change NA to none, test fixes

eureka_ml_insights/configs/model_configs.py

@@ -199,4 +199,4 @@
         },
         "model_name": "Mistral-large-2407",
     },
-)
+)

eureka_ml_insights/metrics/ba_calendar_metrics.py

@@ -98,7 +98,7 @@ def run_programmatic_tests(self, instance):
         for key, value in result.items():
             if value == 0:
                 all_correct = 0
-            if value != 'NA' and pd.notna(value) and isinstance(value, int):
+            if value is not None and value != 'NA' and pd.notna(value) and isinstance(value, int):
                 passed_constraints.append(value)
         result['all_correct'] = all_correct
         result['fraction_passed'] = np.mean(passed_constraints)
@@ -114,7 +114,8 @@ def is_formatted(self, solution):
 
     def check_availability_programmatic(self, instance, solution):
         if not instance['constraints'].get('availability', True):
-            result = {'availability_programmatic_check': 'NA'}
+            # result = {'availability_programmatic_check': 'NA'}
+            result = {'availability_programmatic_check': None}
             return result
 
         if not self.is_formatted(solution):
@@ -144,7 +145,8 @@ def check_availability_programmatic(self, instance, solution):
 
     def check_meeting_duration_programmatic(self, instance, solution):
         if not instance['constraints'].get('meeting_duration', True):
-            result = {'meeting_duration_programmatic_check': 'NA'}
+            # result = {'meeting_duration_programmatic_check': 'NA'}
+            result = {'meeting_duration_programmatic_check': None}
             return result
 
         if not self.is_formatted(solution):
@@ -162,7 +164,8 @@ def check_meeting_duration_programmatic(self, instance, solution):
     def check_buffer_time_programmatic(self, instance, solution):
         buffer_time = instance['constraints'].get('buffer_time_before_and_after_meeting', True)
         if buffer_time is None or not buffer_time:
-            result = {'buffer_time_programmatic_check': 'NA'}
+            # result = {'buffer_time_programmatic_check': 'NA'}
+            result = {'buffer_time_programmatic_check': None}
             return result
 
         if not self.is_formatted(solution):
@@ -195,7 +198,8 @@ def check_buffer_time_programmatic(self, instance, solution):
 
     def check_no_weekends_programmatic(self, instance, solution):
         if not instance['constraints'].get('no_meetings_on_weekends', True):
-            return {'no_weekends_programmatic_check': 'NA'}
+            # return {'no_weekends_programmatic_check': 'NA'}
+            return {'no_weekends_programmatic_check': None}
 
         if not self.is_formatted(solution):
             return {'no_weekends_programmatic_check': 0}
@@ -207,7 +211,8 @@ def check_no_weekends_programmatic(self, instance, solution):
 
     def check_time_restrictions_programmatic(self, instance, solution):
         if not instance['constraints'].get('no_meetings_before', True) and not instance['constraints'].get('no_meetings_after', True):
-            return {'time_restrictions_programmatic_check': 'NA'}
+            # return {'time_restrictions_programmatic_check': 'NA'}
+            return {'time_restrictions_programmatic_check': None}
 
         if not self.is_formatted(solution):
             return {'time_restrictions_programmatic_check': 0}
@@ -231,7 +236,8 @@ def check_time_restrictions_programmatic(self, instance, solution):
 
     def check_priority_programmatic(self, instance, solution):
         if not instance['constraints'].get('high_priority_meeting', False):
-            return {'priority_programmatic_check': 'NA'}
+            # return {'priority_programmatic_check': 'NA'}
+            return {'priority_programmatic_check': None}
 
         if not self.is_formatted(solution):
             return {'priority_programmatic_check': 0}
@@ -269,7 +275,8 @@ def check_priority_programmatic(self, instance, solution):
 
     def check_specific_times_programmatic(self, instance, solution):
         if not instance['constraints'].get('no_meetings_during_specific_times', True):
-            return {'specific_times_programmatic_check': 'NA'}
+            # return {'specific_times_programmatic_check': 'NA'}
+            return {'specific_times_programmatic_check': None}
 
         if not self.is_formatted(solution):
             return {'specific_times_programmatic_check': 0}

eureka_ml_insights/metrics/reports.py

@@ -348,12 +348,13 @@ def _aggregate_grouped(self, data):
         divided_result = (gb[self.numerator_column_name].sum() / gb[self.denominator_column_name].sum()).to_dict()
         self.aggregated_result = {"ratio": divided_result}
 
-class NAFilteredAggregator(Aggregator):
-    def __init__(self, agg_class, column_names, output_dir, group_by=None, ignore_non_numeric=False, filename_base=None, **kwargs):
+class ValueFilteredAggregator(Aggregator):
+    def __init__(self, agg_class, value, column_names, output_dir, group_by=None, ignore_non_numeric=False, filename_base=None, **kwargs):
         """
-        Aggregator that filters out "NA" values before aggregating the data.
+        Aggregator that filters out a particular value before aggregating the data.
         args:
             agg_class: Aggregator class to use for aggregation
+            value: value to filter out
             column_names: column names to filter and aggregate
             output_dir: str. directory to save the report
             group_by: str. or list of str. column(s) to group by before aggregating
@@ -362,19 +363,19 @@ def __init__(self, agg_class, column_names, output_dir, group_by=None, ignore_no
         """
 
         self.base_aggregator = agg_class(column_names, output_dir, group_by, ignore_non_numeric, filename_base, **kwargs)
+        self.value = value
         self.column_names = column_names
         self.group_by = group_by
         self.output_dir = output_dir
         self.aggregated_result = None
         self.ignore_non_numeric = ignore_non_numeric
         self.filename_base = filename_base
-        # super().__init__(self.input_column_names, output_dir, group_by, ignore_non_numeric, filename_base, **kwargs)
 
     def aggregate(self, data):
         agg_results = {}
         for col in self.column_names:
             # workaround to process one column at a time
-            filtered_data = data[data[col] != "NA"].copy()
+            filtered_data = data[data[col] != self.value].copy()
             self.base_aggregator.column_names = [col]
             self.base_aggregator.aggregate(filtered_data)
             agg_results.update(self.base_aggregator.aggregated_result)

eureka_ml_insights/prompt_templates/ba_calendar_templates/calendar_scheduling_brief.jinja

@@ -2,6 +2,7 @@ You are a scheduling assistant. Given the availability schedules of multiple par
 Make sure you use the availability schedules to generate your response.
 High priority meetings should be scheduled as early as possible.
 Buffer time refers to the required remaining available time before and after a meeting. For example, if buffer time is 15 minutes, a meeting from 9:00-10:00 will require availability from 8:45-10:15.
-Respond with "[day] [start_time]-[end_time]" or "No common time slot available"
-Do not respond with any additional information or comments.
+The final time slot solution should be "[day] [start_time]-[end_time]" or "No common time slot available".
+Provide your answer in the format:
+Final Answer: <answer>
 {{prompt}}

eureka_ml_insights/prompt_templates/ba_calendar_templates/calendar_scheduling_cot.jinja

@@ -3,7 +3,7 @@ Make sure you use the availability schedules to generate your response.
 High priority meetings should be scheduled as early as possible.
 Buffer time refers to the required remaining available time before and after a meeting. For example, if buffer time is 15 minutes, a meeting from 9:00-10:00 will require availability from 8:45-10:15.
 The final time slot solution should be "[day] [start_time]-[end_time]" or "No common time slot available".
-Think through and provide your your answer in the format:
+Think through and provide your answer in the format:
 Reason: <reasoning>
 Final Answer: <answer>
 {{prompt}}

eureka_ml_insights/user_configs/ba_calendar.py

@@ -19,8 +19,6 @@
 from eureka_ml_insights.metrics.reports import (
     AverageAggregator,
     BiLevelMaxAggregator,
-    MaxAggregator,
-    NAFilteredAggregator,
 )
 
 from ..configs.config import (
@@ -99,17 +97,7 @@ def configure_pipeline(self, model_config=None, resume_from=None, **kwargs) -> P
                     {
                         "column_names": [
                             "BACalendarMetric_all_correct",
-                            "BACalendarMetric_fraction_passed"
-                        ],
-                        "filename_base": "BaCal_OverallMetrics_SeparateRuns",
-                        "group_by": "data_repeat_id",
-                    },
-                ),
-                AggregatorConfig(
-                    NAFilteredAggregator,
-                    {
-                        "agg_class": AverageAggregator,
-                        "column_names": [
+                            "BACalendarMetric_fraction_passed",
                             "BACalendarMetric_availability_programmatic_check",
                             "BACalendarMetric_meeting_duration_programmatic_check",
                             "BACalendarMetric_buffer_time_programmatic_check",
@@ -118,7 +106,7 @@ def configure_pipeline(self, model_config=None, resume_from=None, **kwargs) -> P
                             "BACalendarMetric_specific_times_programmatic_check",
                             "BACalendarMetric_priority_programmatic_check"
                         ],
-                        "filename_base": "BaCal_Constraint_Level_SeprateRuns",
+                        "filename_base": "BaCal_OverallMetrics_SeparateRuns",
                         "group_by": "data_repeat_id",
                     },
                 ),
@@ -142,18 +130,7 @@ def configure_pipeline(self, model_config=None, resume_from=None, **kwargs) -> P
                     {
                         "column_names": [
                             "BACalendarMetric_all_correct",
-                            "BACalendarMetric_fraction_passed"
-                        ],
-                        "first_groupby": "data_point_id",
-                        "filename_base": "BaCal_BestOfN_Aggregated",
-                        "normalize": True,
-                    },
-                ),
-                AggregatorConfig(
-                    NAFilteredAggregator,
-                    {
-                        "agg_class": MaxAggregator,
-                        "column_names": [
+                            "BACalendarMetric_fraction_passed",
                             "BACalendarMetric_availability_programmatic_check",
                             "BACalendarMetric_meeting_duration_programmatic_check",
                             "BACalendarMetric_buffer_time_programmatic_check",
@@ -162,18 +139,17 @@ def configure_pipeline(self, model_config=None, resume_from=None, **kwargs) -> P
                             "BACalendarMetric_specific_times_programmatic_check",
                             "BACalendarMetric_priority_programmatic_check"
                         ],
-                        "filename_base": "BaCal_Constraint_Level_BestOfN_Aggregated",
-                        "group_by": "data_repeat_id",
+                        "first_groupby": "data_point_id",
+                        "filename_base": "BaCal_BestOfN_Aggregated",
+                        "normalize": True,
                     },
                 ),
-
-
             ],
             output_dir=os.path.join(self.log_dir, "bestofn_eval_report"),
         )
 
         # Aggregate the results by a majority vote
-        self.data_post_processing_addmv = DataProcessingConfig(
+        self.maj_vote_data_post_processing = DataProcessingConfig(
             component_type=DataProcessing,
             data_reader_config=DataSetConfig(
                 DataReader,
@@ -197,12 +173,12 @@ def configure_pipeline(self, model_config=None, resume_from=None, **kwargs) -> P
             output_dir=os.path.join(self.log_dir, "data_majvote_output"),
         )
         # Second, compute eaxct match
-        self.postevalprocess_comp = EvalReportingConfig(
+        self.majvote_evalreporting_comp = EvalReportingConfig(
             component_type=EvalReporting,
             data_reader_config=DataSetConfig(
                 DataReader,
                 {
-                    "path": os.path.join(self.data_post_processing_addmv.output_dir, "transformed_data.jsonl"),
+                    "path": os.path.join(self.maj_vote_data_post_processing.output_dir, "transformed_data.jsonl"),
                     "format": ".jsonl",
                     "transform": SequenceTransform(
                         [
@@ -218,17 +194,7 @@ def configure_pipeline(self, model_config=None, resume_from=None, **kwargs) -> P
                     {
                         "column_names": [
                             "BACalendarMetric_all_correct",
-                            "BACalendarMetric_fraction_passed"
-                        ],
-                        "filename_base": "BaCal_MajVote_OverallMetrics_Aggregated",
-                        "group_by": "data_repeat_id",
-                    },
-                ),
-                AggregatorConfig(
-                    NAFilteredAggregator,
-                    {
-                        "agg_class": AverageAggregator,
-                        "column_names": [
+                            "BACalendarMetric_fraction_passed",
                             "BACalendarMetric_availability_programmatic_check",
                             "BACalendarMetric_meeting_duration_programmatic_check",
                             "BACalendarMetric_buffer_time_programmatic_check",
@@ -237,7 +203,7 @@ def configure_pipeline(self, model_config=None, resume_from=None, **kwargs) -> P
                             "BACalendarMetric_specific_times_programmatic_check",
                             "BACalendarMetric_priority_programmatic_check"
                         ],
-                        "filename_base": "BaCal_MajVote_Constraint_Level_Aggregated",
+                        "filename_base": "BaCal_MajVote_OverallMetrics_Aggregated",
                         "group_by": "data_repeat_id",
                     },
                 ),
@@ -252,8 +218,8 @@ def configure_pipeline(self, model_config=None, resume_from=None, **kwargs) -> P
                 self.inference_comp,
                 self.evalreporting_comp,
                 self.bon_evalreporting_comp,
-                self.data_post_processing_addmv,
-                self.postevalprocess_comp
+                self.maj_vote_data_post_processing,
+                self.majvote_evalreporting_comp
             ],
             self.log_dir,
 

tests/metric_utils_tests/aggregator_tests.py

@@ -15,7 +15,7 @@
     SumAggregator,
     TwoColumnSumAverageAggregator,
 )
-from eureka_ml_insights.metrics.reports import BiLevelMaxAggregator, MaxAggregator, NAFilteredAggregator
+from eureka_ml_insights.metrics.reports import BiLevelMaxAggregator, MaxAggregator, ValueFilteredAggregator
 
 PRECISION = 3
 
@@ -419,7 +419,7 @@ def test_average_aggregator_group_by_multiple_columns(self):
         self.assertTrue(os.path.exists(avg_agg.output_file))
 
 
-class NAFilteredAggregatorTestData:
+class ValueFilteredAggregatorTestData:
     def setUp(self):
         self.data = pd.DataFrame(
             {
@@ -430,18 +430,14 @@ def setUp(self):
                 "col3": [5, 8, 'NA', 3, 'abc', 8, 3, 4, 5, 8, 4, 2],
                 "categorical_metric": ["x", "y", "z", "z", "y", "y", "z", "y", "x", "y", "y", "x"],
                 "group": ["a", "a", "b", "b", "a", "a", "b", "b", "a", "a", "b", "b"],
-                # [5, 6, 8, 5, 8, ]
-                # [2, 3, 3, 4, 2]
-                # [5, 8, 6, 8, 5, 8, ]
-                # [2, 3, 4, 2]
             }
         )
         self.output_dir = "output_dir"
         self.precision = PRECISION
 
-class TestNAFilteredAggregator(NAFilteredAggregatorTestData, unittest.TestCase):
+class TestValueFilteredAggregator(ValueFilteredAggregatorTestData, unittest.TestCase):
     def test_average_aggregator(self):
-        avg_agg = NAFilteredAggregator(AverageAggregator, ["col1", "col2"], self.output_dir)
+        avg_agg = ValueFilteredAggregator(AverageAggregator, "NA", ["col1", "col2"], self.output_dir)
         avg_agg.aggregate(self.data)
         x = [a for a in self.data["col1"] if a != 'NA']
         y = [a for a in self.data["col2"] if a != 'NA']
@@ -451,12 +447,12 @@ def test_average_aggregator(self):
         )
 
     def test_average_aggregator_input_validation(self):
-        avg_agg = NAFilteredAggregator(AverageAggregator, ["col3"], self.output_dir)
+        avg_agg = ValueFilteredAggregator(AverageAggregator, 'NA', ["col3"], self.output_dir)
         self.assertRaises(ValueError, avg_agg.aggregate, self.data)
 
     def test_average_aggregator_group_by(self):
-        self.output_dir = create_logdir("NAFilteredAggregatorTests")
-        avg_agg = NAFilteredAggregator(AverageAggregator, ["col1", "col2"], self.output_dir, group_by="group")
+        self.output_dir = create_logdir("ValueFilteredAggregatorTests")
+        avg_agg = ValueFilteredAggregator(AverageAggregator, 'NA', ["col1", "col2"], self.output_dir, group_by="group")
         avg_agg.aggregate(self.data)
         self.assertEqual(avg_agg.aggregated_result, {"col1": {"a": 6.4, "b": 2.8}, "col2": {"a": 6.667, "b": 2.75}})
         avg_agg.write_results()

tests/pipeline_tests.py

@@ -487,7 +487,7 @@ def get_config(self):
 
     def setUp(self) -> None:
         super().setUp()
-        self.eval_configs = [self.test_pipeline.evalreporting_comp]
+        self.eval_configs = [self.test_pipeline.evalreporting_comp,self.test_pipeline.bon_evalreporting_comp, self.test_pipeline.majvote_evalreporting_comp]
 
     def test_outputs_exist(self) -> None:
         logging.info("Running test_outputs_exist test in PipelineTest")