ajoubrel-ensae/BDC-team-1
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

generate train and test dataset for all companies

Browse Source
This commit is contained in:
Alexis REVELLE 2024-02-22 14:57:34 +00:00
parent 44fef6d618
commit 79dc4f13ff
1 changed files with 71 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

89
0_2_Dataset_construction.py
View File

@ -6,6 +6,7 @@ import os
import s3fs
import re
import warnings
from datetime import date, timedelta, datetime
# Create filesystem object
S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"]
@ -18,6 +19,47 @@ exec(open('0_KPI_functions.py').read())
# Ignore warning
warnings.filterwarnings('ignore')
def display_covering_time(df, company, datecover):
"""
This function draws the time coverage of each company
"""
min_date = df['purchase_date'].min().strftime("%Y-%m-%d")
max_date = df['purchase_date'].max().strftime("%Y-%m-%d")
datecover[company] = [datetime.strptime(min_date, "%Y-%m-%d") + timedelta(days=x) for x in range((datetime.strptime(max_date, "%Y-%m-%d") - datetime.strptime(min_date, "%Y-%m-%d")).days)]
print(f'Couverture Company {company} : {min_date} - {max_date}')
return datecover
def compute_time_intersection(datecover):
"""
This function returns the time coverage for all companies
"""
timestamps_sets = [set(timestamps) for timestamps in datecover.values()]
intersection = set.intersection(*timestamps_sets)
intersection_list = list(intersection)
formated_dates = [dt.strftime("%Y-%m-%d") for dt in intersection_list]
return sorted(formated_dates)
def df_coverage_modelization(sport, coverage_train = 0.7):
"""
This function returns start_date, end_of_features and final dates
that help to construct train and test datasets
"""
datecover = {}
for company in sport:
df_products_purchased_reduced = display_databases(company, file_name = "products_purchased_reduced",
datetime_col = ['purchase_date'])
datecover = display_covering_time(df_products_purchased_reduced, company, datecover)
#print(datecover.keys())
dt_coverage = compute_time_intersection(datecover)
start_date = dt_coverage[0]
end_of_features = dt_coverage[int(0.7 * len(dt_coverage))]
final_date = dt_coverage[-1]
return start_date, end_of_features, final_date
def dataset_construction(min_date, end_features_date, max_date, directory_path):
# Import customerplus
@ -97,32 +139,43 @@ def dataset_construction(min_date, end_features_date, max_date, directory_path):
## Exportation
companies = {'musee' : ['1', '2', '3', '4', '101'],
'sport': ['5', '6', '7', '8', '9'],
'musique' : ['10', '11', '12', '13', '14']}
type_of_comp = input('Choisissez le type de compagnie : sport ? musique ? musee ?')
list_of_comp = companies[type_of_comp]
# Dossier d'exportation
BUCKET_OUT = "projet-bdc2324-team1/2_Output/Logistique Regression databases - First approach"
BUCKET_OUT = f'projet-bdc2324-team1/Generalization/{type_of_comp}'
# Dataset test
dataset_test = dataset_construction(min_date = "2021-08-01", end_features_date = "2023-08-01", max_date = "2023-11-01", directory_path = "1")
# Create test dataset and train dataset for sport companies
# Exportation
FILE_KEY_OUT_S3 = "dataset_test.csv"
FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3
start_date, end_of_features, final_date = df_coverage_modelization(list_of_comp, coverage_train = 0.7)
with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
dataset_test.to_csv(file_out, index = False)
for company in list_of_comp:
dataset_test = dataset_construction(min_date = start_date, end_features_date = end_of_features,
max_date = final_date, directory_path = company)
print("Exportation dataset test : SUCCESS")
# Exportation
FILE_KEY_OUT_S3 = "dataset_test" + company + ".csv"
FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3
with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
dataset_test.to_csv(file_out, index = False)
print("Exportation dataset test : SUCCESS")
# Dataset train
dataset_train = dataset_construction(min_date = "2021-05-01", end_features_date = "2023-05-01", max_date = "2023-08-01", directory_path = "1")
# Export
FILE_KEY_OUT_S3 = "dataset_train.csv"
FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3
with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
dataset_train.to_csv(file_out, index = False)
dataset_train = dataset_construction(min_date = start_date, end_features_date = end_of_features,
max_date = final_date, directory_path = company)
# Export
FILE_KEY_OUT_S3 = "dataset_train" + company + ".csv"
FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + FILE_KEY_OUT_S3
print("Exportation dataset train : SUCCESS")
with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
dataset_train.to_csv(file_out, index = False)
print("Exportation dataset train : SUCCESS")
print("FIN DE LA GENERATION DES DATASETS : SUCCESS")