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load.py
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load.py
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# Copyright (c) 2015-2016, The Authors and Contributors
# <see AUTHORS file>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
# following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the
# following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the
# following disclaimer in the documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote
# products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import datetime
import logging
import multiprocessing
import os
from decimal import Decimal
from supplychainpy import model_inventory
from supplychainpy._helpers._config_file_paths import ABS_FILE_PATH_APPLICATION_CONFIG
from supplychainpy._helpers._pickle_config import deserialise_config
from supplychainpy.bi.recommendation_generator import run_sku_recommendation, run_profile_recommendation
from supplychainpy.inventory.analyse_uncertain_demand import UncertainDemand
from supplychainpy.inventory.summarise import Inventory
from supplychainpy.reporting.app import create_app
from supplychainpy.reporting.blueprints.models import Currency
from supplychainpy.reporting.blueprints.models import Forecast
from supplychainpy.reporting.blueprints.models import ForecastBreakdown
from supplychainpy.reporting.blueprints.models import ForecastStatistics
from supplychainpy.reporting.blueprints.models import ForecastType
from supplychainpy.reporting.blueprints.models import InventoryAnalysis
from supplychainpy.reporting.blueprints.models import MasterSkuList
from supplychainpy.reporting.blueprints.models import Orders
from supplychainpy.reporting.blueprints.models import TransactionLog, Recommendations, ProfileRecommendation
from supplychainpy.reporting.extensions import db
from supplychainpy.sample_data.config import ABS_FILE_PATH
log = logging.getLogger(__name__)
log.addHandler(logging.NullHandler())
def currency_codes() -> dict:
""" Retrives HTML Entity (decimal) for currency symbol.
Returns:
dict: Currency Symbols.
"""
codes = {"AED": ("United Arab Emirates Dirham", "\u062f."),
"ANG": ("Netherlands Antilles Guilder", "ƒ"),
"EUR": ("Euro Member Countries", "€"),
"GBP": ("United Kingdom Pound", "£"),
"USD": ("United States Dollar", "$"),
}
return codes
def _analysis_forecast_simple(analysis: UncertainDemand) -> dict:
""" Retrieves simple_exponentions_forecast from an instance of UncertainDemand.
Function only required for Concurrent.futures.
Args:
analysis (UncertainDemand): Instance of UncertainDemand
Returns:
dict: Forecast breakdown.
"""
logging.log(logging.INFO,
"Simple exponential smoothing forecast for SKU: {}\nObject id: {} ".format(
analysis.sku_id,
id(analysis)
)
)
return analysis.simple_exponential_smoothing_forecast
def _analysis_forecast_holt(analysis: UncertainDemand) -> dict:
""" Retrieves simple_exponentions_forecast from an instance of UncertainDemand.
Function only required for Concurrent.futures.
Args:
analysis (UncertainDemand): Instance of UncertainDemand
Returns:
dict: Forecast breakdown.
"""
logging.log(logging.INFO,
"Holt's trend corrected exponential smoothing forecast for SKU: {}\nObject id: {} ".format(
analysis.sku_id,
id(analysis)
)
)
return analysis.holts_trend_corrected_forecast
def load_currency(fx_codes: currency_codes(), ctx: db):
"""Loads Currency Symbols"""
for key, value in fx_codes.items():
codes = Currency()
codes.country = value[0]
codes.symbol = value[1]
codes.currency_code = key
ctx.session.add(codes)
ctx.session.commit()
def load(file_path: str, location: str = None):
""" Loads analysis and forecast into local database for reporting suite.
Args:
file_path (str): File path to source file containing data for analysis.
location (str): Location of database to populate.
"""
try:
app = create_app()
db.init_app(app)
if location is not None and os.name in ['posix', 'mac']:
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///{}/reporting.db'.format(location)
elif location is not None and os.name == 'nt':
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///{}\\reporting.db'.format(location)
log.log(logging.DEBUG, 'Loading data analysis for reporting suite... \n')
with app.app_context():
db.create_all()
log.log(logging.DEBUG, 'loading currency symbols...\n')
print('loading currency symbols...', end="")
fx = currency_codes()
load_currency(fx, db)
print('[COMPLETED]\n')
config = deserialise_config(ABS_FILE_PATH_APPLICATION_CONFIG)
currency = config.get('currency')
log.log(logging.DEBUG, 'Analysing file: {}...\n'.format(file_path))
print('Analysing file: {}...'.format(file_path), end="")
orders_analysis = model_inventory.analyse(file_path=file_path,
z_value=Decimal(1.28),
reorder_cost=Decimal(5000),
file_type="csv", length=12, currency=currency)
ia = [analysis.orders_summary() for analysis in orders_analysis]
date_now = datetime.datetime.now()
analysis_summary = Inventory(processed_orders=orders_analysis)
print('[COMPLETED]\n')
log.log(logging.DEBUG, 'Calculating Forecasts...\n')
print('Calculating Forecasts...', end="")
cores = int(multiprocessing.cpu_count())
cores -= 1
import multiprocessing as mp
simple_forecast = {}
with mp.Pool(processes=cores) as pool:
simple_forecast_gen = {analysis.sku_id: pool.apply_async(_analysis_forecast_simple, args = (analysis,)) for analysis in orders_analysis}
simple_forecast = {key: simple_forecast_gen[key].get() for key in simple_forecast_gen}
holts_forecast_gen = {analysis.sku_id: pool.apply_async(_analysis_forecast_holt, args = (analysis,)) for analysis in orders_analysis}
holts_forecast = {key: holts_forecast_gen[key].get() for key in holts_forecast_gen}
# with ProcessPoolExecutor(max_workers=cores) as executor:
# simple_forecast_futures = { analysis.sku_id: executor.submit(_analysis_forecast_simple, analysis) for analysis in orders_analysis}
# simple_forecast_gen = {future: concurrent.futures.as_completed(simple_forecast_futures[future]) for future in simple_forecast_futures}
# simple_forecast = {value: simple_forecast_futures[value].result() for value in simple_forecast_gen}
# holts_forecast_futures = { analysis.sku_id: executor.submit(_analysis_forecast_holt, analysis) for analysis in orders_analysis}
# holts_forecast_gen = { future: concurrent.futures.as_completed(holts_forecast_futures[future]) for future in holts_forecast_futures}
# holts_forecast = {value: holts_forecast_futures[value].result() for value in holts_forecast_gen}
# executor.shutdown(wait=False)
transact = TransactionLog()
transact.date = date_now
db.session.add(transact)
db.session.commit()
transaction_sub = db.session.query(db.func.max(TransactionLog.date))
transaction_id = db.session.query(TransactionLog).filter(TransactionLog.date == transaction_sub).first()
load_profile_recommendations(analysed_order=orders_analysis, forecast=holts_forecast,
transaction_log_id=transaction_id)
# d = _Orchestrate()
# d.update_database(int(transaction_id.id))
forecast_types = ('ses', 'htces')
for f_type in forecast_types:
forecast_type = ForecastType()
forecast_type.type = f_type
db.session.add(forecast_type)
db.session.commit()
ses_id = db.session.query(ForecastType.id).filter(ForecastType.type == forecast_types[0]).first()
htces_id = db.session.query(ForecastType.id).filter(ForecastType.type == forecast_types[1]).first()
print('[COMPLETED]\n')
log.log(logging.DEBUG, 'loading database ...\n')
print('loading database ...', end="")
for item in ia:
re = 0
skus_description = [summarised for summarised in analysis_summary.describe_sku(item['sku'])]
denom = db.session.query(Currency.id).filter(Currency.currency_code == item['currency']).first()
master_sku = MasterSkuList()
master_sku.sku_id = item['sku']
db.session.add(master_sku)
i_up = InventoryAnalysis()
mk = db.session.query(MasterSkuList.id).filter(MasterSkuList.sku_id == item['sku']).first()
i_up.sku_id = mk.id
tuple_orders = item['orders']
# print(tuple_orders)
i_up.abc_xyz_classification = item['ABC_XYZ_Classification']
i_up.standard_deviation = item['standard_deviation']
i_up.backlog = item['backlog']
i_up.safety_stock = item['safety_stock']
i_up.reorder_level = item['reorder_level']
i_up.economic_order_quantity = item['economic_order_quantity']
i_up.demand_variability = item['demand_variability']
i_up.average_orders = round(float(item['average_orders']))
i_up.shortages = item['shortages']
i_up.excess_stock = item['excess_stock']
i_up.reorder_quantity = item['reorder_quantity']
i_up.economic_order_variable_cost = item['economic_order_variable_cost']
i_up.unit_cost = item['unit_cost']
i_up.revenue = item['revenue']
i_up.date = date_now
i_up.safety_stock_rank = skus_description[0]['safety_stock_rank']
i_up.shortage_rank = skus_description[0]['shortage_rank']
i_up.excess_cost = skus_description[0]['excess_cost']
i_up.percentage_contribution_revenue = skus_description[0]['percentage_contribution_revenue']
i_up.excess_rank = skus_description[0]['excess_rank']
i_up.retail_price = skus_description[0]['retail_price']
i_up.gross_profit_margin = skus_description[0]['gross_profit_margin']
i_up.min_order = skus_description[0]['min_order']
i_up.safety_stock_cost = skus_description[0]['safety_stock_cost']
i_up.revenue_rank = skus_description[0]['revenue_rank']
i_up.markup_percentage = skus_description[0]['markup_percentage']
i_up.max_order = skus_description[0]['max_order']
i_up.shortage_cost = skus_description[0]['shortage_cost']
i_up.quantity_on_hand = item['quantity_on_hand']
i_up.currency_id = denom.id
i_up.traffic_light = skus_description[0]['inventory_traffic_light']
i_up.inventory_turns = skus_description[0]['inventory_turns']
i_up.transaction_log_id = transaction_id.id
db.session.add(i_up)
inva = db.session.query(InventoryAnalysis.id).filter(InventoryAnalysis.sku_id == mk.id).first()
for i, t in enumerate(tuple_orders['demand'], 1):
orders_data = Orders()
# print(r)
orders_data.order_quantity = t
orders_data.rank = i
orders_data.analysis_id = inva.id
db.session.add(orders_data)
# need to select sku id
for i, forecasted_demand in enumerate(simple_forecast, 1):
if forecasted_demand == item['sku']:
forecast_stats = ForecastStatistics()
forecast_stats.analysis_id = inva.id
forecast_stats.mape = simple_forecast.get(forecasted_demand)['mape']
forecast_stats.forecast_type_id = ses_id.id
forecast_stats.slope = simple_forecast.get(forecasted_demand)['statistics']['slope']
forecast_stats.p_value = simple_forecast.get(forecasted_demand)['statistics']['pvalue']
forecast_stats.test_statistic = simple_forecast.get(forecasted_demand)['statistics'][
'test_statistic']
forecast_stats.slope_standard_error = simple_forecast.get(forecasted_demand)['statistics'][
'slope_standard_error']
forecast_stats.intercept = simple_forecast.get(forecasted_demand)['statistics']['intercept']
forecast_stats.standard_residuals = simple_forecast.get(forecasted_demand)['statistics'][
'std_residuals']
forecast_stats.trending = simple_forecast.get(forecasted_demand)['statistics']['trend']
forecast_stats.optimal_alpha = simple_forecast.get(forecasted_demand)['optimal_alpha']
forecast_stats.optimal_gamma = 0
db.session.add(forecast_stats)
for p in range(0, len(simple_forecast.get(forecasted_demand)['forecast'])):
forecast_data = Forecast()
forecast_data.forecast_quantity = simple_forecast.get(forecasted_demand)['forecast'][p]
forecast_data.analysis_id = inva.id
forecast_data.forecast_type_id = ses_id.id
forecast_data.period = p + 1
forecast_data.create_date = date_now
db.session.add(forecast_data)
for q, sesf in enumerate(simple_forecast.get(forecasted_demand)['forecast_breakdown']):
forecast_breakdown = ForecastBreakdown()
forecast_breakdown.analysis_id = inva.id
forecast_breakdown.forecast_type_id = ses_id.id
forecast_breakdown.trend = 0
forecast_breakdown.period = sesf['t']
forecast_breakdown.level_estimates = \
sesf['level_estimates']
forecast_breakdown.one_step_forecast = \
sesf['one_step_forecast']
forecast_breakdown.forecast_error = \
sesf['forecast_error']
forecast_breakdown.squared_error = sesf['squared_error']
forecast_breakdown.regression = simple_forecast.get(forecasted_demand)['regression'][q]
db.session.add(forecast_breakdown)
break
for i, holts_forecast_demand in enumerate(holts_forecast, 1):
if holts_forecast_demand == item['sku']:
forecast_stats = ForecastStatistics()
forecast_stats.analysis_id = inva.id
forecast_stats.mape = holts_forecast.get(holts_forecast_demand)['mape']
forecast_stats.forecast_type_id = htces_id.id
forecast_stats.slope = holts_forecast.get(holts_forecast_demand)['statistics']['slope']
forecast_stats.p_value = holts_forecast.get(holts_forecast_demand)['statistics']['pvalue']
forecast_stats.test_statistic = holts_forecast.get(holts_forecast_demand)['statistics'][
'test_statistic']
forecast_stats.slope_standard_error = holts_forecast.get(holts_forecast_demand)['statistics'][
'slope_standard_error']
forecast_stats.intercept = holts_forecast.get(holts_forecast_demand)['statistics']['intercept']
forecast_stats.standard_residuals = holts_forecast.get(holts_forecast_demand)['statistics'][
'std_residuals']
forecast_stats.trending = holts_forecast.get(holts_forecast_demand)['statistics']['trend']
forecast_stats.optimal_alpha = holts_forecast.get(holts_forecast_demand)['optimal_alpha']
forecast_stats.optimal_gamma = holts_forecast.get(holts_forecast_demand)['optimal_gamma']
db.session.add(forecast_stats)
for p in range(0, len(holts_forecast.get(holts_forecast_demand)['forecast'])):
forecast_data = Forecast()
forecast_data.forecast_quantity = holts_forecast.get(holts_forecast_demand)['forecast'][p]
forecast_data.analysis_id = inva.id
forecast_data.forecast_type_id = htces_id.id
forecast_data.period = p + 1
forecast_data.create_date = date_now
db.session.add(forecast_data)
for i, htcesf in enumerate(holts_forecast.get(holts_forecast_demand)['forecast_breakdown']):
forecast_breakdown = ForecastBreakdown()
forecast_breakdown.analysis_id = inva.id
forecast_breakdown.forecast_type_id = htces_id.id
forecast_breakdown.trend = htcesf['trend']
forecast_breakdown.period = htcesf['t']
forecast_breakdown.level_estimates = \
htcesf['level_estimates']
forecast_breakdown.one_step_forecast = \
htcesf['one_step_forecast']
forecast_breakdown.forecast_error = \
htcesf['forecast_error']
forecast_breakdown.squared_error = htcesf['squared_error']
forecast_breakdown.regression = holts_forecast.get(holts_forecast_demand)['regression'][i]
db.session.add(forecast_breakdown)
break
db.session.commit()
print('[COMPLETED]\n')
loading = 'Loading recommendations into database... '
print(loading, end="")
load_recommendations(summary=ia, forecast=holts_forecast, analysed_order=orders_analysis)
print('[COMPLETED]\n')
log.log(logging.DEBUG, "Analysis ...\n")
print("Analysis ... [COMPLETED]")
except OSError as e:
print(e)
def load_recommendations(summary, forecast, analysed_order):
recommend = run_sku_recommendation(analysed_orders=analysed_order, forecast=forecast)
for item in summary:
rec = Recommendations()
mk = db.session.query(MasterSkuList.id).filter(MasterSkuList.sku_id == item['sku']).first()
inva = db.session.query(InventoryAnalysis.id).filter(InventoryAnalysis.sku_id == mk.id).first()
rec.analysis_id = inva.id
reco = 'There are no recommendation at this time.' if recommend.get(item['sku'],
'There are no recommendation at this time.'
) == '' else recommend.get(item['sku'],
'None')
rec.statement = reco
db.session.add(rec)
db.session.commit()
def load_profile_recommendations(analysed_order, forecast, transaction_log_id):
recommend = run_profile_recommendation(analysed_orders=analysed_order, forecast=forecast)
rec = ProfileRecommendation()
rec.transaction_id = int(transaction_log_id.id)
rec.statement = recommend.get('profile')
db.session.add(rec)
db.session.commit()
if __name__ == '__main__':
load(ABS_FILE_PATH['COMPLETE_CSV_LG'])