rename to batch_first

pts/feature/transform.py

@@ -667,7 +667,7 @@ class InstanceSplitter(FlatMapTransformation):
         length of the target seen before making prediction
     future_length
         length of the target that must be predicted
-    output_NTC
+    batch_first
         whether to have time series output in (time, dimension) or in
         (dimension, time) layout
     time_series_fields
@@ -690,7 +690,7 @@ class InstanceSplitter(FlatMapTransformation):
             train_sampler: InstanceSampler,
             past_length: int,
             future_length: int,
-            output_NTC: bool = True,
+            batch_first: bool = True,
             time_series_fields: Optional[List[str]] = None,
             pick_incomplete: bool = True,
     ) -> None:
@@ -700,7 +700,7 @@ class InstanceSplitter(FlatMapTransformation):
         self.train_sampler = train_sampler
         self.past_length = past_length
         self.future_length = future_length
-        self.output_NTC = output_NTC
+        self.batch_first = batch_first
         self.ts_fields = time_series_fields if time_series_fields is not None else []
         self.target_field = target_field
         self.is_pad_field = is_pad_field
@@ -764,7 +764,7 @@ class InstanceSplitter(FlatMapTransformation):
             if pad_length > 0:
                 pad_indicator[:pad_length] = 1
 
-            if self.output_NTC:
+            if self.batch_first:
                 for ts_field in slice_cols:
                     d[self._past(ts_field)] = d[self._past(ts_field)].transpose()
                     d[self._future(ts_field)] = d[self._future(ts_field)].transpose()
@@ -807,7 +807,7 @@ class CanonicalInstanceSplitter(FlatMapTransformation):
         instance sampler that provides sampling indices given a time-series
     instance_length
         length of the target seen before making prediction
-    output_NTC
+    batch_first
         whether to have time series output in (time, dimension) or in
         (dimension, time) layout
     time_series_fields
@@ -832,7 +832,7 @@ class CanonicalInstanceSplitter(FlatMapTransformation):
             forecast_start_field: str,
             instance_sampler: InstanceSampler,
             instance_length: int,
-            output_NTC: bool = True,
+            batch_first: bool = True,
             time_series_fields: List[str] = [],
             allow_target_padding: bool = False,
             pad_value: float = 0.0,
@@ -841,7 +841,7 @@ class CanonicalInstanceSplitter(FlatMapTransformation):
     ) -> None:
         self.instance_sampler = instance_sampler
         self.instance_length = instance_length
-        self.output_NTC = output_NTC
+        self.batch_first = batch_first
         self.dynamic_feature_fields = time_series_fields
         self.target_field = target_field
         self.allow_target_padding = allow_target_padding
@@ -911,14 +911,14 @@ class CanonicalInstanceSplitter(FlatMapTransformation):
                 else:
                     past_ts = full_ts[..., (i - self.instance_length): i]
 
-                past_ts = past_ts.transpose() if self.output_NTC else past_ts
+                past_ts = past_ts.transpose() if self.batch_first else past_ts
                 d[self._past(ts_field)] = past_ts
 
                 if self.use_prediction_features and not is_train:
                     if not ts_field == self.target_field:
                         future_ts = full_ts[..., i: i + self.prediction_length]
                         future_ts = (
-                            future_ts.transpose() if self.output_NTC else future_ts
+                            future_ts.transpose() if self.batch_first else future_ts
                         )
                         d[self._future(ts_field)] = future_ts