def run(self): scrapper = Scrapper() global folder_path global test_df test_df = generate_test_data(self.username, self.threshold) folder_path = scrapper.dowload_data(self.username, self.threshold) #user_account="skyemcalpine" folder_path = folder_path.replace("\\", "/") print(folder_path) self.signals.result.emit(True)
def test(self,username,threshold): scrapper=Scrapper() folder_path=scrapper.dowload_data(username,threshold) dataProcessor=DataProcessor(folder_path) data=dataProcessor.create_dataframe_input() #print(data) class_names=['food and drink', 'entertainment', 'business and industry', 'family and relationships', 'fitness and wellness', 'hobbies and activities', 'shopping and fashion', 'sports and outdoors', 'technology'] model_path="./last_cnn_model.h5" cnnModel=CnnModel(class_names,model_path,data) model=cnnModel.load_model() test_generator=cnnModel.create_generator() prediction=cnnModel.getPrediction(model,test_generator) result=np.sum(prediction,axis=0) result*=(1/len(prediction)) return result
from scrapper import Scrapper from dataProcessor import DataProcessor from cnnModel import CnnModel import os import numpy as np scrapper = Scrapper() username = "******" threshold = 3 folder_path = scrapper.dowload_data(username, threshold) dataProcessor = DataProcessor(folder_path) data = dataProcessor.create_dataframe_input() print(data) class_names = [ 'food and drink', 'entertainment', 'business and industry', 'family and relationships', 'fitness and wellness', 'hobbies and activities', 'shopping and fashion', 'sports and outdoors', 'technology' ] model_path = "./last_cnn_model.h5" cnnModel = CnnModel(class_names, model_path, data) #cnnModel.visualise_data() model = cnnModel.load_model() test_generator = cnnModel.create_generator() prediction = cnnModel.getPrediction(model, test_generator) print(prediction) #prediction=[[0.2,0.5,0.3],[0.4,0.3,0.3]] result = np.sum(prediction, axis=0) result *= (1 / len(prediction)) print(result)