def classify_button_multiple(self):
address = self.address_display.get()
if len(address) < 1:
messagebox.showinfo("Error", "Please choose a dataset")
else:
importObject = ImportAndClean()
simple_predictor = KNNClassifier()
# Check User Input
file_name = "sample_input.txt"
self.output.delete(0.0, END)
result = simple_predictor.buffer_for_prediction(
importObject.clean_for_prediction(address, True))
self.save_results(result)
self.output.insert(0.0, result)
def classify_button_single(self):
entered_text = self.textentry.get()
# print(entered_text)
if len(entered_text) < 1:
messagebox.showinfo("Error", "Please enter a headline")
else:
self.output.delete(0.0, END)
importObject = ImportAndClean()
simple_predictor = KNNClassifier()
# Check User Input
# test_string = "Justin Bieber arrested after concert"
result = simple_predictor.buffer_for_prediction(
importObject.clean_for_prediction(entered_text, False))
self.save_results(result)
self.output.insert(0.0, result)
def train_button(self):
entered_text = self.textentry.get()
# print(entered_text)
if len(entered_text) == 0:
messagebox.showinfo("Error", "Please enter a relevant value")
elif float(entered_text) > 0.31:
messagebox.showinfo("Error", "Enter a value smaller than 0.3")
else:
self.output.delete(0.0, END)
if len(self.address_display.get()) < 1:
messagebox.showinfo("Error", "Please choose a dataset")
else:
importObject = ImportAndClean(self.address_display.get())
text_column = importObject.get_text_column()
numeric_column = importObject.get_label_column()
trainer = NBModelBuilder(text_column, numeric_column,
float(entered_text))
self.output.insert(END, trainer.train())
from Classifier_Code.back_import_clean import ImportAndClean
from Classifier_Code.back_train_get_accuracy_multinaivebayes import NBModelBuilder
from Classifier_Code.back_classify_multinaivebayes import NBClassifier
from Classifier_Code.back_train_get_accuracy_knn import KNNModelBuilder
from Classifier_Code.back_classify_knn import KNNClassifier
if __name__ == '__main__':
# Check User Input
# test_string = "Justin Bieber arrested after concert"
test_string = "All the Times Ryan Reynolds And Blake Lively Roasted Each Other"
# file_name = "sample_input.txt"
# KNN
# KNN Train
importObject = ImportAndClean()
text_column = importObject.get_text_column()
numeric_column = importObject.get_label_column()
# KNN Training and Testing
knn_object_train = KNNModelBuilder(text_column, numeric_column, 0.1)
print(knn_object_train.train())
# KNN Classify
# knn_object_classify = KNNClassifier()
# print(knn_object_classify.buffer_for_prediction(
# importObject.clean_for_prediction(test_string, False)))
# print(knn_object_classify.buffer_for_prediction(
# importObject.clean_for_prediction(file_name, True)))