id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
602 | import logging
import pathlib
import pickle
import typing as t
import numpy as np
from deepchecks.vision.utils.test_utils import hash_image
from deepchecks.vision.vision_data import VisionData
def mnist_generator(shuffle: bool = False, batch_size: int = 64, train: bool = True, n_samples: int = None,
... | Return MNIST VisionData, containing prediction produced by a simple fully connected model. Model and data are taken from https://www.tensorflow.org/tutorials/quickstart/beginner. Parameters ---------- train : bool, default : True Train or Test dataset with_predictions : bool, default : True Whether the returned VisonDa... |
603 | from enum import Enum
from collections import defaultdict
from typing import Any, Dict, List
from deepchecks.core.errors import DeepchecksValueError
The provided code snippet includes necessary dependencies for implementing the `calc_vision_properties` function. Write a Python function `def calc_vision_properties(raw_... | Calculate the image properties for a batch of images. Parameters ---------- raw_data : torch.Tensor Batch of images to transform to image properties. properties_list: List[Dict] , default: None A list of properties to calculate. Returns ------ batch_properties: dict[str, List] A dict of property name, property value pe... |
604 | from enum import Enum
from collections import defaultdict
from typing import Any, Dict, List
from deepchecks.core.errors import DeepchecksValueError
class DeepchecksValueError(DeepchecksBaseError):
"""Exception class that represent a fault parameter was passed to Deepchecks."""
pass
The provided code snippet... | Validate structure of measurements. |
605 | from typing import List
from deepchecks.core.errors import ModelValidationError
from deepchecks.vision.utils.vision_properties import PropertiesInputType
from deepchecks.vision.vision_data import TaskType
from deepchecks.vision.vision_data.batch_wrapper import BatchWrapper
class ModelValidationError(DeepchecksBaseErro... | Transform the data to the relevant format and calculate the properties on it. Intended for the checks PropertyLabelCorrelation and PropertyLabelCorrelationChange. |
606 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_get_samples_per_class_classification` function. Write a Python function `def _get_samples_per_class_classification(labels: Union[np.ndarray, List]) -> List[int]` to solve the fol... | Return a list containing the class per image in batch. |
607 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_get_samples_per_class_object_detection` function. Write a Python function `def _get_samples_per_class_object_detection(labels: List[np.ndarray]) -> List[List[int]]` to solve the ... | Return a list containing the classes in batch. |
608 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_get_bbox_area` function. Write a Python function `def _get_bbox_area(labels: List[np.ndarray]) -> List[List[int]]` to solve the following problem:
Return a list containing the ar... | Return a list containing the area of bboxes in batch. |
609 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_count_num_bboxes` function. Write a Python function `def _count_num_bboxes(labels: List[np.ndarray]) -> List[int]` to solve the following problem:
Return a list containing the nu... | Return a list containing the number of bboxes in per sample batch. |
610 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_get_samples_per_class_semantic_segmentation` function. Write a Python function `def _get_samples_per_class_semantic_segmentation(labels: List[np.ndarray]) -> List[List[int]]` to ... | Return a list containing the classes in batch. |
611 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_get_segment_area` function. Write a Python function `def _get_segment_area(labels: List[np.ndarray]) -> List[List[int]]` to solve the following problem:
Return a list containing ... | Return a list containing the area of segments in batch. |
612 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_count_classes_by_segment_in_image` function. Write a Python function `def _count_classes_by_segment_in_image(labels: List[np.ndarray]) -> List[int]` to solve the following proble... | Return a list containing the number of unique classes per image for semantic segmentation. |
613 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_get_predicted_classes_per_image_classification` function. Write a Python function `def _get_predicted_classes_per_image_classification(predictions: List[np.ndarray]) -> List[int]... | Return a list of the predicted class per image in the batch. |
614 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_get_predicted_classes_per_image_object_detection` function. Write a Python function `def _get_predicted_classes_per_image_object_detection(predictions: List[np.ndarray]) -> List[... | Return a list containing the classes in batch. |
615 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_get_predicted_bbox_area` function. Write a Python function `def _get_predicted_bbox_area(predictions: List[np.ndarray]) -> List[List[int]]` to solve the following problem:
Return... | Return a list of the predicted bbox sizes per image in the batch. |
616 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_get_predicted_classes_per_image_semantic_segmentation` function. Write a Python function `def _get_predicted_classes_per_image_semantic_segmentation(predictions: List[np.ndarray]... | Return a list containing the classes in batch. |
617 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_get_segment_pred_area` function. Write a Python function `def _get_segment_pred_area(predictions: List[np.ndarray]) -> List[List[int]]` to solve the following problem:
Return a l... | Return a list containing the area of segments in batch. |
618 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_count_pred_classes_by_segment_in_image` function. Write a Python function `def _count_pred_classes_by_segment_in_image(predictions: List[np.ndarray]) -> List[int]` to solve the f... | Return a list containing the number of unique classes per image for semantic segmentation. |
619 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `get_column_type` function. Write a Python function `def get_column_type(output_type)` to solve the following problem:
Get column type to use in drift functions.
Here is the funct... | Get column type to use in drift functions. |
620 | from typing import List, Sequence, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `properties_flatten` function. Write a Python function `def properties_flatten(in_list: Sequence) -> List` to solve the following problem:
Flatten a list of lists into a single lev... | Flatten a list of lists into a single level list. |
621 | import io
import typing as t
from numbers import Number
from pathlib import Path
import cv2
import numpy as np
import PIL.Image as pilimage
import PIL.ImageDraw as pildraw
import PIL.ImageOps as pilops
import plotly.graph_objects as go
from PIL import ImageColor, ImageFont
from deepchecks.core.errors import DeepchecksV... | Return an image to show as output of the display. Parameters ---------- image : np.ndarray The image to draw, must be a [H, W, C] 3D numpy array. label : 2-dim labels tensor for the image to draw on top of the image, shape depends on task type. task_type : TaskType The task type associated with the label. label_map: La... |
622 | import io
import typing as t
from numbers import Number
from pathlib import Path
import cv2
import numpy as np
import PIL.Image as pilimage
import PIL.ImageDraw as pildraw
import PIL.ImageOps as pilops
import plotly.graph_objects as go
from PIL import ImageColor, ImageFont
from deepchecks.core.errors import DeepchecksV... | Create heatmap graph object from given numpy array data. |
623 | import io
import typing as t
from numbers import Number
from pathlib import Path
import cv2
import numpy as np
import PIL.Image as pilimage
import PIL.ImageDraw as pildraw
import PIL.ImageOps as pilops
import plotly.graph_objects as go
from PIL import ImageColor, ImageFont
from deepchecks.core.errors import DeepchecksV... | For heatmap and grayscale images, need to add those properties which on Image exists automatically. |
624 | import io
import typing as t
from numbers import Number
from pathlib import Path
import cv2
import numpy as np
import PIL.Image as pilimage
import PIL.ImageDraw as pildraw
import PIL.ImageOps as pilops
import plotly.graph_objects as go
from PIL import ImageColor, ImageFont
from deepchecks.core.errors import DeepchecksV... | Return the cropped numpy array image by x, y, w, h coordinates (top left corner, width and height. |
625 | from collections import Counter
from typing import Iterable, List, Sequence, Tuple, Union
import numpy as np
from PIL.Image import Image
from deepchecks.vision.vision_data.utils import is_torch_object
def verify_bbox_format_notation(notation: str) -> Tuple[bool, List[str]]:
"""Verify and tokenize bbox format notati... | Convert batch of bboxes to the required format. Parameters ---------- batch : iterable of tuple like object with two items - image, list of bboxes batch of images and bboxes corresponding to them notation : str bboxes format notation Returns ------- List[np.ndarray] list of transformed bboxes |
626 | from typing import Dict, List, Tuple
import numpy as np
from cv2 import CV_64F, Laplacian
from skimage.color import rgb2gray
def _sizes(batch: List[np.ndarray]):
"""Return list of tuples of image height and width."""
return [get_size(img) for img in batch]
The provided code snippet includes necessary dependenc... | Return list of floats of image height to width ratio. |
627 | from typing import Dict, List, Tuple
import numpy as np
from cv2 import CV_64F, Laplacian
from skimage.color import rgb2gray
def get_size(img) -> Tuple[int, int]:
"""Get size of image as (height, width) tuple."""
return img.shape[0], img.shape[1]
The provided code snippet includes necessary dependencies for im... | Return list of integers of image areas (height multiplied by width). |
628 | from typing import Dict, List, Tuple
import numpy as np
from cv2 import CV_64F, Laplacian
from skimage.color import rgb2gray
def _is_grayscale(img):
return get_dimension(img) == 1
The provided code snippet includes necessary dependencies for implementing the `brightness` function. Write a Python function `def brig... | Calculate brightness on each image in the batch. |
629 | from typing import Dict, List, Tuple
import numpy as np
from cv2 import CV_64F, Laplacian
from skimage.color import rgb2gray
def _is_grayscale(img):
return get_dimension(img) == 1
The provided code snippet includes necessary dependencies for implementing the `rms_contrast` function. Write a Python function `def rm... | Return RMS contrast of image. |
630 | from typing import Dict, List, Tuple
import numpy as np
from cv2 import CV_64F, Laplacian
from skimage.color import rgb2gray
def _rgb_relative_intensity_mean(batch: List[np.ndarray]) -> List[Tuple[float, float, float]]:
"""Calculate normalized mean for each channel (rgb) in image.
The normalized mean of each ch... | Return the mean of the red channel relative intensity. |
631 | from typing import Dict, List, Tuple
import numpy as np
from cv2 import CV_64F, Laplacian
from skimage.color import rgb2gray
def _rgb_relative_intensity_mean(batch: List[np.ndarray]) -> List[Tuple[float, float, float]]:
"""Calculate normalized mean for each channel (rgb) in image.
The normalized mean of each ch... | Return the mean of the green channel relative intensity. |
632 | from typing import Dict, List, Tuple
import numpy as np
from cv2 import CV_64F, Laplacian
from skimage.color import rgb2gray
def _rgb_relative_intensity_mean(batch: List[np.ndarray]) -> List[Tuple[float, float, float]]:
"""Calculate normalized mean for each channel (rgb) in image.
The normalized mean of each ch... | Return the mean of the blue channel relative intensity. |
633 | from typing import Dict, List, Tuple
import numpy as np
from cv2 import CV_64F, Laplacian
from skimage.color import rgb2gray
def _is_grayscale(img):
return get_dimension(img) == 1
The provided code snippet includes necessary dependencies for implementing the `texture_level` function. Write a Python function `def t... | Calculate the sharpness of each image in the batch. |
634 | from typing import Dict, List, Tuple
import numpy as np
from cv2 import CV_64F, Laplacian
from skimage.color import rgb2gray
def _sizes_array(batch: List[np.ndarray]):
"""Return an array of height and width per image (Nx2)."""
return np.array(_sizes(batch))
def _rgb_relative_intensity_mean_array(batch: List[np.... | Speed up the calculation for the default image properties by sharing common actions. |
635 | import logging
import os
import time
import typing as t
from functools import lru_cache
import plotly.io as pio
import tqdm
from ipykernel.zmqshell import ZMQInteractiveShell
from IPython import get_ipython
from IPython.display import display
from IPython.terminal.interactiveshell import TerminalInteractiveShell
from t... | Check if we're in an interactive context (Notebook, GUI support) or terminal-based. Returns ------- bool True if we are in a notebook context, False otherwise |
636 | import logging
import os
import time
import typing as t
from functools import lru_cache
import plotly.io as pio
import tqdm
from ipykernel.zmqshell import ZMQInteractiveShell
from IPython import get_ipython
from IPython.display import display
from IPython.terminal.interactiveshell import TerminalInteractiveShell
from t... | Check if we're in a sphinx gallery env. Returns ------- bool True if we are in a sphinx gallery context, False otherwise |
637 | import logging
import os
import time
import typing as t
from functools import lru_cache
import plotly.io as pio
import tqdm
from ipykernel.zmqshell import ZMQInteractiveShell
from IPython import get_ipython
from IPython.display import display
from IPython.terminal.interactiveshell import TerminalInteractiveShell
from t... | Check whether we are in a terminal interactive shell or not. |
638 | import logging
import os
import time
import typing as t
from functools import lru_cache
import plotly.io as pio
import tqdm
from ipykernel.zmqshell import ZMQInteractiveShell
from IPython import get_ipython
from IPython.display import display
from IPython.terminal.interactiveshell import TerminalInteractiveShell
from t... | Check if the system can support GUI. Returns ------- bool True if we cannot support GUI, False otherwise |
639 | import logging
import os
import time
import typing as t
from functools import lru_cache
import plotly.io as pio
import tqdm
from ipykernel.zmqshell import ZMQInteractiveShell
from IPython import get_ipython
from IPython.display import display
from IPython.terminal.interactiveshell import TerminalInteractiveShell
from t... | Check if we are in the google colab environment. |
640 | import logging
import os
import time
import typing as t
from functools import lru_cache
import plotly.io as pio
import tqdm
from ipykernel.zmqshell import ZMQInteractiveShell
from IPython import get_ipython
from IPython.display import display
from IPython.terminal.interactiveshell import TerminalInteractiveShell
from t... | Check if we are in the kaggle environment. |
641 | import logging
import os
import time
import typing as t
from functools import lru_cache
import plotly.io as pio
import tqdm
from ipykernel.zmqshell import ZMQInteractiveShell
from IPython import get_ipython
from IPython.display import display
from IPython.terminal.interactiveshell import TerminalInteractiveShell
from t... | Check if we are in the databricks environment. |
642 | import logging
import os
import time
import typing as t
from functools import lru_cache
import plotly.io as pio
import tqdm
from ipykernel.zmqshell import ZMQInteractiveShell
from IPython import get_ipython
from IPython.display import display
from IPython.terminal.interactiveshell import TerminalInteractiveShell
from t... | Check if we are in the AWS Sagemaker environment. |
643 | import logging
import os
import time
import typing as t
from functools import lru_cache
import plotly.io as pio
import tqdm
from ipykernel.zmqshell import ZMQInteractiveShell
from IPython import get_ipython
from IPython.display import display
from IPython.terminal.interactiveshell import TerminalInteractiveShell
from t... | Create a progress bar instance. |
644 | import math
from collections import Counter
from typing import List, Union
import numpy as np
import pandas as pd
from scipy.stats import entropy
from deepchecks.utils.distribution.preprocessing import value_frequency
def theil_u_correlation(x: Union[List, np.ndarray, pd.Series], y: Union[List, np.ndarray, pd.Series]) ... | Calculate the symmetric Theil's U correlation of y to x. Parameters ---------- x: Union[List, np.ndarray, pd.Series] A sequence of a categorical variable values without nulls y: Union[List, np.ndarray, pd.Series] A sequence of a categorical variable values without nulls Returns ------- float Representing the symmetric ... |
645 | import math
from collections import Counter
from typing import List, Union
import numpy as np
import pandas as pd
from scipy.stats import entropy
from deepchecks.utils.distribution.preprocessing import value_frequency
The provided code snippet includes necessary dependencies for implementing the `correlation_ratio` fu... | Calculate the correlation ratio of numerical_variable to categorical_variable. Correlation ratio is a symmetric grouping based method that describe the level of correlation between a numeric variable and a categorical variable. returns a value in [0,1]. For more information see https://en.wikipedia.org/wiki/Correlation... |
646 | import contextlib
import typing as t
WANDB_INSTALLATION_CMD = 'pip install wandb'
from typing import List
The provided code snippet includes necessary dependencies for implementing the `wandb_run` function. Write a Python function `def wandb_run( project: t.Optional[str] = None, **kwargs ) -> t.Iterator[t.Any... | Create new one or use existing wandb run instance. Parameters ---------- project : Optional[str], default None project name **kwargs : additional parameters that will be passed to the 'wandb.init' Returns ------- Iterator[wandb.sdk.wandb_run.Run] |
647 | import copy
import warnings
from collections import Counter
from typing import List, Tuple, Union
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from deepchecks.core.errors import Dee... | Convert multi-label predictions to multi class format like predictions. |
648 | from numbers import Number
from typing import Dict, Optional, Tuple, Union
import numpy as np
import pandas as pd
from plotly.graph_objs import Figure
from plotly.subplots import make_subplots
from scipy.stats import chi2_contingency, wasserstein_distance
from deepchecks.core import ConditionCategory, ConditionResult
f... | Calculate drift score per column. Parameters ---------- train_column: pd.Series column from train dataset test_column: pd.Series same column from test dataset value_name: str title of the x axis, if plot_title is None then also the title of the whole plot. column_type: str type of column (either "numerical" or "categor... |
649 | from numbers import Number
from typing import Dict, Optional, Tuple, Union
import numpy as np
import pandas as pd
from plotly.graph_objs import Figure
from plotly.subplots import make_subplots
from scipy.stats import chi2_contingency, wasserstein_distance
from deepchecks.core import ConditionCategory, ConditionResult
f... | Create a condition function to be used in drift check's conditions. Parameters ---------- max_allowed_categorical_score: float Max value allowed for categorical drift max_allowed_numeric_score: float Max value allowed for numerical drift subject_single: str String that represents the subject being tested as single (fea... |
650 |
The provided code snippet includes necessary dependencies for implementing the `sort_dict` function. Write a Python function `def sort_dict(x: dict, reverse=True)` to solve the following problem:
Sort dictionary by values. Returns ------- Dict: sorted dictionary
Here is the function:
def sort_dict(x: dict, reverse=... | Sort dictionary by values. Returns ------- Dict: sorted dictionary |
651 | from typing import List
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from sklearn.metrics import confusion_matrix
from deepchecks import ConditionCategory, ConditionResult
from deepchecks.core import CheckResult
from deepchecks.core.errors import DeepchecksValueError
from deepchecks.utils.st... | Calculate confusion matrix based on predictions and true label values. |
652 | from typing import List
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from sklearn.metrics import confusion_matrix
from deepchecks import ConditionCategory, ConditionResult
from deepchecks.core import CheckResult
from deepchecks.core.errors import DeepchecksValueError
from deepchecks.utils.st... | Condition function that checks if the misclassified samples in the confusion matrix is below threshold. Parameters ---------- value: pd.DataFrame Dataframe containing the confusion matrix misclassified_samples_threshold: float Ratio of samples to be used for comparison in the condition (Value should be between 0 - 1 in... |
653 | import gc
import torch.cuda
The provided code snippet includes necessary dependencies for implementing the `empty_gpu` function. Write a Python function `def empty_gpu(device)` to solve the following problem:
Empty GPU or MPS memory and run garbage collector.
Here is the function:
def empty_gpu(device):
"""Empty... | Empty GPU or MPS memory and run garbage collector. |
654 | import typing as t
import jsonpickle
from deepchecks.core.check_result import BaseCheckResult
from deepchecks.core.suite import SuiteResult
from typing import List
class BaseCheckResult:
"""Generic class for any check output, contains some basic functions."""
check: Optional['BaseCheck']
header: Optional... | Convert a json object that was returned from one of our classes to_json. Parameters ---------- json_data: Union[str, Dict] Json data Returns ------- Union[BaseCheckResult, SuiteResult] A check output or a suite result object. |
655 | import typing as t
T = t.TypeVar("T")
from typing import List
The provided code snippet includes necessary dependencies for implementing the `to_ordional_enumeration` function. Write a Python function `def to_ordional_enumeration(data: t.List[T]) -> t.Dict[T, int]` to solve the following problem:
Enumarate each uniqu... | Enumarate each unique item. |
656 | from functools import lru_cache
from inspect import Signature, signature
from typing import Any, Callable, Dict
def extract_signature(obj: Callable[..., Any]) -> Signature:
"""Extract signature object from a callable instance.
Getting a callable signature is a heavy and not cheap op
therefore we are caching... | Return object __dict__ variables that was passed throw constructor (__init__ method). Parameters ---------- obj : object include_defaults : bool, default False wherether to include vars with default value or not Returns ------- Dict[Any, Any] subset of the obj __dict__ |
657 | from typing import Sequence, Tuple, Union
import numpy as np
from deepchecks.core.errors import DeepchecksValueError
EPS = 0.001
class DeepchecksValueError(DeepchecksBaseError):
"""Exception class that represent a fault parameter was passed to Deepchecks."""
pass
The provided code snippet includes necessary ... | Calculate outliers range on the data given using IQR. Parameters ---------- data: np.ndarray Data to calculate outliers range for. iqr_range: Tuple[int, int] Two percentiles which define the IQR range scale: float The scale to multiply the IQR range for the outliers' detection. When the percentiles values are the same ... |
658 | from typing import Sequence, Tuple, Union
import numpy as np
from deepchecks.core.errors import DeepchecksValueError
EPS = 0.001
class DeepchecksValueError(DeepchecksBaseError):
"""Exception class that represent a fault parameter was passed to Deepchecks."""
pass
The provided code snippet includes necessary ... | Calculate outliers range on the data given using sharp drop. Parameters ---------- data_percents : np.ndarray Counts of data to calculate outliers range for. The data is assumed to be sorted from the most common to the least common. sharp_drop_ratio : float , default 0.9 The sharp drop threshold to use for the outliers... |
659 | import numpy as np
def create_proba_result(predictions, classes):
def prediction_to_proba(y_pred):
proba = np.zeros(len(classes))
proba[classes.index(y_pred)] = 1
return proba
return np.apply_along_axis(prediction_to_proba, axis=1, arr=predictions.reshape(-1, 1)) | null |
660 | import base64
from deepchecks.utils.logger import get_logger
import sys
from io import StringIO
import pkg_resources
The provided code snippet includes necessary dependencies for implementing the `imagetag` function. Write a Python function `def imagetag(img: bytes) -> str` to solve the following problem:
Return html ... | Return html image tag with embedded image. |
661 | import base64
from deepchecks.utils.logger import get_logger
import sys
from io import StringIO
import pkg_resources
def get_logger() -> logging.Logger:
"""Retutn the deepchecks logger."""
return _logger
The provided code snippet includes necessary dependencies for implementing the `display_in_gui` function. ... | Display suite result or check result in a new python gui window. |
662 | import textwrap
import typing as t
from functools import wraps
from deepchecks.utils.logger import get_logger
INDENT = ' '
from typing import List
def indent(
text: t.Optional[str],
indents: int = 1,
prefix: bool = False
) -> str:
if not text or not isinstance(text, str):
return ''
iden... | null |
663 | import textwrap
import typing as t
from functools import wraps
from deepchecks.utils.logger import get_logger
F = t.TypeVar('F', bound=t.Callable[..., t.Any])
from typing import List
def get_logger() -> logging.Logger:
"""Retutn the deepchecks logger."""
return _logger
The provided code snippet includes nece... | Decorate a function with deprecated kwargs. Parameters ---------- old_arg_name : str Name of argument in function to deprecate new_arg_name : Optional[str], default None Name of preferred argument in function. |
664 | import textwrap
import typing as t
from functools import wraps
from deepchecks.utils.logger import get_logger
from typing import List
def get_routine_name(it: t.Any) -> str:
if hasattr(it, '__qualname__'):
return it.__qualname__
elif callable(it) or isinstance(it, type):
return it.__name__
... | null |
665 | from typing import Hashable, List
import numpy as np
import pandas as pd
from deepchecks.utils.array_math import fast_sum_by_row
def calculate_distance(vec1: np.array, vec2: np.array, range_per_feature: np.array) -> float:
"""Calculate distance between two vectors using Gower's method.
Parameters
----------... | Calculate distance matrix for a dataset using Gower's method. Gowers distance is a measurement for distance between two samples. It returns the average of their distances per feature. For numeric features it calculates the absolute distance divide by the range of the feature. For categorical features it is an indicator... |
666 | from typing import Hashable, List
import numpy as np
import pandas as pd
from deepchecks.utils.array_math import fast_sum_by_row
def _calculate_distances_to_sample(categorical_sample: np.ndarray, numeric_sample: np.ndarray, cat_data: np.ndarray,
numeric_data: np.ndarray, numeric_featu... | Calculate distance matrix for a dataset using Gower's method. Gowers distance is a measurement for distance between two samples. It returns the average of their distances per feature. For numeric features it calculates the absolute distance divide by the range of the feature. For categorical features it is an indicator... |
667 | import typing as t
import numpy as np
import pandas as pd
from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype, is_numeric_dtype
from deepchecks.core.errors import DeepchecksValueError
from deepchecks.utils.type_inference import infer_categorical_features
from deepchecks.utils.typing import Hashable
f... | Fill NaN values per column type. |
668 | import typing as t
import numpy as np
import pandas as pd
from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype, is_numeric_dtype
from deepchecks.core.errors import DeepchecksValueError
from deepchecks.utils.type_inference import infer_categorical_features
from deepchecks.utils.typing import Hashable
f... | Return a series that if the type is int converted to float. Parameters ---------- ser : pd.Series series to convert Raises ------ pd.Series the converted series |
669 | import typing as t
import numpy as np
import pandas as pd
from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype, is_numeric_dtype
from deepchecks.core.errors import DeepchecksValueError
from deepchecks.utils.type_inference import infer_categorical_features
from deepchecks.utils.typing import Hashable
f... | Compute pairwise correlation. Pairwise correlation is computed between columns of one DataFrame with columns of another DataFrame. Pandas' method corrwith only applies when both dataframes have the same column names, this generalized method applies to any two Dataframes with the same number of rows, regardless of the c... |
670 | import typing as t
import numpy as np
import pandas as pd
from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype, is_numeric_dtype
from deepchecks.core.errors import DeepchecksValueError
from deepchecks.utils.type_inference import infer_categorical_features
from deepchecks.utils.typing import Hashable
f... | Check if a column must be a float - meaning does it contain fractions. Parameters ---------- col : pd.Series The column to check. Returns ------- bool True if the column is float, False otherwise. |
671 | import typing as t
import numpy as np
import pandas as pd
from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype, is_numeric_dtype
from deepchecks.core.errors import DeepchecksValueError
from deepchecks.utils.type_inference import infer_categorical_features
from deepchecks.utils.typing import Hashable
f... | Cast categorical columns to the object dtype. |
672 | from typing import Union
from sklearn.pipeline import Pipeline
from deepchecks.utils.typing import BasicModel
class BasicModel(Protocol):
"""Traits of a model that are necessary for deepchecks."""
def predict(self, X) -> List[Hashable]:
"""Predict on given X."""
...
The provided code snippet ... | Return the model of a given Pipeline or itself if a BaseEstimator is given. Parameters ---------- model : Union[Pipeline, BasicModel] a Pipeline or a BasicModel Returns ------- Union[Pipeline, BasicModel] the inner BaseEstimator of the Pipeline or itself |
673 | import io
import itertools
import json
import os
import random
import re
import sys
import typing as t
from collections import defaultdict
from copy import copy
from datetime import datetime
from decimal import Decimal
from string import ascii_uppercase, digits
import numpy as np
import pandas as pd
from ipywidgets imp... | Return the docs summary if available. Parameters ---------- obj an object with_doc_link : bool , default: True if to add doc link Returns ------- str the object summary. |
674 | import io
import itertools
import json
import os
import random
import re
import sys
import typing as t
from collections import defaultdict
from copy import copy
from datetime import datetime
from decimal import Decimal
from string import ascii_uppercase, digits
import numpy as np
import pandas as pd
from ipywidgets imp... | Transform widget into html string. Parameters ---------- widget: Widget The widget to save as html. title: str The title of the html file. requirejs: bool , default: True If to save with all javascript dependencies connected : bool, default True whether to use CDN or not full_html: bool, default True whether to return ... |
675 | import io
import itertools
import json
import os
import random
import re
import sys
import typing as t
from collections import defaultdict
from copy import copy
from datetime import datetime
from decimal import Decimal
from string import ascii_uppercase, digits
import numpy as np
import pandas as pd
from ipywidgets imp... | Determine whether a pandas series is string type. |
676 | import io
import itertools
import json
import os
import random
import re
import sys
import typing as t
from collections import defaultdict
from copy import copy
from datetime import datetime
from decimal import Decimal
from string import ascii_uppercase, digits
import numpy as np
import pandas as pd
from ipywidgets imp... | Transform camel case indentifier into snake case. Parameters ---------- value : str string to transform Returns ------- str transformed value |
677 | import io
import itertools
import json
import os
import random
import re
import sys
import typing as t
from collections import defaultdict
from copy import copy
from datetime import datetime
from decimal import Decimal
from string import ascii_uppercase, digits
import numpy as np
import pandas as pd
from ipywidgets imp... | Create dict of base-form of the uniques to their values. function gets a set of strings, and returns a dictionary of shape Dict[str, Set] the key being the "base_form" (a clean version of the string), and the value being a set of all existing original values. This is done using the StringCategory class. |
678 | import io
import itertools
import json
import os
import random
import re
import sys
import typing as t
from collections import defaultdict
from copy import copy
from datetime import datetime
from decimal import Decimal
from string import ascii_uppercase, digits
import numpy as np
import pandas as pd
from ipywidgets imp... | Split string by another substring into a list. Like str.split(), but keeps the separator occurrences in the list. Parameters ---------- s : str the string to split separators : t.Union[str, t.Iterable[str]] the substring to split by Returns ------- t.List[str] list of substrings, including the separator occurrences in ... |
679 | import io
import itertools
import json
import os
import random
import re
import sys
import typing as t
from collections import defaultdict
from copy import copy
from datetime import datetime
from decimal import Decimal
from string import ascii_uppercase, digits
import numpy as np
import pandas as pd
from ipywidgets imp... | Split string by a list of substrings, each used once as a separator. Parameters ---------- s : str the string to split separators : t.Iterable[str] list of substrings to split by keep : bool , default: True whether to keep the separators in list as well. Default is True. Returns ------- t.List[str] list of substrings |
680 | import io
import itertools
import json
import os
import random
import re
import sys
import typing as t
from collections import defaultdict
from copy import copy
from datetime import datetime
from decimal import Decimal
from string import ascii_uppercase, digits
import numpy as np
import pandas as pd
from ipywidgets imp... | Format datetime object or timestamp value. Parameters ---------- value : Union[datetime, int, float] datetime (timestamp) to format Returns ------- str string representation of the provided value Raises ------ ValueError if unexpected value type was passed to the function |
681 | import typing as t
from deepchecks import __version__
links = {
'default': {
'supported-metrics-by-string': 'https://docs.deepchecks.com/stable/general/guides/metrics_guide.html#list-of-supported-strings', # pylint: disable=line-too-long # noqa
'supported-prediction-format': 'https://docs.deepcheck... | Get documentation link. Parameters ---------- name: str the name of the required link as appears in the links' dictionary. default_link: t.Optional[str], default: None default like to use if no link corresponding to name was found. template: t.Optional[str], default: None a string template in which to incorporate the l... |
682 | from typing import List, Optional
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksValueError
The provided code snippet includes necessary dependencies for implementing the `calculate_neg_mse_per_sample` function. Write a Python function `def calculate_neg_mse_per_sample(labels, pred... | Calculate negative mean squared error per sample. |
683 | from typing import List, Optional
import numpy as np
import pandas as pd
from deepchecks.core.errors import DeepchecksValueError
class DeepchecksValueError(DeepchecksBaseError):
"""Exception class that represent a fault parameter was passed to Deepchecks."""
pass
The provided code snippet includes necessary ... | Calculate negative cross entropy per sample. |
684 | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.cm import ScalarMappable
from matplotlib.colors import LinearSegmentedColormap
def shifted_color_map(cmap, start=0, midpoint=0.5, stop=1.0, name: str = 'shiftedcmap', transparent_from: float = None):
"""Offset the "center" of a colormap.
Parame... | Output a colorbar barchart using matplotlib. Parameters ---------- x: np.ndarray array containing x axis data. y: np.ndarray array containing y axis data. ylabel: str , default: Result Name of y axis xlabel : str , default: Features Name of x axis color_map : str , default: RdYlGn_r color_map name. See https://matplotl... |
685 | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.cm import ScalarMappable
from matplotlib.colors import LinearSegmentedColormap
The provided code snippet includes necessary dependencies for implementing the `hex_to_rgba` function. Write a Python function `def hex_to_rgba(h, alpha)` to solve the follo... | Convert color value in hex format to rgba format with alpha transparency. |
686 | import numpy as np
The provided code snippet includes necessary dependencies for implementing the `round_sig` function. Write a Python function `def round_sig(x: float, sig: int = 2)` to solve the following problem:
Round a number to a given number of significant digits.
Here is the function:
def round_sig(x: float,... | Round a number to a given number of significant digits. |
687 | from collections import defaultdict
from copy import deepcopy
from typing import Callable, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from sklearn.tree import _tree
from deepchecks.tabular.dataset import Dataset
from deepchecks.utils.strings import format_number
from deepchecks.utils.typing imp... | Merge two DeepChecksFilters into one, an intersection of both filters. |
688 | from collections import defaultdict
from copy import deepcopy
from typing import Callable, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from sklearn.tree import _tree
from deepchecks.tabular.dataset import Dataset
from deepchecks.utils.strings import format_number
from deepchecks.utils.typing imp... | Split given series into segments containing specified segment. Tries to create segments as balanced as possible in size. Parameters ---------- column : pd.Series Series to be partitioned. segment : List[float] Segment to be included in the partition. max_additional_segments : int, default = 4 Maximum number of segments... |
689 | from collections import defaultdict
from copy import deepcopy
from typing import Callable, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from sklearn.tree import _tree
from deepchecks.tabular.dataset import Dataset
from deepchecks.utils.strings import format_number
from deepchecks.utils.typing imp... | Split column into segments. For categorical we'll have a max of max_segments + 1, for the 'Others'. We take the largest categories which cumulative percent in data is equal/larger than `max_cat_proportions`. the rest will go to 'Others' even if less than max_segments. For numerical we split into maximum number of `max_... |
690 | from collections import defaultdict
from copy import deepcopy
from typing import Callable, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from sklearn.tree import _tree
from deepchecks.tabular.dataset import Dataset
from deepchecks.utils.strings import format_number
from deepchecks.utils.typing imp... | Extract the leaves from a sklearn tree and covert them into DeepchecksBaseFilter. The function goes over the tree from root to leaf and concatenates (by intersecting) the relevant filters along the way. The function returns a list in which each element is a DeepchecksFilter representing the path between the root to a d... |
691 | from typing import Any, Callable, Dict, Hashable, List, Optional, Tuple
import numpy as np
import pandas as pd
import plotly.express as px
from category_encoders import TargetEncoder
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer... | Calculate features contributing to model error. |
692 | from typing import Any, Callable, Dict, Hashable, List, Optional, Tuple
import numpy as np
import pandas as pd
import plotly.express as px
from category_encoders import TargetEncoder
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer... | Wrap dataframe with tabular.Dataset for error_model_display with no scorer. |
693 | import logging
import warnings
_logger = logging.getLogger('deepchecks')
_logger.addHandler(_stream_handler)
_logger.setLevel(logging.INFO)
The provided code snippet includes necessary dependencies for implementing the `set_verbosity` function. Write a Python function `def set_verbosity(level: int)` to solve the foll... | Set the deepchecks logger verbosity level. Same as doing logging.getLogger('deepchecks').setLevel(level). Control the package wide log level and the progrees bars - progress bars are level INFO. Examples -------- >>> import logging >>> import deepchecks >>> # will disable progress bars >>> deepchecks.set_verbosity(logg... |
694 | import random
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
import jax
import jax.numpy as jnp
import numpy as np
from braceexpand import braceexpand
from datasets import Dataset, load_dataset
from .model.text import TextNormalizer
def blank_caption_function(example, t... | null |
695 | import random
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
import jax
import jax.numpy as jnp
import numpy as np
from braceexpand import braceexpand
from datasets import Dataset, load_dataset
from .model.text import TextNormalizer
def normalize_function(example, text_... | null |
696 | import random
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
import jax
import jax.numpy as jnp
import numpy as np
from braceexpand import braceexpand
from datasets import Dataset, load_dataset
from .model.text import TextNormalizer
def filter_function(
example,
... | null |
697 | import random
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
import jax
import jax.numpy as jnp
import numpy as np
from braceexpand import braceexpand
from datasets import Dataset, load_dataset
from .model.text import TextNormalizer
def shift_tokens_right(input_ids: np.a... | null |
698 | import html
import math
import random
import re
from pathlib import Path
import emoji
import ftfy
from huggingface_hub import hf_hub_download
from unidecode import unidecode
person_token = [("a person", 282265), ("someone", 121194), ("somebody", 12219)]
The provided code snippet includes necessary dependencies for imp... | Used for CC12M |
699 | import html
import math
import random
import re
from pathlib import Path
import emoji
import ftfy
from huggingface_hub import hf_hub_download
from unidecode import unidecode
def fix_html(t):
# from OpenAI CLIP
return html.unescape(html.unescape(t)) | null |
700 | import html
import math
import random
import re
from pathlib import Path
import emoji
import ftfy
from huggingface_hub import hf_hub_download
from unidecode import unidecode
def replace_punctuation_with_commas(t):
return re.sub("[()[\].,|:;?!=+~\-\/{}]", ",", t) | null |
701 | import html
import math
import random
import re
from pathlib import Path
import emoji
import ftfy
from huggingface_hub import hf_hub_download
from unidecode import unidecode
def simplify_quotes(t):
return re.sub("""['"`]""", ' " ', t) | null |
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