def run(self) -> Table:
"""
calculates metric table for all techniques and applies post processing techinques defined in module
:return: metric table with metrics
"""
Cache.CACHE_ON = True
dataset_name = prompt_for_dataset()
metric_table = calculate_technique_metric_table(dataset_name)
metric_table.sort(DATASET_COLUMN_ORDER).save(
create_export_path(dataset_name))
# export metric table
aggregate_metric_table = MetricTable(
Table.aggregate_intermediate_files(PATH_TO_METRIC_TABLES).sort(
DATASET_COLUMN_ORDER).table).save(
PATH_TO_METRIC_TABLE_AGGREGATE)
# create graphable metrics and export table
aggregate_metric_table.create_lag_norm_inverted(
drop_old=True).melt_metrics().col_values_to_upper(
METRIC_COLNAME).save(PATH_TO_GRAPH_METRIC_TABLE_AGGREGATE)
self.export_paths.append(create_export_path(dataset_name))
self.export_paths.append(PATH_TO_METRIC_TABLE_AGGREGATE)
Cache.cleanup(dataset_name)
return metric_table
def test_crate_lag_norm_inverted(self):
metric_table = MetricTable(self.data)
values = metric_table.create_lag_norm_inverted().table.set_index(
[DATASET_COLNAME, NAME_COLNAME])
for d_name in self.DATASETS:
for name, e_value in self.expected_values:
value = values.loc[(d_name,
name)][LAG_NORMALIZED_INVERTED_COLNAME]
self.assertEqual(e_value, value)
def run(self) -> Table:
"""
Returns a metric table containing all of the metrics calculated for each technique in df
:return: metric table with single query metrics for each technique applied to specified dataset in row
"""
tracer = Tracer()
metric_table = MetricTable()
for dataset_name in DATASET_COLUMN_ORDER:
hybrid_query_metrics: List[Metrics] = tracer.get_metrics(
dataset_name, BEST_OVERALL_TECHNIQUE, summary_metrics=False)
metric_table.add(
hybrid_query_metrics,
other={
DATASET_COLNAME: dataset_name,
TECHNIQUE_TYPE_COLNAME: HYBRID_ID,
},
create_index=True,
)
direct_query_metrics: List[Metrics] = tracer.get_metrics(
dataset_name,
get_best_direct_technique(dataset_name),
summary_metrics=False,
)
metric_table.add(
direct_query_metrics,
other={
DATASET_COLNAME: dataset_name,
TECHNIQUE_TYPE_COLNAME: DIRECT_ID,
},
create_index=True,
)
individual_queries_aggregate = (metric_table.create_lag_norm_inverted(
drop_old=True).melt_metrics(
metric_value_col_name=METRIC_SCORE_COLNAME).sort(
DATASET_COLUMN_ORDER).col_values_to_upper(
METRIC_COLNAME).save(EXPORT_PATH))
self.export_paths.append(PATH_TO_INDIVIDUAL_QUERIES_AGG)
return individual_queries_aggregate
