Data leakage problem

[vishnu020]

!pip install lightning !pip install pytorch-forecasting !pip install torch !pip install optuna==3.4 !pip install torch==2.0.1

max_prediction_length = 6
max_encoder_length = 12
training_cutoff = df["time_idx"].max() - max_prediction_length
training =TimeSeriesDataSet(
    df[lambda x: x.time_idx <= training_cutoff],
    time_idx="time_idx",
    target="total_sales",
    group_ids=['group_ids'],
    max_encoder_length=max_encoder_length,
    
    max_prediction_length=max_prediction_length,
    static_categoricals=["group_ids"],

    time_varying_known_reals=['retail_food_sales_vol_bil_ch_usd__Month_MA2',
                              'retail_sales_food_services_bil_usd',
                              'change_private_inv_minus_retail_trade_bil_ch_usd__Month_MA2',
                              'month', 'year', 'day_of_week', 'quarter', '12th_month'],
    time_varying_unknown_reals=["total_sales"],
    predict_mode=False,
    allow_missing_timesteps=True
)

validation = TimeSeriesDataSet.from_dataset(training, df, predict=True, stop_randomization=True)
batch_size = 10
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 * 10, num_workers=0)


early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=10, verbose=False, mode="min")
lr_logger = LearningRateMonitor()  # log the learning rate
logger = TensorBoardLogger("lightning_logs")  # logging results to a tensorboard

trainer = pl.Trainer(
    max_epochs=10,
    accelerator="cpu",
    enable_model_summary=True,
    gradient_clip_val=0.1,
    limit_train_batches=50,  # coment in for training, running valiation every 30 batches
    # fast_dev_run=True,  # comment in to check that networkor dataset has no serious bugs
    callbacks=[lr_logger, early_stop_callback],
    logger=logger,
)

tft = TemporalFusionTransformer.from_dataset(
    training,
    learning_rate=0.03,
    hidden_size=16,
    attention_head_size=2,
    dropout=0.1,
    hidden_continuous_size=8,
    loss=QuantileLoss(),
    log_interval=10,  # uncomment for learning rate finder and otherwise, e.g. to 10 for logging every 10 batches
    optimizer="Ranger",
    reduce_on_plateau_patience=4,
)
print(f"Number of parameters in network: {tft.size()/1e3:.1f}k")

trainer.fit(
    tft,
    train_dataloaders=train_dataloader,
    val_dataloaders=val_dataloader,
)

best_model_path = trainer.checkpoint_callback.best_model_path
print(best_model_path)
best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path)

predictions = best_tft.predict(val_dataloader, return_y=True, trainer_kwargs=dict(accelerator="cpu"))
MAE()(predictions.output, predictions.y)

**This code is working fine but when i tried to forecast on unseen data below it is giving error **

params = training.get_parameters()
inference_ts_dataset = TimeSeriesDataSet.from_parameters(data= filtered_base_data, parameters=params, predict=True)
predictions = model.predict(inference_ts_dataset.to_dataloader(train=False), raw=True)

##It is giving the following error

KeyError Traceback (most recent call last) ~\AppData\Roaming\Python\Python39\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance) 3801 try: -> 3802 return self._engine.get_loc(casted_key) 3803 except KeyError as err:

~\AppData\Roaming\Python\Python39\site-packages\pandas_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

~\AppData\Roaming\Python\Python39\site-packages\pandas_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'total_sales'

The above exception was the direct cause of the following exception:

KeyError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_27744\1361187920.py in ----> 1 inference_ts_dataset = TimeSeriesDataSet.from_parameters(data= filtered_base_data, parameters=params, predict=True) 2 predictions = model.predict(inference_ts_dataset.to_dataloader(train=False), raw=True)

~\AppData\Roaming\Python\Python39\site-packages\pytorch_forecasting\data\timeseries.py in from_parameters(cls, parameters, data, stop_randomization, predict, **update_kwargs) 1198 parameters.update(update_kwargs) 1199 -> 1200 new = cls(data, **parameters) 1201 return new 1202

~\AppData\Roaming\Python\Python39\site-packages\pytorch_forecasting\data\timeseries.py in init(self, data, time_idx, target, group_ids, weight, max_encoder_length, min_encoder_length, min_prediction_idx, min_prediction_length, max_prediction_length, static_categoricals, static_reals, time_varying_known_categoricals, time_varying_known_reals, time_varying_unknown_categoricals, time_varying_unknown_reals, variable_groups, constant_fill_strategy, allow_missing_timesteps, lags, add_relative_time_idx, add_target_scales, add_encoder_length, target_normalizer, categorical_encoders, scalers, randomize_length, predict_mode) 474 475 # preprocess data --> 476 data = self._preprocess_data(data) 477 for target in self.target_names: 478 assert target not in self.scalers, "Target normalizer is separate and not in scalers."

~\AppData\Roaming\Python\Python39\site-packages\pytorch_forecasting\data\timeseries.py in _preprocess_data(self, data) 742 f"target{target}" not in data.columns 743 ), f"target{target} is a protected column and must not be present in data" --> 744 data[f"target{target}"] = data[target] 745 if self.weight is not None: 746 data["weight"] = data[self.weight]

~\AppData\Roaming\Python\Python39\site-packages\pandas\core\frame.py in getitem(self, key) 3805 if self.columns.nlevels > 1: 3806 return self._getitem_multilevel(key) -> 3807 indexer = self.columns.get_loc(key) 3808 if is_integer(indexer): 3809 indexer = [indexer]

~\AppData\Roaming\Python\Python39\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance) 3802 return self._engine.get_loc(casted_key) 3803 except KeyError as err: -> 3804 raise KeyError(key) from err 3805 except TypeError: 3806 # If we have a listlike key, _check_indexing_error will raise

KeyError: 'total_sales'

[ivanightingale]

I'm assuming filtered_base_data does not have the "total_sales" column. Since you are using the same parameters as training (in which target is set to "total_sales") to create inference_ts_dataset, it complains because it can't find "total_sales" in filtered_base_data.

I don't think this necessarily means there is data leakage in predict.

[ivanightingale]

@vishnu020 if that solved your problem, consider closing the issue.