Merge branch 'correction_variables'
This commit is contained in:
commit
f8dc99df99
|
|
@ -22,52 +22,12 @@ exec(open('0_KPI_functions.py').read())
|
|||
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_features = 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
|
||||
df_customerplus_clean_0 = display_databases(directory_path, file_name = "customerplus_cleaned")
|
||||
df_campaigns_information = display_databases(directory_path, file_name = "campaigns_information", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])
|
||||
df_products_purchased_reduced = display_databases(directory_path, file_name = "products_purchased_reduced", datetime_col = ['purchase_date'])
|
||||
df_customerplus_clean_0 = display_input_databases(directory_path, file_name = "customerplus_cleaned")
|
||||
df_campaigns_information = display_input_databases(directory_path, file_name = "campaigns_information", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])
|
||||
df_products_purchased_reduced = display_input_databases(directory_path, file_name = "products_purchased_reduced", datetime_col = ['purchase_date'])
|
||||
|
||||
# if directory_path == "101":
|
||||
# df_products_purchased_reduced_1 = display_databases(directory_path, file_name = "products_purchased_reduced_1", datetime_col = ['purchase_date'])
|
||||
|
|
@ -90,7 +50,7 @@ def dataset_construction(min_date, end_features_date, max_date, directory_path):
|
|||
# Fusion de l'ensemble et creation des KPI
|
||||
|
||||
# KPI sur les campagnes publicitaires
|
||||
df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information)
|
||||
df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information, max_date = end_features_date)
|
||||
|
||||
# KPI sur le comportement d'achat
|
||||
df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_features)
|
||||
|
|
|
|||
|
|
@ -74,7 +74,7 @@ def preprocessing_customerplus(directory_path):
|
|||
cleaning_date(customerplus_copy, 'last_visiting_date')
|
||||
|
||||
# Selection des variables
|
||||
customerplus_copy.drop(['lastname', 'firstname', 'birthdate', 'profession', 'language', 'age', 'email', 'civility', 'note', 'extra', 'reference', 'extra_field', 'need_reload', 'preferred_category', 'preferred_supplier', 'preferred_formula', 'zipcode', 'last_visiting_date'], axis = 1, inplace=True)
|
||||
customerplus_copy.drop(['lastname', 'firstname', 'birthdate', 'language', 'email', 'civility', 'note', 'extra', 'reference', 'extra_field', 'need_reload', 'preferred_category', 'preferred_supplier', 'preferred_formula', 'mcp_contact_id' 'last_visiting_date', 'deleted_at'], axis = 1, inplace=True)
|
||||
customerplus_copy.rename(columns = {'id' : 'customer_id'}, inplace = True)
|
||||
|
||||
return customerplus_copy
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@
|
|||
def custom_date_parser(date_string):
|
||||
return pd.to_datetime(date_string, utc = True, format = 'ISO8601')
|
||||
|
||||
def display_databases(directory_path, file_name, datetime_col = None):
|
||||
def display_input_databases(directory_path, file_name, datetime_col = None):
|
||||
"""
|
||||
This function returns the file from s3 storage
|
||||
"""
|
||||
|
|
@ -13,14 +13,16 @@ def display_databases(directory_path, file_name, datetime_col = None):
|
|||
df = pd.read_csv(file_in, sep=",", parse_dates = datetime_col, date_parser=custom_date_parser)
|
||||
return df
|
||||
|
||||
def campaigns_kpi_function(campaigns_information = None):
|
||||
def campaigns_kpi_function(campaigns_information = None, max_date = None):
|
||||
|
||||
# Nombre de campagnes de mails
|
||||
nb_campaigns = campaigns_information[['customer_id', 'campaign_name']].groupby('customer_id').count().reset_index()
|
||||
nb_campaigns.rename(columns = {'campaign_name' : 'nb_campaigns'}, inplace = True)
|
||||
|
||||
# Temps d'ouverture moyen (en minutes)
|
||||
campaigns_information['time_to_open'] = pd.to_datetime(campaigns_information['opened_at'], utc = True, format = 'ISO8601') - pd.to_datetime(campaigns_information['delivered_at'], utc = True, format = 'ISO8601')
|
||||
campaigns_information['time_to_open'] = (pd.to_datetime(campaigns_information['opened_at'], utc = True, format = 'ISO8601') - pd.to_datetime(campaigns_information['delivered_at'], utc = True, format = 'ISO8601')) / np.timedelta64(1, 'h')
|
||||
campaigns_information['time_to_open'] = campaigns_information['time_to_open'].fillna((pd.to_datetime(campaigns_information['delivered_at'], utc = True, format = 'ISO8601') - pd.to_datetime(max_date, utc = True, format = 'ISO8601')) / np.timedelta64(1, 'h'))
|
||||
|
||||
time_to_open = campaigns_information[['customer_id', 'time_to_open']].groupby('customer_id').mean().reset_index()
|
||||
|
||||
# Nombre de mail ouvert
|
||||
|
|
@ -33,8 +35,11 @@ def campaigns_kpi_function(campaigns_information = None):
|
|||
campaigns_reduced = pd.merge(nb_campaigns, opened_campaign, on = 'customer_id', how = 'left')
|
||||
campaigns_reduced = pd.merge(campaigns_reduced, time_to_open, on = 'customer_id', how = 'left')
|
||||
|
||||
# Taux de mails ouvert
|
||||
campaigns_reduced['taux_ouverture_mail'] = campaigns_reduced['nb_campaigns_opened'] / campaigns_reduced['nb_campaigns']
|
||||
|
||||
# Fill NaN values
|
||||
campaigns_reduced[['nb_campaigns', 'nb_campaigns_opened']] = campaigns_reduced[['nb_campaigns', 'nb_campaigns_opened']].fillna(0)
|
||||
campaigns_reduced[['nb_campaigns', 'nb_campaigns_opened', 'taux_ouverture_mail']] = campaigns_reduced[['nb_campaigns', 'nb_campaigns_opened', 'taux_ouverture_mail']].fillna(0)
|
||||
# Remplir les NaT : time_to_open (??)
|
||||
|
||||
return campaigns_reduced
|
||||
|
|
@ -49,33 +54,20 @@ def tickets_kpi_function(tickets_information = None):
|
|||
tickets_information_copy['vente_internet'] = tickets_information_copy['supplier_name'].fillna('').str.contains('|'.join(liste_mots), case=False).astype(int)
|
||||
|
||||
# Proportion de vente en ligne
|
||||
prop_vente_internet = tickets_information_copy[tickets_information_copy['vente_internet'] == 1].groupby(['customer_id'])['ticket_id'].count().reset_index()
|
||||
prop_vente_internet.rename(columns = {'ticket_id' : 'nb_tickets_internet'}, inplace = True)
|
||||
|
||||
# Average amount
|
||||
# avg_amount = (tickets_information_copy.groupby(["event_type_id", 'name_event_types'])
|
||||
# .agg({"amount" : "mean"}).reset_index()
|
||||
# .rename(columns = {'amount' : 'avg_amount'}))
|
||||
|
||||
prop_vente_internet = tickets_information_copy[tickets_information_copy['vente_internet'] == 1].groupby(['customer_id'])['purchase_id'].nunique().reset_index()
|
||||
prop_vente_internet.rename(columns = {'purchase_id' : 'nb_purchases_internet'}, inplace = True)
|
||||
|
||||
# Mixte KPI comportement achat
|
||||
tickets_kpi = (tickets_information_copy[['customer_id', 'purchase_id' ,'ticket_id','supplier_name', 'purchase_date', 'amount', 'vente_internet']]
|
||||
.groupby(['customer_id'])
|
||||
.agg({'ticket_id': 'count',
|
||||
'purchase_id' : 'nunique',
|
||||
'amount' : 'sum',
|
||||
'supplier_name': 'nunique',
|
||||
'vente_internet' : 'max',
|
||||
'purchase_date' : ['min', 'max']})
|
||||
.reset_index()
|
||||
)
|
||||
|
||||
tickets_kpi.columns = tickets_kpi.columns.map('_'.join)
|
||||
|
||||
tickets_kpi.rename(columns = {'ticket_id_count' : 'nb_tickets',
|
||||
'purchase_id_nunique' : 'nb_purchases',
|
||||
'amount_sum' : 'total_amount',
|
||||
'supplier_name_nunique' : 'nb_suppliers',
|
||||
'customer_id_' : 'customer_id'}, inplace = True)
|
||||
.agg(nb_tickets=('ticket_id', 'nunique'),
|
||||
nb_purchases=('purchase_id', 'nunique'),
|
||||
total_amount=('amount', 'sum'),
|
||||
nb_suppliers=('supplier_name', 'nunique'),
|
||||
achat_internet=('vente_internet', 'max'),
|
||||
purchase_date_min=('purchase_date', 'min'),
|
||||
purchase_date_max=('purchase_date', 'max'))
|
||||
.reset_index())
|
||||
|
||||
tickets_kpi['time_between_purchase'] = tickets_kpi['purchase_date_max'] - tickets_kpi['purchase_date_min']
|
||||
tickets_kpi['time_between_purchase'] = tickets_kpi['time_between_purchase'] / np.timedelta64(1, 'D') # En nombre de jours
|
||||
|
|
@ -85,30 +77,27 @@ def tickets_kpi_function(tickets_information = None):
|
|||
tickets_kpi['purchase_date_max'] = (max_date - tickets_kpi['purchase_date_max']) / np.timedelta64(1, 'D')
|
||||
tickets_kpi['purchase_date_min'] = (max_date - tickets_kpi['purchase_date_min']) / np.timedelta64(1, 'D')
|
||||
|
||||
|
||||
# Proportion de ticket internet
|
||||
tickets_kpi = tickets_kpi.merge(prop_vente_internet, on = ['customer_id'], how = 'left')
|
||||
tickets_kpi['nb_tickets_internet'] = tickets_kpi['nb_tickets_internet'].fillna(0)
|
||||
|
||||
# tickets_kpi = tickets_kpi.merge(avg_amount, how='left', on= 'event_type_id')
|
||||
|
||||
#Taux de ticket payé par internet selon les compagnies
|
||||
|
||||
#tickets_kpi["Taux_ticket_internet"] = tickets_kpi["nb_tickets_internet"]*100 / tickets_kpi["nb_tickets"]
|
||||
#tickets_kpi['Taux_ticket_internet'] = tickets_kpi['Taux_ticket_internet'].fillna(0)
|
||||
tickets_kpi['nb_purchases_internet'] = tickets_kpi['nb_purchases_internet'].fillna(0)
|
||||
tickets_kpi['prop_purchases_internet'] = tickets_kpi['nb_purchases_internet'] / tickets_kpi['nb_purchases']
|
||||
|
||||
return tickets_kpi
|
||||
|
||||
def customerplus_kpi_function(customerplus_clean = None):
|
||||
# KPI sur les données socio-demographique
|
||||
## Le genre
|
||||
|
||||
# Le genre
|
||||
customerplus_clean["gender_label"] = customerplus_clean["gender"].map({
|
||||
0: 'female',
|
||||
1: 'male',
|
||||
2: 'other'
|
||||
})
|
||||
|
||||
gender_dummies = pd.get_dummies(customerplus_clean["gender_label"], prefix='gender').astype(int)
|
||||
customerplus_clean = pd.concat([customerplus_clean, gender_dummies], axis=1)
|
||||
|
||||
customerplus_clean['opt_in'] = np.multiply(customersplus['opt_in'], 1)
|
||||
## Indicatrice si individue vit en France
|
||||
customerplus_clean["country_fr"] = customerplus_clean["country"].apply(lambda x : int(x=="fr") if pd.notna(x) else np.nan)
|
||||
|
||||
|
|
|
|||
|
|
@ -524,6 +524,65 @@
|
|||
"export_in_temporary(target_agg, 'Target_kpi_concatenate')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cb6f06e6-78de-4b8d-a103-8366eff0493a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"v"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c5e864b1-adad-4267-b956-3f7ef371d677",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"def display_covering_time(df, company, datecover):\n",
|
||||
" \"\"\"\n",
|
||||
" This function draws the time coverage of each company\n",
|
||||
" \"\"\"\n",
|
||||
" min_date = df['purchase_date'].min().strftime(\"%Y-%m-%d\")\n",
|
||||
" max_date = df['purchase_date'].max().strftime(\"%Y-%m-%d\")\n",
|
||||
" 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)]\n",
|
||||
" print(f'Couverture Company {company} : {min_date} - {max_date}')\n",
|
||||
" return datecover\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def compute_time_intersection(datecover):\n",
|
||||
" \"\"\"\n",
|
||||
" This function returns the time coverage for all companies\n",
|
||||
" \"\"\"\n",
|
||||
" timestamps_sets = [set(timestamps) for timestamps in datecover.values()]\n",
|
||||
" intersection = set.intersection(*timestamps_sets)\n",
|
||||
" intersection_list = list(intersection)\n",
|
||||
" formated_dates = [dt.strftime(\"%Y-%m-%d\") for dt in intersection_list]\n",
|
||||
" return sorted(formated_dates)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def df_coverage_modelization(sport, coverage_features = 0.7):\n",
|
||||
" \"\"\"\n",
|
||||
" This function returns start_date, end_of_features and final dates\n",
|
||||
" that help to construct train and test datasets\n",
|
||||
" \"\"\"\n",
|
||||
" datecover = {}\n",
|
||||
" for company in sport:\n",
|
||||
" df_products_purchased_reduced = display_input_databases(company, file_name = \"products_purchased_reduced\",\n",
|
||||
" datetime_col = ['purchase_date'])\n",
|
||||
" datecover = display_covering_time(df_products_purchased_reduced, company, datecover)\n",
|
||||
" #print(datecover.keys())\n",
|
||||
" dt_coverage = compute_time_intersection(datecover)\n",
|
||||
" start_date = dt_coverage[0]\n",
|
||||
" end_of_features = dt_coverage[int(0.7 * len(dt_coverage))]\n",
|
||||
" final_date = dt_coverage[-1]\n",
|
||||
" return start_date, end_of_features, final_date\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2435097a-95a5-43e1-84d0-7f6b701441ba",
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user