Change make_samples of RNNNet and MLPNet to speed up the conver…

…sion of data into `torch.Dataset` (#1281)

CHANGELOG.md

@@ -18,7 +18,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Inherit `SaveMixin` from `AbstractSaveable` for mypy checker ([#1261](https://github.com/tinkoff-ai/etna/pull/1261))
 - Update requirements for `holidays` and `scipy`, change saving library from `pickle` to `dill` in `SaveMixin` ([#1268](https://github.com/tinkoff-ai/etna/pull/1268))
 - Update requirement for `ruptures`, add requirement for `sqlalchemy` ([#1276](https://github.com/tinkoff-ai/etna/pull/1276))
--
+- Optimize `make_samples` of `RNNNet` and `MLPNet` ([#1281](https://github.com/tinkoff-ai/etna/pull/1281))
+- 
 ### Fixed
 - Fix `plot_backtest` and `plot_backtest_interactive` on one-step forecast ([1260](https://github.com/tinkoff-ai/etna/pull/1260))
 - Fix `BaseReconciliator` to work on `pandas==1.1.5` ([#1229](https://github.com/tinkoff-ai/etna/pull/1229))

etna/models/nn/mlp.py

@@ -109,30 +109,34 @@ def step(self, batch: MLPBatch, *args, **kwargs):  # type: ignore
 
     def make_samples(self, df: pd.DataFrame, encoder_length: int, decoder_length: int) -> Iterable[dict]:
         """Make samples from segment DataFrame."""
+        values_real = (
+            df.select_dtypes(include=[np.number]).pipe(lambda x: x[[i for i in x.columns if i != "target"]]).values
+        )
+        values_target = df["target"].values
+        segment = df["segment"].values[0]
+
+        def _make(
+            values_target: np.ndarray, values_real: np.ndarray, segment: str, start_idx: int, decoder_length: int
+        ) -> Optional[dict]:
 
-        def _make(df: pd.DataFrame, start_idx: int, decoder_length: int) -> Optional[dict]:
             sample: Dict[str, Any] = {"decoder_real": list(), "decoder_target": list(), "segment": None}
             total_length = len(df["target"])
             total_sample_length = decoder_length
 
             if total_sample_length + start_idx > total_length:
                 return None
 
-            sample["decoder_real"] = (
-                df.select_dtypes(include=[np.number])
-                .pipe(lambda x: x[[i for i in x.columns if i != "target"]])
-                .values[start_idx : start_idx + decoder_length]
-            )
-
-            target = df["target"].values[start_idx : start_idx + decoder_length].reshape(-1, 1)
-            sample["decoder_target"] = target
-            sample["segment"] = df["segment"].values[0]
+            sample["decoder_real"] = values_real[start_idx : start_idx + decoder_length]
+            sample["decoder_target"] = values_target[start_idx : start_idx + decoder_length].reshape(-1, 1)
+            sample["segment"] = segment
             return sample
 
         start_idx = 0
         while True:
             batch = _make(
-                df=df,
+                values_target=values_target,
+                values_real=values_real,
+                segment=segment,
                 start_idx=start_idx,
                 decoder_length=decoder_length,
             )
@@ -142,7 +146,13 @@ def _make(df: pd.DataFrame, start_idx: int, decoder_length: int) -> Optional[dic
             start_idx += decoder_length
         if start_idx < len(df):
             resid_length = len(df) - decoder_length
-            batch = _make(df=df, start_idx=resid_length, decoder_length=decoder_length)
+            batch = _make(
+                values_target=values_target,
+                values_real=values_real,
+                segment=segment,
+                start_idx=resid_length,
+                decoder_length=decoder_length,
+            )
             if batch is not None:
                 yield batch
 

etna/models/nn/rnn.py

@@ -131,52 +131,59 @@ def step(self, batch: RNNBatch, *args, **kwargs):  # type: ignore
 
     def make_samples(self, df: pd.DataFrame, encoder_length: int, decoder_length: int) -> Iterator[dict]:
         """Make samples from segment DataFrame."""
+        values_real = (
+            df.select_dtypes(include=[np.number])
+            .assign(target_shifted=df["target"].shift(1))
+            .drop(["target"], axis=1)
+            .pipe(lambda x: x[["target_shifted"] + [i for i in x.columns if i != "target_shifted"]])
+            .values
+        )
+        values_target = df["target"].values
+        segment = df["segment"].values[0]
+
+        def _make(
+            values_real: np.ndarray,
+            values_target: np.ndarray,
+            segment: str,
+            start_idx: int,
+            encoder_length: int,
+            decoder_length: int,
+        ) -> Optional[dict]:
 
-        def _make(df: pd.DataFrame, start_idx: int, encoder_length: int, decoder_length: int) -> Optional[dict]:
             sample: Dict[str, Any] = {
                 "encoder_real": list(),
                 "decoder_real": list(),
                 "encoder_target": list(),
                 "decoder_target": list(),
                 "segment": None,
             }
-            total_length = len(df["target"])
+            total_length = len(values_target)
             total_sample_length = encoder_length + decoder_length
 
             if total_sample_length + start_idx > total_length:
                 return None
 
             # Get shifted target and concatenate it with real values features
-            sample["decoder_real"] = (
-                df.select_dtypes(include=[np.number])
-                .pipe(lambda x: x[["target"] + [i for i in x.columns if i != "target"]])
-                .values[start_idx + encoder_length : start_idx + encoder_length + decoder_length]
-            )
-            sample["decoder_real"][:, 0] = (
-                df["target"].shift(1).values[start_idx + encoder_length : start_idx + encoder_length + decoder_length]
-            )
+            sample["decoder_real"] = values_real[start_idx + encoder_length : start_idx + total_sample_length]
 
             # Get shifted target and concatenate it with real values features
-            sample["encoder_real"] = (
-                df.select_dtypes(include=[np.number])
-                .pipe(lambda x: x[["target"] + [i for i in x.columns if i != "target"]])
-                .values[start_idx : start_idx + encoder_length]
-            )
-            sample["encoder_real"][:, 0] = df["target"].shift(1).values[start_idx : start_idx + encoder_length]
+            sample["encoder_real"] = values_real[start_idx : start_idx + encoder_length]
             sample["encoder_real"] = sample["encoder_real"][1:]
 
-            target = df["target"].values[start_idx : start_idx + encoder_length + decoder_length].reshape(-1, 1)
+            target = values_target[start_idx : start_idx + encoder_length + decoder_length].reshape(-1, 1)
             sample["encoder_target"] = target[1:encoder_length]
             sample["decoder_target"] = target[encoder_length:]
 
-            sample["segment"] = df["segment"].values[0]
+            sample["segment"] = segment
 
             return sample
 
         start_idx = 0
         while True:
             batch = _make(
-                df=df,
+                values_target=values_target,
+                values_real=values_real,
+                segment=segment,
                 start_idx=start_idx,
                 encoder_length=encoder_length,
                 decoder_length=decoder_length,