repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/flows.py | import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
def create_masks(
input_size, hidden_size, n_hidden, input_order="sequential", input_degrees=None
):
# MADE paper sec 4:
# degrees of connections between layers -- ensure at m... | 13,996 | 32.646635 | 177 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/feature.py | from typing import List, Optional
import torch
import torch.nn as nn
class FeatureEmbedder(nn.Module):
def __init__(self, cardinalities: List[int], embedding_dims: List[int],) -> None:
super().__init__()
assert len(cardinalities) == len(embedding_dims), 'the length of two variables should match'... | 2,938 | 32.397727 | 100 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/activation.py | from typing import Optional, Union, List, Tuple
# Third-party imports
import torch.nn as nn
from torch import Tensor
class Activation(nn.Module):
"""
Activation fuction
Parameters
----------
activation
Activation function to use.
"""
def __init__(
self,
activati... | 979 | 19.416667 | 55 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/cnn.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 5,557 | 26.37931 | 83 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/mlp.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 2,023 | 26.726027 | 94 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/encoder.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 12,696 | 26.188437 | 118 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/enc2dec.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 2,929 | 25.636364 | 77 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/decoder.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 5,368 | 25.979899 | 100 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/quantile_output.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 5,592 | 25.258216 | 79 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/block/__init__.py | 0 | 0 | 0 | py | |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/distribution/constant.py | import torch
from torch.distributions.distribution import Distribution
class ConstantDistribution(Distribution):
r"""
Creates a constant distribution, i.e. Var(x) = 0
Args:
loss_type: L1 or L2
mu (Tensor): mean
"""
def __init__(self, loss_type, mu, validate_args=None):
... | 1,045 | 25.15 | 92 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/distribution/tweedie.py | import torch
import numpy as np
from torch.distributions.distribution import Distribution
def est_lambda(mu, p):
return mu ** (2 - p) / (2 - p)
def est_alpha(p):
return (2 - p) / (p - 1)
def est_beta(mu, p):
return mu ** (1 - p) / (p - 1)
class Tweedie(Distribution):
r"""
Creates a Tweedie ... | 1,660 | 24.166667 | 79 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/modules/distribution/__init__.py | from .constant import ConstantDistribution
from .tweedie import Tweedie
| 72 | 23.333333 | 42 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/evaluation/evaluator.py | from itertools import chain, tee
from typing import (
Any,
Dict,
Iterable,
Iterator,
List,
Optional,
Tuple,
Union,
Callable,
)
# Third-party imports
import numpy as np
import pandas as pd
from tqdm import tqdm
from pts.feature import get_seasonality
from pts.model import Quantile, ... | 20,928 | 33.708126 | 119 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/evaluation/__init__.py | from .backtest import make_evaluation_predictions, backtest_metrics
from .evaluator import Evaluator, MultivariateEvaluator
| 124 | 40.666667 | 67 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/evaluation/backtest.py | # Standard library imports
import logging
from typing import Dict, Iterator, NamedTuple, Optional, Tuple, Union
# Third-party imports
import pandas as pd
from pts.dataset import (
DataEntry,
Dataset,
DatasetStatistics,
calculate_dataset_statistics,
)
from pts.model import Estimator, Predictor, Forecas... | 8,129 | 33.449153 | 112 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/core/serde.py | 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
import torch
from pts.core import fqname_for
bad_type_msg = textwrap.dedent(
"""
Cannot serialize type {}. See the docum... | 10,036 | 26.49863 | 78 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/core/logging.py | import os
import socket
from datetime import datetime
import logging
from pathlib import Path
def get_log_path(log_dir, log_comment='temp', trial='t0', mkdir=True):
if log_comment=='':
log_comment='temp'
base_path = os.path.join('logs', log_dir, log_comment)
trial_path = trial
full_lo... | 1,140 | 27.525 | 86 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/core/component.py | 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):
"""
Base Pydantic model for components with :func:`validated` ... | 5,487 | 32.668712 | 86 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/core/_base.py | def fqname_for(cls: type) -> str:
"""
Returns the fully qualified name of ``cls``.
Parameters
----------
cls
The class we are interested in.
Returns
-------
str
The fully qualified name of ``cls``.
"""
return f"{cls.__module__}.{cls.__qualname__}" | 305 | 19.4 | 49 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/core/__init__.py | # Relative imports
from ._base import fqname_for
__all__ = ["fqname_for"]
# fix Sphinx issues, see https://bit.ly/2K2eptM
for item in __all__:
if hasattr(item, "__module__"):
setattr(item, "__module__", __name__) | 226 | 24.222222 | 47 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/artificial.py | import math
import os
import random
from typing import Callable, List, NamedTuple, Optional, Tuple, Union
import numpy as np
import pandas as pd
import rapidjson as json
from .common import (
MetaData,
CategoricalFeatureInfo,
BasicFeatureInfo,
FieldName,
Dataset,
TrainDatasets,
DataEntry,
... | 30,342 | 35.958587 | 118 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/utils.py | import shutil
from pathlib import Path
import numpy as np
import pandas as pd
import rapidjson as json
from .common import TrainDatasets, MetaData
from .file_dataset import FileDataset
def frequency_add(ts: pd.Timestamp, amount: int) -> pd.Timestamp:
return ts + ts.freq * amount
def forecast_start(entry):
... | 3,603 | 26.097744 | 81 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/transformed_iterable_dataset.py | import itertools
from typing import Dict, Iterable, Iterator, Optional
import numpy as np
import torch
from pts.transform.transform import Transformation
from .common import DataEntry, Dataset
class TransformedIterableDataset(torch.utils.data.IterableDataset):
def __init__(
self, dataset: Dataset, is_tr... | 2,707 | 30.488372 | 99 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/common.py | from typing import Any, Dict, Iterable, NamedTuple, List, Optional
import pandas as pd
from pydantic import BaseModel
# Dictionary used for data flowing through the transformations.
DataEntry = Dict[str, Any]
# A Dataset is an iterable of DataEntry.
Dataset = Iterable[DataEntry]
class SourceContext(NamedTuple):
... | 1,997 | 22.785714 | 76 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/stat.py | import math
from collections import defaultdict
from typing import Any, List, NamedTuple, Optional, Set
import numpy as np
from tqdm import tqdm
from pts.exception import assert_pts
from .common import FieldName
class ScaleHistogram:
"""
Scale histogram of a timeseries dataset
This counts the number of ... | 12,942 | 36.625 | 88 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/file_dataset.py | import functools
import glob
import random
from pathlib import Path
from typing import Iterator, List
from typing import NamedTuple
import rapidjson as json
from .common import Dataset, DataEntry, SourceContext
from .process import ProcessDataEntry
def load(file_obj):
for line in file_obj:
yield json.lo... | 3,319 | 26.666667 | 87 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/__init__.py | from .artificial import (
ArtificialDataset,
ConstantDataset,
ComplexSeasonalTimeSeries,
RecipeDataset,
constant_dataset,
default_synthetic,
generate_sf2,
)
from .common import (
DataEntry,
FieldName,
Dataset,
MetaData,
TrainDatasets,
DateConstants,
)
from .file_datas... | 864 | 25.212121 | 92 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/process.py | from functools import lru_cache
from typing import Callable, List, cast
import numpy as np
import pandas as pd
from pandas.tseries.offsets import Tick
from .common import DataEntry
class ProcessStartField:
def __init__(self, name: str, freq: str) -> None:
self.name = name
self.freq = freq
d... | 3,843 | 33.321429 | 100 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/loader.py | 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
from pts.transform.transform import Transformation
# First-party imports
from .common import DataEntry, Dataset
DataBatch = Dict[st... | 7,684 | 30.756198 | 87 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/recipe.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 18,316 | 29.276033 | 88 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/multivariate_grouper.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 8,108 | 37.25 | 95 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/list_dataset.py | import random
import torch
from typing import Iterable
from .common import DataEntry, Dataset, SourceContext
from .process import ProcessDataEntry
class ListDataset(Dataset):
def __init__(
self,
data_iter: Iterable[DataEntry],
freq: str,
one_dim_target: bool = True,
shuffl... | 945 | 26.028571 | 78 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/repository/_m4.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 2,966 | 33.5 | 104 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/repository/_gp_copula_2019.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 5,230 | 31.490683 | 138 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/repository/_lstnet.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 5,944 | 29.025253 | 115 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/repository/_util.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 2,063 | 25.126582 | 75 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/repository/datasets.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 5,882 | 32.617143 | 105 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/repository/_artificial.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 1,627 | 32.22449 | 88 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/dataset/repository/__init__.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 626 | 43.785714 | 75 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/transform/split.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 20,103 | 36.64794 | 106 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/transform/field.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 3,366 | 27.294118 | 79 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/transform/sampler.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 6,374 | 30.25 | 91 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/transform/transform.py | from abc import ABC, abstractmethod
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
class Transformation(ABC):
@abstractmethod
def __call__(
self, data_it: Iterable... | 4,564 | 30.923077 | 91 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/transform/dataset.py | from typing import Iterator, List
from pts.dataset import DataEntry, Dataset
from .transform import Chain, Transformation
class TransformedDataset(Dataset):
"""
A dataset that corresponds to applying a list of transformations to each
element in the base_dataset.
This only supports SimpleTransformatio... | 918 | 26.029412 | 76 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/transform/convert.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 22,563 | 30.602241 | 86 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/transform/__init__.py | from .convert import (
AsNumpyArray,
ExpandDimArray,
VstackFeatures,
ConcatFeatures,
SwapAxes,
ListFeatures,
TargetDimIndicator,
SampleTargetDim,
CDFtoGaussianTransform,
cdf_to_gaussian_forward_transform,
)
from .dataset import TransformedDataset
from .feature import (
target... | 1,130 | 19.944444 | 39 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/transform/feature.py | # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license... | 8,489 | 31.906977 | 85 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/quantile.py | import re
from typing import NamedTuple, Union
class Quantile(NamedTuple):
value: float
name: str
@property
def loss_name(self):
return f"QuantileLoss[{self.name}]"
@property
def weighted_loss_name(self):
return f"wQuantileLoss[{self.name}]"
@property
def coverage_na... | 2,496 | 28.376471 | 84 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/predictor.py | 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
import pts
from pts.core.serde import dump_json, fqname_for, load_json
from pts.dataset import Dataset, DataEntry, Inf... | 6,040 | 33.129944 | 81 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/forecast_generator.py | from abc import ABC, abstractmethod
from typing import Any, Callable, Iterator, List, Optional
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 impo... | 6,330 | 33.785714 | 125 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/utils.py | import inspect
from typing import Optional
import torch
import torch.nn as nn
def get_module_forward_input_names(module: nn.Module):
params = inspect.signature(module.forward).parameters
return list(params)
def copy_parameters(net_source: nn.Module, net_dest: nn.Module) -> None:
net_dest.load_state_dic... | 1,032 | 26.918919 | 74 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/forecast.py | from abc import ABC, abstractmethod
from enum import Enum
from typing import Dict, List, Optional, Set, Union, Callable
import numpy as np
import pandas as pd
import torch
from pydantic import BaseModel, Field
from torch.distributions import Distribution
from .quantile import Quantile
class OutputType(str, Enum):
... | 16,436 | 29.495362 | 99 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/__init__.py | from .estimator import Estimator, PTSEstimator
from .forecast import Forecast, SampleForecast, QuantileForecast, DistributionForecast
from .predictor import Predictor, PTSPredictor
from .quantile import Quantile
from .utils import get_module_forward_input_names, copy_parameters, weighted_average
| 297 | 48.666667 | 86 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/estimator.py | from abc import ABC, abstractmethod
from typing import NamedTuple, Optional
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from pts.core.component import validated
from pts import Trainer
from pts.dataset import Dataset, TransformedIterableDataset, TransformedListDataset... | 4,526 | 26.436364 | 83 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/deepar/deepar_network.py | from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch.distributions import Distribution
from pts.core.component import validated
from pts.model import weighted_average
from pts.modules import DistributionOutput, MeanScaler, NOPScaler, FeatureEmbedder
def pr... | 28,509 | 41.936747 | 179 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/deepar/deepar_estimator.py | from typing import List, Optional
import numpy as np
import torch
import torch.nn as nn
from pts.core.component import validated
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 impor... | 9,762 | 38.686992 | 144 | py |
M5_Accuracy_3rd | M5_Accuracy_3rd-master/pts/model/deepar/__init__.py | from .deepar_estimator import DeepAREstimator
from .deepar_network import DeepARNetwork, RolledDeepARTrainingNetwork
| 117 | 38.333333 | 70 | py |
NM-sparsity | NM-sparsity-main/devkit/__init__.py | 0 | 0 | 0 | py | |
NM-sparsity | NM-sparsity-main/devkit/core/lr_scheduler.py | """Learning Rate Schedulers"""
from __future__ import division
from math import pi, cos
class LRScheduler(object):
r"""Learning Rate Scheduler
For mode='step', we multiply lr with `decay_factor` at each epoch in `step`.
For mode='poly'::
lr = targetlr + (baselr - targetlr) * (1 - iter / maxiter) ^ ... | 3,992 | 41.478723 | 102 | py |
NM-sparsity | NM-sparsity-main/devkit/core/dist_utils.py | import os
import torch
import torch.multiprocessing as mp
import torch.distributed as dist
__all__ = [
'init_dist', 'broadcast_params','average_gradients']
def init_dist(backend='nccl',
master_ip='127.0.0.1',
port=29500):
if mp.get_start_method(allow_none=True) is None:
mp.... | 945 | 28.5625 | 60 | py |
NM-sparsity | NM-sparsity-main/devkit/core/utils.py | import torch
import os
import shutil
def save_checkpoint(model_dir, state, is_best):
epoch = state['epoch']
path = os.path.join(model_dir, 'model.pth-' + str(epoch))
torch.save(state, path)
checkpoint_file = os.path.join(model_dir, 'checkpoint')
checkpoint = open(checkpoint_file, 'w+')
checkpo... | 2,861 | 40.478261 | 102 | py |
NM-sparsity | NM-sparsity-main/devkit/core/__init__.py | from .lr_scheduler import *
from .dist_utils import *
from .utils import *
| 75 | 18 | 27 | py |
NM-sparsity | NM-sparsity-main/devkit/dataset/imagenet_dataset.py | from torch.utils.data import Dataset
from PIL import Image
import torch
def pil_loader(filename):
with Image.open(filename) as img:
img = img.convert('RGB')
return img
class ImagenetDataset(Dataset):
def __init__(self, root_dir, meta_file, transform=None):
self.root_dir = root_dir
... | 1,758 | 29.327586 | 71 | py |
NM-sparsity | NM-sparsity-main/devkit/dataset/__init__.py | 0 | 0 | 0 | py | |
NM-sparsity | NM-sparsity-main/devkit/sparse_ops/sparse_ops.py | import torch
from torch import autograd, nn
import torch.nn.functional as F
from itertools import repeat
from torch._six import container_abcs
class Sparse(autograd.Function):
"""" Prune the unimprotant weight for the forwards phase but pass the gradient to dense weight using SR-STE in the backwards phase"""
... | 3,245 | 26.982759 | 159 | py |
NM-sparsity | NM-sparsity-main/devkit/sparse_ops/__init__.py | from .syncbn_layer import SyncBatchNorm2d
from .sparse_ops import SparseConv
| 77 | 25 | 41 | py |
NM-sparsity | NM-sparsity-main/devkit/sparse_ops/syncbn_layer.py | import torch
from torch.autograd import Function
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.distributed as dist
import torch.nn as nn
class SyncBNFunc(Function):
@staticmethod
def forward(ctx, in_data, scale_data, shift_data, running_mean, running_var, eps... | 3,824 | 37.25 | 159 | py |
NM-sparsity | NM-sparsity-main/classification/train_imagenet.py | from __future__ import division
import argparse
import os
import time
import torch.distributed as dist
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data.distributed import DistributedSampler
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
i... | 10,642 | 33.003195 | 131 | py |
NM-sparsity | NM-sparsity-main/classification/models/resnet.py | import torch.nn as nn
import math
import sys
import os.path as osp
sys.path.append(osp.abspath(osp.join(__file__, '../../../')))
#from devkit.ops import SyncBatchNorm2d
import torch
import torch.nn.functional as F
from torch import autograd
from torch.nn.modules.utils import _pair as pair
from torch.nn import init
from... | 5,446 | 28.603261 | 95 | py |
NM-sparsity | NM-sparsity-main/classification/models/__init__.py | from .resnet import *
| 22 | 10.5 | 21 | py |
NM-sparsity | NM-sparsity-main/RAFT/evaluate.py | import sys
sys.path.append('core')
from PIL import Image
import argparse
import os
import time
import numpy as np
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
import datasets
from utils import flow_viz
from utils import frame_utils
from raft import RAFT
from utils.utils import InputPa... | 6,618 | 32.429293 | 112 | py |
NM-sparsity | NM-sparsity-main/RAFT/demo.py | import sys
sys.path.append('core')
import argparse
import os
import cv2
import glob
import numpy as np
import torch
from PIL import Image
from raft import RAFT
from utils import flow_viz
from utils.utils import InputPadder
DEVICE = 'cuda'
def load_image(imfile):
img = np.array(Image.open(imfile)).astype(np.ui... | 2,073 | 26.289474 | 112 | py |
NM-sparsity | NM-sparsity-main/RAFT/train.py | from __future__ import print_function, division
import sys
sys.path.append('core')
import argparse
import os
import cv2
import time
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader... | 8,244 | 31.333333 | 103 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/lr_scheduler.py | import types
import math
from torch._six import inf
from functools import wraps
import warnings
import weakref
from collections import Counter
from bisect import bisect_right
#from torch.optim.optimizer import Optimizer
class _LRScheduler(object):
def __init__(self, optimizer, last_epoch=-1, verbose=False):
... | 19,353 | 43.800926 | 128 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/sparse_update.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
import os.path as osp
sys.path.append(osp.abspath(osp.join(__file__, '../../../')))
from devkit.sparse_ops import SparseConv
class FlowHead(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256):
super(FlowHead, self).__i... | 5,385 | 36.402778 | 88 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/sparse_raft.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sparse_update import BasicUpdateBlock, SmallUpdateBlock
from sparse_extractor import BasicEncoder, SmallEncoder
from corr import CorrBlock, AlternateCorrBlock
from utils.utils import bilinear_sampler, coords_grid, upflow8
try:
... | 4,950 | 33.144828 | 102 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/corr.py | import torch
import torch.nn.functional as F
from utils.utils import bilinear_sampler, coords_grid
try:
import alt_cuda_corr
except:
# alt_cuda_corr is not compiled
pass
class CorrBlock:
def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
self.num_levels = num_levels
self.radius... | 3,085 | 32.543478 | 74 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/update.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class FlowHead(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256):
super(FlowHead, self).__init__()
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
... | 5,227 | 36.342857 | 87 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/extractor.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, in_planes, planes, norm_fn='group', stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
s... | 8,847 | 32.014925 | 93 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/datasets.py | # Data loading based on https://github.com/NVIDIA/flownet2-pytorch
import numpy as np
import torch
import torch.utils.data as data
import torch.nn.functional as F
import os
import math
import random
from glob import glob
import os.path as osp
from utils import frame_utils
from utils.augmentor import FlowAugmentor, S... | 9,242 | 38.165254 | 111 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/raft.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from update import BasicUpdateBlock, SmallUpdateBlock
from extractor import BasicEncoder, SmallEncoder
from corr import CorrBlock, AlternateCorrBlock
from utils.utils import bilinear_sampler, coords_grid, upflow8
try:
autocast =... | 4,924 | 32.965517 | 102 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/__init__.py | 0 | 0 | 0 | py | |
NM-sparsity | NM-sparsity-main/RAFT/core/sparse_extractor.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
import os.path as osp
sys.path.append(osp.abspath(osp.join(__file__, '../../../')))
from devkit.sparse_ops import SparseConv
class ResidualBlock(nn.Module):
def __init__(self, in_planes, planes, norm_fn='group', stride=1):
supe... | 8,997 | 31.959707 | 94 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/utils/utils.py | import torch
import torch.nn.functional as F
import numpy as np
from scipy import interpolate
class InputPadder:
""" Pads images such that dimensions are divisible by 8 """
def __init__(self, dims, mode='sintel'):
self.ht, self.wd = dims[-2:]
pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
... | 2,489 | 29 | 93 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/utils/augmentor.py | import numpy as np
import random
import math
from PIL import Image
import cv2
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
import torch
from torchvision.transforms import ColorJitter
import torch.nn.functional as F
class FlowAugmentor:
def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=Tru... | 9,108 | 35.878543 | 97 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/utils/__init__.py | 0 | 0 | 0 | py | |
NM-sparsity | NM-sparsity-main/RAFT/core/utils/flow_viz.py | # Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization
# MIT License
#
# Copyright (c) 2018 Tom Runia
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software wi... | 4,318 | 31.719697 | 90 | py |
NM-sparsity | NM-sparsity-main/RAFT/core/utils/frame_utils.py | import numpy as np
from PIL import Image
from os.path import *
import re
import cv2
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
TAG_CHAR = np.array([202021.25], np.float32)
def readFlow(fn):
""" Read .flo file in Middlebury format"""
# Code adapted from:
# http://stackoverflow.com/questions/28013200... | 4,024 | 28.379562 | 109 | py |
NM-sparsity | NM-sparsity-main/RAFT/alt_cuda_corr/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
setup(
name='correlation',
ext_modules=[
CUDAExtension('alt_cuda_corr',
sources=['correlation.cpp', 'correlation_kernel.cu'],
extra_compile_args={'cxx': [], 'nvcc': ['-O3']}),
]... | 381 | 22.875 | 67 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/my_mpo.py | import numpy as np
import tensornetwork as tn
from tensornetwork.backends.abstract_backend import AbstractBackend
tn.set_default_backend("pytorch")
#tn.set_default_backend("numpy")
from typing import List, Union, Text, Optional, Any, Type
Tensor = Any
import tequila as tq
import torch
EPS = 1e-12
class SubOperator... | 14,354 | 36.480418 | 99 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/do_annealing.py | import tequila as tq
import numpy as np
import pickle
from pathos.multiprocessing import ProcessingPool as Pool
from parallel_annealing import *
#from dummy_par import *
from mutation_options import *
from single_thread_annealing import *
def find_best_instructions(instructions_dict):
"""
This function finds... | 13,258 | 46.185053 | 187 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/scipy_optimizer.py | import numpy, copy, scipy, typing, numbers
from tequila import BitString, BitNumbering, BitStringLSB
from tequila.utils.keymap import KeyMapRegisterToSubregister
from tequila.circuit.compiler import change_basis
from tequila.utils import to_float
import tequila as tq
from tequila.objective import Objective
from tequi... | 24,489 | 42.732143 | 144 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/generate_orbital_optimization_data.py | import tequila as tq
import numpy
def opt_mol(mol, U, guess=None, threshold=1.e-5):
delta=1.0
energy=1.0
while(delta>threshold):
opt = tq.chemistry.optimize_orbitals(molecule=mol, circuit=U, initial_guess=guess, silent=True)
guess = opt.mo_coeff
delta = abs(opt.energy-energy)
... | 1,383 | 29.086957 | 103 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/energy_optimization.py | import tequila as tq
import numpy as np
from tequila.objective.objective import Variable
import openfermion
from hacked_openfermion_qubit_operator import ParamQubitHamiltonian
from typing import Union
from vqe_utils import convert_PQH_to_tq_QH, convert_tq_QH_to_PQH,\
fold_unitary_into_hamiltonian
... | 12,442 | 42.968198 | 225 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/parallel_annealing.py | import tequila as tq
import multiprocessing
import copy
from time import sleep
from mutation_options import *
from pathos.multiprocessing import ProcessingPool as Pool
def evolve_population(hamiltonian,
type_energy_eval,
cluster_circuit,
process_id,
... | 6,456 | 38.371951 | 146 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/single_thread_annealing.py | import tequila as tq
import copy
from mutation_options import *
def st_evolve_population(hamiltonian,
type_energy_eval,
cluster_circuit,
num_offsprings,
actions_ratio,
tasks):
"""
This function carrie... | 4,005 | 40.298969 | 138 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/mutation_options.py | import argparse
import numpy as np
import random
import copy
import tequila as tq
from typing import Union
from collections import Counter
from time import time
from vqe_utils import convert_PQH_to_tq_QH, convert_tq_QH_to_PQH,\
fold_unitary_into_hamiltonian
from energy_optimization import minimi... | 26,784 | 33.967363 | 191 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/plot_term_increase.py | import numpy as np
import matplotlib.pyplot as plt
# For now, this is all when varying _one_ Clifford gate only
def find_envelope(in_list):
upper, lower = [], []
for li in in_list:
lower += [np.min(li)]
upper += [np.max(li)]
return upper, lower
# >>>>>>>>>>>>>>>>>> BEGIN DATA >>>>... | 29,307 | 286.333333 | 4,705 | py |
partitioning-with-cliffords | partitioning-with-cliffords-main/code/hacked_openfermion_qubit_operator.py | import tequila as tq
import sympy
import copy
#from param_hamiltonian import get_geometry, generate_ucc_ansatz
from hacked_openfermion_symbolic_operator import SymbolicOperator
# Define products of all Pauli operators for symbolic multiplication.
_PAULI_OPERATOR_PRODUCTS = {
('I', 'I'): (1., 'I'),
('I', 'X')... | 9,918 | 29.614198 | 85 | py |
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