def main(filename, category_filename, answer_col, predictor_col, hidden_nodes): df = pd.read_csv(filename, usecols=[answer_col, predictor_col]) categories = pd.read_csv(category_filename, usecols=[predictor_col])[predictor_col].values vectorizer = Vectorizer(df, categories, predictor_col, answer_col) vectorizer.format(0.6, 0.2) batch_size = 1000 epocs = 50 learning_rate = 1e-3 model = build_and_train(vectorizer, batch_size, epocs, learning_rate, hidden_nodes) validate(model, vectorizer) joblib.dump(model, filename + '.joblib')
batch_size = 1000 epocs = 50 learning_rate = 1e-3 model = build_and_train(vectorizer, batch_size, epocs, learning_rate, hidden_nodes) validate(model, vectorizer) joblib.dump(model, filename + '.joblib') # main(filename, category_filename, answer_col, predictor_col, 600) df = pd.read_csv(filename, usecols=[answer_col, predictor_col]) categories = pd.read_csv(category_filename, usecols=[predictor_col])[predictor_col].values vectorizer = Vectorizer(df, categories, predictor_col, answer_col) vectorizer.format(0.6, 0.2) # interactive_test(filename, vectorizer ) test_cases = [['Amie Adams', 'Amy Adams'], ['Michael Fox', 'Michael J. Fox'], ['Minny Driver', 'Minnie Driver'], ['BLair Underwood', 'Blair Underwood'], ['Ralph Finnes', 'Ralph Fiennes'], ['Kate Blanchette', 'Cate Blanchett'], ['Joakin Pheonix', 'Joaquin Phoenix'], ['Ane Hathaway', 'Anne Hathaway'], ['Mickey Rorke', 'Mickey Rourke'], ['Collin Farrell', 'Colin Farrell'], ['Ben Stiler', 'Ben Stiller'], ['Cate Winslet', 'Kate Winslet'], ['John Hawks', 'John Hawkes'], ['George Cloney', 'George Clooney'], ['Cathlene Turner', 'Kathleen Turner'],