def save_data_to_file(datos, ruta_directorio, nombre_fichero, delimitador, extension):
'''This function allows to store data in filex with the specified extension
datos: data to store
ruta_directorio: path to the output directory
nombre_fichero: name of the file that stores the data
delimitador: delimiter for the fields of the file
extension: extension for the output file
'''
if extension == 'txt':
nombre_fichero = nombre_fichero + "." + extension
ruta_destino = os.path.join(ruta_directorio, nombre_fichero)
np.savetxt(ruta_destino, datos, delimiter=delimitador)
elif extension == 'csv':
nombre_fichero = nombre_fichero + "." + extension
ruta_destino = os.path.join(ruta_directorio, nombre_fichero)
datos.to_csv(ruta_destino, index=False, sep=delimitador)
elif extension == 'numpy':
nombre_fichero = nombre_fichero + "." + 'txt'
ruta_destino = os.path.join(ruta_directorio, nombre_fichero)
file_to_write = open(ruta_destino, glod.get_write_mode())
file_to_write.write("\t Predicted label \n")
filas = datos.tolist()
for fila in filas:
file_to_write.write('\t' + str(fila) + '\n')
file_to_write.close()
def confusion_matrix_to_csv(matrix, list_labels, path, fillvalue=''):
'''
It generates a csv file in which each column belongs to the
elements of the class i that were classified as elements of the
j class
Arguments:
matrix -- list shape(n, n) beign n the number of differente classes and
in each element (i,j) one list with the value of one column for
each instance of the class i that was classified as j.
path -- strign with the path to the output file
fillvalue -- value to fill where there are non-existent values
Return: None
'''
data = itertools.chain.from_iterable(matrix)
tuples = itertools.zip_longest(*data, fillvalue=fillvalue)
column_names = [
'TL%s_PL%s' %
(map_values(real, list_labels), map_values(predicted, list_labels))
for real, predicted in itertools.product(range(len(matrix)),
range(len(matrix)))
]
with open(path, glod.get_write_mode()) as fichero:
writer = csv.writer(fichero, delimiter=',')
writer.writerow(column_names)
for tupla in tuples:
writer.writerow(tupla)
def create_report_prediction(report_dict, event_target, ruta_relativa_datos_auxiliares,
ruta_directorio_informes, enco):
'''This funcion allows to get the pdf for the current model with the information
obtained fate rthe prediction phase'''
env = Environment(loader=FileSystemLoader('.'))
ruta_plantilla_temporal = os.path.join(ruta_relativa_datos_auxiliares, 'temp_html.html')
template = env.get_template(ruta_relativa_datos_auxiliares + '/' + glod.get_prediction_template_name())
event = event_target[0]
target_to_predict = event_target[1]
dic_info_event = report_dict[event]
summary_target_to_predict = glod.get_empty_string()
template_vars = {glod.get_title_key(): "Prediction report for " + event,
glod.get_logo_key():\
encode_image(report_dict[glod.get_logo_key()].replace('\'', glod.get_empty_string())),
}
if target_to_predict in dic_info_event:
model = str(dic_info_event[target_to_predict][glod.get_best_model_key()])
model = model.split("(")
model = model[0]
summary_target_to_predict = "<p><strong>Target: <strong>" + ' ' +\
target_to_predict + "</br></br>"
summary_target_to_predict += "<p><strong>Model: <strong>" + ' ' + model + "</br>"
summary_target_to_predict += "<p><strong>Accuracy: <strong>" + ' ' +\
str(dic_info_event[target_to_predict][glod.get_accuracy_parameter_name()]) + "</br>"
summary_target_to_predict += "<strong>Correct classifications: <strong>" + ' ' +\
str(dic_info_event[target_to_predict]['Correct']) + "</br>"
summary_target_to_predict += "<strong>Total number of observations: <strong>" + ' ' +\
str(dic_info_event[target_to_predict]['Total']) + "</br>"
summary_target_to_predict += "<strong>Total number of unknown observations classified: <strong>" + ' ' + str(dic_info_event[target_to_predict]['Predicted']) + "</br>"
cm_target = encode_image(dic_info_event[target_to_predict]\
['target_to_predict_cm'].replace('\'', glod.get_empty_string()))
template_vars['target_to_predict_cm'] = cm_target
template_vars['target'] = summary_target_to_predict
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, mode=glod.get_read_mode(), encoding=enco) as read_html:
pdf_resultante = os.path.join(ruta_directorio_informes,\
"Prediction_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 create_score_report(directorio_salida, nombre_informe, report, enco):
'''This funcion allows to get the score fot each relevant feature in
csv format'''
try:
nombre_informe = nombre_informe + '_report_.csv'
ruta_relativa_informe = os.path.join(directorio_salida, nombre_informe)
with codecs.open(ruta_relativa_informe, glod.get_write_mode(), encoding=enco) as output_score_report:
cabecera = u'feature'+u','+u'weight'+u'\n'
output_score_report.write(cabecera)
for elemento in report:
variable_act = elemento[0]
score = elemento[1]
output_score_report.write(variable_act + u',' + str(score) + u'\n')
mensaje = "Score report created succesfully"
except Exception as excep:
print(excep)
pass
return mensaje
def register_log(array_rutas_ficheros, mensaje, opcion, enco):
'''
This function allows to register information in the logs
Parameters:
:param list array_rutas_ficheros: list with the path to the lgos files
:param str mensaje: Message to register in the log files
:param str/int opcion: Mode for operating
:param str enco: encoding
:return: updated report_dict
:rtype: dict<python_hashable_type:python_type>
'''
modo = glod.get_append_mode()
file_to_operate = glod.get_empty_string()
for ruta_fichero in array_rutas_ficheros:
if opcion == 0:
modo = glod.get_write_mode()
if enco != glod.get_none_encoding():
file_to_operate = codecs.open(ruta_fichero, modo, encoding=enco)
else:
file_to_operate = open(ruta_fichero, modo)
file_to_operate.write(mensaje)
file_to_operate.close()
def create_report_current_dictionary_models(dictionary_of_models, basic_paths,
list_of_parameters_models_events_dict, logo_path, enco):
'''This funcion allows to get the pdf file with the current status of the models,
relevant features and the events to which are applied'''
ruta_relativa_datos_auxiliares = basic_paths[0]
ruta_directorio_resultados = basic_paths[1]
env = Environment(loader=FileSystemLoader('.'))
ruta_plantilla_temporal = os.path.join(ruta_relativa_datos_auxiliares, 'temp_html.html')
template = env.get_template(ruta_relativa_datos_auxiliares + '/' +\
glod.get_dictionary_models_template_name())
tabulacion = " "
template_vars = {glod.get_title_key(): "Report of the information of the Dictionary of models",
glod.get_logo_key(): encode_image(logo_path.replace('\'', glod.get_empty_string()))
}
list_elements = [list_of_parameters_models_events_dict[0],
list_of_parameters_models_events_dict[3],
list_of_parameters_models_events_dict[1]]
informacion = glod.get_empty_string()
for event in dictionary_of_models:
informacion += "<strong><u>"+ event +"</u></strong></br></br>"
for target in dictionary_of_models[event]:
informacion += tabulacion + tabulacion + "<strong><i>Target:</i></strong>" + " " + target + "</br>"
for key in list_elements:
informacion += tabulacion + tabulacion + "<strong><i>" + key + ": </i></strong>"
if type(list()) == type(dictionary_of_models[event][target][key]):
informacion += "<br>"
contador = 0
ordered_list_features = sorted(dictionary_of_models[event][target][key])
while contador < len(ordered_list_features):
element = ordered_list_features[contador]
informacion += tabulacion + tabulacion + tabulacion +tabulacion + element + "</br>"
contador += 1
else:
informacion += dictionary_of_models[event][target][key] + "</br>"
if key == list_of_parameters_models_events_dict[0]:
informacion += tabulacion + tabulacion + "<strong><i>best model: </i></strong> " + dictionary_of_models[event][target][list_of_parameters_models_events_dict[1]].split('_')[-1].split('.')[0] + "</br>" #get model name
if dictionary_of_models[event][target][key] == glod.get_unsupervised_name():
informacion += tabulacion + tabulacion + "<strong><i>dic_reassingment: </i></strong> " + str(dictionary_of_models[event][target][list_of_parameters_models_events_dict[2]]) + "</br>"
informacion += "</br>"
if informacion == glod.get_empty_string():
informacion = "No models were created yet"
template_vars[glod.get_info_key()] = informacion
#html
with codecs.open(ruta_plantilla_temporal, glod.get_write_mode(), encoding=enco) as output_file:
renderizado = template.render(template_vars)
output_file.write(renderizado)
#pdf
with codecs.open(ruta_plantilla_temporal, mode=glod.get_read_mode(), encoding=enco) as read_html:
pdf_resultante = os.path.join(ruta_directorio_resultados, "Current_status_dictionary_events_and_models.pdf")
with open(pdf_resultante, mode=glod.get_writebyte_mode()) as pdf_gen:
pisa.CreatePDF(read_html.read().encode(enco, 'ignore').decode(enco), pdf_gen)
if os.path.exists(ruta_plantilla_temporal):
os.remove(ruta_plantilla_temporal)
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 create_report_current_execution(report_dict, basic_lists, diccionario_aprendizajes,
ruta_relativa_datos_auxiliares, ruta_directorio_resultados):
'''This funcion allows to create a pdf with the information about the current
process that is going to take place'''
env = Environment(loader=FileSystemLoader('.'))
ruta_plantilla_temporal = os.path.join(ruta_relativa_datos_auxiliares, 'temp_html.html')
template = env.get_template(ruta_relativa_datos_auxiliares + '/' +\
glod.get_general_execution_template_name())
template_vars = {glod.get_title_key(): report_dict[glod.get_title_key()],
glod.get_logo_key():encode_image(report_dict[glod.get_logo_key()].replace('\'', glod.get_empty_string())),
glod.get_general_info_execution_key():glod.get_empty_string()
}
lista_eventos = basic_lists[0]
lista_variables_usuario = basic_lists[1]
lista_listas_variables_descartadas = basic_lists[2]
lista_aprendizajes = basic_lists[3]
lista_modelos = basic_lists[4]
#General parameters (target,umbral,variables_descartadas)
target = report_dict[glod.get_objective_target_key()]
umbral = report_dict[glod.get_umbral_key()]
main_metric = report_dict[glod.get_main_metric_key()]
feature_selection_method = report_dict[glod.get_feature_selection_key()]
penalize_falses = report_dict[glod.get_penalization_name()]
lista_variables_descartadas = report_dict[glod.get_variables_key()]\
[glod.get_deleted_by_user_key()]
tabulacion = " "
informacion = "<h3>Common Parameters </h3></p>"
informacion += tabulacion+tabulacion + "<i>Objective Target: </i>" + target + "</br></br>"
informacion += tabulacion+tabulacion + "<i>Percentil for Scoring Function: </i>" + umbral +\
"</br></br>"
informacion += tabulacion+tabulacion + "<i>Main metric: </i>" + main_metric + "</br></br>"
informacion += tabulacion+tabulacion + "<i>Feature selection method: </i>" + \
feature_selection_method + "</br></br>"
informacion += tabulacion+tabulacion + "<i>Penalize falses: </i>" + penalize_falses +\
"</br></br>"
informacion += tabulacion+tabulacion + "<i>Common Discarded Variables:</i></br>"
for variable_descartada in lista_variables_descartadas:
informacion += tabulacion+tabulacion+tabulacion + variable_descartada + "</br>"
if lista_variables_descartadas == []:
informacion += tabulacion+"No variables were selected to be discarded</br>"
informacion += "</p>"
informacion += "<h3>Events to be processed: </h3><p>"
for indice in range(len(lista_eventos)):
informacion += tabulacion+"<strong>"+ lista_eventos[indice] + "</strong></br>"
informacion += tabulacion+tabulacion+"<i>Important features for the user:</i> </br>"
if lista_variables_usuario[indice]:
for variable in lista_variables_usuario[indice]:
informacion += tabulacion+tabulacion+tabulacion+variable + "</br>"
else:
informacion += tabulacion+tabulacion+tabulacion + \
"No important features were specified</br>"
informacion += "</br>"
informacion += tabulacion+tabulacion+"<i>Discarded variables by the user:</i> </br>"
if lista_listas_variables_descartadas[indice]:
for variable in lista_listas_variables_descartadas[indice]:
informacion += tabulacion+tabulacion+tabulacion+variable + "</br>"
else:
informacion += tabulacion+tabulacion+tabulacion+"No variables were discarded</br>"
informacion += "</br>"
informacion += tabulacion+tabulacion+"<i>Learnings to be applied: </i></br>"
aprendizaje = lista_aprendizajes[indice]
modelos = lista_modelos[indice]
if aprendizaje == glod.get_all_learning_modes_name():#looping supervised models
informacion += tabulacion+tabulacion+tabulacion+"<u>" +\
str(diccionario_aprendizajes[1]) + "</u>:</br>"
modelos_sup = modelos[0]
for modelo_act in modelos_sup:
informacion += tabulacion+tabulacion+tabulacion+tabulacion + modelo_act + "</br>"
informacion += "</br>"
else:
informacion += tabulacion+tabulacion+tabulacion+"<u>"+aprendizaje + "</u>:</br>"
for modelo_act in modelos:
informacion += tabulacion+tabulacion+tabulacion+tabulacion + modelo_act + "</br>"
informacion += "</p>"
template_vars[glod.get_general_info_execution_key()] = informacion
with codecs.open(ruta_plantilla_temporal, glod.get_write_mode()) as output_file:
output_file.write(template.render(template_vars))
with codecs.open(ruta_plantilla_temporal, glod.get_read_mode()) as html_leido:
pdf_resultante = os.path.join(ruta_directorio_resultados,
"General_execution_report_"+ target +".pdf")
with open(pdf_resultante, glod.get_writebyte_mode()) as gen_report:
pisa.CreatePDF(html_leido.read(), gen_report)
logging.getLogger("xhtml2pdf").addHandler(PisaNullHandler())
if os.path.exists(ruta_plantilla_temporal):
os.remove(ruta_plantilla_temporal)
