def analyse_func(self):
"""
variables to change:
result_land
self.result_land = magic(input_text)
just copy the above code under the input_text variable
"""
try:
input_text = self.text_box.get(1.0, tkinter.END)
self.result_country = writing_style_analyzer.predict_geo_location(input_text, 'data/model/')
self.open_results()
except ZeroDivisionError as e:
print(e)
def main():
count = 0
true_positive_count = 0
country_occurrences = {}
test = []
prediction = []
test_country_list = []
used_countries = pickle.load(open("../data/model/used_countries", "rb"))
result_file = open("../result/result_stat.csv", "w")
for file_name in os.listdir("../data/test_pickles"):
if file_name == ".DS_Store":
continue
if os.path.isdir("../data/test_pickles" + "/" + file_name):
continue
test_country_list.append(file_name)
page = pickle.load(open("../data/test_pickles/" + file_name, "rb"))
# Iterate over all revisions, predict the geo-location and count the positive occurrences
for revision in page.revisions:
test_content = revision.diff_content
# country was not trained
test_country = revision.country
if test_country not in used_countries:
continue
predicted_geo_location = predict_geo_location(test_content)
test.append(revision.country)
prediction.append(predicted_geo_location)
local_true_positive_count = 0
if predicted_geo_location == revision.country:
true_positive_count += 1
local_true_positive_count = 1
# Also count the occurrences for each geo-location
if predicted_geo_location in country_occurrences:
country_occurrences[predicted_geo_location] = [
country_occurrences[predicted_geo_location][0] + 1,
country_occurrences[predicted_geo_location][1] + local_true_positive_count,
]
else:
country_occurrences[predicted_geo_location] = [1, local_true_positive_count]
count += 1
print("Processed: " + page.title)
# Output results
print()
print(12 * "-" + " Result " + 12 * "-")
print()
trained_data_stat_to_csv()
print()
print("Countries with [Positive Count, True Positive Count]")
print()
result_file.write("country, positive_count, true_positive_count\n")
for country, amount in country_occurrences.items():
print(country + ": " + str(amount))
result_file.write(country + ", " + str(amount[0]) + ", " + str(amount[1]) + "\n")
result_file.write("Total, " + str(count) + ", " + str(true_positive_count) + "\n")
result_file.write("F-Score, " + str(f1_score(test, prediction, average="macro") * 100) + ", " + str(-1) + "\n")
result_file.close()
test_countries_to_file(test_country_list)
print()
print("Count Revisions: " + str(count))
print("True positive count: {0}".format(str(true_positive_count)))
print("Accuracy: %.4f" % ((true_positive_count / count) * 100) + "%")
print("F1-Score with macro-average: %.4f" % (f1_score(test, prediction, average="macro") * 100) + "%")
cm = confusion_matrix(test, prediction, test_country_list)
pl.matshow(cm)
pl.title("Confusion Matrix")
pl.colorbar()
pl.ylabel("True label")
pl.xlabel("Predicted label")
pl.savefig("../result/cm.png")
pl.xlabel("Predicted label \n\n" + str(test_country_list))
pl.show()