id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
402 | import argparse
import speakeasy
import logging
def get_logger():
"""
Get the default logger for speakeasy
"""
logger = logging.getLogger('emu_exe')
if not logger.handlers:
sh = logging.StreamHandler()
logger.addHandler(sh)
logger.setLevel(logging.INFO)
return logger
The... | API hook that is installed to intercept MessageBox calls as an example Args: api_name: The full name including module of the hooked API func: the real emulated function provided by the framework Users can call this by passing in "params" whenever they choose params: the argments passed to the function |
403 | import argparse
import speakeasy
import logging
def get_logger():
"""
Get the default logger for speakeasy
"""
logger = logging.getLogger('emu_dll')
if not logger.handlers:
sh = logging.StreamHandler()
logger.addHandler(sh)
logger.setLevel(logging.INFO)
return logger
The... | API hook that is installed to intercept MessageBox calls as an example Args: api_name: The full name including module of the hooked API func: the real emulated function provided by the framework Users can call this by passing in "params" whenever they choose params: the argments passed to the function |
404 | import argparse
import speakeasy
import logging
def get_logger():
"""
Get the default logger for speakeasy
"""
logger = logging.getLogger('emu_dll')
if not logger.handlers:
sh = logging.StreamHandler()
logger.addHandler(sh)
logger.setLevel(logging.INFO)
return logger
The... | Hook that is called whenever memory is written to Args: access: memory access requested address: Memory address that is being written to size: Size of the data being written value: data that is being written to "address" |
405 | import pathlib
import re
import typing as t
from functools import lru_cache
import setuptools
VERSION_FILE = DEEPCHECKS_DIR / "VERSION"
def is_correct_version_string(value: str) -> bool:
def get_version_string() -> str:
if not (VERSION_FILE.exists() and VERSION_FILE.is_file()):
raise RuntimeError(
... | null |
406 | import pathlib
import re
import typing as t
from functools import lru_cache
import setuptools
DESCRIPTION_FILE = DEEPCHECKS_DIR / "DESCRIPTION.rst"
def get_description() -> t.Tuple[str, str]:
if not (DESCRIPTION_FILE.exists() and DESCRIPTION_FILE.is_file()):
raise RuntimeError(
"DESCRIPTION.rst... | null |
407 | import pathlib
import re
import typing as t
from functools import lru_cache
import setuptools
DEEPCHECKS_DIR = SETUP_MODULE.parent
def read_requirements_file(path):
dependencies = []
dependencies_links = []
for line in path.open("r").readlines():
if "-f" in line or "--find-links" in line:
... | null |
408 | from deepchecks.tabular.checks import DatasetsSizeComparison
import pandas as pd
from deepchecks.tabular import Dataset
from deepchecks.tabular.suites import train_test_validation
import pandas as pd
from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import DatasetsSizeComparison
from deepchecks.core... | null |
409 | from deepchecks.tabular.checks import DatasetsSizeComparison
import pandas as pd
from deepchecks.tabular import Dataset
from deepchecks.tabular.suites import train_test_validation
import pandas as pd
from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import DatasetsSizeComparison
from deepchecks.core... | null |
410 | import warnings
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import CalibrationScore
from deepchecks.tabular.datasets.classificatio... | null |
411 | import warnings
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import RocReport
def custom_formatwarning(msg, *args, **kwargs):
... | null |
412 | import numpy as np
import pandas as pd
from deepchecks.tabular.datasets.classification import adult
def insert_new_values_types(col: pd.Series, ratio_to_replace: float, values_list):
col = col.to_numpy().astype(object)
indices_to_replace = np.random.choice(range(len(col)), int(len(col) * ratio_to_replace), repl... | null |
413 | import numpy as np
import pandas as pd
from deepchecks.tabular.datasets.classification import adult
def insert_new_values_types(col: pd.Series, ratio_to_replace: float, values_list):
col = col.to_numpy().astype(object)
indices_to_replace = np.random.choice(range(len(col)), int(len(col) * ratio_to_replace), repl... | null |
414 | import numpy as np
import pandas as pd
from deepchecks.tabular.datasets.classification import adult
def insert_new_values_types(col: pd.Series, ratio_to_replace: float, values_list):
from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import MixedDataTypes
def insert_number_types(col: pd.Series, rati... | null |
415 | from deepchecks.vision.checks import PredictionDrift
from deepchecks.vision.datasets.classification.mnist_torch import load_dataset
from deepchecks.vision.checks import ClassPerformance
import numpy as np
import torch
np.random.seed(42)
from deepchecks.vision.datasets.detection.coco_torch import load_dataset
def gener... | null |
416 | from deepchecks.tabular import datasets
import pandas as pd
from deepchecks.tabular import Dataset
from deepchecks.tabular.suites import data_integrity
from deepchecks.tabular.checks import IsSingleValue, DataDuplicates
def add_dirty_data(df):
# change strings
df.loc[df[df['type'] == 'organic'].sample(frac=0.1... | null |
417 | import functools
import inspect
import os
import pathlib
import sys
import typing as t
import re
from subprocess import check_output
import plotly.io as pio
from plotly.io._sg_scraper import plotly_sg_scraper
import deepchecks
from deepchecks import vision
from deepchecks.utils.strings import to_snake_case
os.environ['... | null |
418 | import functools
import inspect
import os
import pathlib
import sys
import typing as t
import re
from subprocess import check_output
import plotly.io as pio
from plotly.io._sg_scraper import plotly_sg_scraper
import deepchecks
from deepchecks import vision
from deepchecks.utils.strings import to_snake_case
os.environ['... | null |
419 | import functools
import inspect
import os
import pathlib
import sys
import typing as t
import re
from subprocess import check_output
import plotly.io as pio
from plotly.io._sg_scraper import plotly_sg_scraper
import deepchecks
from deepchecks import vision
from deepchecks.utils.strings import to_snake_case
os.environ['... | null |
420 | import functools
import inspect
import os
import pathlib
import sys
import typing as t
import re
from subprocess import check_output
import plotly.io as pio
from plotly.io._sg_scraper import plotly_sg_scraper
import deepchecks
from deepchecks import vision
from deepchecks.utils.strings import to_snake_case
def get_chec... | null |
421 | from deepchecks.vision.datasets.segmentation.segmentation_coco import CocoSegmentationDataset, load_model
model = load_model(pretrained=True)
import torch
import torchvision.transforms.functional as F
from deepchecks.vision.vision_data import BatchOutputFormat
from torch.utils.data import DataLoader
from deepchecks.vis... | Return a batch of images, labels and predictions for a batch of data. The expected format is a dictionary with the following keys: 'images', 'labels' and 'predictions', each value is in the deepchecks format for the task. You can also use the BatchOutputFormat class to create the output. |
422 | import os
import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset
import albumentations as A
from albumentations.pytorch import ToTensorV2
from PIL import Image
import xml.etree.ElementTree as ET
import urllib.request
import zipfile
from functools import partial
from torch import nn
import torc... | Return a batch of images, labels and predictions in the deepchecks format. |
423 | import os
import urllib.request
import zipfile
import albumentations as A
import numpy as np
import PIL.Image
import torch
import torchvision
from albumentations.pytorch import ToTensorV2
from torch import nn
from torch.utils.data import DataLoader
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")... | Return a batch of images, labels and predictions for a batch of data. The expected format is a dictionary with the following keys: 'images', 'labels' and 'predictions', each value is in the deepchecks format for the task. You can also use the BatchOutputFormat class to create the output. |
424 | import typing as t
import numpy as np
from deepchecks.core.check_result import CheckResult
from deepchecks.core.checks import DatasetKind
from deepchecks.core.condition import ConditionCategory
from deepchecks.vision.base_checks import TrainTestCheck
from deepchecks.vision.context import Context
from deepchecks.vision.... | Initialize the color averages dicts. |
425 | import typing as t
import numpy as np
from deepchecks.core.check_result import CheckResult
from deepchecks.core.checks import DatasetKind
from deepchecks.core.condition import ConditionCategory
from deepchecks.vision.base_checks import TrainTestCheck
from deepchecks.vision.context import Context
from deepchecks.vision.... | Initialize the pixel counts dicts. |
426 | import typing as t
import numpy as np
from deepchecks.core.check_result import CheckResult
from deepchecks.core.checks import DatasetKind
from deepchecks.core.condition import ConditionCategory
from deepchecks.vision.base_checks import TrainTestCheck
from deepchecks.vision.context import Context
from deepchecks.vision.... | Sum the values of all the pixels in the batch, returning a numpy array with an entry per channel. |
427 | import typing as t
import numpy as np
from deepchecks.core.check_result import CheckResult
from deepchecks.core.checks import DatasetKind
from deepchecks.core.condition import ConditionCategory
from deepchecks.vision.base_checks import TrainTestCheck
from deepchecks.vision.context import Context
from deepchecks.vision.... | Count the pixels in the batch. |
428 | import contextlib
import os
import typing as t
from pathlib import Path
import albumentations as A
import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision.transforms.functional as F
from albumentations.pytorch.transforms import ToTensorV2
from PIL import Image, ImageDraw
from torch.utils.data... | Return a batch of images, labels and predictions for a batch of data. The expected format is a dictionary with the following keys: 'images', 'labels' and 'predictions', each value is in the deepchecks format for the task. You can also use the BatchOutputFormat class to create the output. |
429 | import contextlib
import os
import typing as t
from pathlib import Path
import albumentations as A
import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision.transforms.functional as F
from albumentations.pytorch.transforms import ToTensorV2
from PIL import Image, ImageDraw
from torch.utils.data... | Return a list containing the number of detections per sample in batch. |
430 | from datetime import datetime
import joblib
import pandas as pd
from airflow.decorators import dag, task, short_circuit_task
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
def model_training_dag():
@short_circuit_task
def validate_data(**context):
from deepchecks.tabular.suites import data_i... | null |
431 | from datetime import datetime, timedelta
import os
from airflow import DAG
from airflow.operators.python import PythonOperator
import joblib
import pandas as pd
from deepchecks.tabular.datasets.classification import adult
dir_path = "suite_results"
data_path = os.path.join(os.getcwd(), "data")
def load_adult_dataset(*... | null |
432 | from datetime import datetime, timedelta
import os
from airflow import DAG
from airflow.operators.python import PythonOperator
import joblib
import pandas as pd
from deepchecks.tabular.datasets.classification import adult
data_path = os.path.join(os.getcwd(), "data")
def load_fitted_model(pretrained=True):
"""Load... | null |
433 | from datetime import datetime, timedelta
import os
from airflow import DAG
from airflow.operators.python import PythonOperator
import joblib
import pandas as pd
from deepchecks.tabular.datasets.classification import adult
dir_path = "suite_results"
_target = 'income'
_CAT_FEATURES = ['workclass', 'education', 'marital... | null |
434 | from datetime import datetime, timedelta
import os
from airflow import DAG
from airflow.operators.python import PythonOperator
import joblib
import pandas as pd
from deepchecks.tabular.datasets.classification import adult
dir_path = "suite_results"
_target = 'income'
_CAT_FEATURES = ['workclass', 'education', 'marital... | null |
435 | import torch
from transformers import DetrForObjectDetection
from typing import Union, List, Iterable
import numpy as np
from deepchecks.vision import VisionData
import torchvision.transforms as T
class COCODETRData:
"""Class for loading the COCO dataset meant for the DETR ResNet50 model`.
Implement the necessa... | Generates a collate function that converts the batch to the deepchecks format, using the given model. |
436 | import inspect
from typing import Callable
from deepchecks.core import DatasetKind
from deepchecks.core.errors import DeepchecksBaseError
from deepchecks.tabular import Context, SingleDatasetCheck, checks
from deepchecks.tabular.datasets.classification import lending_club
from deepchecks.tabular.datasets.regression imp... | null |
437 | import inspect
from typing import Callable
from deepchecks.core import DatasetKind
from deepchecks.core.errors import DeepchecksBaseError
from deepchecks.tabular import Context, SingleDatasetCheck, checks
from deepchecks.tabular.datasets.classification import lending_club
from deepchecks.tabular.datasets.regression imp... | null |
438 | import inspect
from typing import Callable
from deepchecks.core import DatasetKind
from deepchecks.core.errors import DeepchecksBaseError
from deepchecks.tabular import Context, SingleDatasetCheck, checks
from deepchecks.tabular.datasets.classification import lending_club
from deepchecks.tabular.datasets.regression imp... | null |
439 | from typing import Callable
import torch
from deepchecks.core.errors import DeepchecksBaseError
from deepchecks.vision import SingleDatasetCheck, TrainTestCheck
from deepchecks.vision.datasets.classification import mnist_torch as mnist
from deepchecks.vision.datasets.detection import coco_torch as coco
from deepchecks.... | null |
440 | from typing import Callable
import torch
from deepchecks.core.errors import DeepchecksBaseError
from deepchecks.vision import SingleDatasetCheck, TrainTestCheck
from deepchecks.vision.datasets.classification import mnist_torch as mnist
from deepchecks.vision.datasets.detection import coco_torch as coco
from deepchecks.... | null |
441 | from typing import Callable
import torch
from deepchecks.core.errors import DeepchecksBaseError
from deepchecks.vision import SingleDatasetCheck, TrainTestCheck
from deepchecks.vision.datasets.classification import mnist_torch as mnist
from deepchecks.vision.datasets.detection import coco_torch as coco
from deepchecks.... | null |
442 | import warnings
import numpy as np
import pandas as pd
from pandas.api.types import (is_bool_dtype, is_categorical_dtype, is_datetime64_any_dtype, is_numeric_dtype,
is_object_dtype, is_string_dtype, is_timedelta64_dtype)
from sklearn import preprocessing, tree
from sklearn.metrics import f... | In case of MAE, calculates the baseline score for y and derives the PPS. |
443 | import warnings
import numpy as np
import pandas as pd
from pandas.api.types import (is_bool_dtype, is_categorical_dtype, is_datetime64_any_dtype, is_numeric_dtype,
is_object_dtype, is_string_dtype, is_timedelta64_dtype)
from sklearn import preprocessing, tree
from sklearn.metrics import f... | In case of F1, calculates the baseline score for y and derives the PPS. |
444 | import warnings
import numpy as np
import pandas as pd
from pandas.api.types import (is_bool_dtype, is_categorical_dtype, is_datetime64_any_dtype, is_numeric_dtype,
is_object_dtype, is_string_dtype, is_timedelta64_dtype)
from sklearn import preprocessing, tree
from sklearn.metrics import f... | Calculate the Predictive Power Score (PPS) matrix for all columns in the dataframe. Args: df : pandas.DataFrame The dataframe that contains the data output: str - potential values: "df", "list" Control the type of the output. Either return a pandas.DataFrame (df) or a list with the score dicts sorted: bool Whether or n... |
445 | from deepchecks.nlp import Suite
from deepchecks.nlp.checks import (ConflictingLabels, FrequentSubstrings, LabelDrift, MetadataSegmentsPerformance,
PredictionDrift, PropertyDrift, PropertyLabelCorrelation,
PropertySegmentsPerformance, SpecialCharacte... | Create a suite that includes many of the implemented checks, for a quick overview of your model and data. |
446 | import collections
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple, Type, cast
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksValueError, ValidationError
from deepchecks.nlp.task_type import TaskType, TTextLabel
from deepchecks.utils.log... | Validate tokenized text format. |
447 | import collections
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple, Type, cast
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksValueError, ValidationError
from deepchecks.nlp.task_type import TaskType, TTextLabel
from deepchecks.utils.log... | Validate text format. |
448 | import collections
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple, Type, cast
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksValueError, ValidationError
from deepchecks.nlp.task_type import TaskType, TTextLabel
from deepchecks.utils.log... | Validate and process label to accepted formats. |
449 | import collections
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple, Type, cast
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksValueError, ValidationError
from deepchecks.nlp.task_type import TaskType, TTextLabel
from deepchecks.utils.log... | Validate length of numpy array and type. |
450 | import collections
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple, Type, cast
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksValueError, ValidationError
from deepchecks.nlp.task_type import TaskType, TTextLabel
from deepchecks.utils.log... | Validate length of data table and calculate column types. |
451 | import collections
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple, Type, cast
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksValueError, ValidationError
from deepchecks.nlp.task_type import TaskType, TTextLabel
from deepchecks.utils.log... | Compare two dataframes and return a difference. |
452 | import collections
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple, Type, cast
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksValueError, ValidationError
from deepchecks.nlp.task_type import TaskType, TTextLabel
from deepchecks.utils.log... | null |
453 | import collections
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple, Type, cast
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksValueError, ValidationError
from deepchecks.nlp.task_type import TaskType, TTextLabel
from deepchecks.utils.log... | null |
454 | import collections
from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple, Type, cast
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksValueError, ValidationError
from deepchecks.nlp.task_type import TaskType, TTextLabel
from deepchecks.utils.log... | null |
455 | import typing as t
from collections.abc import Sequence
from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
from seqeval.scheme import Token
from sklearn.metrics import make_scorer
from deepchecks.core.errors import DeepchecksValueError
def get_scorer_dict(
suffix: bool = False,
... | Validate the given scorer list. |
456 | import typing as t
from collections.abc import Sequence
from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score
from seqeval.scheme import Token
from sklearn.metrics import make_scorer
from deepchecks.core.errors import DeepchecksValueError
DEFAULT_AVG_SCORER_NAMES = ('f1_macro', 'recall_mac... | Return the default scorers for token classification. |
457 | import typing as t
import numpy as np
from deepchecks.nlp.task_type import TaskType
from deepchecks.nlp.text_data import TextData
from deepchecks.tabular.metric_utils import DeepcheckScorer
from deepchecks.tabular.metric_utils.scorers import _transform_to_multi_label_format
from deepchecks.utils.metrics import is_label... | Initialize scorers and return all of them as deepchecks scorers. Parameters ---------- scorers : Mapping[str, Union[str, Callable]] dict of scorers names to scorer sklearn_name/function or a list without a name model_classes : t.Optional[t.List] possible classes output for model. None for regression tasks. observed_cla... |
458 | import typing as t
import numpy as np
from deepchecks.nlp.task_type import TaskType
from deepchecks.nlp.text_data import TextData
from deepchecks.tabular.metric_utils import DeepcheckScorer
from deepchecks.tabular.metric_utils.scorers import _transform_to_multi_label_format
from deepchecks.utils.metrics import is_label... | Infer using DeepcheckScorer on NLP TextData using an NLP context _DummyModel. |
459 | import pathlib
import typing as t
import warnings
import numpy as np
import pandas as pd
from deepchecks.nlp import TextData
from deepchecks.utils.builtin_datasets_utils import read_and_save_data
def load_all_data() -> t.Dict[str, t.Dict[str, t.Any]]:
"""Load a dict of all the text data, labels and predictions. One... | Load and return a precalculated predictions for the dataset. Returns ------- predictions : Tuple[List[str], List[str]] The IOB predictions of the tokens in the train and test datasets. |
460 | import pathlib
import typing as t
import warnings
import numpy as np
import pandas as pd
from deepchecks.nlp import TextData
from deepchecks.utils.builtin_datasets_utils import read_and_save_data
def load_all_data() -> t.Dict[str, t.Dict[str, t.Any]]:
"""Load a dict of all the text data, labels and predictions. One... | Load and returns the SCIERC Abstract NER dataset (token classification). Parameters ---------- data_format : str, default: 'TextData' Represent the format of the returned value. Can be 'TextData'|'Dict' 'TextData' will return the data as a TextData object 'Dict' will return the data as a dict of tokenized texts and IOB... |
461 | import pathlib
import typing as t
import warnings
import numpy as np
import pandas as pd
from deepchecks.nlp import TextData
from deepchecks.utils.builtin_datasets_utils import read_and_save_data
_PREDICTIONS_URL = 'https://ndownloader.figshare.com/files/39264461'
ASSETS_DIR = pathlib.Path(__file__).absolute().parent.p... | Load and return a precalculated predictions for the dataset. Parameters ---------- pred_format : str, default: 'predictions' Represent the format of the returned value. Can be 'predictions' or 'probabilities'. 'predictions' will return the predicted class for each sample. 'probabilities' will return the predicted proba... |
462 | import pathlib
import typing as t
import warnings
import numpy as np
import pandas as pd
from deepchecks.nlp import TextData
from deepchecks.utils.builtin_datasets_utils import read_and_save_data
def load_data(data_format: str = 'TextData', as_train_test: bool = True,
include_properties: bool = True, incl... | Load and return the test data, modified to have under annotated segment. |
463 | import pathlib
import typing as t
import warnings
import numpy as np
import pandas as pd
from deepchecks.nlp import TextData
from deepchecks.utils.builtin_datasets_utils import read_and_save_data
_SHORT_PROBAS_URL = 'https://figshare.com/ndownloader/files/40578866'
ASSETS_DIR = pathlib.Path(__file__).absolute().parent.... | Load and return a precalculated predictions for the dataset. Parameters ---------- pred_format : str, default: 'predictions' Represent the format of the returned value. Can be 'predictions' or 'probabilities'. 'predictions' will return the predicted class for each sample. 'probabilities' will return the predicted proba... |
464 | import pathlib
import typing as t
import warnings
import numpy as np
import pandas as pd
from deepchecks.nlp import TextData
from deepchecks.utils.builtin_datasets_utils import read_and_save_data
_FULL_DATA_URL = 'https://figshare.com/ndownloader/files/40564895'
_SHORT_DATA_URL = 'https://figshare.com/ndownloader/files... | Load and returns the Just Dance Comment Analysis dataset (multi-label classification). Parameters ---------- data_format : str, default: 'TextData' Represent the format of the returned value. Can be 'TextData'|'DataFrame' 'TextData' will return the data as a TextData object 'Dataframe' will return the data as a pandas ... |
465 | import contextlib
import pathlib
import typing as t
import warnings
from numbers import Number
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksNotSupportedError, DeepchecksValueError
from deepchecks.nlp.input_validations import (ColumnTypes, validate_length_and_calculate_column_types... | Disable deepchecks root logger. |
466 | import warnings
from typing import Hashable, List, Optional, Union
import numpy as np
from deepchecks.core.errors import DeepchecksNotSupportedError, DeepchecksProcessError
from deepchecks.nlp import TextData
from deepchecks.utils.dataframes import select_from_dataframe
def _warn_n_top_columns(data_type: str, n_top_fea... | Get relevant data table from the database. |
467 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return text length. |
468 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return average word length. |
469 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return percentage of special characters (as float between 0 and 1). |
470 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return percentage of punctuation (as float between 0 and 1). |
471 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return max word length. |
472 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return whether text is in English or not. |
473 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return float representing subjectivity. |
474 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return float representing toxicity. |
475 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return float representing fluency. |
476 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return float representing formality. |
477 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return a float representing lexical density. Lexical density is the percentage of unique words in a given text. For more information: https://en.wikipedia.org/wiki/Lexical_density |
478 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return the number of unique noun words in the text. |
479 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return a float representing the Flesch Reading-Ease score per text sample. In the Flesch reading-ease test, higher scores indicate material that is easier to read whereas lower numbers mark texts that are more difficult to read. For more information: https://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_test... |
480 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return the average words per sentence in the text. |
481 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return the number of unique URLS in the text. |
482 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return the number of URLS in the text. |
483 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return the number of unique email addresses in the text. |
484 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return the number of email addresses in the text. |
485 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return the number of unique syllables in the text. |
486 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return an integer representing time in seconds to read the text. The formula is based on Demberg & Keller, 2008 where it is assumed that reading a character taken 14.69 milliseconds on average. |
487 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return the number of sentences in the text. |
488 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Return a the average number of syllables per sentences per text sample. |
489 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Calculate properties on provided text samples. Parameters ---------- raw_text : Sequence[str] The text to calculate the properties for. include_properties : List[str], default None The properties to calculate. If None, all default properties will be calculated. Cannot be used together with ignore_properties parameter. ... |
490 | import pathlib
import pickle as pkl
import re
import string
import warnings
from collections import defaultdict
from importlib.util import find_spec
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import pandas as pd
import textblob
import torch.cuda
from nltk import co... | Get the names of all the available builtin properties. Returns ------- Dict[str, str] A dictionary with the property name as key and the property's type as value. |
491 | from typing import List, Sequence
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
from deepchecks.nlp import TextData
from deepchecks.nlp.task_type import TaskType
from deepchecks.nlp.utils.text import break_to_lines_and_trim
from deepchecks.nlp.utils.text_properties im... | Create a distribution / bar graph of the data and its outliers. Parameters ---------- dist : Sequence The distribution of the data. data : Sequence[str] The data (used to give samples of it in hover). lower_limit : float The lower limit of the common part of the data (under it is an outlier). upper_limit : float The up... |
492 | from typing import List, Sequence
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `call_open_ai_completion_api` function. Write a Python function `def call_open_ai_completion_api(inputs: Sequence[str], max_tokens=200, batch_size=20, # api limit of 20 requests ... | Call the open ai completion api with the given inputs batch by batch. Parameters ---------- inputs : Sequence[str] The inputs to send to the api. max_tokens : int, default 200 The maximum number of tokens to return for each input. batch_size : int, default 20 The number of inputs to send in each batch. model : str, def... |
493 | import re
import sys
import warnings
from itertools import islice
from typing import Optional
import numpy as np
from tqdm import tqdm
EMBEDDING_MODEL = 'text-embedding-ada-002'
EMBEDDING_DIM = 1536
EMBEDDING_CTX_LENGTH = 8191
EMBEDDING_ENCODING = 'cl100k_base'
def encode_text(text, encoding_name):
"""Encode tokens... | Get the built-in embeddings for the dataset. Parameters ---------- text : np.array The text to get embeddings for. model : str, default 'miniLM' The type of embeddings to return. Can be either 'miniLM' or 'open_ai'. For 'open_ai' option, the model used is 'text-embedding-ada-002' and requires to first set an open ai ap... |
494 | import re
import string
import typing as t
import unicodedata
import warnings
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
def normalize_text(
text_sample: str,
*,
ignore_case: bool = True,
remove_punct: bool = True,
normalize_uni: bool = True,
remove_sto... | Normalize given sequence of text samples. |
495 | import re
import string
import typing as t
import unicodedata
import warnings
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
def hash_text(text: str) -> int:
"""Hash a text sample."""
assert isinstance(text, str)
return hash(text)
from typing import List
The provided... | Hash a sequence of text samples. |
496 | from typing import List, Optional
import numpy as np
import pandas as pd
import plotly.graph_objs as go
from plotly.subplots import make_subplots
from deepchecks.nlp.task_type import TaskType, TTextLabel
from deepchecks.nlp.utils.text import break_to_lines_and_trim
from deepchecks.nlp.utils.text_properties import TEXT_... | Return a plotly figure instance. Parameters ---------- properties: pd.DataFrame The DataFrame consisting of the text properties data. If no prooperties are there, you can pass an empty DataFrame as well. n_samples: int The total number of samples present in the TextData object. max_num_labels_to_show : int The threshol... |
497 | import warnings
from typing import List, Tuple
import numpy as np
import pandas as pd
from seqeval.metrics.sequence_labeling import get_entities
from sklearn.base import BaseEstimator
from deepchecks.core.errors import DeepchecksValueError
from deepchecks.nlp.task_type import TaskType
class DeepchecksValueError(Deepch... | Infer the observed labels from the given datasets and predictions. Parameters ---------- train_dataset : Union[TextData, None], default None TextData object, representing data an estimator was fitted on test_dataset : Union[TextData, None], default None TextData object, representing data an estimator predicts on model ... |
498 | import warnings
import numpy as np
import pandas as pd
from numba import NumbaDeprecationWarning
from sklearn.decomposition import PCA
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from deepchecks.core.check_utils.m... | Calculate multivariable drift on embeddings. |
499 | import io
import traceback
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union, cast
import jsonpickle
import jsonpickle.ext.pandas as jsonpickle_pd
import pandas as pd
from ipywidgets import Widget
from pandas.io.formats.style import Styler
from plotly.basedatatypes import BaseFigure
from deep... | null |
500 | import abc
import io
import json
import pathlib
import warnings
from collections import OrderedDict
from typing import Any, Dict, List, Optional, Sequence, Set, Tuple, Type, Union, cast
import jsonpickle
from bs4 import BeautifulSoup
from ipywidgets import Widget
from typing_extensions import Self, TypedDict
from deepc... | Sort sequence of 'CheckResult' instances. Returns ------- List[check_types.CheckResult] |
501 | import typing as t
import numpy as np
import pandas as pd
from deepchecks.core import ConditionResult
from deepchecks.core.condition import ConditionCategory
from deepchecks.core.errors import DeepchecksValueError
from deepchecks.utils.dict_funcs import get_dict_entry_by_value
from deepchecks.utils.strings import forma... | Add condition - test metric scores are greater than the threshold. Parameters ---------- min_score : float Minimum score to pass the check. Returns ------- Callable the condition function |
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