id int64 0 328k | repository_name stringlengths 7 58 | file_path stringlengths 9 302 | class_name stringlengths 5 256 | human_written_code stringlengths 16 2.16M | class_skeleton stringlengths 18 1.49M ⌀ | total_program_units int64 1 1.76k | total_doc_str int64 0 771 | AvgCountLine float64 0 7.89k | AvgCountLineBlank float64 0 297 | AvgCountLineCode float64 0 7.89k | AvgCountLineComment float64 0 7.89k | AvgCyclomatic float64 0 130 | CommentToCodeRatio float64 0 168 | CountClassBase float64 0 40 | CountClassCoupled float64 0 583 | CountClassCoupledModified float64 0 575 | CountClassDerived float64 0 5.35k | CountDeclInstanceMethod float64 0 529 | CountDeclInstanceVariable float64 0 296 | CountDeclMethod float64 0 599 | CountDeclMethodAll float64 0 1.12k | CountLine float64 1 40.4k | CountLineBlank float64 0 8.16k | CountLineCode float64 1 25.7k | CountLineCodeDecl float64 1 8.15k | CountLineCodeExe float64 0 24.2k | CountLineComment float64 0 16.5k | CountStmt float64 1 9.71k | CountStmtDecl float64 1 8.15k | CountStmtExe float64 0 9.69k | MaxCyclomatic float64 0 759 | MaxInheritanceTree float64 0 16 | MaxNesting float64 0 34 | SumCyclomatic float64 0 2.9k |
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325,200 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/model/han.py | model.han.HAN | from torch.nn import functional as F
import torch
import torch.nn as nn
class HAN(nn.Module):
def __init__(self, embedding_in_channels: int, embedding_hidden_channels: int, gru_hidden_channels: int, class_num: int, dropout=0.2):
super().__init__()
self.embedding = nn.Embedding(embedding_in_channel... |
class HAN(nn.Module):
def __init__(self, embedding_in_channels: int, embedding_hidden_channels: int, gru_hidden_channels: int, class_num: int, dropout=0.2):
pass
def forward(self, x):
pass | 3 | 0 | 37 | 1 | 36 | 0 | 1 | 0 | 1 | 3 | 0 | 0 | 2 | 9 | 2 | 2 | 76 | 3 | 73 | 30 | 63 | 0 | 40 | 23 | 37 | 1 | 1 | 0 | 2 |
325,201 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/model/mlp.py | model.mlp.MLP | from typing import Any, Callable, Dict, List, Optional, Union
from torch import nn
import torch
from torch_geometric.nn.resolver import activation_resolver
class MLP(nn.Module):
"""
Multi-Layer Perceptron (MLP) with configurable layers and activation functions.
"""
def __init__(self, in_channels: int,... | null | 5 | 3 | 27 | 3 | 17 | 7 | 5 | 0.43 | 1 | 7 | 0 | 0 | 2 | 3 | 3 | 3 | 89 | 13 | 53 | 22 | 38 | 23 | 36 | 11 | 32 | 8 | 1 | 3 | 14 |
325,202 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/preprocessing/featurizer/deprecated/organic_atomic_num_one_hot_deprecated.py | organic_atomic_num_one_hot_deprecated.OrganicAtomicNumberOneHotFeaturizer | from chemtorch.components.preprocessing.featurizer.featurizer_base import FeaturizerBase
from rdkit.Chem import Atom
class OrganicAtomicNumberOneHotFeaturizer(FeaturizerBase[Atom]):
"""Atom featurizer using only atomic number."""
def __init__(self):
features = [(Atom.GetAtomicNum, list(range(1, 37)) +... |
class OrganicAtomicNumberOneHotFeaturizer(FeaturizerBase[Atom]):
'''Atom featurizer using only atomic number.'''
def __init__(self):
pass | 2 | 1 | 3 | 0 | 3 | 0 | 1 | 0.25 | 1 | 3 | 0 | 0 | 1 | 0 | 1 | 7 | 6 | 1 | 4 | 3 | 2 | 1 | 4 | 3 | 2 | 1 | 2 | 0 | 1 |
325,203 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/model/gnn/pool/pma.py | pool.pma.MultiheadAttentionBlock | from torch.nn import LayerNorm, Linear, MultiheadAttention
from torch import nn
from typing import Optional
import torch
class MultiheadAttentionBlock(nn.Module):
def __init__(self, channels: int, heads: int=1, layer_norm: bool=True, dropout: float=0.0):
super().__init__()
self.channels = channels... |
class MultiheadAttentionBlock(nn.Module):
def __init__(self, channels: int, heads: int=1, layer_norm: bool=True, dropout: float=0.0):
pass
def reset_parameters(self):
pass
def forward(self, x: torch.Tensor, y: torch.Tensor, x_mask: Optional[torch.Tensor]=None, y_mask: Optional[torch.Tens... | 5 | 1 | 16 | 2 | 13 | 0 | 3 | 0.02 | 1 | 5 | 0 | 0 | 4 | 7 | 4 | 4 | 67 | 12 | 54 | 25 | 37 | 1 | 32 | 13 | 27 | 5 | 1 | 1 | 12 |
325,204 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/model/gnn/pool/pma.py | pool.pma.PMA | from torch_geometric.data import Batch
import torch
from torch import nn
from torch_geometric.utils import to_dense_batch
class PMA(nn.Module):
def __init__(self, channels: int, num_seed_points: int=1, heads: int=1, layer_norm: bool=True, dropout: float=0.0, num_decoder_blocks: int=0):
super().__init__()
... |
class PMA(nn.Module):
def __init__(self, channels: int, num_seed_points: int=1, heads: int=1, layer_norm: bool=True, dropout: float=0.0, num_decoder_blocks: int=0):
pass
def reset_parameters(self):
pass
def forward(self, batch: Batch) -> torch.Tensor:
pass
def __repr__(self)... | 5 | 0 | 11 | 1 | 11 | 0 | 2 | 0 | 1 | 8 | 2 | 0 | 4 | 4 | 4 | 4 | 49 | 5 | 44 | 21 | 31 | 0 | 23 | 12 | 18 | 2 | 1 | 1 | 6 |
325,205 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/model/gnn/pool/pma.py | pool.pma.SetAttentionBlock | from typing import Optional
import torch
class SetAttentionBlock(torch.nn.Module):
"""The Set Attention Block (SAB) from the `"Set Transformer: A
Framework for Attention-based Permutation-Invariant Neural Networks"
<https://arxiv.org/abs/1810.00825>`_ paper.
.. math::
\\mathrm{SAB}(\\mathbf{X... |
class SetAttentionBlock(torch.nn.Module):
'''The Set Attention Block (SAB) from the `"Set Transformer: A
Framework for Attention-based Permutation-Invariant Neural Networks"
<https://arxiv.org/abs/1810.00825>`_ paper.
.. math::
\mathrm{SAB}(\mathbf{X}) = \mathrm{MAB}(\mathbf{x}, \mathbf{y})
... | 5 | 1 | 6 | 0 | 6 | 0 | 1 | 0.61 | 1 | 6 | 1 | 0 | 4 | 1 | 4 | 4 | 44 | 7 | 23 | 12 | 12 | 14 | 10 | 6 | 5 | 1 | 1 | 0 | 4 |
325,206 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/model/gnn/pool/pool.py | pool.pool.AtomTypePool | from chemtorch.utils.atom_mapping import AtomOriginType
from torch import nn
from typing import Literal, Dict, Callable
import torch
from torch_geometric.data import Batch
class AtomTypePool(nn.Module):
"""
Type-specific pooling based on atom origin type.
"""
TYPE_MAPPING = {'reactants': AtomOriginType... |
class AtomTypePool(nn.Module):
'''
Type-specific pooling based on atom origin type.
'''
def __init__(self, pool_type: Literal['reactants', 'products', 'dummy', 'reactant_product'], aggr: Literal['add', 'mean', 'max']='add'):
pass
def forward(self, batch: Batch) -> torch.Tensor:
pa... | 3 | 1 | 10 | 0 | 10 | 0 | 2 | 0.12 | 1 | 3 | 0 | 0 | 2 | 2 | 2 | 2 | 32 | 3 | 26 | 11 | 19 | 3 | 13 | 7 | 10 | 3 | 1 | 1 | 4 |
325,207 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/model/gnn/pool/pool.py | pool.pool.GlobalPool | from torch import nn
from torch_geometric.data import Batch
import torch
from typing import Literal, Dict, Callable
class GlobalPool(nn.Module):
"""
Global pooling across all nodes in a batch.
"""
def __init__(self, aggr: Literal['add', 'mean', 'max']='add'):
super().__init__()
if aggr... |
class GlobalPool(nn.Module):
'''
Global pooling across all nodes in a batch.
'''
def __init__(self, aggr: Literal['add', 'mean', 'max']='add'):
pass
def forward(self, batch: Batch) -> torch.Tensor:
pass | 3 | 1 | 5 | 0 | 5 | 0 | 2 | 0.3 | 1 | 3 | 0 | 0 | 2 | 1 | 2 | 2 | 15 | 2 | 10 | 4 | 7 | 3 | 8 | 4 | 5 | 2 | 1 | 1 | 3 |
325,208 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/representation/abstract_representation.py | representation.abstract_representation.AbstractRepresentation | from abc import ABC, abstractmethod
from typing import TypeVar, Generic
class AbstractRepresentation(ABC, Generic[T]):
"""
Abstract base class for all stateless representation creators.
Subclasses should implement `forward` with the arguments they require.
The only requirement is that the return type ... |
class AbstractRepresentation(ABC, Generic[T]):
'''
Abstract base class for all stateless representation creators.
Subclasses should implement `forward` with the arguments they require.
The only requirement is that the return type is compatible with the dataset and transforms.
Raises:
TypeEr... | 4 | 2 | 8 | 1 | 2 | 5 | 1 | 5 | 2 | 0 | 0 | 4 | 2 | 0 | 2 | 24 | 46 | 10 | 6 | 4 | 2 | 30 | 5 | 3 | 2 | 1 | 4 | 0 | 2 |
325,209 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/representation/fingerprint/drfp.py | representation.fingerprint.drfp.DRFP | import torch
from chemtorch.components.representation import AbstractRepresentation
class DRFP(AbstractRepresentation[torch.Tensor]):
"""
Stateless class for constructing DRFP (Differential Reaction Fingerprints).
This class provides a `forward()` method that takes a reaction SMILES string
and returns... |
class DRFP(AbstractRepresentation[torch.Tensor]):
'''
Stateless class for constructing DRFP (Differential Reaction Fingerprints).
This class provides a `forward()` method that takes a reaction SMILES string
and returns a PyTorch Tensor representing the folded DRFP fingerprint.
It utilizes the DrfpE... | 3 | 3 | 34 | 3 | 15 | 16 | 1 | 1.23 | 1 | 4 | 1 | 0 | 2 | 6 | 2 | 26 | 78 | 9 | 31 | 19 | 20 | 38 | 12 | 11 | 9 | 1 | 5 | 0 | 2 |
325,210 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/representation/fingerprint/drfp.py | representation.fingerprint.drfp.DRFPUtil | from rdkit.Chem.rdchem import Mol
import numpy as np
from tqdm import tqdm
from hashlib import blake2b
from collections import defaultdict
from typing import Dict, Iterable, List, Set, Tuple, Union
from rdkit.Chem import AllChem
class DRFPUtil:
"""
A utility class for encoding SMILES as drfp fingerprints.
... |
class DRFPUtil:
'''
A utility class for encoding SMILES as drfp fingerprints.
'''
@staticmethod
def shingling_from_mol(in_mol: Mol, radius: int=3, rings: bool=True, min_radius: int=0, get_atom_indices: bool=False, root_central_atom: bool=True, include_hydrogens: bool=False) -> Union[List[str], ... | 11 | 6 | 73 | 12 | 52 | 10 | 11 | 0.21 | 0 | 11 | 0 | 0 | 0 | 0 | 5 | 5 | 380 | 64 | 264 | 93 | 216 | 55 | 148 | 51 | 142 | 21 | 0 | 6 | 54 |
325,211 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/representation/graph/cgr.py | representation.graph.cgr.CGR | from chemtorch.components.representation.abstract_representation import AbstractRepresentation
from typing import List, Tuple
from chemtorch.utils.atom_mapping import AtomOriginType, EdgeOriginType, make_mol, map_reac_to_prod
from chemtorch.components.preprocessing.featurizer.featurizer_base import FeaturizerBase
from ... |
class CGR(AbstractRepresentation[Data]):
'''
Stateless class for constructing Condensed Graph of Reaction (CGR) representations.
This class does not hold any data itself. Instead, it provides a `forward()` method
that takes a sample (e.g., a dict or pd.Series) and returns a PyTorch Geometric Data objec... | 5 | 4 | 39 | 7 | 23 | 10 | 3 | 0.58 | 1 | 9 | 4 | 0 | 3 | 2 | 3 | 27 | 134 | 25 | 72 | 37 | 62 | 42 | 55 | 31 | 51 | 6 | 5 | 3 | 8 |
325,212 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/representation/graph/rtsp_3d_graph.py | representation.graph.rtsp_3d_graph.RTSP3DGraph | from torch_geometric.data import Data
from chemtorch.components.representation.abstract_representation import AbstractRepresentation
import os.path as osp
class RTSP3DGraph(AbstractRepresentation[Data]):
"""
Constructs a 3D representation of a reaction from XYZ files.
This representation reads the 3D stru... |
class RTSP3DGraph(AbstractRepresentation[Data]):
'''
Constructs a 3D representation of a reaction from XYZ files.
This representation reads the 3D structures for a reactant, transition state (ts),
and product from their respective .xyz files and packages them into a single
PyTorch Geometric `Data` ... | 4 | 3 | 28 | 4 | 15 | 10 | 3 | 1 | 1 | 3 | 0 | 0 | 2 | 1 | 2 | 26 | 74 | 12 | 31 | 13 | 27 | 31 | 21 | 12 | 18 | 4 | 5 | 1 | 6 |
325,213 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/representation/token/simple_token_representation.py | representation.token.simple_token_representation.SimpleTokenRepresentation | from typing import Dict, List
from chemtorch.components.representation.abstract_representation import AbstractRepresentation
import torch
import collections
class SimpleTokenRepresentation(AbstractRepresentation[torch.Tensor]):
def __init__(self, vocab_path: str, tokenizer, max_sentence_length: int, pad_token: st... |
class SimpleTokenRepresentation(AbstractRepresentation[torch.Tensor]):
def __init__(self, vocab_path: str, tokenizer, max_sentence_length: int, pad_token: str, unk_token: str, *args, **kwargs):
pass
def _load_vocab(self, vocab_file_path: str) -> Dict[str, int]:
'''Loads a vocabulary file into... | 5 | 3 | 18 | 2 | 14 | 2 | 3 | 0.12 | 1 | 7 | 0 | 0 | 4 | 8 | 4 | 28 | 75 | 10 | 58 | 29 | 44 | 7 | 36 | 19 | 31 | 4 | 5 | 4 | 11 |
325,214 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/core/routine/regression_routine.py | routine.regression_routine.RegressionRoutine | from typing import Any, Tuple, Literal
from torchmetrics import Metric, MetricCollection
from chemtorch.utils.standardizer import Standardizer
import torch
from chemtorch.core.routine.supervised_routine import SupervisedRoutine
class RegressionRoutine(SupervisedRoutine):
"""
Extends SupervisedRoutine for regre... |
class RegressionRoutine(SupervisedRoutine):
'''
Extends SupervisedRoutine for regression tasks by allowing the use of an optional standardizer.
This class is intended for regression models where outputs may need to be destandardized
(e.g., to return predictions in the original scale). If a `Standardize... | 12 | 2 | 6 | 0 | 4 | 1 | 2 | 1.03 | 1 | 4 | 1 | 0 | 7 | 1 | 7 | 22 | 86 | 13 | 36 | 16 | 23 | 37 | 29 | 12 | 20 | 2 | 2 | 1 | 13 |
325,215 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/core/routine/supervised_routine.py | routine.supervised_routine.SupervisedRoutine | from torchmetrics import Metric, MetricCollection
import warnings
import os
from torch.optim import Optimizer
from torch import nn
from torch.optim.lr_scheduler import LRScheduler
from typing import Dict, Tuple, Callable, Iterator, Literal, Union, Mapping, Any
import torch
import lightning as L
class SupervisedRoutine... |
class SupervisedRoutine(L.LightningModule):
'''
A flexible LightningModule wrapper for supervised tasks, supporting both training and inference.
This class can be used for:
- Full training/validation/testing with loss, optimizer, scheduler, and metrics.
- Inference-only (prediction), requiring ... | 16 | 8 | 22 | 2 | 12 | 8 | 3 | 0.78 | 1 | 11 | 0 | 1 | 15 | 8 | 15 | 15 | 366 | 45 | 180 | 55 | 151 | 141 | 126 | 42 | 110 | 12 | 1 | 3 | 52 |
325,216 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/core/scheduler/sequential_lr_wrapper.py | sequential_lr_wrapper.SequentialLRWrapper | from torch.optim.lr_scheduler import SequentialLR
class SequentialLRWrapper(SequentialLR):
"""
A wrapper around SequentialLR that recursively instantiates any schedulers or
scheduler factories with a shared optimizer passed to this wrapper optimizer.
"""
def __init__(self, optimizer, schedulers, ... |
class SequentialLRWrapper(SequentialLR):
'''
A wrapper around SequentialLR that recursively instantiates any schedulers or
scheduler factories with a shared optimizer passed to this wrapper optimizer.
'''
def __init__(self, optimizer, schedulers, milestones, **kwargs):
'''
Initial... | 2 | 2 | 16 | 1 | 6 | 9 | 2 | 1.86 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 22 | 2 | 7 | 2 | 5 | 13 | 4 | 2 | 2 | 2 | 1 | 0 | 2 |
325,217 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/preprocessing/tokenizer/simple_tokenizer.py | simple_tokenizer.MoleculeRegexTokenizer | from typing import List
import re
class MoleculeRegexTokenizer:
"""
Tokenizes a single molecule SMILES string using a regex pattern.
"""
def __init__(self, regex_pattern: str=SMILES_ATOM_WISE_PATTERN):
"""
Args:
regex_pattern: The regex pattern to use for tokenization.
... |
class MoleculeRegexTokenizer:
'''
Tokenizes a single molecule SMILES string using a regex pattern.
'''
def __init__(self, regex_pattern: str=SMILES_ATOM_WISE_PATTERN):
'''
Args:
regex_pattern: The regex pattern to use for tokenization.
'''
pass
def toke... | 3 | 3 | 12 | 2 | 5 | 6 | 2 | 1.4 | 0 | 1 | 0 | 0 | 2 | 1 | 2 | 2 | 30 | 6 | 10 | 5 | 7 | 14 | 10 | 5 | 7 | 3 | 0 | 1 | 4 |
325,218 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/preprocessing/tokenizer/simple_tokenizer.py | simple_tokenizer.SimpleTokenizer | from typing import List
class SimpleTokenizer:
"""
Tokenizes a reaction SMILES string (e.g., "R1.R2>>P1.P2").
"""
def __init__(self, unk_token: str=DEFAULT_UNK_TOKEN, molecule_tokenizer_pattern: str=SMILES_ATOM_WISE_PATTERN):
self.unk_token = unk_token
self._molecule_tokenizer = Molecu... |
class SimpleTokenizer:
'''
Tokenizes a reaction SMILES string (e.g., "R1.R2>>P1.P2").
'''
def __init__(self, unk_token: str=DEFAULT_UNK_TOKEN, molecule_tokenizer_pattern: str=SMILES_ATOM_WISE_PATTERN):
pass
def _tokenize_side(self, side_smiles: str) -> List[str]:
'''
Token... | 4 | 3 | 18 | 4 | 11 | 4 | 3 | 0.41 | 0 | 3 | 1 | 0 | 3 | 2 | 3 | 3 | 62 | 14 | 34 | 18 | 26 | 14 | 28 | 14 | 24 | 5 | 0 | 2 | 9 |
325,219 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/transform/abstract_transform.py | transform.abstract_transform.AbstractTransform | from abc import ABC, abstractmethod
from typing import Generic, TypeVar
class AbstractTransform(ABC, Generic[T]):
"""
Abstract base class for transforms in the chemtorch framework.
This class serves as a base for creating transforms that operate single objects.
Raises:
TypeError: If the subcla... |
class AbstractTransform(ABC, Generic[T]):
'''
Abstract base class for transforms in the chemtorch framework.
This class serves as a base for creating transforms that operate single objects.
Raises:
TypeError: If the subclass does not implement the :attr:`__call__` method.
Example (correct u... | 3 | 2 | 12 | 2 | 2 | 8 | 1 | 7 | 2 | 0 | 0 | 2 | 1 | 0 | 1 | 23 | 38 | 6 | 4 | 3 | 1 | 28 | 3 | 2 | 1 | 1 | 4 | 0 | 1 |
325,220 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/transform/graph_transform/dummy.py | transform.graph_transform.dummy.DummyNodeTransform | from torch_geometric.data import Data
from chemtorch.components.transform.abstract_transform import AbstractTransform
import torch
from typing import Dict, Optional
class DummyNodeTransform(AbstractTransform[Data]):
def __init__(self, mode: str, connection_type: Optional[str]='to_dummy', dummy_dummy_connection: O... |
class DummyNodeTransform(AbstractTransform[Data]):
def __init__(self, mode: str, connection_type: Optional[str]='to_dummy', dummy_dummy_connection: Optional[str]=None, feature_init: str='zeros'):
pass
def __call__(self, x: Data) -> Data:
pass
def _get_pretransform_attributes(self, data: ... | 7 | 0 | 19 | 2 | 17 | 0 | 3 | 0 | 1 | 6 | 0 | 0 | 6 | 5 | 6 | 29 | 119 | 14 | 105 | 44 | 79 | 0 | 55 | 25 | 48 | 8 | 5 | 2 | 19 |
325,221 | heid-lab/chemtorch | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/heid-lab_chemtorch/src/chemtorch/components/transform/graph_transform/randomwalkpe.py | transform.graph_transform.randomwalkpe.RandomWalkPETransform | from chemtorch.components.transform.abstract_transform import AbstractTransform
import torch
from torch_geometric.data import Data
import torch_geometric
from torch_geometric.utils import get_self_loop_attr, is_torch_sparse_tensor, scatter, to_edge_index, to_torch_coo_tensor, to_torch_csr_tensor
class RandomWalkPETran... |
class RandomWalkPETransform(AbstractTransform[Data]):
'''
This code includes implementations adapted from PyTorch Geometric
(https://github.com/pyg-team/pytorch_geometric)
'''
def __init__(self, walk_length: int, attr_name=None, type: str='graph') -> None:
pass
def __call__(self, data... | 4 | 1 | 19 | 2 | 17 | 1 | 4 | 0.15 | 1 | 4 | 0 | 0 | 2 | 2 | 2 | 25 | 62 | 9 | 48 | 21 | 39 | 7 | 38 | 16 | 34 | 8 | 5 | 2 | 11 |
325,222 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/graph/pipeline_graph.py | kamae.graph.pipeline_graph.PipelineGraph | from kamae.tensorflow.layers import IdentityLayer
import keras_tuner
import tensorflow as tf
import networkx as nx
from packaging.version import Version
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
class PipelineGraph:
"""
PipelineGraph is a class that constructs a graph of the pipeline... |
class PipelineGraph:
'''
PipelineGraph is a class that constructs a graph of the pipeline stages.
This is used to determine the order in which the layers should be constructed.
The graph is built by adding edges between layers that have the same input column
as the output column of a previous layer... | 19 | 15 | 29 | 3 | 16 | 11 | 2 | 0.78 | 0 | 10 | 1 | 0 | 12 | 5 | 14 | 14 | 465 | 58 | 229 | 93 | 179 | 178 | 114 | 58 | 97 | 6 | 0 | 2 | 38 |
325,223 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/estimators/standard_scale.py | kamae.sklearn.estimators.standard_scale.StandardScaleEstimator | from kamae.sklearn.transformers import BaseTransformerMixin
import tensorflow as tf
import pandas as pd
from sklearn.preprocessing import StandardScaler
from kamae.tensorflow.layers import StandardScaleLayer
from kamae.sklearn.params import SingleInputSingleOutputMixin
from typing import Any
class StandardScaleEstimat... |
class StandardScaleEstimator(StandardScaler, BaseTransformerMixin, SingleInputSingleOutputMixin):
'''
Standard Scikit-Learn Estimator for use in Scikit-Learn pipelines.
Wrapper over the existing implementation of the StandardScaler in Scikit-Learn,
however operates on array columns and returns array co... | 5 | 5 | 14 | 1 | 5 | 8 | 1 | 1.72 | 3 | 5 | 1 | 0 | 4 | 4 | 4 | 34 | 81 | 13 | 25 | 18 | 14 | 43 | 17 | 11 | 12 | 1 | 5 | 0 | 4 |
325,224 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/params/base.py | kamae.sklearn.params.base.MultiInputMixin | from typing import List
class MultiInputMixin:
"""
Mixin class containing set methods for the multiple input columns scenario.
"""
_input_cols: List[str]
@property
def input_cols(self) -> List[str]:
"""
Gets the input column names.
:returns: List of strings of input co... |
class MultiInputMixin:
'''
Mixin class containing set methods for the multiple input columns scenario.
'''
@property
def input_cols(self) -> List[str]:
'''
Gets the input column names.
:returns: List of strings of input column names.
'''
pass
@input_c... | 5 | 3 | 8 | 1 | 2 | 5 | 1 | 1.5 | 0 | 1 | 0 | 2 | 2 | 0 | 2 | 2 | 25 | 5 | 8 | 5 | 3 | 12 | 6 | 3 | 3 | 1 | 0 | 0 | 2 |
325,225 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/params/base.py | kamae.sklearn.params.base.MultiInputMultiOutputMixin | class MultiInputMultiOutputMixin(MultiInputMixin, MultiOutputMixin):
"""
Mixin for a layer that takes multiple inputs and returns multiple outputs
""" | class MultiInputMultiOutputMixin(MultiInputMixin, MultiOutputMixin):
'''
Mixin for a layer that takes multiple inputs and returns multiple outputs
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 4 | 0 | 1 | 1 | 0 | 3 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
325,226 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/params/base.py | kamae.sklearn.params.base.MultiInputSingleOutputMixin | class MultiInputSingleOutputMixin(MultiInputMixin, SingleOutputMixin):
"""
Mixin for a layer that takes multiple inputs and returns a single output
""" | class MultiInputSingleOutputMixin(MultiInputMixin, SingleOutputMixin):
'''
Mixin for a layer that takes multiple inputs and returns a single output
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 6 | 4 | 0 | 1 | 1 | 0 | 3 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
325,227 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/params/base.py | kamae.sklearn.params.base.MultiOutputMixin | from .name import LayerNameMixin
from typing import List
class MultiOutputMixin(LayerNameMixin):
"""
Mixin class containing set methods for the multiple output columns scenario.
"""
_output_cols: List[str]
@property
def output_cols(self) -> List[str]:
"""
Gets the output column... |
class MultiOutputMixin(LayerNameMixin):
'''
Mixin class containing set methods for the multiple output columns scenario.
'''
@property
def output_cols(self) -> List[str]:
'''
Gets the output column names.
:returns: List of strings of output column names.
'''
... | 7 | 4 | 13 | 1 | 6 | 6 | 2 | 0.91 | 1 | 2 | 0 | 2 | 3 | 1 | 3 | 4 | 51 | 7 | 23 | 8 | 16 | 21 | 12 | 5 | 8 | 3 | 1 | 1 | 6 |
325,228 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/params/base.py | kamae.sklearn.params.base.SingleInputMixin | class SingleInputMixin:
"""
Mixin class containing set methods for the single input column scenario.
"""
_input_col: str
@property
def input_col(self) -> str:
"""
Gets the input column name.
:returns: Input column name.
"""
return self._input_col
@i... | class SingleInputMixin:
'''
Mixin class containing set methods for the single input column scenario.
'''
@property
def input_col(self) -> str:
'''
Gets the input column name.
:returns: Input column name.
'''
pass
@input_col.setter
def input_col(sel... | 5 | 3 | 8 | 1 | 2 | 5 | 1 | 1.5 | 0 | 1 | 0 | 2 | 2 | 0 | 2 | 2 | 25 | 5 | 8 | 5 | 3 | 12 | 6 | 3 | 3 | 1 | 0 | 0 | 2 |
325,229 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/params/base.py | kamae.sklearn.params.base.SingleInputMultiOutputMixin | class SingleInputMultiOutputMixin(SingleInputMixin, MultiOutputMixin):
"""
Mixin for a layer that takes a single input and returns multiple outputs
""" | class SingleInputMultiOutputMixin(SingleInputMixin, MultiOutputMixin):
'''
Mixin for a layer that takes a single input and returns multiple outputs
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 6 | 4 | 0 | 1 | 1 | 0 | 3 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
325,230 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/params/base.py | kamae.sklearn.params.base.SingleInputSingleOutputMixin | class SingleInputSingleOutputMixin(SingleInputMixin, SingleOutputMixin):
"""
Mixin for a layer that takes a single input and returns a single output
""" | class SingleInputSingleOutputMixin(SingleInputMixin, SingleOutputMixin):
'''
Mixin for a layer that takes a single input and returns a single output
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 4 | 0 | 0 | 0 | 6 | 4 | 0 | 1 | 1 | 0 | 3 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
325,231 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/params/base.py | kamae.sklearn.params.base.SingleOutputMixin | from .name import LayerNameMixin
class SingleOutputMixin(LayerNameMixin):
"""
Mixin class containing set methods for the single output column scenario.
"""
_output_col: str
@property
def output_col(self) -> str:
"""
Gets the output column name.
:returns: List of string... |
class SingleOutputMixin(LayerNameMixin):
'''
Mixin class containing set methods for the single output column scenario.
'''
@property
def output_col(self) -> str:
'''
Gets the output column name.
:returns: List of strings of output column names.
'''
pass
... | 7 | 4 | 13 | 1 | 6 | 6 | 2 | 0.96 | 1 | 2 | 0 | 2 | 3 | 1 | 3 | 4 | 52 | 7 | 23 | 8 | 16 | 22 | 14 | 5 | 10 | 3 | 1 | 1 | 7 |
325,232 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/params/name.py | kamae.sklearn.params.name.LayerNameMixin | from typing import Optional
class LayerNameMixin:
"""
Mixin class for a layer name.
"""
_layer_name: Optional[str]
@property
def layer_name(self) -> str:
"""
Gets the layer name.
:returns: String of layer name.
"""
return self._layer_name |
class LayerNameMixin:
'''
Mixin class for a layer name.
'''
@property
def layer_name(self) -> str:
'''
Gets the layer name.
:returns: String of layer name.
'''
pass | 3 | 2 | 7 | 1 | 2 | 4 | 1 | 1.4 | 0 | 1 | 0 | 3 | 1 | 0 | 1 | 1 | 15 | 3 | 5 | 3 | 2 | 7 | 4 | 2 | 2 | 1 | 0 | 0 | 1 |
325,233 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/params/utils.py | kamae.sklearn.params.utils.InputOutputExtractor | from typing import List, Tuple
class InputOutputExtractor:
"""
Mixin class containing methods for extracting input and output column names.
"""
def get_layer_inputs_outputs(self) -> Tuple[List[str], List[str]]:
"""
Gets the input & output information of the layer. Returns a tuple of li... |
class InputOutputExtractor:
'''
Mixin class containing methods for extracting input and output column names.
'''
def get_layer_inputs_outputs(self) -> Tuple[List[str], List[str]]:
'''
Gets the input & output information of the layer. Returns a tuple of lists,
the first cont... | 2 | 2 | 24 | 4 | 14 | 6 | 5 | 0.6 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 29 | 5 | 15 | 4 | 13 | 9 | 11 | 4 | 9 | 5 | 0 | 1 | 5 |
325,234 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/pipeline/Pipeline.py | kamae.sklearn.pipeline.Pipeline.KamaeSklearnPipeline | import joblib
from kamae.graph import PipelineGraph
import tensorflow as tf
from sklearn.pipeline import Pipeline
from kamae.sklearn.transformers import BaseTransformer
import keras_tuner as kt
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
class KamaeSklearnPipeline(Pipeline):
"""
KamaeS... |
class KamaeSklearnPipeline(Pipeline):
'''
KamaeSklearnPipeline is a subclass of sklearn.pipeline.Pipeline that is used to
chain together BaseTransformers. It maintains the same functionality
as sklearn.pipeline.Pipeline e.g. serialisation.
'''
def __init__(self, steps: List[Tuple[str, BaseTran... | 5 | 5 | 20 | 1 | 9 | 10 | 1 | 1.25 | 1 | 7 | 2 | 0 | 4 | 0 | 4 | 4 | 89 | 8 | 36 | 24 | 16 | 45 | 13 | 9 | 8 | 1 | 1 | 0 | 4 |
325,235 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/pipeline/pipeline.py | kamae.sklearn.pipeline.pipeline.KamaeSklearnPipeline | import tensorflow as tf
from kamae.sklearn.transformers import BaseTransformer
from sklearn.pipeline import Pipeline
import keras_tuner as kt
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import joblib
from kamae.graph import PipelineGraph
class KamaeSklearnPipeline(Pipeline):
"""
KamaeS... |
class KamaeSklearnPipeline(Pipeline):
'''
KamaeSklearnPipeline is a subclass of sklearn.pipeline.Pipeline that is used to
chain together BaseTransformers. It maintains the same functionality
as sklearn.pipeline.Pipeline e.g. serialisation.
'''
def __init__(self, steps: List[Tuple[str, BaseTran... | 5 | 5 | 20 | 1 | 9 | 10 | 1 | 1.25 | 1 | 7 | 2 | 0 | 4 | 0 | 4 | 4 | 89 | 8 | 36 | 24 | 16 | 45 | 13 | 9 | 8 | 1 | 1 | 0 | 4 |
325,236 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/transformers/array_concatenate.py | kamae.sklearn.transformers.array_concatenate.ArrayConcatenateTransformer | from .base import BaseTransformer
from kamae.sklearn.params import MultiInputSingleOutputMixin
from kamae.tensorflow.layers import ArrayConcatenateLayer
from typing import List
import numpy as np
import pandas as pd
import tensorflow as tf
class ArrayConcatenateTransformer(BaseTransformer, MultiInputSingleOutputMixin)... |
class ArrayConcatenateTransformer(BaseTransformer, MultiInputSingleOutputMixin):
'''
Vector Assembler Scikit-Learn Transformer for use in Scikit-Learn pipelines.
This transformer assembles multiple columns into a single array column.
'''
def __init__(self, input_cols: List[str], output_col: str, l... | 5 | 4 | 16 | 2 | 7 | 8 | 2 | 1.17 | 2 | 4 | 1 | 0 | 4 | 3 | 4 | 34 | 76 | 11 | 30 | 16 | 22 | 35 | 22 | 13 | 17 | 3 | 6 | 2 | 6 |
325,237 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/transformers/array_split.py | kamae.sklearn.transformers.array_split.ArraySplitTransformer | from kamae.tensorflow.layers import ArraySplitLayer
from kamae.sklearn.params import SingleInputMultiOutputMixin
from .base import BaseTransformer
import pandas as pd
from typing import List
import tensorflow as tf
class ArraySplitTransformer(BaseTransformer, SingleInputMultiOutputMixin):
"""
VectorSlicer Scik... |
class ArraySplitTransformer(BaseTransformer, SingleInputMultiOutputMixin):
'''
VectorSlicer Scikit-Learn Transformer for use in Scikit-Learn pipelines.
This transformer slices an array column into multiple columns.
'''
def __init__(self, input_col: str, output_cols: List[str], layer_name: str) -> ... | 5 | 4 | 9 | 1 | 3 | 5 | 1 | 1.5 | 2 | 3 | 1 | 0 | 4 | 3 | 4 | 34 | 47 | 7 | 16 | 11 | 8 | 24 | 13 | 8 | 8 | 1 | 6 | 0 | 4 |
325,238 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/transformers/base.py | kamae.sklearn.transformers.base.BaseTransformer | from sklearn.base import BaseEstimator, TransformerMixin
from abc import ABC, abstractmethod
class BaseTransformer(BaseTransformerMixin, BaseEstimator, TransformerMixin, ABC):
"""
Abstract class for all scikit-learn transformers. Specifically, this class extends
the required scikit-learn classes BaseEstima... |
class BaseTransformer(BaseTransformerMixin, BaseEstimator, TransformerMixin, ABC):
'''
Abstract class for all scikit-learn transformers. Specifically, this class extends
the required scikit-learn classes BaseEstimator and TransformerMixin adding in the
kamae BaseTransformerMixin which defines the metho... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 23 | 4 | 0 | 0 | 5 | 0 | 0 | 0 | 25 | 28 | 4 | 1 | 1 | 0 | 23 | 1 | 1 | 0 | 0 | 5 | 0 | 0 |
325,239 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/transformers/base.py | kamae.sklearn.transformers.base.BaseTransformerMixin | from kamae.sklearn.params import InputOutputExtractor, LayerNameMixin
import tensorflow as tf
from typing import Any, Dict, List, Union
from abc import ABC, abstractmethod
class BaseTransformerMixin(ABC, LayerNameMixin, InputOutputExtractor):
"""
Mixin abstract class defining methods needed for all kamae sciki... |
class BaseTransformerMixin(ABC, LayerNameMixin, InputOutputExtractor):
'''
Mixin abstract class defining methods needed for all kamae scikit-learn
transformers.
'''
def __init__(self, **kwargs: Any) -> None:
'''
Initializes the transformer.
'''
pass
@abstractmet... | 5 | 4 | 9 | 0 | 4 | 5 | 1 | 1.36 | 3 | 4 | 0 | 2 | 3 | 0 | 3 | 25 | 37 | 4 | 14 | 6 | 9 | 19 | 8 | 5 | 4 | 1 | 4 | 0 | 3 |
325,240 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/transformers/identity.py | kamae.sklearn.transformers.identity.IdentityTransformer | from .base import BaseTransformer
from kamae.tensorflow.layers import IdentityLayer
from kamae.sklearn.params import SingleInputSingleOutputMixin
import pandas as pd
import tensorflow as tf
class IdentityTransformer(BaseTransformer, SingleInputSingleOutputMixin):
"""
Identity Scikit-Learn Transformer for use i... |
class IdentityTransformer(BaseTransformer, SingleInputSingleOutputMixin):
'''
Identity Scikit-Learn Transformer for use in Scikit-Learn pipelines.
This transformer simply passes the input to the output unchanged.
Used for cases where you want to keep the input the same.
'''
def __init__(self, ... | 5 | 5 | 11 | 1 | 4 | 7 | 1 | 2.13 | 2 | 3 | 1 | 0 | 4 | 3 | 4 | 34 | 55 | 8 | 15 | 8 | 10 | 32 | 13 | 8 | 8 | 1 | 6 | 0 | 4 |
325,241 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/sklearn/transformers/log.py | kamae.sklearn.transformers.log.LogTransformer | from kamae.sklearn.params import SingleInputSingleOutputMixin
import pandas as pd
from .base import BaseTransformer
from typing import Optional
import numpy as np
from kamae.tensorflow.layers import LogLayer
import tensorflow as tf
class LogTransformer(BaseTransformer, SingleInputSingleOutputMixin):
"""
Log Sc... |
class LogTransformer(BaseTransformer, SingleInputSingleOutputMixin):
'''
Log Scikit-Learn Transformer for use in Scikit-Learn pipelines.
This transformer applies a log(alpha + x) transform to the input column.
'''
def __init__(self, input_col: str, output_col: str, layer_name: str, alpha: Optional... | 5 | 5 | 13 | 1 | 5 | 7 | 1 | 1.48 | 2 | 4 | 1 | 0 | 4 | 4 | 4 | 34 | 61 | 9 | 21 | 16 | 10 | 31 | 15 | 10 | 10 | 2 | 6 | 0 | 5 |
325,242 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/common/spark_operation.py | kamae.spark.common.spark_operation.SparkOperation | from pyspark.sql.types import DataType, NumericType
from random import choice
from kamae.spark.utils import get_element_type, single_input_single_output_scalar_transform
from typing import Any, List, Optional, Tuple
from string import ascii_uppercase
import pyspark.sql.functions as F
from pyspark.sql import Column, Dat... |
class SparkOperation(ABC, HasLayerName, HasInputDtype, HasOutputDtype, InputOutputExtractor):
'''
Abstract class used in Spark transformers and estimators. Provides common utils for
param setting, input/output dtype casting, and layer name setting.
'''
def __init__(self) -> None:
'''
... | 22 | 14 | 23 | 1 | 13 | 9 | 3 | 0.7 | 5 | 10 | 0 | 2 | 10 | 2 | 13 | 44 | 331 | 29 | 179 | 62 | 143 | 125 | 80 | 39 | 66 | 6 | 4 | 3 | 36 |
325,243 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/estimators/base.py | kamae.spark.estimators.base.BaseEstimator | from pyspark.ml import Estimator
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
from kamae.spark.transformers import BaseTransformer
from pyspark.sql import DataFrame
from kamae.spark.common import SparkOperation
class BaseEstimator(Estimator, SparkOperation):
def __init__(self) -> None... |
class BaseEstimator(Estimator, SparkOperation):
def __init__(self) -> None:
'''
Initializes the estimator.
'''
pass
def fit(self, dataset: DataFrame, params: Optional[Union['ParamMap', List['ParamMap'], Tuple['ParamMap']]]=None) -> BaseTransformer:
'''
Override... | 4 | 3 | 26 | 3 | 15 | 8 | 1 | 0.52 | 2 | 5 | 1 | 9 | 3 | 1 | 3 | 47 | 81 | 11 | 46 | 13 | 38 | 24 | 18 | 7 | 14 | 2 | 5 | 1 | 4 |
325,244 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/estimators/conditional_standard_scale.py | kamae.spark.estimators.conditional_standard_scale.ConditionalStandardScaleEstimator | from pyspark.sql import Column, DataFrame
import pyspark.sql.functions as F
from kamae.spark.utils import construct_nested_elements_for_scaling
from kamae.spark.transformers import ConditionalStandardScaleTransformer
from pyspark.sql.types import ArrayType, DataType, DoubleType, FloatType
from kamae.utils import get_co... |
class ConditionalStandardScaleEstimator(BaseEstimator, SingleInputSingleOutputParams, ConditionalStandardScaleEstimatorParams, StandardScaleSkipZerosParams, NanFillValueParams):
'''
Conditional standard scaler estimator for use in Spark pipelines.
This is used to calculate the mean and standard deviation w... | 10 | 8 | 50 | 2 | 35 | 13 | 4 | 0.44 | 5 | 8 | 1 | 0 | 7 | 0 | 7 | 75 | 385 | 20 | 253 | 78 | 210 | 112 | 84 | 43 | 76 | 8 | 6 | 3 | 26 |
325,245 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/estimators/conditional_standard_scale.py | kamae.spark.estimators.conditional_standard_scale.ConditionalStandardScaleEstimatorParams | from typing import List, Optional
from pyspark.ml.param import Param, Params, TypeConverters
class ConditionalStandardScaleEstimatorParams(Params):
"""
Mixin class containing conditional standard scale parameters,
needed for single feature array scaler layers.
"""
scalingFunction = Param(Params._du... |
class ConditionalStandardScaleEstimatorParams(Params):
'''
Mixin class containing conditional standard scale parameters,
needed for single feature array scaler layers.
'''
def setScalingFunction(self, value: str) -> 'ConditionalStandardScaleEstimatorParams':
'''
Sets the scalingFun... | 11 | 11 | 11 | 1 | 4 | 6 | 1 | 0.73 | 1 | 3 | 0 | 1 | 10 | 0 | 10 | 10 | 168 | 24 | 83 | 27 | 63 | 61 | 33 | 18 | 22 | 3 | 1 | 2 | 14 |
325,246 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/estimators/impute.py | kamae.spark.estimators.impute.ImputeEstimator | from .base import BaseEstimator
import pyspark.sql.functions as F
from kamae.spark.utils import flatten_nested_arrays
from pyspark.sql.types import ArrayType, DataType, DoubleType, FloatType
from kamae.spark.transformers import ImputeTransformer
from typing import List, Optional, Union
from pyspark import keyword_only
... |
class ImputeEstimator(BaseEstimator, SingleInputSingleOutputParams, MaskValueParams, ImputeMethodParams):
'''
Imputation estimator for use in Spark pipelines.
This estimator is used to calculate the chosen statistic of the input
feature column. When fit is called it returns a ImputeTransformer
whic... | 6 | 4 | 33 | 3 | 20 | 11 | 2 | 0.61 | 4 | 5 | 1 | 0 | 3 | 1 | 3 | 60 | 119 | 11 | 67 | 29 | 47 | 41 | 22 | 13 | 18 | 4 | 6 | 1 | 6 |
325,247 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/estimators/min_max_scale.py | kamae.spark.estimators.min_max_scale.MinMaxScaleEstimator | from typing import List, Optional
from kamae.spark.params import MaskValueParams, SingleInputSingleOutputParams
from kamae.spark.transformers import MinMaxScaleTransformer
import numpy as np
import pyspark.sql.functions as F
from pyspark.sql.types import ArrayType, DataType, DoubleType, FloatType
from pyspark import ke... |
class MinMaxScaleEstimator(BaseEstimator, SingleInputSingleOutputParams, MaskValueParams):
'''
Min max estimator for use in Spark pipelines.
This estimator is used to calculate the min and max of the input
feature column. When fit is called it returns a MinMaxScaleTransformer
which can be used to s... | 6 | 4 | 34 | 3 | 21 | 10 | 1 | 0.54 | 3 | 5 | 1 | 0 | 3 | 0 | 3 | 57 | 120 | 14 | 69 | 29 | 51 | 37 | 23 | 15 | 19 | 2 | 6 | 1 | 4 |
325,248 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/estimators/one_hot_encode.py | kamae.spark.estimators.one_hot_encode.OneHotEncodeEstimator | from kamae.spark.params import DropUnseenParams, SingleInputSingleOutputParams, StringIndexParams
from typing import List, Optional
from .base import BaseEstimator
from kamae.spark.utils import collect_labels_array
from pyspark.sql.types import DataType, IntegerType, LongType, ShortType, StringType
import pyspark.sql.f... |
class OneHotEncodeEstimator(BaseEstimator, DropUnseenParams, SingleInputSingleOutputParams, StringIndexParams):
'''
One-hot encoder Spark Estimator for use in Spark pipelines.
This estimator is used to collect all the string labels for a given column.
When fit is called it returns a OneHotEncodeTransfo... | 6 | 4 | 32 | 2 | 17 | 13 | 1 | 0.79 | 4 | 5 | 1 | 0 | 3 | 0 | 3 | 67 | 113 | 9 | 58 | 26 | 35 | 46 | 13 | 7 | 9 | 1 | 6 | 0 | 3 |
325,249 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/estimators/shared_one_hot_encode.py | kamae.spark.estimators.shared_one_hot_encode.SharedOneHotEncodeEstimator | from kamae.spark.transformers import SharedOneHotEncodeTransformer
from kamae.spark.params import DropUnseenParams, MultiInputMultiOutputParams, StringIndexParams
from typing import List, Optional
from pyspark import keyword_only
from pyspark.sql.types import DataType, IntegerType, LongType, ShortType, StringType
from ... |
class SharedOneHotEncodeEstimator(BaseEstimator, MultiInputMultiOutputParams, DropUnseenParams, StringIndexParams):
'''
Shared One-hot encoder Spark Estimator for use in Spark pipelines.
This estimator is used to collect all the string labels for a given set of input
columns. When fit is called it retu... | 6 | 4 | 31 | 1 | 17 | 13 | 1 | 0.76 | 4 | 5 | 1 | 0 | 3 | 0 | 3 | 67 | 111 | 7 | 59 | 26 | 36 | 45 | 13 | 7 | 9 | 1 | 6 | 0 | 3 |
325,250 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/estimators/shared_string_index.py | kamae.spark.estimators.shared_string_index.SharedStringIndexEstimator | from kamae.spark.transformers import SharedStringIndexTransformer
import pyspark.sql.functions as F
from pyspark.sql import DataFrame
from kamae.spark.utils import collect_labels_array_from_multiple_columns
from .base import BaseEstimator
from kamae.spark.params import MultiInputMultiOutputParams, StringIndexParams
fro... |
class SharedStringIndexEstimator(BaseEstimator, MultiInputMultiOutputParams, StringIndexParams):
'''
Shared vocab String indexer Spark Estimator for use in Spark pipelines.
This estimator is used to collect all the string labels across multiple columns
and keeps a shared list of string labels.
When... | 6 | 4 | 29 | 2 | 16 | 11 | 1 | 0.73 | 3 | 4 | 1 | 0 | 3 | 0 | 3 | 65 | 103 | 8 | 55 | 24 | 34 | 40 | 13 | 7 | 9 | 1 | 6 | 0 | 3 |
325,251 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/estimators/single_feature_array_standard_scale.py | kamae.spark.estimators.single_feature_array_standard_scale.SingleFeatureArrayStandardScaleEstimator | from typing import Dict, List, Optional
from pyspark.sql import DataFrame
from .base import BaseEstimator
from kamae.spark.params import MaskValueParams, SingleInputSingleOutputParams
import numpy as np
from kamae.spark.transformers import StandardScaleTransformer
import pyspark.sql.functions as F
from pyspark import k... |
class SingleFeatureArrayStandardScaleEstimator(BaseEstimator, SingleInputSingleOutputParams, MaskValueParams):
'''
Single feature array standard scaler estimator for use in Spark pipelines.
This estimator is used to calculate the mean and standard deviation of the input
feature column when it is an arr... | 6 | 4 | 32 | 3 | 19 | 11 | 1 | 0.65 | 3 | 6 | 1 | 0 | 3 | 0 | 3 | 57 | 115 | 11 | 63 | 25 | 45 | 41 | 18 | 11 | 14 | 2 | 6 | 1 | 4 |
325,252 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/estimators/standard_scale.py | kamae.spark.estimators.standard_scale.StandardScaleEstimator | from .base import BaseEstimator
import pyspark.sql.functions as F
from pyspark import keyword_only
from pyspark.sql import DataFrame
from pyspark.sql.types import ArrayType, DataType, DoubleType, FloatType
import numpy as np
from kamae.spark.transformers import StandardScaleTransformer
from typing import List, Optional... |
class StandardScaleEstimator(BaseEstimator, SingleInputSingleOutputParams, MaskValueParams):
'''
Standard scaler estimator for use in Spark pipelines.
This estimator is used to calculate the mean and standard deviation of the input
feature column. When fit is called it returns a StandardScaleTransforme... | 6 | 4 | 33 | 3 | 21 | 9 | 1 | 0.51 | 3 | 5 | 1 | 0 | 3 | 0 | 3 | 57 | 118 | 14 | 69 | 29 | 51 | 35 | 23 | 15 | 19 | 2 | 6 | 1 | 4 |
325,253 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/estimators/string_index.py | kamae.spark.estimators.string_index.StringIndexEstimator | from kamae.spark.utils import collect_labels_array
import pyspark.sql.functions as F
from .base import BaseEstimator
from typing import List, Optional
from pyspark import keyword_only
from kamae.spark.transformers import StringIndexTransformer
from kamae.spark.params import SingleInputSingleOutputParams, StringIndexPar... |
class StringIndexEstimator(BaseEstimator, SingleInputSingleOutputParams, StringIndexParams):
'''
String indexer Spark Estimator for use in Spark pipelines.
This estimator is used to collect all the string labels for a given column.
When fit is called it returns a StringIndexerLayerModel which can be us... | 6 | 4 | 28 | 1 | 16 | 11 | 1 | 0.72 | 3 | 4 | 1 | 0 | 3 | 0 | 3 | 65 | 100 | 7 | 54 | 24 | 33 | 39 | 13 | 7 | 9 | 1 | 6 | 0 | 3 |
325,254 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/base.py | kamae.spark.params.base.HasInputDtype | from pyspark.ml.param import Param, Params, TypeConverters
from kamae.utils import DType
from typing import List, Optional
class HasInputDtype(Params):
"""
Mixin class for a transformer input datatype.
"""
inputDtype = Param(Params._dummy(), 'inputDtype', 'Input datatype of the transformer', typeConver... |
class HasInputDtype(Params):
'''
Mixin class for a transformer input datatype.
'''
def setInputDtype(self, value: str) -> 'HasInputDtype':
'''
Sets the parameter inputDtype to the given string value.
:param value: String to set the inputDtype parameter to.
:raises V... | 4 | 4 | 11 | 0 | 5 | 5 | 2 | 0.83 | 1 | 3 | 1 | 1 | 3 | 0 | 3 | 3 | 47 | 5 | 23 | 8 | 19 | 19 | 15 | 8 | 11 | 2 | 1 | 1 | 5 |
325,255 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/base.py | kamae.spark.params.base.HasOutputDtype | from typing import List, Optional
from kamae.utils import DType
from pyspark.ml.param import Param, Params, TypeConverters
class HasOutputDtype(Params):
"""
Mixin class for a transformer output datatype.
"""
outputDtype = Param(Params._dummy(), 'outputDtype', 'Output datatype of the transformer', typeC... |
class HasOutputDtype(Params):
'''
Mixin class for a transformer output datatype.
'''
def setOutputDtype(self, value: str) -> 'HasOutputDtype':
'''
Sets the parameter outputDtype to the given string value.
:param value: String to set the outputDtype parameter to.
:ra... | 4 | 4 | 11 | 1 | 5 | 5 | 2 | 0.83 | 1 | 3 | 1 | 3 | 3 | 0 | 3 | 3 | 48 | 6 | 23 | 8 | 19 | 19 | 15 | 8 | 11 | 2 | 1 | 1 | 5 |
325,256 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/base.py | kamae.spark.params.base.MultiInputMultiOutputParams | from .default_read_write import KamaeDefaultParamsReadable, KamaeDefaultParamsWritable
from .utils import InputOutputExtractor
class MultiInputMultiOutputParams(MultiInputParams, MultiOutputParams, InputOutputExtractor, KamaeDefaultParamsReadable, KamaeDefaultParamsWritable):
"""
Mixin class containing set met... |
class MultiInputMultiOutputParams(MultiInputParams, MultiOutputParams, InputOutputExtractor, KamaeDefaultParamsReadable, KamaeDefaultParamsWritable):
'''
Mixin class containing set methods for the multiple input
and multiple output columns scenario.
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.57 | 5 | 0 | 0 | 5 | 0 | 0 | 0 | 13 | 11 | 0 | 7 | 7 | 0 | 4 | 1 | 1 | 0 | 0 | 3 | 0 | 0 |
325,257 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/base.py | kamae.spark.params.base.MultiInputParams | from typing import List, Optional
from pyspark.ml.param.shared import HasInputCol, HasInputCols, HasOutputCol, HasOutputCols
class MultiInputParams(HasInputCols):
"""
Mixin class containing set methods for the multiple input columns scenario.
"""
def setInputCols(self, value: List[str]) -> 'MultiInput... |
class MultiInputParams(HasInputCols):
'''
Mixin class containing set methods for the multiple input columns scenario.
'''
def setInputCols(self, value: List[str]) -> 'MultiInputParams':
'''
Sets the parameter inputCols to the given list of strings.
:param value: List of str... | 2 | 2 | 8 | 1 | 2 | 5 | 1 | 2.67 | 1 | 1 | 0 | 2 | 1 | 0 | 1 | 1 | 13 | 2 | 3 | 2 | 1 | 8 | 3 | 2 | 1 | 1 | 1 | 0 | 1 |
325,258 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/base.py | kamae.spark.params.base.MultiInputSingleOutputParams | from .utils import InputOutputExtractor
from .default_read_write import KamaeDefaultParamsReadable, KamaeDefaultParamsWritable
class MultiInputSingleOutputParams(MultiInputParams, SingleOutputParams, InputOutputExtractor, KamaeDefaultParamsReadable, KamaeDefaultParamsWritable):
"""
Mixin class containing set m... |
class MultiInputSingleOutputParams(MultiInputParams, SingleOutputParams, InputOutputExtractor, KamaeDefaultParamsReadable, KamaeDefaultParamsWritable):
'''
Mixin class containing set methods for the multiple input
and single output column scenario.
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.57 | 5 | 0 | 0 | 29 | 0 | 0 | 0 | 13 | 11 | 0 | 7 | 7 | 0 | 4 | 1 | 1 | 0 | 0 | 3 | 0 | 0 |
325,259 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/base.py | kamae.spark.params.base.MultiOutputParams | from pyspark.ml.param.shared import HasInputCol, HasInputCols, HasOutputCol, HasOutputCols
from typing import List, Optional
from .name import HasLayerName
class MultiOutputParams(HasLayerName, HasOutputCols, HasOutputDtype):
"""
Mixin class containing set methods for the multiple output columns scenario.
... |
class MultiOutputParams(HasLayerName, HasOutputCols, HasOutputDtype):
'''
Mixin class containing set methods for the multiple output columns scenario.
'''
def setLayerName(self, value: str) -> 'MultiOutputParams':
'''
Sets the parameter layerName to the given string value.
... | 3 | 3 | 15 | 1 | 7 | 7 | 3 | 1.13 | 3 | 2 | 0 | 2 | 2 | 0 | 2 | 6 | 36 | 4 | 15 | 3 | 12 | 17 | 11 | 3 | 8 | 3 | 2 | 2 | 6 |
325,260 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/base.py | kamae.spark.params.base.SingleInputMultiOutputParams | from .utils import InputOutputExtractor
from .default_read_write import KamaeDefaultParamsReadable, KamaeDefaultParamsWritable
class SingleInputMultiOutputParams(SingleInputParams, MultiOutputParams, InputOutputExtractor, KamaeDefaultParamsReadable, KamaeDefaultParamsWritable):
"""
Mixin class containing set m... |
class SingleInputMultiOutputParams(SingleInputParams, MultiOutputParams, InputOutputExtractor, KamaeDefaultParamsReadable, KamaeDefaultParamsWritable):
'''
Mixin class containing set methods for the single input
and multiple output columns scenario.
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.57 | 5 | 0 | 0 | 2 | 0 | 0 | 0 | 13 | 11 | 0 | 7 | 7 | 0 | 4 | 1 | 1 | 0 | 0 | 3 | 0 | 0 |
325,261 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/base.py | kamae.spark.params.base.SingleInputParams | from pyspark.ml.param.shared import HasInputCol, HasInputCols, HasOutputCol, HasOutputCols
class SingleInputParams(HasInputCol):
"""
Mixin class containing set methods for the single input column scenario.
"""
def setInputCol(self, value: str) -> 'SingleInputParams':
"""
Sets the param... |
class SingleInputParams(HasInputCol):
'''
Mixin class containing set methods for the single input column scenario.
'''
def setInputCol(self, value: str) -> 'SingleInputParams':
'''
Sets the parameter inputCol to the given string value.
:param value: String to set the inputC... | 2 | 2 | 8 | 1 | 2 | 5 | 1 | 2.67 | 1 | 1 | 0 | 2 | 1 | 0 | 1 | 1 | 13 | 2 | 3 | 2 | 1 | 8 | 3 | 2 | 1 | 1 | 1 | 0 | 1 |
325,262 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/base.py | kamae.spark.params.base.SingleInputSingleOutputParams | from .default_read_write import KamaeDefaultParamsReadable, KamaeDefaultParamsWritable
from .utils import InputOutputExtractor
class SingleInputSingleOutputParams(SingleInputParams, SingleOutputParams, InputOutputExtractor, KamaeDefaultParamsReadable, KamaeDefaultParamsWritable):
"""
Mixin class containing set... |
class SingleInputSingleOutputParams(SingleInputParams, SingleOutputParams, InputOutputExtractor, KamaeDefaultParamsReadable, KamaeDefaultParamsWritable):
'''
Mixin class containing set methods for the single input
and single output column scenario.
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.57 | 5 | 0 | 0 | 66 | 0 | 0 | 0 | 13 | 11 | 0 | 7 | 7 | 0 | 4 | 1 | 1 | 0 | 0 | 3 | 0 | 0 |
325,263 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/base.py | kamae.spark.params.base.SingleOutputParams | from .name import HasLayerName
from pyspark.ml.param.shared import HasInputCol, HasInputCols, HasOutputCol, HasOutputCols
class SingleOutputParams(HasLayerName, HasOutputCol, HasOutputDtype):
"""
Mixin class containing set methods for the single output column scenario.
"""
def setLayerName(self, value... |
class SingleOutputParams(HasLayerName, HasOutputCol, HasOutputDtype):
'''
Mixin class containing set methods for the single output column scenario.
'''
def setLayerName(self, value: str) -> 'SingleOutputParams':
'''
Sets the parameter layerName to the given string value.
Th... | 3 | 3 | 13 | 1 | 5 | 7 | 3 | 1.55 | 3 | 2 | 0 | 2 | 2 | 0 | 2 | 6 | 32 | 4 | 11 | 3 | 8 | 17 | 11 | 3 | 8 | 3 | 2 | 2 | 6 |
325,264 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/default_read_write.py | kamae.spark.params.default_read_write.KamaeDefaultParamsReadable | from pyspark.ml.util import DefaultParamsReadable, DefaultParamsReader, DefaultParamsWritable, DefaultParamsWriter, MLWriter
class KamaeDefaultParamsReadable(DefaultParamsReadable):
"""
DefaultParamsReadable with a workaround for slow metadata writes in Databricks.
Replicates the functionality of DefaultPa... |
class KamaeDefaultParamsReadable(DefaultParamsReadable):
'''
DefaultParamsReadable with a workaround for slow metadata writes in Databricks.
Replicates the functionality of DefaultParamsReadable in PySpark 3.5.0 since
Databricks uses different functionality
'''
@classmethod
def read(cls) ->... | 3 | 2 | 3 | 0 | 2 | 1 | 1 | 1.5 | 1 | 1 | 1 | 4 | 0 | 0 | 1 | 1 | 11 | 1 | 4 | 3 | 1 | 6 | 3 | 2 | 1 | 1 | 1 | 0 | 1 |
325,265 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/default_read_write.py | kamae.spark.params.default_read_write.KamaeDefaultParamsReader | from typing import Any, Dict, Optional
from pyspark import SparkContext
import os
from pyspark.ml.util import DefaultParamsReadable, DefaultParamsReader, DefaultParamsWritable, DefaultParamsWriter, MLWriter
class KamaeDefaultParamsReader(DefaultParamsReader):
"""
DefaultParamsReadable with a workaround for slo... |
class KamaeDefaultParamsReader(DefaultParamsReader):
'''
DefaultParamsReadable with a workaround for slow metadata writes in Databricks.
Replicates the functionality of DefaultParamsReadable in PySpark 3.5.0 since
Databricks uses different functionality
'''
@staticmethod
def loadMetadata(pa... | 3 | 2 | 17 | 0 | 7 | 10 | 1 | 1.67 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 25 | 1 | 9 | 8 | 4 | 15 | 6 | 5 | 4 | 1 | 1 | 0 | 1 |
325,266 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/default_read_write.py | kamae.spark.params.default_read_write.KamaeDefaultParamsWritable | from pyspark.ml.param import Params
from pyspark.ml.util import DefaultParamsReadable, DefaultParamsReader, DefaultParamsWritable, DefaultParamsWriter, MLWriter
class KamaeDefaultParamsWritable(DefaultParamsWritable):
"""
DefaultParamsWritable with a workaround for slow metadata writes in Databricks.
Repli... |
class KamaeDefaultParamsWritable(DefaultParamsWritable):
'''
DefaultParamsWritable with a workaround for slow metadata writes in Databricks.
Replicates the functionality of DefaultParamsWritable in PySpark 3.5.0 since
Databricks uses different functionality
'''
def write(self) -> MLWriter:
... | 2 | 2 | 12 | 1 | 10 | 1 | 2 | 0.55 | 1 | 3 | 1 | 4 | 1 | 0 | 1 | 1 | 19 | 2 | 11 | 3 | 8 | 6 | 6 | 3 | 3 | 2 | 1 | 1 | 2 |
325,267 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/default_read_write.py | kamae.spark.params.default_read_write.KamaeDefaultParamsWriter | from pyspark.ml.util import DefaultParamsReadable, DefaultParamsReader, DefaultParamsWritable, DefaultParamsWriter, MLWriter
from typing import Any, Dict, Optional
import os
from pyspark import SparkContext
class KamaeDefaultParamsWriter(DefaultParamsWriter):
"""
DefaultParamsWriter with a workaround for slow ... |
class KamaeDefaultParamsWriter(DefaultParamsWriter):
'''
DefaultParamsWriter with a workaround for slow metadata writes in Databricks.
Replicates the functionality of DefaultParamsWriter in PySpark 3.5.0 since
Databricks uses different functionality
'''
@staticmethod
def saveMetadata(instan... | 3 | 2 | 29 | 0 | 12 | 17 | 1 | 1.57 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 37 | 1 | 14 | 11 | 5 | 22 | 5 | 4 | 3 | 1 | 1 | 0 | 1 |
325,268 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/name.py | kamae.spark.params.name.HasLayerName | from pyspark.ml.param import Param, Params, TypeConverters
class HasLayerName(Params):
"""
Mixin class for a layer name.
"""
layerName = Param(Params._dummy(), 'layerName', 'Name of the layer', typeConverter=TypeConverters.toString)
def getLayerName(self) -> str:
"""
Gets the value... |
class HasLayerName(Params):
'''
Mixin class for a layer name.
'''
def getLayerName(self) -> str:
'''
Gets the value of the layerName parameter.
:returns: Layer name.
'''
pass | 2 | 2 | 7 | 1 | 2 | 4 | 1 | 0.78 | 1 | 1 | 0 | 3 | 1 | 0 | 1 | 1 | 19 | 3 | 9 | 3 | 7 | 7 | 4 | 3 | 2 | 1 | 1 | 0 | 1 |
325,269 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.AutoBroadcastParams | from pyspark.ml.param import Param, Params, TypeConverters
class AutoBroadcastParams(Params):
"""
Mixin class for the auto broadcast parameter.
"""
autoBroadcast = Param(Params._dummy(), 'autoBroadcast', 'Whether to enable auto broadcast for the layer.\n If `True`, will broadcast the input tenso... |
class AutoBroadcastParams(Params):
'''
Mixin class for the auto broadcast parameter.
'''
def setAutoBroadcast(self, value: bool) -> 'AutoBroadcastParams':
'''
Sets the autoBroadcast parameter.
:param value: autoBroadcast.
:returns: Instance of class mixed in.
... | 3 | 3 | 7 | 0 | 2 | 5 | 1 | 0.86 | 1 | 1 | 0 | 1 | 2 | 0 | 2 | 2 | 29 | 3 | 14 | 4 | 11 | 12 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,270 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.ConstantStringArrayParams | from typing import List, Union
from pyspark.ml.param import Param, Params, TypeConverters
class ConstantStringArrayParams(Params):
"""
Mixin class containing separator parameter needed for constant string array
transforms.
"""
constantStringArray = Param(Params._dummy(), 'constantStringArray', 'Val... |
class ConstantStringArrayParams(Params):
'''
Mixin class containing separator parameter needed for constant string array
transforms.
'''
def setConstantStringArray(self, value: List[str]) -> 'ConstantStringArrayParams':
'''
Sets the constantStringArray parameter.
:param val... | 3 | 3 | 8 | 1 | 2 | 5 | 1 | 1.18 | 1 | 1 | 0 | 3 | 2 | 0 | 2 | 2 | 29 | 5 | 11 | 4 | 8 | 13 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,271 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.DateTimeParams | from pyspark.ml.param import Param, Params, TypeConverters
class DateTimeParams(Params):
"""
Mixin class for a datetime transformation
"""
includeTime = Param(Params._dummy(), 'includeTime', 'Whether to include the time in the output datetime.\n If False, only the date is included in the format ... |
class DateTimeParams(Params):
'''
Mixin class for a datetime transformation
'''
def setIncludeTime(self, value: bool) -> 'DateTimeParams':
'''
Sets the includeTime parameter.
:param value: includeTime.
:returns: Instance of class mixed in.
'''
pass
... | 3 | 3 | 7 | 0 | 2 | 5 | 1 | 0.92 | 1 | 1 | 0 | 1 | 2 | 0 | 2 | 2 | 28 | 3 | 13 | 4 | 10 | 12 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,272 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.DefaultIntValueParams | from pyspark.ml.param import Param, Params, TypeConverters
class DefaultIntValueParams(Params):
"""
Mixin class containing default integer parameter.
"""
defaultValue = Param(Params._dummy(), 'defaultValue', '\n Default int value to use in the transformer.\n ', typeConverter=TypeConverter... |
class DefaultIntValueParams(Params):
'''
Mixin class containing default integer parameter.
'''
def setDefaultValue(self, value: int) -> 'DefaultIntValueParams':
'''
Sets the defaultValue parameter.
:param value: Value to set the defaultValue parameter to.
:returns: ... | 3 | 3 | 7 | 0 | 2 | 5 | 1 | 0.92 | 1 | 1 | 0 | 2 | 2 | 0 | 2 | 2 | 28 | 3 | 13 | 4 | 10 | 12 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,273 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.DropUnseenParams | from pyspark.ml.param import Param, Params, TypeConverters
class DropUnseenParams(Params):
"""
Mixin class containing parameters needed to drop unseen index in the
one hot encoder layer.
"""
dropUnseen = Param(Params._dummy(), 'dropUnseen', 'Whether to the drop unseen label index in the one hot enc... |
class DropUnseenParams(Params):
'''
Mixin class containing parameters needed to drop unseen index in the
one hot encoder layer.
'''
def setDropUnseen(self, value: bool) -> 'DropUnseenParams':
'''
Sets the dropUnseen parameter.
:param value: Bool value of whether to drop uns... | 3 | 3 | 9 | 1 | 2 | 6 | 1 | 1.36 | 1 | 1 | 0 | 4 | 2 | 0 | 2 | 2 | 31 | 5 | 11 | 4 | 8 | 15 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,274 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.HashIndexParams | from pyspark.ml.param import Param, Params, TypeConverters
class HashIndexParams(Params):
"""
Mixin class containing bin parameter needed for hash indexing layers.
"""
numBins = Param(Params._dummy(), 'numBins', 'Number of bins to use for hash indexing', typeConverter=TypeConverters.toInt)
maskValu... |
class HashIndexParams(Params):
'''
Mixin class containing bin parameter needed for hash indexing layers.
'''
def setNumBins(self, value: int) -> 'HashIndexParams':
'''
Sets the numBins parameter.
:param value: Integer value for the number of bins to use for hash indexing.
... | 5 | 5 | 8 | 1 | 3 | 5 | 1 | 0.91 | 1 | 3 | 0 | 2 | 4 | 0 | 4 | 4 | 54 | 10 | 23 | 7 | 18 | 21 | 13 | 7 | 8 | 2 | 1 | 1 | 5 |
325,275 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.ImputeMethodParams | from pyspark.ml.param import Param, Params, TypeConverters
class ImputeMethodParams(Params):
"""
Mixin class containing imputeParam parameter for imputation layer.
This parameter is used to select the method to estimate the value
that should be imputed over the mask.
"""
imputeMethod = Param(Pa... |
class ImputeMethodParams(Params):
'''
Mixin class containing imputeParam parameter for imputation layer.
This parameter is used to select the method to estimate the value
that should be imputed over the mask.
'''
def __init__(self) -> None:
pass
def setImputeMethod(self, value: st... | 4 | 3 | 7 | 0 | 4 | 2 | 1 | 0.55 | 1 | 3 | 0 | 1 | 3 | 1 | 3 | 3 | 38 | 4 | 22 | 6 | 18 | 12 | 11 | 6 | 7 | 2 | 1 | 1 | 4 |
325,276 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.LabelsArrayParams | from typing import List, Union
from pyspark.ml.param import Param, Params, TypeConverters
class LabelsArrayParams(Params):
labelsArray = Param(Params._dummy(), 'labelsArray', 'Ordered list of labels to use for the indexer', typeConverter=TypeConverters.toListString)
def setLabelsArray(self, value: List[str]) ... |
class LabelsArrayParams(Params):
def setLabelsArray(self, value: List[str]) -> 'LabelsArrayParams':
'''
Sets the labelArray parameter.
:param value: List of strings to use in indexing transformers.
:returns: Instance of class mixed in.
'''
pass
def getLabelsArr... | 3 | 2 | 8 | 1 | 2 | 5 | 1 | 0.82 | 1 | 1 | 0 | 1 | 2 | 0 | 2 | 2 | 24 | 4 | 11 | 4 | 8 | 9 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,277 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.LatLonConstantParams | from pyspark.ml.param import Param, Params, TypeConverters
from typing import List, Union
class LatLonConstantParams(Params):
"""
Mixin class containing lat and lon constant parameters.
"""
latLonConstant = Param(Params._dummy(), 'latLonConstant', 'Constant lat & lon to use in haversine distance calcul... |
class LatLonConstantParams(Params):
'''
Mixin class containing lat and lon constant parameters.
'''
def setLatLonConstant(self, value: List[float]) -> 'LatLonConstantParams':
'''
Sets the latLonConstant parameter.
:param value: List of float lat and lon values.
:ret... | 3 | 3 | 13 | 0 | 8 | 5 | 3 | 0.57 | 1 | 2 | 0 | 2 | 2 | 0 | 2 | 2 | 39 | 3 | 23 | 4 | 20 | 13 | 11 | 4 | 8 | 5 | 1 | 1 | 6 |
325,278 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.ListwiseParams | from pyspark.ml.param import Param, Params, TypeConverters
class ListwiseParams(Params):
"""
Mixin class containing the parameters needed for Listwise transformers.
"""
queryIdCol = Param(Params._dummy(), 'queryIdCol', "Column name to aggregate summary statistics upon,\n such as 'search_id'.", t... |
class ListwiseParams(Params):
'''
Mixin class containing the parameters needed for Listwise transformers.
'''
def setQueryIdCol(self, value: str) -> 'ListwiseParams':
'''
Sets the query id parameter.
:param value: String for column name to aggregate upon.
:returns: ... | 5 | 5 | 10 | 1 | 4 | 5 | 1 | 0.71 | 1 | 2 | 0 | 2 | 4 | 0 | 4 | 4 | 63 | 10 | 31 | 8 | 26 | 22 | 14 | 8 | 9 | 2 | 1 | 1 | 5 |
325,279 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.ListwiseStatisticsParams | from pyspark.ml.param import Param, Params, TypeConverters
from typing import List, Union
class ListwiseStatisticsParams(ListwiseParams):
"""
Mixin class containing the parameters needed for Listwise Statistics transformers.
"""
topN = Param(Params._dummy(), 'topN', 'Limit to how far into the list to a... |
class ListwiseStatisticsParams(ListwiseParams):
'''
Mixin class containing the parameters needed for Listwise Statistics transformers.
'''
def setInputCols(self, value: List[str]) -> 'ListwiseStatisticsParams':
'''
Overrides setting the input columns for the transformer.
Th... | 6 | 6 | 10 | 1 | 4 | 5 | 1 | 0.72 | 1 | 3 | 0 | 5 | 5 | 0 | 5 | 9 | 72 | 10 | 36 | 8 | 30 | 26 | 17 | 8 | 11 | 3 | 2 | 1 | 7 |
325,280 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.MaskStringValueParams | from pyspark.ml.param import Param, Params, TypeConverters
class MaskStringValueParams(Params):
"""
Mixin class containing maskValue parameter needed
for MinHashIndexTransformer and other transformers that require a string mask value.
"""
maskValue = Param(Params._dummy(), 'maskValue', '\n V... |
class MaskStringValueParams(Params):
'''
Mixin class containing maskValue parameter needed
for MinHashIndexTransformer and other transformers that require a string mask value.
'''
def setMaskValue(self, value: str) -> 'MaskStringValueParams':
'''
Sets the maskValue parameter.
... | 3 | 3 | 7 | 0 | 2 | 5 | 1 | 1 | 1 | 1 | 0 | 1 | 2 | 0 | 2 | 2 | 29 | 3 | 13 | 4 | 10 | 13 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,281 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.MaskValueParams | from pyspark.ml.param import Param, Params, TypeConverters
class MaskValueParams(Params):
"""
Mixin class containing maskValue parameter needed
for standard scale and imputation layers.
This parameter is used to ignore certain values in the scaling process.
For imputation, the value is ignored by t... |
class MaskValueParams(Params):
'''
Mixin class containing maskValue parameter needed
for standard scale and imputation layers.
This parameter is used to ignore certain values in the scaling process.
For imputation, the value is ignored by the estimator and imputed over at
training and inference... | 3 | 3 | 7 | 0 | 2 | 5 | 1 | 1.23 | 1 | 1 | 0 | 6 | 2 | 0 | 2 | 2 | 32 | 3 | 13 | 4 | 10 | 16 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,282 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.MathFloatConstantParams | from pyspark.ml.param import Param, Params, TypeConverters
class MathFloatConstantParams(Params):
"""
Mixin class for a math float constant.
"""
mathFloatConstant = Param(Params._dummy(), 'mathFloatConstant', 'Float constant used for math operations', typeConverter=TypeConverters.toFloat)
def __in... |
class MathFloatConstantParams(Params):
'''
Mixin class for a math float constant.
'''
def __init__(self) -> None:
pass
def getMathFloatConstant(self) -> float:
'''
Gets the value of the mathFloatConstant parameter.
:returns: Float constant used for math operati... | 4 | 3 | 6 | 1 | 2 | 3 | 1 | 0.86 | 1 | 2 | 0 | 7 | 3 | 0 | 3 | 3 | 32 | 6 | 14 | 5 | 10 | 12 | 9 | 5 | 5 | 1 | 1 | 0 | 3 |
325,283 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.NanFillValueParams | from pyspark.ml.param import Param, Params, TypeConverters
class NanFillValueParams(Params):
nanFillValue = Param(Params._dummy(), 'nanFillValue', '\n The value to fill Nan with.\n ', typeConverter=TypeConverters.toFloat)
def setNanFillValue(self, value: float) -> 'NanFillValueParams':
"... |
class NanFillValueParams(Params):
def setNanFillValue(self, value: float) -> 'NanFillValueParams':
'''
Sets the nanFillValue parameter.
:param value: Float value to use as the fill value.
:returns: Instance of class mixed in.
'''
pass
def getNanFillValue(self) ... | 3 | 2 | 8 | 0 | 3 | 5 | 2 | 0.6 | 1 | 2 | 0 | 6 | 2 | 0 | 2 | 2 | 26 | 2 | 15 | 4 | 12 | 9 | 8 | 4 | 5 | 2 | 1 | 1 | 3 |
325,284 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.NegationParams | from pyspark.ml.param import Param, Params, TypeConverters
class NegationParams(Params):
"""
Mixin class containing negation parameter needed for transforms that output a
boolean.
"""
negation = Param(Params._dummy(), 'negation', 'Whether to negate the operation.', typeConverter=TypeConverters.toBo... |
class NegationParams(Params):
'''
Mixin class containing negation parameter needed for transforms that output a
boolean.
'''
def setNegation(self, value: bool) -> 'NegationParams':
'''
Sets the negation parameter.
:param value: Bool value of whether to negate the operation.... | 3 | 3 | 8 | 1 | 2 | 5 | 1 | 1.18 | 1 | 1 | 0 | 3 | 2 | 0 | 2 | 2 | 29 | 5 | 11 | 4 | 8 | 13 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,285 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.PadValueParams | from pyspark.ml.param import Param, Params, TypeConverters
from typing import List, Union
class PadValueParams(Params):
"""
Mixin class containing pad value parameters needed
for ordinal array encoder transformers and array crop transformers.
"""
padValue = Param(Params._dummy(), 'padValue', 'The v... |
class PadValueParams(Params):
'''
Mixin class containing pad value parameters needed
for ordinal array encoder transformers and array crop transformers.
'''
def setPadValue(self, value: Union[str, int, float]) -> 'PadValueParams':
'''
Sets the parameter pad value to the given value... | 3 | 3 | 7 | 0 | 2 | 5 | 1 | 1.18 | 1 | 3 | 0 | 2 | 2 | 0 | 2 | 2 | 27 | 3 | 11 | 4 | 8 | 13 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,286 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.StandardScaleParams | from typing import List, Union
from pyspark.ml.param import Param, Params, TypeConverters
class StandardScaleParams(MaskValueParams):
"""
Mixin class containing mean and standard deviation parameters needed
for standard scaler layers.
"""
mean = Param(Params._dummy(), 'mean', 'Mean of the feature v... |
class StandardScaleParams(MaskValueParams):
'''
Mixin class containing mean and standard deviation parameters needed
for standard scaler layers.
'''
def setMean(self, value: List[float]) -> 'StandardScaleParams':
'''
Sets the parameter mean to the given Vector value.
Saves ... | 5 | 5 | 10 | 1 | 4 | 5 | 2 | 0.89 | 1 | 4 | 0 | 2 | 4 | 0 | 4 | 6 | 61 | 10 | 27 | 9 | 22 | 24 | 17 | 9 | 12 | 2 | 2 | 1 | 6 |
325,287 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.StandardScaleSkipZerosParams | from pyspark.ml.param import Param, Params, TypeConverters
class StandardScaleSkipZerosParams(Params):
"""
Mixin class containing maskValue parameter needed for conditional standard scale
layers. This parameter is used to ignore zeros when scaling.
"""
skipZeros = Param(Params._dummy(), 'skipZeros'... |
class StandardScaleSkipZerosParams(Params):
'''
Mixin class containing maskValue parameter needed for conditional standard scale
layers. This parameter is used to ignore zeros when scaling.
'''
def setSkipZeros(self, value: bool) -> 'StandardScaleSkipZerosParams':
'''
Sets the skip... | 5 | 5 | 8 | 1 | 2 | 5 | 1 | 0.85 | 1 | 2 | 0 | 2 | 4 | 0 | 4 | 4 | 58 | 10 | 26 | 7 | 21 | 22 | 11 | 7 | 6 | 1 | 1 | 0 | 4 |
325,288 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.StringConstantParams | from pyspark.ml.param import Param, Params, TypeConverters
class StringConstantParams(Params):
"""
Mixin class for a string constant.
"""
stringConstant = Param(Params._dummy(), 'stringConstant', 'String constant to use in many string transformers', typeConverter=TypeConverters.toString)
def setSt... |
class StringConstantParams(Params):
'''
Mixin class for a string constant.
'''
def setStringConstant(self, value: str) -> 'StringConstantParams':
'''
Sets the stringConstant parameter.
:param value: String constant value to use in different string transformers.
:ret... | 3 | 3 | 8 | 1 | 2 | 5 | 1 | 1.09 | 1 | 1 | 0 | 1 | 2 | 0 | 2 | 2 | 28 | 5 | 11 | 4 | 8 | 12 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,289 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.StringIndexParams | from pyspark.ml.param import Param, Params, TypeConverters
class StringIndexParams(LabelsArrayParams):
"""
Mixin class containing parameters needed for string indexer and one hot encoder
layers.
"""
stringOrderType = Param(Params._dummy(), 'stringOrderType', "How to order the strings. Options are\n... |
class StringIndexParams(LabelsArrayParams):
'''
Mixin class containing parameters needed for string indexer and one hot encoder
layers.
'''
def setStringOrderType(self, value: str) -> 'StringIndexParams':
'''
Sets the stringOrderType parameter to the given value.
Must be on... | 9 | 9 | 10 | 1 | 4 | 6 | 1 | 0.88 | 1 | 3 | 0 | 8 | 8 | 0 | 8 | 10 | 125 | 20 | 56 | 14 | 47 | 49 | 28 | 14 | 19 | 2 | 2 | 1 | 11 |
325,290 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.StringRegexParams | from pyspark.ml.param import Param, Params, TypeConverters
class StringRegexParams(Params):
"""
Mixin class for string transformers that use regex.
"""
regex = Param(Params._dummy(), 'regex', 'Whether to use regex in the string contains operation.', typeConverter=TypeConverters.toBoolean)
def setR... |
class StringRegexParams(Params):
'''
Mixin class for string transformers that use regex.
'''
def setRegex(self, value: bool) -> 'StringRegexParams':
'''
Sets the regex parameter.
:param value: Bool value of whether to use regex in the string contains
operation.
... | 3 | 3 | 9 | 1 | 2 | 6 | 1 | 1.27 | 1 | 1 | 0 | 1 | 2 | 0 | 2 | 2 | 30 | 5 | 11 | 4 | 8 | 14 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
325,291 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/shared.py | kamae.spark.params.shared.UnixTimestampParams | from pyspark.ml.param import Param, Params, TypeConverters
class UnixTimestampParams(Params):
"""
Mixin class for a unix timestamp
"""
unit = Param(Params._dummy(), 'unit', 'Unit of the timestamp.\n Can be `milliseconds` or `seconds`. Can also use short-hand `ms` and `s`.\n Default is `s`... |
class UnixTimestampParams(Params):
'''
Mixin class for a unix timestamp
'''
def setUnit(self, value: str) -> 'UnixTimestampParams':
'''
Sets the unit parameter.
:param value: unit.
:returns: Instance of class mixed in.
'''
pass
def getUnit(self)... | 3 | 3 | 11 | 1 | 6 | 5 | 3 | 0.6 | 1 | 2 | 0 | 3 | 2 | 0 | 2 | 2 | 37 | 5 | 20 | 5 | 17 | 12 | 13 | 5 | 10 | 4 | 1 | 1 | 5 |
325,292 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/params/utils.py | kamae.spark.params.utils.InputOutputExtractor | from pyspark.ml.param import Params
import pyspark.sql.functions as F
from pyspark.sql.types import DataType
from typing import List, Optional, Tuple
from pyspark.sql import Column, DataFrame
class InputOutputExtractor(Params):
"""
Mixin class for extracting input & output information from a transformer/estima... |
class InputOutputExtractor(Params):
'''
Mixin class for extracting input & output information from a transformer/estimator.
Used across all transformers/estimators to facilitate the construction
of the pipeline graph.
'''
def _get_single_or_multi_col(self, ingress: bool) -> List[str]:
... | 6 | 5 | 21 | 1 | 12 | 9 | 3 | 0.81 | 1 | 4 | 0 | 5 | 3 | 0 | 4 | 4 | 96 | 9 | 48 | 13 | 40 | 39 | 22 | 10 | 17 | 4 | 1 | 2 | 10 |
325,293 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/pipeline/pipeline.py | kamae.spark.pipeline.pipeline.KamaeSparkPipeline | from kamae.spark.pipeline import KamaeSparkPipelineModel
import networkx as nx
from pyspark.ml import Pipeline
from pyspark.ml.param import Params
from pyspark import keyword_only
from pyspark.ml.util import DefaultParamsReader, MLWriter
from typing import TYPE_CHECKING, List, Optional, Type
from pyspark.sql import Dat... |
class KamaeSparkPipeline(Pipeline):
'''
KamaeSparkPipeline is a subclass of pyspark.ml.Pipeline that is used to chain
together BaseTransformers.
It maintains the same functionality as pyspark.ml.Pipeline e.g. serialisation.
'''
@keyword_only
def __init__(self, *, stages: Optional[List['Kama... | 15 | 11 | 16 | 2 | 8 | 6 | 2 | 0.84 | 1 | 12 | 6 | 0 | 8 | 0 | 10 | 10 | 175 | 26 | 81 | 38 | 62 | 68 | 56 | 30 | 45 | 7 | 1 | 3 | 20 |
325,294 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/pipeline/pipeline.py | kamae.spark.pipeline.pipeline.KamaeSparkPipelineReader | from pyspark.ml.util import DefaultParamsReader, MLWriter
from typing import TYPE_CHECKING, List, Optional, Type
from pyspark.ml.pipeline import PipelineReader, PipelineSharedReadWrite, PipelineWriter
class KamaeSparkPipelineReader(PipelineReader):
"""
Util class for reading a pipeline from a persistent storag... |
class KamaeSparkPipelineReader(PipelineReader):
'''
Util class for reading a pipeline from a persistent storage path.
'''
def __init__(self, cls: Type[KamaeSparkPipeline]) -> None:
pass
def load(self, path: str) -> KamaeSparkPipeline:
'''
Loads a pipeline from a given ... | 3 | 2 | 6 | 1 | 3 | 3 | 1 | 1.14 | 1 | 3 | 1 | 0 | 2 | 0 | 2 | 2 | 18 | 3 | 7 | 5 | 4 | 8 | 7 | 5 | 4 | 1 | 1 | 0 | 2 |
325,295 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/pipeline/pipeline.py | kamae.spark.pipeline.pipeline.KamaeSparkPipelineWriter | from pyspark.ml.pipeline import PipelineReader, PipelineSharedReadWrite, PipelineWriter
class KamaeSparkPipelineWriter(PipelineWriter):
"""
Util class for writing a pipeline to a persistent storage path.
"""
def __init__(self, instance: KamaeSparkPipeline) -> None:
super().__init__(instance=in... |
class KamaeSparkPipelineWriter(PipelineWriter):
'''
Util class for writing a pipeline to a persistent storage path.
'''
def __init__(self, instance: KamaeSparkPipeline) -> None:
pass | 2 | 1 | 2 | 0 | 2 | 0 | 1 | 1 | 1 | 2 | 1 | 0 | 1 | 0 | 1 | 1 | 7 | 1 | 3 | 2 | 1 | 3 | 3 | 2 | 1 | 1 | 1 | 0 | 1 |
325,296 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/pipeline/pipeline_model.py | kamae.spark.pipeline.pipeline_model.KamaeSparkPipelineModel | import keras_tuner as kt
from pyspark.ml import PipelineModel
from kamae.graph import PipelineGraph
from pyspark.ml.util import DefaultParamsReader, MLWriter
from kamae.spark.transformers import BaseTransformer
import tensorflow as tf
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Type, Union, c... |
class KamaeSparkPipelineModel(PipelineModel):
'''
KamaeSparkPipelineModel is a subclass of pyspark.ml.PipelineModel that is used to
chain together LayerTransformers. It maintains the same functionality
as pyspark.ml.PipelineModel e.g. serialisation.
'''
def __init__(self, stages: List[BaseTran... | 10 | 9 | 14 | 1 | 6 | 6 | 1 | 1.1 | 1 | 8 | 4 | 0 | 7 | 1 | 8 | 8 | 123 | 16 | 51 | 27 | 32 | 56 | 30 | 17 | 21 | 3 | 1 | 2 | 11 |
325,297 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/pipeline/pipeline_model.py | kamae.spark.pipeline.pipeline_model.KamaeSparkPipelineModelReader | from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Type, Union, cast
from pyspark.ml.util import DefaultParamsReader, MLWriter
from pyspark.ml.pipeline import PipelineModelReader, PipelineModelWriter, PipelineSharedReadWrite
from kamae.spark.transformers import BaseTransformer
class KamaeSparkPipel... |
class KamaeSparkPipelineModelReader(PipelineModelReader):
'''
Util class for reading a pipeline model from a persistent storage path.
'''
def __init__(self, cls: Type['KamaeSparkPipelineModel']) -> None:
pass
def load(self, path: str) -> 'KamaeSparkPipelineModel':
'''
... | 3 | 2 | 7 | 1 | 4 | 3 | 1 | 0.89 | 1 | 4 | 2 | 0 | 2 | 0 | 2 | 2 | 20 | 3 | 9 | 5 | 6 | 8 | 7 | 5 | 4 | 1 | 1 | 0 | 2 |
325,298 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/pipeline/pipeline_model.py | kamae.spark.pipeline.pipeline_model.KamaeSparkPipelineModelWriter | from pyspark.ml.pipeline import PipelineModelReader, PipelineModelWriter, PipelineSharedReadWrite
class KamaeSparkPipelineModelWriter(PipelineModelWriter):
"""
Util class for writing a pipeline model to a persistent storage path.
"""
def __init__(self, instance: 'KamaeSparkPipelineModel') -> None:
... |
class KamaeSparkPipelineModelWriter(PipelineModelWriter):
'''
Util class for writing a pipeline model to a persistent storage path.
'''
def __init__(self, instance: 'KamaeSparkPipelineModel') -> None:
pass | 2 | 1 | 2 | 0 | 2 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 7 | 1 | 3 | 2 | 1 | 3 | 3 | 2 | 1 | 1 | 1 | 0 | 1 |
325,299 | ExpediaGroup/kamae | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/ExpediaGroup_kamae/src/kamae/spark/transformers/absolute_value.py | kamae.spark.transformers.absolute_value.AbsoluteValueTransformer | from pyspark.sql import DataFrame
from pyspark.sql.types import ByteType, DataType, DoubleType, FloatType, IntegerType, LongType, ShortType
from typing import List, Optional
import pyspark.sql.functions as F
from kamae.spark.utils import single_input_single_output_scalar_transform
from kamae.spark.params import SingleI... |
class AbsoluteValueTransformer(BaseTransformer, SingleInputSingleOutputParams):
'''
Absolute value Spark Transformer for use in Spark pipelines.
This transformer applies abs(x) operation to the input.
'''
@keyword_only
def __init__(self, inputCol: Optional[str]=None, outputCol: Optional[str]=No... | 7 | 5 | 17 | 1 | 9 | 7 | 1 | 0.76 | 2 | 3 | 1 | 0 | 4 | 0 | 4 | 57 | 82 | 8 | 42 | 20 | 25 | 32 | 13 | 8 | 8 | 1 | 6 | 0 | 4 |
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