accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# initializing all variables
init = tf.global_variables_initializer()
# launch the graph
saver = tf.train.Saver(tf.global_variables(), max_to_keep=1)
tf.add_to_collection('accuracy', accuracy)
tf.add_to_collection('x1', x1)
tf.add_to_collection('x2', x2)
tf.add_to_collection('y', y)
with tf.Session() as sess:
sess.run(init)
step = 1
model = data_helpers.load_model(
'./Data/GoogleNews-vectors-negative300.bin')
if test == 'PDTB':
sentences1, sentences2, labels = data_helpers.load_labels_and_data_PDTB(
model, './Data/PDTB_implicit/train.txt')
elif test == 'SICK':
sentences1, sentences2, labels = data_helpers.load_data_SICK(
model, './Data/SICK/train.txt')
total = 0
while total < training_iters:
start = total % len(sentences1)
end = (total + batch_size) % len(sentences1)
if end <= start:
end = len(sentences1)
batch_x1 = sentences1[start:end]
batch_x2 = sentences2[start:end]
def forecast_oot(req_dict):
# Get all future forecast , future forecast analysis, retailer forecast, retailer forecast analysis
future_steps = int(req_dict.futurePeriod)
past_steps = int(req_dict.historicalPeriod)
start = req_dict.forecastStart
analysis_windows = get_window_periods(start,
future_steps=future_steps,
past_steps=past_steps)
# Load data & model
filename = req_dict.data
country = req_dict.market
category = req_dict.category
skus = req_dict.sku
df_skus, df_retailers = load_data(filename=filename,
skus=skus,
country=country,
category=category)
models = load_model()
forecast_sku = _forecast_sku(df=df_skus,
models=models,
analysis_windows=analysis_windows,
category=category,
country=country)
disp_sku_for_cols = [
'sku', 'market', 'category', 'forecastWeek', 'forecastVolume'
]
sku_forecast_week = forecast_sku['weeklyForecast']
sku_list = (sku_forecast_week['sku'].unique()).tolist()
sku_forecast_week = sku_forecast_week[disp_sku_for_cols].to_dict('records')
disp_sku_for_cols = [
'sku', 'market', 'category', 'month', 'forecastVolume'
]
sku_forecast_month = forecast_sku['monthlyForecast']
sku_forecast_month = sku_forecast_month[disp_sku_for_cols].to_dict(
'records')
forecast_retailer_sku = _forecast_retailer_sku(
dfs=forecast_sku['weeklyForecast'],
dfr=df_retailers,
analysis_windows=analysis_windows,
category=category,
country=country)
disp_ret_sku_for_cols = [
'sku', 'forecastWeek', 'category', 'market',
'accountPlanningGroupCode', 'retForecastVolume'
]
ret_sku_forecast_week = forecast_retailer_sku['weeklyForecast']
ret_list = (
ret_sku_forecast_week['accountPlanningGroupCode'].unique()).tolist()
ret_sku_analysis = forecast_retailer_sku['weeklyForecast'].copy()
ret_sku_forecast_week = ret_sku_forecast_week[disp_ret_sku_for_cols]
ret_sku_forecast_week.rename(
columns={'retForecastVolume': 'forecastVolume'}, inplace=True)
ret_sku_forecast_week = ret_sku_forecast_week.to_dict('records')
ret_sku_forecast_month = forecast_retailer_sku['monthlyForecast']
disp_ret_sku_for_cols = [
'sku', 'month', 'category', 'market', 'accountPlanningGroupCode',
'retForecastVolume'
]
ret_sku_forecast_month = ret_sku_forecast_month[disp_ret_sku_for_cols]
ret_sku_forecast_month.rename(
columns={'retForecastVolume': 'forecastVolume'}, inplace=True)
ret_sku_forecast_month = ret_sku_forecast_month.to_dict('records')
forecast_sku_analysis = _forecast_sku_analysis(
df=df_skus,
models=models,
analysis_windows=analysis_windows,
category=category,
country=country)
_ft_sku_act = forecast_sku_analysis['weeklyForecast'].copy()
_ft_sku_act['actualVolume'] = round(_ft_sku_act['actualVolume'], 0)
_ft_sku_act['forecastVolume'] = round(_ft_sku_act['forecastVolume'], 0)
_ft_sku_act['forecastError'] = _ft_sku_act.apply(lambda x: calc_error(
actual=x['actualVolume'], forecast=x['forecastVolume']),
axis=1)
_ft_sku_act['ape'] = _ft_sku_act.apply(lambda x: calc_ape(
actual=x['actualVolume'], forecast=x['forecastVolume']),
axis=1)
_ft_sku_act['accuracy'] = round((100 - _ft_sku_act['ape']), 0)
_ft_sku_act['bias'] = _ft_sku_act.apply(lambda x: calc_bias(
actual=x['actualVolume'], forecast=x['forecastVolume']),
axis=1)
_ft_sku_act['extremeBias'] = _ft_sku_act.apply(
lambda x: calc_extreme_bias(x['actualVolume'], x['forecastVolume']),
axis=1)
overall_accuracy = round(_ft_sku_act['accuracy'].mean(), 0)
# overall_bias = round(_ft_sku_act['bias'].mean(), 0)
overall_bias = calc_total_bias(_ft_sku_act)
__sku_forecast = forecast_sku['weeklyForecast']
__sku_forecast['actualVolume'] = np.nan
# sku_analysis_df = pd.concat([_ft_sku_act, __sku_forecast])
# Append % to ape, bias, accuracy
_ft_sku_act_disp = _ft_sku_act.copy()
_ft_sku_act_disp['accuracy'] = _ft_sku_act_disp.apply(
lambda x: (str(int(x['accuracy'])) + '%'), axis=1)
_ft_sku_act_disp['bias'] = _ft_sku_act_disp.apply(
lambda x: (str(int(x['bias'])) + '%'), axis=1)
_ft_sku_act_disp['ape'] = _ft_sku_act_disp.apply(
lambda x: (str(int(x['ape'])) + '%'), axis=1)
# sku_analysis_df = pd.concat([__sku_forecast.sort_values(by='forecastWeek', ascending=False), _ft_sku_act])
sku_analysis_df = pd.concat([
__sku_forecast.sort_values(by='forecastWeek', ascending=False),
_ft_sku_act_disp
])
sku_analysis_df.fillna('N/A', inplace=True)
sku_analysis_df = sku_analysis_df.drop_duplicates()
sku_analysis_df_dict = sku_analysis_df.to_dict('records')
# # Total Graph
act_graph_sales = _ft_sku_act.groupby(
'forecastWeek', as_index=False)['actualVolume'].sum()
act_graph_week = act_graph_sales['forecastWeek'].apply(
lambda x: format_graph_week(x))
act_graph_sales = act_graph_sales['actualVolume']
# Split forecast in actual-forecast & future-forecast
df_act_fr = sku_analysis_df[
sku_analysis_df['forecastWeek'] < int(start)].copy()
df_act_fr = df_act_fr.groupby('forecastWeek',
as_index=False)['forecastVolume'].sum()
fct_graph_week = df_act_fr['forecastWeek'].apply(
lambda x: format_graph_week(x))
fct_graph_sales = df_act_fr['forecastVolume']
df_fut_fr = sku_analysis_df[
sku_analysis_df['forecastWeek'] >= int(start)].copy()
df_fut_fr = df_fut_fr.groupby('forecastWeek',
as_index=False)['forecastVolume'].sum()
fut_fr_graph_week = df_fut_fr['forecastWeek'].apply(
lambda x: format_graph_week(x))
fut_fr_graph_sales = df_fut_fr['forecastVolume']
# fct_graph_sales = sku_analysis_df.groupby('forecastWeek', as_index=False)['forecastVolume'].sum()
# fct_graph_week = fct_graph_sales['forecastWeek']
# fct_graph_sales = fct_graph_sales['forecastVolume']
dict_graph = {}
dict_graph['actualVolume'] = {
'x': np.array(act_graph_week).tolist(),
'y': np.array(act_graph_sales).tolist()
}
dict_graph['forecastVolume'] = {
'x': np.array(fct_graph_week).tolist(),
'y': np.array(fct_graph_sales).tolist()
}
dict_graph['futureForecastVolume'] = {
'x': np.array(fut_fr_graph_week).tolist(),
'y': np.array(fut_fr_graph_sales).tolist()
}
## REGION: Forecast Retailer SKU Analysis
res_ret_sku_analysis = _forecast_retailer_sku_analysis(
dfs=forecast_sku_analysis['weeklyForecast'],
dfr=df_retailers,
analysis_windows=analysis_windows,
category=category,
country=country,
future=ret_sku_analysis)
# res_ret_sku_analysis_dict = res_ret_sku_analysis['table'].to_dict('records')
res_ret_sku_analysis_dict = res_ret_sku_analysis['table']
# res_ret_sku_analysis_dict.rename(columns={'retForecastVolume': 'forecastVolume'}, inplace=True)
res_ret_sku_analysis_dict = res_ret_sku_analysis_dict.to_dict('records')
result = {
'forecastResults': {
'forecastSku': {
'weeklyForecast': sku_forecast_week,
'monthlyForecast': sku_forecast_month
# 'skuList': sku_list
},
'forecastRetailerSku': {
'weeklyForecast': ret_sku_forecast_week,
'monthlyForecast': ret_sku_forecast_month
# 'skuList': sku_list,
# 'retailerList': ret_list
},
'forecastSkuAnalysis': {
'table': sku_analysis_df_dict,
'graph': dict_graph,
'totalAccuracy': overall_accuracy,
'totalBias': overall_bias
# 'skuList': sku_list
},
'forecastRetailerSkuAnalysis': {
'table': res_ret_sku_analysis_dict,
'graph': {
'forecastAccuracy': res_ret_sku_analysis['accuracy_graph'],
'forecastBias': res_ret_sku_analysis['bias_graph']
}
# 'skuList': sku_list,
# 'retailerList': ret_list
}
}
}
return result
