code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
from typing import Literal, Tuple, Any, Optional
import hydra
import omegaconf
import pytorch_lightning as pl
import rul_datasets
from rul_adapt.approach import LatentAlignApproach
def get_latent_align(
dataset: Literal["cmapss", "xjtu-sy"],
source_fd: int,
target_fd: int,
xjtu_sy_subtask: Optional[... | /rul_adapt-0.2.0-py3-none-any.whl/rul_adapt/construct/latent_align/functional.py | 0.905128 | 0.760406 | functional.py | pypi |
from typing import List, Type, Optional
import torch
from torch import nn
from rul_adapt import utils
from rul_adapt.utils import pairwise
class FullyConnectedHead(nn.Module):
"""A fully connected (FC) network that can be used as a RUL regressor or a domain
discriminator.
This network is a stack of fu... | /rul_adapt-0.2.0-py3-none-any.whl/rul_adapt/model/head.py | 0.965495 | 0.784649 | head.py | pypi |
from typing import List, Optional, Union, Type
import torch
from torch import nn
from rul_adapt import utils
from rul_adapt.utils import pairwise
class CnnExtractor(nn.Module):
"""A Convolutional Neural Network (CNN) based network that extracts a feature
vector from same-length time windows.
This feat... | /rul_adapt-0.2.0-py3-none-any.whl/rul_adapt/model/cnn.py | 0.97631 | 0.796728 | cnn.py | pypi |
from copy import deepcopy
from typing import Dict, List, Optional, Tuple, Any, Callable
import numpy as np
import pytorch_lightning as pl
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset, get_worker_info
from rul_datasets import utils
from rul_datasets.reader import AbstractReader
... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/core.py | 0.954594 | 0.849566 | core.py | pypi |
import warnings
from typing import Any, Optional
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from rul_datasets.adaption import AdaptionDataset
from rul_datasets.core import RulDataModule
class SemiSupervisedDataModule(pl.LightningDataModule):
"""
A higher-order [data module][pytor... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/ssl.py | 0.9274 | 0.81119 | ssl.py | pypi |
import warnings
from copy import deepcopy
from typing import List, Optional, Any, Tuple, Callable, Sequence, Union, cast
import numpy as np
import pytorch_lightning as pl
import torch
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.dataset import ConcatDataset, TensorDataset
from rul_datasets ... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/adaption.py | 0.941251 | 0.749156 | adaption.py | pypi |
import warnings
from copy import deepcopy
from typing import List, Optional, Any
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from rul_datasets.core import PairedRulDataset, RulDataModule
class BaselineDataModule(pl.LightningDataModule):
"""
A higher-order [data module][pytorch_lig... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/baseline.py | 0.907168 | 0.712657 | baseline.py | pypi |
import os
from typing import List, Optional, Callable, Dict, Tuple
import numpy as np
import requests # type: ignore
import torch
from tqdm import tqdm # type: ignore
def get_files_in_path(path: str, condition: Optional[Callable] = None) -> List[str]:
"""
Return the paths of all files in a path that satisf... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/utils.py | 0.932222 | 0.498108 | utils.py | pypi |
from typing import List, Tuple, Iterable, Union, Optional
import numpy as np
def truncate_runs(
features: List[np.ndarray],
targets: List[np.ndarray],
percent_broken: Optional[float] = None,
included_runs: Optional[Union[float, Iterable[int]]] = None,
degraded_only: bool = False,
) -> Tuple[List... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/reader/truncating.py | 0.927802 | 0.826327 | truncating.py | pypi |
import os
import tempfile
import warnings
import zipfile
from typing import Union, List, Tuple, Dict, Optional
import numpy as np
from sklearn import preprocessing as scalers # type: ignore
from rul_datasets.reader import scaling
from rul_datasets.reader.data_root import get_data_root
from rul_datasets.reader.abstra... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/reader/cmapss.py | 0.880129 | 0.684521 | cmapss.py | pypi |
that want to extend this package with their own dataset. """
import abc
from copy import deepcopy
from typing import Optional, Union, List, Dict, Any, Iterable, Tuple, Literal
import numpy as np
from rul_datasets.reader import truncating
class AbstractReader(metaclass=abc.ABCMeta):
"""
This reader is the ab... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/reader/abstract.py | 0.953719 | 0.547283 | abstract.py | pypi |
import copy
import pickle
from typing import List, Optional, Union, Tuple
import numpy as np
from sklearn import preprocessing as scalers # type: ignore
from sklearn.base import BaseEstimator, TransformerMixin # type: ignore
_Scaler = (
scalers.StandardScaler,
scalers.MinMaxScaler,
scalers.MaxAbsScaler,... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/reader/scaling.py | 0.941895 | 0.60013 | scaling.py | pypi |
import os.path
import tempfile
import zipfile
from typing import Tuple, List, Union, Dict, Optional
import numpy as np
from sklearn import preprocessing as scalers # type: ignore
from rul_datasets import utils
from rul_datasets.reader import saving, scaling
from rul_datasets.reader.abstract import AbstractReader
fro... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/reader/xjtu_sy.py | 0.887668 | 0.602822 | xjtu_sy.py | pypi |
import os.path
from typing import Tuple, List, Dict, Literal, Optional
import numpy as np
from tqdm import tqdm # type: ignore
def save(save_path: str, features: np.ndarray, targets: np.ndarray) -> None:
"""
Save features and targets of a run to .npy files.
The arrays are saved to separate .npy files t... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/reader/saving.py | 0.837952 | 0.516108 | saving.py | pypi |
from typing import Tuple, List, Optional, Union
import numpy as np
from sklearn import preprocessing # type: ignore
from rul_datasets import utils
from rul_datasets.reader import AbstractReader, scaling
class DummyReader(AbstractReader):
"""
This reader represents a simple, small dummy dataset that can be ... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/reader/dummy.py | 0.961043 | 0.979255 | dummy.py | pypi |
import os
import re
import tempfile
import warnings
import zipfile
from typing import List, Tuple, Union, Dict, Optional
import numpy as np
import sklearn.preprocessing as scalers # type: ignore
from rul_datasets import utils
from rul_datasets.reader import scaling, saving
from rul_datasets.reader.data_root import g... | /rul_datasets-0.10.5.tar.gz/rul_datasets-0.10.5/rul_datasets/reader/femto.py | 0.86712 | 0.645246 | femto.py | pypi |
import math
import numpy as np
from rul_pm.dataset.lives_dataset import AbstractLivesDataset, FoldedDataset
from tqdm.auto import tqdm
from sklearn.base import clone
from sklearn.model_selection import ParameterGrid
from sklearn.model_selection._split import _BaseKFold
import multiprocessing
class RULScorerWrapper:... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/selection.py | 0.705075 | 0.39321 | selection.py | pypi |
from typing import Optional
import numpy as np
from rul_pm.results.results import FittedLife
from temporis.dataset.transformed import TransformedDataset
class BaselineModel:
"""Predict the RUL using the mean of the median value of the duration
of the dataset
Parameters
----------
mode: str
... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/baseline.py | 0.960639 | 0.54952 | baseline.py | pypi |
from typing import Optional
import numpy as np
from rul_pm.iterators.batcher import get_batcher
from rul_pm.models.model import TrainableModel
from torchsummary import summary as model_summary
from tqdm.auto import tqdm
import torch
import torch.nn.functional as F
LOSSES = {
'mae': F.l1_loss,
'mse': F.mse_lo... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/torch/model.py | 0.9226 | 0.232125 | model.py | pypi |
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from rul_pm.models.keras.keras import KerasTrainableModel
from rul_pm.models.keras.losses import weighted_categorical_crossentropy
from rul_pm.models.keras.weibull import WeibullLayer
from sklearn.base import BaseEstimator, TransformerMixin... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/keras/extras.py | 0.941021 | 0.431884 | extras.py | pypi |
import logging
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from temporis.iterators.iterators import WindowedDatasetIterator
from rul_pm.graphics.plots import plot_predictions
from tensorflow.keras.callbacks import Callback
from temporis.iterators.utils import true_v... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/keras/callbacks.py | 0.919156 | 0.419351 | callbacks.py | pypi |
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.layers import (Add, Conv1D, Dense, Dropout, Lambda,
Permute)
class Attention(tf.keras.Model):
"""
Temporal pattern attention for multivariate time series forecasting
Shun-Yao Shih, ... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/keras/attention.py | 0.905109 | 0.607692 | attention.py | pypi |
import numpy as np
import tensorflow as tf
from scipy.special import loggamma
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Concatenate, Dense, Lambda, Multiply
class TFWeibullDistribution:
@staticmethod
def log_likelihood(x: tf.Tensor, alpha: tf.Tensor, beta: tf.Tensor):
... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/keras/weibull.py | 0.932114 | 0.578924 | weibull.py | pypi |
from typing import Tuple
from rul_pm.models.keras.keras import KerasTrainableModel
from tensorflow.keras import Input, Model, optimizers
from tensorflow.keras.layers import (Concatenate, Conv2D, Dense, Dropout,
Flatten, Permute, Reshape)
def MVCNN(input_shape:Tuple[int, int],
... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/keras/models/MVCNN.py | 0.920692 | 0.617195 | MVCNN.py | pypi |
import numpy as np
import tensorflow as tf
from tensorflow.python.keras.layers import (BatchNormalization, Concatenate, MaxPool1D, Activation)
from rul_pm.models.keras.keras import KerasTrainableModel
from rul_pm.models.keras.layers import ExpandDimension, RemoveDimension
from tensorflow.keras import Input, Model, op... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/keras/models/InceptionTime.py | 0.855746 | 0.484685 | InceptionTime.py | pypi |
import numpy as np
import tensorflow as tf
from rul_pm.models.keras.keras import KerasTrainableModel
from rul_pm.models.keras.layers import ExpandDimension, RemoveDimension
from tensorflow.keras import Input, Model, optimizers
from tensorflow.keras.layers import (Layer, LayerNormalization, MultiHeadAttention,
... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/keras/models/VisionTransformer.py | 0.858259 | 0.554893 | VisionTransformer.py | pypi |
from typing import List
import tensorflow as tf
from rul_pm.models.keras.keras import KerasTrainableModel
from rul_pm.models.keras.losses import time_to_failure_rul
from tensorflow.keras import Input, Model, optimizers
from tensorflow.keras.layers import LSTM, Dense
class MultiTaskRUL(KerasTrainableModel):
"""
... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/keras/models/MultiTaskRUL.py | 0.960842 | 0.60775 | MultiTaskRUL.py | pypi |
import tensorflow as tf
from rul_pm.models.keras.keras import KerasTrainableModel
from tcn import TCN
from tensorflow.keras import Input, Model, optimizers
from tensorflow.keras.layers import (AveragePooling1D, Concatenate, Conv1D,
Dense, Dropout, Flatten, Lambda,
... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/models/keras/models/ConvEncoderDecoder.py | 0.883855 | 0.439326 | ConvEncoderDecoder.py | pypi |
from typing import List, Tuple
import numpy as np
class Segment:
def __init__(self, initial_point:Tuple[float, float], not_increasing:bool = False):
self.n = 1
self.initial = initial_point
self.xx = 0
self.xy = 0
self.yy = 0
self.B = 0
self.segment_error = ... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/results/picewise_regression.py | 0.889259 | 0.442998 | picewise_regression.py | pypi |
import logging
from dataclasses import dataclass
from typing import Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from rul_pm.results.picewise_regression import (PiecewesieLinearFunction,
PiecewiseLinearRegression)
from sklearn.metri... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/results/results.py | 0.90484 | 0.505127 | results.py | pypi |
import gzip
import io
import logging
import os
import pickle
import tarfile
from enum import Enum
from pathlib import Path
from typing import List, Optional, Union
import gdown
import pandas as pd
from joblib import Memory
from rul_pm import CACHE_PATH, DATASET_PATH
from rul_pm.datasets.lives_dataset import AbstractLi... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/datasets/PHMDataset2018.py | 0.725746 | 0.185062 | PHMDataset2018.py | pypi |
from typing import List, Optional, Union
import numpy as np
import pandas as pd
from rul_pm.datasets.lives_dataset import AbstractLivesDataset
from temporis import DATA_PATH
CMAPSS_PATH = DATA_PATH / "C_MAPSS"
# Features used by
# Multiobjective Deep Belief Networks Ensemble forRemaining Useful Life Estimation in
# ... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/datasets/CMAPSS.py | 0.860369 | 0.453746 | CMAPSS.py | pypi |
import math
from typing import Dict, Iterable, List, Optional, Union
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from rul_pm.graphics.utils.curly_brace import curlyBrace
from rul_pm.results.results import (FittedLife, PredictionResult,
... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/graphics/plots.py | 0.839504 | 0.349158 | plots.py | pypi |
from copy import copy
from typing import Callable, List, Optional, Tuple, Union
import matplotlib.patheffects as PathEffects
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from temporis.dataset.ts_dataset import AbstractTimeSeriesDataset
def add_vertical_line(ax, v_x, label, color, line, n_... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/graphics/duration.py | 0.945676 | 0.538983 | duration.py | pypi |
from typing import List, Optional, Type
import matplotlib.cm as cm
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LogNorm, Normalize
def time_series_importance(
n_features:int,
window_size:int,
coefficients:np.ndarray,
column_name... | /rul_pm-1.1.0.tar.gz/rul_pm-1.1.0/rul_pm/graphics/feature_importance.py | 0.960431 | 0.692291 | feature_importance.py | pypi |
class _UNDEFINED(object):
def __bool__(self):
return False
__name__ = 'UNDEFINED'
__nonzero__ = __bool__
def __repr__(self):
return self.__name__
UNDEFINED = _UNDEFINED()
"""
A sentinel value to specify that something is undefined. When evaluated, the value is falsy.
.. versionadded:: 2.0.0
"""
class EngineE... | /rule-engine-4.1.0.tar.gz/rule-engine-4.1.0/lib/rule_engine/errors.py | 0.796015 | 0.30571 | errors.py | pypi |
import collections
import collections.abc
import datetime
import decimal
import functools
import math
import random
from ._utils import parse_datetime, parse_float, parse_timedelta
from . import ast
from . import errors
from . import types
import dateutil.tz
def _builtin_filter(function, iterable):
return tuple(fi... | /rule-engine-4.1.0.tar.gz/rule-engine-4.1.0/lib/rule_engine/builtins.py | 0.636127 | 0.285908 | builtins.py | pypi |
import ast as pyast
import collections
import threading
import types as pytypes
from . import ast
from . import errors
from ._utils import timedelta_regex
import ply.lex as lex
import ply.yacc as yacc
literal_eval = pyast.literal_eval
class _DeferredAstNode(object):
__slots__ = ('cls', 'args', 'kwargs', 'method')... | /rule-engine-4.1.0.tar.gz/rule-engine-4.1.0/lib/rule_engine/parser.py | 0.632049 | 0.260866 | parser.py | pypi |
__all__ = [
'BinarySplit',
'IsInSplit',
'GreaterThanSplit',
'GreaterEqualThanSplit',
'LesserThanSplit',
'LesserEqualThanSplit',
'RangeSplit',
'MultiRangeSplit',
'MultiRangeAnySplit'
]
from typing import Union, List, Dict, Tuple
import numpy as np
import pandas as pd
from igrap... | /rule_estimator-0.4.1-py3-none-any.whl/rule_estimator/splits.py | 0.801159 | 0.248352 | splits.py | pypi |
__all__ = ['RuleClassifierDashboard']
from math import log10, floor
from typing import List, Tuple, Dict, Union
import numpy as np
import pandas as pd
from pandas.api.types import is_numeric_dtype
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as d... | /rule_estimator-0.4.1-py3-none-any.whl/rule_estimator/dashboard.py | 0.891369 | 0.514156 | dashboard.py | pypi |
__all__ = [
'CaseWhen',
'EmptyRule',
'PredictionRule',
'IsInRule',
'GreaterThan',
'GreaterEqualThan',
'LesserThan',
'LesserEqualThan',
'RangeRule',
'MultiRange',
'MultiRangeAny'
]
from typing import Union, List, Dict, Tuple
import numpy as np
import pandas as pd
from ig... | /rule_estimator-0.4.1-py3-none-any.whl/rule_estimator/rules.py | 0.740831 | 0.268695 | rules.py | pypi |
__all__ = [
'plot_model_graph',
'plot_label_pie',
'plot_parallel_coordinates',
'plot_density',
'plot_cats_density',
'plot_confusion_matrix',
'get_metrics_df',
'get_coverage_df',
]
from typing import List, Tuple, Union
import numpy as np
import pandas as pd
from pandas.api.types import ... | /rule_estimator-0.4.1-py3-none-any.whl/rule_estimator/plotting.py | 0.878627 | 0.487734 | plotting.py | pypi |
__all__ = ['BusinessRule']
from typing import Union, List, Dict, Tuple
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator
from sklearn.metrics import accuracy_score, mean_squared_error
from igraph import Graph
from .storable import Storable
def generate_range_... | /rule_estimator-0.4.1-py3-none-any.whl/rule_estimator/businessrule.py | 0.893193 | 0.642573 | businessrule.py | pypi |
import math
from typing import Dict, List, Tuple
import numpy as np
from pandas import DataFrame
def support(subset: List[str], data_df: DataFrame) -> float:
"""Calculates the support for a given itemset over all transactions.
Args:
subset (List[str]): List containing a candidate itemset
dat... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/util.py | 0.908873 | 0.715958 | util.py | pypi |
import pkg_resources
import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
def load_store_data() -> pd.DataFrame:
""" Loads the stored_data.csv file and binarizes the data.
Returns:
pd.DataFrame: One-hot encoded store data, where each column
corresponds to an item.
"""... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/data.py | 0.700178 | 0.419648 | data.py | pypi |
from copy import deepcopy
from math import floor
import random
from typing import Any, Dict, List, Tuple
import numpy as np
import pandas as pd
class Gene:
"""Store the information associated with an individual attribute.
For categorical attributes lower, upper is meaningless same goes for
numerical ones... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/gar.py | 0.92183 | 0.628208 | gar.py | pypi |
from collections import defaultdict
from typing import Dict, Iterator, List, Tuple
import numpy as np
import pandas as pd
from pandas import DataFrame
from algs.util import get_frequent_1_itemsets
def ais(dataframe: DataFrame, support_threshold: float = 0.005) -> DataFrame:
"""Calculates the frequent itemsets sat... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/ais.py | 0.938032 | 0.577436 | ais.py | pypi |
from itertools import chain, combinations
from typing import Any, Dict, Iterator, List, Tuple
import pandas as pd
from pandas import DataFrame, Series
from algs.util import confidence, measure_dict
def generate_rules(frequent_itemsets: DataFrame,
min_conf: float = 0.5) -> DataFrame:
"""Genera... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/rule_gen.py | 0.893152 | 0.565479 | rule_gen.py | pypi |
from copy import deepcopy
import random
from math import floor
from typing import Any, Dict, List, Tuple
import numpy as np
import pandas as pd
from algs.gar import Gene, _amplitude, _get_fittest, _get_lower_upper_bound
from algs.util import measure_dict
class RuleIndividuum:
def __init__(self, items: Dict[str,... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/gar_plus.py | 0.888487 | 0.46721 | gar_plus.py | pypi |
from typing import DefaultDict, Dict, List, Tuple
import numpy as np
from pandas import DataFrame
from collections import defaultdict
from algs.util import get_frequent_1_itemsets
class FPNode:
"""Node used in a fp tree.
"""
def __init__(self, item: str, parent: "FPNode", count: int = 1) -> None:
... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/fp_tree.py | 0.950457 | 0.482673 | fp_tree.py | pypi |
import pandas as pd
import numpy as np
from typing import Dict, Iterator, List, Tuple
from pandas import DataFrame
from algs.util import get_frequent_1_itemsets
from algs.hash_tree import HashTree
def apriori(dataframe: DataFrame, support_threshold: float = 0.005) -> DataFrame:
"""Calculate all frequent itemsets... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/apriori.py | 0.879134 | 0.574484 | apriori.py | pypi |
from typing import Dict, Tuple
import numpy as np
import pandas as pd
from pandas import DataFrame
from algs.apriori import _count_transactions, _generate_itemsets_by_join, _is_candidate
from algs.hash_tree import HashTree
from algs.util import get_frequent_1_itemsets
def hclique(dataframe: DataFrame, hconf_threshol... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/hclique.py | 0.93161 | 0.567128 | hclique.py | pypi |
from math import ceil, floor
from typing import Any, Dict, Iterator, Set, Tuple
import numpy as np
import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from pandas import DataFrame
from sklearn.cluster import Birch
def partition_intervals(
num_intervals: int, attribute: str, db: DataFrame, eq... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/quantitative.py | 0.942242 | 0.560463 | quantitative.py | pypi |
from typing import Dict, List, Tuple
class HashTree:
def __init__(self, depth: int = 0, leaf: bool = True, max_size: int = 57) -> None:
self.children = {}
self.itemsets = {}
self.leaf = leaf
self.max_size = max_size
self.depth = depth
def add_itemset(self, itemset: Tup... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/hash_tree.py | 0.935391 | 0.594963 | hash_tree.py | pypi |
from typing import Any, Callable, Dict
from algs.apriori import a_close
from algs.fp_tree import fp_growth
from algs.gar import gar
from algs.gar_plus import gar_plus
from algs.hclique import hclique
from algs.quantitative import quantitative_itemsets
from algs.rule_gen import generate_rules, minimal_non_redundant_rule... | /rule_mining_algs-0.1.1-py3-none-any.whl/algs/models.py | 0.91366 | 0.32536 | models.py | pypi |
class Rule34Post:
"""
The data structure for images on rule34. By default, all items are none,
they will only be something else if rule34.xxx specifies a value.
if ``initialised`` is False, that means somehow this object wasn't initialised properly, and you should discard it
"""
initialised = F... | /rule34_new-1.0.4-py3-none-any.whl/rule34/objectClasses.py | 0.503418 | 0.384508 | objectClasses.py | pypi |
class Rule34Post:
"""
The data structure for images on rule34. By default, all items are none,
they will only be something else if rule34.xxx specifies a value.
if ``initialised`` is False, that means somehow this object wasn't initialised properly, and you should discard it
"""
initialised = F... | /rule34-1.7.4.tar.gz/rule34-1.7.4/Rule34/objectClasses.py | 0.525369 | 0.384739 | objectClasses.py | pypi |
from ruleau import All, ApiAdapter, OverrideLevel, execute, rule
@rule(rule_id="rul_child", name="Has children")
def has_children(_, payload):
"""
Checks whether the custom has any children.
>>> has_children(None, {"data": {"number_of_children": 0}})
False
>>> has_children(None, {"data": {"number... | /ruleau-0.7.1-py3-none-any.whl/examples/kitchen_sink/lending_rules.py | 0.800497 | 0.292739 | lending_rules.py | pypi |
from lending_rules import (
ccjs_check_required,
fico_score_greater_than_threshold,
has_no_ccjs,
has_sufficient_capital,
kyc_risk_greater_than_threshold,
)
from ruleau import All, ApiAdapter, Process, execute, rule
@rule("rul-101A", "Causes skipped")
def causes_skip(_, __):
return False
@ru... | /ruleau-0.7.1-py3-none-any.whl/examples/kitchen_sink/rules.py | 0.526099 | 0.19112 | rules.py | pypi |
from rule_based_block_rules import account_status
from ruleau import Process, execute
if __name__ == "__main__":
execution_result = execute(
account_status,
{
"loc_record": {
"loc_number": 12345,
"current_dnp": 0,
"current_cons_full_pmt": ... | /ruleau-0.7.1-py3-none-any.whl/examples/account_status_rules/main.py | 0.482917 | 0.288723 | main.py | pypi |
from datetime import datetime, timedelta
from ruleau import All, rule
@rule(rule_id="MP-001-B1", name="Days Not Paid")
def days_not_paid(_, payload):
"""
Block if the latest payment is now late (i.e. Days not paid is greater than zero).
>>> days_not_paid(None, {"loc_record": {"current_dnp": 1}})
Fal... | /ruleau-0.7.1-py3-none-any.whl/examples/account_status_rules/rule_based_block_rules.py | 0.738575 | 0.413773 | rule_based_block_rules.py | pypi |
 
# Rule Check
Rule Check (aka rulecheck or source rule check) is a command line system for running custom static an... | /rulecheck-0.6.1.tar.gz/rulecheck-0.6.1/README.md | 0.492188 | 0.954942 | README.md | pypi |
from __future__ import annotations
from random import randint, shuffle
import string
try:
from secrets import choice
except ImportError:
from random import choice
class PasswordGenerator:
"""Random password generator that follows the rules
Args:
length (int): Length of the password (one or... | /ruled_password_generator-1.0.1-py3-none-any.whl/ruled_password_generator.py | 0.847053 | 0.307332 | ruled_password_generator.py | pypi |
# Getting Started
[](https://pepy.tech/project/rules-engine) 
## Description
Simple rules engine inspired by [M... | /rules-engine-0.2.5.tar.gz/rules-engine-0.2.5/README.md | 0.596198 | 0.907845 | README.md | pypi |
rules
^^^^^
``rules`` is a tiny but powerful app providing object-level permissions to
Django, without requiring a database. At its core, it is a generic framework
for building rule-based systems, similar to `decision trees`_. It can also be
used as a standalone library in other contexts and frameworks.
.. image:: ht... | /rules-3.3.tar.gz/rules-3.3/README.rst | 0.936198 | 0.689482 | README.rst | pypi |
# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of... | /ruleskit-1.0.125.tar.gz/ruleskit-1.0.125/CODE_OF_CONDUCT.md | 0.574872 | 0.684277 | CODE_OF_CONDUCT.md | pypi |
# Rules protocol
Core smart contracts of the Rules protocol.
- for marketplace contracts, see [marketplace](https://github.com/ruleslabs/marketplace) repository.
- for pack opening contracts, see [pack-opener](https://github.com/ruleslabs/pack-opener) repository.
## Overview
Rules protocol is composed of 4 contracts... | /ruleslabs-core-1.0.1.tar.gz/ruleslabs-core-1.0.1/README.md | 0.582847 | 0.947769 | README.md | pypi |
import itertools
import json
import ruly
from ruly_dmn import common
class DMN:
"""Class that contains the DMN implementation.
Args:
handler (ruly_dmn.ModelHandler): model handler
rule_factory_cb (Optional[Callable]): function that creates a rule
factory - if None, a factory that... | /ruly-dmn-0.0.6.tar.gz/ruly-dmn-0.0.6/ruly_dmn/dmn.py | 0.702938 | 0.317373 | dmn.py | pypi |
import json
import ruly
import uuid
import xml.etree.ElementTree
from ruly_dmn import common
_tags = {
'decision': '{https://www.omg.org/spec/DMN/20191111/MODEL/}decision',
'decisionTable': '{https://www.omg.org/spec/DMN/20191111/MODEL/}'
'decisionTable',
'input': '{https://www.omg.o... | /ruly-dmn-0.0.6.tar.gz/ruly-dmn-0.0.6/ruly_dmn/handlers/camunda_modeler.py | 0.439747 | 0.280672 | camunda_modeler.py | pypi |
import abc
from collections import namedtuple
import enum
import json
class Rule(namedtuple('Rule', ['antecedent', 'consequent'])):
"""Knowledge base rule
Attributes:
antecedent (Union[ruly.Condition, ruly.Expression]): expression or a
condition that, if evaluated to True, fires assignmen... | /ruly-zlatsic-0.0.1.tar.gz/ruly-zlatsic-0.0.1/ruly/common.py | 0.774669 | 0.377828 | common.py | pypi |
from ruly import common
def backward_chain(knowledge_base, output_name, post_eval_cb=None, **kwargs):
"""Evaulates the output using backward chaining
The algorithm is depth-first-search, if goal variable assigment is
contained within a rule that has a depending derived variable, this
variable is solv... | /ruly-zlatsic-0.0.1.tar.gz/ruly-zlatsic-0.0.1/ruly/evaluator.py | 0.906818 | 0.545407 | evaluator.py | pypi |
from rumboot.images.imageFormatBase import ImageFormatBase
class ImageFormatV2(ImageFormatBase):
"""
This class works with version 2.0 images.
struct __attribute__((packed)) rumboot_bootheader {
uint32_t magic; /* 0xb0ldface */
uint8_t version;
uint8_t reserved;
uint8_t chip_id;
uin... | /rumboot-tools-0.9.30.tar.gz/rumboot-tools-0.9.30/rumboot/images/imageFormatV2.py | 0.560493 | 0.353317 | imageFormatV2.py | pypi |
from rumboot.images.imageFormatBase import ImageFormatBase
import os
class ImageFormatLegacyNM6408(ImageFormatBase):
MAGIC = 0x12345678
name = "NM6408 (Legacy)"
format = [
[4, "magic", "0x%x", "Magic"],
[4, "data_length", "%d", "Data Length"],
]
def __init__(self, inFile):
... | /rumboot-tools-0.9.30.tar.gz/rumboot-tools-0.9.30/rumboot/images/imageFormatLegacyNM6408.py | 0.486819 | 0.293253 | imageFormatLegacyNM6408.py | pypi |
from rumboot.ops.base import base
import tqdm
import time
class basic_uploader(base):
formats = {
"first_upload" : "boot: host: Hit '{}' for X-Modem upload",
"first_upload_basis" : "boot: host: Hit 'X' for xmodem upload",
"upload_uboot": "Trying to boot from UART",
"uboot_xmod... | /rumboot-tools-0.9.30.tar.gz/rumboot-tools-0.9.30/rumboot/ops/xfer.py | 0.486332 | 0.169715 | xfer.py | pypi |
import argparse
from distutils.util import strtobool
import rumboot_packimage
import rumboot
from rumboot.ImageFormatDb import ImageFormatDb
class RumbootPackimage:
"""RumbootPackimage tool frontend"""
def __init__(self, opts):
pass
def cli():
parser = argparse.ArgumentParser(formatter_class=argp... | /rumboot-tools-0.9.30.tar.gz/rumboot-tools-0.9.30/rumboot_packimage/frontend.py | 0.517571 | 0.158663 | frontend.py | pypi |
from decimal import Decimal
import requests
from . import exceptions
TIMEOUT = 3
API_HOST = 'https://rumetr.com/api/v1/'
class ApptList(dict):
"""
Abstract list of flats. Useful for working with a plain list of flats
"""
def add(self, complex: str, house: str, id, **kwargs):
self._get_house... | /rumetr-client-0.2.4.tar.gz/rumetr-client-0.2.4/rumetr/roometr.py | 0.71721 | 0.253618 | roometr.py | pypi |
import hashlib
import re
from scrapy.spiders import XMLFeedSpider
from rumetr.scrapy.item import ApptItem as Item
class YandexFeedSpider(XMLFeedSpider):
"""Base Spider to parse yandex-realty feed"""
name = 'spider'
namespaces = [('yandex', 'http://webmaster.yandex.ru/schemas/feed/realty/2010-06')]
i... | /rumetr-client-0.2.4.tar.gz/rumetr-client-0.2.4/rumetr/scrapy/yandex.py | 0.402862 | 0.159021 | yandex.py | pypi |
# rumi
> Not the ones speaking the same language, but the ones sharing the same feeling understand each other. —Rumi
Rumi is a static site translation monitoring tool designed to support the localization (l10n) and internationalization (i18n) of documentation, and to facilitation the long-term maintenance of ... | /rumi-i18n-0.1.3a1.post1.tar.gz/rumi-i18n-0.1.3a1.post1/README.md | 0.426083 | 0.875148 | README.md | pypi |
# Basic Usage
## Overview
Rummage is designed to be easy to pick up. Its interface consists of three tabs: Search, Files, and Content. In the
**Search** tab, a user specifies where they want to search, what they want to search for, and optionally what they want
to replace it with. Search features can be tweaked wit... | /rummage-4.18.tar.gz/rummage-4.18/docs/src/markdown/usage.md | 0.71721 | 0.89783 | usage.md | pypi |
# Installation
## Requirements
Rummage, when installed via `pip`, will install all of your required dependencies, but there are a few optional
dependencies. If desired, you can install these dependencies manually, or install them automatically with
[`pip`](#installation_1).
Name | Details
---------... | /rummage-4.18.tar.gz/rummage-4.18/docs/src/markdown/installation.md | 0.487307 | 0.809728 | installation.md | pypi |
# Search Features
## Search Options
Rummage supports the default regular expression library ([Re][re]) that comes with Python and the 3rd party
[Regex][regex] library, and though the basic syntax and features are similar between the two, Regex provides many
additional features, some of which causes the syntax to devi... | /rummage-4.18.tar.gz/rummage-4.18/docs/src/markdown/search.md | 0.4856 | 0.905865 | search.md | pypi |
import copy
import logging
import itertools
import numpy
from scipy.sparse import csc_array
import pandas
import networkx as nx
from pyvis.network import Network
logger = logging.getLogger(__name__)
SIZE=500
NX_OPTIONS_DEFAULT = dict(
height=f'{SIZE}px', width=f'{SIZE}px', bgcolor='#05131e', font_color='white', n... | /rumor_view-0.0.3.tar.gz/rumor_view-0.0.3/rumor_view/view.py | 0.5794 | 0.439747 | view.py | pypi |
import argparse
from typing import List
from run_across_america import (
RunAcrossAmerica,
Team,
Activity,
Goal,
Member,
MemberStats,
User,
)
def main() -> None:
parser = argparse.ArgumentParser(
description="Lookup info from `Run Across America`."
)
subparsers = pars... | /run-across-america-0.1.0.tar.gz/run-across-america-0.1.0/run_across_america/cli.py | 0.612889 | 0.228307 | cli.py | pypi |
import os
import pathlib
class DirectoryHelper:
def __init__(self, top_dir, param_dict):
"""Small class for manipulating a standard directory structure for BRER
runs.
Parameters
----------
top_dir :
the path to the directory containing all the ensemble members.... | /run_brer-2.0.0b2-py3-none-any.whl/run_brer/directory_helper.py | 0.831896 | 0.391639 | directory_helper.py | pypi |
import json
import typing
from run_brer.metadata import MetaData
from run_brer.pair_data import PairData
class GeneralParams(MetaData):
"""Stores the parameters that are shared by all restraints in a single
simulation.
These include some of the "Voth" parameters: tau, A, tolerance
.. versionadded::... | /run_brer-2.0.0b2-py3-none-any.whl/run_brer/run_data.py | 0.825906 | 0.454896 | run_data.py | pypi |
import json
import warnings
from abc import ABC
class MetaData(ABC):
def __init__(self, name):
"""Construct metadata object. and give it a name.
Parameters
----------
name :
Give your MetaData class a descriptive name.
"""
self.__name = name
sel... | /run_brer-2.0.0b2-py3-none-any.whl/run_brer/metadata.py | 0.77223 | 0.264186 | metadata.py | pypi |
from pytorch_lightning import Trainer
from pytorch_lightning import loggers as pl_loggers
from pytorch_lightning.strategies import DDPStrategy
import argparse
import os
from run_crom.simulation import SimulationDataModule
from run_crom.cromnet import CROMnet
from run_crom.callbacks import *
def prepare_Trainer(arg... | /run_crom-1.0.0-py3-none-any.whl/run_crom/run_crom.py | 0.450601 | 0.390069 | run_crom.py | pypi |
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from pytorch_lightning.callbacks import LearningRateMonitor, Callback, TQDMProgressBar
from pytorch_lightning.utilities import rank_zero_info
from pytorch_lightning.utilities.rank_zero import rank_zero_only
import time
import warnings
from run_c... | /run_crom-1.0.0-py3-none-any.whl/run_crom/callbacks.py | 0.484624 | 0.547343 | callbacks.py | pypi |
import numpy as np
from numpy import ndarray
from typing import Optional
from skfem.mesh import Mesh, MeshTri, MeshQuad, MeshTet, MeshHex
from dataclasses import replace
MESH_TYPE_MAPPING = {
MeshTet: '504',
MeshHex: '808',
MeshTri: '303',
MeshQuad: '404',
}
BOUNDARY_TYPE_MAPPING = {
MeshTet: '... | /run_elmer-0.2.0.tar.gz/run_elmer-0.2.0/run_elmer/export.py | 0.866118 | 0.364184 | export.py | pypi |
import numpy as np
from .run import run
from skfem import Mesh, MeshTri, MeshTet, MeshQuad, MeshHex
def mesh(arg1=None, arg2=None):
if arg2 is None:
if isinstance(arg1, str):
return Mesh.load(arg1)
if isinstance(arg1, list) and isinstance(arg2, list):
arg1 = np.array(arg1, np.f... | /run_elmer-0.2.0.tar.gz/run_elmer-0.2.0/run_elmer/__init__.py | 0.551091 | 0.540136 | __init__.py | pypi |
import os
import tarfile
import tempfile
import json
from typing import Optional
import docker
import meshio
def get_container(image: str, tag: str, verbose: bool):
"""Pull and/or start a container that has `ElmerSolver`.
Parameters
----------
image
The container image name to use.
tag... | /run_elmer-0.2.0.tar.gz/run_elmer-0.2.0/run_elmer/runners/docker.py | 0.753739 | 0.189071 | docker.py | pypi |
import logging
import math
import signal
import sys
import timeit
import traceback
import memory_profiler
import mock
from six import StringIO
from run_lambda import context as context_module
def run_lambda(handle, event, context=None, timeout_in_seconds=None, patches=None):
"""
Run the Lambda function ``ha... | /run_lambda-0.1.7.2.tar.gz/run_lambda-0.1.7.2/run_lambda/call.py | 0.629775 | 0.183392 | call.py | pypi |
from dataclasses import astuple, dataclass
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
import yaml
from gql import gql
from run_logger import HasuraLogger
@dataclass
class NewParams:
config_params: Optional[dict]
sweep_params: Optional[dict]
load_params: Optional[dict]... | /run_logger-0.1.8-py3-none-any.whl/run_logger/main.py | 0.944715 | 0.328637 | main.py | pypi |
import time
from dataclasses import dataclass
from itertools import cycle, islice
from pathlib import Path
from typing import List, Optional
import numpy as np
from gql import Client as GQLClient
from gql import gql
from gql.transport.requests import RequestsHTTPTransport
from run_logger.logger import Logger
from run... | /run_logger-0.1.8-py3-none-any.whl/run_logger/hasura_logger.py | 0.864611 | 0.286482 | hasura_logger.py | pypi |
import re
import logging
import yaml
import marathon.cached as cached
log = logging.getLogger(__name__)
VAR_REGEX = re.compile("\${(.*?)}")
def get_marathon_config():
if not cached.marathon_config:
with open("run.yaml", "r") as f:
cached.marathon_config = yaml.safe_load(f)
return cach... | /run_marathon-0.3.2-py3-none-any.whl/marathon/utils.py | 0.400046 | 0.17441 | utils.py | pypi |
def run_regressors(df, target_column):
'''
df: Data (dataFrame)
target_column: Target variable/column name (str)
1. All regression models are ranked by RMSE (Root Mean Squared Error)
2. Categorical variables are dummy encoded for regression models except for Catboost and LightGBM.
'''
imp... | /run_models-0.0.4-py3-none-any.whl/run_models/__init__.py | 0.552298 | 0.696275 | __init__.py | pypi |
import configparser
import contextlib
import enum
import functools
import logging
import os
import random
import socket
import sys
import time
from os import path
from typing import Dict, Sequence, Optional, Union, KeysView
import grpc
from google.protobuf import duration_pb2
from grpc._channel import _InactiveRpcErro... | /run_once-0.4.2.tar.gz/run_once-0.4.2/run_once.py | 0.731059 | 0.179836 | run_once.py | pypi |
import ast
from collections import defaultdict
import csv
from datetime import datetime, timezone
from itertools import tee
from pathlib import Path
from typing import Callable, Iterable, TypeVar
import uuid
import pandas as pd
from run_one.util.config import Config
_T = TypeVar('_T')
uid = str(uuid.uuid1())
counte... | /run_one-1.0.17.tar.gz/run_one-1.0.17/run_one/util/util.py | 0.782953 | 0.302881 | util.py | pypi |
from builtins import range
import numpy as np
import pandas as pd
from sklearn import preprocessing
class Encode(object):
"""
Encode all columns where the values are categorical.
Parameters
----------
strategy: string, optional (default='oneHotEncoder')
available options: 'oneHotEncoder' ... | /run_regression-0.7.tar.gz/run_regression-0.7/run_regression/data_preprocessing/encode_categorical_data.py | 0.788543 | 0.558026 | encode_categorical_data.py | pypi |
from builtins import range
import numpy as np
import pandas as pd
class Outliers(object):
"""
remove all rows where the values of a certain column are within an specified
standard deviation from mean/median.
Parameters
----------
m: float, optional (default=3.0)
the outlier threshold w... | /run_regression-0.7.tar.gz/run_regression-0.7/run_regression/data_preprocessing/remove_outliers.py | 0.920153 | 0.657456 | remove_outliers.py | pypi |
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