ETNA-752: pytorch-forecasting

etna/models/nn/deepar.py

@@ -0,0 +1,140 @@
+from typing import List
+from typing import Optional
+from typing import Union
+
+import pandas as pd
+import pytorch_lightning as pl
+from pytorch_forecasting.data import TimeSeriesDataSet
+from pytorch_forecasting.models import DeepAR
+
+from etna.datasets.tsdataset import TSDataset
+from etna.models.base import Model
+
+
+class DeepARModel(Model):
+    """Wrapper for DeepAR from Pytorch Forecasting library.
+    Notes
+    -----
+    We save TimeSeriesDataSet in instance to use it in the model.
+    It`s not right pattern of using Transforms and TSDataset.
+    """
+
+    def __init__(
+        self,
+        batch_size: int = 64,
+        context_length: Optional[int] = None,
+        max_epochs: int = 10,
+        gpus: Union[int, List[int]] = 0,
+        gradient_clip_val: float = 0.1,
+        learning_rate: List[float] = [0.001],
+        cell_type: str = "LSTM",
+        hidden_size: int = 10,
+        rnn_layers: int = 2,
+        dropout: float = 0.1,
+    ):
+        """
+        Initialize DeepAR wrapper.
+
+        Parameters
+        ----------
+        batch_size:
+            Batch size.
+        context_length:
+            Max encoder length, if None max encoder length is equal to 2 horizons.
+        max_epochs:
+            Max epochs.
+        gpus:
+            0 - is CPU, or [n_{i}] - to choose n_{i} GPU from cluster.
+        gradient_clip_val:
+            Cliping by norm is using, choose 0 to not clip.
+        learning_rate:
+            Learning rate.
+        cell_type:
+            One of 'LSTM', 'GRU'.
+        hidden_size:
+            Hidden size of network which can range from 8 to 512.
+        rnn_layers:
+            Number of LSTM layers.
+        dropout:
+            Dropout rate.
+        """
+        self.max_epochs = max_epochs
+        self.gpus = gpus
+        self.gradient_clip_val = gradient_clip_val
+        self.learning_rate = learning_rate
+        self.batch_size = batch_size
+        self.context_length = context_length
+        self.cell_type = cell_type
+        self.hidden_size = hidden_size
+        self.rnn_layers = rnn_layers
+        self.dropout = dropout
+
+    def _from_dataset(self, ts_dataset: TimeSeriesDataSet) -> DeepAR:
+        """
+        Construct DeepAR.
+
+        Returns
+        -------
+        DeepAR
+            Class instance.
+        """
+        return DeepAR.from_dataset(
+            ts_dataset,
+            learning_rate=self.learning_rate,
+            cell_type=self.cell_type,
+            hidden_size=self.hidden_size,
+            rnn_layers=self.rnn_layers,
+            dropout=self.dropout,
+        )
+
+    def fit(self, ts: TSDataset) -> "DeepARModel":
+        """
+        Fit model.
+
+        Parameters
+        ----------
+        ts:
+            TSDataset to fit.
+
+        Returns
+        -------
+        DeepARModel
+        """
+        self.model = self._from_dataset(ts.transforms[-1].pf_dataset_train)
+
+        self.trainer = pl.Trainer(
+            logger=False,
+            max_epochs=self.max_epochs,
+            gpus=self.gpus,
+            checkpoint_callback=False,
+            gradient_clip_val=self.gradient_clip_val,
+        )
+
+        train_dataloader = ts.transforms[-1].pf_dataset_train.to_dataloader(train=True, batch_size=self.batch_size)
+
+        self.trainer.fit(self.model, train_dataloader)
+
+        return self
+
+    def forecast(self, ts: TSDataset) -> TSDataset:
+        """
+        Predict future.
+
+        Parameters
+        ----------
+        ts:
+            TSDataset to forecast.
+
+        Returns
+        -------
+        TSDataset
+            TSDataset with predictions.
+        """
+        prediction_dataloader = ts.transforms[-1].pf_dataset_predict.to_dataloader(
+            train=False, batch_size=self.batch_size * 2
+        )
+
+        predicts = self.model.predict(prediction_dataloader).numpy()  # shape (segments, encoder_lenght)
+
+        ts.loc[:, pd.IndexSlice[:, "target"]] = predicts.T[-len(ts.df) :]
+        return ts

etna/models/nn/tft.py

@@ -0,0 +1,148 @@
+from typing import List
+from typing import Optional
+from typing import Union
+
+import pandas as pd
+import pytorch_lightning as pl
+from pytorch_forecasting.data import TimeSeriesDataSet
+from pytorch_forecasting.models import TemporalFusionTransformer
+
+from etna.datasets.tsdataset import TSDataset
+from etna.models.base import Model
+
+
+class TFTModel(Model):
+    """Wrapper for TemporalFusionTransformer from Pytorch Forecasting library.
+    Notes
+    -----
+    We save TimeSeriesDataSet in instance to use it in the model.
+    It`s not right pattern of using Transforms and TSDataset.
+    """
+
+    def __init__(
+        self,
+        max_epochs: int = 10,
+        gpus: Union[int, List[int]] = 0,
+        gradient_clip_val: float = 0.1,
+        learning_rate: List[float] = [0.001],
+        batch_size: int = 64,
+        context_length: Optional[int] = None,
+        hidden_size: int = 16,
+        lstm_layers: int = 1,
+        attention_head_size: int = 4,
+        dropout: float = 0.1,
+        hidden_continuous_size: int = 8,
+        *args,
+        **kwargs,
+    ):
+        """
+        Initialize TFT wrapper.
+
+        Parameters
+        ----------
+        batch_size:
+            Batch size.
+        context_length:
+            Max encoder length, if None max encoder length is equal to 2 horizons.
+        max_epochs:
+            Max epochs.
+        gpus:
+            0 - is CPU, or [n_{i}] - to choose n_{i} GPU from cluster.
+        gradient_clip_val:
+            Cliping by norm is using, choose 0 to not clip.
+        learning_rate:
+            Learning rate.
+        hidden_size:
+            Hidden size of network which can range from 8 to 512.
+        lstm_layers:
+            Number of LSTM layers.
+        attention_head_size:
+            Number of attention heads.
+        dropout:
+            Dropout rate.
+        hidden_continuous_size:
+            Hidden size for processing continous variables.
+        """
+        self.max_epochs = max_epochs
+        self.gpus = gpus
+        self.gradient_clip_val = gradient_clip_val
+        self.learning_rate = learning_rate
+        self.horizon = None
+        self.batch_size = batch_size
+        self.context_length = context_length
+        self.hidden_size = hidden_size
+        self.lstm_layers = lstm_layers
+        self.attention_head_size = attention_head_size
+        self.dropout = dropout
+        self.hidden_continuous_size = hidden_continuous_size
+
+    def _from_dataset(self, ts_dataset: TimeSeriesDataSet) -> TemporalFusionTransformer:
+        """
+        Construct TemporalFusionTransformer.
+
+        Returns
+        -------
+        TemporalFusionTransformer
+            Class instance.
+        """
+        return TemporalFusionTransformer.from_dataset(
+            ts_dataset,
+            learning_rate=self.learning_rate,
+            hidden_size=self.hidden_size,
+            lstm_layers=self.lstm_layers,
+            attention_head_size=self.attention_head_size,
+            dropout=self.dropout,
+            hidden_continuous_size=self.hidden_continuous_size,
+        )
+
+    def fit(self, ts: TSDataset) -> "TFTModel":
+        """
+        Fit model.
+
+        Parameters
+        ----------
+        ts:
+            TSDataset to fit.
+
+        Returns
+        -------
+        TFTModel
+        """
+        self.model = self._from_dataset(ts.transforms[-1].pf_dataset_train)
+
+        self.trainer = pl.Trainer(
+            logger=False,
+            max_epochs=self.max_epochs,
+            gpus=self.gpus,
+            checkpoint_callback=False,
+            gradient_clip_val=self.gradient_clip_val,
+        )
+
+        train_dataloader = ts.transforms[-1].pf_dataset_train.to_dataloader(train=True, batch_size=self.batch_size)
+
+        self.trainer.fit(self.model, train_dataloader)
+
+        return self
+
+    def forecast(self, ts: TSDataset) -> pd.DataFrame:
+        """
+        Predict future.
+
+        Parameters
+        ----------
+        ts:
+            TSDataset to forecast.
+
+        Returns
+        -------
+        TSDataset
+            TSDataset with predictions.
+        """
+        prediction_dataloader = ts.transforms[-1].pf_dataset_predict.to_dataloader(
+            train=False, batch_size=self.batch_size * 2
+        )
+
+        predicts = self.model.predict(prediction_dataloader).numpy()  # shape (segments, encoder_lenght)
+
+        ts.loc[:, pd.IndexSlice[:, "target"]] = predicts.T[-len(ts.df) :]
+        return ts