def update_report_user_discarded_features(report_dict,lista_descartadas):
report_dict[glod.get_report_general_info_key()][glod.get_variables_key()][glod.get_user_discarded_key()] = lista_descartadas
return report_dict
def update_report_training_models_features(report_dict,diccionario_variables_scores):
report_dict[glod.get_report_general_info_key()][glod.get_variables_key()][glod.get_score_relevant_key()] = diccionario_variables_scores
return report_dict
def update_report_empty_constant_features(report_dict,lista_vacias_constantes):
report_dict[glod.get_report_general_info_key()][glod.get_variables_key()][glod.get_empty_or_constant_key()] = lista_vacias_constantes
return report_dict
def update_report_relevant_user_features(report_dict,lista_importantes):
report_dict[glod.get_report_general_info_key()][glod.get_variables_key()][glod.get_user_requested_key()] = lista_importantes
return report_dict
def create_report_current_model(report_dict, lista_modelos, ruta_relativa_datos_auxiliares,
ruta_directorio_informes, enco):
'''This funcion allows to get information of the current model in pdf format
with the full charactristics fo the model'''
env = Environment(loader=FileSystemLoader('.'))
ruta_plantilla_temporal = os.path.join(ruta_relativa_datos_auxiliares, 'temp_html.html')
if lista_modelos == []: #if process not completed
template = env.get_template(ruta_relativa_datos_auxiliares + '/' +\
glod.get_incomplete_event_report_template_name())
template_vars = {glod.get_title_key(): "Incomplete Execution Report",
glod.get_logo_key(): \
encode_image(report_dict[glod.get_logo_key()].replace('\'', glod.get_empty_string())),
glod.get_report_generic_target_key(): report_dict[glod.get_objective_target_key()],
glod.get_event_key(): report_dict[glod.get_event_key()],
glod.get_info_key(): " " +\
report_dict[glod.get_warning_key()]
}
with codecs.open(ruta_plantilla_temporal, glod.get_write_mode(), encoding=enco) as output_file:
output_file.write(template.render(template_vars))
with codecs.open(ruta_plantilla_temporal, glod.get_read_mode(), encoding=enco) as html_leido:
pdf_resultante = os.path.join(ruta_directorio_informes, "report_" +\
report_dict[glod.get_event_key()]+"_incomplete.pdf")
with open(pdf_resultante, glod.get_writebyte_mode()) as incomplete_rep:
pisa.CreatePDF(html_leido.read(), incomplete_rep)
logging.getLogger("xhtml2pdf").addHandler(PisaNullHandler())
else:
lista_pares_modelo_indice = auxf.order_models_by_score_and_time(report_dict, lista_modelos)
template = env.get_template(ruta_relativa_datos_auxiliares + '/' + glod.get_report_template_name())
for modelo in lista_modelos:
if modelo in report_dict:
observations_targets = "<p><strong>Target distribution of observations\
</strong></br>"
final_targets_list = list(report_dict[glod.get_report_general_info_key()]\
[glod.get_report_generic_target_key()].keys())
for ob_target in auxf.natsorted(final_targets_list):
observations_targets += " "+ "With target " +\
str(ob_target) + " :"+ str(report_dict[glod.get_report_general_info_key()]\
[glod.get_report_generic_target_key()][ob_target]) + "</br>"
observations_targets += "</p>"
variables_summary = "<p><strong>Summary of variables</strong></br>"
discarded_for_event = report_dict[glod.get_report_general_info_key()]\
[glod.get_variables_key()][glod.get_user_discarded_key()]
variables_summary += "<br><i><u>Deleted by the user at the begining:</i></u></br>"
for deleted_var in report_dict[glod.get_report_general_info_key()]\
[glod.get_variables_key()][glod.get_deleted_by_user_key()]:
variable_dis = glod.get_empty_string()
if deleted_var in discarded_for_event:
variable_dis = "<strong>" + deleted_var + "</strong>"
else:
variable_dis = deleted_var
variables_summary += " "+ variable_dis + "</br>"
variables_summary += " <i>*variables in bold were\
specified by the user to be discarded specifically for this event<i></br>"
variables_summary += "</br>"
variables_summary += "<br><i><u>Deleted in execution time(Empty or Constant)\
:</i></u></br>"
for emp_con_var in report_dict[glod.get_report_general_info_key()]\
[glod.get_variables_key()][glod.get_empty_or_constant_key()]:
variables_summary += " "+ emp_con_var + "</br>"
variables_summary += "</br>"
variables_summary += "<br><i><u>Requested for the event by the user:</i></u></br>"
for req_var in report_dict[glod.get_report_general_info_key()]\
[glod.get_variables_key()][glod.get_user_requested_key()]:
variables_summary += " "+ req_var + "</br>"
variables_summary += "</br>"
variables_summary += "<br><i><u>Used during the process:</i></u></br>"
diccionario_relevantes_mif = report_dict[glod.get_report_general_info_key()]\
[glod.get_variables_key()][glod.get_score_relevant_key()]
sorted_relevant_vars = sorted(diccionario_relevantes_mif.items(),
key=operator.itemgetter(1),
reverse=True)
for relevant_var in sorted_relevant_vars:
rel_variable = relevant_var[0]
rel_variable = "<strong>" + rel_variable +' \
'+\
str(diccionario_relevantes_mif[rel_variable]) +"</strong>"
variables_summary += " "+ rel_variable + "</br>"
for relevant_var in report_dict[glod.get_report_general_info_key()][glod.get_variables_key()][glod.get_used_in_process()]:
if relevant_var not in diccionario_relevantes_mif:
variables_summary += " "+ relevant_var + "</br>"
variables_summary += " <i>*variables in bold were used\
to train the models<i></br>"
variables_summary += "</p>"
#Information about the model
accuracy = "</br></br> \
<strong>Accuracy: "+\
str(float(round(report_dict[modelo][glod.get_accuracy_parameter_name()], 5)))+\
"</strong>"
ranking = get_string_with_ranking_of_models(lista_pares_modelo_indice, modelo)
model_info = "<p><strong>Parameters used to configure the model</strong></br>"
for param in report_dict[modelo][glod.get_parameters_key()]:
model_info += " <i>"+ param + "</i>: " +\
str(report_dict[modelo][glod.get_parameters_key()][param]) + "</br>"
model_info += "</p>"
time_info = "<p><strong>Time elapsed</strong></br>"
tiempo_seleccion_parametros = report_dict[modelo][glod.get_time_parameters_key()][glod.get_time_sel_finish_key()] - report_dict[modelo][glod.get_time_parameters_key()][glod.get_time_sel_init_key()]
tiempo_entrenamiento = report_dict[modelo][glod.get_time_parameters_key()][glod.get_time_train_finish_key()] - report_dict[modelo][glod.get_time_parameters_key()][glod.get_time_train_init_key()]
time_info += " "+ "Parameters selection time: "+\
str(tiempo_seleccion_parametros) + "</br>"
time_info += " "+ "Training time: "+\
str(tiempo_entrenamiento) + "</br>"
time_info += "</p>"
total_train = 0.0
vector_of_targets = []
vector_of_values_by_target = []
vector_of_percentages_by_target = []
train_distribution_info = "<p></br><strong>Training Data Distribution\
</strong></br>"
for train_target in auxf.natsorted(list(report_dict[glod.get_report_general_info_key()][glod.get_training_division_key()].keys())):
train_distribution_info += " "+ "With target " + str(train_target) + " :"+ str(report_dict[glod.get_report_general_info_key()][glod.get_training_division_key()][train_target]) + "</br>"
vector_of_targets.append(train_target)
vector_of_values_by_target.append(float(report_dict[glod.get_report_general_info_key()][glod.get_training_division_key()][train_target]))
total_train += float(report_dict[glod.get_report_general_info_key()][glod.get_training_division_key()][train_target])
train_distribution_info += "</p>"
#getting null train accuracy
null_train_accuracy = 0.0
for indice_t in range(len(vector_of_values_by_target)):
vector_of_percentages_by_target.append(round(vector_of_values_by_target[indice_t]/total_train, 4))
null_train_accuracy = max(vector_of_percentages_by_target)
total_test = 0.0
vector_of_targets = []
vector_of_values_by_target = []
vector_of_percentages_by_target = []
test_distribution_info = "<p><strong>Test Data Distribution</strong></br>"
for test_target in auxf.natsorted(list(report_dict[glod.get_report_general_info_key()][glod.get_test_division_key()].keys())):
test_distribution_info += " "+ "With target " + str(test_target) + " :"+ str(report_dict[glod.get_report_general_info_key()][glod.get_test_division_key()][test_target]) + "</br>"
vector_of_targets.append(test_target)
vector_of_values_by_target.append(float(report_dict[glod.get_report_general_info_key()][glod.get_test_division_key()][test_target]))
total_test += float(report_dict[glod.get_report_general_info_key()][glod.get_test_division_key()][test_target])
test_distribution_info += "</p>"
null_test_accuracy = 0.0
for indice_t in range(len(vector_of_values_by_target)):
vector_of_percentages_by_target.append(round(vector_of_values_by_target[indice_t]/total_test, 4))
null_test_accuracy = max(vector_of_percentages_by_target)
event = report_dict[glod.get_event_key()]
template_vars = {glod.get_title_key(): "Execution Report",
glod.get_logo_key():encode_image(report_dict[glod.get_logo_key()].replace('\'', glod.get_empty_string())),
glod.get_model_key(): modelo,
glod.get_report_generic_target_key():\
report_dict[glod.get_objective_target_key()],
glod.get_event_key(): event,
glod.get_accuracy_parameter_name():\
str(accuracy)+"<br> \
<strong>Null train acc: "+\
str(null_train_accuracy)+"</strong>"+"<br> \
\
<strong>Null test acc: "+ str(null_test_accuracy)+\
"</strong></p>",
glod.get_models_ranking_key(): ranking,
glod.get_observations_targets_key(): observations_targets,
glod.get_variables_summary_key(): variables_summary,
glod.get_models_info_key(): model_info,
glod.get_time_info_key(): time_info,
glod.get_train_distribution_info_key(): train_distribution_info,
glod.get_test_distribution_info_key(): test_distribution_info
}
template_vars[glod.get_metrics_info_key()] = glod.get_empty_string()
for metric in report_dict[modelo][glod.get_metrics_micro_avg_key()]:
template_vars[glod.get_metrics_info_key()] += "<p>"+"<strong>"+metric+"</strong>: " + report_dict[modelo][glod.get_metrics_micro_avg_key()][metric] +"</br>"
template_vars[glod.get_metrics_info_key()] += "</p>"
if glod.get_model_parameters_plot_name() in report_dict[modelo]:
template_vars[glod.get_image_parameters_accuracy_key()] = encode_image(report_dict[modelo][glod.get_model_parameters_plot_name()].replace('\'', glod.get_empty_string()))
if glod.get_confussion_matrix_train_path_key() in report_dict[modelo]:
template_vars[glod.get_conf_train_img_key()] = encode_image(report_dict[modelo][glod.get_confussion_matrix_train_path_key()].replace('\'', glod.get_empty_string()))
if glod.get_confussion_matrix_test_path_key() in report_dict[modelo]:
template_vars[glod.get_conf_test_img_key()] = encode_image(report_dict[modelo][glod.get_confussion_matrix_test_path_key()].replace('\'', glod.get_empty_string()))
if glod.get_learning_curve_key() in report_dict[modelo]:
template_vars[glod.get_learning_curve_key()] = encode_image(report_dict[modelo][glod.get_learning_curve_key()].replace('\'', glod.get_empty_string()))
metrics_by_label = "<table width='100%' border='1' cellspacing='0' cellpadding='5'>"
keys = glod.get_empty_string()
for elemento in auxf.natsorted(list(report_dict[modelo][glod.get_metrics_key()].keys())):
if keys == glod.get_empty_string():
keys = report_dict[modelo][glod.get_metrics_key()][elemento].keys()
metrics_by_label += "<tr><td align='center' class='black'>"+ glod.get_report_generic_target_key() +"</td>"
for cabecera in keys:
metrics_by_label += "<td align='center' class='black'>" + cabecera +"</td>"
metrics_by_label += "</tr>"
metrics_by_label += "<tr><td>" + elemento.replace('target_', glod.get_empty_string()) + "</td>"
for key in keys:
metrics_by_label += "<td>"+str(report_dict[modelo][glod.get_metrics_key()][elemento][key])+"</td>"
metrics_by_label += "</tr>"
metrics_by_label += "</table>"
template_vars[glod.get_metrics_by_label_key()] = metrics_by_label
#generamos el html
with codecs.open(ruta_plantilla_temporal, glod.get_write_mode(), encoding=enco) as output_file:
output_file.write(template.render(template_vars))
#generamos el pdf
with codecs.open(ruta_plantilla_temporal, mode=glod.get_read_mode(), encoding=enco) as read_html:
pdf_resultante = os.path.join(ruta_directorio_informes, modelo + "_report_for_"+ event +".pdf")
with open(pdf_resultante, mode=glod.get_writebyte_mode()) as pdf_gen:
pisa.CreatePDF(read_html.read(), pdf_gen)
logging.getLogger("xhtml2pdf").addHandler(PisaNullHandler())
if os.path.exists(ruta_plantilla_temporal):
os.remove(ruta_plantilla_temporal)
def update_test_division(report_dict, key, valor):
'''This funcion allows to register the distribution of observations that will be used
to test the models in the report'''
report_dict[glod.get_report_general_info_key()][glod.get_test_division_key()][key] = valor
return report_dict
def update_report_user_discarded_features(report_dict, lista_descartadas):
'''This funcion allows to register the discarded features in the report'''
report_dict[glod.get_report_general_info_key()][glod.get_variables_key()]\
[glod.get_user_discarded_key()] = lista_descartadas
return report_dict
def update_report_relevant_user_features(report_dict, lista_importantes):
'''This funcion allows to register the relevant features in the report'''
report_dict[glod.get_report_general_info_key()][glod.get_variables_key()]\
[glod.get_user_requested_key()] = lista_importantes
return report_dict
def update_report_empty_constant_features(report_dict, lista_vacias_constantes):
'''This funcion allows to register the empty or constant features in the report'''
report_dict[glod.get_report_general_info_key()][glod.get_variables_key()]\
[glod.get_empty_or_constant_key()] = lista_vacias_constantes
return report_dict
