def solution_saving(df, sel_model, client_lvl_cols_in, client_lvl_sels):
truncate_query_part_2 = ' '.join([
'and {} = \'{}\''.format(x, y)
for x, y in zip(client_lvl_cols_in, client_lvl_sels) if y != '-'
])
df = client_replacement(
df, client_lvl_cols_in, client_lvl_sels
) # Replaces the values of Client's Levels by the actual values selected for this solution
level_1_e_deployment.sql_truncate(
options_file.DSN_SRV3_PRD,
options_file,
options_file.sql_info['database_source'],
options_file.sql_info['optimization_solution_table'],
query=truncate_query.format(sel_model) + truncate_query_part_2)
level_1_e_deployment.sql_inject(
df,
options_file.DSN_SRV3_PRD,
options_file.sql_info['database_source'],
options_file.sql_info['optimization_solution_table'],
options_file,
configuration_parameters + client_lvl_cols_in +
['Quantity', 'Average_Score_Euros', 'ML_VehicleData_Code'],
check_date=1)
st.write('Sugestão gravada com sucesso.')
return
def deployment(df, db, view):
performance_info_append(time.time(), 'Section_E_Start')
log_record('Início Secção E...', project_id)
if df is not None:
df['NLR_Code'] = level_2_optionals_cdsu_options.nlr_code
# df = column_rename(df, list(level_2_optionals_cdsu_options.column_sql_renaming.keys()), list(level_2_optionals_cdsu_options.column_sql_renaming.values()))
df = df.rename(
columns=level_2_optionals_cdsu_options.column_sql_renaming)
control_prints(df, 'before deployment, after renaming', head=1)
sql_delete(
level_2_optionals_cdsu_options.DSN_MLG_PRD, db, view,
level_2_optionals_cdsu_options,
{'NLR_Code': '{}'.format(level_2_optionals_cdsu_options.nlr_code)})
sql_inject(df,
level_2_optionals_cdsu_options.DSN_MLG_PRD,
db,
view,
level_2_optionals_cdsu_options,
list(level_2_optionals_cdsu_options.
column_checkpoint_sql_renaming.values()),
check_date=1)
log_record('Fim Secção E.', project_id)
performance_info_append(time.time(), 'Section_E_End')
return
def sql_upload(df, db, view):
df['Totals'] = df.sum(axis=1)
df.index.rename('Actual', inplace=True)
df.reset_index(inplace=True)
sql_inject(df, options_file.DSN_MLG_PRD, db, view, options_file, list(df), truncate=1, check_date=1)
return
def update_family(df, new_family_classification, df_product_group):
new_family_classification_code = family_code_convertion(new_family_classification, df_product_group)
df['New_Product_Group_DW'] = new_family_classification_code
df.rename(columns={'Product_Group_DW': 'Old_Product_Group_DW'}, inplace=True)
level_1_e_deployment.sql_inject(df, options_file.DSN_MLG_PRD, options_file.sql_info['database_final'], options_file.sql_info['parts_classification_refs'], options_file, ['Part_Ref', 'Part_Description', 'Part_Cost', 'Part_PVP', 'Client_ID', 'Old_Product_Group_DW', 'New_Product_Group_DW', 'Classification', 'Classification_Prob'], check_date=1)
st.write('Famílias das referências selecionadas alteradas com sucesso.')
return
def deployment(df):
performance_info_append(time.time(), 'Section_E_Start')
log_record('Início Secção E...', options_file.project_id)
df = df.astype(object).where(pd.notnull(df), None)
sql_inject(df, options_file.DSN_SRV3_PRD, options_file.sql_info['database_source'], options_file.sql_info['final_table'], options_file, ['Request_Num', 'StemmedDescription', 'Description', 'Language', 'Open_Date', 'Label', 'Classification_Flag'], truncate=1)
log_record('Fim Secção E.', options_file.project_id)
performance_info_append(time.time(), 'Section_E_End')
return
def model_performance_saving(df, options_file):
level_1_e_deployment.sql_inject(
df,
level_0_performance_report.DSN_MLG_PRD,
level_0_performance_report.performance_sql_info['DB'],
level_0_performance_report.
performance_sql_info['performance_algorithm_results'],
options_file,
list(df),
check_date=1)
return
def deployment(df, main_families_cm, other_families_cm):
sql_upload(main_families_cm, options_file.sql_info['database_final'], options_file.sql_info['matrix_lvl_1'])
sql_upload(other_families_cm, options_file.sql_info['database_final'], options_file.sql_info['matrix_lvl_2'])
df.rename(columns={'Client_Id': 'Client_ID', 'Part_Desc_concat': 'Part_Description', 'Average_Cost_avg': 'Part_Cost', 'PVP_1_avg': 'Part_PVP', 'prediction': 'Classification', 'Max_Prob': 'Classification_Prob'}, inplace=True)
df['Classification_Flag'] = 0
df['Classification_Prob'] = df['Classification_Prob'].round(2)
df['Part_Cost'] = df['Part_Cost'].round(2)
df['Part_PVP'] = df['Part_PVP'].round(2)
df = df.astype({'Part_Ref': 'str', 'Client_ID': 'str', 'Part_Cost': 'str', 'Part_PVP': 'str', 'Classification_Prob': 'str'})
df['Part_Description'] = df['Part_Description'].fillna("")
df.dropna(subset=['Classification'], axis=0, inplace=True)
sql_inject(df, options_file.DSN_MLG_PRD, options_file.sql_info['database_final'], options_file.sql_info['parts_classification_table'], options_file, columns=['Part_Ref', 'Part_Description', 'Part_Cost', 'Part_PVP', 'Client_ID', 'Product_Group_DW', 'Classification', 'Classification_Prob', 'Classification_Flag'], truncate=1, check_date=1)
sql_inject(df, options_file.DSN_SRV3_PRD, options_file.sql_info['database_BI_GSC'], options_file.sql_info['parts_classification_table'], options_file, columns=['Part_Ref', 'Part_Description', 'Part_Cost', 'Part_PVP', 'Client_ID', 'Product_Group_DW', 'Classification', 'Classification_Prob', 'Classification_Flag'], truncate=1, check_date=1)
sql_sp_run(options_file.DSN_SRV3_PRD, options_file.sql_info['database_BI_GSC'], options_file)
return
def deployment(df, db, view):
performance_info_append(time.time(), 'Section_E_Start')
log_record('Início Secção E...', project_id)
for col in list(df):
df[col] = df[col].astype(str)
df['NLR_Code'] = level_2_optionals_baviera_options.nlr_code
if df is not None:
df = column_rename(
df,
list(level_2_optionals_baviera_options.column_sql_renaming.keys()),
list(level_2_optionals_baviera_options.column_sql_renaming.values(
)))
if model_training_check:
sql_delete(
level_2_optionals_baviera_options.DSN_MLG_PRD, db, view,
level_2_optionals_baviera_options, {
'NLR_Code':
'{}'.format(level_2_optionals_baviera_options.nlr_code)
})
sql_inject(df,
level_2_optionals_baviera_options.DSN_MLG_PRD,
db,
view,
level_2_optionals_baviera_options,
level_2_optionals_baviera_options.columns_for_sql,
check_date=1)
else:
sql_delete(
level_2_optionals_baviera_options.DSN_MLG_PRD, db, view,
level_2_optionals_baviera_options, {
'NLR_Code':
'{}'.format(level_2_optionals_baviera_options.nlr_code)
})
sql_inject(df,
level_2_optionals_baviera_options.DSN_MLG_PRD,
db,
view,
level_2_optionals_baviera_options,
level_2_optionals_baviera_options.columns_for_sql_temp,
check_date=1)
log_record('Fim Secção E.', project_id)
performance_info_append(time.time(), 'Section_E_End')
return
def deployment(df, db, view):
performance_info_append(time.time(), 'Section_E_Start')
log_record('Início Secção E...', options_file.project_id)
if df is not None:
sel_df = df.loc[:, options_file.sql_columns_vhe_fact_bi].copy()
sel_df['NLR_Posting_Date'] = sel_df['NLR_Posting_Date'].astype(
object).where(sel_df['NLR_Posting_Date'].notnull(), None)
sel_df['SLR_Document_Date_CHS'] = sel_df[
'SLR_Document_Date_CHS'].astype(object).where(
sel_df['SLR_Document_Date_CHS'].notnull(), None)
sel_df['SLR_Document_Date_RGN'] = sel_df[
'SLR_Document_Date_RGN'].astype(object).where(
sel_df['SLR_Document_Date_RGN'].notnull(), None)
sel_df['Ship_Arrival_Date'] = sel_df['Ship_Arrival_Date'].astype(
object).where(sel_df['Ship_Arrival_Date'].notnull(), None)
sel_df['Registration_Request_Date'] = sel_df[
'Registration_Request_Date'].astype(object).where(
sel_df['Registration_Request_Date'].notnull(), None)
sel_df['Registration_Date'] = sel_df['Registration_Date'].astype(
object).where(sel_df['Registration_Date'].notnull(), None)
sel_df['PDB_Start_Order_Date'] = sel_df['PDB_Start_Order_Date'].astype(
object).where(sel_df['PDB_Start_Order_Date'].notnull(), None)
sel_df['PDB_End_Order_Date'] = sel_df['PDB_End_Order_Date'].astype(
object).where(sel_df['PDB_End_Order_Date'].notnull(), None)
sel_df['Fixed_Margin_II'] = sel_df['Fixed_Margin_II'].round(2)
sel_df = sel_df.where(sel_df.notnull(), None)
sel_df.rename(columns={
'prev_sales_check': 'Previous_Sales_Flag',
'number_prev_sales': 'Previous_Sales_Count'
},
inplace=True)
sql_inject(sel_df,
options_file.DSN_SRV3_PRD,
db,
view,
options_file,
list(sel_df),
truncate=1,
check_date=1)
log_record('Fim Secção E.', options_file.project_id)
performance_info_append(time.time(), 'Section_E_End')
def solution_saving(df_solution, group_name, group_name_original):
level_1_e_deployment.sql_truncate(
options_file.DSN_MLG_PRD,
options_file,
options_file.sql_info['database_final'],
options_file.sql_info['optimization_solution_table'],
query=truncate_query.format(
options_file.sql_info['optimization_solution_table'], group_name))
level_1_e_deployment.sql_inject(
df_solution,
options_file.DSN_MLG_PRD,
options_file.sql_info['database_final'],
options_file.sql_info['optimization_solution_table'],
options_file,
list(df_solution[options_file.columns_sql_solver_solution]),
check_date=1)
st.write('Sugestão gravada com sucesso - {}'.format(group_name_original))
return
def deployment(df_solver, df_part_ref_ta, pse_code):
performance_info_append(time.time(), 'Section_E_Start')
log_record('Início Secção E...', options_file.project_id)
df_solver = column_rename(df_solver, list(options_file.column_sql_renaming.keys()), list(options_file.column_sql_renaming.values()))
df_solver = df_solver.dropna(subset=[options_file.column_sql_renaming['Group']])
df_solver['Cost'] = pd.to_numeric(df_solver['Cost'], errors='coerce')
df_solver.dropna(axis=0, subset=['Cost'], inplace=True)
df_part_ref_ta = column_rename(df_part_ref_ta, ['Group'], [options_file.column_sql_renaming['Group']])
sql_truncate(options_file.DSN_MLG_DEV, options_file, options_file.sql_info['database_final'], options_file.sql_info['final_table'], query=options_file.truncate_table_query.format(options_file.sql_info['final_table'], pse_code))
sql_inject(df_solver, options_file.DSN_MLG_DEV, options_file.sql_info['database_final'], options_file.sql_info['final_table'], options_file, columns=list(options_file.column_sql_renaming.values()), check_date=1)
sql_truncate(options_file.DSN_MLG_DEV, options_file, options_file.sql_info['database_final'], options_file.sql_info['ta_table'], query=options_file.truncate_table_query.format(options_file.sql_info['ta_table'], pse_code))
df_part_ref_ta.dropna(subset=['Part_Ref_Group_Desc'], inplace=True)
sql_inject(df_part_ref_ta, options_file.DSN_MLG_DEV, options_file.sql_info['database_final'], options_file.sql_info['ta_table'], options_file, columns=list(df_part_ref_ta), check_date=1)
log_record('Fim Secção E.', options_file.project_id)
performance_info_append(time.time(), 'Section_E_End')
return
def save_classification_rule(df_product_group, text, text_option, sel_family_sel_overwrite, sel_cost_max, max_cost, sel_cost_min, min_cost, sel_pvp_max, max_pvp, sel_pvp_min, min_pvp):
family_code = family_code_convertion(sel_family_sel_overwrite, df_product_group)
time_tag, _ = level_1_e_deployment.time_tags(format_date="%Y%m%d")
# st.write(text, text_option, family_code, sel_cost_max, max_cost, sel_cost_min, min_cost, sel_pvp_max, max_pvp, sel_pvp_min, min_pvp, time_tag)
df_rules = pd.DataFrame()
df_rules['Matching_Rule'] = [text_option]
df_rules['Word'] = text
df_rules['Product_Group_DW'] = family_code
df_rules['Sel_Max_Cost'] = sel_cost_max
df_rules['Max_Cost'] = max_cost
df_rules['Sel_Min_Cost'] = sel_cost_min
df_rules['Min_Cost'] = min_cost
df_rules['Sel_Max_PVP'] = sel_pvp_max
df_rules['Max_PVP'] = max_pvp
df_rules['Sel_Min_PVP'] = sel_pvp_min
df_rules['Min_PVP'] = min_pvp
df_rules['Date'] = time_tag
level_1_e_deployment.sql_inject(df_rules, options_file.DSN_MLG_PRD, options_file.sql_info['database_final'], options_file.sql_info['parts_classification_rules'], options_file, columns=list(df_rules))
return
def model_choice_upload(flag, name, value, options_file):
df_model_result = pd.DataFrame(columns={
'Model_Choice_Flag', 'Chosen_Model', 'Metric', 'Value', 'Message'
})
message = None
df_model_result['Model_Choice_Flag'] = [flag]
df_model_result['Project_Id'] = [options_file.project_id]
if not flag:
message = 'Nenhum dos modelos treinados atinge os valores mínimos definidos.'
df_model_result['Chosen_Model'] = [0]
df_model_result['Metric'] = [0]
df_model_result['Value'] = [0]
elif flag:
if flag == 1:
message = 'Modelo anterior com melhor performance do que o atual.'
if flag == 2:
message = 'Modelo anterior substituído pelo atual.'
if flag == 3:
message = 'Modelo anterior substituído pelo atual, com pequenas variações de performance.'
if flag == 4:
message = 'Novo modelo com performance igual ao anterior.'
df_model_result['Chosen_Model'] = [name]
df_model_result['Metric'] = [options_file.metric]
df_model_result['Value'] = [value]
df_model_result['Message'] = [message]
level_1_e_deployment.sql_inject(
df_model_result,
options_file.DSN_MLG_PRD,
level_0_performance_report.performance_sql_info['DB'],
level_0_performance_report.performance_sql_info['model_choices'],
options_file,
list(df_model_result),
check_date=1)
return message
def performance_evaluation_classification(models, best_models, running_times,
datasets, options_file, project_id):
# models -> list with models names;
# best_models -> dict with models name as key and the best clf after gridsearch as values;
# classes -> clf.classes_
# running_times -> dict with models name as key and the training time as values
# datasets -> dict with the datasets required - train_x, test_x, train_y, test_y
results_train, results_test = [], []
predictions, feat_importance = {}, pd.DataFrame(index=list(
datasets['train_x']),
columns={'Importance'})
for model in models:
prediction_train = best_models[model].predict(datasets['train_x'])
prediction_test = best_models[model].predict(datasets['test_x'])
evaluation_training = ClassificationEvaluation(
groundtruth=datasets['train_y'], prediction=prediction_train)
evaluation_test = ClassificationEvaluation(
groundtruth=datasets['test_y'], prediction=prediction_test)
predictions[model] = [
prediction_train.astype(int, copy=False),
prediction_test.astype(int, copy=False)
]
# plot_conf_matrix(datasets['train_y'], prediction_train, classes, model, project_id)
# plot_conf_matrix(datasets['test_y'], prediction_test, classes, model, project_id)
try:
feat_importance['Importance'] = best_models[
model].feature_importances_
feat_importance.sort_values(by='Importance',
ascending=False,
inplace=True)
feat_importance.to_csv(base_path + '/output/' +
'feature_importance_' + str(model) + '.csv')
except AttributeError:
pass
row_train = {
'Micro_F1':
getattr(evaluation_training, 'micro'),
'Average_F1':
getattr(evaluation_training, 'average'),
'Macro_F1':
getattr(evaluation_training, 'macro'),
'Accuracy':
getattr(evaluation_training, 'accuracy'),
'ROC_Curve':
getattr(evaluation_training, 'roc_auc_curve'),
# ('Precision_Class_' + str(classes[0])): getattr(evaluation_training, 'precision')[0],
('Precision_Class_' + str(best_models[model].classes_[0])):
getattr(evaluation_training, 'precision')[0],
('Precision_Class_' + str(best_models[model].classes_[1])):
getattr(evaluation_training, 'precision')[1],
('Recall_Class_' + str(best_models[model].classes_[0])):
getattr(evaluation_training, 'recall')[0],
('Recall_Class_' + str(best_models[model].classes_[1])):
getattr(evaluation_training, 'recall')[1],
# 'Precision_Micro_Class_' + str(classes[0]): getattr(evaluation_training, 'precision_multiclass_micro')[0],
# 'Precision_Micro_Class_' + str(classes[1]): getattr(evaluation_training, 'precision_multiclass_micro')[1],
# 'Precision_Macro_Class_' + str(classes[0]): getattr(evaluation_training, 'precision_multiclass_macro')[0],
# 'Precision_Macro_Class_' + str(classes[1]): getattr(evaluation_training, 'precision_multiclass_macro')[1],
# 'Precision_Average_Class_' + str(classes[0]): getattr(evaluation_training, 'precision_multiclass_average')[0],
# 'Precision_Average_Class_' + str(classes[1]): getattr(evaluation_training, 'precision_multiclass_average')[1],
#
# 'Recall_Micro_Class_' + str(classes[0]): getattr(evaluation_training, 'recall_multiclass_micro')[0],
# 'Recall_Micro_Class_' + str(classes[1]): getattr(evaluation_training, 'recall_multiclass_micro')[1],
# 'Recall_Macro_Class_' + str(classes[0]): getattr(evaluation_training, 'recall_multiclass_macro')[0],
# 'Recall_Macro_Class_' + str(classes[1]): getattr(evaluation_training, 'recall_multiclass_macro')[1],
# 'Recall_Average_Class_' + str(classes[0]): getattr(evaluation_training, 'recall_multiclass_average')[0],
# 'Recall_Average_Class_' + str(classes[1]): getattr(evaluation_training, 'recall_multiclass_average')[1],
'Running_Time':
running_times[model]
}
row_test = {
'Micro_F1':
getattr(evaluation_test, 'micro'),
'Average_F1':
getattr(evaluation_test, 'average'),
'Macro_F1':
getattr(evaluation_test, 'macro'),
'Accuracy':
getattr(evaluation_test, 'accuracy'),
'ROC_Curve':
getattr(evaluation_test, 'roc_auc_curve'),
('Precision_Class_' + str(best_models[model].classes_[0])):
getattr(evaluation_test, 'precision')[0],
('Precision_Class_' + str(best_models[model].classes_[1])):
getattr(evaluation_test, 'precision')[1],
('Recall_Class_' + str(best_models[model].classes_[0])):
getattr(evaluation_test, 'recall')[0],
('Recall_Class_' + str(best_models[model].classes_[1])):
getattr(evaluation_test, 'recall')[1],
# 'Precision_Micro_Class_' + str(classes[0]): getattr(evaluation_test, 'precision_multiclass_micro')[0],
# 'Precision_Micro_Class_' + str(classes[1]): getattr(evaluation_test, 'precision_multiclass_micro')[1],
# 'Precision_Macro_Class_' + str(classes[0]): getattr(evaluation_test, 'precision_multiclass_macro')[0],
# 'Precision_Macro_Class_' + str(classes[1]): getattr(evaluation_test, 'precision_multiclass_macro')[1],
# 'Precision_Average_Class_' + str(classes[0]): getattr(evaluation_test, 'precision_multiclass_average')[0],
# 'Precision_Average_Class_' + str(classes[1]): getattr(evaluation_test, 'precision_multiclass_average')[1],
#
# 'Recall_Micro_Class_' + str(classes[0]): getattr(evaluation_test, 'recall_multiclass_micro')[0],
# 'Recall_Micro_Class_' + str(classes[1]): getattr(evaluation_test, 'recall_multiclass_micro')[1],
# 'Recall_Macro_Class_' + str(classes[0]): getattr(evaluation_test, 'recall_multiclass_macro')[0],
# 'Recall_Macro_Class_' + str(classes[1]): getattr(evaluation_test, 'recall_multiclass_macro')[1],
# 'Recall_Average_Class_' + str(classes[0]): getattr(evaluation_test, 'recall_multiclass_average')[0],
# 'Recall_Average_Class_' + str(classes[1]): getattr(evaluation_test, 'recall_multiclass_average')[1],
'Running_Time':
running_times[model]
}
results_train.append(row_train)
results_test.append(row_test)
df_results_train = pd.DataFrame(results_train, index=models)
df_results_train['Algorithms'] = df_results_train.index
df_results_train['Dataset'] = ['Train'] * df_results_train.shape[0]
df_results_train['Project_Id'] = [project_id] * df_results_train.shape[0]
df_results_test = pd.DataFrame(results_test, index=models)
df_results_test['Algorithms'] = df_results_test.index
df_results_test['Dataset'] = ['Test'] * df_results_train.shape[0]
df_results_test['Project_Id'] = [project_id] * df_results_train.shape[0]
metric_bar_plot(df_results_train,
'project_{}_train_dataset'.format(project_id))
metric_bar_plot(df_results_test,
'project_{}_test_dataset'.format(project_id))
model_performance_saving(pd.concat([df_results_train, df_results_test]),
options_file)
level_1_e_deployment.sql_inject(
pd.concat([df_results_train, df_results_test]),
level_0_performance_report.performance_sql_info['DSN'],
level_0_performance_report.performance_sql_info['DB'],
level_0_performance_report.
performance_sql_info['performance_algorithm_results'],
options_file,
list(df_results_train),
check_date=1)
return df_results_train, df_results_test, predictions
def feature_contribution(df, configuration_parameters, col_to_group_by,
options_file, project_id):
configuration_parameters.remove(col_to_group_by)
boolean_parameters = [
x for x in configuration_parameters
if list(df[x].unique()) == [0, 1] or list(df[x].unique()) == [1, 0]
]
non_boolean_parameters = [
x for x in configuration_parameters if x not in boolean_parameters
]
df_feature_contribution_total = pd.DataFrame()
for model in df[col_to_group_by].unique():
model_mask = df[col_to_group_by] == model
df_model = df.loc[df[model_mask].index, :]
mask_class_1 = df_model['score_class_gt'] == 1
mask_class_0 = df_model['score_class_gt'] == 0
class_1 = df_model.loc[df_model[mask_class_1].index, :]
class_0 = df_model.loc[df_model[mask_class_0].index, :]
differences_boolean, differences_non_boolean, features_boolean, features_non_boolean = [], [], [], []
differences_feature, features, model_tag = [], [], []
for feature in configuration_parameters:
if feature in boolean_parameters:
c1_f1 = class_1.loc[class_1[feature] == 1, :].shape[0]
c1_f0 = class_1.loc[class_1[feature] == 0, :].shape[0]
c0_f1 = class_0.loc[class_0[feature] == 1, :].shape[0]
c0_f0 = class_0.loc[class_0[feature] == 0, :].shape[0]
f1 = c1_f1 + c0_f1
f0 = c1_f0 + c0_f0
try:
p_c1_f1 = c1_f1 / f1 * 1.
p_c1_f0 = c1_f0 / f0 * 1.
differences_boolean.append(p_c1_f1 - p_c1_f0)
features_boolean.append(feature + '_sim')
except ZeroDivisionError:
continue
elif feature in non_boolean_parameters:
for value in df_model[feature].unique():
if value == 'outros':
continue
c1_f1 = class_1.loc[class_1[feature] == value, :].shape[0]
c1_f0 = class_1.loc[class_1[feature] != value, :].shape[0]
c0_f1 = class_0.loc[class_0[feature] == value, :].shape[0]
c0_f0 = class_0.loc[class_0[feature] != value, :].shape[0]
# ToDo: There might be cases where only one value for a feature is available if the df is too small (Only Preto as Cor_Interior, e.g.). I should add a try/exception to catch these for the conditions there feature != value
f1 = c1_f1 + c0_f1
f0 = c1_f0 + c0_f0
try:
p_c1_f1 = c1_f1 / f1 * 1.
p_c1_f0 = c1_f0 / f0 * 1.
except ZeroDivisionError:
# log_record('Insufficient data for feature ' + str(feature) + ' and value ' + str(value) + '.', project_id, flag=1)
level_0_performance_report.log_record(
'Dados insuficientes para a feature {} com valor {}.'
.format(feature, value),
project_id,
flag=1)
continue
differences_non_boolean.append(p_c1_f1 - p_c1_f0)
features_non_boolean.append(feature + '_' + value)
differences_feature.extend(differences_boolean)
differences_feature.extend(differences_non_boolean)
features.extend(features_boolean)
features.extend(features_non_boolean)
model_tag.extend(
[model] *
(len(differences_boolean) + len(differences_non_boolean)))
df_feature_contribution = pd.DataFrame()
df_feature_contribution['Features'] = features
df_feature_contribution['Differences'] = differences_feature
df_feature_contribution['Model_Code'] = model_tag
if abs(df_feature_contribution['Differences'].min()
) > df_feature_contribution['Differences'].max():
max_range_value = abs(df_feature_contribution['Differences'].min())
min_range_value = df_feature_contribution['Differences'].min()
else:
max_range_value = df_feature_contribution['Differences'].max()
min_range_value = df_feature_contribution['Differences'].max() * -1
df_feature_contribution[
'Differences_Normalized'] = 2 * df_feature_contribution[
'Differences'] / (max_range_value - min_range_value)
df_feature_contribution_total = pd.concat(
[df_feature_contribution_total, df_feature_contribution])
level_1_e_deployment.sql_inject(
df_feature_contribution_total,
options_file.DSN_MLG_PRD,
options_file.sql_info['database_final'],
options_file.sql_info['feature_contribution'],
options_file,
list(df_feature_contribution_total),
truncate=1)
def performance_evaluation_regression(models, best_models, running_times,
datasets, datasets_non_ohe, options_file,
project_id):
results_train, results_test = [], []
predictions, feat_importance = {}, pd.DataFrame(index=list(
datasets['train_x']),
columns={'Importance'})
for model in models:
if model == 'lgb':
train_x, test_x = datasets_non_ohe['train_x'], datasets_non_ohe[
'test_x']
train_y, test_y = datasets_non_ohe['train_y'], datasets_non_ohe[
'test_y']
else:
train_x, test_x = datasets['train_x'], datasets['test_x']
train_y, test_y = datasets['train_y'], datasets['test_y']
prediction_train = best_models[model].predict(train_x)
prediction_test = best_models[model].predict(test_x)
evaluation_training = RegressionEvaluation(groundtruth=train_y,
prediction=prediction_train)
evaluation_test = RegressionEvaluation(groundtruth=test_y,
prediction=prediction_test)
predictions[model] = [
prediction_train.astype(int, copy=False),
prediction_test.astype(int, copy=False)
]
# try:
# feat_importance['Importance'] = best_models[model].feature_importances_
# feat_importance.sort_values(by='Importance', ascending=False, inplace=True)
# feat_importance.to_csv(base_path + '/output/' + 'feature_importance_' + str(model) + '.csv')
# except AttributeError:
# pass
row_train = {
'R2': getattr(evaluation_training, 'r2_score'),
'MSE': getattr(evaluation_training, 'mse'),
'RMSE': np.sqrt(getattr(evaluation_training, 'mse')),
'Running_Time': running_times[model]
}
row_test = {
'R2': getattr(evaluation_test, 'r2_score'),
'MSE': getattr(evaluation_test, 'mse'),
'RMSE': np.sqrt(getattr(evaluation_test, 'mse')),
'Running_Time': running_times[model]
}
results_train.append(row_train)
results_test.append(row_test)
df_results_train = pd.DataFrame(results_train, index=models)
df_results_train['Algorithms'] = df_results_train.index
df_results_train['Dataset'] = ['Train'] * df_results_train.shape[0]
df_results_train['Project_Id'] = [project_id] * df_results_train.shape[0]
df_results_test = pd.DataFrame(results_test, index=models)
df_results_test['Algorithms'] = df_results_test.index
df_results_test['Dataset'] = ['Test'] * df_results_train.shape[0]
df_results_test['Project_Id'] = [project_id] * df_results_train.shape[0]
# metric_bar_plot(df_results_train, 'project_{}_train_dataset'.format(project_id))
# metric_bar_plot(df_results_test, 'project_{}_test_dataset'.format(project_id))
level_1_e_deployment.sql_inject(
pd.concat([df_results_train, df_results_test]),
level_0_performance_report.performance_sql_info['DSN'],
level_0_performance_report.performance_sql_info['DB'],
level_0_performance_report.
performance_sql_info['performance_algorithm_results'],
options_file,
list(df_results_train),
check_date=1)
return df_results_train, df_results_test, predictions
