optimized the imports

pts/__init__.py

@@ -1,8 +1,9 @@
 from pkgutil import extend_path
-from pkg_resources import get_distribution, DistributionNotFound
-from .trainer import Trainer
-from .exception import assert_pts
 
+from pkg_resources import get_distribution, DistributionNotFound
+
+from .exception import assert_pts
+from .trainer import Trainer
 
 __path__ = extend_path(__path__, __name__)  # type: ignore
 

pts/core/component.py

@@ -1,10 +1,12 @@
-import inspect
-from pydantic import BaseConfig, BaseModel, create_model
-from typing import Any
-from collections import OrderedDict
-from pts.core.serde import dump_code
 import functools
+import inspect
+from collections import OrderedDict
+from typing import Any
+
 import torch
+from pydantic import BaseConfig, BaseModel, create_model
+
+from pts.core.serde import dump_code
 
 
 class BaseValidatedInitializerModel(BaseModel):

pts/core/serde.py

@@ -1,13 +1,14 @@
-from typing import Any, Optional, cast, NamedTuple
-import json
-from functools import singledispatch
-from pts.core import fqname_for
-import numpy as np
-import textwrap
-from pydoc import locate
-import math
 import itertools
+import json
+import math
+import textwrap
+from functools import singledispatch
+from pydoc import locate
+from typing import Any, Optional, cast, NamedTuple
 
+import numpy as np
+
+from pts.core import fqname_for
 
 bad_type_msg = textwrap.dedent(
     """

pts/dataset/__init__.py

@@ -1,24 +1,3 @@
-from .common import (
-    DataEntry,
-    FieldName,
-    Dataset,
-    MetaData,
-    TrainDatasets,
-    DateConstants,
-)
-from .list_dataset import ListDataset
-from .file_dataset import FileDataset
-from .loader import TrainDataLoader, InferenceDataLoader
-from .process import ProcessStartField, ProcessDataEntry
-from .utils import (
-    to_pandas,
-    load_datasets,
-    save_datasets,
-    serialize_data_entry,
-    frequency_add,
-    forecast_start,
-)
-from .stat import DatasetStatistics, ScaleHistogram, calculate_dataset_statistics
 from .artificial import (
     ArtificialDataset,
     ConstantDataset,
@@ -28,5 +7,26 @@ from .artificial import (
     default_synthetic,
     generate_sf2,
 )
+from .common import (
+    DataEntry,
+    FieldName,
+    Dataset,
+    MetaData,
+    TrainDatasets,
+    DateConstants,
+)
+from .file_dataset import FileDataset
+from .list_dataset import ListDataset
+from .loader import TrainDataLoader, InferenceDataLoader
 from .multivariate_grouper import MultivariateGrouper
+from .process import ProcessStartField, ProcessDataEntry
+from .stat import DatasetStatistics, ScaleHistogram, calculate_dataset_statistics
 from .transformed_iterable_dataset import TransformedIterableDataset
+from .utils import (
+    to_pandas,
+    load_datasets,
+    save_datasets,
+    serialize_data_entry,
+    frequency_add,
+    forecast_start,
+)

pts/dataset/artificial.py

@@ -1,5 +1,5 @@
-import os
 import math
+import os
 import random
 from typing import Callable, List, NamedTuple, Optional, Tuple, Union
 
@@ -17,7 +17,6 @@ from .common import (
     DataEntry,
 )
 from .list_dataset import ListDataset
-from .stat import DatasetStatistics, calculate_dataset_statistics
 from .recipe import (
     BinaryHolidays,
     BinaryMarkovChain,
@@ -31,6 +30,7 @@ from .recipe import (
     generate,
     take_as_list,
 )
+from .stat import DatasetStatistics, calculate_dataset_statistics
 
 
 class DatasetInfo(NamedTuple):

pts/dataset/common.py

@@ -1,5 +1,4 @@
-from abc import ABC, abstractmethod
-from typing import Any, Dict, Iterable, NamedTuple, Sized, List, Optional, Iterator
+from typing import Any, Dict, Iterable, NamedTuple, List, Optional
 
 import pandas as pd
 from pydantic import BaseModel

pts/dataset/file_dataset.py

@@ -1,9 +1,9 @@
 import functools
-from pathlib import Path
-from typing import NamedTuple
-from typing import Iterator, List
 import glob
 import random
+from pathlib import Path
+from typing import Iterator, List
+from typing import NamedTuple
 
 import rapidjson as json
 

pts/dataset/list_dataset.py

@@ -1,5 +1,5 @@
-from typing import Iterable
 import random
+from typing import Iterable
 
 from .common import DataEntry, Dataset, SourceContext
 from .process import ProcessDataEntry

pts/dataset/loader.py

@@ -3,13 +3,12 @@ from collections import defaultdict
 from typing import Any, Dict, Iterable, Iterator, List, Optional  # noqa: F401
 
 import numpy as np
-
 # Third-party imports
 import torch
 
+from pts.transform.transform import Transformation
 # First-party imports
 from .common import DataEntry, Dataset
-from pts.transform import Transformation
 
 DataBatch = Dict[str, Any]
 

pts/dataset/multivariate_grouper.py

@@ -13,9 +13,10 @@
 
 # Standard library imports
 import logging
+from typing import Callable, Optional
+
 import numpy as np
 import pandas as pd
-from typing import Callable, Optional
 
 # First-party imports
 from .common import DataEntry, Dataset, FieldName, DateConstants

pts/dataset/repository/_gp_copula_2019.py

@@ -18,14 +18,13 @@ large files in GluonTS master.
 """
 import json
 import os
-import tarfile
 import shutil
+import tarfile
 from pathlib import Path
 from typing import NamedTuple, Optional
 from urllib import request
 
 from pts.dataset import FileDataset, FieldName
-
 from ._util import save_to_file, to_dict, metadata
 
 

pts/dataset/repository/_lstnet.py

@@ -23,7 +23,6 @@ from typing import List, NamedTuple, Optional
 import pandas as pd
 
 from pts.dataset import frequency_add
-
 from ._util import save_to_file, to_dict, metadata
 
 

pts/dataset/repository/_m4.py

@@ -11,12 +11,12 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
-from pathlib import Path
-import os
 import json
+import os
+from pathlib import Path
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 
 from ._util import save_to_file, to_dict, metadata
 

pts/dataset/repository/datasets.py

@@ -17,11 +17,10 @@ from functools import partial
 from pathlib import Path
 
 from pts.dataset import ConstantDataset, TrainDatasets, load_datasets
-
 from ._artificial import generate_artificial_dataset
+from ._gp_copula_2019 import generate_gp_copula_dataset
 from ._lstnet import generate_lstnet_dataset
 from ._m4 import generate_m4_dataset
-from ._gp_copula_2019 import generate_gp_copula_dataset
 from ._util import get_download_path
 
 m4_freq = "Hourly"

pts/dataset/stat.py

@@ -5,8 +5,8 @@ from typing import Any, List, NamedTuple, Optional, Set
 import numpy as np
 from tqdm import tqdm
 
-from .common import FieldName
 from pts.exception import assert_pts
+from .common import FieldName
 
 
 class ScaleHistogram:

pts/dataset/transformed_iterable_dataset.py

@@ -1,12 +1,10 @@
 import itertools
 from typing import Dict, Iterable, Iterator, Optional
-import random
 
 import numpy as np
 import torch
 
-from pts.transform import Transformation
-
+from pts.transform.transform import Transformation
 from .common import DataEntry, Dataset
 
 

pts/dataset/utils.py

@@ -1,5 +1,5 @@
-from pathlib import Path
 import shutil
+from pathlib import Path
 
 import numpy as np
 import pandas as pd

pts/evaluation/__init__.py

@@ -1,2 +1,2 @@
-from .evaluator import Evaluator, MultivariateEvaluator
 from .backtest import make_evaluation_predictions, backtest_metrics
+from .evaluator import Evaluator, MultivariateEvaluator

pts/evaluation/backtest.py

@@ -5,16 +5,15 @@ from typing import Dict, Iterator, NamedTuple, Optional, Tuple, Union
 # Third-party imports
 import pandas as pd
 
-# First-party imports
-from pts.transform import AdhocTransform, TransformedDataset
 from pts.dataset import (
     DataEntry,
     Dataset,
-    InferenceDataLoader,
     DatasetStatistics,
     calculate_dataset_statistics,
 )
-from pts.model import Estimator, PTSEstimator, PTSPredictor, Predictor, Forecast
+from pts.model import Estimator, Predictor, Forecast
+# First-party imports
+from pts.transform import AdhocTransform, TransformedDataset
 from .evaluator import Evaluator
 
 

pts/evaluation/evaluator.py

@@ -10,15 +10,14 @@ from typing import (
     Union,
     Callable,
 )
-from tqdm import tqdm
 
 # Third-party imports
 import numpy as np
 import pandas as pd
 from tqdm import tqdm
 
-from pts.model import Quantile, Forecast
 from pts.feature import get_seasonality
+from pts.model import Quantile, Forecast
 
 
 class Evaluator:

pts/feature/__init__.py

@@ -1,3 +1,4 @@
+from .holiday import SPECIAL_DATE_FEATURES, SpecialDateFeatureSet
 from .lag import get_lags_for_frequency, get_fourier_lags_for_frequency
 from .time_feature import (
     DayOfMonth,
@@ -12,5 +13,4 @@ from .time_feature import (
     time_features_from_frequency_str,
     fourier_time_features_from_frequency_str,
 )
-from .holiday import SPECIAL_DATE_FEATURES, SpecialDateFeatureSet
 from .utils import get_granularity, get_seasonality

pts/feature/lag.py

@@ -12,7 +12,7 @@
 # permissions and limitations under the License.
 
 # Standard library imports
-from typing import List, Optional, Tuple
+from typing import List, Optional
 
 # Third-party imports
 import numpy as np

pts/feature/time_feature.py

@@ -4,6 +4,7 @@ from typing import List
 import numpy as np
 import pandas as pd
 from pandas.tseries.frequencies import to_offset
+
 from pts.core.component import validated
 from .utils import get_granularity
 

pts/feature/utils.py

@@ -1,6 +1,6 @@
 import re
-from typing import Tuple
 from functools import lru_cache
+from typing import Tuple
 
 
 def get_granularity(freq_str: str) -> Tuple[int, str]:

pts/model/deepar/deepar_estimator.py

@@ -1,16 +1,18 @@
 from typing import List, Optional
 
 import numpy as np
-
 import torch
 import torch.nn as nn
 
 from pts import Trainer
+from pts.dataset import FieldName
 from pts.feature import (
     TimeFeature,
     get_lags_for_frequency,
     time_features_from_frequency_str,
 )
+from pts.model import PTSEstimator, Predictor, PTSPredictor, copy_parameters
+from pts.modules import DistributionOutput, StudentTOutput
 from pts.transform import (
     Transformation,
     Chain,
@@ -24,10 +26,6 @@ from pts.transform import (
     InstanceSplitter,
     ExpectedNumInstanceSampler,
 )
-from pts.dataset import FieldName
-from pts.model import PTSEstimator, Predictor, PTSPredictor, copy_parameters
-from pts.modules import DistributionOutput, StudentTOutput
-
 from .deepar_network import DeepARTrainingNetwork, DeepARPredictionNetwork
 
 

pts/model/deepar/deepar_network.py

@@ -1,13 +1,13 @@
 from typing import List, Optional, Tuple, Union
 
+import numpy as np
 import torch
 import torch.nn as nn
 from torch.distributions import Distribution
 
-import numpy as np
 from pts.core.component import validated
-from pts.modules import DistributionOutput, MeanScaler, NOPScaler, FeatureEmbedder
 from pts.model import weighted_average
+from pts.modules import DistributionOutput, MeanScaler, NOPScaler, FeatureEmbedder
 
 
 def prod(xs):

pts/model/deepvar/deepvar_estimator.py

@@ -1,14 +1,16 @@
 from typing import List, Optional
 
-import numpy as np
-import pandas as pd
 import torch
-import torch.nn as nn
 
 from pts import Trainer
+from pts.dataset import FieldName
+from pts.feature import (
+    TimeFeature,
+    fourier_time_features_from_frequency_str,
+    get_fourier_lags_for_frequency,
+)
 from pts.model import PTSEstimator, PTSPredictor, copy_parameters
 from pts.modules import DistributionOutput, LowRankMultivariateNormalOutput
-from pts.dataset import FieldName
 from pts.transform import (
     Transformation,
     Chain,
@@ -25,12 +27,6 @@ from pts.transform import (
     SetFieldIfNotPresent,
     TargetDimIndicator,
 )
-from pts.feature import (
-    TimeFeature,
-    fourier_time_features_from_frequency_str,
-    get_fourier_lags_for_frequency,
-)
-
 from .deepvar_network import DeepVARTrainingNetwork, DeepVARPredictionNetwork
 
 

pts/model/deepvar/deepvar_network.py

@@ -2,12 +2,10 @@ from typing import List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
-from torch.distributions import Distribution
 
-import numpy as np
 from pts.core.component import validated
-from pts.modules import DistributionOutput, MeanScaler, NOPScaler
 from pts.model import weighted_average
+from pts.modules import DistributionOutput, MeanScaler, NOPScaler
 
 
 class DeepVARTrainingNetwork(nn.Module):

pts/model/estimator.py

@@ -6,10 +6,9 @@ import torch
 import torch.nn as nn
 from torch.utils.data import DataLoader
 
+from pts import Trainer
 from pts.dataset import Dataset, TransformedIterableDataset
 from pts.transform import Transformation
-from pts import Trainer
-
 from .predictor import Predictor
 from .utils import get_module_forward_input_names
 

pts/model/forecast.py

@@ -2,10 +2,10 @@ from abc import ABC, abstractmethod
 from enum import Enum
 from typing import Dict, List, Optional, Set, Union, Callable
 
-from pydantic import BaseModel, Field
 import numpy as np
 import pandas as pd
 import torch
+from pydantic import BaseModel, Field
 from torch.distributions import Distribution
 
 from .quantile import Quantile

pts/model/forecast_generator.py

@@ -5,10 +5,10 @@ import numpy as np
 import torch
 import torch.nn as nn
 
+from pts.core.component import validated
 from pts.dataset import InferenceDataLoader, DataEntry, FieldName
 from pts.modules import DistributionOutput
 from .forecast import Forecast, DistributionForecast, QuantileForecast, SampleForecast
-from pts.core.component import validated
 
 OutputTransform = Callable[[DataEntry, np.ndarray], np.ndarray]
 

pts/model/n_beats/__init__.py

@@ -1,2 +1,2 @@
-from .n_beats_estimator import NBEATSEstimator
 from .n_beats_ensemble import NBEATSEnsembleEstimator
+from .n_beats_estimator import NBEATSEstimator

pts/model/n_beats/n_beats_ensemble.py

@@ -1,16 +1,15 @@
-from typing import List, Optional, Iterator
-from itertools import product
 import copy
 import logging
+from itertools import product
+from typing import List, Optional, Iterator
 
 import numpy as np
 
-from pts.model import Predictor, SampleForecast, Forecast, Estimator
 from pts import Trainer
 from pts.dataset import Dataset, FieldName
-
-from .n_beats_network import VALID_LOSS_FUNCTIONS
+from pts.model import Predictor, SampleForecast, Forecast, Estimator
 from .n_beats_estimator import NBEATSEstimator
+from .n_beats_network import VALID_LOSS_FUNCTIONS
 
 AGGREGATION_METHODS = "median", "mean", "none"
 

pts/model/n_beats/n_beats_estimator.py

@@ -1,11 +1,10 @@
 from typing import List, Optional
 
-import numpy as np
-
 import torch
 import torch.nn as nn
 
 from pts import Trainer
+from pts.dataset import FieldName
 from pts.model import PTSEstimator, Predictor, PTSPredictor, copy_parameters
 from pts.transform import (
     InstanceSplitter,
@@ -14,15 +13,13 @@ from pts.transform import (
     RemoveFields,
     ExpectedNumInstanceSampler,
 )
-from pts.dataset import FieldName
-
 from .n_beats_network import (
     NBEATSPredictionNetwork,
     NBEATSTrainingNetwork,
     VALID_N_BEATS_STACK_TYPES,
-    VALID_LOSS_FUNCTIONS,
 )
 
+
 class NBEATSEstimator(PTSEstimator):
     def __init__(
         self,

pts/model/n_beats/n_beats_network.py

@@ -1,7 +1,6 @@
 from typing import List, Tuple
 
 import numpy as np
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

pts/model/predictor.py

@@ -1,19 +1,20 @@
-import pts
 import json
+from abc import ABC, abstractmethod
+from pathlib import Path
+from pydoc import locate
+from typing import Iterator, Callable, Optional
+
 import numpy as np
 import torch
 import torch.nn as nn
-from abc import ABC, abstractmethod
-from pydoc import locate
-from typing import Iterator, Callable, Optional
+
+import pts
+from pts.core.serde import dump_json, fqname_for, load_json
 from pts.dataset import Dataset, DataEntry, InferenceDataLoader
 from pts.transform import Transformation
-from pathlib import Path
 from .forecast import Forecast
 from .forecast_generator import ForecastGenerator, SampleForecastGenerator
 from .utils import get_module_forward_input_names
-from pts.core.serde import dump_json, fqname_for, load_json
-
 
 OutputTransform = Callable[[DataEntry, np.ndarray], np.ndarray]
 

pts/model/simple_feedforward/simple_feedforward_estimator.py

@@ -4,16 +4,15 @@ import torch
 import torch.nn as nn
 
 from pts import Trainer
+from pts.dataset import FieldName
 from pts.model import PTSEstimator, PTSPredictor, copy_parameters
 from pts.modules import DistributionOutput, StudentTOutput
-from pts.dataset import FieldName
 from pts.transform import (
     Transformation,
     Chain,
     InstanceSplitter,
     ExpectedNumInstanceSampler,
 )
-
 from .simple_feedforward_network import (
     SimpleFeedForwardTrainingNetwork,
     SimpleFeedForwardPredictionNetwork,

pts/model/simple_feedforward/simple_feedforward_network.py

@@ -3,8 +3,8 @@ from typing import List
 import torch
 import torch.nn as nn
 from torch.distributions import Distribution
-from pts.core.component import validated
 
+from pts.core.component import validated
 from pts.modules import MeanScaler, NOPScaler, DistributionOutput, LambdaLayer
 
 

pts/model/tempflow/tempflow_estimator.py

@@ -1,21 +1,20 @@
 from typing import List, Optional
 
-import numpy as np
-import pandas as pd
 import torch
-import torch.nn as nn
 
 from pts import Trainer
-from pts.model import PTSEstimator, PTSPredictor, copy_parameters
-from pts.modules import RealNVP
 from pts.dataset import FieldName
+from pts.feature import (
+    TimeFeature,
+    fourier_time_features_from_frequency_str,
+    get_fourier_lags_for_frequency,
+)
+from pts.model import PTSEstimator, PTSPredictor, copy_parameters
 from pts.transform import (
     Transformation,
     Chain,
     InstanceSplitter,
     ExpectedNumInstanceSampler,
-    CDFtoGaussianTransform,
-    cdf_to_gaussian_forward_transform,
     RenameFields,
     AsNumpyArray,
     ExpandDimArray,
@@ -25,12 +24,6 @@ from pts.transform import (
     SetFieldIfNotPresent,
     TargetDimIndicator,
 )
-from pts.feature import (
-    TimeFeature,
-    fourier_time_features_from_frequency_str,
-    get_fourier_lags_for_frequency,
-)
-
 from .tempflow_network import TempFlowTrainingNetwork, TempFlowPredictionNetwork
 
 

pts/model/tempflow/tempflow_network.py

@@ -3,10 +3,9 @@ from typing import List, Optional, Tuple, Union
 import torch
 import torch.nn as nn
 
-import numpy as np
 from pts.core.component import validated
-from pts.modules import RealNVP, MAF, FlowOutput, MeanScaler, NOPScaler
 from pts.model import weighted_average
+from pts.modules import RealNVP, MAF, FlowOutput, MeanScaler, NOPScaler
 
 
 class TempFlowTrainingNetwork(nn.Module):

pts/model/transformer/transformer_estimator.py

@@ -1,39 +1,31 @@
 from typing import List, Optional
 
 import numpy as np
-import pandas as pd
 import torch
 import torch.nn as nn
 
 from pts import Trainer
-from pts.model import PTSEstimator, Predictor, PTSPredictor, copy_parameters
-from pts.modules import DistributionOutput, StudentTOutput
 from pts.dataset import FieldName
-from pts.transform import (
-    Transformation,
-    Chain,
-    InstanceSplitter,
-    ExpectedNumInstanceSampler,
-    CDFtoGaussianTransform,
-    cdf_to_gaussian_forward_transform,
-    RenameFields,
-    RemoveFields,
-    AddAgeFeature,
-    AsNumpyArray,
-    ExpandDimArray,
-    AddObservedValuesIndicator,
-    AddTimeFeatures,
-    VstackFeatures,
-    SetFieldIfNotPresent,
-    TargetDimIndicator,
-    SetField,
-)
 from pts.feature import (
     TimeFeature,
     fourier_time_features_from_frequency_str,
     get_fourier_lags_for_frequency,
 )
-
+from pts.model import PTSEstimator, Predictor, PTSPredictor, copy_parameters
+from pts.modules import DistributionOutput, StudentTOutput
+from pts.transform import (
+    Transformation,
+    Chain,
+    InstanceSplitter,
+    ExpectedNumInstanceSampler,
+    RemoveFields,
+    AddAgeFeature,
+    AsNumpyArray,
+    AddObservedValuesIndicator,
+    AddTimeFeatures,
+    VstackFeatures,
+    SetField,
+)
 from .transformer_network import (
     TransformerTrainingNetwork,
     TransformerPredictionNetwork,

pts/model/transformer/transformer_network.py

@@ -1,13 +1,10 @@
-from typing import List, Optional, Tuple, Union
+from typing import List, Optional, Tuple
 
 import torch
 import torch.nn as nn
-from torch.distributions import Distribution
 
-import numpy as np
 from pts.core.component import validated
 from pts.modules import DistributionOutput, MeanScaler, NOPScaler, FeatureEmbedder
-from pts.model import weighted_average
 
 
 def prod(xs):

pts/model/transformer_tempflow/transformer_tempflow_estimator.py

@@ -1,21 +1,20 @@
 from typing import List, Optional
 
-import numpy as np
-import pandas as pd
 import torch
-import torch.nn as nn
 
 from pts import Trainer
-from pts.model import PTSEstimator, PTSPredictor, copy_parameters
-from pts.modules import RealNVP
 from pts.dataset import FieldName
+from pts.feature import (
+    TimeFeature,
+    fourier_time_features_from_frequency_str,
+    get_fourier_lags_for_frequency,
+)
+from pts.model import PTSEstimator, PTSPredictor, copy_parameters
 from pts.transform import (
     Transformation,
     Chain,
     InstanceSplitter,
     ExpectedNumInstanceSampler,
-    CDFtoGaussianTransform,
-    cdf_to_gaussian_forward_transform,
     RenameFields,
     AsNumpyArray,
     ExpandDimArray,
@@ -25,12 +24,6 @@ from pts.transform import (
     SetFieldIfNotPresent,
     TargetDimIndicator,
 )
-from pts.feature import (
-    TimeFeature,
-    fourier_time_features_from_frequency_str,
-    get_fourier_lags_for_frequency,
-)
-
 from .transformer_tempflow_network import TransformerTempFlowTrainingNetwork, TransformerTempFlowPredictionNetwork
 
 

pts/model/transformer_tempflow/transformer_tempflow_network.py

@@ -1,12 +1,10 @@
-from typing import List, Optional, Tuple, Union
+from typing import List, Optional, Tuple
 
 import torch
 import torch.nn as nn
 
-import numpy as np
 from pts.core.component import validated
 from pts.modules import RealNVP, MAF, FlowOutput, MeanScaler, NOPScaler
-from pts.model import weighted_average
 
 
 class TransformerTempFlowTrainingNetwork(nn.Module):

pts/model/utils.py

@@ -1,5 +1,5 @@
-from typing import Optional
 import inspect
+from typing import Optional
 
 import torch
 import torch.nn as nn

pts/modules/__init__.py

@@ -11,7 +11,7 @@ from .distribution_output import (
     MultivariateNormalOutput,
     FlowOutput,
 )
-from .lambda_layer import LambdaLayer
 from .feature import FeatureEmbedder, FeatureAssembler
-from .scaler import MeanScaler, NOPScaler
 from .flows import RealNVP, MAF
+from .lambda_layer import LambdaLayer
+from .scaler import MeanScaler, NOPScaler

pts/modules/distribution_output.py

@@ -5,10 +5,8 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from pts.core.component import validated
 from torch.distributions import (
     Distribution,
-    Normal,
     Beta,
     NegativeBinomial,
     StudentT,
@@ -20,8 +18,8 @@ from torch.distributions import (
     AffineTransform,
 )
 
+from pts.core.component import validated
 from .lambda_layer import LambdaLayer
-from .flows import RealNVP
 
 
 class ArgProj(nn.Module):

pts/modules/feature.py

@@ -1,4 +1,4 @@
-from typing import Callable, List, Optional
+from typing import List, Optional
 
 import torch
 import torch.nn as nn

pts/modules/flows.py

@@ -1,6 +1,5 @@
 import copy
 import math
-from abc import ABC, abstractmethod
 
 import torch
 import torch.nn as nn

pts/modules/lambda_layer.py

@@ -1,4 +1,3 @@
-import torch
 import torch.nn as nn
 
 

pts/modules/scaler.py

@@ -1,5 +1,5 @@
-from typing import Tuple
 from abc import ABC, abstractmethod
+from typing import Tuple
 
 import torch
 import torch.nn as nn

pts/trainer.py

@@ -1,12 +1,12 @@
 import time
-from typing import Any, List, NamedTuple, Optional, Union
+from typing import List, Optional
 
 import torch
 import torch.nn as nn
+from torch.utils.data import DataLoader
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
-from torch.utils.data import DataLoader
 
 class Trainer:
     def __init__(

pts/transform/__init__.py

@@ -1,14 +1,3 @@
-from .transform import (
-    Transformation,
-    Chain,
-    Identity,
-    MapTransformation,
-    SimpleTransformation,
-    AdhocTransform,
-    FlatMapTransformation,
-    FilterTransformation,
-)
-
 from .convert import (
     AsNumpyArray,
     ExpandDimArray,
@@ -21,9 +10,7 @@ from .convert import (
     CDFtoGaussianTransform,
     cdf_to_gaussian_forward_transform,
 )
-
 from .dataset import TransformedDataset
-
 from .feature import (
     target_transformation_length,
     AddObservedValuesIndicator,
@@ -31,7 +18,6 @@ from .feature import (
     AddTimeFeatures,
     AddAgeFeature,
 )
-
 from .field import (
     RemoveFields,
     RenameFields,
@@ -39,8 +25,6 @@ from .field import (
     SetFieldIfNotPresent,
     SelectFields,
 )
-
-
 from .sampler import (
     InstanceSampler,
     UniformSplitSampler,
@@ -50,10 +34,19 @@ from .sampler import (
     ContinuousTimePointSampler,
     ContinuousTimeUniformSampler,
 )
-
 from .split import (
     shift_timestamp,
     InstanceSplitter,
     CanonicalInstanceSplitter,
     ContinuousTimeInstanceSplitter,
 )
+from .transform import (
+    Transformation,
+    Chain,
+    Identity,
+    MapTransformation,
+    SimpleTransformation,
+    AdhocTransform,
+    FlatMapTransformation,
+    FilterTransformation,
+)

pts/transform/convert.py

@@ -14,14 +14,12 @@
 from typing import Iterator, List, Tuple, Optional
 
 import numpy as np
+import torch
 from scipy.special import erf, erfinv
 
-import torch
-
-from pts.exception import assert_pts
 from pts.core.component import validated
 from pts.dataset import DataEntry
-
+from pts.exception import assert_pts
 from .transform import (
     SimpleTransformation,
     MapTransformation,

pts/transform/feature.py

@@ -16,11 +16,11 @@ from typing import List
 import numpy as np
 import pandas as pd
 
+from pts.core.component import validated
 from pts.dataset import DataEntry
 from pts.feature import TimeFeature
-from pts.core.component import validated
-from .transform import SimpleTransformation, MapTransformation
 from .split import shift_timestamp
+from .transform import SimpleTransformation, MapTransformation
 
 
 def target_transformation_length(

pts/transform/field.py

@@ -14,8 +14,8 @@
 from collections import Counter
 from typing import Any, Dict, List
 
-from pts.dataset import DataEntry
 from pts.core.component import validated
+from pts.dataset import DataEntry
 from .transform import SimpleTransformation, MapTransformation
 
 

pts/transform/sampler.py

@@ -15,8 +15,8 @@ from abc import ABC, abstractmethod
 
 import numpy as np
 
-from pts.dataset.stat import ScaleHistogram
 from pts.core.component import validated
+from pts.dataset.stat import ScaleHistogram
 
 
 class InstanceSampler(ABC):

pts/transform/split.py

@@ -17,11 +17,10 @@ from typing import Iterator, List, Optional
 import numpy as np
 import pandas as pd
 
-from pts.dataset import DataEntry, FieldName
-
-from .transform import FlatMapTransformation
-from .sampler import InstanceSampler, ContinuousTimePointSampler
 from pts.core.component import validated
+from pts.dataset import DataEntry, FieldName
+from .sampler import InstanceSampler, ContinuousTimePointSampler
+from .transform import FlatMapTransformation
 
 
 def shift_timestamp(ts: pd.Timestamp, offset: int) -> pd.Timestamp:

pts/transform/transform.py

@@ -1,11 +1,10 @@
 from abc import ABC, abstractmethod
-from typing import Callable, Iterator, Iterable, List
 from functools import reduce
+from typing import Callable, Iterator, Iterable, List
+
 from pts.core.component import validated
-
 from pts.dataset import DataEntry
 
-
 MAX_IDLE_TRANSFORMS = 100
 
 

setup.py

@@ -21,7 +21,6 @@ setup(
         'pandas',
         'scipy',
         'tqdm',
-        'ujson',
         'pydantic',
         'matplotlib',
         'python-rapidjson',

test/dataset/test_multivariate_grouper.py

@@ -11,9 +11,9 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
+import numpy as np
 # Standard library imports
 import pytest
-import numpy as np
 
 # First-party imports
 from pts.dataset import ListDataset, MultivariateGrouper

test/dataset/test_process.py

@@ -1,5 +1,5 @@
-import pytest
 import pandas as pd
+import pytest
 
 from pts.dataset import ProcessStartField
 

test/model/deepar/test_auxillary_outputs.py

@@ -15,12 +15,11 @@ from itertools import islice
 
 import torch
 
-from pts.modules import StudentTOutput
-from pts.dataset import constant_dataset, TrainDataLoader
-from pts.model.deepar import DeepAREstimator
-from pts.model import get_module_forward_input_names
 from pts import Trainer
-
+from pts.dataset import constant_dataset, TrainDataLoader
+from pts.model import get_module_forward_input_names
+from pts.model.deepar import DeepAREstimator
+from pts.modules import StudentTOutput
 
 ds_info, train_ds, test_ds = constant_dataset()
 freq = ds_info.metadata.freq

test/model/test_deepvar.py

@@ -14,17 +14,17 @@
 # First-party imports
 import pytest
 
+from pts import Trainer
+from pts.dataset import TrainDatasets, MultivariateGrouper
 from pts.dataset.artificial import constant_dataset
+from pts.evaluation import MultivariateEvaluator
+from pts.evaluation import backtest_metrics
+from pts.model.deepvar import DeepVAREstimator
 from pts.modules import (
     IndependentNormalOutput,
     LowRankMultivariateNormalOutput,
     MultivariateNormalOutput,
 )
-from pts.evaluation import backtest_metrics
-from pts.model.deepvar import DeepVAREstimator
-from pts.dataset import TrainDatasets, MultivariateGrouper
-from pts import Trainer
-from pts.evaluation import MultivariateEvaluator
 
 
 def load_multivariate_constant_dataset():

test/model/test_forecast.py

@@ -16,6 +16,7 @@ import numpy as np
 import pandas as pd
 import pytest
 import torch
+from torch.distributions import Uniform
 
 # First-party imports
 from pts.model import (
@@ -24,8 +25,6 @@ from pts.model import (
     DistributionForecast,
 )
 
-from torch.distributions import Uniform
-
 QUANTILES = np.arange(1, 100) / 100
 SAMPLES = np.arange(101).reshape(101, 1) / 100
 START_DATE = pd.Timestamp(2017, 1, 1, 12)

test/modules/test_distribution_output.py

@@ -1,13 +1,9 @@
-import pytest
-from typing import Iterable, List, Tuple
+from typing import List, Tuple
 
 import numpy as np
-
+import pytest
 import torch
 import torch.nn as nn
-from torch.nn.utils import clip_grad_norm_
-from torch.utils.data import TensorDataset, DataLoader
-from torch.optim import SGD
 from torch.distributions import (
     StudentT,
     Beta,
@@ -17,6 +13,9 @@ from torch.distributions import (
     Independent,
     Normal,
 )
+from torch.nn.utils import clip_grad_norm_
+from torch.optim import SGD
+from torch.utils.data import TensorDataset, DataLoader
 
 from pts.modules import (
     DistributionOutput,

test/modules/test_feature.py

@@ -1,10 +1,8 @@
-import pytest
 from itertools import chain, combinations
 
-
+import pytest
 import torch
 import torch.nn as nn
-from torch.distributions import Uniform
 
 from pts.modules import FeatureEmbedder, FeatureAssembler
 

test/modules/test_scaler.py

@@ -11,14 +11,12 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
-import pytest
 import numpy as np
-
+import pytest
 import torch
 
 from pts.modules import MeanScaler, NOPScaler
 
-
 test_cases = [
     (
         MeanScaler(),

test/test_transform.py

@@ -17,9 +17,10 @@ from typing import Tuple
 # Third-party imports
 import numpy as np
 import pandas as pd
-import torch
 import pytest
+import torch
 
+from pts import transform
 # First-party imports
 from pts.dataset import (
     ProcessStartField,
@@ -29,7 +30,6 @@ from pts.dataset import (
     calculate_dataset_statistics,
     ScaleHistogram,
 )
-from pts import transform
 from pts.feature import time_feature
 
 FREQ = "1D"