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feat: Added normalizaton.

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Daniel Szemerey
2021-11-16 10:20:35 +01:00
parent 1d6eed3a94
commit b0b0d5bbba
1 changed files with 10 additions and 5 deletions
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lightning/forecasting.py
+10 -5
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@@ -15,7 +15,7 @@ print("success")
#%% #%%
# load data # load data
data = load_files('data/', add_features=True, log_returns=False, narrow_format=True) data = load_files('../data/', add_features=True, log_returns=False, narrow_format=True)
# need to treat time as an independent column # need to treat time as an independent column
data = data.reset_index().rename({'index':'time'}, axis = 'columns') data = data.reset_index().rename({'index':'time'}, axis = 'columns')
# we need a `time_idx` column for pytorch-forecasting, so we convert the time column to a time index by encoding the dates as consecutive days from the first date. # we need a `time_idx` column for pytorch-forecasting, so we convert the time column to a time index by encoding the dates as consecutive days from the first date.
@@ -37,6 +37,11 @@ max_prediction_length = 6
# training_cutoff = data["time_idx"].max() - max_prediction_length # training_cutoff = data["time_idx"].max() - max_prediction_length
#%%
data.head()
data.describe()
#%%
training = TimeSeriesDataSet( training = TimeSeriesDataSet(
data, # data[lambda x: x.date < training_cutoff], data, # data[lambda x: x.date < training_cutoff],
time_idx= 'time_idx', time_idx= 'time_idx',
@@ -54,8 +59,8 @@ training = TimeSeriesDataSet(
time_varying_unknown_categoricals=[ ], time_varying_unknown_categoricals=[ ],
time_varying_unknown_reals=[ 'returns' ], time_varying_unknown_reals=[ 'returns' ],
allow_missing_timesteps=True, allow_missing_timesteps=True,
# target_normalizer=GroupNormalizer( target_normalizer=GroupNormalizer(
# groups=['ADA_returns', 'BTC_returns'], transformation="softplus") groups=['ticker'], transformation="softplus")
) )
#%% #%%
@@ -67,8 +72,8 @@ print(training.index.time.max())
validation = TimeSeriesDataSet.from_dataset(training, data, predict=True, stop_randomization=True) validation = TimeSeriesDataSet.from_dataset(training, data, predict=True, stop_randomization=True)
batch_size = 128 batch_size = 128
train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=2) train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=0)
val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=2) val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=0)
# define trainer with early stopping # define trainer with early stopping
early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=1, verbose=False, mode="min") early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=1, verbose=False, mode="min")