def input_metrics(targetxml, referencexml):
rvars = pull_tune_variables(referencexml)
tvars = pull_tune_variables(targetxml, referencexml)
rvars.variables.sort(key=lambda x: x.group)
tvars.variables.sort(key=lambda x: x.group)
data = {'target': [], 'reference': [], 'min': [], 'max': [], 'key': []}
for r, t in zip(rvars.variables, tvars.variables):
if r.group == t.group:
key = ';'.join([r.idfclass, r.idfobject, r.idffield])
data['target'].append(float(t.value))
data['reference'].append(float(r.value))
data['min'].append(r.minimum)
data['max'].append(r.maximum)
data['key'].append(key)
paes = metrics.pae(data['target'], data['reference'], data['min'],
data['max'])
m = {
'pae': {},
'rmse': {},
'cvrmse': {},
'mbe': {},
'nmbe': {},
'mape': {}
}
for k, p in zip(data['key'], paes):
m['pae'][k] = p
m['rmse']['all inputs'] = metrics.rmse(data['target'], data['reference'])
m['cvrmse']['all inputs'] = metrics.cvrmse(data['target'],
data['reference'])
m['mbe']['all inputs'] = metrics.mbe(data['target'], data['reference'])
m['nmbe']['all inputs'] = metrics.nmbe(data['target'], data['reference'])
m['mape']['all inputs'] = metrics.mape(data['target'], data['reference'])
return m
def main():
if sys.argv[1] == 'daily':
print('Using daily data...')
path_to_dataset = '../data/household_power_consumption_daily.csv'
model, y_test, predictions = run(path_to_dataset, 10, 50, 1.0)
elif sys.argv[1] == 'monthly':
print('Using monthly data...')
path_to_dataset = '../data/household_power_consumption_monthly.csv'
model, y_test, predictions = run(path_to_dataset, 30, 5, 1.0)
elif sys.argv[1] == 'hourly':
print('Using hourly data...')
path_to_dataset = '../data/household_power_consumption_hourly.csv'
model, y_test, predictions = run(path_to_dataset, 30, 50, 1.0)
else:
print('Using minute data...')
path_to_dataset = '../data/household_power_consumption.csv'
model, y_test, predictions = run(path_to_dataset)
# save for later use
model.save_weights('../output/lstm.h5', overwrite=True)
# model.load_weights('../output/lstm.h5')
graph_utils.plot('lstm', predictions, y_test)
print('RMSE: %.4f'% metrics.rmse(predictions, y_test))
print('MAPE: %.4f'% metrics.mape(predictions, y_test))
def test_TRTF():
dense_mat = pd.read_csv('./datasets/Seattle-data-set/mat.csv', index_col=0)
rm = pd.read_csv('./datasets/Seattle-data-set/RM_mat.csv', index_col=0)
dense_mat = dense_mat.values
rm = rm.values
binary_mat2 = np.round(rm + 0.5 - 0.2)
nan_mat2 = binary_mat2.copy()
nan_mat2[nan_mat2 == 0] = np.nan
sparse_mat2 = np.multiply(nan_mat2, dense_mat)
pos2 = np.where((dense_mat != 0) & (binary_mat2 == 0))
sparse_tensor2 = sparse_mat2.reshape([sparse_mat2.shape[0], 28, 288])
# sparse_tensor_ori, rank = 30, time_lags = (1, 2, 24),
# burn_iter = 1100, gibbs_iter = 100
# TRTF(sparse_tensor_ori, rank=30, time_lags=(1, 2, 24),
# lambda_u=500, lambda_v=500, lambda_ar=500,
# eta=2e-2, lambda_theta=100, maxiter=1000)
TRTF_res2 = TRTF(sparse_tensor2,
rank=50,
time_lags=(1, 2, 288),
maxiter=200).reshape(dense_mat.shape)
TRTF_res2_mape2 = mape(dense_mat[pos2], TRTF_res2[pos2])
TRTF_res2_rmse2 = rmse(dense_mat[pos2], TRTF_res2[pos2])
print("TRTF_res2_mape2", TRTF_res2_mape2)
print("TRTF_res2_rmse2", TRTF_res2_rmse2)
def input_metrics(targetxml, referencexml):
rvars = pull_tune_variables(referencexml)
tvars = pull_tune_variables(targetxml, referencexml)
rvars.variables.sort(key=lambda x: x.group)
tvars.variables.sort(key=lambda x: x.group)
data = {'target': [], 'reference': [], 'min': [], 'max': [], 'key': []}
for r, t in zip(rvars.variables, tvars.variables):
if r.group == t.group:
key = ';'.join([r.idfclass, r.idfobject, r.idffield])
data['target'].append(float(t.value))
data['reference'].append(float(r.value))
data['min'].append(r.minimum)
data['max'].append(r.maximum)
data['key'].append(key)
paes = metrics.pae(data['target'], data['reference'], data['min'], data['max'])
m = {'pae': {},
'rmse': {},
'cvrmse': {},
'mbe': {},
'nmbe': {},
'mape': {}}
for k, p in zip(data['key'], paes):
m['pae'][k] = p
m['rmse']['all inputs'] = metrics.rmse(data['target'], data['reference'])
m['cvrmse']['all inputs'] = metrics.cvrmse(data['target'], data['reference'])
m['mbe']['all inputs'] = metrics.mbe(data['target'], data['reference'])
m['nmbe']['all inputs'] = metrics.nmbe(data['target'], data['reference'])
m['mape']['all inputs'] = metrics.mape(data['target'], data['reference'])
return m
def cross_val_score(model=None, data=None, cv=10, scorer=rmse):
data=np.array(data)
print(data.shape)
chunks=chunk(data, cv)
#print chunks
score=list()
for i in range(10):
iter_data=list()
for j in range(len(chunks)):
if j!=i:
iter_data.extend(chunks[j])
pred_data=np.array(chunks[i])
iter_data=np.array(iter_data)
model.fit(iter_data)
pred=model.predict(pred_data)
score.append(rmse(pred_data[ : , model.formatizer['value']], pred))
print(score[i])
return np.mean(score)
def test_HaLRTC():
dense_mat = pd.read_csv('./datasets/Seattle-data-set/mat.csv', index_col=0)
rm = pd.read_csv('./datasets/Seattle-data-set/RM_mat.csv', index_col=0)
dense_mat = dense_mat.values
rm = rm.values
binary_mat2 = np.round(rm + 0.5 - 0.2)
nan_mat2 = binary_mat2.copy()
nan_mat2[nan_mat2 == 0] = np.nan
sparse_mat2 = np.multiply(nan_mat2, dense_mat)
pos2 = np.where((dense_mat != 0) & (binary_mat2 == 0))
sparse_tensor2 = sparse_mat2.reshape([sparse_mat2.shape[0], 28, 288])
# sparse_tensor_ori, rank = 30, time_lags = (1, 2, 24),
# burn_iter = 1100, gibbs_iter = 100
HaLRTC_res2 = HaLRTC(sparse_tensor2, rho=1e-5, epsilon=1e-4,
maxiter=200).reshape(dense_mat.shape)
HaLRTC_res2_mape2 = mape(dense_mat[pos2], HaLRTC_res2[pos2])
HaLRTC_res2_rmse2 = rmse(dense_mat[pos2], HaLRTC_res2[pos2])
print("HaLRTC_res2_mape2", HaLRTC_res2_mape2)
print("HaLRTC_res2_rmse2", HaLRTC_res2_rmse2)
def test_TRMF():
dense_mat = pd.read_csv('./datasets/Seattle-data-set/mat.csv', index_col=0)
rm = pd.read_csv('./datasets/Seattle-data-set/RM_mat.csv', index_col=0)
dense_mat = dense_mat.values
rm = rm.values
binary_mat2 = np.round(rm + 0.5 - 0.2)
nan_mat2 = binary_mat2.copy()
nan_mat2[nan_mat2 == 0] = np.nan
sparse_mat2 = np.multiply(nan_mat2, dense_mat)
pos2 = np.where((dense_mat != 0) & (binary_mat2 == 0))
# sparse_tensor2 = sparse_mat2.reshape([sparse_mat2.shape[0], 28, 288])
# def TRMF(sparse_mat, lambda_w=500,
# lambda_x=500,
# lambda_theta=500,
# eta=0.03, time_lags=(1, 2, 144), maxiter=200)
TRMF_res2 = TRMF(sparse_mat2,
lambda_w=500,
lambda_x=500,
lambda_theta=500,
eta=0.03,
time_lags=(1, 2, 3, 4, 144),
maxiter=200)
# print(TRMF_res2)
# print(dense_mat)
TRMF_res2_mape2 = mape(dense_mat[pos2], TRMF_res2[pos2])
TRMF_res2_rmse2 = rmse(dense_mat[pos2], TRMF_res2[pos2])
print("TRMF_res2_mape2", TRMF_res2_mape2)
print("TRMF_res2_rmse2", TRMF_res2_rmse2)
def test_BTRMF():
dense_mat = pd.read_csv('./datasets/Seattle-data-set/mat.csv', index_col=0)
rm = pd.read_csv('./datasets/Seattle-data-set/RM_mat.csv', index_col=0)
dense_mat = dense_mat.values
rm = rm.values
binary_mat2 = np.round(rm + 0.5 - 0.2)
nan_mat2 = binary_mat2.copy()
nan_mat2[nan_mat2 == 0] = np.nan
sparse_mat2 = np.multiply(nan_mat2, dense_mat)
pos2 = np.where((dense_mat != 0) & (binary_mat2 == 0))
# sparse_tensor2 = sparse_mat2.reshape([sparse_mat2.shape[0], 28, 288])
BTRMF_res2 = BTRMF(sparse_mat2,
rank=50,
time_lags=(1, 2, 288),
burn_iter=100,
gibbs_iter=20)
BTRMF_res2_mape2 = mape(dense_mat[pos2], BTRMF_res2[pos2])
BTRMF_res2_rmse2 = rmse(dense_mat[pos2], BTRMF_res2[pos2])
print("BTRMF_res2_mape2", BTRMF_res2_mape2)
print("BTRMF_res2_rmse2", BTRMF_res2_rmse2)
def val_model(model, criterion):
dset_sizes = len(val_dataset)
model.eval()
running_loss = 0.0
running_corrects = 0
cont = 0
outPre = []
outLabel = []
pres_list = []
labels_list = []
for data in val_loader:
inputs, labels, month = data['X']['x'], data['Y'], data['X']['m']
x = inputs[0]
labels = labels.type(torch.float).cuda()
inputs = [x.cuda()]
outputs = model(inputs)
loss = criterion(outputs, labels)
if cont == 0:
outPre = outputs.data.cpu()
outLabel = labels.data.cpu()
else:
outPre = torch.cat((outPre, outputs.data.cpu()), 0)
outLabel = torch.cat((outLabel, labels.data.cpu()), 0)
pres_list += outputs.cpu().numpy().tolist()
labels_list += labels.data.cpu().numpy().tolist()
running_loss += loss.item() * outputs.size(0)
cont += 1
#
labels_arr = np.array(labels_list)
pre_arr = np.array(pres_list)
val_score = score(labels_arr, pre_arr)
val_rmse = rmse(labels_arr, pre_arr)
return val_score, val_rmse
def crossval_and_predict(self, n_folds: int, df: pd.DataFrame,
df_test: pd.DataFrame, feature_col: list,
target_col: str, model_params: dict):
oof = np.zeros((len(df)))
cv_preds = np.zeros((len(df_test)))
kfold = KFold(n_splits=n_folds,
random_state=self.random_state,
shuffle=True)
for train_idx, valid_idx in kfold.split(df):
X_train, y_train = df[feature_col].iloc[train_idx], df[
target_col].iloc[train_idx]
X_valid, y_valid = df[feature_col].iloc[valid_idx], df[
target_col].iloc[valid_idx]
model_params['n_estimators'] = 5000
model_params['learning_rate'] = 1e-2
model = LGBMRegressor(**model_params)
model.fit(X_train,
y_train,
eval_set=((X_valid, y_valid)),
early_stopping_rounds=500,
verbose=0)
oof[valid_idx] = model.predict(X_valid)
cv_preds += model.predict(df_test[feature_col]) / n_folds
rmse_score = rmse(df[target_col], oof)
return rmse_score, cv_preds
def prediction_pipeline(model, X_train, X_test, y_train, y_test):
"""
This function performs a pipeline for prediction and score on test set.
param model: Estimator
param X_train: Train dataframe without target.
param X_test: Test dataframe without target.
param y_train: Train target.
param y_test: Test target.
return: Predictions and score
"""
model.fit(X_train, y_train)
predict_train = model.predict(X_train)
predict_test = model.predict(X_test)
score_train = rmse(y_train, predict_train)
score_test = rmse(y_test, predict_test)
return predict_train, predict_test, score_train, score_test
def update(self, test_predictions, val_predictions=None, year=None):
self.test_predictions = self.test_predictions.append(test_predictions)
try:
self.test_metrics[str(year + 2014) + '/' + str(year + 15)] = [
metrics.crps(test_predictions),
metrics.nll(test_predictions),
metrics.mae(test_predictions),
metrics.rmse(test_predictions),
metrics.smape(test_predictions),
metrics.corr(test_predictions),
np.ma.masked_invalid(metrics.mb_log(test_predictions)).mean(),
metrics.sdp(test_predictions)
]
except:
pass
if year == 3:
self.test_metrics['Average'] = self.test_metrics.mean(1)
self.test_metrics['Average'].loc['SDP'] = np.abs(
self.test_metrics.loc['SDP'].values[-1]).mean()
try:
self.val_predictions = self.val_predictions.append(val_predictions)
self.val_metrics[str(year + 2013) + '/' + str(year + 14)] = [
metrics.crps(val_predictions),
metrics.nll(val_predictions),
metrics.mae(val_predictions),
metrics.rmse(val_predictions),
metrics.smape(val_predictions),
metrics.corr(val_predictions),
metrics.mb_log(val_predictions).mean(),
metrics.sdp(val_predictions)
]
except:
pass
self.val_metrics['Average'] = self.val_metrics.mean(1)
self.val_metrics['Average'].loc['SDP'] = np.abs(
self.val_metrics.loc['SDP'].values[-1]).mean()
self.test_metrics['Average'] = self.test_metrics.mean(1)
self.test_metrics['Average'].loc['SDP'] = np.abs(
self.test_metrics.loc['SDP'].values[-1]).mean()
def crossval_and_predict(self, n_folds: int, df: pd.DataFrame,
df_test: pd.DataFrame, feature_col: list,
target_col: str, model_params: dict):
oof = np.zeros((len(df)))
cv_preds = np.zeros((len(df_test)))
kfold = KFold(n_splits=n_folds,
random_state=self.random_state,
shuffle=True)
for train_idx, valid_idx in kfold.split(df):
X_train, y_train = df[feature_col].values[train_idx], df[
target_col].values[train_idx].reshape(-1, 1)
X_valid, y_valid = df[feature_col].values[valid_idx], df[
target_col].values[valid_idx].reshape(-1, 1)
X_test = df_test[feature_col].values
params = self.default_params()
params['seed'] = self.random_state
params['n_d'] = model_params['n_d']
params['n_a'] = model_params['n_d']
params['gamma'] = model_params['gamma']
params['momentum'] = model_params['momentum']
params['n_steps'] = model_params['n_steps']
params['n_shared'] = model_params['n_shared']
params['n_independent'] = model_params['n_independent']
logging.info(
f'Parameters used for TabNet supervised training: {params}')
unsupervised_model = TabNetPretrainer(**params)
unsupervised_model.fit(X_train=X_train,
eval_set=[X_valid],
pretraining_ratio=0.5,
max_epochs=20)
model = TabNetRegressor(**params)
model.fit(X_train=X_train,
y_train=y_train,
eval_set=[(X_valid, y_valid)],
eval_name=['valid'],
eval_metric=['rmse'],
max_epochs=100,
patience=10,
batch_size=1024,
from_unsupervised=unsupervised_model)
oof[valid_idx] = model.predict(X_valid).squeeze()
cv_preds += model.predict(X_test).squeeze() / n_folds
logging.info(
f'Finished fold with score {rmse(y_valid, oof[valid_idx])}')
rmse_score = rmse(df[target_col], oof)
return rmse_score, cv_preds
def main():
# minute
y_test, predictions = run()
# hourly
# y_test, predictions = run(50, 1.0)
# daily
# y_test, predictions = run(50, 1.0)
graph_utils.plot('linear', predictions, y_test)
print('RMSE: %.4f'% metrics.rmse(predictions, y_test))
print('MAPE: %.4f'% metrics.mape(predictions, y_test))
def output_metrics(estresults, actresults):
m = {'rmse': {}, 'cvrmse': {}, 'mbe': {}, 'nmbe': {}, 'mape': {}}
estres = column_vectors(estresults)
actres = column_vectors(actresults)
for col in actres:
try:
m['rmse'][col] = metrics.rmse(estres[col], actres[col])
m['cvrmse'][col] = metrics.cvrmse(estres[col], actres[col])
m['mbe'][col] = metrics.mbe(estres[col], actres[col])
m['nmbe'][col] = metrics.nmbe(estres[col], actres[col])
m['mape'][col] = metrics.mape(estres[col], actres[col])
except:
# If anything crashes it here, just ignore the column in the output.
pass
return m
def main():
# minute
model, y_test, predictions = run()
# hourly
# model, y_test, predictions = run(30, 50, 1.0)
# daily
# model, y_test, predictions = run(100, 50, 1.0)
# save for later use
model.save_weights('../output/lstm.h5', overwrite=True)
# model.load_weights('../output/lstm.h5')
graph_utils.plot('lstm', predictions, y_test)
print('RMSE: %.4f'% metrics.rmse(predictions, y_test))
print('MAPE: %.4f'% metrics.mape(predictions, y_test))
def _run_base_model_dfm(dfTrain, dfTest, folds, dfm_params):
fd = FeatureDictionary(dfTrain=dfTrain, dfTest=dfTest,
numeric_cols=config.NUMERIC_COLS,
ignore_cols=config.IGNORE_COLS)
data_parser = DataParser(feat_dict=fd)
Xi_train, Xv_train, y_train = data_parser.parse(df=dfTrain, has_label=True)
Xi_test, Xv_test, ids_test = data_parser.parse(df=dfTest)
dfm_params["feature_size"] = fd.feat_dim
dfm_params["field_size"] = len(Xi_train[0])
y_train_meta = np.zeros((dfTrain.shape[0], 1), dtype=float)
y_test_meta = np.zeros((dfTest.shape[0], 1), dtype=float)
_get = lambda x, l: [x[i] for i in l]
gini_results_cv = np.zeros(len(folds), dtype=float)
gini_results_epoch_train = np.zeros((len(folds), dfm_params["epoch"]), dtype=float)
gini_results_epoch_valid = np.zeros((len(folds), dfm_params["epoch"]), dtype=float)
for i, (train_idx, valid_idx) in enumerate(folds):
Xi_train_, Xv_train_, y_train_ = _get(Xi_train, train_idx), _get(Xv_train, train_idx), _get(y_train, train_idx)
Xi_valid_, Xv_valid_, y_valid_ = _get(Xi_train, valid_idx), _get(Xv_train, valid_idx), _get(y_train, valid_idx)
dfm = DeepFM(**dfm_params)
dfm.fit(Xi_train_, Xv_train_, y_train_, Xi_valid_, Xv_valid_, y_valid_)
y_train_meta[valid_idx,0] = dfm.predict(Xi_valid_, Xv_valid_)
y_test_meta[:,0] += dfm.predict(Xi_test, Xv_test)
gini_results_cv[i] = rmse(y_valid_, y_train_meta[valid_idx])
gini_results_epoch_train[i] = dfm.train_result
gini_results_epoch_valid[i] = dfm.valid_result
y_test_meta /= float(len(folds))
# save result
if dfm_params["use_fm"] and dfm_params["use_deep"]:
clf_str = "DeepFM"
elif dfm_params["use_fm"]:
clf_str = "FM"
elif dfm_params["use_deep"]:
clf_str = "DNN"
print("%s: %.5f (%.5f)"%(clf_str, gini_results_cv.mean(), gini_results_cv.std()))
filename = "%s_Mean%.5f_Std%.5f.csv"%(clf_str, gini_results_cv.mean(), gini_results_cv.std())
_make_submission(ids_test, y_test_meta, filename)
_plot_fig(gini_results_epoch_train, gini_results_epoch_valid, clf_str)
return y_train_meta, y_test_meta
def main():
# dataset has format like [user_id, song_id, play_count]
file = 'train_triplets.txt'
print("Loading data...")
load_data(file)
print("Starting evaluation...")
calc_neighbours()
print("Finished evaluations.")
print_top_songs_for_user(1)
print("Starting cross validation...")
print("RMSE result: ", str(rmse(train_set, test_set)))
print("MAE result: ", str(mae(train_set, test_set)))
print("NDCG result: ", str(ndcg(train_set, test_set)))
def evaluate(self, observed, estimated, zone, res = 0.5):
'''
Returns evaluation between observed and estimated rainfall maps
by computing following metrics:
['BIAS', 'CORREALTION', 'Nash-Sutcliffe', 'RMSE', 'MAE',
'MEAN_OBS','MEAN_EST']
Inputs:
observed - 2D array. Observed rainfall map.
estimated - 2D array. Estimated rainfall map.
zone - (2,2) tuple. Evaluation study zone [km x km]
Optional:
res - scalar. Resolution for comparison, [km].
Outputs:
metrics - dictionary. Statistical metrics:
['bias', 'corr', 'nash', 'rmse', 'mae',
'mean_obs','mean_est']
'''
# We neglect area that is not estimated for comparison purpose.
estimated[estimated <= -999] = -999
observed[estimated <= -999] = -999
((x0, x1), (y0, y1)) = zone
# Cut the zone for evaluation
t1, t2, t3, t4 = int(y0/res), int(y1/res), int(x0/res), int(x1/res)
observed = observed[t1:t2, t3:t4]
estimated = estimated[t1:t2, t3:t4]
est = estimated[estimated<>-999].flatten()
obs = observed[observed<>-999].flatten()
stats = dict()
stats['bias'] = metrics.bias(obs, est)
stats['corr'] = metrics.corr(obs, est)
stats['nash'] = metrics.nash(obs, est)
stats['rmse'] = metrics.rmse(obs, est)
stats['mae'] = metrics.mae(obs, est)
stats['mean_obs'] = metrics.average(obs)
stats['mean_est'] = metrics.average(est)
# additional metrics can be added
##stats['likelihood'] = metrics.likelihood(obs, est)
##stats['mape'] = metrics.mape(obs, est)
##stats['mse'] = metrics.mse(obs, est)
##stats['mspe'] = metrics.mspe(obs, est)
##stats['rmspe'] = metrics.rmspe(obs, est)
return stats
def output_metrics(estresults, actresults):
m = {'rmse': {},
'cvrmse':{},
'mbe': {},
'nmbe':{},
'mape': {}}
estres = column_vectors(estresults)
actres = column_vectors(actresults)
for col in actres:
try:
m['rmse'][col] = metrics.rmse(estres[col], actres[col])
m['cvrmse'][col] = metrics.cvrmse(estres[col], actres[col])
m['mbe'][col] = metrics.mbe(estres[col], actres[col])
m['nmbe'][col] = metrics.nmbe(estres[col], actres[col])
m['mape'][col] = metrics.mape(estres[col], actres[col])
except:
# If anything crashes it here, just ignore the column in the output.
pass
return m
def forward(self, u, v, r_matrix):
u_z, v_z = self.gcl1(self.u_features, self.v_features,
range(self.num_users), range(self.num_items), r_matrix)
u_z, v_z = self.gcl2(u_z, v_z, u, v, r_matrix)
u_f = torch.relu(self.denseu1(self.u_features_side[u]))
v_f = torch.relu(self.densev1(self.v_features_side[v]))
u_h = self.denseu2(F.dropout(torch.cat((u_z, u_f), 1), self.dropout))
v_h = self.densev2(F.dropout(torch.cat((v_z, v_f), 1), self.dropout))
output, m_hat = self.bilin_dec(u_h, v_h, u, v)
r_mx = r_matrix.index_select(1, u).index_select(2, v)
loss = softmax_cross_entropy(output, r_mx.float())
rmse_loss = rmse(m_hat, r_mx.float())
return output, loss, rmse_loss
def lambdas_cross_validate(self, lambdas, fold_ct):
# Use cross-validation to test the lambdas. Set self.lambda_cv to a
# series with index (lambda, fold index) and one column RMSE.
index = pd.MultiIndex.from_product([lambdas, range(fold_ct)],
names=["lambda", "fold"])
out = pd.Series(index=index, name="RMSE", dtype=np.float64)
for lambda_ in lambdas:
folds = sklearn.model_selection.KFold(fold_ct, shuffle=False) \
.split(self.features_train, self.incidence_train)
for (fold_idx, (cv_train_is, cv_test_is)) in enumerate(folds):
X_train = self.features_train.iloc[cv_train_is]
y_train = self.incidence_train.iloc[cv_train_is]
X_test = self.features_train.iloc[cv_test_is]
y_test = self.incidence_train.iloc[cv_test_is]
betas = linear_fit(X_train, y_train, self.penalty, lambda_)
y_predicted = linear_predict(X_test, betas, y_test.index)
out.loc[lambda_, fold_idx] = metrics.rmse(y_test, y_predicted)
self.lambda_cv = out
self.lambda_cv_means = out.groupby(["lambda"
]).mean() # avg across folds
def main():
if sys.argv[1] == 'daily':
print('Using daily data...')
path_to_dataset = '../data/household_power_consumption_daily.csv'
y_test, predictions = run(path_to_dataset, 50, 1.0)
elif sys.argv[1] == 'monthly':
print('Using monthly data...')
path_to_dataset = '../data/household_power_consumption_monthly.csv'
y_test, predictions = run(path_to_dataset, 5, 1.0)
elif sys.argv[1] == 'hourly':
print('Using hourly data...')
path_to_dataset = '../data/household_power_consumption_hourly.csv'
y_test, predictions = run(path_to_dataset, 50, 1.0)
else:
print('Using minute data...')
path_to_dataset = '../data/household_power_consumption.csv'
y_test, predictions = run(path_to_dataset)
graph_utils.plot('linear', predictions, y_test)
print('RMSE: %.4f'% metrics.rmse(predictions, y_test))
print('MAPE: %.4f'% metrics.mape(predictions, y_test))
def test_PPCA():
dense_mat = pd.read_csv('./datasets/Seattle-data-set/mat.csv', index_col=0)
rm = pd.read_csv('./datasets/Seattle-data-set/RM_mat.csv', index_col=0)
dense_mat = dense_mat.values
rm = rm.values
binary_mat2 = np.round(rm + 0.5 - 0.2)
nan_mat2 = binary_mat2.copy()
nan_mat2[nan_mat2 == 0] = np.nan
sparse_mat2 = np.multiply(nan_mat2, dense_mat)
pos2 = np.where((dense_mat != 0) & (binary_mat2 == 0))
# sparse_tensor2 = sparse_mat2.reshape([sparse_mat2.shape[0], 28, 288])
PPCA_res2 = PPCA(sparse_mat2, 20)
PPCA_res2_mape2 = mape(dense_mat[pos2], PPCA_res2[pos2])
PPCA_res2_rmse2 = rmse(dense_mat[pos2], PPCA_res2[pos2])
print("PPCA_res2_mape2", PPCA_res2_mape2)
print("PPCA_res2_rmse2", PPCA_res2_rmse2)
def __call__(self, trial):
df_train, df_valid = train_test_split(self.df,
test_size=0.1,
random_state=self.random_state)
X_train, y_train = df_train[self.feature_col].values, df_train[
self.target_col].values.reshape(-1, 1)
X_valid, y_valid = df_valid[self.feature_col].values, df_valid[
self.target_col].values.reshape(-1, 1)
logging.info(
f'Train/valid split: {X_train.shape[0]} for training, {X_valid.shape[0]} for validation'
)
n_d = trial.suggest_int('n_d', 8, 64)
params = self.default_params
params['n_d'] = n_d
params['n_a'] = n_d
params['seed'] = self.random_state
params['n_steps'] = trial.suggest_int('n_steps', 3, 10)
params['n_shared'] = trial.suggest_int('n_shared', 2, 5)
params['n_independent'] = trial.suggest_int('n_independent', 2, 5)
params['momentum'] = trial.suggest_float('momentum', 0.01, 0.4)
params['gamma'] = trial.suggest_float('gamma', 1.0, 2.0)
model = TabNetRegressor(**params)
model.fit(X_train=X_train,
y_train=y_train,
eval_set=[(X_valid, y_valid)],
eval_metric=['rmse'],
max_epochs=20,
patience=10,
batch_size=1024)
score = rmse(y_valid, model.predict(X_valid).squeeze())
return score
def cross_val_score(model=None, data=None, cv=10, scorer=rmse):
data = np.array(data)
print(data.shape)
chunks = chunk(data, cv)
#print chunks
score = list()
for i in range(10):
iter_data = list()
for j in range(len(chunks)):
if j != i:
iter_data.extend(chunks[j])
pred_data = np.array(chunks[i])
iter_data = np.array(iter_data)
model.fit(iter_data)
pred = model.predict(pred_data)
score.append(rmse(pred_data[:, model.formatizer['value']], pred))
print(score[i])
return np.mean(score)
utilMat = svd(utilMat,k=15)
pred = [] #to store the predicted ratings
for _,row in test.iterrows():
user = row['userId']
item = row['movieId']
if user in user_index:
u_index = user_index[user]
if item in item_index:
i_index = item_index[item]
pred_rating = utilMat[u_index, i_index]
else:
pred_rating = np.mean(utilMat[u_index, :])
else:
if item in item_index:
i_index = item_index[item]
pred_rating = np.mean(utilMat[:, i_index])
else:
pred_rating = np.mean(utilMat[:, :])
pred.append(pred_rating)
error = rmse(test['rating'], pred)
print(error)
errors.append(error)
del error, pred
print np.mean(errors)
def fit(self, X, y, validation_data=None):
y = y.reshape(-1, 1)
start_time = time.time()
l = y.shape[0]
train_idx_shuffle = np.arange(l)
epoch_best_ = 4
rmsle_best_ = 10.
cycle_num = 0
decay_steps = self.params["first_decay_steps"]
global_step = 0
global_step_exp = 0
global_step_total = 0
snapshot_num = 0
learning_rate_need_big_jump = False
total_rmse = 0.
rmse_decay = 0.9
for epoch in range(self.params["epoch"]):
print("epoch: %d" % (epoch + 1))
np.random.seed(epoch)
if snapshot_num >= self.params["snapshot_before_restarts"] and self.params["shuffle_with_replacement"]:
train_idx_shuffle = np.random.choice(np.arange(l), l)
else:
np.random.shuffle(train_idx_shuffle)
batches = self._get_batch_index(train_idx_shuffle, self.params["batch_size_train"])
for i, idx in enumerate(batches):
if snapshot_num >= self.params["max_snapshot_num"]:
break
if learning_rate_need_big_jump:
learning_rate = self.params["lr_jump_rate"] * self.params["max_lr_exp"]
learning_rate_need_big_jump = False
else:
learning_rate = self.params["max_lr_exp"]
lr = _exponential_decay(learning_rate=learning_rate,
global_step=global_step_exp,
decay_steps=decay_steps, # self.params["num_update_each_epoch"],
decay_rate=self.params["lr_decay_each_epoch_exp"])
feed_dict = self._get_feed_dict(X, idx, dropout=0.1, training=False)
feed_dict[self.target] = y[idx]
feed_dict[self.learning_rate] = lr
feed_dict[self.training] = True
rmse_, opt = self.sess.run((self.rmse, self.train_op), feed_dict=feed_dict)
if self.params["RUNNING_MODE"] != "submission":
# scaling rmsle' = (1/scale_) * (raw rmsle)
# raw rmsle = scaling rmsle' * scale_
total_rmse = rmse_decay * total_rmse + (1. - rmse_decay) * rmse_ * (self.target_scaler.scale_)
self.logger.info("[batch-%d] train-rmsle=%.5f, lr=%.5f [%.1f s]" % (
i + 1, total_rmse,
lr, time.time() - start_time))
# save model
global_step += 1
global_step_exp += 1
global_step_total += 1
if self.params["enable_snapshot_ensemble"]:
if global_step % decay_steps == 0:
cycle_num += 1
if cycle_num % self.params["snapshot_every_num_cycle"] == 0:
snapshot_num += 1
print("snapshot num: %d" % snapshot_num)
self._save_state()
self.logger.info("[model-%d] cycle num=%d, current lr=%.5f [%.5f]" % (
snapshot_num, cycle_num, lr, time.time() - start_time))
# reset global_step and first_decay_steps
decay_steps = self.params["first_decay_steps"]
if self.params["lr_jump_exp"] or snapshot_num >= self.params["snapshot_before_restarts"]:
learning_rate_need_big_jump = True
if snapshot_num >= self.params["snapshot_before_restarts"]:
global_step = 0
global_step_exp = 0
decay_steps *= self.params["t_mul"]
if validation_data is not None and global_step_total % self.params["eval_every_num_update"] == 0:
y_pred = self._predict(validation_data[0])
y_valid_inv = self.target_scaler.inverse_transform(validation_data[1])
y_pred_inv = self.target_scaler.inverse_transform(y_pred)
rmsle = rmse(y_valid_inv, y_pred_inv)
self.logger.info("[step-%d] train-rmsle=%.5f, valid-rmsle=%.5f, lr=%.5f [%.1f s]" % (
global_step_total, total_rmse, rmsle, lr, time.time() - start_time))
if rmsle < rmsle_best_:
rmsle_best_ = rmsle
epoch_best_ = epoch + 1
return rmsle_best_, epoch_best_
os.close(cfile)
runner = eplus.EnergyPlus()
eplus_data = runner.run(candidate_filepath, eplus_weather, eplus_schedule, eplus_params['output_directory'])
if eplus_data is None or user_data is None:
if eplus_data is None:
logger.error('evaluator() :: EnergyPlus output is None.')
elif user_data is None:
logger.error('evaluator() :: User data is None.')
fitness = WORST_FITNESS
else:
ep = utilities.column_vectors(eplus_data)
ud = utilities.column_vectors(user_data)
errors = {}
for key in ud:
if 'Date/Time' not in key:
errors[key] = metrics.rmse(ep[key], ud[key])
if eplus_tune_keys is None or len(eplus_tune_keys) == 0:
fitness = sum([errors[k] for k in errors if errors[k] is not None])
else:
fitness = 0
for k in eplus_tune_keys:
k = k.strip()
try:
fitness += errors[k]
except KeyError:
logger.warning('evaluator() :: Tune key {} does not exist in model output.'.format(k))
except TypeError:
logger.warning('evaluator() :: Tune key {} has error value None and is excluded from fitness.'.format(k))
try:
os.remove(candidate_filepath)
except:
if __name__ == "__main__":
# Matrix of movie ratings
train_data, test_data = get_movie_matrix()
# Get all user mean values
user_mean = get_user_mean(train_data)
start = time.time()
prediction_matrix = pd.DataFrame(index=train_data.index)
for name, data in train_data.iteritems():
prediction_matrix[name] = main(train_data, name, k)
# break
logging.info("Process done in: {0:.2f} seconds".format(time.time() -
start))
inter_columns = np.intersect1d(prediction_matrix.columns.values,
test_data.columns.values)
small_pred = prediction_matrix[inter_columns].dropna(how='all')
small_test = test_data.loc[:, inter_columns].dropna(how='all')
print("Test Matrix\n", small_test)
print("Predicted Matrix\n", small_pred.loc[small_test.index, :])
logging.info('\nMetric Calculations RMSE and MAE')
rmse_value = metrics.rmse(test_data, prediction_matrix)
print(f'RMSE:\t{rmse_value}')
mae_value = metrics.mae(test_data, prediction_matrix)
print(f'MAE:\t{mae_value}')
def main():
#getmodel
model = lstm()
adam = Adam(lr=lr)
model.compile(loss='mse',
optimizer=adam,
metrics=[metrics.rmse, metrics.mape, metrics.ma])
all_scores_train = []
all_scores_test = []
#model.summary()
#get data
data = load_data_() #nodes x slots
ts1 = time.time()
trueY_train = []
predictY_train = []
trueY_test = []
predictY_test = []
for i in range(len(data)):
print('grid %d.....' % (i))
ts = time.time()
#makedata set
testslots = T * days_test
trainx, trainy = makedataset(data[i, :-testslots])
testx, testy = makedataset(data[i, -testslots:])
print('trainx shape:', (trainx.shape))
print('trainy shape:', (trainy.shape))
print('testx shape:', (testx.shape))
print('testy shape:', (testy.shape))
#scaler
print(trainy, testy)
mmn = MinMaxScaler(feature_range=(-1, 1))
trainlen = len(trainy)
Y = np.concatenate([trainy, testy], axis=0)
Y = mmn.fit_transform(Y.reshape(-1, 1))
trainy, testy = Y[:trainlen], Y[trainlen:]
print(trainy.shape, testy.shape)
#train
adam = Adam(lr=lr)
model.compile(loss='mse',
optimizer=adam,
metrics=[metrics.rmse, metrics.mape, metrics.ma])
early_stopping = EarlyStopping(monitor='val_rmse',
patience=patience,
mode='min')
history = model.fit(trainx,
trainy,
epochs=nb_epoch,
batch_size=batch_size,
validation_split=0.1,
callbacks=[early_stopping],
verbose=0)
#evalute
predict_y_train = model.predict([trainx],
batch_size=batch_size,
verbose=0)[:, 0:1]
score = model.evaluate(trainx,
trainy,
batch_size=batch_size,
verbose=0)
print(
'Train score: %.6f rmse (norm): %.6f rmse (real): %.6f nrmse : %.6f mape: %.6f ma: %.6f'
% (score[0], score[1], score[1] *
(mmn._max - mmn._min) / 2., score[1] *
(mmn._max - mmn._min) / 2. /
mmn.inverse_transform(np.mean(y_train)), score[2], score[3]))
predict_y_test = model.predict([testx],
batch_size=batch_size,
verbose=0)[:, 0:1]
score = model.evaluate(testx, testy, batch_size=batch_size, verbose=0)
print(
'Test score: %.6f rmse (norm): %.6f rmse (real): %.6f nrmse : %.6f mape: %.6f ma: %.6f'
% (score[0], score[1], score[1] *
(mmn._max - mmn._min) / 2., score[1] *
(mmn._max - mmn._min) / 2. /
mmn.inverse_transform(np.mean(y_test)), score[2], score[3]))
predictY_train.append(
mmn.inverse_transform(predict_y_train).reshape(-1).tolist())
predictY_test.append(
mmn.inverse_transform(predict_y_test).reshape(-1).tolist())
trueY_train.append(mmn.inverse_transform(trainy).reshape(-1).tolist())
trueY_test.append(mmn.inverse_transform(testy).reshape(-1).tolist())
print("\nestimate on grid%d ,elapsed time (eval): %.3f seconds\n" %
(i, time.time() - ts))
#all_scores_train = np.asarray(all_scores_train)
#all_scores_train = np.mean(all_scores_train, axis = 0)
#all_scores_test = np.asarray(all_scores_test)
#all_Scores_test = np.mean(all_scores_test,axis = 0)
print('\n\n')
evaluate = lambda y1, y2: (metrics.rmse(y1, y2), metrics.rmse(
y1, y2) / np.mean(y1), metrics.mape(y1, y2), metrics.ma(y1, y2))
print('All Train rmse (real): %.6f nrmse : %.6f mape: %.6f ma: %.6f' %
(evaluate(trueY_train, predictY_train)))
print('All Test rmse (real): %.6f nrmse : %.6f mape: %.6f ma: %.6f' %
(evaluate(trueY_test, predictY_test)))
print('elapsed time: %3f seconds\n' % (time.time() - ts1))
x_test = x_test[-days_test:, :, -30:]
# y_test = y_test[-days_test:,:]
x_train = x_train[-days_train:, :, -30:]
y_train = y_train[-days_train:, :]
model = model_builder(x_train, y_train, args)
model.fit(x_train, y_train)
pred = model.predict(x_test, y_test)
results[str(fold_num + 2013) + '/' + str(fold_num + 14)] = [
metrics.crps(pred),
metrics.nll(pred),
metrics.mae(pred),
metrics.rmse(pred),
metrics.smape(pred),
metrics.corr(pred),
metrics.mb_log(pred),
metrics.sdp(pred)
]
tf.keras.backend.clear_session()
results['Average'] = results.mean(1)
results['Average'].loc['SDP'] = np.abs(results.loc['SDP'].values[-1]).mean()
plt.plot(pred.index, pred['True'], color='black')
plt.plot(pred.index, pred['Pred'], color='red')
plt.fill_between(pred.index,
pred['Pred'] - pred['Std'],
pred['Pred'] + pred['Std'],
def do_model(all_data, steps, run_model=True):
_steps = steps
print("steps:", _steps)
scaler = MinMaxScaler()
all_data = scaler.fit_transform(all_data)
if not run_model:
return None, None, scaler
features = all_data[:-_steps]
labels = all_data[_steps:, -1:]
tts = train_test_split(features, labels, test_size=0.4)
X_train = tts[0]
X_test = tts[1]
Y_train = tts[2].astype(np.float64)
Y_test = tts[3].astype(np.float64)
optimiser = 'adam'
hidden_neurons = 200
loss_function = 'mse'
batch_size = 105
dropout = 0.056
inner_hidden_neurons = 269
dropout_inner = 0.22
X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))
X_test = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])
print("X train shape:\t", X_train.shape)
print("X test shape:\t", X_test.shape)
# print("Y train shape:\t", Y_train.shape)
# print("Y test shape:\t", Y_test.shape)
# print("Steps:\t", _steps)
in_neurons = X_train.shape[2]
out_neurons = 1
model = Sequential()
gpu_cpu = 'cpu'
best_weight = BestWeight()
model.add(
LSTM(output_dim=hidden_neurons,
input_dim=in_neurons,
return_sequences=True,
init='uniform',
consume_less=gpu_cpu))
model.add(Dropout(dropout))
dense_input = inner_hidden_neurons
model.add(
LSTM(output_dim=dense_input,
input_dim=hidden_neurons,
return_sequences=False,
consume_less=gpu_cpu))
model.add(Dropout(dropout_inner))
model.add(Activation('relu'))
model.add(Dense(output_dim=out_neurons, input_dim=dense_input))
model.add(Activation('relu'))
model.compile(loss=loss_function, optimizer=optimiser)
history = model.fit(X_train,
Y_train,
verbose=0,
batch_size=batch_size,
nb_epoch=30,
validation_split=0.3,
shuffle=False,
callbacks=[best_weight])
model.set_weights(best_weight.get_best())
predicted = model.predict(X_test) + EPS
rmse_val = rmse(Y_test, predicted)
metrics = OrderedDict([
# ('hidden', hidden_neurons),
('steps', _steps),
('geh', geh(Y_test, predicted)),
('rmse', rmse_val),
('mape', mean_absolute_percentage_error(Y_test, predicted)),
# ('smape', smape(predicted, _Y_test)),
# ('median_pe', median_percentage_error(predicted, Y_test)),
# ('mase', MASE(_Y_train, _Y_test, predicted)),
# ('mae', mean_absolute_error(y_true=Y_test, y_pred=predicted)),
# ('batch_size', batch_size),
# ('optimiser', optimiser),
# ('dropout', dropout),
# ('extra_layer_dropout', dropout_inner),
# ('extra_layer_neurons', inner_hidden_neurons),
# ('loss function', loss_function)
# 'history': history.history
])
return metrics, model, scaler
def get_scores(y_true, y_predict):
return dice_coef(y_true, y_predict), sensitivity(y_true, y_predict), specificity(y_true, y_predict), MeanSurfaceDistance(y_true, y_predict), mutual_information(y_true, y_predict), rmse(y_true, y_predict)
# Preprocessing
X = shuffled.iloc[:, :-1].squeeze()
y = (shuffled.iloc[:, -1:]).T.squeeze()
len_estate = len(y)
# Splitting data
X_train, y_train = X.loc[:split*len_estate], y.loc[:split*len_estate]
X_test, y_test = X.loc[split*len_estate+1:].reset_index(
drop=True), y.loc[split*len_estate+1:].reset_index(drop=True)
# Learning tree
print("Please wait for some time, it takes time, you can change max depth if it takes too long time.")
tree = DecisionTree(criterion="information_gain", max_depth=max_depth)
tree.fit(X_train, y_train)
tree.plot()
# Printing accuracies for different depths
for depth in range(2, max_depth+1):
y_hat = tree.predict(X_test, max_depth=depth)
print("Depth: ", depth)
print('\tRMSE: ', rmse(y_hat, y_test))
print('\tMAE: ', mae(y_hat, y_test))
# Decision Tree Regressor from Sci-kit learn
dt = DecisionTreeRegressor(random_state=0)
dt.fit(X_train, y_train)
y_hat = pd.Series(dt.predict(X_test))
print('Sklearn RMSE: ', rmse(y_hat, y_test))
print('Sklearn MAE: ', mae(y_hat, y_test))
k: np.array(v[split_idx:]) for k, v in predictions.items()
}
print()
table = []
print(' & '.join(['step', 'geh', 'mape', 'rmse'])+' \\\\')
for step in steps:
# true values
stepped_vals = flow_values[step:len(predictions)]
# predicted values
pred_vals = predictions[step][:-step] + eps
table.append(OrderedDict([
('steps', step),
('geh', geh(stepped_vals, pred_vals)),
('mape', mape(stepped_vals, pred_vals)),
('rmse', rmse(stepped_vals, pred_vals))
]))
print(tabulate.tabulate(table, 'keys', 'latex'))
print("Loading matplotlib")
import matplotlib.pyplot as plt
true_y = []
true_x = []
pred_y = []
print("Predicting data rows: {}".format(data_len - row_count))
progress = pyprind.ProgBar(data_len - row_count, width=50, stream=1)
for row in it:
progress.update()
preds = model.run(row)
def calculate_train_and_forecast_metrics(
self, train: pd.DataFrame, oos: pd.DataFrame, target_index: int,
hps: dict, horizon: int,
mae_rmse_ignore_when_actual_and_pred_are_zero: bool,
mape_ignore_when_actual_is_zero: bool):
train_dataset = TrainDataset(train_df=train,
target_index=target_index,
hyperparams=hps,
horizon=horizon)
train_loader = DataLoader(train_dataset, batch_size=1, num_workers=1)
inputs, train_actual = next(iter(train_loader))
inputs = inputs.to(device=self.device)
self.net = self.net.to(device=self.device)
train_pred = self.net(inputs.float())
train_actual = train_actual[0, 0, :].cpu().numpy()
train_pred = train_pred[0, 0, :].cpu().detach().numpy()
forecast_actual = oos.iloc[:horizon, target_index].values
forecast_pred = self.predict(train_df, target_index, hps, horizon)
assert (train_actual.shape == train_pred.shape)
assert (forecast_actual.shape == forecast_pred.shape)
train_dict = {
'mae':
metrics.mae(train_actual, train_pred,
mae_rmse_ignore_when_actual_and_pred_are_zero),
'rmse':
metrics.rmse(train_actual, train_pred,
mae_rmse_ignore_when_actual_and_pred_are_zero),
'mape':
metrics.mape(train_actual, train_pred,
mape_ignore_when_actual_is_zero),
'presence_accuracy':
metrics.presence_accuracy(train_actual, train_pred),
'peak_accuracy':
metrics.peak_accuracy(train_actual, train_pred),
'total_volume':
int(metrics.total_actual_volume(train_actual)),
'num_timestamps_predicted_on':
int(train_pred.shape[0])
}
forecast_dict = {
'mae':
metrics.mae(forecast_actual, forecast_pred,
mae_rmse_ignore_when_actual_and_pred_are_zero),
'rmse':
metrics.rmse(forecast_actual, forecast_pred,
mae_rmse_ignore_when_actual_and_pred_are_zero),
'mape':
metrics.mape(forecast_actual, forecast_pred,
mape_ignore_when_actual_is_zero),
'presence_accuracy':
metrics.presence_accuracy(forecast_actual, forecast_pred),
'peak_accuracy':
metrics.peak_accuracy(forecast_actual, forecast_pred),
'total_volume':
int(metrics.total_actual_volume(forecast_actual)),
'num_time_stamps_predicted_on':
int(forecast_pred.shape[0])
}
train_metrics = pd.DataFrame.from_dict(train_dict,
columns=[None],
orient='index').iloc[:,
0].round(3)
forecast_metrics = pd.DataFrame.from_dict(
forecast_dict, columns=[None], orient='index').iloc[:, 0].round(3)
return train_metrics, forecast_metrics
print('Items factorization matrix is:') # item factorization matrix
print(pd.DataFrame(algo.qi))
print()
# predict a single score
# algo.predict(192, 302, 4, verbose=True)
# show predicted score and actual score side by side
# randomly choose first 20 records with percentage 20/100000(# of records in total)
print('Randomly choose 1-10 records to compare side by side:')
limit = 10
for uid, iid, r, timestamp in data.raw_ratings:
if random.random() > limit / 100000:
continue
if limit == 0:
break
algo.predict(uid, iid, r, verbose=True)
limit -= 1
print()
# show rmse
print('RMSE value of all rated scores is:')
predictions = []
for uid, iid, r, timestamp in data.raw_ratings:
prediction = (r, algo.predict(uid, iid, r, verbose=False))
predictions.append(prediction)
metrics.rmse(predictions, verbose=True)
import metrics
import numpy as np
import tensorflow as tf
import math
import tensorflow.keras.backend as K
from tensorflow.keras.layers import Reshape
y_true = np.random.rand(1, 55, 74)
# y_pred = np.random.rand(1, 55, 74)
y_true_tensor = tf.constant(y_true, dtype='float32')
y_pred_tensor = tf.constant(y_true, dtype='float32')
print("y_true_tensor: " + str(y_true_tensor))
print("y_pred_tensor: " + str(y_pred_tensor))
abs_relative_diff = metrics.abs_relative_diff(y_true_tensor, y_pred_tensor)
squared_relative_diff = metrics.squared_relative_diff(y_true_tensor,
y_pred_tensor)
rmse = metrics.rmse(y_true_tensor, y_pred_tensor)
rmse_log = metrics.rmse_log(y_true_tensor, y_pred_tensor)
rmse_scale_invariance_log = metrics.rmse_scale_invariance_log(
y_true_tensor, y_pred_tensor)
print("abs_relative_diff: " + str(abs_relative_diff))
print("squared_relative_diff: " + str(squared_relative_diff))
print("rmse: " + str(rmse))
print("rmse_log: " + str(rmse_log))
print("rmse_scale_invariance_log: " + str(rmse_scale_invariance_log))
def main():
"""
Main function for mpc-scheme with receding horizion.
"""
conf = utils.parse_config()
logpath = None
log = input("Do you wish to log this run? ")
if log in ["y", "yes", "Yes"]:
foldername = input("Do you wish to name logfolder? (enter to skip)")
logpath = utils.create_logs_folder(conf["logpath"], foldername)
openloop = False
predictions = conf["predictions"]
print("Using {} predictions.".format(predictions))
actions_per_hour = conf["actions_per_hour"]
horizon = conf["simulation_horizon"]
simulation_horizon = horizon * actions_per_hour
start_time = time.time()
step_time = start_time
PV, PV_pred, PL, PL_pred, grid_buy, grid_sell = utils.load_data()
T = conf["prediction_horizon"]
N = conf["prediction_horizon"] * actions_per_hour
xk = conf["x_inital"]
xk_sim = conf["x_inital"]
x_opt = np.asarray([xk])
x_sim = np.asarray([xk])
u0 = np.asarray([])
u1 = np.asarray([])
u2 = np.asarray([])
u3 = np.asarray([])
solver = OptiSolver(N)
x, lbx, ubx, lbg, ubg = solver.build_nlp(
T,
N,
)
net_cost_grid = 0
net_cost_bat = 0
J = 0
pv_preds = [PV[0]]
pl_preds = [PL[0]]
pv_error = []
pl_error = []
plt.figure()
if predictions in ["arima", "best"]:
pv_model = Arima("PV", order=(3, 1, 2))
pl_model = Arima("PL", order=(1, 1, 4), seasonal_order=(0, 0, 0, 0))
for step in range(simulation_horizon - N):
# Update NLP parameters
x[0] = xk
lbx[0] = xk
ubx[0] = xk
PV_true = PV[step:step + N]
PL_true = PL[step:step + N]
if predictions == "constant": # Predicted values equal to measurement
pv_ref = np.ones(N) * PV[step]
pl_ref = np.ones(N) * PL[step]
elif predictions == "arima": # Estimate using ARIMA
pv_model.update(PV[step])
pl_model.update(PL[step])
pv_ref = pv_model.predict(T)
pl_ref = pl_model.predict(T)
elif predictions == "data":
pv_ref = PV_pred[step:step + N]
pl_ref = PL_pred[step:step + N]
elif predictions == "scaled_mean":
pv_ref = (PV[step] / PV_pred[step]) * PV_pred[step:step + N]
pl_ref = (PL[step] / PL_pred[step]) * PL_pred[step:step + N]
elif predictions == "best":
pv_model.update(PV[step])
pv_ref = pv_model.predict(T)
pl_ref = (PL[step] / PL_pred[step]) * PL_pred[step:step + N]
else: # Use true predictions
pv_ref = PV_true
pl_ref = PL_true
pv_preds.append(pv_ref[1])
pl_preds.append(pl_ref[1])
pv_error.append(metrics.rmse(PV_true[0:4], pv_ref[0:4]))
pl_error.append(metrics.rmse(PL_true[0:4], pl_ref[0:4]))
plt.plot(range(step, step + N), pv_ref, c="b")
plt.plot(range(step, step + N), PV_true, c="r")
xk_opt, Uk_opt, J_opt = solver.solve_nlp([x, lbx, ubx, lbg, ubg],
vertcat(pv_ref, pl_ref))
J += J_opt
x_opt = np.append(x_opt, xk_opt[1])
xk_sim, Uk_sim = simulate_SOC(
xk_sim,
Uk_opt,
PV[step],
PL[step],
solver.F,
)
x_sim = np.append(x_sim, xk_sim)
if openloop:
xk = xk_opt[1] # xk is optimal
else:
xk = xk_sim
uk = [u[0] for u in Uk_opt]
u0 = np.append(u0, uk[0])
u1 = np.append(u1, uk[1])
u2 = np.append(u2, uk[2])
u3 = np.append(u3, uk[3])
net_cost_grid += metrics.net_spending_grid(uk, 1.5, actions_per_hour)
net_cost_bat += metrics.net_cost_battery(
uk, conf["system"]["battery_cost"], actions_per_hour)
if step % 50 == 0:
print("\nFinshed iteration step {}. Current step took {}s".format(
step, np.around(time.time() - step_time, 2)))
print("xsim {}%, x_opt {}%".format(np.around(xk_sim, 2),
np.around(xk_opt[1], 2)))
step_time = time.time()
peak_power = np.around(np.max(u2), 2) * 70
E_start = conf["x_inital"] * conf["system"]["C_MAX"]
E_end = xk * conf["system"]["C_MAX"]
battery_change = np.around(grid_buy * (E_end - E_start), 2)
print()
print("Error PV prediction:", np.mean(pv_error))
print("Error PL prediction:", np.mean(pl_error))
print("Net spending grid: {} kr".format(np.around(net_cost_grid, 2)))
print("Peak power cost: {} kr".format(peak_power))
print("Net spending battery: {} kr".format(np.around(net_cost_bat, 2)))
print("Grid + battery spending: {} kr".format(
np.around(net_cost_grid + net_cost_bat, 2), ))
print("Change in battery energy {} kr".format(battery_change))
print("Total spending:",
net_cost_grid + net_cost_bat - battery_change + peak_power)
# Plotting
u = np.asarray([-u0, u1, u2, -u3])
u_bat = np.asarray([-u0, u1])
u_grid = np.asarray([u2, -u3])
p.plot_control_actions(u, horizon - T, actions_per_hour, logpath)
p.plot_control_actions(
u_bat,
horizon - T,
actions_per_hour,
logpath,
title="Battery Controls",
legends=["Battery Charge", "Battery Discharge"],
)
p.plot_control_actions(
u_grid,
horizon - T,
actions_per_hour,
logpath,
title="Grid Controls",
legends=["Grid Buy", "Grid Sell"],
)
p.plot_SOC(x_sim, horizon - T, logpath)
p.plot_data(
[x_opt, x_sim],
logpath=logpath,
legends=["SOC optimal", "SOC simulated"],
title="Simulated vs optimal SOC",
)
p.plot_data(
[PV[:simulation_horizon - N], PL[:simulation_horizon - N]],
logpath=logpath,
legends=["PV Production", "Load Demands"],
title="PV Production & Load Demands",
)
p.plot_SOC_control_subplots(x_sim, u, horizon - T, logpath=logpath)
stop = time.time()
print("\nFinished optimation in {}s".format(np.around(
stop - start_time, 2)))
utils.save_datafile(
[x_opt, x_sim, u0, u1, u2, u3, PV, PV_pred, PL, PL_pred],
names=[
"x_opt",
"x_sim",
"u0",
"u1",
"u2",
"u3",
"PV",
"PV_pred",
"PL",
"PL_pred",
],
logpath=logpath,
)
print("One-step PV RMSE:", metrics.rmse_predictions(PV, pv_preds))
print("One-step Load RMSE:", metrics.rmse_predictions(PL, pl_preds))
if conf["plot_predictions"]:
p.plot_predictions_subplots(PV, pv_preds, PL, pl_preds, logpath)
plt.show(block=True)
plt.ion()
plt.close("all")