Error: max() arg is an empty sequence #352 (#394)

fix Error: max() arg is an empty sequence #352

analytics/analytics/models/drop_model.py

@@ -81,7 +81,7 @@ class DropModel(Model):
             if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']):
                 convol_data = utils.get_interval(data, segment, self.state['WINDOW_SIZE'])
                 percent_of_nans = convol_data.isnull().sum() / len(convol_data)
-                if percent_of_nans > 0.5:
+                if len(convol_data) == 0 or percent_of_nans > 0.5:
                     delete_list.append(segment)
                     continue
                 elif 0 < percent_of_nans <= 0.5:

analytics/analytics/models/general_model.py

@@ -63,7 +63,8 @@ class GeneralModel(Model):
         data = utils.cut_dataframe(dataframe)
         data = data['value']
         pat_data = self.model_gen
-        y = max(pat_data)
+        if pat_data.count(0) == len(pat_data):
+            raise ValueError('Labeled patterns must not be empty')
 
         for i in range(self.state['WINDOW_SIZE'] * 2, len(data)):
             watch_data = data[i - self.state['WINDOW_SIZE'] * 2: i]

analytics/analytics/models/jump_model.py

@@ -87,7 +87,7 @@ class JumpModel(Model):
             if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']):
                 convol_data = utils.get_interval(data, segment, self.state['WINDOW_SIZE'])
                 percent_of_nans = convol_data.isnull().sum() / len(convol_data)
-                if percent_of_nans > 0.5:
+                if len(convol_data) == 0 or percent_of_nans > 0.5:
                     delete_list.append(segment)
                     continue
                 elif 0 < percent_of_nans <= 0.5:

analytics/analytics/utils/common.py

@@ -5,6 +5,7 @@ from scipy.fftpack import fft
 from scipy.signal import argrelextrema
 from scipy.stats import gaussian_kde
 from scipy.stats.stats import pearsonr
+import math
 from typing import Union
 import utils
 
@@ -81,7 +82,6 @@ def ar_mean(numbers):
 def get_av_model(patterns_list):
     if len(patterns_list) == 0:
         return []
-
     x = len(patterns_list[0])
     if len(patterns_list) > 1 and len(patterns_list[1]) != x:
         raise NameError(
@@ -210,8 +210,10 @@ def get_convolve(segments: list, av_model: list, data: pd.Series, window_size: i
         labeled_segment = utils.check_nan_values(labeled_segment)
         auto_convolve = scipy.signal.fftconvolve(labeled_segment, labeled_segment)
         convolve_segment = scipy.signal.fftconvolve(labeled_segment, av_model)
-        convolve_list.append(max(auto_convolve))
-        convolve_list.append(max(convolve_segment))
+        if len(auto_convolve) > 0:
+            convolve_list.append(max(auto_convolve))
+        if len(convolve_segment) > 0:
+            convolve_list.append(max(convolve_segment))
     return convolve_list
 
 def get_correlation(segments: list, av_model: list, data: pd.Series, window_size: int) -> list:
@@ -228,7 +230,7 @@ def get_correlation(segments: list, av_model: list, data: pd.Series, window_size
     return correlation_list
 
 def get_distribution_density(segment: pd.Series) -> float:
-    if len(segment) < 2:
+    if len(segment) < 2 or len(segment.nonzero()[0]) == 0:
         return (0, 0, 0)
     min_jump = min(segment)
     max_jump = max(segment)
@@ -264,8 +266,11 @@ def find_parameters(segment_data: pd.Series, segment_from_index: int, pat_type:
 def find_pattern_center(segment_data: pd.Series, segment_from_index: int, pattern_type: str):
     segment_median = utils.get_distribution_density(segment_data)[0]
     cen_ind = utils.pattern_intersection(segment_data.tolist(), segment_median, pattern_type)
-    pat_center = cen_ind[0]
-    segment_cent_index = pat_center + segment_from_index
+    if len(cen_ind) > 0:
+        pat_center = cen_ind[0]
+        segment_cent_index = pat_center + segment_from_index
+    else: 
+        segment_cent_index = math.ceil((len(segment_data)) / 2)
     return segment_cent_index
 
 def find_length(segment_data: pd.Series, segment_min_line: float, segment_max_line: float, pat_type: str) -> int:

analytics/tests/test_dataset.py

@@ -90,6 +90,32 @@ class TestDataset(unittest.TestCase):
         except ValueError:
             self.fail('Model {} raised unexpectedly'.format(model_name))
     
+    def test_jump_empty_segment(self):
+        data_val = [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 9.0, 9.0, 9.0, 9.0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+        dataframe = create_dataframe(data_val)
+        segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000019, 'to': 1523889000025, 'labeled': True, 'deleted': False},
+                    {'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, '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))
+    
+    def test_drop_empty_segment(self):
+        data_val = [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 9.0, 9.0, 9.0, 9.0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+        dataframe = create_dataframe(data_val)
+        segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000019, 'to': 1523889000025, 'labeled': True, 'deleted': False},
+                    {'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, 'labeled': True, 'deleted': False}]
+
+        try:
+            model = models.DropModel()
+            model_name = model.__class__.__name__
+            model.fit(dataframe, segments, dict())
+        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)
@@ -125,6 +151,22 @@ class TestDataset(unittest.TestCase):
             self.assertTrue(np.isnan(item))
         for item in return_data_none.value:
             self.assertTrue(np.isnan(item))
+    
+    def test_three_value_segment(self):
+        data_val = [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 2.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 9.0, 9.0, 9.0, 9.0, 2.0, 3.0, 4.0, 5.0, 4.0, 2.0, 1.0, 3.0, 4.0]
+        dataframe = create_dataframe(data_val)
+        segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000004, 'to': 1523889000006, 'labeled': True, 'deleted': False}]
+
+        model_instances = [
+            models.GeneralModel(),
+            models.PeakModel(),
+        ]
+        try:
+            for model in model_instances:
+                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

@@ -196,6 +196,17 @@ class TestUtils(unittest.TestCase):
         median = 3.0
         result = 3
         self.assertEqual(result, utils.find_pattern_center(data, 0, 'jump'))
+    
+    def test_get_convolve_wrong_index(self):
+        data = [1.0, 5.0, 2.0, 1.0, 6.0, 2.0]
+        data = pd.Series(data)
+        segemnts = [1, 11]
+        av_model = [0.0, 4.0, 0.0]
+        window_size = 1
+        try:
+            utils.get_convolve(segemnts, av_model, data, window_size)
+        except ValueError:
+            self.fail('Method get_convolve raised unexpectedly')
 
 if __name__ == '__main__':
     unittest.main()