From 66258bdf1b182bc2e5b6e08fe60ec34d4d7e982a Mon Sep 17 00:00:00 2001
From: Fengrui-Liu <liufengrui18z@ict.ac.cn>
Date: Sun, 6 Nov 2022 19:35:37 +0800
Subject: [PATCH 1/3] Func: ray-based deployment

---
 README.md                                     |   2 +-
 docs/static/metric-detector.png               | Bin 0 -> 40118 bytes
 .../models/metric/detectors/spot_detector.py  |  78 ++++++++++--------
 engine/models/metric/serve/deployment.py      |  58 +++++++++++++
 engine/models/tests/test_detector.py          |  41 +++++++--
 5 files changed, 138 insertions(+), 41 deletions(-)
 create mode 100644 docs/static/metric-detector.png
 create mode 100644 engine/models/metric/serve/deployment.py

diff --git a/README.md b/README.md
index 4f46032..37d5225 100644
--- a/README.md
+++ b/README.md
@@ -67,7 +67,7 @@ OSPP 2022 and GSOC 2022 student research outcomes.
 
 **Metric Anomaly Detection and Visualizations**
 
-TBD - Soon to be added
+![img.png](docs/static/metric-detector.png)
 
 ### Roadmap
 
diff --git a/docs/static/metric-detector.png b/docs/static/metric-detector.png
new file mode 100644
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diff --git a/engine/models/metric/detectors/spot_detector.py b/engine/models/metric/detectors/spot_detector.py
index eb41d99..8e2449e 100644
--- a/engine/models/metric/detectors/spot_detector.py
+++ b/engine/models/metric/detectors/spot_detector.py
@@ -22,7 +22,7 @@
 
 from ..base import BaseDetector
 
-np.seterr(divide='ignore', invalid='ignore')
+np.seterr(divide="ignore", invalid="ignore")
 
 
 class SpotDetector(BaseDetector):
@@ -53,19 +53,19 @@ def __init__(
         self.back_mean_window = deque(maxlen=self.back_mean_len)
         assert (
             self.window_len > 100
-        ), 'window_len is too small, default value is 200'
+        ), "window_len is too small, default value is 200"
 
         self.num_threshold = {
-            'up': num_threshold_up,
-            'down': num_threshold_down,
+            "up": num_threshold_up,
+            "down": num_threshold_down,
         }
 
-        nonedict = {'up': None, 'down': None}
+        nonedict = {"up": None, "down": None}
 
         self.extreme_quantile = dict.copy(nonedict)
         self.init_threshold = dict.copy(nonedict)
         self.peaks = dict.copy(nonedict)
-        self.history_peaks = {'up': [], 'down': []}
+        self.history_peaks = {"up": [], "down": []}
         # self.peaks = {'up':deque(maxlen=20),'down':deque(maxlen=20)}
         self.gamma = dict.copy(nonedict)
         self.sigma = dict.copy(nonedict)
@@ -96,7 +96,7 @@ def jac_w(y, t):
             ) * (1 - vs)
             jac_vs = np.divide(
                 1, t, out=np.array(1 / epsilon), where=t != 0
-            ) * (-vs + np.mean(1 / s**2))
+            ) * (-vs + np.mean(1 / s ** 2))
             return us * jac_vs + vs * jac_us
 
         self.peaks[side][self.peaks[side] == 0] = epsilon
@@ -117,7 +117,7 @@ def jac_w(y, t):
         )
         c = 2 * np.divide(
             y_mean - y_min,
-            y_min**2,
+            y_min ** 2,
             out=np.array((y_mean - y_min) / epsilon + epsilon),
             where=y_min != 0,
         )
@@ -130,7 +130,7 @@ def jac_w(y, t):
             lambda t: jac_w(self.peaks[side], t),
             (d, e) if d < e else (e, d),
             n_points,
-            'regular',
+            "regular",
         )
 
         right_zeros = self._roots_finder(
@@ -138,7 +138,7 @@ def jac_w(y, t):
             lambda t: jac_w(self.peaks[side], t),
             (b, c) if b < c else (c, b),
             n_points,
-            'regular',
+            "regular",
         )
 
         # all the possible roots
@@ -183,13 +183,13 @@ def _roots_finder(self, fun, jac, bounds, npoints, method):
         numpy.array
             possible roots of the function
         """
-        if method == 'regular':
+        if method == "regular":
             step = (bounds[1] - bounds[0]) / (npoints + 1)
             try:
                 x_sampled = np.arange(bounds[0] + step, bounds[1], step)
             except BaseException:
                 x_sampled = np.random.uniform(bounds[0], bounds[1], npoints)
-        elif method == 'random':
+        elif method == "random":
             x_sampled = np.random.uniform(bounds[0], bounds[1], npoints)
 
         def obj_fun(x_sampled, f, jac):
@@ -198,7 +198,7 @@ def obj_fun(x_sampled, f, jac):
             i = 0
             for x in x_sampled:
                 fx = f(x)
-                g = g + fx**2
+                g = g + fx ** 2
                 j[i] = 2 * fx * jac(x)
                 i = i + 1
             return g, j
@@ -206,7 +206,7 @@ def obj_fun(x_sampled, f, jac):
         opt = minimize(
             lambda X: obj_fun(X, fun, jac),
             x_sampled,
-            method='L-BFGS-B',
+            method="L-BFGS-B",
             jac=True,
             bounds=[bounds] * len(x_sampled),
         )
@@ -245,7 +245,7 @@ def _log_likelihood(self, y, gamma, sigma):
 
     def _quantile(self, side, gamma, sigma):
 
-        if side == 'up':
+        if side == "up":
             r = self.window_len * self.prob / self.num_threshold[side]
 
             if gamma != 0:
@@ -254,7 +254,7 @@ def _quantile(self, side, gamma, sigma):
                 )
             else:
                 return self.init_threshold[side] - sigma * log(r)
-        elif side == 'down':
+        elif side == "down":
             r = self.window_len * self.prob / self.num_threshold[side]
 
             if gamma != 0:
@@ -264,18 +264,18 @@ def _quantile(self, side, gamma, sigma):
             else:
                 return self.init_threshold[side] + sigma * log(r)
         else:
-            raise ValueError('The side is not right')
+            raise ValueError("The side is not right")
 
     def _init_drift(self, verbose=False):
 
-        for side in ['up', 'down']:
+        for side in ["up", "down"]:
             self._update_one_side(side)
 
         return self
 
     def _update_one_side(self, side: str):
 
-        if side == 'up':
+        if side == "up":
             self.history_peaks[side] = heapq.nlargest(
                 self.num_threshold[side],
                 list(self.window) + self.history_peaks[side],
@@ -284,9 +284,9 @@ def _update_one_side(self, side: str):
             self.peaks[side] = np.array(self.history_peaks[side]) - np.array(
                 self.init_threshold[side]
             )
-        elif side == 'down':
+        elif side == "down":
             self.history_peaks[side] = heapq.nsmallest(
-                self.num_threshold['down'],
+                self.num_threshold["down"],
                 list(self.window) + self.history_peaks[side],
             )
             self.init_threshold[side] = self.history_peaks[side][-1]
@@ -312,7 +312,6 @@ def _cal_back_mean(self, data):
 
     def fit(self, data: Union[float, int]):
 
-
         self.back_mean_window.append(data)
 
         if self.index >= self.back_mean_len:
@@ -333,25 +332,27 @@ def fit(self, data: Union[float, int]):
             if (
                 abs(
                     np.divide(
-                        data - last_data, last_data, np.array(data), where=last_data != 0
+                        data - last_data,
+                        last_data,
+                        np.array(data),
+                        where=last_data != 0,
                     )
                 )
                 < self.deviance_ratio
             ):
                 return self
 
-            if self.normal_X > self.init_threshold['up']:
-                self._update_one_side('up')
+            if self.normal_X > self.init_threshold["up"]:
+                self._update_one_side("up")
 
-            elif self.normal_X < self.init_threshold['down']:
+            elif self.normal_X < self.init_threshold["down"]:
 
-                self._update_one_side('down')
+                self._update_one_side("down")
 
         return self
 
     def score(self, data: Union[float, int]) -> float:
 
-
         last_data = (
             self.window[-2]
             if self.back_mean_len == 0
@@ -359,19 +360,26 @@ def score(self, data: Union[float, int]) -> float:
         )
 
         if (
-            abs(np.divide(data - last_data, last_data, np.array(data), where=last_data != 0))
+            abs(
+                np.divide(
+                    data - last_data,
+                    last_data,
+                    np.array(data),
+                    where=last_data != 0,
+                )
+            )
             < self.deviance_ratio
         ):
             score = 0.0
 
         elif (
-            self.normal_X > self.extreme_quantile['up']
-            or self.normal_X < self.extreme_quantile['down']
+            self.normal_X > self.extreme_quantile["up"]
+            or self.normal_X < self.extreme_quantile["down"]
         ):
             score = 1.0
 
-        elif self.normal_X > self.init_threshold['up']:
-            side = 'up'
+        elif self.normal_X > self.init_threshold["up"]:
+            side = "up"
             score = np.divide(
                 self.normal_X - self.init_threshold[side],
                 (self.extreme_quantile[side] - self.init_threshold[side]),
@@ -381,8 +389,8 @@ def score(self, data: Union[float, int]) -> float:
                 ),
             )
 
-        elif self.normal_X < self.init_threshold['down']:
-            side = 'down'
+        elif self.normal_X < self.init_threshold["down"]:
+            side = "down"
             score = np.divide(
                 self.init_threshold[side] - self.normal_X,
                 (self.init_threshold[side] - self.extreme_quantile[side]),
diff --git a/engine/models/metric/serve/deployment.py b/engine/models/metric/serve/deployment.py
new file mode 100644
index 0000000..6387e6e
--- /dev/null
+++ b/engine/models/metric/serve/deployment.py
@@ -0,0 +1,58 @@
+# Copyright 2022 SkyAPM org
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import ray
+
+
+@ray.remote(num_cpus=1)
+class RayMetricConsumer(object):
+    def __init__(self, detector) -> None:
+        self.detector = detector
+
+    def run(self, timestamp: str, data: float) -> float:
+        score = self.detector.fit_score(timestamp=timestamp, data=float(data))
+        return score
+
+
+if __name__ == "__main__":
+
+    from engine.models.metric.detectors import SpotDetector
+    import pandas as pd
+
+    df = pd.read_csv(
+        "experiments/metric/data/univarate_dataset.csv", index_col="timestamp"
+    )
+
+    # This for loop can be replaced by a MQ
+    for index, row in df.iterrows():
+        timestamp = index
+        data = row.value
+
+        # Here we setup multi-consumer according to unique name
+        detector = RayMetricConsumer.options(
+            name="cpu.load", lifetime="detached", get_if_exists=True
+        ).remote(SpotDetector())
+
+        score1 = ray.get(
+            detector.run.remote(timestamp=timestamp, data=float(data))
+        )
+
+        detector = RayMetricConsumer.options(
+            name="mem.load", lifetime="detached", get_if_exists=True
+        ).remote(SpotDetector())
+        score2 = ray.get(
+            detector.run.remote(timestamp=timestamp, data=float(data))
+        )
+
+        assert score1 == score2
diff --git a/engine/models/tests/test_detector.py b/engine/models/tests/test_detector.py
index b1228e5..620643e 100644
--- a/engine/models/tests/test_detector.py
+++ b/engine/models/tests/test_detector.py
@@ -13,24 +13,55 @@
 #  limitations under the License.
 
 
-
 import sys
 
 import pandas as pd
 
 from engine.models.metric.detectors import SpotDetector
+from engine.models.metric.serve.deployment import RayMetricConsumer
+import ray
 
-sys.path.append('../../')
+sys.path.append("../../")
 
 
 def test_detector_uni_dataset():
     df = pd.read_csv(
-        'experiments/metric/data/univarate_dataset.csv', index_col='timestamp'
+        "experiments/metric/data/univarate_dataset.csv", index_col="timestamp"
     )
-    detector = SpotDetector()
+    detector = SpotDetector.remote()
 
     for index, row in df.iterrows():
         timestamp = index
         data = row.value
-        score = detector.fit_score(timestamp=timestamp, data=float(data))
+
+        score = ray.get(
+            detector.fit_score.remote(timestamp=timestamp, data=float(data))
+        )
         assert score is None or 0 <= score <= 1
+
+
+def test_multi_detector_uni_dataset():
+    df = pd.read_csv(
+        "experiments/metric/data/univarate_dataset.csv", index_col="timestamp"
+    )
+
+    for index, row in df.iterrows():
+        timestamp = index
+        data = row.value
+
+        detector = RayMetricConsumer.options(
+            name="cpu.load", lifetime="detached", get_if_exists=True
+        ).remote(SpotDetector())
+
+        score1 = ray.get(
+            detector.run.remote(timestamp=timestamp, data=float(data))
+        )
+
+        detector = RayMetricConsumer.options(
+            name="mem.load", lifetime="detached", get_if_exists=True
+        ).remote(SpotDetector())
+        score2 = ray.get(
+            detector.run.remote(timestamp=timestamp, data=float(data))
+        )
+
+        assert score1 == score2

From b5008d9d3f0d15bf258fedefd3122b98d9b085d9 Mon Sep 17 00:00:00 2001
From: Fengrui-Liu <liufengrui18z@ict.ac.cn>
Date: Sun, 6 Nov 2022 19:58:35 +0800
Subject: [PATCH 2/3] Fix: quota formatter

---
 .../models/metric/detectors/spot_detector.py  | 60 +++++++++----------
 engine/models/metric/serve/deployment.py      | 12 ++--
 engine/models/tests/test_detector.py          | 14 ++---
 3 files changed, 43 insertions(+), 43 deletions(-)

diff --git a/engine/models/metric/detectors/spot_detector.py b/engine/models/metric/detectors/spot_detector.py
index 8e2449e..2829fb1 100644
--- a/engine/models/metric/detectors/spot_detector.py
+++ b/engine/models/metric/detectors/spot_detector.py
@@ -1,13 +1,13 @@
 #  Copyright 2022 SkyAPM org
 #
-#  Licensed under the Apache License, Version 2.0 (the "License");
+#  Licensed under the Apache License, Version 2.0 (the 'License');
 #  you may not use this file except in compliance with the License.
 #  You may obtain a copy of the License at
 #
 #  http://www.apache.org/licenses/LICENSE-2.0
 #
 #  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
+#  distributed under the License is distributed on an 'AS IS' BASIS,
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
@@ -22,7 +22,7 @@
 
 from ..base import BaseDetector
 
-np.seterr(divide="ignore", invalid="ignore")
+np.seterr(divide='ignore', invalid='ignore')
 
 
 class SpotDetector(BaseDetector):
@@ -53,19 +53,19 @@ def __init__(
         self.back_mean_window = deque(maxlen=self.back_mean_len)
         assert (
             self.window_len > 100
-        ), "window_len is too small, default value is 200"
+        ), 'window_len is too small, default value is 200'
 
         self.num_threshold = {
-            "up": num_threshold_up,
-            "down": num_threshold_down,
+            'up': num_threshold_up,
+            'down': num_threshold_down,
         }
 
-        nonedict = {"up": None, "down": None}
+        nonedict = {'up': None, 'down': None}
 
         self.extreme_quantile = dict.copy(nonedict)
         self.init_threshold = dict.copy(nonedict)
         self.peaks = dict.copy(nonedict)
-        self.history_peaks = {"up": [], "down": []}
+        self.history_peaks = {'up': [], 'down': []}
         # self.peaks = {'up':deque(maxlen=20),'down':deque(maxlen=20)}
         self.gamma = dict.copy(nonedict)
         self.sigma = dict.copy(nonedict)
@@ -130,7 +130,7 @@ def jac_w(y, t):
             lambda t: jac_w(self.peaks[side], t),
             (d, e) if d < e else (e, d),
             n_points,
-            "regular",
+            'regular',
         )
 
         right_zeros = self._roots_finder(
@@ -138,7 +138,7 @@ def jac_w(y, t):
             lambda t: jac_w(self.peaks[side], t),
             (b, c) if b < c else (c, b),
             n_points,
-            "regular",
+            'regular',
         )
 
         # all the possible roots
@@ -183,13 +183,13 @@ def _roots_finder(self, fun, jac, bounds, npoints, method):
         numpy.array
             possible roots of the function
         """
-        if method == "regular":
+        if method == 'regular':
             step = (bounds[1] - bounds[0]) / (npoints + 1)
             try:
                 x_sampled = np.arange(bounds[0] + step, bounds[1], step)
             except BaseException:
                 x_sampled = np.random.uniform(bounds[0], bounds[1], npoints)
-        elif method == "random":
+        elif method == 'random':
             x_sampled = np.random.uniform(bounds[0], bounds[1], npoints)
 
         def obj_fun(x_sampled, f, jac):
@@ -206,7 +206,7 @@ def obj_fun(x_sampled, f, jac):
         opt = minimize(
             lambda X: obj_fun(X, fun, jac),
             x_sampled,
-            method="L-BFGS-B",
+            method='L-BFGS-B',
             jac=True,
             bounds=[bounds] * len(x_sampled),
         )
@@ -245,7 +245,7 @@ def _log_likelihood(self, y, gamma, sigma):
 
     def _quantile(self, side, gamma, sigma):
 
-        if side == "up":
+        if side == 'up':
             r = self.window_len * self.prob / self.num_threshold[side]
 
             if gamma != 0:
@@ -254,7 +254,7 @@ def _quantile(self, side, gamma, sigma):
                 )
             else:
                 return self.init_threshold[side] - sigma * log(r)
-        elif side == "down":
+        elif side == 'down':
             r = self.window_len * self.prob / self.num_threshold[side]
 
             if gamma != 0:
@@ -264,18 +264,18 @@ def _quantile(self, side, gamma, sigma):
             else:
                 return self.init_threshold[side] + sigma * log(r)
         else:
-            raise ValueError("The side is not right")
+            raise ValueError('The side is not right')
 
     def _init_drift(self, verbose=False):
 
-        for side in ["up", "down"]:
+        for side in ['up', 'down']:
             self._update_one_side(side)
 
         return self
 
     def _update_one_side(self, side: str):
 
-        if side == "up":
+        if side == 'up':
             self.history_peaks[side] = heapq.nlargest(
                 self.num_threshold[side],
                 list(self.window) + self.history_peaks[side],
@@ -284,9 +284,9 @@ def _update_one_side(self, side: str):
             self.peaks[side] = np.array(self.history_peaks[side]) - np.array(
                 self.init_threshold[side]
             )
-        elif side == "down":
+        elif side == 'down':
             self.history_peaks[side] = heapq.nsmallest(
-                self.num_threshold["down"],
+                self.num_threshold['down'],
                 list(self.window) + self.history_peaks[side],
             )
             self.init_threshold[side] = self.history_peaks[side][-1]
@@ -342,12 +342,12 @@ def fit(self, data: Union[float, int]):
             ):
                 return self
 
-            if self.normal_X > self.init_threshold["up"]:
-                self._update_one_side("up")
+            if self.normal_X > self.init_threshold['up']:
+                self._update_one_side('up')
 
-            elif self.normal_X < self.init_threshold["down"]:
+            elif self.normal_X < self.init_threshold['down']:
 
-                self._update_one_side("down")
+                self._update_one_side('down')
 
         return self
 
@@ -373,13 +373,13 @@ def score(self, data: Union[float, int]) -> float:
             score = 0.0
 
         elif (
-            self.normal_X > self.extreme_quantile["up"]
-            or self.normal_X < self.extreme_quantile["down"]
+            self.normal_X > self.extreme_quantile['up']
+            or self.normal_X < self.extreme_quantile['down']
         ):
             score = 1.0
 
-        elif self.normal_X > self.init_threshold["up"]:
-            side = "up"
+        elif self.normal_X > self.init_threshold['up']:
+            side = 'up'
             score = np.divide(
                 self.normal_X - self.init_threshold[side],
                 (self.extreme_quantile[side] - self.init_threshold[side]),
@@ -389,8 +389,8 @@ def score(self, data: Union[float, int]) -> float:
                 ),
             )
 
-        elif self.normal_X < self.init_threshold["down"]:
-            side = "down"
+        elif self.normal_X < self.init_threshold['down']:
+            side = 'down'
             score = np.divide(
                 self.init_threshold[side] - self.normal_X,
                 (self.init_threshold[side] - self.extreme_quantile[side]),
diff --git a/engine/models/metric/serve/deployment.py b/engine/models/metric/serve/deployment.py
index 6387e6e..a78dee2 100644
--- a/engine/models/metric/serve/deployment.py
+++ b/engine/models/metric/serve/deployment.py
@@ -1,13 +1,13 @@
 # Copyright 2022 SkyAPM org
 #
-# Licensed under the Apache License, Version 2.0 (the "License");
+# Licensed under the Apache License, Version 2.0 (the 'License');
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #      https://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
+# distributed under the License is distributed on an 'AS IS' BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
@@ -25,13 +25,13 @@ def run(self, timestamp: str, data: float) -> float:
         return score
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
 
     from engine.models.metric.detectors import SpotDetector
     import pandas as pd
 
     df = pd.read_csv(
-        "experiments/metric/data/univarate_dataset.csv", index_col="timestamp"
+        'experiments/metric/data/univarate_dataset.csv', index_col='timestamp'
     )
 
     # This for loop can be replaced by a MQ
@@ -41,7 +41,7 @@ def run(self, timestamp: str, data: float) -> float:
 
         # Here we setup multi-consumer according to unique name
         detector = RayMetricConsumer.options(
-            name="cpu.load", lifetime="detached", get_if_exists=True
+            name='cpu.load', lifetime='detached', get_if_exists=True
         ).remote(SpotDetector())
 
         score1 = ray.get(
@@ -49,7 +49,7 @@ def run(self, timestamp: str, data: float) -> float:
         )
 
         detector = RayMetricConsumer.options(
-            name="mem.load", lifetime="detached", get_if_exists=True
+            name='mem.load', lifetime='detached', get_if_exists=True
         ).remote(SpotDetector())
         score2 = ray.get(
             detector.run.remote(timestamp=timestamp, data=float(data))
diff --git a/engine/models/tests/test_detector.py b/engine/models/tests/test_detector.py
index 620643e..86f4f48 100644
--- a/engine/models/tests/test_detector.py
+++ b/engine/models/tests/test_detector.py
@@ -1,13 +1,13 @@
 #  Copyright 2022 SkyAPM org
 #
-#  Licensed under the Apache License, Version 2.0 (the "License");
+#  Licensed under the Apache License, Version 2.0 (the 'License');
 #  you may not use this file except in compliance with the License.
 #  You may obtain a copy of the License at
 #
 #  http://www.apache.org/licenses/LICENSE-2.0
 #
 #  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
+#  distributed under the License is distributed on an 'AS IS' BASIS,
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
@@ -21,12 +21,12 @@
 from engine.models.metric.serve.deployment import RayMetricConsumer
 import ray
 
-sys.path.append("../../")
+sys.path.append('../../')
 
 
 def test_detector_uni_dataset():
     df = pd.read_csv(
-        "experiments/metric/data/univarate_dataset.csv", index_col="timestamp"
+        'experiments/metric/data/univarate_dataset.csv', index_col='timestamp'
     )
     detector = SpotDetector.remote()
 
@@ -42,7 +42,7 @@ def test_detector_uni_dataset():
 
 def test_multi_detector_uni_dataset():
     df = pd.read_csv(
-        "experiments/metric/data/univarate_dataset.csv", index_col="timestamp"
+        'experiments/metric/data/univarate_dataset.csv', index_col='timestamp'
     )
 
     for index, row in df.iterrows():
@@ -50,7 +50,7 @@ def test_multi_detector_uni_dataset():
         data = row.value
 
         detector = RayMetricConsumer.options(
-            name="cpu.load", lifetime="detached", get_if_exists=True
+            name='cpu.load', lifetime='detached', get_if_exists=True
         ).remote(SpotDetector())
 
         score1 = ray.get(
@@ -58,7 +58,7 @@ def test_multi_detector_uni_dataset():
         )
 
         detector = RayMetricConsumer.options(
-            name="mem.load", lifetime="detached", get_if_exists=True
+            name='mem.load', lifetime='detached', get_if_exists=True
         ).remote(SpotDetector())
         score2 = ray.get(
             detector.run.remote(timestamp=timestamp, data=float(data))

From 7ae1e0ee049f7c494eeec870da28489503cda51f Mon Sep 17 00:00:00 2001
From: Fengrui-Liu <liufengrui18z@ict.ac.cn>
Date: Sun, 6 Nov 2022 20:22:47 +0800
Subject: [PATCH 3/3] Fix: quota formatter

---
 engine/models/metric/detectors/spot_detector.py | 4 ++--
 engine/models/metric/serve/deployment.py        | 4 ++--
 engine/models/tests/test_detector.py            | 4 ++--
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/engine/models/metric/detectors/spot_detector.py b/engine/models/metric/detectors/spot_detector.py
index 2829fb1..ce4cd02 100644
--- a/engine/models/metric/detectors/spot_detector.py
+++ b/engine/models/metric/detectors/spot_detector.py
@@ -1,13 +1,13 @@
 #  Copyright 2022 SkyAPM org
 #
-#  Licensed under the Apache License, Version 2.0 (the 'License');
+#  Licensed under the Apache License, Version 2.0 (the "License");
 #  you may not use this file except in compliance with the License.
 #  You may obtain a copy of the License at
 #
 #  http://www.apache.org/licenses/LICENSE-2.0
 #
 #  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an 'AS IS' BASIS,
+#  distributed under the License is distributed on an "AS IS" BASIS,
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
diff --git a/engine/models/metric/serve/deployment.py b/engine/models/metric/serve/deployment.py
index a78dee2..76a3cb6 100644
--- a/engine/models/metric/serve/deployment.py
+++ b/engine/models/metric/serve/deployment.py
@@ -1,13 +1,13 @@
 # Copyright 2022 SkyAPM org
 #
-# Licensed under the Apache License, Version 2.0 (the 'License');
+# Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #      https://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an 'AS IS' BASIS,
+# distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
diff --git a/engine/models/tests/test_detector.py b/engine/models/tests/test_detector.py
index 86f4f48..a669676 100644
--- a/engine/models/tests/test_detector.py
+++ b/engine/models/tests/test_detector.py
@@ -1,13 +1,13 @@
 #  Copyright 2022 SkyAPM org
 #
-#  Licensed under the Apache License, Version 2.0 (the 'License');
+#  Licensed under the Apache License, Version 2.0 (the "License");
 #  you may not use this file except in compliance with the License.
 #  You may obtain a copy of the License at
 #
 #  http://www.apache.org/licenses/LICENSE-2.0
 #
 #  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an 'AS IS' BASIS,
+#  distributed under the License is distributed on an "AS IS" BASIS,
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.