def add_model_to_report(report_dict,modelo): report_dict[modelo]={glod.get_parameters_key():{}, glod.get_time_parameters_key():{}, glod.get_accuracy_parameter_name():0, glod.get_decision_index_key():0, } return report_dict
def add_model_to_report(report_dict, modelo): '''This funcion allows to register the information about the trained model in the report''' report_dict[modelo] = {glod.get_parameters_key():{}, glod.get_time_parameters_key():{}, glod.get_accuracy_parameter_name():0, glod.get_decision_index_key():0, } return report_dict
def create_report_current_model(report_dict,lista_modelos,ruta_relativa_datos_auxiliares,ruta_directorio_informes,enco): 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 + '/' + 'incomplete_event_report_template.html') #usamos la plantilla de informes incompletos template_vars = {glod.get_title_key(): "Incomplete Execution Report", glod.get_logo_key(): encode_image(report_dict[glod.get_logo_key()].replace('\'','')), 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['Warning_info'] } #html with codecs.open(ruta_plantilla_temporal,'w',encoding=enco) as output_file: output_file.write(template.render(template_vars)) #pdf with codecs.open(ruta_plantilla_temporal, 'r') as html_leido: pdf_resultante=os.path.join(ruta_directorio_informes,"report_"+report_dict[glod.get_event_key()]+"_incomplete.pdf") with open(pdf_resultante, "wb") 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 + '/' +'report_template.html') #using standard template for modelo in lista_modelos: if(modelo in report_dict): observations_targets="<p><strong>Target distribution of observations</strong></br>" for ob_target in auxf.natsorted(report_dict[glod.get_report_general_info_key()][glod.get_report_generic_target_key()].keys()): 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='' 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(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(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_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()] ="" 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('\'','')) 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('\'','')) 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('\'','')) 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('\'','')) metrics_by_label = "<table width='100%' border='1' cellspacing='0' cellpadding='5'>" keys = '' for elemento in auxf.natsorted(report_dict[modelo][glod.get_metrics_key()].keys()): if(keys == ''): 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_','') + "</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,'w',encoding=enco) as output_file: output_file.write(template.render(template_vars)) #generamos el pdf with codecs.open(ruta_plantilla_temporal, mode='r',encoding=enco) as read_html: pdf_resultante=os.path.join(ruta_directorio_informes,modelo + "_report_for_"+ event +".pdf") with open(pdf_resultante, mode='wb') 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_model_parameters(report_dict,modelo,parametro,valor): report_dict[modelo][glod.get_parameters_key()][parametro] = valor return report_dict
def update_model_parameters(report_dict, modelo, parametro, valor): '''This funcion allows to register the information about the parameters of the trained model in the report''' report_dict[modelo][glod.get_parameters_key()][parametro] = valor return report_dict