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partially converted to M2S (multi inputs)

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wassname
2022-11-22 16:42:58 +08:00
parent 00ae1d8b8f
commit 9072613d03
7 changed files with 647 additions and 38 deletions
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data/datasets.py
+31 -17
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@@ -43,14 +43,15 @@ class ForecastDataset(Dataset):
"""
assert flag in ('train', 'val', 'test'), \
f"flag should be one of (train, val, test)"
assert features in ('M', 'S'), \
f"features should be one of (M: multivar, S: univar)"
assert features in ('M', 'S', 'M2S'), \
f"features should be one of (M: multivar, S: univar, M2S: multi inputs 2 single output)"
assert (lookback_len is not None) ^ (lookback_mult is not None), \
f"only 'lookback_len' xor 'lookback_mult' should be specified"
self.flag = flag
self.lookback_len = int(horizon_len * lookback_mult) if lookback_mult is not None else lookback_len
self.lookback_aux_len = lookback_aux_len
self.gap = 0
self.horizon_len = horizon_len
self.scale = scale
self.cross_learn = cross_learn
@@ -84,8 +85,11 @@ class ForecastDataset(Dataset):
elif self.features == 'S':
df_data = df_raw[[self.target]]
self.n_dims = 1
elif self.features == 'M2S':
df_data = df_raw[cols + [self.target]]
self.n_dims = 1 # len(cols + [self.target])
else:
raise ValueError
raise NotImplementedError(self.features)
self.scaler = StandardScaler()
if self.scale:
@@ -95,13 +99,15 @@ class ForecastDataset(Dataset):
else:
data = df_data.values
self.data_x = data[border1:border2]
self.data_y = data[border1:border2]
# is will be our past data, including the y col
self.data_x = data[border1:border2]
# y is just the col we predict
self.data_y = data[border1:border2][:, [-1]]
self.timestamps = get_time_features(pd.to_datetime(df_raw.date[border1:border2].values),
normalise=self.normalise_time_features,
features=self.time_features)
self.n_time = len(self.data_x)
self.n_time_samples = self.n_time - self.lookback_len - self.horizon_len + 1
self.n_time_samples = self.n_time - self.lookback_len * 2 - self.horizon_len + 1 + self.gap
def get_borders(self, df_raw: pd.DataFrame) -> Tuple[List[int], List[int], List[int], List[int]]:
set_type = {'train': 0, 'val': 1, 'test': 2}[self.flag]
@@ -133,18 +139,26 @@ class ForecastDataset(Dataset):
idx = idx % self.n_time_samples
else:
dim_slice = slice(None)
cx_start = idx
cx_end = cx_start + self.lookback_len
c_start = cx_end + self.gap
c_end = c_start + self.horizon_len
qx_start = cx_end + self.gap
qx_end = qx_start + self.lookback_len
q_start = qx_end + self.gap
q_end = q_start + self.horizon_len
x_start = idx
x_end = x_start + self.lookback_len
y_start = x_end - self.lookback_aux_len
y_end = y_start + self.lookback_aux_len + self.horizon_len
x = self.data_x[x_start:x_end, dim_slice]
y = self.data_y[y_start:y_end, dim_slice]
x_time = self.timestamps[x_start:x_end]
y_time = self.timestamps[y_start:y_end]
return x, y, x_time, y_time
context_past_x = self.data_x[cx_start:cx_end, dim_slice]
context_y = self.data_y[c_start:c_end, dim_slice]
context_time = self.timestamps[c_start:c_end]
query_past_x = self.data_x[qx_start:qx_end, dim_slice]
query_y = self.data_y[q_start:q_end, dim_slice]
query_time = self.timestamps[q_start:q_end]
return context_past_x, context_y, query_past_x, query_y, context_time, query_time
def inverse_transform(self, data):
return self.scaler.inverse_transform(data)