Exemplos de write_file em Python

Exemplo n.º 1

#Y Scaled
Y_train_scaled = np.log1p(Y_train)
Y_val_scaled = np.log1p(Y_val)

# Y_train_scaled = Y_train
# Y_val_scaled = Y_val

print('Training Models')
model.fit(X_train, Y_train_scaled)
model_val_pred = model.predict(X_val)
model_pred = np.expm1(model.predict(X_test))
score = MAPE_score(Y_val_scaled, model_val_pred, Y_inv_transform_fn=np.expm1)
print('\n V Model Score: {:.4f} ({:.4f})\n'.format(score.mean(), score.std()))

Sub.append(part_sub(test_ID, model_pred))
mapes.append(score)

if not (val and tcv and cv):
    write_file(Sub, 'hdb_whole', date=False)
mapes = np.array(mapes)
cv_mapes = np.array(cv_mapes)
tcv_mapes = np.array(tcv_mapes)
print(tcv_mapes)
print(cv_mapes)
print(mapes)
print('Model TCV MAPE on training dataset: {} ({})'.format(
    tcv_mapes.mean(), tcv_mapes.std()))
print('Model CV MAPE on training dataset: {} ({})'.format(
    cv_mapes.mean(), cv_mapes.std()))
print('MAPE on validation dataset: {} ({})'.format(mapes.mean(), mapes.std()))

Exemplo n.º 2

Arquivo: Private_whole.py Projeto: Soufedd/ML2_pr

#Y Scaled
Y_train_scaled = np.log1p(Y_train)
Y_val_scaled = np.log1p(Y_val)

# Y_train_scaled = Y_train
# Y_val_scaled = Y_val

print('Training Models')
model.fit(X_train, Y_train_scaled)
model_val_pred = model.predict(X_val)
model_pred = np.expm1(model.predict(X_test))
score = MAPE_score(Y_val_scaled, model_val_pred, Y_inv_transform_fn=np.expm1)
print('\n V Model Score: {:.4f} ({:.4f})\n'.format(score.mean(), score.std()))

Sub.append(part_sub(test_ID, model_pred))
mapes.append(score)

if not (val and tcv and cv):
    write_file(Sub, 'private_whole', date=False)
mapes = np.array(mapes)
cv_mapes = np.array(cv_mapes)
tcv_mapes = np.array(tcv_mapes)
print(tcv_mapes)
print(cv_mapes)
print(mapes)
print('Model TCV MAPE on training dataset: {} ({})'.format(
    tcv_mapes.mean(), tcv_mapes.std()))
print('Model CV MAPE on training dataset: {} ({})'.format(
    cv_mapes.mean(), cv_mapes.std()))
print('MAPE on validation dataset: {} ({})'.format(mapes.mean(), mapes.std()))

Exemplo n.º 3

  print('Training Models')
  model.fit(X_train, Y_train_scaled)
  model_val_pred = np.expm1(model.predict(X_val))-constant
  model_pred = np.expm1(model.predict(X_test[:,:-1]))-constant+X_test[:,-1]
  score = MAE_score(Y_val_scaled, model_val_pred)
  print('\n V Model Score: {:.4f} ({:.4f})\n'.format(score.mean(), score.std()))


  Sub.append(part_sub(test_ID, model_pred))
  mapes.append(score)


if not (val and tcv and cv):
  write_file(Sub, 'hdb', date = False)
mapes = np.array(mapes)
cv_mapes = np.array(cv_mapes)
tcv_mapes = np.array(tcv_mapes)
print(tcv_mapes)
print(cv_mapes)
print(mapes)
print('Model TCV MAE on training dataset: {} ({})'.format(tcv_mapes.mean(),tcv_mapes.std()))
print('Model CV MAE on training dataset: {} ({})'.format(cv_mapes.mean(),cv_mapes.std()))
print('MAE on validation dataset: {} ({})'.format(mapes.mean(),mapes.std()))

Exemplo n.º 4

Arquivo: HDB_town.py Projeto: Soufedd/ML2_pr

        # Y_train_scaled = Y_train
        # Y_val_scaled = Y_val

        print('Training Models')
        model.fit(X_train, Y_train_scaled)
        model_val_pred = model.predict(X_val)
        model_preds.append(np.expm1(model.predict(X_test)))
        score = MAPE_score(Y_val_scaled,
                           model_val_pred,
                           Y_inv_transform_fn=np.expm1)
        vs.append(score)
        print('\n V Model {} Score: {:.4f} ({:.4f})\n'.format(
            m + 1, score.mean(), score.std()))

    Sub.append(part_sub(test_ID, model_preds[np.argmin(vs)]))
    mapes.append(np.min(vs))

if not (val and tcv and cv):
    write_file(Sub, 'hdb_town', date=False)
mapes = np.array(mapes)
cv_mapes = np.array(cv_mapes)
tcv_mapes = np.array(tcv_mapes)
print(tcv_mapes)
print(cv_mapes)
print(mapes)
print('Model TCV MAPE on training dataset: {} ({})'.format(
    tcv_mapes.mean(), tcv_mapes.std()))
print('Model CV MAPE on training dataset: {} ({})'.format(
    cv_mapes.mean(), cv_mapes.std()))
print('MAPE on validation dataset: {} ({})'.format(mapes.mean(), mapes.std()))

Exemplo n.º 5

        # Y_train_scaled = Y_train
        # Y_val_scaled = Y_val

        print('Training Models')
        model.fit(X_train, Y_train_scaled)
        model_val_pred = model.predict(X_val)
        model_preds.append(np.expm1(model.predict(X_test)))
        score = MAPE_score(Y_val_scaled,
                           model_val_pred,
                           Y_inv_transform_fn=np.expm1)
        vs.append(score)
        print('\n V Model {} Score: {:.4f} ({:.4f})\n'.format(
            m + 1, score.mean(), score.std()))

    Sub.append(part_sub(test_ID, model_preds[np.argmin(vs)]))
    mapes.append(np.min(vs))

if not (val and tcv and cv):
    write_file(Sub, 'private_town', date=False)
mapes = np.array(mapes)
cv_mapes = np.array(cv_mapes)
tcv_mapes = np.array(tcv_mapes)
print(tcv_mapes)
print(cv_mapes)
print(mapes)
print('Model TCV MAPE on training dataset: {} ({})'.format(
    tcv_mapes.mean(), tcv_mapes.std()))
print('Model CV MAPE on training dataset: {} ({})'.format(
    cv_mapes.mean(), cv_mapes.std()))
print('MAPE on validation dataset: {} ({})'.format(mapes.mean(), mapes.std()))