start_time = time.time() filter_pairs = {} @@ -67,7 +67,7 @@ def fetch_data( logging.warning("No data found for keys %s", filter_pairs) return pd.DataFrame() - df["timestamp"] = pd.to_datetime(df["timestamp"]).astype("int64") // 10**6 + df["timestamp"] = pd.to_datetime(df["timestamp"]).astype("int64") // 10 ** 6 if group_by: df = df.groupby(by=group_by).sum().reset_index() @@ -85,4 +85,19 @@ def fetch_data( filter_pairs, df.shape, ) - return df \ No newline at end of file + return df + + +fetcher = DruidFetcher("https://getafix.odldruid-prd.a.intuit.com/", "druid/v2") +df = fetcher.fetch_data( + datasource="tech-ip-customer-interaction-metrics", + filter_keys=["assetId"], + filter_values=["5984175597303660107"], + dimensions=["ciStatus"], + group_by=["timestamp", "ciStatus"], + pivot=Pivot(index="timestamp", columns=["ciStatus"], value=["count"]), + aggregations={"count": {"type": "doubleSum", "fieldName": "count", "name": "count"}}, + hours=240, +) +df.to_csv("test.csv") +print(df) \ No newline at end of file diff --git a/udf/anomaly-detection/src/entities.py b/udf/anomaly-detection/src/entities.py index db4acc16..1fd56be1 100644 --- a/udf/anomaly-detection/src/entities.py +++ b/udf/anomaly-detection/src/entities.py @@ -42,7 +42,7 @@ class _BasePayload: @dataclass class TrainerPayload(_BasePayload): - metric: str + metrics: List[str] header: Header = Header.TRAIN_REQUEST def to_json(self): @@ -55,9 +55,9 @@ class StreamPayload(_BasePayload): raw_data: Matrix metrics: List[str] timestamps: List[int] - status: Dict[str, Status] = field(default_factory=dict) - header: Dict[str, Header] = field(default_factory=dict) - metadata: Dict[str, Dict[str, Any]] = field(default_factory=dict) + status: Status = field(default_factory=lambda x: Status.RAW) + header: Header = field(default_factory=lambda x: Header.MODEL_INFERENCE) + metadata: Dict[str, Any] = field(default_factory=dict) def get_df(self, original=False) -> pd.DataFrame: return pd.DataFrame(self.get_data(original), columns=self.metrics) @@ -70,25 +70,16 @@ def set_metric_data(self, metric: str, arr: Matrix) -> None: _df[metric] = arr self.set_data(np.asarray(_df.values.tolist())) - def get_metric_arr(self, metric: str) -> npt.NDArray[float]: - return self.get_df()[metric].values - def get_data(self, original=False) -> npt.NDArray[float]: if original: return np.asarray(self.raw_data) return np.asarray(self.data) - def set_status(self, metric: str, status: Status) -> None: - self.status[metric] = status - - def set_header(self, metric: str, header: Header) -> None: - self.header[metric] = header + def set_status(self, status: Status) -> None: + self.status = status - def set_metric_metadata(self, metric: str, key: str, value) -> None: - if metric in self.metadata.keys(): - self.metadata[metric][key] = value - else: - self.metadata[metric] = {key: value} + def set_header(self, header: Header) -> None: + self.header = header def set_metadata(self, key: str, value) -> None: self.metadata[key] = value diff --git a/udf/anomaly-detection/src/tools.py b/udf/anomaly-detection/src/tools.py index 609830d6..e7afa2ab 100644 --- a/udf/anomaly-detection/src/tools.py +++ b/udf/anomaly-detection/src/tools.py @@ -14,7 +14,7 @@ from numalogic.config import PostprocessFactory, ModelInfo from numalogic.models.threshold import SigmoidThreshold -from src import get_logger, MetricConf +from src import get_logger from src._config import StaticThreshold from src.connectors.prometheus import Prometheus from src.entities import TrainerPayload, Matrix @@ -76,7 +76,6 @@ def is_host_reachable(hostname: str, port=None, max_retries=5, sleep_sec=5) -> b def fetch_data( payload: TrainerPayload, - metric_config: MetricConf, labels: dict, return_labels=None, hours: int = 36, @@ -89,12 +88,12 @@ def fetch_data( start_dt = end_dt - timedelta(hours=hours) df = datafetcher.query_metric( - metric_name=payload.composite_keys["name"], + metric_name=payload.composite_keys[1], labels_map=labels, return_labels=return_labels, start=start_dt.timestamp(), end=end_dt.timestamp(), - step=metric_config.scrape_interval, + step=prometheus_conf.scrape_interval, ) _LOGGER.info( "%s - Time taken to fetch data: %s, for df shape: %s", @@ -170,7 +169,7 @@ def get_static_score(self, x_arr) -> float: static_score = np.mean(static_scores) return self.postproc_clf.transform(static_score) - def get_norm_score(self, x_arr: Matrix): + def get_norm_score(self, x_arr: Matrix) -> float: """ Returns the normalized window score @@ -180,6 +179,7 @@ def get_norm_score(self, x_arr: Matrix): Returns: Score for the window """ + win_score = np.mean(x_arr) return self.postproc_clf.transform(win_score) diff --git a/udf/anomaly-detection/src/udf/inference.py b/udf/anomaly-detection/src/udf/inference.py index 9a15aaf1..c59fc3e1 100644 --- a/udf/anomaly-detection/src/udf/inference.py +++ b/udf/anomaly-detection/src/udf/inference.py @@ -33,14 +33,13 @@ def __init__(self): def _run_inference( cls, keys: list[str], - metric: str, payload: StreamPayload, artifact_data: ArtifactData, ) -> np.ndarray: model = artifact_data.artifact - win_size = ConfigManager.get_ds_config(config_name=keys[0]).window_size - metric_arr = payload.get_metric_arr(metric=metric).reshape(-1, 1) - stream_loader = DataLoader(StreamingDataset(metric_arr, win_size)) + win_size = ConfigManager.get_stream_config(config_name=keys[0]).window_size + data_arr = payload.get_data().reshape(-1, 1) + stream_loader = DataLoader(StreamingDataset(data_arr, win_size)) trainer = AutoencoderTrainer() try: @@ -50,35 +49,35 @@ def _run_inference( "%s - Runtime error while performing inference: Keys: %s, Metric: %s, Error: %r", payload.uuid, keys, - metric, + payload.metrics, err, ) raise RuntimeError("Failed to infer") from err - _LOGGER.info("%s - Successfully inferred: Keys: %s, Metric: %s", payload.uuid, keys, metric) + _LOGGER.info("%s - Successfully inferred: Keys: %s, Metric: %s", payload.uuid, keys, payload.metrics) return recon_err.numpy().flatten() def inference( - self, keys: List[str], metric: str, payload: StreamPayload + self, keys: List[str], payload: StreamPayload ) -> (np.ndarray, Status, Header, int): static_response = (None, Status.ARTIFACT_NOT_FOUND, Header.STATIC_INFERENCE, -1) # Check if metric needs static inference - if payload.header[metric] == Header.STATIC_INFERENCE: + if payload.header == Header.STATIC_INFERENCE: _LOGGER.debug( - "%s - Models not found in the previous steps, forwarding for static thresholding. Keys: %s, Metric: %s", + "%s - Models not found in the previous steps, forwarding for static thresholding. Keys: %s, Metrics: %s", payload.uuid, payload.composite_keys, - metric, + payload.metrics, ) return static_response # Load config - retrain_config = ConfigManager.get_retrain_config(config_name=keys[0], metric_name=metric) - numalogic_conf = ConfigManager.get_numalogic_config(config_name=keys[0], metric_name=metric) + retrain_config = ConfigManager.get_retrain_config(config_name=keys[0]) + numalogic_conf = ConfigManager.get_numalogic_config(config_name=keys[0]) # Load inference artifact try: artifact_data = self.model_registry.load( - skeys=keys + [metric], + skeys=keys, dkeys=[numalogic_conf.model.name], ) except RedisRegistryError as err: @@ -86,7 +85,7 @@ def inference( "%s - Error while fetching inference artifact, Keys: %s, Metric: %s, Error: %r", payload.uuid, payload.composite_keys, - metric, + payload.metrics, err, ) return static_response @@ -97,7 +96,7 @@ def inference( "%s - Inference artifact not found, forwarding for static thresholding. Keys: %s, Metric: %s", payload.uuid, payload.composite_keys, - metric, + payload.metrics, ) return static_response @@ -115,19 +114,19 @@ def inference( "%s - Inference artifact found is stale, Keys: %s, Metric: %s", payload.uuid, payload.composite_keys, - metric, + payload.metrics, ) header = Header.MODEL_STALE # Generate predictions try: - x_infered = self._run_inference(keys, metric, payload, artifact_data) + x_infered = self._run_inference(keys, payload, artifact_data) except RuntimeError: _LOGGER.info( "%s - Failed to infer, forwarding for static thresholding. Keys: %s, Metric: %s", payload.uuid, payload.composite_keys, - metric, + payload.metrics, ) return None, Status.RUNTIME_ERROR, Header.STATIC_INFERENCE, -1 @@ -145,15 +144,14 @@ def run(self, keys: List[str], datum: Datum) -> Messages: _LOGGER.info("%s - Received Msg: { Keys: %s, Payload: %r }", payload.uuid, keys, payload) messages = Messages() - # Perform inference for each metric - for metric in payload.metrics: - x_infered, status, header, version = self.inference(keys, metric, payload) - payload.set_status(metric=metric, status=status) - payload.set_header(metric=metric, header=header) - payload.set_metric_metadata(metric=metric, key="model_version", value=version) - - if x_infered is not None: - payload.set_metric_data(metric=metric, arr=x_infered) + # Perform inference + x_infered, status, header, version = self.inference(keys, payload) + payload.set_status(status=status) + payload.set_header(header=header) + payload.set_metadata(key="model_version", value=version) + + if x_infered is not None: + payload.set_data(arr=x_infered) messages.append(Message(keys=keys, value=payload.to_json())) _LOGGER.info("%s - Sending Msg: { Keys: %s, Payload: %r }", payload.uuid, keys, payload) diff --git a/udf/anomaly-detection/src/udf/postprocess.py b/udf/anomaly-detection/src/udf/postprocess.py index 57d84468..9eaa2d07 100644 --- a/udf/anomaly-detection/src/udf/postprocess.py +++ b/udf/anomaly-detection/src/udf/postprocess.py @@ -15,45 +15,53 @@ class Postprocess: @classmethod - def postprocess(cls, keys: list[str], metric: str, payload: StreamPayload) -> float: - static_thresh = ConfigManager.get_static_threshold_config( - config_name=keys[0], metric_name=metric - ) - postprocess_conf = ConfigManager.get_postprocess_config( - config_name=keys[0], metric_name=metric - ) + def postprocess(cls, keys: list[str], payload: StreamPayload) -> (float, dict): + static_thresh = ConfigManager.get_static_threshold_config(config_name=keys[0]) + postprocess_conf = ConfigManager.get_postprocess_config(config_name=keys[0]) + unified_config = ConfigManager.get_stream_config(config_name=keys[0]).unified_config + + # TODO: Implement weighted average or max strategy for unified anomaly + # if weights: + # weighted_anomalies = np.multiply(scores, unified_weights) + # unified_anomaly = float(np.sum(weighted_anomalies) / np.sum(unified_weights)) # Compute score using static thresholding - metric_arr = payload.get_metric_arr(metric=metric) + metric_arr = payload.get_data() win_scorer = WindowScorer(static_thresh, postprocess_conf) - if payload.header[metric] == Header.STATIC_INFERENCE: + if payload.header == Header.STATIC_INFERENCE: final_score = win_scorer.get_norm_score(metric_arr) _LOGGER.info( - "%s - Final static threshold score: %s, keys: %s, metric: %s", + "%s - Final static threshold score: %s, keys: %s, metrics: %s", payload.uuid, final_score, keys, - metric, + payload.metrics, ) # Compute ensemble score otherwise else: final_score = win_scorer.get_ensemble_score(metric_arr) _LOGGER.info( - "%s - Final ensemble score: %s, static thresh wt: %s, keys: %s, metric: %s", + "%s - Final ensemble score: %s, static thresh wt: %s, keys: %s, metrics: %s", payload.uuid, final_score, static_thresh.weight, keys, - metric, + payload.metrics, ) - return final_score + + # TODO: construct map + metric_scores = {} + for metric in payload.metrics: + metric_scores[metric] = 0 + + return final_score, metric_scores @classmethod def get_unified_anomaly( - cls, keys: List[str], scores: list[float], payload: StreamPayload + cls, keys: List[str], scores: list[float], payload: StreamPayload ) -> float: - unified_config = ConfigManager.get_ds_config(config_name=keys[0]).unified_config + unified_config = ConfigManager.get_stream_config(config_name=keys[0]).unified_config unified_weights = unified_config.weights if unified_weights: weighted_anomalies = np.multiply(scores, unified_weights) @@ -86,24 +94,13 @@ def run(self, keys: List[str], datum: Datum) -> Messages: messages = Messages() - scores = [] - metric_data = {} - - # Perform postprocess for each metric - for metric in payload.metrics: - final_score = self.postprocess(keys, metric, payload) - scores.append(final_score) - metric_data[metric] = { - "anomaly_score": final_score, - "model_version": payload.get_metadata(key=metric)["model_version"], - } - - unified_anomaly = self.get_unified_anomaly(keys, scores, payload) + # Perform postprocess + final_score, metric_scores = self.postprocess(keys, payload) out_payload = OutputPayload( timestamp=payload.timestamps[-1], - unified_anomaly=unified_anomaly, - data=metric_data, + unified_anomaly=final_score, + data=metric_scores, metadata=payload.metadata, ) diff --git a/udf/anomaly-detection/src/udf/preprocess.py b/udf/anomaly-detection/src/udf/preprocess.py index 4e28dddb..0a679e03 100644 --- a/udf/anomaly-detection/src/udf/preprocess.py +++ b/udf/anomaly-detection/src/udf/preprocess.py @@ -19,6 +19,7 @@ _LOGGER = get_logger(__name__) LOCAL_CACHE_TTL = int(os.getenv("LOCAL_CACHE_TTL", 3600)) + class Preprocess: @classmethod def get_df(cls, data_payload: dict, features: List[str]) -> (pd.DataFrame, List[int]): @@ -45,25 +46,24 @@ def get_df(cls, data_payload: dict, features: List[str]) -> (pd.DataFrame, List[ return df, [*timestamps] def preprocess( - self, keys: List[str], metric: str, payload: StreamPayload + self, keys: List[str], payload: StreamPayload ) -> (np.ndarray, Status): - preprocess_cfgs = ConfigManager.get_preprocess_config( - config_name=keys[0], metric_name=metric - ) + preprocess_cfgs = ConfigManager.get_preprocess_config(config_name=keys[0]) + local_cache = LocalLRUCache(ttl=LOCAL_CACHE_TTL) model_registry = RedisRegistry(client=get_redis_client_from_conf(master_node=False), cache_registry=local_cache) # Load preproc artifact try: preproc_artifact = model_registry.load( - skeys=keys + [metric], + skeys=keys, dkeys=[_conf.name for _conf in preprocess_cfgs], ) except RedisRegistryError as err: _LOGGER.error( - "%s - Error while fetching preproc artifact, Keys: %s, Metric: %s, Error: %r", + "%s - Error while fetching preproc artifact, Keys: %s, Metrics: %s, Error: %r", payload.uuid, keys, - metric, + payload.metrics, err, ) return None, Status.RUNTIME_ERROR @@ -71,22 +71,22 @@ def preprocess( # Check if artifact is found if not preproc_artifact: _LOGGER.info( - "%s - Preprocess artifact not found, forwarding for static thresholding. Keys: %s, Metric: %s", + "%s - Preprocess artifact not found, forwarding for static thresholding. Keys: %s, Metrics: %s", payload.uuid, keys, - metric, + payload.metrics, ) return None, Status.ARTIFACT_NOT_FOUND # Perform preprocessing - x_raw = payload.get_metric_arr(metric).reshape(-1, 1) + x_raw = payload.get_data().reshape(-1, 1) preproc_clf = preproc_artifact.artifact x_scaled = preproc_clf.transform(x_raw).flatten() _LOGGER.info( - "%s - Successfully preprocessed, Keys: %s, Metric: %s, x_scaled: %s", + "%s - Successfully preprocessed, Keys: %s, Metrics: %s, x_scaled: %s", payload.uuid, keys, - metric, + payload.metrics, list(x_scaled), ) return x_scaled, Status.PRE_PROCESSED @@ -108,7 +108,7 @@ def run(self, keys: List[str], datum: Datum) -> Messages: return messages # Load config - stream_conf = ConfigManager.get_ds_config(config_name=keys[0]) + stream_conf = ConfigManager.get_stream_config(config_name=keys[0]) raw_df, timestamps = self.get_df(data_payload, stream_conf.metrics) # Prepare payload for forwarding @@ -130,17 +130,16 @@ def run(self, keys: List[str], datum: Datum) -> Messages: keys, ) - # Perform preprocessing for each metric - for metric in payload.metrics: - x_scaled, status = self.preprocess(keys, metric, payload) - payload.set_status(metric=metric, status=status) - - # If preprocess failed, forward for static thresholding - if x_scaled is None: - payload.set_header(metric=metric, header=Header.STATIC_INFERENCE) - else: - payload.set_header(metric=metric, header=Header.MODEL_INFERENCE) - payload.set_metric_data(metric=metric, arr=x_scaled) + # Perform preprocessing + x_scaled, status = self.preprocess(keys, payload) + payload.set_status(status=status) + + # If preprocess failed, forward for static thresholding + if x_scaled is None: + payload.set_header(header=Header.STATIC_INFERENCE) + else: + payload.set_header(header=Header.MODEL_INFERENCE) + payload.set_data(arr=x_scaled) messages.append(Message(keys=keys, value=payload.to_json())) _LOGGER.info("%s - Sending Msg: { Keys: %s, Payload: %r }", payload.uuid, keys, payload) diff --git a/udf/anomaly-detection/src/udf/threshold.py b/udf/anomaly-detection/src/udf/threshold.py index 3df06d76..4f7e8e51 100644 --- a/udf/anomaly-detection/src/udf/threshold.py +++ b/udf/anomaly-detection/src/udf/threshold.py @@ -29,21 +29,19 @@ def __init__(self): def threshold( self, keys: List[str], metric: str, payload: StreamPayload ) -> (np.ndarray, Status, Header, int): - metric_arr = payload.get_metric_arr(metric=metric) + metric_arr = payload.get_data() # Load config - static_thresh = ConfigManager.get_static_threshold_config( - config_name=keys[0], metric_name=metric - ) - thresh_cfg = ConfigManager.get_threshold_config(config_name=keys[0], metric_name=metric) + static_thresh = ConfigManager.get_static_threshold_config(config_name=keys[0]) + thresh_cfg = ConfigManager.get_threshold_config(config_name=keys[0]) # Check if metric needs static inference - if payload.header[metric] == Header.STATIC_INFERENCE: + if payload.header == Header.STATIC_INFERENCE: _LOGGER.info( "%s - Sending to trainer and performing static thresholding. Keys: %s, Metric: %s", payload.uuid, payload.composite_keys, - metric, + payload.metrics, ) static_scores = calculate_static_thresh(metric_arr, static_thresh) return static_scores, Status.ARTIFACT_NOT_FOUND, Header.STATIC_INFERENCE, -1 @@ -51,7 +49,7 @@ def threshold( # Load threshold artifact try: thresh_artifact = self.model_registry.load( - skeys=keys + [metric], + skeys=keys, dkeys=[thresh_cfg.name], ) except RedisRegistryError as err: @@ -59,7 +57,7 @@ def threshold( "%s - Error while fetching threshold artifact, Keys: %s, Metric: %s, Error: %r", payload.uuid, payload.composite_keys, - metric, + payload.metrics, err, ) static_scores = calculate_static_thresh(metric_arr, static_thresh) @@ -76,15 +74,15 @@ def threshold( return static_scores, Status.ARTIFACT_NOT_FOUND, Header.STATIC_INFERENCE, -1 # Calculate anomaly score - recon_err = payload.get_metric_arr(metric=metric) + recon_err = payload.get_data() thresh_clf = thresh_artifact.artifact y_score = thresh_clf.score_samples(recon_err) return ( y_score, Status.THRESHOLD, - payload.header[metric], - payload.get_metadata(key=metric)["model_version"], + payload.header, + payload.get_metadata(key="model_version"), ) def run(self, keys: List[str], datum: Datum) -> Messages: @@ -103,16 +101,16 @@ def run(self, keys: List[str], datum: Datum) -> Messages: # Perform threshold for each metric for metric in payload.metrics: y_score, status, header, version = self.threshold(keys, metric, payload) - payload.set_status(metric=metric, status=status) - payload.set_header(metric=metric, header=header) - payload.set_metric_metadata(metric=metric, key="model_version", value=version) + payload.set_status(status=status) + payload.set_header(header=header) + payload.set_metadata(key="model_version", value=version) if y_score is not None: - payload.set_metric_data(metric=metric, arr=y_score) + payload.set_data(arr=y_score) if y_score is None or header == Header.MODEL_STALE or status == Status.ARTIFACT_NOT_FOUND: train_payload = TrainerPayload( - uuid=payload.uuid, composite_keys=keys, metric=metric + uuid=payload.uuid, composite_keys=keys, metrics=payload.metrics ) _LOGGER.info( "%s - Sending Msg: { Keys: %s, Tags:%s, Payload: %s }", diff --git a/udf/anomaly-detection/src/udsink/train.py b/udf/anomaly-detection/src/udsink/train.py index 352ad32c..2c38783b 100644 --- a/udf/anomaly-detection/src/udsink/train.py +++ b/udf/anomaly-detection/src/udsink/train.py @@ -1,8 +1,5 @@ -import json import os import time -from datetime import datetime - import orjson import pandas as pd from typing import List, Iterator @@ -45,14 +42,14 @@ def fetch_prometheus_data(cls, payload: TrainerPayload) -> pd.DataFrame: return pd.DataFrame() data_fetcher = PrometheusDataFetcher(prometheus_conf.server) return data_fetcher.fetch_data( - metric=payload.metric, + metric=payload.metrics[0], labels={"namespace": payload.composite_keys[1]}, return_labels=["rollouts_pod_template_hash"], ) @classmethod def fetch_druid_data(cls, payload: TrainerPayload) -> pd.DataFrame: - stream_config = ConfigManager.get_ds_config(payload.composite_keys[0]) + stream_config = ConfigManager.get_stream_config(payload.composite_keys[0]) druid_conf = ConfigManager.get_druid_config() fetcher_conf = stream_config.druid_fetcher if druid_conf is None: @@ -62,8 +59,8 @@ def fetch_druid_data(cls, payload: TrainerPayload) -> pd.DataFrame: return data_fetcher.fetch_data( datasource=fetcher_conf.datasource, - filter_keys=stream_config.composite_keys + fetcher_conf.dimensions, - filter_values=payload.composite_keys[1:] + [payload.metric], # skip config name and add metric name + filter_keys=stream_config.composite_keys, + filter_values=payload.composite_keys[1:], # skip config name and add metric name dimensions=OmegaConf.to_container(fetcher_conf.dimensions), granularity=fetcher_conf.granularity, aggregations=OmegaConf.to_container(fetcher_conf.aggregations), @@ -74,7 +71,7 @@ def fetch_druid_data(cls, payload: TrainerPayload) -> pd.DataFrame: @classmethod def fetch_data(cls, payload: TrainerPayload) -> pd.DataFrame: - stream_config = ConfigManager.get_ds_config(payload.composite_keys[0]) + stream_config = ConfigManager.get_stream_config(payload.composite_keys[0]) if stream_config.source == DataSource.PROMETHEUS: return cls.fetch_prometheus_data(payload) elif stream_config.source == DataSource.DRUID: @@ -89,7 +86,7 @@ def fetch_data(cls, payload: TrainerPayload) -> pd.DataFrame: @classmethod def _is_new_request(cls, redis_client: redis_client_t, payload: TrainerPayload) -> bool: - _ckeys = ":".join(payload.composite_keys + [payload.metric]) + _ckeys = ":".join(payload.composite_keys) r_key = f"train::{_ckeys}" value = redis_client.get(r_key) if value: @@ -147,12 +144,14 @@ def _train_and_save( "%s - Starting Training for keys: %s, metric: %s", payload.uuid, payload.composite_keys, - payload.metric, + payload.metrics, ) model_cfg = numalogic_conf.model preproc_cfgs = numalogic_conf.preprocess + # TODO: filter the metrics here + x_train, preproc_clf = self._preprocess(train_df.to_numpy(), preproc_cfgs) trainer_cfg = numalogic_conf.trainer @@ -163,7 +162,7 @@ def _train_and_save( thresh_cfg = numalogic_conf.threshold thresh_clf = self._find_threshold(x_reconerr, thresh_cfg) - skeys = payload.composite_keys + [payload.metric] + skeys = payload.composite_keys # TODO if one of the models fail to save, delete the previously saved models and transition stage # Save main model @@ -247,20 +246,17 @@ def run(self, datums: Iterator[Datum]) -> Responses: responses.append(Response.as_success(_datum.id)) continue - metric_config = ConfigManager.get_metric_config( - payload.composite_keys[0], payload.metric - ) - retrain_config = metric_config.retrain_conf - numalogic_config = metric_config.numalogic_conf + retrain_config = ConfigManager.get_retrain_config(payload.composite_keys[0]) + numalogic_config = ConfigManager.get_numalogic_config(payload.composite_keys[0]) try: train_df = self.fetch_data(payload) except Exception as err: _LOGGER.error( - "%s - Error while fetching data for keys: %s, metric: %s, err: %r", + "%s - Error while fetching data for keys: %s, metrics: %s, err: %r", payload.uuid, payload.composite_keys, - payload.metric, + payload.metrics, err, ) responses.append(Response.as_success(_datum.id)) diff --git a/udf/anomaly-detection/src/watcher.py b/udf/anomaly-detection/src/watcher.py index 12d26eee..740f4f18 100644 --- a/udf/anomaly-detection/src/watcher.py +++ b/udf/anomaly-detection/src/watcher.py @@ -8,9 +8,9 @@ from watchdog.events import FileSystemEventHandler from src import PipelineConf, Configs -from src.connectors import RedisConf, PrometheusConf, RegistryConf +from src.connectors import PrometheusConf from src._constants import CONFIG_DIR -from src import DataStreamConf, get_logger, MetricConf, UnifiedConf +from src import StreamConf, get_logger, UnifiedConf from src.connectors._config import DruidConf _LOGGER = get_logger(__name__) @@ -55,51 +55,34 @@ def update_configs(cls): return cls.config @classmethod - def get_ds_config(cls, config_name: str) -> DataStreamConf: + def get_stream_config(cls, config_name: str) -> StreamConf: if not cls.config: cls.update_configs() - ds_config = None + stream_conf = None # search and load from user configs if config_name in cls.config["user_configs"]: - ds_config = cls.config["user_configs"][config_name] + stream_conf = cls.config["user_configs"][config_name] # if not search and load from default configs - if not ds_config and config_name in cls.config["default_configs"]: - ds_config = cls.config["default_configs"][config_name] + if not stream_conf and config_name in cls.config["default_configs"]: + stream_conf = cls.config["default_configs"][config_name] # if not in default configs, initialize conf with default values - if not ds_config: - ds_config = OmegaConf.structured(DataStreamConf) + if not stream_conf: + stream_conf = OmegaConf.structured(StreamConf) # loading and setting default numalogic config - for metric_config in ds_config.metric_configs: - if OmegaConf.is_missing(metric_config, "numalogic_conf"): - metric_config.numalogic_conf = cls.config["default_numalogic"] + if OmegaConf.is_missing(stream_conf, "numalogic_conf"): + stream_conf.numalogic_conf = cls.config["default_numalogic"] - return ds_config - - @classmethod - def get_metric_config(cls, config_name: str, metric_name: str) -> MetricConf: - ds_config = cls.get_ds_config(config_name) - metric_config = list( - filter(lambda conf: (conf.metric == metric_name), ds_config.metric_configs) - ) - - # if no config found for metric, initialize with default values - if not metric_config: - metric_config = OmegaConf.structured(MetricConf(metric=metric_name)) - if OmegaConf.is_missing(metric_config, "numalogic_conf"): - metric_config.numalogic_conf = cls.config["default_numalogic"] - - return metric_config - return metric_config[0] + return stream_conf @classmethod def get_unified_config(cls, config_name: str) -> UnifiedConf: - ds_config = cls.get_ds_config(config_name) - return ds_config.unified_config + stream_conf = cls.get_stream_config(config_name) + return stream_conf.unified_config @classmethod def get_pipeline_config(cls) -> PipelineConf: @@ -120,40 +103,32 @@ def get_druid_config(cls) -> Optional[DruidConf]: return None @classmethod - def get_numalogic_config(cls, config_name: str, metric_name: str): - return cls.get_metric_config( - config_name=config_name, metric_name=metric_name - ).numalogic_conf + def get_numalogic_config(cls, config_name: str): + return cls.get_stream_config(config_name=config_name).numalogic_conf @classmethod - def get_preprocess_config(cls, config_name: str, metric_name: str): - return cls.get_numalogic_config(config_name=config_name, metric_name=metric_name).preprocess + def get_preprocess_config(cls, config_name: str): + return cls.get_numalogic_config(config_name=config_name).preprocess @classmethod - def get_retrain_config(cls, config_name: str, metric_name: str): - return cls.get_metric_config(config_name=config_name, metric_name=metric_name).retrain_conf + def get_retrain_config(cls, config_name: str,): + return cls.get_stream_config(config_name=config_name).retrain_conf @classmethod - def get_static_threshold_config(cls, config_name: str, metric_name: str): - return cls.get_metric_config( - config_name=config_name, metric_name=metric_name - ).static_threshold + def get_static_threshold_config(cls, config_name: str): + return cls.get_stream_config(config_name=config_name).static_threshold @classmethod - def get_threshold_config(cls, config_name: str, metric_name: str): - return cls.get_metric_config( - config_name=config_name, metric_name=metric_name - ).numalogic_conf.threshold + def get_threshold_config(cls, config_name: str): + return cls.get_stream_config(config_name=config_name).numalogic_conf.threshold @classmethod - def get_postprocess_config(cls, config_name: str, metric_name: str): - return cls.get_numalogic_config( - config_name=config_name, metric_name=metric_name - ).postprocess + def get_postprocess_config(cls, config_name: str): + return cls.get_numalogic_config(config_name=config_name).postprocess @classmethod - def get_trainer_config(cls, config_name: str, metric_name: str): - return cls.get_numalogic_config(config_name=config_name, metric_name=metric_name).trainer + def get_trainer_config(cls, config_name: str): + return cls.get_numalogic_config(config_name=config_name).trainer class ConfigHandler(FileSystemEventHandler): diff --git a/udf/anomaly-detection/tests/__init__.py b/udf/anomaly-detection/tests/__init__.py index 4200b211..cbd66490 100644 --- a/udf/anomaly-detection/tests/__init__.py +++ b/udf/anomaly-detection/tests/__init__.py @@ -6,7 +6,7 @@ from omegaconf import OmegaConf from src._config import PipelineConf, Configs -from src._constants import TESTS_RESOURCES, CONFIG_DIR +from src._constants import TESTS_RESOURCES from src.watcher import ConfigManager server = fakeredis.FakeServer() @@ -16,17 +16,17 @@ def mock_configs(): schema: Configs = OmegaConf.structured(Configs) - conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "users-config.yaml")) + conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "users_config.yaml")) user_configs = OmegaConf.merge(schema, conf).configs - conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "default-config.yaml")) + conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "default_config.yaml")) default_configs = OmegaConf.merge(schema, conf).configs conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "numalogic_config.yaml")) schema: NumalogicConf = OmegaConf.structured(NumalogicConf) default_numalogic = OmegaConf.merge(schema, conf) - conf = OmegaConf.load(os.path.join(CONFIG_DIR, "default-configs", "pipeline_config.yaml")) + conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "pipeline_config.yaml")) schema: PipelineConf = OmegaConf.structured(PipelineConf) pipeline_config = OmegaConf.merge(schema, conf) diff --git a/udf/anomaly-detection/tests/resources/configs/default-config.yaml b/udf/anomaly-detection/tests/resources/configs/default_config.yaml similarity index 51% rename from udf/anomaly-detection/tests/resources/configs/default-config.yaml rename to udf/anomaly-detection/tests/resources/configs/default_config.yaml index 7f1ce8f9..cdfacc7a 100644 --- a/udf/anomaly-detection/tests/resources/configs/default-config.yaml +++ b/udf/anomaly-detection/tests/resources/configs/default_config.yaml @@ -19,21 +19,5 @@ configs: - name: "argo-rollouts" composite_keys: [ "namespace", "name", "app", "rollouts_pod_template_hash" ] metrics: [ "namespace_app_rollouts_http_request_error_rate"] - metric_configs: - - metric: "namespace_app_rollouts_http_request_error_rate" - static_threshold: - weight: 0.7 - - name: "argo-cd" - composite_keys: [ "namespace", "name" ] - metrics: [ "namespace_app_http_server_requests_errors", - "namespace_app_http_server_requests_error_rate", - "namespace_app_http_server_requests_latency", - "namespace_app_cpu_utilization", - "namespace_app_memory_utilization" ] - metric_configs: - - metric: "namespace_app_cpu_utilization" - static_threshold: - upper_limit: 80 - - metric: "namespace_app_memory_utilization" - static_threshold: - upper_limit: 80 \ No newline at end of file + static_threshold: + weight: 0.7 \ No newline at end of file diff --git a/udf/anomaly-detection/tests/resources/configs/pipeline_config.yaml b/udf/anomaly-detection/tests/resources/configs/pipeline_config.yaml new file mode 100644 index 00000000..5b9a4590 --- /dev/null +++ b/udf/anomaly-detection/tests/resources/configs/pipeline_config.yaml @@ -0,0 +1,8 @@ +redis_conf: + host: "isbsvc-redis-isbs-redis-svc.numalogic.svc" + port: 26379 + expiry: 360 + master_name: "mymaster" +druid_conf: + url: "http://localhost:8888" + endpoint: "druid/v2/" \ No newline at end of file diff --git a/udf/anomaly-detection/tests/resources/configs/users-config.yaml b/udf/anomaly-detection/tests/resources/configs/users_config.yaml similarity index 54% rename from udf/anomaly-detection/tests/resources/configs/users-config.yaml rename to udf/anomaly-detection/tests/resources/configs/users_config.yaml index 7a51abb1..a7ded00a 100644 --- a/udf/anomaly-detection/tests/resources/configs/users-config.yaml +++ b/udf/anomaly-detection/tests/resources/configs/users_config.yaml @@ -1,20 +1,14 @@ configs: - name: "sandbox_numalogic_demo1" composite_keys: [ "namespace", "name", "hash_id" ] - metrics: ["rollout_error_rate", "rollout_latency"] - metric_configs: - - metric: "rollout_error_rate" - static_threshold: - weight: 0.6 - - metric: "rollout_latency" + metrics: ["rollout_error_rate"] + static_threshold: + weight: 0.6 - name: "sandbox_numalogic_demo2" composite_keys: [ "namespace", "name", "hash_id" ] - metrics: ["rollout_error_rate", "rollout_latency"] - metric_configs: - - metric: "rollout_error_rate" - static_threshold: - weight: 0.7 - - metric: "rollout_latency" + metrics: ["rollout_error_rate"] + static_threshold: + weight: 0.7 unified_config: weights: [0.7, 0.3] - name: "sandbox_numalogic_demo3" diff --git a/udf/anomaly-detection/tests/resources/data/druid.csv b/udf/anomaly-detection/tests/resources/data/druid.csv index 81c27586..fe4c8f0f 100644 --- a/udf/anomaly-detection/tests/resources/data/druid.csv +++ b/udf/anomaly-detection/tests/resources/data/druid.csv @@ -1,2161 +1,14378 @@ -timestamp,failed -1689001140000,998.0 -1689001200000,1000.0 -1689001260000,1127.0 -1689001320000,1036.0 -1689001380000,1089.0 -1689001440000,978.0 -1689001500000,1053.0 -1689001560000,918.0 -1689001620000,1162.0 -1689001680000,1071.0 -1689001740000,877.0 -1689001800000,898.0 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+1689618840000,123.0,597.0,1182747.0 +1689618900000,,9.0,22706.0 diff --git a/udf/anomaly-detection/tests/test_trainer.py b/udf/anomaly-detection/tests/test_trainer.py index ebee2e8a..dd3fb618 100644 --- a/udf/anomaly-detection/tests/test_trainer.py +++ b/udf/anomaly-detection/tests/test_trainer.py @@ -38,13 +38,13 @@ class TestTrainer(unittest.TestCase): "metric_1", "123456789", ], - "metric": "metric_1", + "metric": ["metric_1"] } train_payload2 = { "uuid": "2", "composite_keys": ["fciAsset", "5984175597303660107"], - "metric": "failed", + "metric": ["failed", "degraded"] } def setUp(self) -> None: diff --git a/udf/anomaly-detection/tests/test_watcher.py b/udf/anomaly-detection/tests/test_watcher.py index be5b2753..c811c6c0 100644 --- a/udf/anomaly-detection/tests/test_watcher.py +++ b/udf/anomaly-detection/tests/test_watcher.py @@ -25,49 +25,19 @@ def test_load_configs(self): def test_get_datastream_config(self): # from users config - ds_config = self.cm.get_ds_config(config_name="sandbox_numalogic_demo1") - self.assertTrue(ds_config) - self.assertEqual(ds_config.name, "sandbox_numalogic_demo1") + stream_conf = self.cm.get_stream_config(config_name="sandbox_numalogic_demo1") + self.assertTrue(stream_conf) + self.assertEqual(stream_conf.name, "sandbox_numalogic_demo1") # from given default config - ds_config = self.cm.get_ds_config(config_name="argo-rollouts") - self.assertTrue(ds_config) - self.assertEqual(ds_config.name, "argo-rollouts") + stream_conf = self.cm.get_stream_config(config_name="argo-rollouts") + self.assertTrue(stream_conf) + self.assertEqual(stream_conf.name, "argo-rollouts") # default config - ds_config = self.cm.get_ds_config(config_name="random") - self.assertTrue(ds_config) - self.assertEqual(ds_config.name, "default") - - def test_get_metric_config(self): - # from given config - metric_config = self.cm.get_metric_config( - config_name="sandbox_numalogic_demo1", metric_name="rollout_latency" - ) - self.assertTrue(metric_config) - self.assertEqual(metric_config.metric, "rollout_latency") - - # from given default config - metric_config = self.cm.get_metric_config( - config_name="argo-rollouts", - metric_name="namespace_app_rollouts_http_request_error_rate", - ) - self.assertTrue(metric_config) - self.assertEqual(metric_config.metric, "namespace_app_rollouts_http_request_error_rate") - - # from given default config - metric_config = self.cm.get_metric_config( - config_name="fciAsset", - metric_name="ciStatus_failed", - ) - self.assertTrue(metric_config) - self.assertEqual(metric_config.metric, "ciStatus_failed") - - # default config - metric_config = self.cm.get_metric_config(config_name="random", metric_name="random_metric") - self.assertTrue(metric_config) - self.assertEqual(metric_config.metric, "random_metric") - self.assertTrue(metric_config.numalogic_conf) + stream_conf = self.cm.get_stream_config(config_name="random") + self.assertTrue(stream_conf) + self.assertEqual(stream_conf.name, "default") def test_get_unified_config(self): # from given config @@ -84,28 +54,15 @@ def test_get_unified_config(self): def test_get_datastream_config_time(self): _start_time = time.perf_counter() - ConfigManager.get_ds_config(config_name="sandbox_numalogic_demo1") + ConfigManager.get_stream_config(config_name="sandbox_numalogic_demo1") time1 = time.perf_counter() - _start_time _start_time = time.perf_counter() - ConfigManager.get_ds_config(config_name="sandbox_numalogic_demo1") + ConfigManager.get_stream_config(config_name="sandbox_numalogic_demo1") time2 = time.perf_counter() - _start_time _start_time = time.perf_counter() self.assertTrue(time2 <= time1) - def test_get_metric_config_time(self): - _start_time = time.perf_counter() - ConfigManager().get_metric_config( - config_name="sandbox_numalogic_demo1", metric_name="rollout_latency" - ) - time1 = time.perf_counter() - _start_time - _start_time = time.perf_counter() - ConfigManager().get_metric_config( - config_name="sandbox_numalogic_demo1", metric_name="rollout_latency" - ) - time2 = time.perf_counter() - _start_time - self.assertTrue(time2 < time1) - def test_get_unified_config_time(self): _start_time = time.perf_counter() ConfigManager().get_unified_config(config_name="sandbox_numalogic_demo1") diff --git a/udf/anomaly-detection/tests/udf/test_inference.py b/udf/anomaly-detection/tests/udf/test_inference.py index 2a6ecfda..e4ad28bc 100644 --- a/udf/anomaly-detection/tests/udf/test_inference.py +++ b/udf/anomaly-detection/tests/udf/test_inference.py @@ -43,10 +43,9 @@ def test_inference(self): self.assertTrue(payload.data) self.assertTrue(payload.raw_data) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.status[metric], Status.INFERRED) - self.assertEqual(payload.header[metric], Header.MODEL_INFERENCE) - self.assertGreater(payload.metadata[metric]["model_version"], 0) + self.assertEqual(payload.status, Status.INFERRED) + self.assertEqual(payload.header, Header.MODEL_INFERENCE) + self.assertGreater(payload.metadata["model_version"], 0) @freeze_time("2022-02-20 12:00:00") @patch.object(RedisRegistry, "load", Mock(return_value=return_mock_lstmae())) @@ -57,10 +56,9 @@ def test_inference_err(self): self.assertTrue(payload.data) self.assertTrue(payload.raw_data) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.status[metric], Status.RUNTIME_ERROR) - self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) - self.assertEqual(payload.metadata[metric]["model_version"], -1) + self.assertEqual(payload.status, Status.RUNTIME_ERROR) + self.assertEqual(payload.header, Header.STATIC_INFERENCE) + self.assertEqual(payload.metadata["model_version"], -1) @patch.object(RedisRegistry, "load", Mock(return_value=None)) def test_no_model(self): @@ -69,10 +67,9 @@ def test_no_model(self): self.assertTrue(payload.data) self.assertTrue(payload.raw_data) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.status[metric], Status.ARTIFACT_NOT_FOUND) - self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) - self.assertEqual(payload.metadata[metric]["model_version"], -1) + self.assertEqual(payload, Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload, Header.STATIC_INFERENCE) + self.assertEqual(payload.metadata["model_version"], -1) @freeze_time("2022-02-20 12:00:00") @patch.object(RedisRegistry, "load", Mock(return_value=return_mock_lstmae())) @@ -83,10 +80,9 @@ def test_no_prev_model(self): self.assertTrue(payload.data) self.assertTrue(payload.raw_data) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.status[metric], Status.ARTIFACT_NOT_FOUND) - self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) - self.assertEqual(payload.metadata[metric]["model_version"], -1) + self.assertEqual(payload.status, Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.header, Header.STATIC_INFERENCE) + self.assertEqual(payload.metadata["model_version"], -1) @patch.object(RedisRegistry, "load", Mock(return_value=return_stale_model())) def test_stale_model(self): @@ -95,10 +91,9 @@ def test_stale_model(self): self.assertTrue(payload.data) self.assertTrue(payload.raw_data) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.status[metric], Status.INFERRED) - self.assertEqual(payload.header[metric], Header.MODEL_STALE) - self.assertGreater(payload.metadata[metric]["model_version"], 0) + self.assertEqual(payload.status, Status.INFERRED) + self.assertEqual(payload.header, Header.MODEL_STALE) + self.assertGreater(payload.metadata["model_version"], 0) if __name__ == "__main__": diff --git a/udf/anomaly-detection/tests/udf/test_postprocess.py b/udf/anomaly-detection/tests/udf/test_postprocess.py index 3d291ac6..a6ede621 100644 --- a/udf/anomaly-detection/tests/udf/test_postprocess.py +++ b/udf/anomaly-detection/tests/udf/test_postprocess.py @@ -27,8 +27,8 @@ def test_postprocess(self): payload = OutputPayload(**orjson.loads(msg.value.decode("utf-8"))) self.assertIsInstance(payload, OutputPayload) self.assertTrue(payload.unified_anomaly) + self.assertGreater(payload.metadata["model_version"], 0) for metric, metric_data in payload.data.items(): - self.assertGreater(metric_data["model_version"], 0) self.assertTrue(metric_data["anomaly_score"]) def test_preprocess_prev_stale_model(self): @@ -37,8 +37,8 @@ def test_preprocess_prev_stale_model(self): payload = OutputPayload(**orjson.loads(msg.value.decode("utf-8"))) self.assertIsInstance(payload, OutputPayload) self.assertTrue(payload.unified_anomaly) + self.assertGreater(payload.metadata["model_version"], 0) for metric, metric_data in payload.data.items(): - self.assertGreater(metric_data["model_version"], 0) self.assertTrue(metric_data["anomaly_score"]) def test_preprocess_no_prev_clf(self): @@ -47,8 +47,8 @@ def test_preprocess_no_prev_clf(self): payload = OutputPayload(**orjson.loads(msg.value.decode("utf-8"))) self.assertIsInstance(payload, OutputPayload) self.assertTrue(payload.unified_anomaly) + self.assertEqual(payload.metadata["model_version"], -1) for metric, metric_data in payload.data.items(): - self.assertEqual(metric_data["model_version"], -1) self.assertTrue(metric_data["anomaly_score"]) diff --git a/udf/anomaly-detection/tests/udf/test_preprocess.py b/udf/anomaly-detection/tests/udf/test_preprocess.py index d824743c..4a2ccc08 100644 --- a/udf/anomaly-detection/tests/udf/test_preprocess.py +++ b/udf/anomaly-detection/tests/udf/test_preprocess.py @@ -38,18 +38,16 @@ def test_preprocess(self): self.assertTrue(payload.data) self.assertTrue(payload.raw_data) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.status[metric], Status.PRE_PROCESSED) - self.assertEqual(payload.header[metric], Header.MODEL_INFERENCE) + self.assertEqual(payload.status, Status.PRE_PROCESSED) + self.assertEqual(payload.header, Header.MODEL_INFERENCE) @patch.object(RedisRegistry, "load", Mock(return_value=None)) def test_preprocess_no_clf(self): _out = Preprocess().run(self.keys, self.preproc_input)[0] payload = StreamPayload(**orjson.loads(_out.value)) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.status[metric], Status.ARTIFACT_NOT_FOUND) - self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) + self.assertEqual(payload.status, Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.header, Header.STATIC_INFERENCE) @patch.object(RedisRegistry, "load", Mock(return_value=return_preproc_clf())) def test_preprocess_with_nan(self): @@ -61,9 +59,8 @@ def test_preprocess_with_nan(self): self.assertTrue(np.isfinite(df.values).all()) self.assertTrue(payload.data) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.status[metric], Status.PRE_PROCESSED) - self.assertEqual(payload.header[metric], Header.MODEL_INFERENCE) + self.assertEqual(payload.status, Status.PRE_PROCESSED) + self.assertEqual(payload.header, Header.MODEL_INFERENCE) if __name__ == "__main__": diff --git a/udf/anomaly-detection/tests/udf/test_threshold.py b/udf/anomaly-detection/tests/udf/test_threshold.py index 1d89b618..21c2c903 100644 --- a/udf/anomaly-detection/tests/udf/test_threshold.py +++ b/udf/anomaly-detection/tests/udf/test_threshold.py @@ -12,7 +12,6 @@ from tests import redis_client, Threshold from tests.tools import get_threshold_input, return_threshold_clf - DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") STREAM_DATA_PATH = os.path.join(DATA_DIR, "stream.json") @@ -36,10 +35,9 @@ def test_threshold(self): self.assertTrue(payload.data) self.assertTrue(payload.raw_data) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.status[metric], Status.THRESHOLD) - self.assertEqual(payload.header[metric], Header.MODEL_INFERENCE) - self.assertGreater(payload.metadata[metric]["model_version"], 0) + self.assertEqual(payload.status, Status.THRESHOLD) + self.assertEqual(payload.header, Header.MODEL_INFERENCE) + self.assertGreater(payload.metadata["model_version"], 0) @patch.object(RedisRegistry, "load", Mock(return_value=return_threshold_clf())) def test_threshold_prev_stale_model(self): @@ -52,10 +50,9 @@ def test_threshold_prev_stale_model(self): else: payload = StreamPayload(**orjson.loads(msg.value.decode("utf-8"))) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.header[metric], Header.MODEL_STALE) - self.assertEqual(payload.status[metric], Status.THRESHOLD) - self.assertGreater(payload.metadata[metric]["model_version"], 0) + self.assertEqual(payload.header, Header.MODEL_STALE) + self.assertEqual(payload.status, Status.THRESHOLD) + self.assertGreater(payload.metadata["model_version"], 0) @patch.object(RedisRegistry, "load", Mock(return_value=None)) def test_threshold_no_prev_clf(self): @@ -68,10 +65,9 @@ def test_threshold_no_prev_clf(self): else: payload = StreamPayload(**orjson.loads(msg.value.decode("utf-8"))) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) - self.assertEqual(payload.status[metric], Status.ARTIFACT_NOT_FOUND) - self.assertEqual(payload.metadata[metric]["model_version"], -1) + self.assertEqual(payload.header, Header.STATIC_INFERENCE) + self.assertEqual(payload.status, Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.metadata["model_version"], -1) @freeze_time("2022-02-20 12:00:00") @patch.object(RedisRegistry, "load", Mock(return_value=None)) @@ -85,10 +81,9 @@ def test_threshold_no_clf(self): else: payload = StreamPayload(**orjson.loads(msg.value.decode("utf-8"))) self.assertIsInstance(payload, StreamPayload) - for metric in payload.metrics: - self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) - self.assertEqual(payload.status[metric], Status.ARTIFACT_NOT_FOUND) - self.assertEqual(payload.metadata[metric]["model_version"], -1) + self.assertEqual(payload.header, Header.STATIC_INFERENCE) + self.assertEqual(payload.status, Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.metadata["model_version"], -1) if __name__ == "__main__":