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Implemented Probabilistic Baseline

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Victor Mylle
2023-11-19 20:20:31 +00:00
parent 1268af47a6
commit 166d3967e1
4 changed files with 182 additions and 36 deletions
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16
src/data/preprocessing.py
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@@ -17,7 +17,7 @@ class DataConfig:
self.NRV_HISTORY: bool = True
### LOAD ###
self.LOAD_FORECAST: bool = True
self.LOAD_FORECAST: bool = False
self.LOAD_HISTORY: bool = False
### PV ###
@@ -51,6 +51,13 @@ class DataProcessor:
self.nrv_scaler = MinMaxScaler(feature_range=(-1, 1))
self.load_forecast_scaler = MinMaxScaler(feature_range=(-1, 1))
self.full_day_skip = False
def set_data_config(self, data_config: DataConfig):
self.data_config = data_config
def set_full_day_skip(self, full_day_skip: bool):
self.full_day_skip = full_day_skip
def set_train_range(self, train_range: tuple):
self.train_range = train_range
@@ -115,7 +122,8 @@ class DataProcessor:
self.batch_size = batch_size
def get_dataloader(self, dataset, shuffle: bool = True):
return torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, shuffle=shuffle, num_workers=4)
batch_size = len(dataset) if self.batch_size is None else self.batch_size
return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=4)
def get_train_dataloader(self, transform: bool = True, predict_sequence_length: int = 96):
train_df = self.all_features.copy()
@@ -131,7 +139,7 @@ class DataProcessor:
train_df['load_forecast'] = self.load_forecast_scaler.fit_transform(train_df['load_forecast'].values.reshape(-1, 1)).reshape(-1)
train_df['total_load'] = self.load_forecast_scaler.transform(train_df['total_load'].values.reshape(-1, 1)).reshape(-1)
train_dataset = NrvDataset(train_df, data_config=self.data_config, predict_sequence_length=predict_sequence_length)
train_dataset = NrvDataset(train_df, data_config=self.data_config, full_day_skip=self.full_day_skip, predict_sequence_length=predict_sequence_length)
return self.get_dataloader(train_dataset)
def get_test_dataloader(self, transform: bool = True, predict_sequence_length: int = 96):
@@ -149,7 +157,7 @@ class DataProcessor:
test_df['load_forecast'] = self.load_forecast_scaler.transform(test_df['load_forecast'].values.reshape(-1, 1)).reshape(-1)
test_df['total_load'] = self.load_forecast_scaler.transform(test_df['total_load'].values.reshape(-1, 1)).reshape(-1)
test_dataset = NrvDataset(test_df, data_config=self.data_config, predict_sequence_length=predict_sequence_length)
test_dataset = NrvDataset(test_df, data_config=self.data_config, full_day_skip=self.full_day_skip, predict_sequence_length=predict_sequence_length)
return self.get_dataloader(test_dataset, shuffle=False)