def plot_decompositions(
signal,
figsize=None,
save_path=None,
measurement_time='month',
measurement_unit="$10^8m^3$",
):
cols = signal.columns.values
logger.info('cols={}'.format(cols))
T = signal.shape[0]
t = np.arange(start=1, stop=T + 1, step=1, dtype=np.float) / T
freqs = t - 0.5 - 1 / T
if figsize == None:
figsize = (7.48, 1 * len(cols))
plt.figure(figsize=figsize)
for i in range(len(cols)):
subsignal = signal[cols[i]].values
plt.subplot(len(cols), 2, 2 * i + 1)
plt.title(cols[i])
plt.plot(subsignal, c='b')
if i == len(cols) - 1:
plt.xlabel('Time(' + measurement_time + ')')
else:
plt.xticks([])
plt.ylabel(r"Streamflow(" + measurement_unit + ")", )
plt.subplot(len(cols), 2, 2 * i + 2)
plt.title(cols[i])
plt.plot(freqs, abs(fft(subsignal)), c='b', lw=0.8, zorder=0)
if i == len(cols) - 1:
plt.xlabel('Frequency(1/' + measurement_time + ')')
else:
plt.xticks([])
plt.ylabel('Amplitude')
plt.tight_layout()
def dum_pred_results(path,
train_y,
train_predictions,
dev_y,
dev_predictions,
test_y,
test_predictions,
time_cost=None):
"""
Dump real records (labels) and predictions as well as evaluation criteria (metrix R2,RMSE,MAE,MAPE,PPTS,time_cost) to csv.
Args:
path: The local disk path to dump data into.
train_y: records of training set with numpy array type.
train_predictions: predictions of training set with numpy array type.
dev_y: records of development set with numpy array type.
dev_predictions: predictions of development set with numpy array type.
test_y: records of testing set with numpy array type.
test_predictions: predictions of testing set with numpy array type.
time_cost: Time cost for profiling.
Return:
A csv file
"""
logger.info('Dump records, predictions and evaluation criteria...')
logger.info('Compute Nash-Sutcliffe efficiency (NSE)...')
train_nse = r2_score(train_y, train_predictions)
dev_nse = r2_score(dev_y, dev_predictions)
test_nse = r2_score(test_y, test_predictions)
logger.info('Compute Mean Square Error (MSE)...')
train_mse = mean_squared_error(y_true=train_y, y_pred=train_predictions)
dev_mse = mean_squared_error(y_true=dev_y, y_pred=dev_predictions)
test_mse = mean_squared_error(y_true=test_y, y_pred=test_predictions)
logger.info('Compute normalized mean square error (NRMSE)...')
train_nrmse = math.sqrt(mean_squared_error(
train_y, train_predictions)) / (sum(train_y) / len(train_y))
dev_nrmse = math.sqrt(mean_squared_error(
dev_y, dev_predictions)) / (sum(dev_y) / len(dev_y))
test_nrmse = math.sqrt(mean_squared_error(
test_y, test_predictions)) / (sum(test_y) / len(test_y))
logger.info('Compute mean absolute error (MAE)...')
train_mae = mean_absolute_error(train_y, train_predictions)
dev_mae = mean_absolute_error(dev_y, dev_predictions)
test_mae = mean_absolute_error(test_y, test_predictions)
logger.info('Compute mean absolute percentage error (MAPE)...')
train_mape = np.mean(np.abs((train_y - train_predictions) / train_y)) * 100
dev_mape = np.mean(np.abs((dev_y - dev_predictions) / dev_y)) * 100
test_mape = np.mean(np.abs((test_y - test_predictions) / test_y)) * 100
logger.info('Compute peak percentage of threshold statistic (PPTS)...')
train_ppts = PPTS(train_y, train_predictions, 5)
dev_ppts = PPTS(dev_y, dev_predictions, 5)
test_ppts = PPTS(test_y, test_predictions, 5)
logger.info('Dumping the model results.')
dump_train_dev_test_to_csv(
path=path,
train_y=train_y,
train_pred=train_predictions,
train_nse=train_nse,
train_mse=train_mse,
train_nrmse=train_nrmse,
train_mae=train_mae,
train_mape=train_mape,
train_ppts=train_ppts,
dev_y=dev_y,
dev_pred=dev_predictions,
dev_nse=dev_nse,
dev_mse=dev_mse,
dev_nrmse=dev_nrmse,
dev_mae=dev_mae,
dev_mape=dev_mape,
dev_ppts=dev_ppts,
test_y=test_y,
test_pred=test_predictions,
test_nse=test_nse,
test_mse=test_mse,
test_nrmse=test_nrmse,
test_mae=test_mae,
test_mape=test_mape,
test_ppts=test_ppts,
time_cost=time_cost,
)
def ensemble(root_path,
original_series,
station,
predictor,
predict_pattern,
variables,
decomposer=None,
wavelet_level='db10-2',
framework='WDDFF'):
if decomposer == 'modwt':
if framework == 'TSDP':
lags_dict = variables['lags_dict'][wavelet_level]
else:
lags_dict = None
elif decomposer == 'dwt':
lags_dict = variables['lags_dict'][wavelet_level]
else:
lags_dict = variables['lags_dict']
full_len = variables['full_len']
train_len = variables['train_len']
dev_len = variables['dev_len']
test_len = variables['test_len']
logger.info('Ensemble forecasting results...')
logger.info('Root path:{}'.format(root_path))
logger.info('original series:\n{}'.format(original_series))
logger.info('Station:{}'.format(station))
logger.info('Decomposer:{}'.format(decomposer))
logger.info('Lags dict:{}'.format(lags_dict))
logger.info('Predictor:{}'.format(predictor))
logger.info('Predict pattern:{}'.format(predict_pattern))
logger.info('Training length:{}'.format(train_len))
logger.info('Development length:{}'.format(test_len))
logger.info('Testing length:{}'.format(test_len))
logger.info('Entire length:{}'.format(full_len))
logger.info(
'Wavelet and decomposition level of WA:{}'.format(wavelet_level))
if decomposer == 'modwt':
models_path = root_path + '/' + station + '_' + decomposer + '/projects/' + predictor + '-' + framework.lower(
) + '/' + wavelet_level + '/' + predict_pattern + '/'
elif decomposer == 'dwt':
models_path = root_path + '/' + station + '_' + decomposer + '/projects/' + predictor + '/' + wavelet_level + '/' + predict_pattern + '/'
elif decomposer == None:
models_path = root_path + '/' + station + '/projects/' + predictor + '/' + predict_pattern + '/'
else:
models_path = root_path + '/' + station + '_' + decomposer + '/projects/' + predictor + '/' + predict_pattern + '/'
logger.info("Model path:{}".format(models_path))
if 'multi_step' not in predict_pattern:
models_history = models_path + 'history/'
optimal_model = ''
min_dev_mse = np.inf
for file_ in os.listdir(models_history):
if '.csv' in file_ and 'optimized_params' not in file_:
logger.info('read model results:{}'.format(file_))
dev_mse = pd.read_csv(models_history + file_)['dev_mse'][0]
if dev_mse < min_dev_mse:
min_dev_mse = dev_mse
optimal_model = file_
logger.info('Optimal model:{}'.format(optimal_model))
logger.info('Minimum MSE={}'.format(min_dev_mse))
res = load(models_history +
(optimal_model.split('.csv')[0] + '_result.pkl'))
dump(res, models_path + 'result.pkl')
optimal_model = pd.DataFrame([optimal_model],
columns=['optimal_model'])
optimal_results = pd.read_csv(models_history +
optimal_model['optimal_model'][0])
if predictor == 'esvr' or predictor == 'gbrt':
optimal_params = pd.read_csv(
models_history +
optimal_model['optimal_model'][0].split('.csv')[0] +
'_optimized_params.csv')
optimal_results = pd.concat(
[optimal_model, optimal_params, optimal_results], axis=1)
elif predictor == 'lstm':
optimal_results = pd.concat([optimal_model, optimal_results],
axis=1)
optimal_results.to_csv(models_path + 'optimal_model_results.csv')
plot_rela_pred(optimal_results['train_y'],
optimal_results['train_pred'],
models_path + 'train_pred.png')
plot_rela_pred(optimal_results['dev_y'][0:data_part['dev_len']],
optimal_results['dev_pred'][0:data_part['dev_len']],
models_path + 'dev_pred.png')
plot_rela_pred(optimal_results['test_y'][0:data_part['test_len']],
optimal_results['test_pred'][0:data_part['test_len']],
models_path + 'test_pred.png')
else:
for i in range(len(lags_dict)):
print(len(lags_dict))
model_path = models_path + 's' + str(i + 1) + '/'
models_history = model_path + 'history/'
optimal_model = ''
min_dev_mse = np.inf
for file_ in os.listdir(models_history):
if '.csv' in file_ and 'optimized_params' not in file_:
logger.info('read model results:{}'.format(file_))
dev_mse = pd.read_csv(models_history + file_)['dev_mse'][0]
if dev_mse < min_dev_mse:
min_dev_mse = dev_mse
optimal_model = file_
logger.info('Optimal model:{}'.format(optimal_model))
logger.info('Minimum MSE={}'.format(min_dev_mse))
res = load(models_history +
(optimal_model.split('.csv')[0] + '_result.pkl'))
dump(res, model_path + 'result.pkl')
optimal_model = pd.DataFrame([optimal_model],
columns=['optimal_model'])
optimal_results = pd.read_csv(models_history +
optimal_model['optimal_model'][0])
if predictor == 'esvr' or predictor == 'gbrt':
optimal_params = pd.read_csv(
models_history +
optimal_model['optimal_model'][0].split('.csv')[0] +
'_optimized_params.csv')
optimal_results = pd.concat(
[optimal_model, optimal_params, optimal_results], axis=1)
elif predictor == 'lstm':
optimal_results = pd.concat([optimal_model, optimal_results],
axis=1)
optimal_results.to_csv(model_path + 'optimal_model_results.csv')
plot_rela_pred(optimal_results['train_y'],
optimal_results['train_pred'],
model_path + 'train_pred.png')
plot_rela_pred(optimal_results['dev_y'][0:data_part['dev_len']],
optimal_results['dev_pred'][0:data_part['dev_len']],
model_path + 'dev_pred.png')
plot_rela_pred(
optimal_results['test_y'][0:data_part['test_len']],
optimal_results['test_pred'][0:data_part['test_len']],
model_path + 'test_pred.png')
train_len_ = train_len - max(lags_dict.values())
train_sum_pred = pd.DataFrame()
dev_sum_pred = pd.DataFrame()
test_sum_pred = pd.DataFrame()
time_cost_sum = 0.0
for i in range(len(lags_dict)):
model_path = models_path + 's' + str(i + 1) + '/'
results = pd.read_csv(model_path + 'optimal_model_results.csv')
time_cost_sum = time_cost_sum + results['time_cost'][0]
train_pred = results['train_pred']
train_pred = train_pred[train_pred.shape[0] - train_len_:]
train_pred = train_pred.reset_index(drop=True)
dev_pred = results['dev_pred'][0:dev_len]
test_pred = results['test_pred'][0:test_len]
train_sum_pred = pd.concat([train_sum_pred, train_pred], axis=1)
dev_sum_pred = pd.concat([dev_sum_pred, dev_pred], axis=1)
test_sum_pred = pd.concat([test_sum_pred, test_pred], axis=1)
train_sum_pred = train_sum_pred.sum(axis=1)
dev_sum_pred = dev_sum_pred.sum(axis=1)
test_sum_pred = test_sum_pred.sum(axis=1)
train_sum_pred[train_sum_pred < 0.0] = 0.0
dev_sum_pred[dev_sum_pred < 0.0] = 0.0
test_sum_pred[test_sum_pred < 0.0] = 0.0
original_series = original_series.reset_index(drop=True)
train_y = original_series[train_len - train_len_:train_len]
dev_y = original_series[train_len:train_len + dev_len]
test_y = original_series[train_len + dev_len:]
train_y = train_y.reset_index(drop=True)
dev_y = dev_y.reset_index(drop=True)
test_y = test_y.reset_index(drop=True)
train_nse = r2_score(train_y.values, train_sum_pred.values)
train_mse = mean_squared_error(train_y.values, train_sum_pred.values)
train_nrmse = math.sqrt(
mean_squared_error(train_y.values, train_sum_pred.values)) / (
sum(train_y.values) / len(train_y.values))
train_ppts = PPTS(train_y.values, train_sum_pred.values, 5)
dev_nse = r2_score(dev_y.values, dev_sum_pred.values)
dev_mse = mean_squared_error(dev_y.values, dev_sum_pred.values)
dev_nrmse = math.sqrt(
mean_squared_error(dev_y.values, dev_sum_pred.values)) / (
sum(dev_y.values) / len(dev_y.values))
dev_ppts = PPTS(dev_y.values, dev_sum_pred.values, 5)
test_nse = r2_score(test_y.values, test_sum_pred.values)
test_mse = mean_squared_error(test_y.values, test_sum_pred.values)
test_nrmse = math.sqrt(
mean_squared_error(test_y.values, test_sum_pred.values)) / (
sum(test_y.values) / len(test_y.values))
test_ppts = PPTS(test_y.values, test_sum_pred.values, 5)
metrics = {
'train_nse': train_nse,
'train_mse': train_mse,
'train_nrmse': train_nrmse,
'train_ppts': train_ppts,
'dev_nse': dev_nse,
'dev_mse': dev_mse,
'dev_nrmse': dev_nrmse,
'dev_ppts': dev_ppts,
'test_nse': test_nse,
'test_mse': test_mse,
'test_nrmse': test_nrmse,
'test_ppts': test_ppts,
'time_cost': time_cost_sum,
}
metrics_df = pd.DataFrame(metrics, index=[0])
print(metrics_df)
train_results = pd.concat([train_y, train_sum_pred], axis=1)
train_results = pd.DataFrame(train_results.values,
columns=['train_y', 'train_pred'])
dev_results = pd.concat([dev_y, dev_sum_pred], axis=1)
dev_results = pd.DataFrame(dev_results.values,
columns=['dev_y', 'dev_pred'])
test_results = pd.concat([test_y, test_sum_pred], axis=1)
test_results = pd.DataFrame(test_results.values,
columns=['test_y', 'test_pred'])
optimal_results = pd.concat(
[train_results, dev_results, test_results, metrics_df], axis=1)
optimal_results.to_csv(models_path + 'optimal_results.csv')
plot_rela_pred(train_y, train_sum_pred, models_path + 'train_pred.png')
plot_rela_pred(dev_y, dev_sum_pred, models_path + 'dev_pred.png')
plot_rela_pred(test_y, test_sum_pred, models_path + 'test_pred.png')
def plot_cv_error(data_path, labels, mode='avg'):
logger.info('Plot cross validation MSE...')
logger.info('Data path:{}'.format(data_path))
logger.info('Labels:{}'.format(labels))
if isinstance(data_path, str):
data_path = [data_path]
labels = [labels]
plt.figure(figsize=(7.48, 7.48))
plt.xlabel('CV')
plt.ylabel('MSE')
for path, label in zip(data_path, labels):
logger.info('Read cv results of {}'.format(path))
dev_cv = {}
test_cv = {}
for file_ in os.listdir(path):
if '.csv' in file_ and 'seed' not in file_ and 'optimized_params' not in file_:
logger.info('cv-file:{}'.format(file_))
cv = int(re.findall(r"(?<=cv)\d+", file_)[0])
logger.info('cv={}'.format(cv))
data = pd.read_csv(path + file_)
dev_metrics = data['dev_nrmse'][0]
test_metrics = data['test_nrmse'][0]
logger.info('Development metrics={}'.format(dev_metrics))
dev_cv[cv] = dev_metrics
test_cv[cv] = test_metrics
logger.debug('Development cv dict before sort:{}'.format(dev_cv))
logger.debug('Testing cv dict before sort:{}'.format(test_cv))
dev_cv = dict(sorted(dev_cv.items()))
test_cv = dict(sorted(test_cv.items()))
logger.info('Cross validation development dict:{}'.format(dev_cv))
logger.info('Cross validation folds:{}'.format(dev_cv.keys()))
logger.info('Cross validation MSE:{}'.format(dev_cv.values()))
plt.plot(list(dev_cv.keys()),
list(dev_cv.values()),
marker='o',
label=label + 'dev')
plt.plot(list(test_cv.keys()),
list(test_cv.values()),
marker='o',
label=label + 'test')
plt.legend()
plt.tight_layout()
print(len(cal_pred))
train_pred = cal_pred[0:train_len]
dev_pred = cal_pred[train_len:]
print(model_fit.summary())
print(len(train_pred))
print(len(dev_pred))
residuals = pd.DataFrame(model_fit.resid)
residuals.plot()
plt.show()
residuals.plot(kind='kde')
plt.show()
print(residuals.describe())
if os.path.exists(model_path + 'arima' + str(order) + '_results.csv'):
logger.info("The arima" + str(order) + " was already tuned")
history = [x for x in cal]
test_pred = list()
for t in range(len(test)):
model = ARIMA(history, order=order)
model_fit = model.fit(disp=0)
output = model_fit.forecast()
yhat = output[0]
test_pred.append(yhat)
obs = test[t]
history.append(obs)
print('predicted=%f, expected=%f' % (yhat, obs))
end = time.process_time()
time_cost = end - start
def plot_rela_pred(records,
predictions,
fig_savepath,
measurement_time='month',
measurement_unit="$10^8m^3$",
figsize=(7.48, 3),
format='PNG',
dpi=300):
"""
Plot the relations between the records and predictions.
Args:
records: the actual measured records.
predictions: the predictions obtained by model
fig_savepath: the path where the plot figure will be saved.
"""
logger.info('Plot predictions and correlations...')
if isinstance(records, pd.DataFrame) or isinstance(records, pd.Series):
records = records.values
elif isinstance(predictions, pd.DataFrame) or isinstance(
predictions, pd.Series):
predictions = predictions.values
length = records.size
t = np.linspace(start=1, stop=length, num=length)
plt.figure(figsize=figsize)
ax1 = plt.subplot2grid((1, 5), (0, 0), colspan=3)
ax2 = plt.subplot2grid((1, 5), (0, 3), colspan=2, aspect='equal')
# ax1.set_xticks([])
# ax1.set_yticks([])
ax1.set_xlabel('Time(' + measurement_time + ')', )
ax1.set_ylabel(r'Streamflow(' + measurement_unit + ')', )
ax1.plot(t, records, '-', color='blue', label='Records', linewidth=1.0)
ax1.plot(t,
predictions,
'--',
color='red',
label='Predictions',
linewidth=1.0)
ax1.legend(
# loc='upper left',
loc=0,
# bbox_to_anchor=(0.005,1.2),
shadow=False,
frameon=False,
)
logger.info('records=\n{}'.format(records))
logger.info('predictions=\n{}'.format(predictions))
pred_min = predictions.min()
pred_max = predictions.max()
record_min = records.min()
record_max = records.max()
if pred_min < record_min:
xymin = pred_min
else:
xymin = record_min
if pred_max > record_max:
xymax = pred_max
else:
xymax = record_max
logger.info('xymin={}'.format(xymin))
logger.info('xymax={}'.format(xymax))
xx = np.arange(start=xymin, stop=xymax + 1, step=1.0)
coeff = np.polyfit(predictions, records, 1)
linear_fit = coeff[0] * xx + coeff[1]
# print('a:{}'.format(coeff[0]))
# print('b:{}'.format(coeff[1]))
# ax2.set_xticks()
# ax2.set_yticks()
ax2.set_xlabel(r'Predictions(' + measurement_unit + ')', )
ax2.set_ylabel(r'Records(' + measurement_unit + ')', )
# ax2.plot(predictions, records, 'o', color='blue', label='',markersize=6.5)
ax2.plot(predictions,
records,
'o',
markerfacecolor='w',
markeredgecolor='blue',
markersize=6.5)
# ax2.plot(predictions, linear_fit, '--', color='red', label='Linear fit',linewidth=1.0)
# ax2.plot(predictions, ideal_fit, '-', color='black', label='Ideal fit',linewidth=1.0)
ax2.plot(xx,
linear_fit,
'--',
color='red',
label='Linear fit',
linewidth=1.0)
ax2.plot([xymin, xymax], [xymin, xymax],
'-',
color='black',
label='Ideal fit',
linewidth=1.0)
ax2.set_xlim([xymin, xymax])
ax2.set_ylim([xymin, xymax])
ax2.legend(
# loc='upper left',
loc=0,
# bbox_to_anchor=(0.05,1),
shadow=False,
frameon=False,
)
# plt.subplots_adjust(left=0.08, bottom=0.12, right=0.98, top=0.98, hspace=0.1, wspace=0.2)
plt.tight_layout()
plt.savefig(fig_savepath, format=format, dpi=dpi)
def dump_pred_results(path,
train_y=None,
train_predictions=None,
dev_y=None,
dev_predictions=None,
test_y=None,
test_predictions=None,
time_cost=None):
"""
Dump real records (labels) and predictions as well as evaluation criteria (metrix R2,RMSE,MAE,MAPE,PPTS,time_cost) to csv.
Args:
path: The local disk path to dump data into.
train_y: records of training set with numpy array type.
train_predictions: predictions of training set with numpy array type.
dev_y: records of development set with numpy array type.
dev_predictions: predictions of development set with numpy array type.
test_y: records of testing set with numpy array type.
test_predictions: predictions of testing set with numpy array type.
time_cost: Time cost for profiling.
Return:
A csv file
"""
logger.info('Dump records, predictions and evaluation criteria...')
logger.info('Compute Nash-Sutcliffe efficiency (NSE)...')
# if train_y==None or train_predictions==None:
# train_nse = None
# train_mse = None
# train_nrmse = None
# train_mae = None
# train_mape = None
# else:
train_nse = r2_score(train_y, train_predictions)
train_mse = mean_squared_error(y_true=train_y, y_pred=train_predictions)
train_nrmse = math.sqrt(mean_squared_error(
train_y, train_predictions)) / (sum(train_y) / len(train_y))
train_mae = mean_absolute_error(train_y, train_predictions)
train_mape = np.mean(np.abs((train_y - train_predictions) / train_y)) * 100
train_ppts = PPTS(train_y, train_predictions, 5)
# if dev_y==None or dev_predictions==None:
# dev_nse = None
# dev_mse = None
# dev_nrmse = None
# dev_mae = None
# dev_mape = None
# else:
dev_nse = r2_score(dev_y, dev_predictions)
dev_mse = mean_squared_error(y_true=dev_y, y_pred=dev_predictions)
dev_nrmse = math.sqrt(mean_squared_error(
dev_y, dev_predictions)) / (sum(dev_y) / len(dev_y))
dev_mae = mean_absolute_error(dev_y, dev_predictions)
dev_mape = np.mean(np.abs((dev_y - dev_predictions) / dev_y)) * 100
dev_ppts = PPTS(dev_y, dev_predictions, 5)
# if test_y==None or test_predictions==None:
# test_nse = None
# test_mse = None
# test_nrmse = None
# test_mae = None
# test_mape = None
# else:
test_nse = r2_score(test_y, test_predictions)
test_mse = mean_squared_error(y_true=test_y, y_pred=test_predictions)
test_nrmse = math.sqrt(mean_squared_error(
test_y, test_predictions)) / (sum(test_y) / len(test_y))
test_mae = mean_absolute_error(test_y, test_predictions)
test_mape = np.mean(np.abs((test_y - test_predictions) / test_y)) * 100
test_ppts = PPTS(test_y, test_predictions, 5)
dump_train_dev_test_to_csv(
path=path,
train_y=train_y,
train_pred=train_predictions,
train_nse=train_nse,
train_mse=train_mse,
train_nrmse=train_nrmse,
train_mae=train_mae,
train_mape=train_mape,
train_ppts=train_ppts,
dev_y=dev_y,
dev_pred=dev_predictions,
dev_nse=dev_nse,
dev_mse=dev_mse,
dev_nrmse=dev_nrmse,
dev_mae=dev_mae,
dev_mape=dev_mape,
dev_ppts=dev_ppts,
test_y=test_y,
test_pred=test_predictions,
test_nse=test_nse,
test_mse=test_mse,
test_nrmse=test_nrmse,
test_mae=test_mae,
test_mape=test_mape,
test_ppts=test_ppts,
time_cost=time_cost,
)
def ensemble(root_path,original_series,station,predictor,predict_pattern,variables,decomposer=None,wavelet_level='db10-2'):
lags_dict = variables['lags_dict']
full_len = variables['full_len']
train_len = variables['train_len']
dev_len = variables['dev_len']
test_len = variables['test_len']
logger.info('Ensemble forecasting results...')
logger.info('Root path:{}'.format(root_path))
logger.info('original series:\n{}'.format(original_series))
logger.info('Station:{}'.format(station))
logger.info('Decomposer:{}'.format(decomposer))
logger.info('Lags dict:{}'.format(lags_dict))
logger.info('Predictor:{}'.format(predictor))
logger.info('Predict pattern:{}'.format(predict_pattern))
logger.info('Training length:{}'.format(train_len))
logger.info('Development length:{}'.format(test_len))
logger.info('Testing length:{}'.format(test_len))
logger.info('Entire length:{}'.format(full_len))
logger.info('Wavelet and decomposition level of WA:{}'.format(wavelet_level))
original = original_series
if decomposer=='dwt' or decomposer=='modwt':
models_path = root_path+'/'+station+'_'+decomposer+'/projects/'+predictor+'/'+wavelet_level+'/'+predict_pattern+'/'
elif decomposer==None:
models_path = root_path+'/'+station+'/projects/'+predictor+'/'+predict_pattern+'/'
else:
models_path = root_path+'/'+station+'_'+decomposer+'/projects/'+predictor+'/'+predict_pattern+'/'
logger.info("Model path:{}".format(models_path))
if 'multi_step' not in predict_pattern:
models_history = models_path+'history/'
optimal_model = ''
min_dev_mse = np.inf
for file_ in os.listdir(models_history):
if '.csv' in file_ and 'optimized_params' not in file_:
logger.info('read model results:{}'.format(file_))
dev_mse = pd.read_csv(models_history+file_)['dev_mse'][0]
if dev_mse < min_dev_mse:
min_dev_mse = dev_mse
optimal_model = file_
logger.info('Optimal model:{}'.format(optimal_model))
logger.info('Minimum MSE={}'.format(min_dev_mse))
optimal_model = pd.DataFrame([optimal_model],columns=['optimal_model'])
optimal_results = pd.read_csv(models_history+optimal_model['optimal_model'][0])
if predictor=='esvr' or predictor=='gbrt':
optimal_params = pd.read_csv(models_history+optimal_model['optimal_model'][0].split('.csv')[0]+'_optimized_params.csv')
optimal_results = pd.concat([optimal_model,optimal_params,optimal_results],axis=1)
elif predictor=='lstm':
optimal_results = pd.concat([optimal_model,optimal_results],axis=1)
optimal_results.to_csv(models_path+'optimal_model_results.csv')
plot_rela_pred(optimal_results['train_y'],optimal_results['train_pred'],models_path+'train_pred.png')
plot_rela_pred(optimal_results['dev_y'][0:data_part['dev_len']],optimal_results['dev_pred'][0:data_part['dev_len']],models_path+'dev_pred.png')
plot_rela_pred(optimal_results['test_y'][0:data_part['test_len']],optimal_results['test_pred'][0:data_part['test_len']],models_path+'test_pred.png')
def read_long_leading_time(station,
decomposer,
mode='pearson',
pearson_threshold=0.2,
wavelet_level="db10-2"):
logger.info('reading long lead time model results...')
logger.info('station:{}'.format(station))
logger.info('decomposer:{}'.format(decomposer))
logger.info('mode:{}'.format(mode))
logger.info('pearson threshold:{}'.format(pearson_threshold))
logger.info('wavelet level:{}'.format(wavelet_level))
records = []
predictions = []
nse = []
nrmse = []
ppts = []
if decomposer == 'modwt':
m1 = read_two_stage(
station=station,
decomposer=decomposer,
predict_pattern="single_hybrid_1_ahead_mi_ts0.1",
)
else:
m1 = read_two_stage(
station=station,
decomposer=decomposer,
predict_pattern="one_step_1_ahead_forecast_pacf",
)
records.append(m1['test_y'])
predictions.append(m1['test_pred'])
nse.append(m1['test_nse'])
nrmse.append(m1['test_nrmse'])
ppts.append(m1['test_ppts'])
# averaging the trained svr with different seed
leading_times = [3, 5, 7, 9]
for leading_time in leading_times:
if decomposer == 'modwt':
model_path = root_path + "\\" + station + "_" + decomposer + "\\projects\\esvr-wddff\\" + wavelet_level + "\\"
elif decomposer == "dwt":
model_path = root_path + "\\" + station + "_" + decomposer + "\\projects\\esvr\\" + wavelet_level + "\\"
else:
model_path = root_path + "\\" + station + "_" + decomposer + "\\projects\\esvr\\"
print("Reading mode:{}".format(mode))
if mode == 'pacf':
model_path = model_path + "one_step_" + str(
leading_time) + "_ahead_forecast_pacf//"
elif mode == 'pearson':
model_path = model_path + "one_step_" + str(
leading_time) + "_ahead_forecast_pearson" + str(
pearson_threshold) + "//"
elif mode == 'mi':
model_path = model_path + "single_hybrid_" + str(
leading_time) + "_ahead_mi_ts0.1//"
logger.info('model path:{}'.format(model_path))
results = pd.read_csv(model_path + 'optimal_model_results.csv')
test_pred = (results['test_pred'][0:120]).values.flatten()
test_y = (results['test_y'][0:120]).values.flatten()
records.append(test_y)
predictions.append(test_pred)
nse.append(results['test_nse'][0])
nrmse.append(results['test_nrmse'][0])
ppts.append(results['test_ppts'][0])
results = {
'records': records,
'predictions': predictions,
'nse': nse,
'nrmse': nrmse,
'ppts': ppts,
}
logger.info('results.records:{}'.format(pd.DataFrame(results)['records']))
logger.info('results.predictions:{}'.format(
pd.DataFrame(results)['predictions']))
return results
import pandas as pd
import numpy as np
import math
from statistics import mean
from sklearn.decomposition import PCA
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, mean_squared_log_error
import os
root_path = os.path.dirname(os.path.abspath('__file__'))
import sys
sys.path.append(root_path)
from tools.metrics_ import PPTS, mean_absolute_percentage_error
from config.globalLog import logger
logger.info('results_reader')
def read_two_stage(station,
decomposer,
predict_pattern,
wavelet_level="db10-2",
framework='WDDFF'):
if decomposer == 'modwt':
model_path = root_path + "\\" + station + "_" + decomposer + "\\projects\\esvr-" + framework.lower(
) + "\\" + wavelet_level + "\\" + predict_pattern + "\\"
elif decomposer == "dwt":
model_path = root_path + "\\" + station + "_" + decomposer + "\\projects\\esvr\\" + wavelet_level + "\\" + predict_pattern + "\\"
else:
model_path = root_path + "\\" + station + "_" + decomposer + "\\projects\\esvr\\" + predict_pattern + "\\"
results = pd.read_csv(model_path + 'optimal_model_results.csv')
test_pred = (results['test_pred'][0:120]).values.flatten()
test_y = (results['test_y'][0:120]).values.flatten()
# cb_ax = fig.add_axes([0.85, 0.06, 0.05, 0.38])#[x,y,width,height]
# cbar = fig.colorbar(im, cax=cb_ax)
# cbar.set_ticks(np.arange(0, 1.1, 0.5))
# cbar.set_label(r"$Corr_{i,j}$")
# # cbar.set_ticklabels(['low', 'medium', 'high'])
# plt.savefig(graphs_path+"Fig.9.Pearson corr of Huaxian.tif",format="TIFF",dpi=1200)
# plt.savefig(graphs_path+"Fig.9.Pearson corr of Huaxian.pdf",format="PDF",dpi=1200)
# plt.show()
fig = plt.figure(figsize=(7.4861,1.7))
for i in range(len(corrs)):
ax = plt.subplot(1,5,i+1)
ax.set_title(titles[i],fontsize=6)
sign_num=corrs[i].shape[1]
logger.info('Number of sub-signals:{}'.format(sign_num))
ticks = list(range(sign_num))
logger.info('ticks:{}'.format(ticks))
labels=[]
for j in ticks:
if titles[i].find('VMD')>=0:
labels.append(r'$IMF_{'+str(j+1)+'}$')
elif titles[i].find('EEMD')>=0:
if j==sign_num-1:
labels.append(r'$R$')
else:
labels.append(r'$IMF_{'+str(j+1)+'}$')
elif titles[i].find('MODWT')>=0:
if j==sign_num-1:
labels.append(r'$V_{'+str(j)+'}$')
else:
