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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/array_crop.py
kamae.tensorflow.layers.array_crop.ArrayCropLayer
from kamae.tensorflow.utils import enforce_single_tensor_input from kamae.tensorflow.typing import Tensor import kamae import tensorflow as tf from .base import BaseLayer from typing import Any, Dict, List, Optional, Union @tf.keras.utils.register_keras_serializable(kamae.__name__) class ArrayCropLayer(BaseLayer): ...
@tf.keras.utils.register_keras_serializable(kamae.__name__) class ArrayCropLayer(BaseLayer): ''' Performs a cropping of the input tensor to a certain length. If the tensor is shorter than the specified length, it is padded with specified pad value. TODO: Currently only supports cropping the final di...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/array_split.py
kamae.tensorflow.layers.array_split.ArraySplitLayer
from typing import Any, Dict, List, Optional from kamae.tensorflow.utils import enforce_single_tensor_input import tensorflow as tf from kamae.tensorflow.typing import Tensor from .base import BaseLayer import kamae @tf.keras.utils.register_keras_serializable(kamae.__name__) class ArraySplitLayer(BaseLayer): """ ...
@tf.keras.utils.register_keras_serializable(kamae.__name__) class ArraySplitLayer(BaseLayer): ''' Performs a splitting of the input tensor into a list of tensors. Expands dimensions to ensure the output tensors are the same shape as the input. ''' def __init__(self, name: Optional[str]=None, input_...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/array_subtract_minimum.py
kamae.tensorflow.layers.array_subtract_minimum.ArraySubtractMinimumLayer
from .base import BaseLayer from kamae.tensorflow.typing import Tensor import tensorflow as tf from kamae.tensorflow.utils import enforce_single_tensor_input import kamae from typing import Any, Dict, List, Optional, Union @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ArraySubtractMinimumLa...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ArraySubtractMinimumLayer(BaseLayer): ''' TensorFlow layer that computes the difference across an axis from the minimum non-paded element in the input tensor. It takes a tensor of numerical value and calculates the differences bet...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/base.py
kamae.tensorflow.layers.base.BaseLayer
import kamae from kamae.tensorflow.typing import Tensor import tensorflow as tf from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input from functools import reduce from typing import Any, Dict, Iterable, List, Optional, Tuple, Union from abc import ABC, abstractmethod @tf.keras.utils.register_keras_s...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class BaseLayer(tf.keras.layers.Layer, ABC): ''' Abstract base layer that performs casting of inputs and outputs to specified data types. All layers should inherit from this class. ''' def __init__(self, name: Optional[str]=Non...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/bearing_angle.py
kamae.tensorflow.layers.bearing_angle.BearingAngleLayer
from kamae.tensorflow.typing import Tensor from typing import Any, Dict, Iterable, List, Optional import math import tensorflow as tf from tensorflow.math import atan2, cos, mod, sin from .base import BaseLayer import kamae from kamae.tensorflow.utils import enforce_multiple_tensor_input @tf.keras.utils.register_keras...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class BearingAngleLayer(BaseLayer): ''' Computes the Bearing angle operation on a given input tensor. If lat_lon_constant is not set, inputs must be a list of 4 tensors, in the order of lat1, lon1, lat2, lon2. If lat_lon_constant is...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/bin.py
kamae.tensorflow.layers.bin.BinLayer
from kamae.tensorflow.utils import enforce_single_tensor_input import kamae import tensorflow as tf from kamae.utils import get_condition_operator from kamae.tensorflow.typing import Tensor from typing import Any, Dict, List, Optional, Union from .base import BaseLayer @tf.keras.utils.register_keras_serializable(packa...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class BinLayer(BaseLayer): ''' Performs a binning operation on a given input tensor. The binning operation is performed by comparing the input tensor to a list of values using a list of operators. The bin label corresponding to the firs...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/bloom_encode.py
kamae.tensorflow.layers.bloom_encode.BloomEncodeLayer
import tensorflow as tf from typing import Any, Dict, List, Optional, Union from .base import BaseLayer from tensorflow.keras.layers import Hashing from kamae.tensorflow.typing import Tensor from kamae.tensorflow.utils import enforce_single_tensor_input import kamae @tf.keras.utils.register_keras_serializable(package=...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class BloomEncodeLayer(BaseLayer): ''' Performs a bloom encoding on the input tensor. Uses multiple hash functions to encode the input tensor, significantly reducing the dimensionality of the input and also avoiding collisions. See pape...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/bucketize.py
kamae.tensorflow.layers.bucketize.BucketizeLayer
from kamae.tensorflow.utils import enforce_single_tensor_input from typing import Any, Dict, List, Optional from kamae.tensorflow.typing import Tensor import tensorflow as tf import kamae from .base import BaseLayer @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class BucketizeLayer(BaseLayer): ...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class BucketizeLayer(BaseLayer): ''' Performs a bucketing operation on the input tensor. Given a list of splits, the input tensor is bucketed into the corresponding bucket. For example, if the splits are [0, 1, 2, 3], then the input...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/conditional_standard_scale.py
kamae.tensorflow.layers.conditional_standard_scale.ConditionalStandardScaleLayer
from kamae.tensorflow.utils import NormalizeLayer, enforce_single_tensor_input from typing import Any, Dict, List, Optional, Union import kamae import tensorflow as tf from kamae.tensorflow.typing import Tensor import numpy as np @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ConditionalStan...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ConditionalStandardScaleLayer(NormalizeLayer): ''' Performs the standard scaling of the input with a masking condition. This layer will shift and scale inputs into a distribution centered around 0 with standard deviation 1. It acc...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/cosine_similarity.py
kamae.tensorflow.layers.cosine_similarity.CosineSimilarityLayer
from kamae.tensorflow.utils import enforce_multiple_tensor_input from .base import BaseLayer import kamae import tensorflow as tf from typing import Any, Dict, Iterable, List, Optional from kamae.tensorflow.typing import Tensor @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class CosineSimilarityL...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class CosineSimilarityLayer(BaseLayer): ''' Computes the cosine similarity between two input tensors. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, axis: int=-1, kee...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/current_date.py
kamae.tensorflow.layers.current_date.CurrentDateLayer
from .base import BaseLayer from typing import Any, Dict, List, Optional from kamae.tensorflow.utils import enforce_single_tensor_input, unix_timestamp_to_datetime import kamae import tensorflow as tf from kamae.tensorflow.typing import Tensor @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class C...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class CurrentDateLayer(BaseLayer): ''' Returns the current UTC date in yyyy-MM-dd format. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, **kwargs: Any) -> None: ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/current_date_time.py
kamae.tensorflow.layers.current_date_time.CurrentDateTimeLayer
import tensorflow as tf from typing import Any, Dict, List, Optional from .base import BaseLayer from kamae.tensorflow.typing import Tensor from kamae.tensorflow.utils import enforce_single_tensor_input, unix_timestamp_to_datetime import kamae @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class C...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class CurrentDateTimeLayer(BaseLayer): ''' Returns the current timestamp in yyyy-MM-dd HH:mm:ss.SSS format. NOTE: Parity between this and its Spark counterpart is very difficult at the millisecond level. We have to round the TensorFlow ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/current_unix_timestamp.py
kamae.tensorflow.layers.current_unix_timestamp.CurrentUnixTimestampLayer
from .base import BaseLayer import kamae import tensorflow as tf from kamae.tensorflow.typing import Tensor from kamae.tensorflow.utils import enforce_single_tensor_input from typing import Any, Dict, List, Optional @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class CurrentUnixTimestampLayer(Bas...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class CurrentUnixTimestampLayer(BaseLayer): ''' Returns the current unix timestamp in either seconds or milliseconds. NOTE: Parity between this and its Spark counterpart is very difficult at the millisecond level. TensorFlow provides mu...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/date_add.py
kamae.tensorflow.layers.date_add.DateAddLayer
from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input, datetime_add_days from typing import Any, Dict, List, Optional from .base import BaseLayer from kamae.tensorflow.typing import Tensor import tensorflow as tf import kamae @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class DateAddLayer(BaseLayer): ''' Adds or subtracts a number of days from a date(time) string. WARNING: This layer destroys the time component of the date column. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optio...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/date_diff.py
kamae.tensorflow.layers.date_diff.DateDiffLayer
from kamae.tensorflow.typing import Tensor from .base import BaseLayer from typing import Any, Dict, List, Optional from kamae.tensorflow.utils import datetime_total_days, enforce_multiple_tensor_input import tensorflow as tf import kamae @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class DateDi...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class DateDiffLayer(BaseLayer): '''A preprocessing layer that returns the difference between two dates in days. The inputs must be in yyyy-MM-dd (HH:mm:ss.SSS) format and must be passed to the layer in the order [start date , end date]. ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/date_parse.py
kamae.tensorflow.layers.date_parse.DateParseLayer
from typing import Any, Dict, List, Optional from kamae.tensorflow.typing import Tensor import tensorflow as tf import kamae from .base import BaseLayer from kamae.tensorflow.utils import datetime_day, datetime_day_of_year, datetime_hour, datetime_millisecond, datetime_minute, datetime_month, datetime_second, datetime_...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class DateParseLayer(BaseLayer): ''' Parses a date(time) string from yyyy-MM-dd (HH:mm:ss.SSS) format into a specified date part tensor. Date parts can be one of the following: - `DayOfWeek` - day of week (Monday = 1, Sunday = 7) ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/date_time_to_unix_timestamp.py
kamae.tensorflow.layers.date_time_to_unix_timestamp.DateTimeToUnixTimestampLayer
import tensorflow as tf from kamae.tensorflow.typing import Tensor import kamae from typing import Any, Dict, List, Optional from .base import BaseLayer from kamae.tensorflow.utils import datetime_to_unix_timestamp, enforce_single_tensor_input @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class D...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class DateTimeToUnixTimestampLayer(BaseLayer): ''' Returns the unix timestamp from a datetime in either yyyy-MM-dd HH:mm:ss.SSS or yyyy-MM-dd format. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None,...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/divide.py
kamae.tensorflow.layers.divide.DivideLayer
from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input from functools import reduce import tensorflow as tf from typing import Any, Dict, Iterable, List, Optional, Union from .base import BaseLayer from kamae.tensorflow.typing import Tensor import kamae @tf.keras.utils.register_keras_serializable(pac...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class DivideLayer(BaseLayer): ''' Performs the divide(x, y) operation on a given input tensor. If divisor is not set, inputs must be a list. If divisor is set, inputs must be a tensor. ''' def __init__(self, name: Optional[str]=Non...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/exp.py
kamae.tensorflow.layers.exp.ExpLayer
import tensorflow as tf from kamae.tensorflow.utils import enforce_single_tensor_input from .base import BaseLayer from typing import Any, Dict, List, Optional import kamae from kamae.tensorflow.typing import Tensor @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ExpLayer(BaseLayer): """ ...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ExpLayer(BaseLayer): ''' Performs the exp(x) operation on a given input tensor ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, **kwargs: Any) -> None: ''...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/exponent.py
kamae.tensorflow.layers.exponent.ExponentLayer
import kamae from kamae.tensorflow.typing import Tensor from typing import Any, Dict, List, Optional from .base import BaseLayer from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input import tensorflow as tf @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ExponentLayer(BaseL...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ExponentLayer(BaseLayer): ''' Performs the x^exponent operation on a given input tensor ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, exponent: Optional[float]...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/hash_index.py
kamae.tensorflow.layers.hash_index.HashIndexLayer
import tensorflow as tf from tensorflow.keras.layers import Hashing from kamae.tensorflow.typing import Tensor from .base import BaseLayer from kamae.tensorflow.utils import enforce_single_tensor_input import kamae from typing import Any, Dict, List, Optional, Union @tf.keras.utils.register_keras_serializable(package=...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class HashIndexLayer(BaseLayer): ''' Wrapper around the Keras Hashing layer which hashes and bins categorical features. This layer transforms categorical inputs to hashed output. It element-wise converts ints or strings to ints in a fix...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/haversine_distance.py
kamae.tensorflow.layers.haversine_distance.HaversineDistanceLayer
from kamae.tensorflow.utils import enforce_multiple_tensor_input from typing import Any, Dict, Iterable, List, Optional from kamae.tensorflow.typing import Tensor import math from .base import BaseLayer import tensorflow as tf import kamae @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class Haver...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class HaversineDistanceLayer(BaseLayer): ''' Computes the haversine distance operation on a given input tensor. If lat_lon_constant is not set, inputs must be a list of 4 tensors, in the order of lat1, lon1, lat2, lon2. If lat_lon_c...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/identity.py
kamae.tensorflow.layers.identity.IdentityLayer
from typing import Any, Dict, List, Optional import tensorflow as tf from kamae.tensorflow.utils import enforce_single_tensor_input from kamae.tensorflow.typing import Tensor from .base import BaseLayer import kamae @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class IdentityLayer(BaseLayer): ...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class IdentityLayer(BaseLayer): ''' Performs an identity transform on the input tensor. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, **kwargs: Any) -> None: ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/if_statement.py
kamae.tensorflow.layers.if_statement.IfStatementLayer
from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input from numbers import Number from kamae.utils import get_condition_operator from .base import BaseLayer import kamae import tensorflow as tf from typing import Any, Dict, Iterable, List, Optional, Union from kamae.tensorflow.typing import Tensor @t...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class IfStatementLayer(BaseLayer): ''' Performs an if statement on the input tensor, returning a tensor of the same shape as the input tensor. The condition operator can be one of the following: - "eq": Equal to - "neq": Not equ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/impute.py
kamae.tensorflow.layers.impute.ImputeLayer
from kamae.tensorflow.typing import Tensor from .base import BaseLayer import tensorflow as tf import kamae from typing import Any, Dict, List, Optional, Union from kamae.tensorflow.utils import enforce_single_tensor_input @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ImputeLayer(BaseLayer)...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ImputeLayer(BaseLayer): ''' Performs imputation on the input. Where the input data is equal to the specified mask value, this layer will replace the data with the impute value calculated at preprocessing time. The impute value...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/lambda_function.py
kamae.tensorflow.layers.lambda_function.LambdaFunctionLayer
import kamae from typing import Any, Callable, Dict, Iterable, List, Optional, Union from .base import BaseLayer from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input import tensorflow as tf from kamae.tensorflow.typing import Tensor @tf.keras.utils.register_keras_serializable(package=kamae.__name__...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class LambdaFunctionLayer(BaseLayer, tf.keras.layers.Lambda): ''' Performs the lambda function operation on a given input tensor WARNING: This layer relies on a `tf.keras.layers.Lambda` layer which have (de)serialization limitations! ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/list_max.py
kamae.tensorflow.layers.list_max.ListMaxLayer
import tensorflow as tf import kamae from kamae.tensorflow.typing import Tensor from .base import BaseLayer from typing import Any, Dict, Iterable, List, Optional from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input, get_top_n @tf.keras.utils.register_keras_serializable(package=kamae.__name__) clas...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ListMaxLayer(BaseLayer): ''' Calculate the max across the axis dimension. - If one tensor is passed, the transformer calculates the max of the tensor based on all the items in the given axis dimension. - If inputCols is set, t...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/list_mean.py
kamae.tensorflow.layers.list_mean.ListMeanLayer
from typing import Any, Dict, Iterable, List, Optional from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input, get_top_n import kamae from kamae.tensorflow.typing import Tensor from .base import BaseLayer import tensorflow as tf @tf.keras.utils.register_keras_serializable(package=kamae.__name__) clas...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ListMeanLayer(BaseLayer): ''' Calculate the mean across the axis dimension. - If one tensor is passed, the transformer calculates the mean of the tensor based on all the items in the given axis dimension. - If inputCols is set...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/list_median.py
kamae.tensorflow.layers.list_median.ListMedianLayer
from typing import Any, Dict, Iterable, List, Optional import kamae from .base import BaseLayer from kamae.tensorflow.typing import Tensor from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input, get_top_n import tensorflow as tf @tf.keras.utils.register_keras_serializable(package=kamae.__name__) clas...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ListMedianLayer(BaseLayer): ''' Calculate the median across the axis dimension. - If one tensor is passed, the transformer calculates the median of the tensor based on all the items in the given axis dimension. - If inputCols ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/list_min.py
kamae.tensorflow.layers.list_min.ListMinLayer
from typing import Any, Dict, Iterable, List, Optional from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input, get_top_n import tensorflow as tf from .base import BaseLayer import kamae from kamae.tensorflow.typing import Tensor @tf.keras.utils.register_keras_serializable(package=kamae.__name__) clas...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ListMinLayer(BaseLayer): ''' Calculate the min across the axis dimension. - If one tensor is passed, the transformer calculates the min of the tensor based on all the items in the given axis dimension. - If inputCols is set, t...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/list_rank.py
kamae.tensorflow.layers.list_rank.ListRankLayer
from kamae.tensorflow.utils import enforce_single_tensor_input import kamae from kamae.tensorflow.typing import Tensor from typing import Any, Dict, Iterable, List, Optional import tensorflow as tf from .base import BaseLayer @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ListRankLayer(BaseL...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ListRankLayer(BaseLayer): ''' Calculate the rank across the axis dimension. Example: calculate the rank of items within a query, given the score. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=Non...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/list_std_dev.py
kamae.tensorflow.layers.list_std_dev.ListStdDevLayer
from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input, get_top_n from typing import Any, Dict, Iterable, List, Optional import kamae from kamae.tensorflow.typing import Tensor import tensorflow as tf from .base import BaseLayer @tf.keras.utils.register_keras_serializable(package=kamae.__name__) clas...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ListStdDevLayer(BaseLayer): ''' Calculate the average across the axis dimension. - If one tensor is passed, the transformer calculates the average of the tensor based on all the items in the given axis dimension. - If inputCol...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/log.py
kamae.tensorflow.layers.log.LogLayer
import tensorflow as tf from kamae.tensorflow.utils import enforce_single_tensor_input from .base import BaseLayer from typing import Any, Dict, List, Optional import kamae from kamae.tensorflow.typing import Tensor @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class LogLayer(BaseLayer): """ ...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class LogLayer(BaseLayer): ''' Performs the log(alpha + x) operation on a given input tensor ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, alpha: float=0.0, **kwargs...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/logical_and.py
kamae.tensorflow.layers.logical_and.LogicalAndLayer
import tensorflow as tf from kamae.tensorflow.utils import enforce_multiple_tensor_input from .base import BaseLayer import kamae from functools import reduce from typing import Any, Dict, Iterable, List, Optional from kamae.tensorflow.typing import Tensor @tf.keras.utils.register_keras_serializable(package=kamae.__na...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class LogicalAndLayer(BaseLayer): ''' Performs the and(x, y) operation on a given input tensor. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, **kwargs: Any) -> None:...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/logical_not.py
kamae.tensorflow.layers.logical_not.LogicalNotLayer
import tensorflow as tf import kamae from kamae.tensorflow.utils import enforce_single_tensor_input from .base import BaseLayer from typing import Any, Dict, List, Optional from kamae.tensorflow.typing import Tensor @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class LogicalNotLayer(BaseLayer): ...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class LogicalNotLayer(BaseLayer): ''' Performs the not operation on a given input tensor. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, **kwargs: Any) -> None: ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/logical_or.py
kamae.tensorflow.layers.logical_or.LogicalOrLayer
from typing import Any, Dict, Iterable, List, Optional from functools import reduce from kamae.tensorflow.typing import Tensor from kamae.tensorflow.utils import enforce_multiple_tensor_input import kamae from .base import BaseLayer import tensorflow as tf @tf.keras.utils.register_keras_serializable(package=kamae.__na...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class LogicalOrLayer(BaseLayer): ''' Performs the or(x, y) operation on a given input tensor. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, **kwargs: Any) -> None: ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/max.py
kamae.tensorflow.layers.max.MaxLayer
from .base import BaseLayer from functools import reduce from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input import tensorflow as tf from typing import Any, Dict, Iterable, List, Optional, Union from kamae.tensorflow.typing import Tensor import kamae @tf.keras.utils.register_keras_serializable(pac...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class MaxLayer(BaseLayer): ''' Performs the max(x, y) operation on a given input tensor. If max_constant is not set, inputs are assumed to be a list of tensors and the max of all the tensors is computed. If max_constant is set, inpu...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/mean.py
kamae.tensorflow.layers.mean.MeanLayer
import tensorflow as tf from functools import reduce from typing import Any, Dict, Iterable, List, Optional, Union from kamae.tensorflow.typing import Tensor from .base import BaseLayer from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input import kamae @tf.keras.utils.register_keras_serializable(pac...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class MeanLayer(BaseLayer): ''' Performs the mean(x, y) operation on a given input tensor. If mean_constant is not set, inputs are assumed to be a list of tensors and the mean of all the tensors is computed. If mean_constant is set,...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/min.py
kamae.tensorflow.layers.min.MinLayer
import tensorflow as tf from .base import BaseLayer from functools import reduce from kamae.tensorflow.typing import Tensor from typing import Any, Dict, Iterable, List, Optional, Union import kamae from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input @tf.keras.utils.register_keras_serializable(pac...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class MinLayer(BaseLayer): ''' Performs the min(x, y) operation on a given input tensor. If min_constant is not set, inputs are assumed to be a list of tensors and the min of all the tensors is computed. If min_constant is set, inpu...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/min_hash_index.py
kamae.tensorflow.layers.min_hash_index.MinHashIndexLayer
from typing import Any, Dict, List, Optional from kamae.tensorflow.utils import enforce_single_tensor_input import tensorflow as tf from .base import BaseLayer from tensorflow.keras.layers import Hashing import kamae from kamae.tensorflow.typing import Tensor @tf.keras.utils.register_keras_serializable(package=kamae._...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class MinHashIndexLayer(BaseLayer): ''' Performs min hashing of the input tensor as described here: https://en.wikipedia.org/wiki/MinHash MinHash approximates the Jaccard similarity between sets by hashing the elements of the sets a...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/min_max_scale.py
kamae.tensorflow.layers.min_max_scale.MinMaxScaleLayer
from kamae.tensorflow.typing import Tensor from kamae.tensorflow.utils import enforce_single_tensor_input, listify_tensors from .base import BaseLayer import tensorflow as tf import kamae import numpy as np from typing import Any, Dict, List, Optional, Tuple, Union @tf.keras.utils.register_keras_serializable(package=k...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class MinMaxScaleLayer(BaseLayer): ''' Performs a min-max scaling operation on the input tensor(s). This is used to standardize/transform the input tensor to the range [0, 1] using the minimum and maximum values. Formula: (x - min)/...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/modulo.py
kamae.tensorflow.layers.modulo.ModuloLayer
from kamae.tensorflow.typing import Tensor from typing import Any, Dict, Iterable, List, Optional, Union import kamae from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input from .base import BaseLayer import tensorflow as tf @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class Mo...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class ModuloLayer(BaseLayer): ''' Performs the modulo(x, y) operation on a given input tensor. If divisor is not set, inputs are assumed to be a list of two tensors and the first tensor is modulo'd by the second. If divisor is set, ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/multiply.py
kamae.tensorflow.layers.multiply.MultiplyLayer
from .base import BaseLayer from kamae.tensorflow.typing import Tensor import kamae import tensorflow as tf from functools import reduce from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input from typing import Any, Dict, Iterable, List, Optional, Union @tf.keras.utils.register_keras_serializable(pac...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class MultiplyLayer(BaseLayer): ''' Performs the multiply(x, y) operation on a given input tensor. If multiplier is not set, inputs are assumed to be a list of tensors and multiplied. If multiplier is set, inputs must be a tensor. '...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/numerical_if_statement.py
kamae.tensorflow.layers.numerical_if_statement.NumericalIfStatementLayer
from kamae.tensorflow.typing import Tensor import tensorflow as tf from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input from typing import Any, Dict, Iterable, List, Optional, Union from kamae.utils import get_condition_operator from .base import BaseLayer import kamae @tf.keras.utils.register_kera...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class NumericalIfStatementLayer(BaseLayer): ''' Performs a numerical if statement on the input tensor, returning a tensor of the same shape as the input tensor. The condition operator can be one of the following: - "eq": Equal to ...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/one_hot_encode.py
kamae.tensorflow.layers.one_hot_encode.OneHotEncodeLayer
import tensorflow as tf from typing import Any, Dict, List, Optional, Union from kamae.tensorflow.utils import enforce_single_tensor_input from .base import BaseLayer from kamae.tensorflow.typing import Tensor import kamae @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class OneHotEncodeLayer(Base...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/one_hot_encode.py
kamae.tensorflow.layers.one_hot_encode.OneHotLayer
from typing import Any, Dict, List, Optional, Union import warnings import kamae import tensorflow as tf @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class OneHotLayer(OneHotEncodeLayer): def __init__(self, *args: Any, **kwargs: Any) -> None: warnings.warn('OneHotLayer is deprecated...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class OneHotLayer(OneHotEncodeLayer): def __init__(self, *args: Any, **kwargs: Any) -> None: pass
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/ordinal_array_encode.py
kamae.tensorflow.layers.ordinal_array_encode.OrdinalArrayEncodeLayer
from kamae.tensorflow.typing import Tensor import tensorflow as tf from kamae.tensorflow.utils import enforce_single_tensor_input, map_fn_w_axis import kamae from .base import BaseLayer from typing import Any, Dict, List, Optional @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class OrdinalArrayEn...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class OrdinalArrayEncodeLayer(BaseLayer): ''' Transformer that encodes an array of strings into an array of integers. The transformer will map each unique string in the array to an integer, according to the order in which they appear in...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/round.py
kamae.tensorflow.layers.round.RoundLayer
import kamae from typing import Any, Dict, List, Optional from .base import BaseLayer from kamae.tensorflow.typing import Tensor import tensorflow as tf from kamae.tensorflow.utils import enforce_single_tensor_input @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class RoundLayer(BaseLayer): ""...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class RoundLayer(BaseLayer): ''' Performs a standard rounding operation on the input tensor. Supported rounding types are 'ceil', 'floor' and 'round'. - 'ceil' rounds up to the nearest integer. - 'floor' rounds down to the nearest i...
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325,448
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/round_to_decimal.py
kamae.tensorflow.layers.round_to_decimal.RoundToDecimalLayer
from .base import BaseLayer from kamae.tensorflow.utils import enforce_single_tensor_input from typing import Any, Dict, List, Optional import tensorflow as tf import kamae from kamae.tensorflow.typing import Tensor @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class RoundToDecimalLayer(BaseLayer...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class RoundToDecimalLayer(BaseLayer): ''' Performs a rounding to the nearest decimal operation on the input tensor. If the specified number of decimals is too large for the input precision type, this operation can result in overflow. Th...
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1.06
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/standard_scale.py
kamae.tensorflow.layers.standard_scale.StandardScaleLayer
from typing import Any, Dict, List, Optional, Union import kamae from kamae.tensorflow.utils import NormalizeLayer, enforce_single_tensor_input import numpy as np from kamae.tensorflow.typing import Tensor import tensorflow as tf @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StandardScaleLa...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StandardScaleLayer(NormalizeLayer): ''' Performs the standard scaling of the input. This layer will shift and scale inputs into a distribution centered around 0 with standard deviation 1. It accomplishes this by precomputing the m...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_affix.py
kamae.tensorflow.layers.string_affix.StringAffixLayer
import tensorflow as tf from .base import BaseLayer from typing import Any, Dict, List, Optional import kamae from kamae.tensorflow.utils import enforce_single_tensor_input from kamae.tensorflow.typing import Tensor @tf.keras.utils.register_keras_serializable(kamae.__name__) class StringAffixLayer(BaseLayer): """ ...
@tf.keras.utils.register_keras_serializable(kamae.__name__) class StringAffixLayer(BaseLayer): ''' Performs a prefixing and suffing on the input tensor. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, prefix: Optional[str]=None, suffix...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_array_constant.py
kamae.tensorflow.layers.string_array_constant.StringArrayConstantLayer
from typing import Any, Dict, List, Optional from kamae.tensorflow.typing import Tensor from .base import BaseLayer import tensorflow as tf from kamae.tensorflow.utils import enforce_single_tensor_input import kamae @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringArrayConstantLayer(Base...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringArrayConstantLayer(BaseLayer): ''' Tensorflow keras layer that outputs a constant string array. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, constant_st...
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325,452
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_case.py
kamae.tensorflow.layers.string_case.StringCaseLayer
from kamae.tensorflow.typing import Tensor from kamae.tensorflow.utils import enforce_single_tensor_input from .base import BaseLayer import kamae from typing import Any, Dict, List, Optional import tensorflow as tf @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringCaseLayer(BaseLayer): ...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringCaseLayer(BaseLayer): ''' Performs a string case transform on the input tensor. Supported string case types are 'upper' and 'lower'. ''' def __init__(self, string_case_type: str='lower', name: Optional[str]=None, input_...
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325,453
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_concatenate.py
kamae.tensorflow.layers.string_concatenate.StringConcatenateLayer
from kamae.tensorflow.typing import Tensor from typing import Any, Dict, Iterable, List, Optional from .base import BaseLayer from kamae.tensorflow.utils import enforce_multiple_tensor_input import tensorflow as tf import kamae @tf.keras.utils.register_keras_serializable(kamae.__name__) class StringConcatenateLayer(Ba...
@tf.keras.utils.register_keras_serializable(kamae.__name__) class StringConcatenateLayer(BaseLayer): ''' Performs a concatenation of the input tensors. ''' def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, separator: str='_', **kwargs: Any) ...
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325,454
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_contains.py
kamae.tensorflow.layers.string_contains.StringContainsLayer
from typing import Any, Dict, Iterable, List, Optional, Union from kamae.tensorflow.typing import Tensor from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input import tensorflow as tf import kamae from .base import BaseLayer @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class St...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringContainsLayer(BaseLayer): ''' Performs a string contains operation on the input tensor, matching against a string constant or element-wise against a second input tensor. WARNING: While it works, the use of tensors in matchin...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_contains_list.py
kamae.tensorflow.layers.string_contains_list.StringContainsListLayer
from typing import Any, Dict, List, Optional import kamae from kamae.tensorflow.typing import Tensor from .base import BaseLayer import tensorflow as tf from kamae.tensorflow.utils import enforce_single_tensor_input @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringContainsListLayer(BaseL...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringContainsListLayer(BaseLayer): ''' Performs a string contains operation on the input tensor over entries in the string constant list. This implementation does not support matching of newline characters or empty strings. ...
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325,456
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_equals_if_statement.py
kamae.tensorflow.layers.string_equals_if_statement.StringEqualsIfStatementLayer
from kamae.tensorflow.typing import Tensor import kamae from typing import Any, Dict, Iterable, List, Optional, Union from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input import tensorflow as tf from .base import BaseLayer @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class St...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringEqualsIfStatementLayer(BaseLayer): ''' Performs a string if equals statement on the input tensor, returning a tensor of the same shape as the input tensor. The value to compare must be a string. We will cast the input tensor...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_index.py
kamae.tensorflow.layers.string_index.StringIndexLayer
from kamae.tensorflow.utils import enforce_single_tensor_input import kamae from typing import Any, Dict, List, Optional, Union from tensorflow.keras.layers import StringLookup import tensorflow as tf from kamae.tensorflow.typing import Tensor from .base import BaseLayer @tf.keras.utils.register_keras_serializable(pac...
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325,458
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_isin_list.py
kamae.tensorflow.layers.string_isin_list.StringIsInListLayer
from typing import Any, Dict, List, Optional import kamae from .base import BaseLayer from kamae.tensorflow.typing import Tensor from kamae.tensorflow.utils import enforce_single_tensor_input import tensorflow as tf @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringIsInListLayer(BaseLayer...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringIsInListLayer(BaseLayer): ''' Performs a string isin operation on the input tensor over entries in the string constant list. ''' def __init__(self, string_constant_list: List[str], name: Optional[str]=None, input_dtype:...
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325,459
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_list_to_string.py
kamae.tensorflow.layers.string_list_to_string.StringListToStringLayer
from kamae.tensorflow.typing import Tensor from kamae.tensorflow.utils import enforce_single_tensor_input from typing import Any, Dict, List, Optional import tensorflow as tf from .base import BaseLayer import kamae @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringListToStringLayer(BaseL...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringListToStringLayer(BaseLayer): ''' A layer that converts a list of strings to a single string along the specified axis. If `keepdims` is `True`, the shape is retained. ''' def __init__(self, name: Optional[str]=None,...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_map.py
kamae.tensorflow.layers.string_map.StringMapLayer
from kamae.tensorflow.typing import Tensor from typing import Any, Dict, List, Optional import tensorflow as tf from kamae.tensorflow.utils import enforce_single_tensor_input import kamae from .base import BaseLayer @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringMapLayer(BaseLayer): ...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringMapLayer(BaseLayer): ''' StringMapLayer layer for TensorFlow. ''' def __init__(self, string_match_values: List[str], string_replace_values: List[str], default_replace_value: Optional[str]=None, name: Optional[str]=None, inp...
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325,461
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_replace.py
kamae.tensorflow.layers.string_replace.StringReplaceLayer
import tensorflow as tf from typing import Any, Dict, Iterable, List, Optional, Union from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input from kamae.tensorflow.typing import Tensor from .base import BaseLayer import kamae @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class St...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringReplaceLayer(BaseLayer): ''' StringReplaceLayer layer for TensorFlow. ''' def __init__(self, string_match_constant: Optional[str]=None, string_replace_constant: Optional[str]=None, regex: bool=False, name: Optional[str]=Non...
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325,462
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/string_to_string_list.py
kamae.tensorflow.layers.string_to_string_list.StringToStringListLayer
from kamae.tensorflow.utils import enforce_single_tensor_input from typing import Any, Dict, List, Optional import kamae from kamae.tensorflow.typing import Tensor import tensorflow as tf from .base import BaseLayer @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringToStringListLayer(BaseL...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class StringToStringListLayer(BaseLayer): ''' A layer that converts a string to a list of strings by splitting on a separator. It takes a default value and a list_length parameter to ensure that the output tensor has the correct shape. ...
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325,463
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/sub_string_delim_at_index.py
kamae.tensorflow.layers.sub_string_delim_at_index.SubStringDelimAtIndexLayer
import tensorflow as tf from .base import BaseLayer from typing import Any, Dict, List, Optional from kamae.tensorflow.utils import enforce_single_tensor_input from kamae.tensorflow.typing import Tensor import kamae @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class SubStringDelimAtIndexLayer(Ba...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class SubStringDelimAtIndexLayer(BaseLayer): ''' Layer which splits a string tensor by a delimiter and returns the substring at the specified index. If the delimiter is the empty string, the string is split into bytes/characters. If...
10
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160
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325,464
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/subtract.py
kamae.tensorflow.layers.subtract.SubtractLayer
from typing import Any, Dict, Iterable, List, Optional, Union from kamae.tensorflow.typing import Tensor import tensorflow as tf from .base import BaseLayer from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input import kamae from functools import reduce @tf.keras.utils.register_keras_serializable(pac...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class SubtractLayer(BaseLayer): def __init__(self, name: Optional[str]=None, input_dtype: Optional[str]=None, output_dtype: Optional[str]=None, subtrahend: Optional[float]=None, **kwargs: Any) -> None: ''' Initializes the SubtractL...
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325,465
ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/sum.py
kamae.tensorflow.layers.sum.SumLayer
from typing import Any, Dict, Iterable, List, Optional, Union import tensorflow as tf import kamae from kamae.tensorflow.utils import allow_single_or_multiple_tensor_input from .base import BaseLayer from kamae.tensorflow.typing import Tensor from functools import reduce @tf.keras.utils.register_keras_serializable(pac...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class SumLayer(BaseLayer): ''' Performs the sum(x, y) operation on a given input tensor. If added is not set, inputs are assumed to be a list of tensors and summed. If added is set, inputs must be a tensor. ''' def __init__(sel...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/layers/unix_timestamp_to_date_time.py
kamae.tensorflow.layers.unix_timestamp_to_date_time.UnixTimestampToDateTimeLayer
from .base import BaseLayer from typing import Any, Dict, List, Optional import tensorflow as tf import kamae from kamae.tensorflow.typing import Tensor from kamae.tensorflow.utils import enforce_single_tensor_input, unix_timestamp_to_datetime @tf.keras.utils.register_keras_serializable(package=kamae.__name__) class U...
@tf.keras.utils.register_keras_serializable(package=kamae.__name__) class UnixTimestampToDateTimeLayer(BaseLayer): ''' Returns the date in yyyy-MM-dd HH:mm:ss.SSS format from a Unix timestamp. If `include_time` is set to `False`, the output will be in yyyy-MM-dd format. ''' def __init__(self, name:...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/tensorflow/utils/layer_utils.py
kamae.tensorflow.utils.layer_utils.NormalizeLayer
from kamae.tensorflow.utils import listify_tensors import numpy as np from typing import Any, Dict, List, Optional, Tuple, Union import tensorflow as tf from kamae.tensorflow.layers.base import BaseLayer class NormalizeLayer(BaseLayer): """ Intermediate layer for normalization layers. Reduces code duplica...
class NormalizeLayer(BaseLayer): ''' Intermediate layer for normalization layers. Reduces code duplication by providing a common interface for normalization layers. ''' def __init__(self, mean: Union[List[float], np.array], variance: Union[List[float], np.array], name: Optional[str]=None, input_dt...
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ExpediaGroup/kamae
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/utils/dtype_enum.py
kamae.utils.dtype_enum.DType
from enum import Enum import tensorflow as tf from typing import Any, Dict from pyspark.sql.types import BooleanType, ByteType, DataType, DoubleType, FloatType, IntegerType, LongType, ShortType, StringType class DType(Enum): """ Enum class for supported data types in Kamae. Contains a string name, the corr...
class DType(Enum): ''' Enum class for supported data types in Kamae. Contains a string name, the corresponding Spark data type, the corresponding TensorFlow data type, and the number of bytes the data type takes up. String is a special case, as it can be of any length, so the number of bytes is...
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/common/deep_next/common/cmd.py
cmd.RunCmdError
class RunCmdError(Exception): """Run command error.""" def __init__(self, message, stdout: str='', stderr: str=''): super().__init__(message) self.stdout = stdout self.stderr = stderr
class RunCmdError(Exception): '''Run command error.''' def __init__(self, message, stdout: str='', stderr: str=''): pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/apps/app/deep_next/app/config.py
config.Label
from enum import Enum class Label(Enum): """State of the DeepNext process.""" TODO = 'deep_next' HUMAN_IN_THE_LOOP = 'deep_next_human_in_the_loop' AUTONOMOUS = 'deep_next_autonomous' IN_PROGRESS = 'deep_next_in_progress' AWAITING_RESPONSE = 'deep_next_awaiting_response' SOLVED = 'deep_next_...
class Label(Enum): '''State of the DeepNext process.''' pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/base_graph.py
core.base_graph.BaseGraph
from typing import Any, Awaitable, Callable, Hashable, Union from langgraph.graph import StateGraph from langchain_core.runnables.base import Runnable, RunnableLike from typing_extensions import Self from pydantic import BaseModel from abc import ABC, abstractmethod from langchain_core.runnables.graph import MermaidDra...
class BaseGraph(StateGraph, ABC): def __init__(self, state_cls: type[BaseModel | BaseModel]): pass def set_setup_fn(self, setup_fn: Callable[[BaseModel], None]) -> Self: pass def set_teardown_fn(self, teardown_fn: Callable[[BaseModel], None]) -> Self: pass @abstractmethod ...
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/base_graph.py
core.base_graph._WrappedCompiledStateGraph
from langgraph.graph.state import CompiledStateGraph from typing import Any, Awaitable, Callable, Hashable, Union from pydantic import BaseModel class _WrappedCompiledStateGraph(CompiledStateGraph): """Wrapper for CompiledStateGraph to add setup and teardown logic.""" def __init__(self, compiled_graph: Compil...
class _WrappedCompiledStateGraph(CompiledStateGraph): '''Wrapper for CompiledStateGraph to add setup and teardown logic.''' def __init__(self, compiled_graph: CompiledStateGraph, setup_fn: Callable[[BaseModel], None], teardown_fn: Callable[[BaseModel], None]): pass def invoke(self, *args: Any, **...
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/common.py
core.common.RemoveThinkingBlocksParser
from langchain_core.output_parsers import BaseOutputParser import re class RemoveThinkingBlocksParser(BaseOutputParser): """Parser that removes <think>...</think> blocks from LLM output.""" def parse(self, text: str) -> str: """Remove <think>...</think> blocks from text. Args: tex...
class RemoveThinkingBlocksParser(BaseOutputParser): '''Parser that removes <think>...</think> blocks from LLM output.''' def parse(self, text: str) -> str: '''Remove <think>...</think> blocks from text. Args: text: The input string with potential <think>...</think> blocks R...
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/config.py
core.config.ImplementationModes
from enum import Enum class ImplementationModes(str, Enum): SINGLE_FILE = 'single_file' ALL_AT_ONCE = 'all_at_once'
class ImplementationModes(str, Enum): pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/config.py
core.config.SRFConfig
class SRFConfig: N_CYCLES = 3 CYCLE_ITERATION_LIMIT = 20
class SRFConfig: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/config.py
core.config.SRSConfig
class SRSConfig: N_CYCLES = 3 CONTEXT_WINDOW = 10
class SRSConfig: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/graph.py
core.graph.DeepNextGraph
from deep_next.core.base_graph import BaseGraph from langgraph.graph import END, START from pathlib import Path class DeepNextGraph(BaseGraph): """Main graph for DeepNext.""" def __init__(self): super().__init__(_State) def _build(self): self.add_quick_node(_Node.gather_project_knowledge)...
class DeepNextGraph(BaseGraph): '''Main graph for DeepNext.''' def __init__(self): pass def _build(self): pass def create_init_state(self, root: Path, issue_title: str, issue_description: str, issue_comments: list[str]=[]) -> _State: pass def __call__(self, *_, issue_tit...
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/graph.py
core.graph.DeepNextResult
from pydantic import BaseModel, Field class DeepNextResult(BaseModel): """Response model for DeepNext.""" git_diff: str = Field(description='Final result: git diff of the changes made to the source code.') reasoning: str = Field(description='Reasoning behind the changes made.') action_plan: str = Field...
class DeepNextResult(BaseModel): '''Response model for DeepNext.''' pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/graph.py
core.graph._Node
from deep_next.core.steps.action_plan import action_plan_graph from deep_next.core.steps.implement.graph import implement_graph from deep_next.core.steps.code_review.graph import code_review_graph import textwrap from deep_next.core.steps.gather_project_knowledge.graph import gather_project_knowledge_graph from loguru ...
class _Node: @staticmethod def gather_project_knowledge(state: _State) -> dict: pass @staticmethod def create_action_plan(state: _State) -> dict: pass @staticmethod def implement(state: _State) -> dict: pass @staticmethod def review_code(state: _State) -> dict: ...
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/graph.py
core.graph._State
from deep_next.core.steps.action_plan.data_model import ActionPlan from deep_next.common.common import prepare_issue_statement from pydantic import BaseModel, Field from pathlib import Path class _State(BaseModel): root_path: Path = Field(description='Path to the root project directory.') issue_title: str = Fi...
class _State(BaseModel): @property def issue_statement(self) -> str: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/graph_hitl.py
core.graph_hitl.DeepNextActionPlanGraph
from deep_next.core.steps.action_plan.data_model import ActionPlan from deep_next.core.base_graph import BaseGraph from pathlib import Path from langgraph.graph import END, START class DeepNextActionPlanGraph(BaseGraph): """ Graph for the first phase of DeepNext. Gather the project knowledge and creating ...
class DeepNextActionPlanGraph(BaseGraph): ''' Graph for the first phase of DeepNext. Gather the project knowledge and creating an action plan. ''' def __init__(self): pass def _build(self): pass def create_init_state(self, root_path: Path, issue_title: str, issue_descript...
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/graph_hitl.py
core.graph_hitl.DeepNextImplementGraph
from deep_next.core.steps.action_plan.data_model import ActionPlan from langgraph.graph import END, START from pathlib import Path from deep_next.core.base_graph import BaseGraph class DeepNextImplementGraph(BaseGraph): """ Graph for the second phase of DeepNext. Implement the action plan and generate the...
class DeepNextImplementGraph(BaseGraph): ''' Graph for the second phase of DeepNext. Implement the action plan and generate the git diff. ''' def __init__(self): pass def _build(self): pass def create_init_state(self, root_path: Path, issue_title: str, issue_description: ...
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/graph_hitl.py
core.graph_hitl._NodeActionPlan
from deep_next.core.steps.gather_project_knowledge.graph import gather_project_knowledge_graph from deep_next.core.steps.action_plan import action_plan_graph class _NodeActionPlan: @staticmethod def gather_project_knowledge(state: _StateActionPlan) -> dict: init_state = gather_project_knowledge_graph....
class _NodeActionPlan: @staticmethod def gather_project_knowledge(state: _StateActionPlan) -> dict: pass @staticmethod def create_action_plan(state: _StateActionPlan) -> dict: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/graph_hitl.py
core.graph_hitl._NodeImplement
from deep_next.core.steps.code_review.graph import code_review_graph from deep_next.core.steps.implement.graph import implement_graph class _NodeImplement: @staticmethod def implement(state: _StateImplement) -> dict: init_state = implement_graph.create_init_state(root_path=state.root_path, issue_state...
class _NodeImplement: @staticmethod def implement(state: _StateImplement) -> dict: pass @staticmethod def review_code(state: _StateImplement) -> dict: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/graph_hitl.py
core.graph_hitl._StateActionPlan
from pathlib import Path from pydantic import BaseModel, Field from deep_next.core.steps.action_plan.data_model import ActionPlan from deep_next.common.common import prepare_issue_statement class _StateActionPlan(BaseModel): root_path: Path = Field(description='Path to the root project directory.') issue_title...
class _StateActionPlan(BaseModel): @property def issue_statement(self) -> str: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/graph_hitl.py
core.graph_hitl._StateImplement
from deep_next.common.common import prepare_issue_statement from pathlib import Path from deep_next.core.steps.action_plan.data_model import ActionPlan from pydantic import BaseModel, Field class _StateImplement(BaseModel): root_path: Path = Field(description='Path to the root project directory.') issue_title:...
class _StateImplement(BaseModel): @property def issue_statement(self) -> str: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/project_info.py
core.project_info.ProjectInfo
from dataclasses import dataclass import re from pathlib import Path import tomllib from typing import Any @dataclass(frozen=True) class ProjectInfo: root_dir: Path pyproject_toml: str = NOT_FOUND setup_py: str = NOT_FOUND setup_cfg: str = NOT_FOUND readme: str = NOT_FOUND def _get_name_from_p...
@dataclass(frozen=True) class ProjectInfo: def _get_name_from_pyproject_toml_tool(self) -> str | None: pass def _get_name_from_pyproject_toml_project(self) -> str | None: pass @property @_log_if_different_than_dir def name(self) -> str: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/action_plan.py
core.steps.action_plan.action_plan.ActionPlanValidationError
class ActionPlanValidationError(Exception): """Raised when action plan is invalid."""
class ActionPlanValidationError(Exception): '''Raised when action plan is invalid.''' pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/action_plan.py
core.steps.action_plan.action_plan._Prompt
import textwrap class _Prompt: role = textwrap.dedent('\n You are an expert software engineer tasked with breaking down a software issue into an ordered action plan with explicit dependencies.\n\n The following steps should be an ordered list of high-level, actionable goals for the developer ...
class _Prompt: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/data_model.py
core.steps.action_plan.data_model.ActionPlan
from pydantic import BaseModel, Field class ActionPlan(BaseModel): reasoning: str ordered_steps: list[Step]
class ActionPlan(BaseModel): pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/data_model.py
core.steps.action_plan.data_model.ExistingCodeContext
from pydantic import BaseModel, Field class ExistingCodeContext(BaseModel): code_context: list[FileCodeContext] = Field(default_factory=list) def dump(self) -> str: return '\n'.join([f'Path: {file_context.path}\nExplanation: {file_context.explanation}\nCode:\n{file_context.code_snippet}\n' for file_co...
class ExistingCodeContext(BaseModel): def dump(self) -> str: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/data_model.py
core.steps.action_plan.data_model.FileCodeContext
from pathlib import Path from pydantic import BaseModel, Field class FileCodeContext(BaseModel): path: Path code_snippet: str explanation: str = ''
class FileCodeContext(BaseModel): pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/data_model.py
core.steps.action_plan.data_model.Step
from pydantic import BaseModel, Field from pathlib import Path class Step(BaseModel): title: str = Field(description='High level step overview.') description: str = Field(description='Detailed step description.') target_file: Path = Field(description='Absolute path to the file to be modified.')
class Step(BaseModel): pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/graph.py
core.steps.action_plan.graph.ActionPlanGraph
from deep_next.core.base_graph import BaseGraph from deep_next.core.steps.action_plan.data_model import ActionPlan, ExistingCodeContext, FileCodeContext from langgraph.constants import START from pathlib import Path from langgraph.graph import END class ActionPlanGraph(BaseGraph): def __init__(self): supe...
class ActionPlanGraph(BaseGraph): def __init__(self): pass def __call__(self, root_path: Path, issue_statement: str, project_knowledge: str) -> ActionPlan: pass def _build(self) -> None: pass def create_init_state(self, root_path: Path, issue_statement: str, project_knowledg...
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/graph.py
core.steps.action_plan.graph._Node
from deep_next.core.steps.action_plan.srf import srf_graph from deep_next.core.config import SRFConfig from langchain_core.runnables import RunnableConfig from deep_next.core.io import read_txt from deep_next.core.steps.action_plan.action_plan import create_action_plan from deep_next.core.steps.action_plan.data_model i...
class _Node: @staticmethod def define_code_context(state: _State) -> dict: pass @staticmethod def create_action_plan(state: _State) -> dict: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/graph.py
core.steps.action_plan.graph._State
from pathlib import Path from pydantic import BaseModel, Field from deep_next.core.steps.action_plan.data_model import ActionPlan, ExistingCodeContext, FileCodeContext class _State(BaseModel): root_path: Path = Field(description='Root path for the project.') issue_statement: str = Field(description='Issue deta...
class _State(BaseModel): pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/srf/file_selection/analysis_model.py
core.steps.action_plan.srf.file_selection.analysis_model.Analysis
import json from pydantic import BaseModel, Field class Analysis(BaseModel): overview: str = Field(default='', description=overview_desc) relevant_files_so_far: list[RelevantFile] = Field(default_factory=list, description=relevant_files_so_far_desc) reasoning: str = Field(default='', description=reasoning_...
class Analysis(BaseModel): @property def json_str(self) -> str: pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/srf/file_selection/analysis_model.py
core.steps.action_plan.srf.file_selection.analysis_model.RelevantFile
from pydantic import BaseModel, Field class RelevantFile(BaseModel): path: str explanation: str def __hash__(self): return hash(self.path) def __eq__(self, other): if isinstance(other, RelevantFile): return self.path == other.path return False
class RelevantFile(BaseModel): def __hash__(self): pass def __eq__(self, other): pass
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stxnext/deep-next
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/stxnext_deep-next/libs/core/deep_next/core/steps/action_plan/srf/file_selection/graph.py
core.steps.action_plan.srf.file_selection.graph.AnalyzeKnowledgePrompt
import textwrap class AnalyzeKnowledgePrompt: role_description = textwrap.dedent('\n You are an advanced codebase analysis agent designed to assist in identifying files within a software repository that are relevant to solving specific issues. When provided with an issue statement and analys...
class AnalyzeKnowledgePrompt: pass
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