Error name parse segment is not defined #315 (#322)

Fixed: Error name parse segment is not defined #315

analytics/analytics/models/drop_model.py

@@ -42,7 +42,7 @@ class DropModel(Model):
                     continue
                 confidence = utils.find_confidence(segment_data)
                 confidences.append(confidence)
-                segment_cent_index, drop_height, drop_length = utils.find_parameters(segment_data, segment_from_index, "drop")
+                segment_cent_index, drop_height, drop_length = utils.find_parameters(segment_data, segment_from_index, 'drop')
                 drop_height_list.append(drop_height)
                 drop_length_list.append(drop_length)
                 self.idrops.append(segment_cent_index)
@@ -59,7 +59,7 @@ class DropModel(Model):
                 segment_from_index, segment_to_index, segment_data = utils.parse_segment(segment, dataframe)
                 if len(segment_data) == 0:
                     continue
-                segment_cent_index = utils.find_drop_parameters(segment_data, segment_from_index)[0]
+                segment_cent_index = utils.find_parameters(segment_data, segment_from_index, 'drop')[0]
                 deleted_drop = utils.get_interval(data, segment_cent_index, self.state['WINDOW_SIZE'])
                 deleted_drop = utils.subtract_min_without_nan(deleted_drop)
                 del_conv_drop = scipy.signal.fftconvolve(deleted_drop, self.model_drop)

analytics/analytics/models/jump_model.py

@@ -1,7 +1,6 @@
 from models import Model
 
 import utils
-from utils.segments import parse_segment
 import numpy as np
 import pandas as pd
 import scipy.signal
@@ -38,13 +37,13 @@ class JumpModel(Model):
         patterns_list = []
         for segment in segments:
             if segment['labeled']:
-                segment_from_index, segment_to_index, segment_data = parse_segment(segment, dataframe)
+                segment_from_index, segment_to_index, segment_data = utils.parse_segment(segment, dataframe)
                 percent_of_nans = segment_data.isnull().sum() / len(segment_data)
                 if percent_of_nans > 0 or len(segment_data) == 0:
                     continue
                 confidence = utils.find_confidence(segment_data)
                 confidences.append(confidence)
-                segment_cent_index, jump_height, jump_length = utils.find_parameters(segment_data, segment_from_index, "jump")
+                segment_cent_index, jump_height, jump_length = utils.find_parameters(segment_data, segment_from_index, 'jump')
                 jump_height_list.append(jump_height)
                 jump_length_list.append(jump_length)
                 self.ijumps.append(segment_cent_index)
@@ -61,7 +60,7 @@ class JumpModel(Model):
                 segment_from_index, segment_to_index, segment_data = utils.parse_segment(segment, dataframe)
                 if len(segment_data) == 0:
                     continue
-                segment_cent_index = utils.find_jump_parameters(segment_data, segment_from_index)[0]
+                segment_cent_index = utils.find_parameters(segment_data, segment_from_index, 'jump')[0]
                 deleted_jump = utils.get_interval(data, segment_cent_index, self.state['WINDOW_SIZE'])
                 deleted_jump = utils.subtract_min_without_nan(labeled_jump)
                 del_conv_jump = scipy.signal.fftconvolve(deleted_jump, self.model_jump)

analytics/analytics/models/peak_model.py

@@ -52,7 +52,7 @@ class PeakModel(Model):
         del_conv_list = []
         for segment in segments:
             if segment['deleted']:
-                segment_from_index, segment_to_index, segment_data = parse_segment(segment, dataframe)
+                segment_from_index, segment_to_index, segment_data = utils.parse_segment(segment, dataframe)
                 if len(segment_data) == 0:
                     continue
                 del_max_index = segment_data.idxmax()

analytics/tests/test_dataset.py

@@ -23,6 +23,78 @@ class TestDataset(unittest.TestCase):
                 model.fit(dataframe, segments, dict())
         except ValueError:
             self.fail('Model {} raised unexpectedly'.format(model_name))
+    
+    def test_peak_antisegments(self):
+        data_val = [1.0, 1.0, 1.0, 2.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 5.0, 7.0, 5.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        dataframe = create_dataframe(data_val)
+        segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000012, 'labeled': True, 'deleted': False},
+                    {'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000003, 'to': 1523889000005, 'labeled': False, 'deleted': True}]
+
+        try:
+            model = models.PeakModel()
+            model_name = model.__class__.__name__
+            model.fit(dataframe, segments, dict())
+        except ValueError:
+            self.fail('Model {} raised unexpectedly'.format(model_name))
+        
+    def test_jump_antisegments(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, 9.0, 1.0, 1.0]
+        dataframe = create_dataframe(data_val)
+        segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000016, 'labeled': True, 'deleted': False},
+                    {'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, 'labeled': False, 'deleted': True}]
+
+        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_trough_antisegments(self):
+        data_val = [9.0, 9.0, 9.0, 9.0, 7.0, 4.0, 7.0, 9.0, 9.0, 9.0, 5.0, 1.0, 5.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0]
+        dataframe = create_dataframe(data_val)
+        segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000012, 'labeled': True, 'deleted': False},
+                    {'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000003, 'to': 1523889000005, 'labeled': False, 'deleted': True}]
+
+        try:
+            model = models.TroughModel()
+            model_name = model.__class__.__name__
+            model.fit(dataframe, segments, dict())
+        except ValueError:
+            self.fail('Model {} raised unexpectedly'.format(model_name))
+    
+    def test_drop_antisegments(self):
+        data_val = [9.0, 9.0, 9.0, 9.0, 9.0, 5.0, 5.0, 5.0, 5.0, 9.0, 9.0, 9.0, 9.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.0, 9.0]
+        dataframe = create_dataframe(data_val)
+        segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000016, 'labeled': True, 'deleted': False},
+                    {'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000002, 'to': 1523889000008, 'labeled': False, 'deleted': True}]
+
+        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_general_antisegments(self):
+        data_val = [1.0, 2.0, 1.0, 2.0, 5.0, 6.0, 3.0, 2.0, 1.0, 1.0, 8.0, 9.0, 8.0, 1.0, 2.0, 3.0, 2.0, 1.0, 1.0, 2.0]
+        dataframe = create_dataframe(data_val)
+        segments = [{'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000010, 'to': 1523889000012, 'labeled': True, 'deleted': False},
+                    {'_id': 'Esl7uetLhx4lCqHa', 'analyticUnitId': 'opnICRJwOmwBELK8', 'from': 1523889000003, 'to': 1523889000005, 'labeled': False, 'deleted': True}]
+
+        try:
+            model = models.GeneralModel()
+            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()
+
+def create_dataframe(data_val: list) -> pd.DataFrame:    
+    data_ind = [1523889000000 + i for i in range(len(data_val))]
+    data = {'timestamp': data_ind, 'value': data_val}
+    dataframe = pd.DataFrame(data)
+    dataframe['timestamp'] = pd.to_datetime(dataframe['timestamp'], unit='ms')
+    return dataframe