def get_num_and_data(args): if not args.num: print('use test data') pre = Preprocessing('digits') pre.load_data(filename='test.csv', name='test') X_test = torch.tensor(pre.get('test').drop(columns=['0']).values, device=device, dtype=dtype) y_test = torch.tensor(pre.get('test')['0'].values, device=device, dtype=dtype) index = random.randint(0, len(y_test)) print('index {}'.format(index)) num = int(y_test[index].item()) print('real_numer {}'.format(num)) data_to_predict = X_test[index:index + 1, :] return num, data_to_predict else: print('use written images') num = int(args.num) im_imp = Image_Importer('digits') im_imp.load_image_as_grey(num) print('real_numer {}'.format(num)) data_to_predict = im_imp.get_image_as_256px_array(num) return num, data_to_predict
from models import LogReg import matplotlib.pyplot as plt if not __name__ == '__main_': parser = argparse.ArgumentParser(description='Digits') parser.add_argument('--s_model', default=True, help='save trained model') args=parser.parse_args() n_classes = 10 n_epochs = 200 pre = Preprocessing('digits') pre.load_data(filename='train.csv', name='train') X_df = pre.get(name='train').drop(columns=['0']) y_df = pre.get(name='train')['0'] dtype = torch.float device = torch.device("cpu") model_name = 'logreg_digits' model = LogReg(model_name, 256, n_classes) learning_rate = 0.0001 batch_size = 32 train_classifier = TrainClassifier(model, X_df, y_df) trained_model , optimizer, criterion, loss_hist, loss_val_hist, best_param = train_classifier.run_train(n_epochs = n_epochs, lr=learning_rate, batch_size=batch_size)
from models import LogReg if not __name__ == '__main_': parser = argparse.ArgumentParser(description='IMDBData') parser.add_argument('--n_feat', default=1000, help='number of features') parser.add_argument('--s_model', default=False, help='save trained model') args=parser.parse_args() pre = Preprocessing('IMDB') n_classes = 2 n_features = int(args.n_feat) n_epochs = 100 pre.load_data(filename=f'training_data_{n_features}.csv', name='training_data') X_df = pre.get(name='training_data').drop(columns=['target']) y_df = pre.get(name='training_data')['target'] model = LogReg('log_reg', n_features, n_classes) train_classifier = TrainClassifier(model, X_df, y_df) trained_model, optimizer, criterion, loss_hist, loss_validate_hist = train_classifier.run_train(n_epochs = n_epochs) pre.save_results(loss_hist, loss_validate_hist, f'log_reg_{100}') m_exporter = ModelExporter('IMDB') m_exporter.save_nn_model(trained_model, optimizer, n_features, n_classes, n_epochs) ##teeeeeest part pre.load_data(filename=f'test_data_{n_features}.csv', name='test_data')
import pickle import torch import torch.nn as nn from preprocessing_utils import Preprocessing if not __name__ == '__main_': pre = Preprocessing('digits') pre.load_data(filename='test.csv', name='test') dtype = torch.float device = torch.device("cpu") X_test = torch.tensor(pre.get('test').drop(columns=['0']).values, device=device, dtype=dtype) y_test = torch.tensor(pre.get('test')['0'].values, device=device, dtype=dtype) #print(y_test) filename = "../data/digits/softmax_torch_digits_model.sav" model = pickle.load(open(filename, 'rb')) y_pred = model(X_test) softmax = torch.nn.Softmax(dim=1) y_pred = softmax(y_pred).argmax(1) #print(y_pred)
from preprocessing_utils import Preprocessing import torch import argparse if not __name__ == '__main_': parser = argparse.ArgumentParser(description='IMDBData') parser.add_argument('--n_feat', default=1000, help='number of features') args = parser.parse_args() n_feat = args.n_feat pre = Preprocessing('IMDB') pre.load_data(filename=f'test_data_{n_feat}.csv', name='test_data') X_test_df = pre.get(name='test_data').drop(columns=['target']) y_test_df = pre.get(name='test_data')['target'] dtype = torch.float device = torch.device("cpu") X_test = torch.tensor(X_test_df.values, device=device, dtype=dtype) y_test = torch.tensor(y_test_df.values, device=device, dtype=torch.long) n_features = X_test.size()[1] name = f'logreg_{n_features}.pt' model = pre.load_model(name) y_pred = model(X_test).argmax(1) accuracy_soft = (y_pred == y_test).float().mean() #results = (y_test == y_pred)
from preprocessing_utils import Preprocessing, ModelImporter import torch if not __name__ == '__main_': pre = Preprocessing('digits') pre.load_data(filename='test_encoded.csv', name='test') X_df = pre.get(name='test').drop(columns=['0']) y_df = pre.get(name='test')['0'] dtype = torch.float device = torch.device("cpu") model_name = 'logreg_digits_encoded' m_importer = ModelImporter('digits') model = m_importer.load_nn_model(model_name, 0, 10, 200) X_test = model.reshape_data(torch.tensor(X_df.values, device=device, dtype=dtype)) y_test = torch.tensor(y_df.values, device=device, dtype=torch.long) y_pred = model(X_test).argmax(1) accuracy_soft = (y_pred == y_test).float().mean()
from models import LogReg import matplotlib.pyplot as plt if not __name__ == '__main_': parser = argparse.ArgumentParser(description='Digits') parser.add_argument('--s_model', default=True, help='save trained model') args = parser.parse_args() n_classes = 10 n_epochs = 200 pre = Preprocessing('digits') pre.load_data(filename='train_encoded.csv', name='train') X_df = pre.get(name='train').drop(columns=['0']) y_df = pre.get(name='train')['0'] dtype = torch.float device = torch.device("cpu") print(len(X_df.columns)) model_name = 'logreg_digits_encoded' model = LogReg(model_name, len(X_df.columns), n_classes) learning_rate = 0.0001 batch_size = 32
from preprocessing_utils import Preprocessing from sklearn.model_selection import train_test_split if not __name__ == '__main_': pre_train = Preprocessing('digits') kwarg = {'header': None, 'sep': ' '} pre_train.load_data(filename='zip.train', name='raw', **kwarg) pre_train.cleanup(name='raw', drop_duplicates=True, dropna={ 'axis': 1, 'thresh': 2 }) print(pre_train.get('clean').head()) #classes = ['0_0.0', '0_1.0', '0_2.0', '0_3.0', '0_4.0', '0_5.0', '0_6.0', '0_7.0', '0_8.0', '0_9.0'] X = pre_train.get('clean').drop(columns=[0]) y = pre_train.get('clean')[0] pre_train.set(name='train', value=pre_train.get('clean')) pre_train.save(name='train') pre_test = Preprocessing('digits') kwarg = {'header': None, 'sep': ' '} pre_test.load_data(filename='zip.test', name='raw', **kwarg) pre_test.cleanup(name='raw',