def setUp(self):
self._states = (
'EXCESS_RANK',
'SHORTAGE_RANK',
'TRAFFIC_LIGHT',
'CLASSIFICATION',
'FORECAST',
'RECOMMENDATION',
'INVENTORY_TURNS',
'START'
)
self.orders_analysis = model_inventory.analyse(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
z_value=Decimal(1.28),
reorder_cost=Decimal(5000),
file_type='csv',
length=12,
currency='USD')
self.forecast = deserialise_config(ABS_FILE_PATH['FORECAST_PICKLE'])
self.recommend = SkuMachine()
self.states = SKUStates(analysed_orders=self.orders_analysis, forecast=self.forecast)
self.recommend.add_state("start", self.states.initialise_machine)
self.recommend.add_state("excess_rank", self.states.excess_rank)
self.recommend.add_state("shortage_rank", self.states.shortage_rank)
self.recommend.add_state("inventory_turns", self.states.inventory_turns)
self.recommend.add_state("classification", self.states.classification)
self.recommend.add_state("traffic_light", self.states.traffic_light)
self.recommend.add_state("forecast", self.states.forecast)
self.recommend.add_state("recommendation", self.recommend, end_state=1)
self.recommend.set_start("start")
def setUp(self):
self.STATES = ('EXCESS_RANK', 'SHORTAGE_RANK', 'TRAFFIC_LIGHT', 'CLASSIFICATION', 'FORECAST', 'RECOMMENDATION',
'INVENTORY_TURNS', 'START')
self.orders_analysis = model_inventory.analyse(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
z_value=Decimal(1.28),
reorder_cost=Decimal(5000),
file_type="csv",
length=12)
self.forecast = deserialise_config(ABS_FILE_PATH['FORECAST_PICKLE'])
self.recommend = SkuMachine()
self.states = SKUStates(analysed_orders=self.orders_analysis, forecast=self.forecast)
self.recommend.add_state("start", self.states.initialise_machine)
self.recommend.add_state("excess_rank", self.states.excess_rank)
self.recommend.add_state("shortage_rank", self.states.shortage_rank)
self.recommend.add_state("inventory_turns", self.states.inventory_turns)
self.recommend.add_state("classification", self.states.classification)
self.recommend.add_state("traffic_light", self.states.traffic_light)
self.recommend.add_state("forecast", self.states.forecast)
self.recommend.add_state("recommendation", self.recommend, end_state=1)
self.recommend.set_start("start")
def run_sku_recommendation(analysed_orders: UncertainDemand,
forecast: dict) -> dict:
""" Runs SKU recommendation state machine and generates recommendations for each sku.
Args:
analysed_orders (UncertainDemand): Analysed Orders.
forecast (dict): forecast.
Returns:
dict: Recommendations for each sku.
"""
recommend = SkuMachine()
states = SKUStates(analysed_orders=analysed_orders, forecast=forecast)
recommend.add_state("start", states.initialise_machine)
recommend.add_state("excess_rank", states.excess_rank)
recommend.add_state("shortage_rank", states.shortage_rank)
recommend.add_state("inventory_turns", states.inventory_turns)
recommend.add_state("classification", states.classification)
recommend.add_state("traffic_light", states.traffic_light)
recommend.add_state("forecast", states.forecast)
recommend.add_state("recommendation", recommend, end_state=1)
recommend.set_start("start")
for sku in analysed_orders:
recommend.run(sku.sku_id)
return deserialise_config(ABS_FILE_PATH['RECOMMENDATION_PICKLE'])
class TestRecommendations(TestCase):
def setUp(self):
self.STATES = ('EXCESS_RANK', 'SHORTAGE_RANK', 'TRAFFIC_LIGHT', 'CLASSIFICATION', 'FORECAST', 'RECOMMENDATION',
'INVENTORY_TURNS', 'START')
self.orders_analysis = model_inventory.analyse(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
z_value=Decimal(1.28),
reorder_cost=Decimal(5000),
file_type="csv",
length=12)
self.forecast = deserialise_config(ABS_FILE_PATH['FORECAST_PICKLE'])
self.recommend = SkuMachine()
self.states = SKUStates(analysed_orders=self.orders_analysis, forecast=self.forecast)
self.recommend.add_state("start", self.states.initialise_machine)
self.recommend.add_state("excess_rank", self.states.excess_rank)
self.recommend.add_state("shortage_rank", self.states.shortage_rank)
self.recommend.add_state("inventory_turns", self.states.inventory_turns)
self.recommend.add_state("classification", self.states.classification)
self.recommend.add_state("traffic_light", self.states.traffic_light)
self.recommend.add_state("forecast", self.states.forecast)
self.recommend.add_state("recommendation", self.recommend, end_state=1)
self.recommend.set_start("start")
def test_add_states(self):
self.assertEqual(8, len(self.recommend.handlers))
def test_add_states_key(self):
for state in self.recommend.handlers.keys():
self.assertIn(state, self.STATES)
def test_recommendations(self):
completed_recommendations =[]
for sku in self.orders_analysis:
completed_recommendations.append(self.recommend.run(sku.sku_id))
self.assertEqual(len(completed_recommendations), len(self.orders_analysis))
def test_recommendations_coverage(self):
completed_recommendations =[]
sku_ids = [i.sku_id for i in self.orders_analysis]
for sku in self.orders_analysis:
completed_recommendations.append(self.recommend.run(sku.sku_id)[1])
for i in completed_recommendations:
self.assertIn(i, sku_ids)
class TestRecommendations(TestCase):
"""A class for testing the Recommendations generator for reporting"""
def setUp(self):
self._states = (
'EXCESS_RANK',
'SHORTAGE_RANK',
'TRAFFIC_LIGHT',
'CLASSIFICATION',
'FORECAST',
'RECOMMENDATION',
'INVENTORY_TURNS',
'START'
)
self.orders_analysis = model_inventory.analyse(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
z_value=Decimal(1.28),
reorder_cost=Decimal(5000),
file_type='csv',
length=12,
currency='USD')
self.forecast = deserialise_config(ABS_FILE_PATH['FORECAST_PICKLE'])
self.recommend = SkuMachine()
self.states = SKUStates(analysed_orders=self.orders_analysis, forecast=self.forecast)
self.recommend.add_state("start", self.states.initialise_machine)
self.recommend.add_state("excess_rank", self.states.excess_rank)
self.recommend.add_state("shortage_rank", self.states.shortage_rank)
self.recommend.add_state("inventory_turns", self.states.inventory_turns)
self.recommend.add_state("classification", self.states.classification)
self.recommend.add_state("traffic_light", self.states.traffic_light)
self.recommend.add_state("forecast", self.states.forecast)
self.recommend.add_state("recommendation", self.recommend, end_state=1)
self.recommend.set_start("start")
def test_add_states(self):
"""Checks length of loaded states"""
self.assertEqual(8, len(self.recommend.handlers))
def test_add_states_key(self):
"""Checks all states are present"""
for state in self.recommend.handlers.keys():
self.assertIn(state, self._states)
def test_recommendations(self):
"""Checks recommendations generated"""
completed_recommendations = []
for sku in self.orders_analysis:
completed_recommendations.append(self.recommend.run(sku.sku_id))
self.assertEqual(len(completed_recommendations), len(self.orders_analysis))
def test_recommendations_coverage(self):
"""Checks coverage of recommendations"""
completed_recommendations = []
sku_ids = [i.sku_id for i in self.orders_analysis]
for sku in self.orders_analysis:
completed_recommendations.append(self.recommend.run(sku.sku_id)[1])
for i in completed_recommendations:
self.assertIn(i, sku_ids)
def run_sku_recommendation(analysed_orders:UncertainDemand, forecast: dict)->dict:
""" Runs SKU recommendation state machine and generates recommendations for each sku.
Args:
analysed_orders (UncertainDemand): Analysed Orders.
forecast (dict): forecast.
Returns:
dict: Recommendations for each sku.
"""
recommend = SkuMachine()
states = SKUStates(analysed_orders=analysed_orders, forecast=forecast)
recommend.add_state("start", states.initialise_machine)
recommend.add_state("excess_rank", states.excess_rank)
recommend.add_state("shortage_rank", states.shortage_rank)
recommend.add_state("inventory_turns", states.inventory_turns)
recommend.add_state("classification", states.classification)
recommend.add_state("traffic_light", states.traffic_light)
recommend.add_state("forecast", states.forecast)
recommend.add_state("recommendation", recommend, end_state=1)
recommend.set_start("start")
for sku in analysed_orders:
recommend.run(sku.sku_id)
return deserialise_config(ABS_FILE_PATH['RECOMMENDATION_PICKLE'])
