def evaluate(data, teX, teY, index_arr, kernelMeans, kernelSigma,
kernelWeights, tau, E, original_P, target_P, mode):
"""model test"""
print("== EVALUATE ==")
f = open('result.txt', 'w')
# recursive application
assert (
target_P % original_P == 0
) # check if target P can be achieved using n step applications of interval original_P
gap = target_P - original_P
loop = gap + 1
print("Iterative Application for {} times".format(loop))
Y_hat = []
print(len(teX))
for idx, x_element in enumerate(teX):
data_copy = copy.deepcopy(data)
data_at = index_arr[idx] - gap
x = x_element
for i in range(0, loop):
y_h = predict(x, kernelMeans, kernelSigma, kernelWeights)
# update value by prediction
data_copy[data_at] = y_h
x, data_at = ft.extracting_on_index(tau, E, original_P, data_copy,
data_at, mode)
Y_hat.append(y_h)
err, rmse, rsq, mae = ft.loss_with_prediction_array(teY, Y_hat)
return Y_hat, rmse, rsq, mae
def evaluate(data, teX, teY, teYdate, index_arr,
num_kernels, kernelMeans, kernelSigma, kernelWeights,
tau, E, original_P, target_P, mode,
formatter, locater
):
"""model test"""
print("== EVALUATE ==")
f = open('result.txt', 'w')
# recursive application
assert(target_P % original_P == 0) # check if target P can be achieved using n step applications of interval original_P
gap = target_P - original_P
loop = gap + 1
print("Iterative Application for {} times".format(loop))
Y_hat = []
print(len(teX))
for idx, x_element in enumerate(teX):
data_copy = copy.deepcopy(data)
data_at = index_arr[idx] - gap
x = x_element
for i in range(0, loop):
y_h = predict(x, kernelMeans, kernelSigma, kernelWeights)
# update value by prediction
data_copy[data_at] = y_h
x, data_at = ft.extracting_on_index(tau, E, original_P, data_copy, data_at, mode)
Y_hat.append(y_h)
err, rmse, rsq, mae = ft.loss_with_prediction_array(teY, Y_hat)
print(format('rmse: %f, R2: %f, MAE: %f') % (rmse, rsq, mae))
f.write(format('rmse: %f, R2: %f, MAE: %f') % (rmse, rsq, mae) + '\n')
"""
plot
"""
dates = [datetime.datetime.strptime(d, "%Y-%m-%d").date() for d in teYdate]
plt.gca().xaxis.set_major_formatter(formatter)
plt.gca().xaxis.set_major_locator(locater)
pre = teY - err
plt.plot(dates, teY, 'r')
plt.plot(dates, pre, 'b')
plt.legend(["Test Data", "Prediction"])
plt.savefig("./kernel" + str(num_kernels) + "_prediction_graph.png")
plt.show()
f.close()
return rmse, rsq, mae
def rolling_forecast(teX, teY, teYdate, num_kernels, kernelMeans, kernelSigma,
kernelWeights, formatter, locater):
"""
model test, rolling forecast
"""
# forecast and update
n = len(teX)
Yest = []
for i in range(n):
# forecast
Yhat = ft.output(teX[i], kernelMeans, kernelSigma, kernelWeights)
Yest.append(Yhat)
# update
kernelMeans, kernelSigma, kernelWeights = \
updateWeights(teX[i], teY[i],
num_kernels,
kernelMeans, kernelSigma, kernelWeights)
# evaluate
f = open('result.txt', 'w')
err, rmse, rsq, mae = ft.loss_with_prediction_array(teY, Yest)
print(format('rmse: %f, R2: %f, MAE: %f') % (rmse, rsq, mae))
f.write(format('rmse: %f, R2: %f, MAE: %f') % (rmse, rsq, mae) + '\n')
"""
plot
"""
dates = [datetime.datetime.strptime(d, "%Y-%m-%d").date() for d in teYdate]
plt.gca().xaxis.set_major_formatter(formatter)
plt.gca().xaxis.set_major_locator(locater)
pre = teY - err
plt.plot(dates, teY, 'r')
plt.plot(dates, pre, 'b')
plt.legend(["Test Data", "Prediction"])
plt.savefig("./kernel" + str(num_kernels) + "_prediction_graph.png")
plt.show()
f.close()
return rmse, rsq, mae
def rolling_forecast(teX, teY, num_kernels, kernelMeans, kernelSigma, kernelWeights, loop):
"""
model test, rolling forecast
"""
# forecast and update
n = len(teX)
Yest = []
for i in range(n):
# forecast
Yhat = ft.output(teX[i], kernelMeans, kernelSigma, kernelWeights)
Yest.append(Yhat)
# update
kernelMeans, kernelSigma, kernelWeights = \
updateWeights(teX[i], teY[i],
num_kernels,
kernelMeans, kernelSigma, kernelWeights,
loop)
# evaluate
f = open('result.txt', 'w')
err, rmse, rsq, mae = ft.loss_with_prediction_array(teY, Yest)
print(format('rmse: %f, R2: %f, MAE: %f') % (rmse, rsq, mae))
f.write(format('rmse: %f, R2: %f, MAE: %f') % (rmse, rsq, mae) + '\n')
# plot
pre = teY - err
plt.plot(teY, 'r')
plt.plot(pre, 'b')
plt.legend(["Test Data", "Prediction"])
plt.savefig("./kernel" + str(num_kernels) + "_prediction_graph.png")
plt.show()
f.close()
return rmse, rsq, mae
def rolling_forecast(teX, teY, num_kernels, kernelMeans, kernelSigma,
kernelWeights):
"""
model test, rolling forecast
"""
# forecast and update
n = len(teX)
Yest = []
for i in range(n):
# forecast
Yhat = ft.output(teX[i], kernelMeans, kernelSigma, kernelWeights)
Yest.append(Yhat)
# update
kernelMeans, kernelSigma, kernelWeights = \
updateWeights(teX[i], teY[i],
num_kernels,
kernelMeans, kernelSigma, kernelWeights)
# evaluate
err, rmse, rsq, mae = ft.loss_with_prediction_array(teY, Yest)
return Yest, rmse, rsq, mae