[GitHub] [incubator-pinot] xiaohui-sun commented on a change in pull request #4202: [TE] Evaluation metrics store

[GitBox <gi...@apache.org>]

xiaohui-sun commented on a change in pull request #4202: [TE] Evaluation metrics store
URL: https://github.com/apache/incubator-pinot/pull/4202#discussion_r283077349
 
 

 ##########
 File path: thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/Evaluation.java
 ##########
 @@ -0,0 +1,49 @@
+/*
+ *
+ *  * Licensed to the Apache Software Foundation (ASF) under one
+ *  * or more contributor license agreements.  See the NOTICE file
+ *  * distributed with this work for additional information
+ *  * regarding copyright ownership.  The ASF licenses this file
+ *  * to you 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, 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.
+ *
+ *
+ */
+
+package org.apache.pinot.thirdeye.detection;
+
+import org.apache.pinot.thirdeye.dataframe.DoubleSeries;
+
+
+/**
+ * The util class for model evaluation
+ */
+public class Evaluation {
+  // Suppresses default constructor, ensuring non-instantiability.
+  private Evaluation() {
+  }
+
+  /**
+   * Calculate the mean absolute percentage error (MAPE).
+   * See https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
+   * @param current current time series
+   * @param predicted baseline time series
+   * @return the mape value
+   */
+  public static double calculateMape(DoubleSeries current, DoubleSeries predicted) {
+    if (current.contains(0.0)) {
+      return Double.POSITIVE_INFINITY;
+    }
+    return predicted.divide(current).subtract(1).abs().mean().getDouble(0);
 
 Review comment:
   predicted.divide(current).subtract(1).abs().mean().value() to get the first double.

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