Get the metrics tests to pass with inferred data as well

- First, add the inference to the intake pipeline in the tests common file
- Next, copy testing tests on cleaned data to a separate test file
- Finally, create a new test for metrics on inferred modes and update the
  expected values for the inferred modes by looking at the current results

emission/analysis/result/metrics/time_grouping.py

@@ -20,6 +20,7 @@
 import emission.storage.timeseries.aggregate_timeseries as estag
 import emission.core.wrapper.motionactivity as ecwm
 import emission.core.wrapper.modestattimesummary as ecwms
+import emission.core.wrapper.modeprediction as ecwmp
 import emission.core.wrapper.localdate as ecwl
 
 import emission.analysis.config as eac
@@ -117,7 +118,10 @@ def grouped_to_summary(time_grouped_df, key_to_fill_fn, summary_fn):
         mode_grouped_df = section_group_df.groupby('sensed_mode')
         mode_results = summary_fn(mode_grouped_df)
         for mode, result in mode_results.items():
-            curr_msts[ecwm.MotionTypes(mode).name] = result
+            if eac.get_section_key_for_analysis_results() == "analysis/inferred_section":
+                curr_msts[ecwmp.PredictedModeTypes(mode).name] = result
+            else:
+                curr_msts[ecwm.MotionTypes(mode).name] = result
         ret_list.append(curr_msts)
     return ret_list
 

emission/core/wrapper/modestattimesummary.py

@@ -8,6 +8,7 @@
 import logging
 import emission.core.wrapper.wrapperbase as ecwb
 import emission.core.wrapper.motionactivity as ecwm
+import emission.core.wrapper.modeprediction as ecwmp
 
 # Used for various metrics such as count, distance, mean speed calorie consumption,
 # median speed calorie consumption
@@ -22,7 +23,10 @@ class ModeStatTimeSummary(ecwb.WrapperBase):
     # Each distance will have
     #
     #
+    # Make this only predicted mode, or remove completely depending on what we
+    # do for mode stuff
     props = dict([(t.name, ecwb.WrapperBase.Access.WORM) for t in ecwm.MotionTypes])
+    props.update(dict([(t.name, ecwb.WrapperBase.Access.WORM) for t in ecwmp.PredictedModeTypes]))
     props.update(
         {'ts': ecwb.WrapperBase.Access.WORM,  # YYYY-MM-DD
          'local_dt': ecwb.WrapperBase.Access.WORM,

emission/tests/common.py

@@ -128,12 +128,14 @@ def runIntakePipeline(uuid):
     import emission.analysis.intake.segmentation.section_segmentation as eaiss
     import emission.analysis.intake.cleaning.location_smoothing as eaicl
     import emission.analysis.intake.cleaning.clean_and_resample as eaicr
+    import emission.analysis.classification.inference.mode.pipeline as eacimp
 
     eaicf.filter_accuracy(uuid)
     eaist.segment_current_trips(uuid)
     eaiss.segment_current_sections(uuid)
     eaicl.filter_current_sections(uuid)
     eaicr.clean_and_resample(uuid)
+    eacimp.predict_mode(uuid)
 
 def configLogging():
     """

emission/tests/netTests/TestMetrics.py → emission/tests/netTests/TestMetricsCleanedSections.py

@@ -8,6 +8,7 @@
 import unittest
 import logging
 import arrow
+import os
 
 import emission.core.get_database as edb
 import emission.core.wrapper.localdate as ecwl
@@ -23,6 +24,8 @@
 
 class TestMetrics(unittest.TestCase):
     def setUp(self):
+        self.analysis_conf_path = \
+            etc.set_analysis_config("analysis.result.section.key", "analysis/cleaned_section")
         etc.setupRealExample(self,
                              "emission/tests/data/real_examples/shankari_2015-aug-21")
         self.testUUID1 = self.testUUID
@@ -39,14 +42,15 @@ def setUp(self):
 
     def tearDown(self):
         self.clearRelatedDb()
+        os.remove(self.analysis_conf_path)
 
     def clearRelatedDb(self):
-        edb.get_timeseries_db().remove({"user_id": self.testUUID})
-        edb.get_analysis_timeseries_db().remove({"user_id": self.testUUID})
-        edb.get_pipeline_state_db().remove({"user_id": self.testUUID})
-        edb.get_timeseries_db().remove({"user_id": self.testUUID1})
-        edb.get_analysis_timeseries_db().remove({"user_id": self.testUUID1})
-        edb.get_pipeline_state_db().remove({"user_id": self.testUUID1})
+        edb.get_timeseries_db().delete_many({"user_id": self.testUUID})
+        edb.get_analysis_timeseries_db().delete_many({"user_id": self.testUUID})
+        edb.get_pipeline_state_db().delete_many({"user_id": self.testUUID})
+        edb.get_timeseries_db().delete_many({"user_id": self.testUUID1})
+        edb.get_analysis_timeseries_db().delete_many({"user_id": self.testUUID1})
+        edb.get_pipeline_state_db().delete_many({"user_id": self.testUUID1})
 
     def testCountTimestampMetrics(self):
         met_result = metrics.summarize_by_timestamp(self.testUUID,