def test_naive2(self):
smape_naive2 = SMAPE(self.test_data, self.naive2_predictions)
mase_naive2 = MASE(self.test_data, self.naive2_predictions, scale)
owa_naive2 = round(OWA(mase_naive2, smape_naive2), 3)
self.assertEqual(round(smape_naive2, 3), 13.564, "Should be 13.564")
self.assertEqual(round(mase_naive2, 3), 1.912, "Should be 1.912")
self.assertEqual(owa_naive2, 1.000, "Should be 1.000")
def test_naive(self):
smape_naive = SMAPE(self.test_data, self.naive_predictions)
mase_naive = MASE(self.test_data, self.naive_predictions, scale)
owa_naive = round(OWA(mase_naive, smape_naive), 3)
self.assertEqual(round(smape_naive, 3), 14.208, "Should be 14.208")
self.assertEqual(round(mase_naive, 3), 2.044, "Should be 2.044")
self.assertEqual(owa_naive, 1.058, "Should be 1.058")
def test_montero(self):
smape_montero = SMAPE(self.test_data, self.montero_predictions)
mase_montero = MASE(self.test_data, self.montero_predictions, scale)
owa_montero = round(OWA(mase_montero, smape_montero), 3)
self.assertEqual(round(smape_montero, 3), 11.720, "Should be 11.720")
self.assertEqual(round(mase_montero, 3), 1.551, "Should be 1.551")
self.assertEqual(owa_montero, 0.838, "Should be 0.838")
def test_smyl(self):
smape_smyl = SMAPE(self.test_data, self.smyl_predictions)
mase_smyl = MASE(self.test_data, self.smyl_predictions, scale)
owa_smyl = OWA(mase_smyl, smape_smyl)
self.assertEqual(round(smape_smyl, 3), 11.374, "Should be 11.374")
self.assertEqual(round(mase_smyl, 3), 1.536, "Should be 1.536")
self.assertEqual(round(owa_smyl, 3), 0.821, "Should be 0.821")
def test_owa(self):
smape_smyl = SMAPE(self.test_data, self.smyl_predictions)
smape_montero = SMAPE(self.test_data, self.montero_predictions)
smape_naive = SMAPE(self.test_data, self.naive_predictions)
smape_naive2 = SMAPE(self.test_data, self.naive2_predictions)
mase_smyl = MASE(self.test_data, self.smyl_predictions, scale)
mase_montero = MASE(self.test_data, self.montero_predictions, scale)
mase_naive = MASE(self.test_data, self.naive_predictions, scale)
mase_naive2 = MASE(self.test_data, self.naive2_predictions, scale)
owa_smyl = round(OWA(mase_smyl, smape_smyl), 3)
owa_montero = round(OWA(mase_montero, smape_montero), 3)
owa_naive = round(OWA(mase_naive, smape_naive), 3)
owa_naive2 = round(OWA(mase_naive2, smape_naive2), 3)
self.assertEqual(owa_smyl, 0.821, "Should be 0.821")
self.assertEqual(owa_montero, 0.838, "Should be 0.838")
self.assertEqual(owa_naive, 1.058, "Should be 1.058")
self.assertEqual(owa_naive2, 1.000, "Should be 1.000")
def score_M4(
predictions: np.array, df_results_name: str = "GPTime/results/M4/test.csv", val:bool=False
) -> Dict:
""" Calculating the OWA. Return dict of scores of subfrequencies also."""
frequency_metrics: Dict[str, Dict[str, float]] = {}
# Read in and prepare the data
if val:
all_test_files = glob.glob(cfg.path.m4_val_test + "*")
all_train_files = glob.glob(cfg.path.m4_val_train + "*")
else:
all_test_files = glob.glob(cfg.path.m4_test + "*")
all_train_files = glob.glob(cfg.path.m4_train + "*")
# Removing hourly for the zero-shot part
#all_train_files = [fname for fname in all_train_files if "hourly" not in fname.lower()]
#all_test_files = [fname for fname in all_test_files if "hourly" not in fname.lower()]
all_test_files.sort()
all_train_files.sort()
crt_pred_index = 0
tot_mase = 0.0
tot_smape = 0.0
for fname_train, fname_test in zip(all_train_files, all_test_files):
#logger.info(fname_test)
#logger.info(fname_train)
df_train = pd.read_csv(fname_train, index_col=0)
df_test = pd.read_csv(fname_test, index_col=0)
period_num, period_str = period_from_fname(
fname=fname_train, period_dict=cfg.scoring.m4.periods
)
horizon = cfg.scoring.m4.horizons[period_str]
Y = df_test.values[:, :horizon]
index = crt_pred_index + Y.shape[0]
predicted = predictions[crt_pred_index:index, :horizon]
#logger.info(f"predicted.shape: {predicted.shape}")
assert np.sum(np.isnan(Y)) == 0, "NaNs in Y"
assert np.sum(np.isnan(predicted)) == 0, f"NaNs in predictions: {np.where(np.isnan(predicted))}"
assert Y.shape == predicted.shape, "Y and predicted have different shapes"
#scale = Scaler().fit(df_train.values, freq=period_num).scale_.flatten()
scale = MASEScaler().fit(df_train.values, freq=period_num).scale_.flatten()
mase_freq = MASE(Y, predicted, scale)
smape_freq = SMAPE(Y, predicted)
owa_freq = OWA(mase=mase_freq, smape=smape_freq, freq=period_str)
tot_mase += mase_freq * Y.shape[0]
tot_smape += smape_freq * Y.shape[0]
#logger.debug(f"mase_freq = {mase_freq}")
#logger.debug(f"smape_freq = {smape_freq}")
frequency_metrics[period_str] = {}
frequency_metrics[period_str]["MASE"] = mase_freq
frequency_metrics[period_str]["SMAPE"] = smape_freq
frequency_metrics[period_str]["OWA"] = owa_freq
crt_pred_index += Y.shape[0]
tot_mase = tot_mase / crt_pred_index
tot_smape = tot_smape / crt_pred_index
tot_owa = OWA(tot_mase, tot_smape, freq="global")
frequency_metrics["GLOBAL"] = {}
frequency_metrics["GLOBAL"]["MASE"] = tot_mase
frequency_metrics["GLOBAL"]["SMAPE"] = tot_smape
frequency_metrics["GLOBAL"]["OWA"] = tot_owa
df = pd.DataFrame(frequency_metrics).T
df.to_csv(df_results_name)
return frequency_metrics
def score_M4(predictions: np.array,
df_results_name: str = "GPTime/results/M4/test.csv") -> Dict:
""" Calculating the OWA. Return dict of scores of subfrequencies also."""
"""
metrics = {}
frequency_metrics = {}
for metric in cfg.scoring.metrics.keys():
if cfg.scoring.metrics[metric]:
metrics[metric] = []
frequency_metrics[metric] = []
"""
frequency_metrics: Dict[str, Dict[str, float]] = {}
# Read in and prepare the data
all_test_files = glob.glob(cfg.path.m4_test + "*")
all_train_files = glob.glob(cfg.path.m4_test + "*")
crt_pred_index = 0
tot_mase = 0.0
tot_smape = 0.0
for fname_train, fname_test in zip(all_train_files, all_test_files):
df_train = pd.read_csv(fname_train, index_col=0)
df_test = pd.read_csv(fname_test, index_col=0)
period_num, period_str = period_num_str_file(
fname=fname_train, period_dict=cfg.scoring.m4.periods)
horizon = cfg.scoring.m4.horizons[period_str]
scale = (df_train.diff(
periods=period_num,
axis=1).abs().mean(axis=1).reset_index(drop=True)).values
Y = df_test.values[:, :horizon]
index = crt_pred_index + Y.shape[0]
predicted = predictions[crt_pred_index:index, :horizon]
assert np.sum(np.isnan(Y)) == 0, "NaNs in Y"
assert np.sum(np.isnan(predicted)) == 0, "NaNs in predictions"
assert Y.shape == predicted.shape, "Y and predicted have different shapes"
mase_freq = MASE(Y, predicted, scale)
smape_freq = SMAPE(Y, predicted)
owa_freq = OWA(mase=mase_freq, smape=smape_freq, freq=period_str)
tot_mase += mase_freq * Y.shape[0]
tot_smape += smape_freq * Y.shape[0]
frequency_metrics[period_str] = {}
frequency_metrics[period_str]["MASE"] = mase_freq
frequency_metrics[period_str]["SMAPE"] = smape_freq
frequency_metrics[period_str]["OWA"] = owa_freq
crt_pred_index += Y.shape[0]
tot_mase = tot_mase / crt_pred_index
tot_smape = tot_smape / crt_pred_index
tot_owa = OWA(tot_mase, tot_smape, freq="global")
frequency_metrics["GLOBAL"] = {}
frequency_metrics["GLOBAL"]["MASE"] = tot_mase
frequency_metrics["GLOBAL"]["SMAPE"] = tot_smape
frequency_metrics["GLOBAL"]["OWA"] = tot_owa
df = pd.DataFrame(frequency_metrics).T
df.to_csv(df_results_name)
return frequency_metrics