Error: ValueError - dataset input should have multiple elements #325 (#327)

analytics/analytics/utils/common.py

@@ -9,6 +9,7 @@ import utils
 
 SHIFT_FACTOR = 0.05
 CONFIDENCE_FACTOR = 0.2
+SMOOTHING_FACTOR = 5
 
 def exponential_smoothing(series, alpha):
     result = [series[0]]
@@ -192,6 +193,8 @@ def get_convolve(segments: list, av_model: list, data: pd.Series, window_size: i
     return convolve_list
 
 def get_distribution_density(segment: pd.Series) -> float:
+    if len(segment) < 2:
+        return (0, 0, 0)
     min_jump = min(segment)
     max_jump = max(segment)
     pdf = gaussian_kde(segment)
@@ -214,9 +217,11 @@ def get_distribution_density(segment: pd.Series) -> float:
     return segment_median, segment_max_line, segment_min_line
 
 def find_parameters(segment_data: pd.Series, segment_from_index: int, pat_type: str) -> [int, float, int]:
-    flat_segment = segment_data.rolling(window=5).mean()
+    segment = segment_data
-    flat_segment_dropna = flat_segment.dropna()
+    if len(segment_data) > SMOOTHING_FACTOR * 3:
-    segment_median, segment_max_line, segment_min_line = utils.get_distribution_density(flat_segment_dropna)
+        flat_segment = segment_data.rolling(window = SMOOTHING_FACTOR).mean()
+        segment = flat_segment.dropna()
+    segment_median, segment_max_line, segment_min_line = utils.get_distribution_density(segment)
     height = 0.95 * (segment_max_line - segment_min_line)
     length = utils.find_length(segment_data, segment_min_line, segment_max_line, pat_type)
     cen_ind = utils.pattern_intersection(segment_data.tolist(), segment_median, pat_type)

analytics/tests/test_dataset.py

@@ -89,6 +89,18 @@ class TestDataset(unittest.TestCase):
         except ValueError:
             self.fail('Model {} raised unexpectedly'.format(model_name))
     
+    def test_value_error_dataset_input_should_have_multiple_elements(self):
+        data_val = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 4.0, 5.0, 5.0, 6.0, 5.0, 1.0, 2.0, 3.0, 4.0, 5.0,3.0,3.0,2.0,7.0,8.0,9.0,8.0,7.0,6.0]
+        dataframe = create_dataframe(data_val)
+        segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000007, 'to': 1523889000011, 'labeled': True, 'deleted': False}]
+
+        try:
+            model = models.JumpModel()
+            model_name = model.__class__.__name__
+            model.fit(dataframe, segments, dict())
+        except ValueError:
+            self.fail('Model {} raised unexpectedly'.format(model_name))
+
 if __name__ == '__main__':
     unittest.main()
 

analytics/tests/test_utils.py

@@ -4,6 +4,8 @@ import numpy as np
 import pandas as pd
 import math
 
+RELATIVE_TOLERANCE = 1e-1
+
 class TestUtils(unittest.TestCase):
 
     #example test for test's workflow purposes
@@ -14,8 +16,7 @@ class TestUtils(unittest.TestCase):
         segment = [1, 2, 0, 6, 8, 5, 3]
         utils_result = utils.find_confidence(segment)
         result = 1.6
-        relative_tolerance = 1e-2
+        self.assertTrue(math.isclose(utils_result, result, rel_tol = RELATIVE_TOLERANCE))
-        self.assertTrue(math.isclose(utils_result, result, rel_tol = relative_tolerance))
     
     def test_confidence_all_nan_value(self):
         segment = [np.NaN, np.NaN, np.NaN, np.NaN]
@@ -25,8 +26,7 @@ class TestUtils(unittest.TestCase):
         data = [np.NaN, np.NaN, 0, 8]
         utils_result = utils.find_confidence(data)
         result = 1.6
-        relative_tolerance = 1e-2
+        self.assertTrue(math.isclose(utils_result, result, rel_tol = RELATIVE_TOLERANCE))
-        self.assertTrue(math.isclose(utils_result, result, rel_tol = relative_tolerance))
     
     def test_interval_all_normal_value(self):
         data = [1, 2, 1, 2, 4, 1, 2, 4, 5, 6]
@@ -87,12 +87,6 @@ class TestUtils(unittest.TestCase):
         self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result)
         self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size_zero), result)
     
-    def test_get_distribution_density(self):
-        segment = [1, 1, 1, 3, 5, 5, 5]
-        segment = pd.Series(segment)
-        result = (3, 5, 1)
-        self.assertEqual(utils.get_distribution_density(segment), result)
-    
     def test_find_jump_parameters_center(self):
         segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
         segment = pd.Series(segment)
@@ -158,5 +152,43 @@ class TestUtils(unittest.TestCase):
         self.assertEqual(utils.find_drop(data, height, length), result)
         self.assertEqual(utils.find_drop(data, height_zero, length_zero), result)
     
+    def test_get_distribution_density(self):
+        segment = [1, 1, 1, 3, 5, 5, 5]
+        segment = pd.Series(segment)
+        result = (3, 5, 1)
+        self.assertEqual(utils.get_distribution_density(segment), result)
+    
+    def test_get_distribution_density_right(self):
+        data = [1.0, 5.0, 5.0, 4.0]
+        data = pd.Series(data)
+        median = 3.0
+        max_line = 5.0
+        min_line = 1.0
+        utils_result = utils.get_distribution_density(data)
+        self.assertTrue(math.isclose(utils_result[0], median, rel_tol = RELATIVE_TOLERANCE))
+        self.assertTrue(math.isclose(utils_result[1], max_line, rel_tol = RELATIVE_TOLERANCE))
+        self.assertTrue(math.isclose(utils_result[2], min_line, rel_tol = RELATIVE_TOLERANCE))
+    
+    def test_get_distribution_density_left(self):
+        data = [1.0, 1.0, 2.0, 1.0, 5.0]
+        data = pd.Series(data)
+        median = 3.0
+        max_line = 5.0
+        min_line = 1.0
+        utils_result = utils.get_distribution_density(data)
+        self.assertTrue(math.isclose(utils_result[0], median, rel_tol = RELATIVE_TOLERANCE))
+        self.assertTrue(math.isclose(utils_result[1], max_line, rel_tol = RELATIVE_TOLERANCE))
+        self.assertTrue(math.isclose(utils_result[2], min_line, rel_tol = RELATIVE_TOLERANCE))
+    
+    def test_get_distribution_density_short_data(self):
+        data = [1.0, 5.0]
+        data = pd.Series(data)
+        segment = [1.0]
+        segment = pd.Series(segment)
+        utils_result_data = utils.get_distribution_density(data)
+        utils_result_segment = utils.get_distribution_density(segment)
+        self.assertEqual(len(utils_result_data), 3)
+        self.assertEqual(utils_result_segment, (0, 0, 0))
+
 if __name__ == '__main__':
     unittest.main()