algo: tweaks

Signed-off-by: Zach Zhu <[email protected]>

algorithm/kapacity/metric/query.py

@@ -171,19 +171,12 @@ def query_metrics(addr, query, start, end):
 
 
 def convert_metric_series_to_dataframe(series):
-    dataframe = None
-    for item in series:
-        array = []
-        for point in item.points:
-            array.append([point.timestamp, point.value])
-        df = pd.DataFrame(array, columns=['timestamp', 'value'], dtype=float)
-        df['timestamp'] = df['timestamp'].map(lambda x: x / 1000).astype('int64')
-        if dataframe is not None:
-            # TODO: consider if it's possible to have multiple series
-            pd.merge(dataframe, df, how='left', on='timestamp')
-        else:
-            dataframe = df
-    return dataframe
+    df_list = []
+    for point in series[0].points:
+        df_list.append([point.timestamp, point.value])
+    df = pd.DataFrame(df_list, columns=['timestamp', 'value'], dtype=float)
+    df['timestamp'] = df['timestamp'].map(lambda x: x / 1000).astype('int64')
+    return df
 
 
 def time_period_to_minutes(time_period):

algorithm/kapacity/portrait/horizontal/predictive/main.py

@@ -33,7 +33,6 @@ class EnvInfo:
 
 class MetricsContext:
     workload_identifier = None
-    resource_name = None
     resource_target = 0
     resource_history = None
     replicas_history = None
@@ -130,7 +129,7 @@ def predict_replicas(args, metric_ctx, pred_traffics):
         pred = estimator.estimate(history,
                                   pred_traffics,
                                   'timestamp',
-                                  metric_ctx.resource_name,
+                                  'resource',
                                   'replicas',
                                   traffic_col,
                                   metric_ctx.resource_target,
@@ -155,12 +154,10 @@ def merge_history_dict(history_dict):
 
 
 def resample_by_freq(old_df, freq, agg_funcs):
-    df = old_df.copy()
-    df = df.sort_values(by='timestamp', ascending=True)
+    df = old_df.sort_values(by='timestamp', ascending=True)
     df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')
-    df = df.resample(rule=freq, on='timestamp').agg(agg_funcs)
-    df = df.rename_axis('timestamp').reset_index()
-    df['timestamp'] = df['timestamp'].astype('int64') // 10 ** 9
+    df = df.resample(rule=freq, on='timestamp').agg(agg_funcs).reset_index()
+    df['timestamp'] = df['timestamp'].astype('int64') // 1e9
     return df
 
 
@@ -185,10 +182,9 @@ def fetch_metrics_history(args, env, hp_cr):
                 resource = metric['containerResource']
             else:
                 raise RuntimeError('MetricTypeError')
-            resource_history = query.fetch_metrics(env.metrics_server_addr, env.namespace, metric, scale_target, start, end)
-            metric_ctx.resource_name = resource['name']
             metric_ctx.resource_target = compute_resource_target(env.namespace, resource, scale_target)
-            metric_ctx.resource_history = resource_history.rename(columns={'value': resource['name']})
+            resource_history = query.fetch_metrics(env.metrics_server_addr, env.namespace, metric, scale_target, start, end)
+            metric_ctx.resource_history = resource_history.rename(columns={'value': 'resource'})
         elif i == 1:
             if metric_type != 'Pods':
                 raise RuntimeError('MetricTypeError')

algorithm/kapacity/portrait/horizontal/predictive/replicas_estimator.py

@@ -631,15 +631,15 @@ class EstimationException(Exception):
     pass
 
 
-def estimate(data,
-             data_pred,
-             time_col,
-             resource_col,
-             replicas_col,
-             traffic_cols,
-             resource_target,
-             time_delta_hours,
-             test_dataset_size_in_seconds=86400):
+def estimate(data: pd.DataFrame,
+             data_pred: pd.DataFrame,
+             time_col: str,
+             resource_col: str,
+             replicas_col: str,
+             traffic_cols: list[str],
+             resource_target: float,
+             time_delta_hours: int,
+             test_dataset_size_in_seconds: int = 86400) -> pd.DataFrame:
     logging.basicConfig(level=logging.INFO,
                         format='%(asctime)s - %(levelname)s: %(message)s')
     logger = logging.getLogger()