def evaluation_on_feature_prediction_all_users(feature_name):
#contains the scores of the different predictors combined for all the users
mixed_evaluations = {}
#contains the scores details of the different predictors for each user
detailed_evaluations = {}
iter = 0
for user_id in MDataExtractor.users_ids_list():
print("user "+str(user_id)+" on working")
if feature_name == "day":
[evaluations,classes] = eodpou(user_id)
file_name = "evaluation_day_prediction"
elif feature_name == "location":
[evaluations,classes] = eolpou(user_id)
file_name = "evaluation_location_prediction"
elif feature_name == "applaunch":
[evaluations,classes] = eoapou(user_id)
file_name = "evaluation_applaunch_prediction"
else:
raise Exception("NOT IMPLEMENTED FEATURE EXCEPTION: the feature "+str(feature_name)+" do not implemented or do not exist")
if len(detailed_evaluations.keys()) == 0:
#instantiate the evaluations objects add the classifier names for detailed_evaluations and detailed_evaluations
for classifier_name, evaluation_results in evaluations.iteritems():
mixed_evaluations[classifier_name] = {"correct predictions":0 , "total predictions":0, "accuracy":0, "average accuracy":0, "average macro_accuracy":0}
detailed_evaluations[classifier_name] = {}
#integrate the evaluations of the current users
for classifier_name, evaluation_results in evaluations.iteritems():
#evaluation_results = [good_predictions, total_predictions, accuracy, macro_average_acc_by_class, accuracy_class1,...,accuracy_classn]
#update the mixed evaluations
mixed_evaluations[classifier_name]["correct predictions"]+=evaluation_results[0]
mixed_evaluations[classifier_name]["total predictions"]+=evaluation_results[1]
mixed_evaluations[classifier_name]["accuracy"]= (mixed_evaluations[classifier_name]["correct predictions"]*1.0)/mixed_evaluations[classifier_name]["total predictions"]
mixed_evaluations[classifier_name]["average accuracy"] = ((mixed_evaluations[classifier_name]["average accuracy"]*iter)+evaluation_results[2])/((iter+1)*1.0)
mixed_evaluations[classifier_name]["average macro_accuracy"] = ((mixed_evaluations[classifier_name]["average macro_accuracy"]*iter)+evaluation_results[3])/((iter+1)*1.0)
#update the detailed evaluations
detailed_evaluations[classifier_name]["user "+str(user_id)]= {"correct predictions": evaluation_results[0], "total predictions":evaluation_results[1], "accuracy":evaluation_results[2],
"macro_accuracy":evaluation_results[3], "accuracy by class": evaluation_results[4:]}
iter+=1
print("user "+str(user_id)+" extracted")
#write the results in a log file
#write the dictionaries into files
out = LogsFileWriter.open(file_name)
LogsFileWriter.write("predictions on the classes "+JsonUtils.dict_as_json_str(classes)+"\n",out)
LogsFileWriter.write("Total scores :\n",out)
LogsFileWriter.write(JsonUtils.dict_as_json_str(mixed_evaluations),out)
LogsFileWriter.write("detailed scores :\n",out)
LogsFileWriter.write(JsonUtils.dict_as_json_str(detailed_evaluations),out)
LogsFileWriter.close(out)
def evaluation_on_feature_prediction_all_users(feature_name):
#contains the scores of the different predictors combined for all the users
mixed_evaluations = {}
#contains the scores details of the different predictors for each user
detailed_evaluations = {}
iter = 0
for user_id in MDataExtractor.users_ids_list():
print("user " + str(user_id) + " on working")
if feature_name == "day":
[evaluations, classes] = eodpou(user_id)
file_name = "evaluation_day_prediction"
elif feature_name == "location":
[evaluations, classes] = eolpou(user_id)
file_name = "evaluation_location_prediction"
elif feature_name == "applaunch":
[evaluations, classes] = eoapou(user_id)
file_name = "evaluation_applaunch_prediction"
else:
raise Exception("NOT IMPLEMENTED FEATURE EXCEPTION: the feature " +
str(feature_name) +
" do not implemented or do not exist")
if len(detailed_evaluations.keys()) == 0:
#instantiate the evaluations objects add the classifier names for detailed_evaluations and detailed_evaluations
for classifier_name, evaluation_results in evaluations.iteritems():
mixed_evaluations[classifier_name] = {
"correct predictions": 0,
"total predictions": 0,
"accuracy": 0,
"average accuracy": 0,
"average macro_accuracy": 0
}
detailed_evaluations[classifier_name] = {}
#integrate the evaluations of the current users
for classifier_name, evaluation_results in evaluations.iteritems():
#evaluation_results = [good_predictions, total_predictions, accuracy, macro_average_acc_by_class, accuracy_class1,...,accuracy_classn]
#update the mixed evaluations
mixed_evaluations[classifier_name][
"correct predictions"] += evaluation_results[0]
mixed_evaluations[classifier_name][
"total predictions"] += evaluation_results[1]
mixed_evaluations[classifier_name]["accuracy"] = (
mixed_evaluations[classifier_name]["correct predictions"] *
1.0) / mixed_evaluations[classifier_name]["total predictions"]
mixed_evaluations[classifier_name]["average accuracy"] = (
(mixed_evaluations[classifier_name]["average accuracy"] * iter)
+ evaluation_results[2]) / ((iter + 1) * 1.0)
mixed_evaluations[classifier_name]["average macro_accuracy"] = (
(mixed_evaluations[classifier_name]["average macro_accuracy"] *
iter) + evaluation_results[3]) / ((iter + 1) * 1.0)
#update the detailed evaluations
detailed_evaluations[classifier_name]["user " + str(user_id)] = {
"correct predictions": evaluation_results[0],
"total predictions": evaluation_results[1],
"accuracy": evaluation_results[2],
"macro_accuracy": evaluation_results[3],
"accuracy by class": evaluation_results[4:]
}
iter += 1
print("user " + str(user_id) + " extracted")
#write the results in a log file
#write the dictionaries into files
out = LogsFileWriter.open(file_name)
LogsFileWriter.write(
"predictions on the classes " + JsonUtils.dict_as_json_str(classes) +
"\n", out)
LogsFileWriter.write("Total scores :\n", out)
LogsFileWriter.write(JsonUtils.dict_as_json_str(mixed_evaluations), out)
LogsFileWriter.write("detailed scores :\n", out)
LogsFileWriter.write(JsonUtils.dict_as_json_str(detailed_evaluations), out)
LogsFileWriter.close(out)
#!/usr/bin/env python from evaluation_on_day_prediction_one_user import evaluation_on_day_prediction_one_user as eodpou print eodpou(1)
#!/usr/bin/env python from evaluation_on_day_prediction_one_user import evaluation_on_day_prediction_one_user as eodpou print eodpou(1)
