ran isort

isort --recursive --atomic --apply pts

pts/__init__.py

@@ -1,2 +1,2 @@
 from .exception import assert_pts
-from .trainer import Trainer
+from .trainer import Trainer

pts/dataset/__init__.py

@@ -1,11 +1,7 @@
 from pts.dataset.common import DataEntry, FieldName
 from pts.dataset.list_dataset import ListDataset
-from pts.dataset.sampler import (
-    UniformSplitSampler,
-    TestSplitSampler,
-    ExpectedNumInstanceSampler,
-    BucketInstanceSampler,
-)
-from pts.dataset.sampler import InstanceSampler, UniformSplitSampler, TestSplitSampler, ExpectedNumInstanceSampler, BucketInstanceSampler
-from pts.dataset.loader import DataLoader, TrainDataLoader, InferenceDataLoader
-from pts.dataset.utils import to_pandas
+from pts.dataset.loader import DataLoader, InferenceDataLoader, TrainDataLoader
+from pts.dataset.sampler import (BucketInstanceSampler,
+                                 ExpectedNumInstanceSampler, InstanceSampler,
+                                 TestSplitSampler, UniformSplitSampler)
+from pts.dataset.utils import to_pandas

pts/dataset/common.py

@@ -1,6 +1,5 @@
 from abc import ABC, abstractmethod
-
-from typing import Any, Dict, Sized, Iterable, NamedTuple
+from typing import Any, Dict, Iterable, NamedTuple, Sized
 
 DataEntry = Dict[str, Any]
 

pts/dataset/list_dataset.py

@@ -1,6 +1,6 @@
 from typing import Iterable
 
-from .common import Dataset, DataEntry, SourceContext
+from .common import DataEntry, Dataset, SourceContext
 from .process import ProcessDataEntry
 
 

pts/dataset/loader.py

@@ -2,13 +2,14 @@ import itertools
 from collections import defaultdict
 from typing import Any, Dict, Iterable, Iterator, List, Optional  # noqa: F401
 
+import numpy as np
 # Third-party imports
 import torch
-import numpy as np
+
+from pts.feature.transform import Transformation
 
 # First-party imports
 from .common import DataEntry, Dataset
-from pts.feature.transform import Transformation
 
 DataBatch = Dict[str, Any]
 

pts/dataset/sampler.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Union, List
+from typing import List, Union
 
 import numpy as np
 

pts/dataset/stat.py

@@ -1,7 +1,6 @@
+import math
 from collections import defaultdict
 from typing import Optional
-import math
-
 
 import numpy as np
 

pts/dataset/transformed_dataset.py

@@ -1,7 +1,8 @@
-from typing import List, Iterator
+from typing import Iterator, List
 
-from .common import Dataset, DataEntry
-from pts.feature import Transformation, Chain
+from pts.feature import Chain, Transformation
+
+from .common import DataEntry, Dataset
 
 
 class TransformedDataset(Dataset):

pts/dataset/utils.py

@@ -23,4 +23,4 @@ def to_pandas(instance: dict, freq: str = None) -> pd.Series:
     if not freq:
         freq = start.freqstr
     index = pd.date_range(start=start, periods=len(target), freq=freq)
-    return pd.Series(target, index=index)
+    return pd.Series(target, index=index)

pts/feature/__init__.py

@@ -1,40 +1,16 @@
-from pts.feature.time_feature import (
-    TimeFeature,
-    MinuteOfHour,
-    HourOfDay,
-    DayOfWeek,
-    DayOfMonth,
-    DayOfYear,
-    MonthOfYear,
-    WeekOfYear,
-    time_features_from_frequency_str,
-)
-
-from pts.feature.transform import (
-    Transformation,
-    Chain,
-    IdentityTransformation,
-    MapTransformation,
-    SimpleTransformation,
-    AdhocTransform,
-    FlatMapTransformation,
-    FilterTransformation,
-    RemoveFields,
-    SetField,
-    AsNumpyArray,
-    ExpandDimArray,
-    VstackFeatures,
-    ConcatFeatures,
-    SwapAxes,
-    ListFeatures,
-    AddObservedValuesIndicator,
-    RenameFields,
-    AddConstFeature,
-    AddTimeFeatures,
-    AddAgeFeature,
-    InstanceSplitter,
-    CanonicalInstanceSplitter,
-    SelectFields,
-)
-
 from pts.feature.lag import get_lags_for_frequency
+from pts.feature.time_feature import (DayOfMonth, DayOfWeek, DayOfYear,
+                                      HourOfDay, MinuteOfHour, MonthOfYear,
+                                      TimeFeature, WeekOfYear,
+                                      time_features_from_frequency_str)
+from pts.feature.transform import (AddAgeFeature, AddConstFeature,
+                                   AddObservedValuesIndicator, AddTimeFeatures,
+                                   AdhocTransform, AsNumpyArray,
+                                   CanonicalInstanceSplitter, Chain,
+                                   ConcatFeatures, ExpandDimArray,
+                                   FilterTransformation, FlatMapTransformation,
+                                   IdentityTransformation, InstanceSplitter,
+                                   ListFeatures, MapTransformation,
+                                   RemoveFields, RenameFields, SelectFields,
+                                   SetField, SimpleTransformation, SwapAxes,
+                                   Transformation, VstackFeatures)

pts/feature/lag.py

@@ -12,7 +12,7 @@
 # permissions and limitations under the License.
 
 # Standard library imports
-from typing import List, Tuple, Optional
+from typing import List, Optional, Tuple
 
 # Third-party imports
 import numpy as np
@@ -122,4 +122,4 @@ def get_lags_for_frequency(freq_str: str,
     ]
     lags = [1, 2, 3, 4, 5, 6, 7] + sorted(list(set(lags)))
 
-    return lags[:num_lags]
+    return lags[:num_lags]

pts/feature/time_feature.py

@@ -6,6 +6,7 @@ import pandas as pd
 
 from .utils import get_granularity
 
+
 class TimeFeature(ABC):
     def __init__(self, normalized: bool = True):
         self.normalized = normalized

pts/feature/transform.py

@@ -1,13 +1,14 @@
 from abc import ABC, abstractmethod
 from collections import Counter
 from functools import lru_cache, reduce
-from typing import Iterator, List, Callable, Any, Optional, Dict, Tuple
+from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple
 
 import numpy as np
 import pandas as pd
 
-from pts.dataset import DataEntry, InstanceSampler
 from pts import assert_pts
+from pts.dataset import DataEntry, InstanceSampler
+
 from .time_feature import TimeFeature
 
 MAX_IDLE_TRANSFORMS = 100

pts/feature/utils.py

@@ -1,5 +1,6 @@
-from typing import Tuple
 import re
+from typing import Tuple
+
 
 def get_granularity(freq_str: str) -> Tuple[int, str]:
     """
@@ -18,4 +19,4 @@ def get_granularity(freq_str: str) -> Tuple[int, str]:
     groups = m.groups()
     multiple = int(groups[1]) if groups[1] is not None else 1
     granularity = groups[2]
-    return multiple, granularity
+    return multiple, granularity

pts/model/__init__.py

@@ -1,4 +1,4 @@
 from pts.model.estimator import Estimator, PTSEstimator
-from pts.model.predictor import Predictor
 from pts.model.forecast import Forecast
+from pts.model.predictor import Predictor
 from pts.model.quantile import Quantile

pts/model/deepar/__init__.py

@@ -1 +1 @@
-from .deepar_estimator import DeepAREstimator
+from .deepar_estimator import DeepAREstimator

pts/model/deepar/deepar_estimator.py

@@ -2,9 +2,11 @@ from typing import List, Optional
 
 import numpy as np
 
-from pts.model import PTSEstimator
-from pts.feature import TimeFeature, get_lags_for_frequency, time_features_from_frequency_str
 from pts import Trainer
+from pts.feature import (TimeFeature, get_lags_for_frequency,
+                         time_features_from_frequency_str)
+from pts.model import PTSEstimator
+
 
 class DeepAREstimator(PTSEstimator):
     def __init__(self,
@@ -67,4 +69,4 @@ class DeepAREstimator(PTSEstimator):
 
         self.history_length = self.context_length + max(self.lags_seq)
 
-        self.num_parallel_samples = num_parallel_samples
+        self.num_parallel_samples = num_parallel_samples

pts/model/deepar/deepar_network.py

@@ -1,5 +1,6 @@
 import torch
 import torch.nn as nn
 
+
 class DeepARNetwork(nn.Module):
-    pass
+    pass

pts/model/estimator.py

@@ -1,14 +1,13 @@
 from abc import ABC, abstractmethod
 
 import numpy as np
-
-from pts.dataset.common import Dataset
-from pts.dataset import TrainDataLoader
-from pts.feature import Transformation
-
 import torch
 import torch.nn as nn
 
+from pts.dataset import TrainDataLoader
+from pts.dataset.common import Dataset
+from pts.feature import Transformation
+
 from .predictor import Predictor
 from .utils import get_module_forward_input_names
 

pts/model/forecast.py

@@ -1,9 +1,8 @@
 from abc import ABC, abstractmethod
-from typing import Optional, Dict, Enum, Set, List
+from typing import Dict, Enum, List, Optional, Set
 
 import numpy as np
 import pandas as pd
-
 import torch
 from torch.distributions.distribution import Distributions
 
@@ -393,4 +392,4 @@ class QuantileForecast(Forecast):
 #             freq=self.freq,
 #             item_id=self.item_id,
 #             info=self.info,
-#         )
+#         )

pts/model/predictor.py

@@ -2,9 +2,11 @@ from abc import ABC, abstractmethod
 from typing import Iterator
 
 from pts.dataset.common import Dataset
+
 from .forecast import Forecast
 from .predictor import Predictor
 
+
 class Predictor(ABC):
     def __init__(self, prediction_length: int, freq: str) -> None:
         self.prediction_length = prediction_length
@@ -13,4 +15,3 @@ class Predictor(ABC):
     @abstractmethod
     def predict(self, dataset: Dataset, **kwargs) -> Iterator[Forecast]:
         pass
-

pts/model/quantile.py

@@ -1,5 +1,5 @@
-from typing import NamedTuple, Union
 import re
+from typing import NamedTuple, Union
 
 
 class Quantile(NamedTuple):

pts/modules/__init__.py

@@ -0,0 +1,2 @@
+from pts.modules.distribution_output import ArgProj
+from pts.modules.lambda_layer import LambdaLayer

pts/modules/distribution_output.py

@@ -1,4 +1,5 @@
 from typing import Callable, Dict, Optional, Tuple
+from abc import ABC, abstractmethod
 
 import numpy as np
 
@@ -8,25 +9,26 @@ import torch.nn as nn
 
 class ArgProj(nn.Module):
     def __init__(
-            self,
-            in_features,
-            args_dim: Dict[str, int],
-            domain_map: Callable[..., Tuple[torch.Tensor]],
-            dtype: np.dtype = np.float32,
-            prefix: Optional[str] = None,
-            **kwargs,
+        self,
+        in_features,
+        args_dim: Dict[str, int],
+        domain_map: Callable[..., Tuple[torch.Tensor]],
+        dtype: np.dtype = np.float32,
+        prefix: Optional[str] = None,
+        **kwargs,
     ):
         super().__init__(**kwargs)
         self.args_dim = args_dim
         self.dtype = dtype
-        self.proj = nn.ModuleList([
-            nn.Linear(in_features, dim)
-            for dim in args_dim.values()])
+        self.proj = nn.ModuleList(
+            [nn.Linear(in_features, dim) for dim in args_dim.values()]
+        )
         self.domain_map = domain_map
-    
+
     def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]:
         params_unbounded = [proj(x) for proj in self.proj]
 
         return self.domain_map(*params_unbounded)
 
-
+class Output(ABC):
+    

pts/modules/lambda_layer.py

@@ -1,10 +1,11 @@
 import torch
 import torch.nn as nn
 
-class Lambda(nn.Module):
+
+class LambdaLayer(nn.Module):
     def __init__(self, function):
         super().__init__()
         self._func = function
-    
+
     def forward(self, x, *args):
         return self._func(x, *args)