def predict_with_dwt(dataset, testnum, featurenum):
ca, cd = dwt.dwt(dataset)
ca_matrix = ca[np.newaxis, :]
print('DWT finish.')
x_train, x_test, y_train, y_test = generate_data(ca_matrix,
int(testnum / 2),
featurenum)
min_max_scaler = MinMaxScaler()
x_train = min_max_scaler.fit_transform(x_train)
x_test = min_max_scaler.transform(x_test)
dbn1 = dbn.DBN(x_train=x_train,
y_train=y_train,
x_test=x_test,
y_test=y_test,
hidden_layer=[250],
learning_rate_rbm=0.0005,
batch_size_rbm=150,
n_epochs_rbm=200,
verbose_rbm=1,
random_seed_rbm=500,
activation_function_nn='tanh',
learning_rate_nn=0.005,
batch_size_nn=150,
n_epochs_nn=1500,
verbose_nn=1,
decay_rate=0)
dbn1.pretraining()
dbn1.finetuning()
ca_pred = dbn1.result[:, 0]
print('Lowpass coefficient estimation finish.')
mu, sigma_2, cd_pred = generateData(cd[0:len(cd) - int(testnum / 2)],
outputnum=int(testnum / 2))
print('Highpass coefficient estimation finish.')
dataset_pred = dwt.idwt(ca_pred, cd_pred)
print('IDWT finish.')
dataset_test = dataset[len(dataset) - testnum:len(dataset)]
ca_test, cd_test = dwt.dwt(dataset_test)
plt.figure(figsize=(12, 9), dpi=100)
plt.subplot(3, 1, 1)
plt.plot(ca_test)
plt.plot(ca_pred)
plt.legend(['lowpass_real', 'lowpass_prediction'], loc='upper right')
plt.title('lowpass coefficient prediction result', fontsize=16)
plt.subplot(3, 1, 2)
plt.plot(cd_test)
plt.plot(cd_pred)
plt.legend(['highpass_real', 'highpass_prediction'], loc='upper right')
plt.title('highpass coefficient prediction result', fontsize=16)
plt.subplot(3, 1, 3)
mse = mean_squared_error(dataset_pred, dataset_test)
plt.plot(dataset_test)
plt.plot(dataset_pred)
plt.legend(['dataset_real', 'dataset_prediction'], loc='upper right')
plt.title('sequence prediction result', fontsize=16)
plt.xlabel('MSE = %f' % mse)
plt.draw()
#plt.show()
return dataset_pred, mse
def train_model_func(learning_rate_rbm, learning_rate, batch_size, feature,
label, path_out_png, pred_num, train_deep):
X_train, X_test, Y_train, Y_test = train_test_split(feature,
label,
test_size=0.2,
shuffle=False)
print("Training model...")
print("RMSE (on training data):")
root_mean_squared_errors = []
message_queue = Queue()
for deep in range(1, train_deep + 1):
RMSE_total = 0
for i in range(0, pred_num):
starttime = datetime.datetime.now()
x_train = np.array(X_train[X_train.shape[0] - i -
deep:X_train.shape[0] - i])
y_trian = np.array(Y_train[Y_train.shape[0] - i -
deep:Y_train.shape[0] - i])
x_test = np.array(X_test)
y_test = np.array(Y_test)
_process = Process(target=train_model,
args=(learning_rate_rbm, learning_rate,
batch_size, x_train, y_trian, x_test,
message_queue))
_process.start()
_process.join()
predictions = message_queue.get()
root_mean_squared_error = math.sqrt(
mean_squared_error(y_test, predictions))
endtime = datetime.datetime.now()
print("\t\ti:\t", root_mean_squared_error, "\t\tusing seconds:\t",
(endtime - starttime).seconds)
RMSE_total += root_mean_squared_error
RMSE_avg = RMSE_total / pred_num
root_mean_squared_errors.append(RMSE_avg)
print("train_deep:", deep, "\tRMSE_avg:", RMSE_avg)
# Output a graph of loss metrics over periods.
# plt.subplot(1, 2, 2)
plt.ylabel('RMSE')
plt.xlabel('train_deep')
plt.title("Root Mean Squared Error vs. Train Deep")
plt.tight_layout()
plt.plot(root_mean_squared_errors)
plt.savefig(path_out_png)
print("finished.")
def _print_regressionMetrics(_linear, _X, _y, _predict):
metrics = [['Regresión Lineal', 'Datos obtenidos'],
['Coeficiente', _linear.coef_],
['Interceptación', _linear.intercept_],
['Calificación (score)', _linear.score(_X, _y)],
['Variance Score', r2_score(_y, _predict)],
['Explained Variance Score', explained_variance_score(_y, _predict)],
['Mean Squared Error', mean_squared_error(_y, _predict)],
['Mean Absolute Error', mean_absolute_error(_y, _predict)], ]
print('\nMinería de Datos - Regresión Lineal - <VORT>', '\n')
print(_linear, '\n')
print(look(metrics))
def neural_net_2(train, test, val, train_out, test_out, val_out, BigSigma_inv):
clf = MLPClassifier(solver='sgd',
alpha=1e-5,
hidden_layer_sizes=(100, 1),
activation='logistic',
batch_size=BATCH_HUMAN,
shuffle=True,
max_iter=5000)
scaler = StandardScaler()
scaler.fit(train)
train1 = scaler.transform(train)
# apply same transformation to test data
test = scaler.transform(test)
train_out = train_out.astype(float)
clf.fit(X=train1, y=train_out)
predict_test = clf.predict(test)
predict_val = clf.predict(val)
print("TEST ERMS ACCURACY", mean_squared_error(test_out, predict_test),
acc_manual(test_out, predict_test))
print("VAL ERMS ACCURACY", mean_squared_error(val_out, predict_val),
acc_manual(val_out, predict_test))
def run_gradient_desc_gsc_con():
train, test, val, train_out, test_out, val_out, BigSigma_inv = process_gsc__data_con(
)
Weight_final = np.ones((31, ), dtype=float64)
erms = []
for start in range(0, len(train), BATCH):
end = start + BATCH
phi_train = getphi(train[start:end], BigSigma_inv)
phi_train = np.insert(phi_train, 0, 1, 1)
hx = phi_train.dot(Weight_final)
hx = hx.astype(np.int64)
Weight_final = gradient_desc(phi_train, hx, Weight_final,
train_out[start:end])
hx = phi_train.dot(Weight_final)
hx = hx.astype(np.int64)
Gx = getGX(hx)
phi_test = getphi(test, BigSigma_inv)
phi_test = np.insert(phi_test, 0, 1, 1)
hx_test = phi_test.dot(Weight_final)
hx_test = hx_test.astype(np.int64)
Gx_test = getGX(hx_test)
phi_val = getphi(val, BigSigma_inv)
phi_val = np.insert(phi_val, 0, 1, 1)
hx_val = phi_val.dot(Weight_final)
hx_val = hx_val.astype(np.int64)
Gx_val = getGX(hx_val)
print(
"=========================Logistic Regression using Gradient Descent for Concatenated GSC Data==========================="
)
print("ERMS Train", mean_squared_error(train_out[start:end], Gx),
"Accuracy", acc_manual(train_out[start:end], Gx))
print("ERMS Test", mean_squared_error(test_out, Gx_test), "Accuracy",
acc_manual(test_out, Gx))
print("ERMS VAL", mean_squared_error(val_out, Gx_val), "Accuracy",
acc_manual(val_out, Gx_val))
def run_logistic_human_sub(train, test, val, train_out, test_out, val_out,
BigSigma_inv):
Weight_final = np.ones((CENTERS + 1, ), dtype=float64)
erms = []
for start in range(0, len(train), BATCH_HUMAN):
end = start + BATCH_HUMAN
phi_train = getphi(train[start:end], BigSigma_inv)
phi_train = np.insert(phi_train, 0, 1, 1)
hx = phi_train.dot(Weight_final)
hx = hx.astype(np.int64)
Weight_final = gradient_desc(phi_train, hx, Weight_final,
train_out[start:end])
hx = phi_train.dot(Weight_final)
hx = hx.astype(np.int64)
Gx = getGX(hx)
phi_test = getphi(test, BigSigma_inv)
phi_test = np.insert(phi_test, 0, 1, 1)
hx_test = phi_test.dot(Weight_final)
hx_test = hx_test.astype(np.int64)
Gx_test = getGX(hx_test)
phi_val = getphi(val, BigSigma_inv)
phi_val = np.insert(phi_val, 0, 1, 1)
hx_val = phi_val.dot(Weight_final)
hx_val = hx_val.astype(np.int64)
Gx_val = getGX(hx_val)
print(
"=========================Logistic Regression using Gradient Descent for Subtracted HOD Data==========================="
)
print("ERMS Train", mean_squared_error(train_out[start:end], Gx),
"Accuracy", acc_manual(train_out[start:end], Gx))
print("ERMS Test", mean_squared_error(test_out, Gx_test), "Accuracy",
acc_manual(test_out, Gx))
print("ERMS VAL", mean_squared_error(val_out, Gx_val), "Accuracy",
acc_manual(val_out, Gx_val))
def score(self, curr_sk_ids=[]):
""" This method take current ids and return Previous application score
"""
orig_data = pd.read_csv(self.path_to_data_store + '/previous_application.csv')
orig_data = orig_data.loc[ (orig_data['SK_ID_CURR'].isin(curr_sk_ids)) & (orig_data['FLAG_LAST_APPL_PER_CONTRACT'] == 'Y') \
& (orig_data['NAME_CONTRACT_STATUS'] != 'Canceled')]
sk_ids_from_payments = orig_data['SK_ID_CURR']
orig_data['NAME_CONTRACT_STATUS'].replace(self.target_map, inplace=True)
test_data = self.__curate__(orig_data)
y_test = test_data['NAME_CONTRACT_STATUS']
X_test = test_data.drop(['NAME_CONTRACT_STATUS'], axis=1)
preds = self.model.predict(X_test)
print('MSE Score : ', mean_squared_error(y_test, preds))
return self.__adjustDuplicates__(preds, sk_ids_from_payments, curr_sk_ids)
def evaluate(ytrue, ypred):
"""
:param ytrue: true value of the dependent variable, numpy array
:param ypred: predictions for the dependent variable, numpy array
:return: different evaluation metrics: R squared, mean square error, mean absolute error, and fraction of variance
explained and Spearman ranking correlation coefficient
"""
r2 = r2_score(ytrue, ypred)
mse = mean_squared_error(ytrue, ypred)
mae = mean_absolute_error(ytrue, ypred)
variance_explained = explained_variance_score(ytrue, ypred)
spearman = spearmanr(ytrue, ypred)[0]
return r2, mse, mae, variance_explained, spearman
def test_DBN(finetune_lr=0.1,
pretraining_epochs=100,
pretrain_lr=0.01,
k=1,
training_epochs=100,
dataset=3,
batch_size=10,
layers=[1000, 1000, 1000]):
# title
temp_title = [
"DNA Methylation", "Gene Expression HTSeq", "miRNA Expression"
]
print("\nSurvival Rate Regression with " + temp_title[dataset - 1] +
" (Tensorflow)\n")
# Loading dataset
X, Y = load_data(dataset)
# Splitting data
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=0.2,
random_state=1337)
# Data scaling
min_max_scaler = MinMaxScaler()
X_train = min_max_scaler.fit_transform(X_train)
# Training
regressor = SupervisedDBNRegression(hidden_layers_structure=layers,
learning_rate_rbm=pretrain_lr,
learning_rate=finetune_lr,
n_epochs_rbm=pretraining_epochs,
n_iter_backprop=training_epochs,
batch_size=batch_size,
activation_function='relu')
regressor.fit(X_train, Y_train)
# Test
X_test = min_max_scaler.transform(X_test)
Y_pred = regressor.predict(X_test)
try:
print('Done.\nR-squared: %f\nMSE: %f' %
(r2_score(Y_test, Y_pred), mean_squared_error(Y_test, Y_pred)))
except Exception as e:
print(
"Infinity or a value too large for dtype('float32'). Please try different layer settings."
)
def update_mse(tmp_input_element, tmp_list):
data_train, label_train, data_test, label_test = \
HSMemory.create_train_and_test_data(tmp_list, tmp_input_element.number_visible_input)
tmp_regression = SupervisedDBNRegression(
hidden_layers_structure=[
tmp_input_element.number_visible_input,
tmp_input_element.number_hidden_input
],
learning_rate_rbm=tmp_input_element.learning_rate_rbm,
learning_rate=tmp_input_element.learning_rate,
n_epochs_rbm=tmp_input_element.n_epochs_rbm,
n_iter_backprop=tmp_input_element.n_iter_back_prop,
contrastive_divergence_iter=tmp_input_element.
contrastive_divergence_iter,
batch_size=tmp_input_element.batch_size,
activation_function=tmp_input_element.activation_function,
n_hidden_layers_mlp=tmp_input_element.n_hidden_layers_mlp,
cost_function_name=tmp_input_element.cost_function_name)
tmp_regression.fit(data_train, label_train) # train data
tmp_input_element.train_mse = sum(
tmp_regression.train_loss) / HSElement.config_n_iter_back_prop
y_pred_test = tmp_regression.predict(data_test)
check_nan = np.isnan(y_pred_test).any()
if check_nan:
tmp_input_element.test_mse = 1000
else:
tmp_input_element.test_mse = mean_squared_error(
label_test, y_pred_test)
if np.isnan(tmp_input_element.train_mse) or np.isinf(
tmp_input_element.train_mse):
tmp_input_element.train_mse = 1000
# add to export result
tmp_result_data = [
tmp_input_element.learning_rate_rbm,
tmp_input_element.learning_rate,
tmp_input_element.number_visible_input,
tmp_input_element.number_hidden_input, tmp_input_element.train_mse,
tmp_input_element.test_mse, '', '', '', '', '', '', '', ''
]
TensorGlobal.followHs.append(tmp_result_data)
TensorGlobal.sessFlg = True
tf.reset_default_graph()
del tmp_regression
return tmp_input_element
def get_regression_metrics(ground_truth_value, predicted_value):
regression_metric_dict = dict({})
regression_metric_dict['r2_score'] = r2_score(ground_truth_value,
predicted_value)
regression_metric_dict['mean_squared_error'] = mean_squared_error(
ground_truth_value, predicted_value)
#regression_metric_dict['mean_squared_log_error'] = mean_squared_log_error(ground_truth_value, predicted_value)
regression_metric_dict['mean_absolute_error'] = mean_absolute_error(
ground_truth_value, predicted_value)
regression_metric_dict[
'explained_variance_score'] = explained_variance_score(
ground_truth_value, predicted_value)
regression_metric_dict['median_absolute_error'] = median_absolute_error(
ground_truth_value, predicted_value)
regression_metric_dict['max_error'] = max_error(ground_truth_value,
predicted_value)
return regression_metric_dict
def read_input_data():
X_train, Y_train, X_test, Y_test = normalize_data()
rng = numpy.random.RandomState(123)
# print(normalized_X_train.values, normalized_Y_train)
# print(normalized_X_train.shape, normalized_Y_train.shape)
dbn = DBN(input=X_train,
label=Y_train,
n_ins=X_train.shape[1],
hidden_layer_sizes=[80] * 10,
n_outs=1,
rng=rng)
dbn.pretrain(lr=0.001, k=1, epochs=1000)
dbn.finetune(lr=0.001, epochs=200)
resutls = dbn.predict(X_test)
print("results", resutls, Y_test)
print(Y_test.shape)
print(r2_score(resutls, Y_test), mean_squared_error(resutls, Y_test))
print(mean_absolute_error(resutls, Y_test))
def get_resutls_column(model, trainfolds_dfs, testfolds_dfs, train_set,
test_set, feature_set, target_col_name):
MSEs = [None] * len(testfolds_dfs)
MAEs = [None] * len(testfolds_dfs)
SPs = [None] * len(testfolds_dfs)
PNs = [None] * len(testfolds_dfs)
for i in range(len(testfolds_dfs)):
train_X = trainfolds_dfs[i].loc[:, feature_set].values
train_Y = trainfolds_dfs[i].loc[:, target_col_name].values
test_X = testfolds_dfs[i].loc[:, feature_set].values
test_Y = testfolds_dfs[i].loc[:, target_col_name].values
model.fit(train_X, train_Y)
test_pred = model.predict(test_X)
MAEs[i] = MAE(test_pred, test_Y)
MSEs[i] = MSE(test_pred, test_Y)
SPs[i] = SPC(test_pred, test_Y)
PNs[i] = PNC(test_pred, test_Y)
train_cvavg_MAE = numpy.mean(MAEs)
train_cvavg_MSE = numpy.mean(MSEs)
train_cvavg_PN = numpy.mean(PNs)
train_cvavg_SP = numpy.mean(SPs)
test_Y = test_set.loc[:, target_col].values
test_X = test_set.loc[:, feature_set].values
train_X = train_set.loc[:, feature_set].values
train_Y = train_set.loc[:, target_col].values
model.fit(train_X, train_Y)
test_pred = model.predict(test_X)
testset_pn, _ = pearsonr(test_Y, test_pred)
testset_sp, _ = spearmanr(test_Y, test_pred)
testset_mae = mean_absolute_error(test_Y, test_pred)
testset_mse = mean_squared_error(test_Y, test_pred)
column = [
testset_mae, testset_mse, testset_pn, testset_sp, train_cvavg_MAE,
train_cvavg_MSE, train_cvavg_PN, train_cvavg_SP, feature_set,
len(feature_set)
]
column += list(test_pred)
return column
def score(self):
test_target = None
test_data = pd.read_csv(self.path_to_data_store +
'/application_test.csv')
sk_id_curr = test_data[['SK_ID_CURR']]
if self.score_type == 'actual':
test_data = self.createEnsembleData(test_data[['SK_ID_CURR']])
else:
test_target = test_data['TARGET']
test_data = self.createEnsembleData(test_data[['SK_ID_CURR']])
score = self.ensemble.predict(test_data)
if test_target is not None:
print(mean_squared_error(test_target.values, score))
output = self.format_output(sk_id_curr, score, test_target)
output.to_csv('submission.csv', index=False)
else:
output = self.format_output(sk_id_curr, score)
output.to_csv('submission.csv', index=False)
def test_DBN(finetune_lr=0.1,
pretraining_epochs=100,
pretrain_lr=0.01,
training_epochs=100,
dataset=6, batch_size=10,
layers=[1000, 1000, 1000],
dropout=0.2,
pca=2,
optimizer=1):
# title
temp_title = ["DNA Methylation Platform GPL8490",
"DNA Methylation Platform GPL16304",
"Gene Expression HTSeq Count",
"Gene Expression HTSeq FPKM",
"Gene Expression HTSeq FPKM-UQ",
"miRNA Expression"]
print("\nSurvival Rate Regression with " + temp_title[dataset-1] + " (Tensorflow)\n")
# Loading dataset
X, Y = load_data(dataset, pca)
# Splitting data
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=1337)
# Training
regressor = SupervisedDBNRegression(hidden_layers_structure=layers,
learning_rate_rbm=pretrain_lr,
learning_rate=finetune_lr,
n_epochs_rbm=pretraining_epochs,
n_iter_backprop=training_epochs,
batch_size=batch_size,
activation_function='relu',
dropout_p=dropout)
regressor.fit(X_train, Y_train)
# Test
Y_pred = regressor.predict(X_test)
Y_pred = numpy.transpose(Y_pred)[0]
Y_pred_train = regressor.predict(X_train)
Y_pred_train = numpy.transpose(Y_pred_train)[0]
print('Done.\nR-squared: %f\nMSE: %f' % (r2_score(Y_test, Y_pred), mean_squared_error(Y_test, Y_pred)))
print('Done. \ntraining R-squared: %f\nMSE: %f' % (r2_score(Y_train, Y_pred_train), mean_squared_error(Y_train, Y_pred_train)))
def test_data(self):
housing_data = self.strat_test_set
X = housing_data.drop("median_house_value", axis=1)
y = housing_data["median_house_value"].copy()
X_prepared = self.full_pipeline.transform(X)
final_predictions = self.final_model.predict(X_prepared)
final_mse = mean_squared_error(y, final_predictions)
final_rmse = np.sqrt(final_mse)
print('\n')
print('final root mean squared error (RMSE):\n{0}\n'.format(final_rmse))
confidence = 0.95
squared_errors = (final_predictions - y) ** 2
mean = squared_errors.mean()
scale = stats.sem(squared_errors)
m = len(squared_errors)
interval95 = np.sqrt(stats.t.interval(confidence, m-1, loc=mean,scale=scale))
print('95% confidence interval for the RMSE:\n{0}\n'.format(interval95))
def score(self, X, y, step=1, method="r2"):
"""
Produce multi-step prediction of y, and compute the metrics against y.
Nan is ignored when computing the metrics.
:param array-like X: exogenous input time series, shape = (n_samples,
n_exog_inputs)
:param array-like y: target time series to predict, shape = (n_samples)
:param int step: prediction step.
:param string method: could be "r2" (R Square) or "mse" (Mean Square
Error).
:return: prediction metric. Nan is ignored when computing the metrics.
"""
ypred = self.predict(X, y, step=step)
mask = np.isnan(y) | np.isnan(ypred)
if method == "r2":
return r2_score(y[~mask], ypred[~mask])
elif method == "mse":
return mean_squared_error(y[~mask], ypred[~mask])
def _get_error(self, used, used_bounds):
if self.cv:
cv_errors = []
for train_idx, test_idx, in self.kf.split(self.X):
self._refit_model(
self.types[used], self.bounds[used_bounds],
self.X[train_idx.reshape(-1, 1), used],
self.y[train_idx]) # refit the model every round
pred = self.model.predict(self.X[test_idx.reshape(-1, 1),
used])[0]
cv_errors.append(
np.sqrt(mean_squared_error(self.y[test_idx], pred)))
cve = np.mean(cv_errors)
else:
cve = np.float('inf')
self._refit_model(self.types[used], self.bounds[used_bounds],
self.X[:,
used], self.y) # refit the model every round
oob = self.model.rf.out_of_bag_error()
return oob, cve
def train(self, data, training):
'En esta funcion se realiza 10-Fold CV para entrenar la red con una expansion de entre 20-75%.'
'El algoritmo de entrenamiento es Descenso por Gradiente Estocastico o Extreme Learning Machine.'
# 10-Fold Cross Validation
folds = 10; iters = 10;
kf = KFold(data.shape[0], n_folds=folds)
hiddenNodes = arange(2*data.shape[1])+1
Error_HNodes = []
Nets_HNodes = []
for j in hiddenNodes:
self.setHiddenNodes([j])
Mean_error_iter = []
Mean_nets_iter = []
for train_index, val_index in kf:
X, Xval = data[train_index], data[val_index]
Error_iter = []
Nets_iter = []
for i in np.arange(iters):
self.initialization() # Inicializaciones comunes
if training == 'elm':
Out,H,N = self.sim(X)
H = H[-1]
pseudoinverse = pinv(H)
beta = np.dot(pseudoinverse,X)
self.Weights[-1] = beta
# Validation
Out_val,H_val,N_val = self.sim(Xval)
# Se guarda el error y la red
MSE = [mean_squared_error(Xval,Out_val)]
Networks = [self.Weights]
Error_iter.append(np.min(MSE))
Nets_iter.append(Networks[np.argmin(MSE)])
Mean_error_iter.append(np.mean(Error_iter))
Mean_nets_iter.append(Nets_iter[np.argmin(Error_iter)])
Error_HNodes.append(np.mean(Mean_error_iter))
Nets_HNodes.append(Mean_nets_iter[np.argmin(Mean_error_iter)])
self.Weights = Nets_HNodes[np.argmin(Error_HNodes)]
Final_Error = np.min(Error_HNodes)
selected_Nodes = hiddenNodes[np.argmin(Error_HNodes)]
self.setHiddenNodes([selected_Nodes])
return Final_Error
def fnLinearRegression(TrainData, Target, Title):
regr = linear_model.LinearRegression()
regr.fit(TrainData, Target)
prediction = regr.predict(TrainData)
plt.figure(1)
plt.title(Title)
plt.scatter(TrainData, Target)
plt.plot(TrainData, prediction, color='blue')
plt.show()
''' Find out mean sqs uared error between prediction and target '''
MSE = mean_squared_error(Target, prediction)
R2_Score = r2_score(Target, prediction)
return MSE, regr.coef_, regr.intercept_, R2_Score
def score(self, X, y, method="r2", verbose=False):
"""
Produce multi-step prediction of y, and compute the metrics against y.
Nan is ignored when computing the metrics.
:param array-like X: exogenous input time series, shape = (n_samples,
n_exog_inputs)
:param array-like y: target time series to predict, shape = (n_samples)
:param string method: could be "r2" (R Square) or "mse" (Mean Square
Error).
:return: prediction metric. Nan is ignored when computing the metrics.
"""
ypred = self.predict(X, y)
mask = np.isnan(y) | np.isnan(ypred)
if verbose:
print('Evaluating {} score, {} of {} data points are evaluated.'.
format(method, np.sum(~mask), y.shape[0]))
if method == "r2":
return r2_score(y[~mask], ypred[~mask])
elif method == "mse":
return mean_squared_error(y[~mask], ypred[~mask])
def predict_without_dwt(dataset, testnum, featurenum):
dataset = dataset[np.newaxis, :]
x_train, x_test, y_train, y_test = generate_data(dataset, testnum,
featurenum)
min_max_scaler = MinMaxScaler()
x_train = min_max_scaler.fit_transform(x_train)
x_test = min_max_scaler.transform(x_test)
dbn1 = dbn.DBN(x_train=x_train,
y_train=y_train,
x_test=x_test,
y_test=y_test,
hidden_layer=[250],
learning_rate_rbm=0.0005,
batch_size_rbm=150,
n_epochs_rbm=200,
verbose_rbm=1,
random_seed_rbm=500,
activation_function_nn='tanh',
learning_rate_nn=0.005,
batch_size_nn=150,
n_epochs_nn=1500,
verbose_nn=1,
decay_rate=0)
dbn1.pretraining()
dbn1.finetuning()
dataset_pred = dbn1.result[:, 0]
dataset_test = dataset[0, dataset.shape[1] - testnum:dataset.shape[1]]
mse = mean_squared_error(dataset_pred, dataset_test)
plt.figure(figsize=(12, 9), dpi=100)
plt.plot(dataset_test)
plt.plot(dataset_pred)
plt.legend(['dataset_real', 'dataset_prediction'], loc='upper right')
plt.title('sequence prediction result', fontsize=16)
plt.xlabel('MSE = %f' % mse)
plt.draw()
#plt.show()
return dataset_pred, mse
def regressionSummary(y_true, y_pred):
""" print regression performance metrics
Input:
y_true: actual values
y_pred: predicted values
"""
y_true = np.asarray(y_true)
y_pred = np.asarray(y_pred)
y_res = y_true - y_pred
metrics = [
('Mean Error (ME)', sum(y_res) / len(y_res)),
('Root Mean Squared Error (RMSE)',
math.sqrt(regression.mean_squared_error(y_true, y_pred))),
('Mean Absolute Error (MAE)', sum(abs(y_res)) / len(y_res)),
('Mean Percentage Error (MPE)',
100 * sum(y_res / y_true) / len(y_res)),
('Mean Absolute Percentage Error (MAPE)',
100 * sum(abs(y_res / y_true) / len(y_res))),
]
fmt1 = '{{:>{}}} : {{:.4f}}'.format(max(len(m[0]) for m in metrics))
print('\nRegression statistics\n')
for metric, value in metrics:
print(fmt1.format(metric, value))
def train_model_func(learning_rate_rbm, learning_rate, batch_size, feature, label, path_out_png, pred_num, train_deep):
X_train, X_test, Y_train, Y_test = train_test_split(feature, label, test_size=0.2, shuffle=False)
print("Training model...")
print("RMSE (on training data):")
root_mean_squared_errors = []
for deep in range(1, train_deep + 1):
RMSE_total = 0
for i in range(0, pred_num):
x_train = np.array(X_train[X_train.shape[0] - i - deep:X_train.shape[0] - i])
y_trian = np.array(Y_train[Y_train.shape[0] - i - deep:Y_train.shape[0] - i])
x_test = np.array(X_test)
y_test = np.array(Y_test)
predictions = train_model(learning_rate_rbm=learning_rate_rbm, learning_rate=learning_rate,
batch_size=batch_size, x_train=x_train,
y_trian=y_trian, x_test=x_test)
root_mean_squared_error = math.sqrt(mean_squared_error(y_test, predictions))
print("\t\ti:\t", root_mean_squared_error)
RMSE_total += root_mean_squared_error
RMSE_avg = RMSE_total / pred_num
root_mean_squared_errors.append(RMSE_avg)
print("train_deep:", deep, "\tRMSE_avg:", RMSE_avg)
# Output a graph of loss metrics over periods.
# plt.subplot(1, 2, 2)
plt.ylabel('RMSE')
plt.xlabel('train_deep')
plt.title("Root Mean Squared Error vs. Train Deep")
plt.tight_layout()
plt.plot(root_mean_squared_errors)
plt.savefig(path_out_png)
print("finished.")
from sklearn.preprocessing import MinMaxScaler
from dbn.tensorflow import SupervisedDBNRegression
# Loading dataset
boston = load_boston()
X, Y = boston.data, boston.target
# Splitting data
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)
# Data scaling
min_max_scaler = MinMaxScaler()
X_train = min_max_scaler.fit_transform(X_train)
# Training
regressor = SupervisedDBNRegression(hidden_layers_structure=[100],
learning_rate_rbm=0.01,
learning_rate=0.01,
n_epochs_rbm=20,
n_iter_backprop=200,
batch_size=16,
activation_function='relu')
regressor.fit(X_train, Y_train)
# Test
X_test = min_max_scaler.transform(X_test)
Y_pred = regressor.predict(X_test)
print 'Done.\nR-squared: %f\nMSE: %f' % (r2_score(Y_test, Y_pred), mean_squared_error(Y_test, Y_pred))
X_train = min_max_scaler.fit_transform(X_train)
#print('x_train number: ', X_train[0])
#print('x_test number: ', X_test.shape[1])
#print('Y_train number: ', Y_train[0])
#print('y_test number: ', Y_test.shape[0])
# Training
regressor = SupervisedDBNRegression(hidden_layers_structure=[100],
learning_rate_rbm=0.01,
learning_rate=0.01,
n_epochs_rbm=20,
n_iter_backprop=200,
batch_size=16,
activation_function='relu')
#regressor.fit(X_train, Y_train)
# Save the model
#regressor.save('model_regression_128.pkl')
# Restore it
regressor = SupervisedDBNRegression.load('models/model_regression.pkl')
# Test
X_test = min_max_scaler.transform(X_test)
Y_pred = regressor.predict(X_test)
print('Done.\nR-squared: %f\nMSE: %f' %
(r2_score(Y_test, Y_pred), mean_squared_error(Y_test, Y_pred)))
#print(Y_pred)
#Computing the RMSE def ComputeRMSE(result, predicted) :
tpr
thresholds
# =============================================================================
# REGRESSION EVALUATION METRICS
# =============================================================================
from sklearn.metrics.regression import (r2_score, explained_variance_score,
mean_absolute_error,
median_absolute_error,
mean_squared_error,
mean_squared_log_error)
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
mean_squared_error(y_true, y_pred)
r2_score(y_true, y_pred)
y_true = [[0.5, 1], [-1, 1], [7, -6]]
y_pred = [[0, 2], [-1, 2], [8, -5]]
mean_squared_error(y_true, y_pred)
r2_score(y_true, y_pred)
# =============================================================================
# CLUSTERING EVALUATION METRICS
# =============================================================================
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris = load_iris()
regressor.fit(data_train, label_train)
tmp_train_mse = sum(regressor.train_loss) / RandomRegression.number_iter_backprop
tmp_min_mse_label = 'MORE BAD'
stop_time = time.time()
print("THE TIME FOR TRAINING: " + str((stop_time - start_time)) + ' second')
# Test
start_time = time.time()
Y_pred_test = regressor.predict(data_test)
print("Begin to call mean_squared_error")
check_nan = np.isnan(Y_pred_test).any()
if check_nan:
tmp_test_mse = 1000
else:
tmp_test_mse = mean_squared_error(label_test, Y_pred_test)
if np.isnan(tmp_train_mse) or np.isinf(tmp_train_mse):
tmp_train_mse = 1000
stop_time = time.time()
print("THE TIME FOR TEST: " + str((stop_time - start_time)) + ' second')
TensorGlobal.sessFlg = True
tf.reset_default_graph()
del regressor
tmp_element_data = [tmp_lrr, tmp_lr, RandomRegression.number_visible_input, RandomRegression.number_hidden_input,
tmp_train_mse, tmp_test_mse]
result_data.append(tmp_element_data)
# Export result to excel file
print("Begin to print result")
now = datetime.now()
def build_predictor(fname):
s = []
d1, d2 = 0, 0
for line in open(fname):
s.append(map(float, line.split()))
# s = s[5000:]
# print '--- Error on complete set'
# y = map(lambda u: u[0], s)
# for idx, val in enumerate(["reqtime", "tsafrir", "sgd"]):
# xi = map(lambda u: u[idx+1], s)
# print regression.mean_squared_error(y, xi), val
# accuracy(y, xi)
# s = []
# for i in range(-100,100,3):
# # s.append([7*(i**3)-5*i*i-17*i+8, i,i*i,i*i*i])
# s.append([7*(i**3)-5*i*i-17*i+8, i])
import random
random.shuffle(s)
# s=[ [1, 6,8,9], [5, 6,8,9], [7, 6,8,9], [2, 7,8,9]]
# keyfunc = lambda u: u[0]
# s.sort(key=keyfunc)
print "original data size", len(s)
# mp = {}
# for r in s:
# key, val = r[0], r[1:]
# if not key in mp:
# mp[key] = []
# mp[key].append(val)
# s = []
# for key in mp:
# l = mp[key]
# if len (l) > 1:
# t = reduce(lambda u,v: [i+j for i,j in izip(u,v)], l)
# t = map(lambda u: 1.0*u/len(l), t)
# mp[key] = t
# else:
# mp[key] = l[0]
# s.append([key] + mp[key])
# print "data size later", len(s)
mp = {}
for r in s:
key, val = r[0], tuple(r[1:])
if not val in mp:
mp[val] = []
mp[val].append(key)
s = []
for key in mp:
l = mp[key]
mp[key] = sum(l)/len(l)
s.append([mp[key]] + list(key))
print "data size later", len(s)
X = map(lambda u: u[1:], s)
# X = map(lambda u: [u[1]], s)
y = map(lambda u: u[0], s)
# print len(X), len(set(tuple(u) for u in X))
import itertools
for x in X:
t = []
for i in x:
t.extend([i*i,i**3])
for a,b in itertools.combinations(x, 2):
t.append(a*b)
for a,b,c in itertools.combinations(x, 3):
t.append(a*b*c)
x.extend(t)
# prepare the training and testing data for the model
nCases = len(y)
nTrain = int(np.floor(nCases * 0.8))
trainX = X[:nTrain]
trainY = y[:nTrain]
testX = X[nTrain:]
testY = y[nTrain:]
# print type(X), X[0]
svr = SVR(kernel='linear', C=1.0, epsilon=0.2)
svr = SVR(kernel='rbf', C=1.0, epsilon=0.2, gamma=.0001)
log = LinearRegression(normalize=True)
# train both models
# svr.fit(trainX, trainY)
log.fit(trainX, trainY)
# predict test labels from both models
predLog = log.predict(testX)
# predSvr = svr.predict(testX)
# show it on the plot
# plt.plot(testY, testY, label='true data')
# # plt.plot(testY, predSvr, 'co', label='SVR')
# plt.plot(testY, predLog, 'mo', label='LogReg')
# plt.legend()
# plt.show()
print '--- Error on test set'
meta_mse = regression.mean_squared_error(testY, predLog)
print int(meta_mse), "meta predictor"
# print regression.mean_squared_error(testY, predSvr)
well_estimated = sum([1 if abs(u-v)<2*u else 0 for u,v in izip(testY, predLog)])
print "well estimated, all, percent: ", well_estimated, len(testY), int(100.0*well_estimated/len(testY)), "%"
exit(0)
for idx, val in enumerate(["reqtime", "tsafrir", "sgd"]):
mse = regression.mean_squared_error(testY, map(lambda u: u[idx], testX))
print "%d %.2f %% %s" % (mse, 100*(mse-meta_mse)/mse, val)
path3 = 'modelo_treinamento_2019_11_01_14_10_48_modelo_1_[90, 90, 90].pkl'
pathCompleto = path1 + path2 + path3
regressor = SupervisedDBNRegression.load(pathCompleto)
# Teste
Y_pred = regressor.predict(X_test)
# if conjTreino == 'degrauUnitario.csv':
# Y_pred = Y_pred / 4.6 # 4.62073146825719
r2Score = r2_score(Y_test, Y_pred)
MSE = mean_squared_error(Y_test, Y_pred)
print('\nDone.\nR-squared: %f\nMSE: %f' % (r2Score, MSE))
arquivoResultados = pd.DataFrame(data={
"Arquivo": [conjTreino],
"r2Score": [r2Score],
"MSE": [MSE]
})
arquivoResultados.to_csv(r'./Resultados/resultados_teste_' +
indiceTreinamento + '.csv',
sep=',',
index=False,
mode='a',
header=primeiraExecucao)
def test_DBN(finetune_lr=0.1,
pretraining_epochs=100,
pretrain_lr=0.01, k=1,
training_epochs=100,
dataset=6,
batch_size=10,
layers=[1000, 1000, 1000],
dropout=0.2,
pca=2,
optimizer=1):
# Title
temp_title = ["DNA Methylation Platform GPL8490",
"DNA Methylation Platform GPL16304",
"Gene Expression HTSeq Count",
"Gene Expression HTSeq FPKM",
"Gene Expression HTSeq FPKM-UQ",
"miRNA Expression"]
print("\Survival Rate Regression with " + temp_title[dataset-1] + " (Theano)\n")
#########################
#### PREPARE DATASET ####
#########################
# Load datasets
datasets = load_data(dataset, pca)
# Split dataset into training and test set
train_input_set, test_input_set, train_label_set, test_label_set = train_test_split(datasets[0], datasets[1], test_size=0.25, random_state=100)
# Size of input layer
_, nr_in = train_input_set.shape
# Number of training batches
n_train_batches = train_input_set.shape[0] // batch_size
# cast inputs and labels as shared variable to accelerate computation
train_set_x, train_set_y = shared_dataset(data_xy = (train_input_set,train_label_set))
test_set_x, test_set_y = shared_dataset(data_xy = (test_input_set,test_label_set))
#########################
##### BUILD NN MODEL ####
#########################
print('Build NN Model')
numpy_rng = numpy.random.RandomState(123)
dbn = DBN(numpy_rng=numpy_rng, n_ins=nr_in, hidden_layers_sizes=layers, n_outs=1)
#########################
### PRETRAIN NN MODEL ###
#########################
print('Pretrain NN Model')
# Get the pretraining functions. It is on the amount of the number of layers.
pretraining_fns = dbn.pretraining_functions(train_set_x=train_set_x, batch_size=batch_size, k=k)
# iterate for each RBMs
for i in range(dbn.n_layers):
# iterate for pretraining epochs
for epoch in range(pretraining_epochs):
c = []
# iterate for number of training batches
for batch_index in range(n_train_batches):
# c is a list of monitoring cost per batch for RBM[i]
c.append(pretraining_fns[i](index=batch_index, lr=pretrain_lr))
print('Pre-training layer %i, epoch %d, cost ' % (i, epoch), end=' ')
print(numpy.mean(c, dtype='float64'))
#########################
### FINETUNE NN MODEL ###
#########################
print('Train NN Model')
# Get the training functions.
train_fn = dbn.build_finetune_functions(train_set_x=train_set_x, train_set_y=train_set_y, batch_size=batch_size, learning_rate=finetune_lr, dropout=dropout, optimizer=optimizer)
# iterate for training epochs
for j in range(training_epochs):
# iterate for number of training batches
for minibatch_index in range(n_train_batches):
train_fn(minibatch_index)
#########################
##### TEST NN MODEL #####
#########################
print('Test NN Model')
# Get the test functions.
test_model = dbn.predict(test_set_x=test_set_x, dropout=dropout)
# take the test result
test_predicted_label_set = test_model()
print(test_label_set)
print(test_predicted_label_set)
# accuracy, p, r, f, s
mse = mean_squared_error(test_label_set, test_predicted_label_set)
r2 = r2_score(test_label_set, test_predicted_label_set)
# print results
print("MSE = " + str(mse))
print("R2 = " + str(r2))
momentum = 1e-4
# do weight updates in imperative
for pname, W, G in zip(cost_classification.list_arguments(),
executor.arg_arrays, executor.grad_arrays):
# Don't update inputs
# MXNet makes no distinction between weights and data.
if pname in ['data', 'lro']:
continue
# what ever fancy update to modify the parameters
auto_momentum = mx.nd.minimum(momentum,
mx.nd.power(mx.nd.sum(G), 2.0))
auto_k = mx.nd.minimum(0, mx.nd.minimum(1, 1 - auto_momentum))
vw = W * auto_momentum - .001 * G
vn = W * momentum - .001 * G
# print(auto_momentum.asnumpy(), auto_k.asnumpy())
W[:] = W + auto_k * vn + (1 - auto_k) * vw
# Evaluation at each epoch
output = []
for x in range(0, len(teIdx), batch_size):
batchX = teIdx[x:x + batch_size]
batchY = teIdy[x:x + batch_size]
if batchX.shape[0] != batch_size:
continue
# use the test executor as we don't care about gradients
executor_test.arg_dict['data'][:] = batchX
executor_test.forward()
output.extend(executor_test.outputs[0].asnumpy().tolist())
# print (str(num_correct) + ",")
print(mean_squared_error(teIdy[:len(output)], output))
#rd = lm.LogisticRegression(penalty='l2', dual=True, tol=0.0001,
# C=1, fit_intercept=True, intercept_scaling=1.0,
# class_weight=None, random_state=None)
rd=SVR(kernel='linear', degree=3, gamma='auto', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1)
print "Training data"
rd.fit(data.toarray(),y_train.values)
scores1 = cross_val_score(rd, data , y_train, cv=cv, scoring='mean_squared_error')
print scores1
pred = rd.predict(tfv.transform(list(np.asarray(x_test))).toarray())
print pred
print sqrt(mean_squared_error(pred,y_test))
pred=np.round(pred)
pred=pred.astype(int)
print 'predicting actual test set...'
predicted=rd.predict(tfv.transform(list(np.asarray(test))).toarray())
#for p in predicted:
#print p
predicted=np.round(predicted)
predicted=predicted.astype(int)
test["publication year"]=predicted
test["record Id"]=record_id
test.to_csv(submission,
columns=['record Id','publication year'],index=False,sep='\t')
'''
norm1 = np.linalg.norm(y_train)
if norm1 != 0:
y_train, y_test = y_train/norm1, y_test/norm1
print norm1
'''
print y_train.shape
model = SVR(C=1.0, gamma=1.0)
model = LinearRegression()
lasso = Lasso(alpha=0.1).fit(X_train, y_train)
enet = ElasticNet(alpha=0.1, l1_ratio=0.7).fit(X_train, y_train)
y_pred = lasso.predict(X_test)
print "MSE", mean_squared_error(y_test, y_pred)
m = np.mean(y_test)
print "MSE (Mean)",mean_squared_error(y_test, m*np.ones(len(y_test)))
print "r^2 on test data", r2_score(y_test, y_pred)
plt.plot(enet.coef_, label='Elastic net coefficients')
plt.plot(lasso.coef_, label='Lasso coefficients')
plt.legend(loc='best')
plt.title("Lasso R^2: %f, Elastic Net R^2: %f"
% (r2_score(y_test, lasso.predict(X_test)), r2_score(y_test, enet.predict(X_test))))
plt.show()
