batch_size=1000,
n_iter=10,
n_stable=10,
verbose=True)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
# print("fitting model...")
mlp.fit(X_train, y_train)
# print("scoring model...")
# print("predicted:", mlp.predict(X_test))
# print("actual:", y_test)
r2s.append(mlp.score(X_test, y_test))
y_pred = mlp.predict(X_test)
mses.append(mse(y_pred, y_test))
mae_score = mae(y_pred, y_test)
maes.append(mae_score)
# print("MAE score =", mae_score)
accs.append(accuracy_score([[round(y[0])] for y in y_pred],
y_test))
mean_mae = np.mean(maes)
mean_mse = np.mean(mses)
mean_r2 = np.mean(r2s)
mean_acc = np.mean(accs)
model = (mean_mse, mean_mae, mean_r2, mean_acc, dropout_rate,
regularize, learning_rule, kernel_shape[0], kernel_shape[1])
if h % 100000 == 0:
print(h)
print("reshaping data...")
Xs = dict()
ys = dict()
for h in feats:
samples = len(feats[h]) // 384
Xs[h] = np.array(feats[h]).reshape((samples, 6, 8, 8))
ys[h] = np.array(labels[h])
mse_scores = dict()
acc_scores = dict()
for h in feats:
y_pred = mlp.predict(np.array(Xs[h]))
mse_scores[h] = mse(y_pred, ys[h])
acc_scores[h] = accuracy_score([[round(y[0])] for y in y_pred], ys[h])
acc_data = []
mse_data = []
h_data = []
for h in sorted(feats.keys()):
h_data.append(h)
mse_data.append(mse_scores[h])
acc_data.append(1 - acc_scores[h])
# fig, ax1 = plt.subplots()
# fig.suptitle('Tree Height v. Prediction Error')
# plt1 = ax1.plot(h_data, mse_data, color='blue', label='mse')
# ax1.set_ylabel('mean squared regression error (MSE)')
X, y = list(zip(*examples))
X = np.array(X)
y = np.array(y)
del examples
kf = KFold(n=len(X), n_folds=5, shuffle=True, random_state=np.random)
train_index, test_index = next(iter(kf))
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
del X
del y
print("fitting model...")
mlp.fit(X_train, y_train)
print("scoring model...")
# print("predicted:", mlp.predict(X_test))
# print("actual:", y_test)
print("R^2 score =", mlp.score(X_test, y_test))
y_pred = mlp.predict(X_test)
print("MSE score =", mse(y_pred, y_test))
print("MAE score =", mae(y_pred, y_test))
print("accuracy_score =", accuracy_score([[round(y[0])] for y in y_pred], y_test))
fn = os.path.join(settings['data-base'], 'nn_tanh3.pickle')
pickle.dump(mlp, open(fn, 'wb'))