def predictor(self,file):
'''
Description: This method takes the csv file from MA_bulk_predict routes in app.py
and calls pre defined preprocessing classes from prediction folder to give output file.
Output: Each method returns an output dataframe along with 4 chart names which are created and
stored in static folder of main directory.
On Failure: Raise Exception.
'''
try:
instance1 = data_ingestion.data_getter()
data = instance1.data_load(file)
instance2 = preprocessingfile.MA_preprocess()
visuals = data_visualization.Data_Visualisation()
visuals.delete_old_graphs('MA')
new_data = instance2.initialize_columns(data)
data_num = new_data.select_dtypes(include='number') # to get list of all numerical features in data
num_col_ls = list(data_num.columns)
# ============ pandas-prof. report ============================
new_data.to_csv(r'graph_input_files\graph_data.csv', index_label=False)
# ==============================================================
final_data = new_data[['CONCAT']]
new_data=instance2.drop_columns(new_data)
model_MA= instance1.decompress_pickle('pickle_files/Mortgage_RE.pbz2')
result = model_MA.predict(new_data)
final_data['output'] = result
return final_data
except Exception as e:
raise e
def predictor(self, file):
'''
Description: This method takes the csv file from LA_bulk_predict routes in app.py
and calls pre defined preprocessing classes from prediction folder to give output file.
Output: Each method returns an output dataframe along with 4 chart names which are created and
stored in static folder of main directory.
On Failure: Raise Exception.
'''
try:
instance1 = data_ingestion.data_getter()
data = instance1.data_load(file)
instance2 = preprocessingfile.LA_preprocess()
visuals = data_visualization.Data_Visualisation()
visuals.delete_old_graphs('LA')
set0 = instance2.initialize_columns(data)
data_num = set0.select_dtypes(
include='number'
) # to get list of all numerical features in data
num_col_ls = list(data_num.columns)
final_data = set0[['ID']]
set1 = instance2.drop_columns(set0)
new_data = instance2.encoder(set1)
# ============ pandas-prof. report ============================
new_data.to_csv(r'graph_input_files\graph_data.csv',
index_label=False)
# ==============================================================
ss_LA = instance1.decompress_pickle(
'pickle_files/LA_Std_scaler.pbz2')
model_LA = instance1.decompress_pickle(
'pickle_files/DTModel-1.pbz2')
ss_result = ss_LA.transform(new_data)
result = model_LA.predict(ss_result)
new_data['output'] = result
chart3_name, imp_feature = visuals.feature_importance(
new_data["output"], new_data.drop('output', axis=1),
'LA') # feature importance
if imp_feature in num_col_ls:
chart4_name = visuals.numeric_summary(
new_data, imp_feature,
'LA') # most important feature graph
else:
chart4_name = visuals.categorical_summary(
new_data, imp_feature, 'LA')
new_data['output'] = np.where(new_data['output'] == 0, "Rejected",
"Accepted")
final_data['Output'] = new_data['output']
chart1_name = visuals.count_plot("Output", final_data,
'LA') # count plot
chart2_name = visuals.heat_map("output", new_data,
'LA') # heat map
return final_data, chart1_name, chart2_name, chart3_name, chart4_name
except Exception as e:
raise e
def predictor(self, file):
'''
Description: This method takes the csv file from LE_bulk_predict routes in app.py
and calls pre defined preprocessing classes from prediction folder to give output file.
Output: Each method returns an output dataframe along with 4 chart names which are created and
stored in static folder of main directory.
On Failure: Raise Exception.
'''
try:
instance1 = data_ingestion.data_getter()
data = instance1.data_load(file)
instance2 = preprocessingfile.LE_preprocess()
visuals = data_visualization.Data_Visualisation()
visuals.delete_old_graphs('LE')
data_final = instance2.initialize_columns(data)
data_num = data_final.select_dtypes(
include='number'
) # to get list of all numerical features in data
num_col_ls = list(data_num.columns)
# ============ pandas-prof. report ============================
data_final.to_csv(r'graph_input_files\graph_data.csv',
index_label=False)
# ==============================================================
le_model = instance1.decompress_pickle(
'pickle_files/LE-DecTreeModel.pbz2')
result = le_model.predict(data_final)
data_final['output'] = result
chart3_name, imp_feature = visuals.feature_importance(
data_final["output"], data_final.drop('output', axis=1),
'LE') # feature importance
if imp_feature in num_col_ls:
chart4_name = visuals.numeric_summary(
data_final, imp_feature,
'LR') # most important feature graph
else:
chart4_name = visuals.categorical_summary(
data_final, imp_feature, 'LR')
data_final['output'] = np.where(data_final['output'] == 0,
"Not Eligible", "Eligible")
data_final['RowID'] = pd.Series(
[i for i in range(len(data_final['output']))])
final_data = {
'RowID': [i for i in data_final['RowID']],
'Output': [i for i in data_final['output']]
}
chart1_name = visuals.count_plot("Output", final_data,
'LE') # count plot
chart2_name = visuals.heat_map("output", data_final,
'LE') # heat map
return pd.DataFrame(
final_data), chart1_name, chart2_name, chart3_name, chart4_name
except Exception as e:
raise e
def predictor(self, file):
'''
Description: This method takes the csv file from LR_bulk_predict routes in app.py
and calls pre defined preprocessing classes from prediction folder to give output file.
Output: Each method returns an output dataframe along with 4 chart names which are created and
stored in static folder of main directory.
On Failure: Raise Exception.
'''
try:
instance1 = data_ingestion.data_getter()
data = instance1.data_load(file)
instance2 = preprocessingfile.LR_preprocess()
visuals = data_visualization.Data_Visualisation()
visuals.delete_old_graphs('LR')
set0 = instance2.initialize_columns(data)
data_num = set0.select_dtypes(
include='number'
) # to get list of all numerical features in data
num_col_ls = list(data_num.columns)
set1 = instance2.drop_col(set0)
set2 = instance2.feature_engg(set1)
set3 = instance2.outlier_removal(set2)
set4 = instance2.imputer(set3)
# ============ pandas-prof. report ============================
set4.to_csv(r'graph_input_files\graph_data.csv', index_label=False)
# ==============================================================
lr_model = instance1.decompress_pickle(
'pickle_files/loan_risk.pbz2')
result = lr_model.predict(set4)
set4['output'] = result
chart3_name, imp_feature = visuals.feature_importance(
set4["output"], set4.drop('output', axis=1),
'LR') # feature importance
if imp_feature in num_col_ls:
chart4_name = visuals.numeric_summary(
set4, imp_feature, 'LR') # most important feature graph
else:
chart4_name = visuals.categorical_summary(
set4, imp_feature, 'LR')
set4['output'] = np.where(set4['output'] == 0, "Risky", "Safe")
final_data = {
'RowID': [i for i in set0['RowID']],
'Output': [i for i in set4['output']]
}
chart1_name = visuals.count_plot("Output", final_data,
'LR') # count plot
chart2_name = visuals.heat_map("output", set4, 'LR') # heat map
print(chart3_name)
print(chart4_name)
return pd.DataFrame(
final_data), chart1_name, chart2_name, chart3_name, chart4_name
except Exception as e:
raise e
def predictor(self,file):
'''
Description: This method takes the csv file from PLR_bulk_predict routes in app.py
and calls pre defined preprocessing classes from prediction folder to give output file.
Output: Each method returns an output dataframe along with 4 chart names which are created and
stored in static folder of main directory.
On Failure: Raise Exception.
'''
try:
instance1 = data_ingestion.data_getter()
visuals = data_visualization.Data_Visualisation()
visuals.delete_old_graphs('LPR')
data = instance1.data_load(file[0])
data1 = data[['SK_ID_CURR']]
bureau=instance1.data_load(file[1])
previos_application=instance1.data_load(file[5])
pos_cash=instance1.data_load(file[4])
insta_payments=instance1.data_load(file[3])
credit_card=instance1.data_load(file[2])
instance2 = preprocessingfile.LPR_preprocess()
data=instance2.error_flag_column(data)
data=instance2.new_columns(data)
application_bureau=instance2.joining_berau_application(bureau,data)
application_bureau=instance2.feature_engineering(bureau,application_bureau)
application_bureau_prev=instance2.joining_previousapplication_to_applicationbereau(previos_application,application_bureau)
application_bureau_prev=instance2.Joining_POS_CASH_balance_to_application_bureau_prev_data(pos_cash,application_bureau_prev)
application_bureau_prev=instance2.joining_InstallmentsPaymentsdata_to_application_bureau_prev_data(insta_payments,application_bureau_prev)
application_bureau_prev=instance2.Joining_Creditcardbalancedata_to_application_bureau_prev(application_bureau_prev,credit_card)
application_bureau_prev=instance2.featurization(application_bureau_prev)
application_bureau_prev=instance2.feature_selection(application_bureau_prev)
data_num = application_bureau_prev.select_dtypes(include='number') # to get list of all numerical features in data
num_col_ls = list(data_num.columns)
model = instance1.decompress_pickle(r'pickle_files\DecTreePLR.pbz2')
output = model.predict(application_bureau_prev)
application_bureau_prev['result'] = output
chart3_name, imp_feature = visuals.feature_importance(application_bureau_prev["result"],
application_bureau_prev.drop('result', axis=1),'LPR') # feature importance
if imp_feature in num_col_ls:
chart4_name = visuals.numeric_summary(application_bureau_prev, imp_feature, 'LPR') # most important feature graph
else:
chart4_name = visuals.categorical_summary(application_bureau_prev, imp_feature, 'LPR') # most imp feature categorical graph
data1['result']=application_bureau_prev["result"]
data1['result'] = np.where(data1['result'] == 0, "Loan Repayed", "Not Repayed")
chart1_name = visuals.count_plot("result", data1,'LPR') # count plot
chart2_name = visuals.heat_map("result", application_bureau_prev, 'LPR') # heat map
return data1,chart1_name,chart2_name,chart3_name,chart4_name
except Exception as e:
raise e
def predictor(self, file):
'''
Description: This method takes the csv file from MA_bulk_predict routes in app.py
and calls pre defined preprocessing classes from prediction folder to give output file.
Output: Each method returns an output dataframe along with 4 chart names which are created and
stored in static folder of main directory.
On Failure: Raise Exception.
'''
try:
instance1 = data_ingestion.data_getter()
data = instance1.data_load(file)
instance2 = preprocessingfile.MS_preprocess()
visuals = data_visualization.Data_Visualisation()
visuals.delete_old_graphs('MS')
set0 = instance2.rename(data)
data_num = set0.select_dtypes(include='number') # to get list of all numerical features in data
num_col_ls = list(data_num.columns)
set1 = instance2.drop_columns(set0)
set2 = instance2.days_passed(set1)
set3 = instance2.job(set2)
set4 = instance2.education_cat(set3)
set5 = instance2.contacted_month(set4)
data_final = instance2.contacts_before_campaign(set5)
data_final = pd.get_dummies(data_final,drop_first=True)
data_final = instance2.columns_match(data_final)
data_final = data_final[['last_call_duration','age','days_passed_recent','contacts_during_campaign','housing_loan_yes','personal_loan_yes','day_of_week_tue','last_contacted_month_may-aug','day_of_week_thu','marital_married','day_of_week_wed','day_of_week_mon','marital_single','contacts_during_campaign']]
MS__model = instance1.decompress_pickle('pickle_files/MS_randomforest_model4.pbz2')
result = MS__model.predict(data_final)
data_final["output"]=result
chart3_name, imp_feature = visuals.feature_importance(data_final["output"], data_final.drop('output', axis=1),'MS') # feature importance
if imp_feature in num_col_ls:
chart4_name = visuals.numeric_summary(data_final, imp_feature, 'MS') # most important feature graph
else:
chart4_name = visuals.categorical_summary(data_final, imp_feature, 'MS')
final_result=pd.DataFrame(result, columns=['Output'])
final_result["SrNo."]=np.arange(len(final_result["Output"]))
pop_col=final_result.pop('Output')
final_result["output"]=pop_col
final_result['output'] = np.where(final_result['output'] == 0, "Not Subscribed", "Term Deposit")
chart1_name = visuals.count_plot("output", final_result, 'MS') # count plot
chart2_name = visuals.heat_map("output", data_final, 'MS') # heat map
return final_result, chart1_name, chart2_name, chart3_name, chart4_name
except Exception as e:
raise e
