class ProductBlockingKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(ProductBlockingKpi, self).__init__(data_provider, config_params=config_params, **kwargs)
self.util = PepsicoUtil(None, data_provider)
self.block = Block(self.data_provider, custom_scif=self.util.filtered_scif, custom_matches=self.util.filtered_matches)
def kpi_type(self):
pass
def calculate(self):
if not self.util.filtered_matches.empty:
self.calculate_product_blocking()
def calculate_product_blocking(self):
external_targets = self.util.all_targets_unpacked[self.util.all_targets_unpacked['type'] == self.util.PRODUCT_BLOCKING]
additional_block_params = {'check_vertical_horizontal': True, 'minimum_facing_for_block': 3,
'include_stacking': True,
'allowed_products_filters': {'product_type': ['Empty']}}
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(self.util.PRODUCT_BLOCKING)
for i, row in external_targets.iterrows():
group_fk = self.util.custom_entities[self.util.custom_entities['name'] == row['Group Name']]['pk'].values[0]
filters = self.util.get_block_and_adjacency_filters(row)
target = row['Target']
additional_block_params.update({'minimum_block_ratio': float(target)/100})
result_df = self.block.network_x_block_together(filters, additional=additional_block_params)
score = max_ratio = 0
result = self.util.commontools.get_yes_no_result(0)
if not result_df.empty:
max_ratio = result_df['facing_percentage'].max()
result_df = result_df[result_df['is_block']==True]
if not result_df.empty:
max_ratio = result_df['facing_percentage'].max()
result_df = result_df[result_df['facing_percentage'] == max_ratio]
result = self.util.commontools.get_yes_no_result(1)
orientation = result_df['orientation'].values[0]
score = self.util.commontools.get_kpi_result_value_pk_by_value(orientation.upper())
self.write_to_db_result(fk=kpi_fk, numerator_id=group_fk, denominator_id=self.util.store_id,
numerator_result=max_ratio * 100,
score=score, result=result, target=target, by_scene=True)
# connection with adjacency kpi
# TODO: setup the dependency
self.util.block_results = self.util.block_results.append(pd.DataFrame([{'Group Name': row['Group Name'],
'Score': result_df['is_block'].values[0] if not result_df.empty else False}]))
class BlocksAdjacencyKpi(UnifiedCalculationsScript):
def __init__(self, data_provider, config_params=None, **kwargs):
super(BlocksAdjacencyKpi, self).__init__(data_provider,
config_params=config_params,
**kwargs)
self.util = PepsicoUtil(None, data_provider)
self.adjacency = Adjancency(self.data_provider,
custom_scif=self.util.filtered_scif,
custom_matches=self.util.filtered_matches)
def kpi_type(self):
pass
def calculate(self):
if not self.util.filtered_matches.empty:
# self.construct_block_results()
self.calculate_adjacency()
def calculate_adjacency(self):
block_pairs = self.get_group_pairs()
if block_pairs:
external_targets = self.util.all_targets_unpacked[
self.util.all_targets_unpacked['type'] ==
self.util.PRODUCT_BLOCKING]
additional_block_params = {
'check_vertical_horizontal': True,
'minimum_facing_for_block': 3,
'minimum_block_ratio': 0.9,
'include_stacking': True,
'allowed_products_filters': {
'product_type': ['Empty']
}
}
kpi_fk = self.util.common.get_kpi_fk_by_kpi_type(
self.util.PRODUCT_BLOCKING_ADJACENCY)
for pair in block_pairs:
pair = list(pair)
group_1_fk = self.util.custom_entities[
self.util.custom_entities['name'] ==
pair[0]]['pk'].values[0]
group_2_fk = self.util.custom_entities[
self.util.custom_entities['name'] ==
pair[1]]['pk'].values[0]
group_1_targets = external_targets[
external_targets['Group Name'] == pair[0]].iloc[0]
group_1_filters = self.util.get_block_and_adjacency_filters(
group_1_targets)
group_2_targets = external_targets[
external_targets['Group Name'] == pair[1]].iloc[0]
group_2_filters = self.util.get_block_and_adjacency_filters(
group_2_targets)
result_df = self.adjacency.network_x_adjacency_calculation(
{
'anchor_products': group_1_filters,
'tested_products': group_2_filters
},
location=None,
additional=additional_block_params)
score = 0
result = self.util.commontools.get_yes_no_result(0)
if not result_df.empty:
score = 1 if result_df['is_adj'].values[0] else 0
result = self.util.commontools.get_yes_no_result(score)
self.write_to_db_result(fk=kpi_fk,
numerator_id=group_1_fk,
denominator_id=group_2_fk,
result=result,
score=score,
by_scene=True)
def get_group_pairs(self):
valid_groups = self.util.block_results[
self.util.block_results['Score'] ==
1]['Group Name'].values.tolist()
result_set = set()
for i, group in enumerate(valid_groups):
[
result_set.add(frozenset([group, valid_groups[j]]))
for j in range(i + 1, len(valid_groups))
]
return list(result_set)
def construct_block_results(self):
block_results = []
for i, result in self.dependencies_data.iterrows():
block = self.util.custom_entities[
self.util.custom_entities['pk'] ==
result['numerator_id']]['name'].values[0]
score = 1 if result['result'] == 4 else 0
block_results.append({'Group Name': block, 'Score': score})
block_res_df = pd.DataFrame.from_records(block_results)
self.util.block_results = self.util.block_results.append(block_res_df)