Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort

Last update: Jan 04, 2023

Overview

Test Suites for Validating ML Models & Data

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Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort. This includes checks related to various types of issues, such as model performance, data integrity, distribution mismatches, and more.

Installation

Using pip

pip install deepchecks -U --user

Using conda

conda install -c deepchecks deepchecks

Try it Out!

Head over to the Quickstart Notebook and choose the or the to have it up and running, and to then apply it on your own data and models.

Usage Examples

Running a Suite

A Suite runs a collection of Checks with optional Conditions added to them.

To see it in action, we recommend trying it out.

To run an existing suite all you need to do is to import the suite and run it with the required (suite-dependent) input parameters. The list of all built-in suites can be found here.

Let's take the "iris" dataset as an example

from sklearn.datasets import load_iris
iris_df = load_iris(return_X_y=False, as_frame=True)['frame']

and run the single_dataset_integrity suite, which requires only a single Dataset and can run also directly on a pd.DataFrame, like in the following example.

from deepchecks.suites import single_dataset_integrity
suite = single_dataset_integrity()
suite.run(iris_df)

Will result in printing the suite's output, that starts with a summary of the check conditions

Single Dataset Integrity Suite

The suite is composed of various checks such as: Mixed Data Types, Is Single Value, String Mismatch, etc...
Each check may contain conditions (which will result in pass / fail / warning, represented by ✓ / ✖ / ! ), as well as other outputs such as plots or tables.
Suites, checks and conditions can all be modified (see the Create a Custom Suite tutorial).

Conditions Summary

Status Check Condition More Info

✖
Single Value in Column - Test Dataset Does not contain only a single value for all columns Columns containing a single value: ['target']

!
Data Duplicates - Test Dataset Duplicate data is not greater than 0% Found 2.00% duplicate data

✓
Mixed Nulls - Test Dataset Not more than 1 different null types for all columns

✓
Mixed Data Types - Test Dataset Rare data types in all columns are either more than 10.00% or less than 1.00% of the data

✓
String Mismatch - Test Dataset No string variants for all columns

✓
String Length Out Of Bounds - Test Dataset Ratio of outliers not greater than 0% string length outliers for all columns

✓
Special Characters - Test Dataset Ratio of entirely special character samples not greater than 0.10% for all columns

Status	Check	Condition	More Info
✖	Single Value in Column - Test Dataset	Does not contain only a single value for all columns	Columns containing a single value: ['target']
!	Data Duplicates - Test Dataset	Duplicate data is not greater than 0%	Found 2.00% duplicate data
✓	Mixed Nulls - Test Dataset	Not more than 1 different null types for all columns
✓	Mixed Data Types - Test Dataset	Rare data types in all columns are either more than 10.00% or less than 1.00% of the data
✓	String Mismatch - Test Dataset	No string variants for all columns
✓	String Length Out Of Bounds - Test Dataset	Ratio of outliers not greater than 0% string length outliers for all columns
✓	Special Characters - Test Dataset	Ratio of entirely special character samples not greater than 0.10% for all columns

Followed by the visual outputs of all of the checks that are in that suite, that isn't appended here for brevity. In the following section you can see an example of how the output of a single check may look.

Running a Check

To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference.

from deepchecks.checks import TrainTestFeatureDrift
import pandas as pd

train_df = pd.read_csv('train_data.csv')
train_df = pd.read_csv('test_data.csv')
# Initialize and run desired check
TrainTestFeatureDrift().run(train_data, test_data)

Will produce output of the type:

Train Test Drift

The Drift score is a measure for the difference between two distributions, in this check - the test and train distributions.
The check shows the drift score and distributions for the features, sorted by feature importance and showing only the top 5 features, according to feature importance. If available, the plot titles also show the feature importance (FI) rank.

Key Concepts

Check

Each check enables you to inspect a specific aspect of your data and models. They are the basic building block of the deepchecks package, covering all kinds of common issues, such as:

Model Error Analysis
Label Ambiguity
Data Sample Leakage

and many more checks.

Each check can have two types of results:

A visual result meant for display (e.g. a figure or a table).
A return value that can be used for validating the expected check results (validations are typically done by adding a "condition" to the check, as explained below).

Condition

A condition is a function that can be added to a Check, which returns a pass ✓, fail ✖ or warning ! result, intended for validating the Check's return value. An example for adding a condition would be:

from deepchecks.checks import BoostingOverfit
BoostingOverfit().add_condition_test_score_percent_decline_not_greater_than(threshold=0.05)

which will return a check failure when running it if there is a difference of more than 5% between the best score achieved on the test set during the boosting iterations and the score achieved in the last iteration (the model's "original" score on the test set).

Suite

An ordered collection of checks, that can have conditions added to them. The Suite enables displaying a concluding report for all of the Checks that ran. See the list of predefined existing suites to learn more about the suites you can work with directly and also to see a code example demonstrating how to build your own custom suite. The existing suites include default conditions added for most of the checks. You can edit the preconfigured suites or build a suite of your own with a collection of checks and optional conditions.

What Do You Need in Order to Start Validating?

Environment

The deepchecks package installed
JupyterLab or Jupyter Notebook

Data / Model

Depending on your phase and what you wish to validate, you'll need a subset of the following:

Raw data (before pre-processing such as OHE, string processing, etc.), with optional labels
The model's training data with labels
Test data (which the model isn't exposed to) with labels
A model compatible with scikit-learn API that you wish to validate (e.g. RandomForest, XGBoost)

Deepchecks validation accompanies you from the initial phase when you have only raw data, through the data splits, and to the final stage of having a trained model that you wish to evaluate. Accordingly, each phase requires different assets for the validation.

See more about typical usage scenarios and the built-in suites in the docs.

Documentation

https://docs.deepchecks.com/ - HTML documentation (stable release)
https://docs.deepchecks.com/en/latest - HTML documentation (latest release)

Community

Join our Slack Community to connect with the maintainers and follow users and interesting discussions
Post a Github Issue to suggest improvements, open an issue, or share feedback.

Comments

[FEAT] Tabular fairness suite
Describe the solution you'd like

(As discussed briefly with @noamzbr.)

A suite of checks for assessing the fairness of classification and regression models. Proposed fairness checks:

Performance Disparity: Check that there are no major performance disparities across a set of pre-defined subgroups (e.g., across sex and race). For certain choices of performance measures, this is equivalent to the equalized odds fairness metric in classification.

Demographic Parity: Check that there are no major differences in the response distribution across subgroups.

Representativity: Check that pre-defined subgroups are sufficiently well represented in the data (i.e., make sure that no subgroup of interest is underrepresented in the data).

These checks should implement functionality comparable to what is other toolkits such as Microsoft's Responsible AI Fairness Dashboard. It can also go much beyond that by:

Evaluating fairness at the intersection of multiple subgroups and allowing for control variables.

Providing high-quality visualizations for evaluating fairness across many subgroups and control variables.

Implementing a broad set of fairness metrics.

Accounting for sampling uncertainty when comparing performance metrics across subgroups.

I think (4) is out of score of deepchecks, but (1)-(3) are achievable.

Describe alternatives you've considered

The SegmentPerformance check looks at performance within subgroups. However, it is now deprecated and it is limited to looking at only two subgroups. Furthermore, it does not provide default conditions that are suited to fairness evaluation. Same problem with WeakSegmentsPerformance which isn't well-suited for fairness analysis.

Additional context

What could this look like?

class PerformanceDisparityReport(SingleDatasetCheck): # List of protected attributes (e.g., sex and age) target_features: List[Hashable] # Optionally, a single variable to use as a control (e.g., education level) control_feature: Hashable = None # Scoring funtions scorers: Union[Mapping[str, Union[str, Callable]], List[str]] = None # For classification, whether to report scores as class averages. class_avg: bool = True # Each feature (in target_features and control_feature) is to be discretized in at most `max_segments` groups. max_segments: int = 10 # Smallest group size on which a performance metric is going to be computed min_group_size: int = 5 # Visual attributes to use for sensitive variables. Defaults to ["x", "color", "facet_col"] facets: List[str] = None # Visual attribute to use for performance dis-aggregated by class. Defaults to "facet_col". class_facet: str = "facet_col"

How can we visualize performance at the intersection of many subgroups?

I would recommend using a strip plot to visualize scores across subgroups. An histogram of subgroup size can be overlayed to provide context.

For example, using the adult dataset and "sex", "race" and "age" as sensitive features, we can visualize performance by class and subgroup. Only the first two subgroups (sex and race) will be assigned distinct visual elements (on the x-axis and color), but there will be one point for each corresponding age group as well.

from deepchecks.tabular.checks.model_evaluation.performance_disparity_report import PerformanceDisparityReport from deepchecks.tabular.datasets.classification import adult check = PerformanceDisparityReport(target_features=["sex", "race", "age"], max_segments=3, scorers=["f1_per_class"]) dataset = adult.load_data()[0] model = adult.load_fitted_model() check.run(dataset=dataset, model=model)

What conditions could be added?

We can threshold the difference between min and max performance across subgroups, or the ratio between min and max performance across subgroups. We can also put a lower boundary on performance for all subgroups.

Why only allow for a single control feature?

Multivariate control variables can be reduced to a univariate variable by regressing on the outcome. I think this is sufficient for fairness analyses and it keeps things simple enough.

What else could be considered?

What I've proposed so far are group fairness checks (see https://en.wikipedia.org/wiki/Fairness_(machine_learning)). Individual or counterfactual fairness checks could also be considered. Additionally, more general definitions of group fairness based on independence and conditional independence between variables could be assessed using statistics such as a $\chi^2$ divergence, KL-divergence, or conditional entropy. It's less easily interpretable than what I proposed, but it's more principled and it's amenable to formal statistical tests.
ds tabular
opened by OlivierBinette 29
[FEAT] Add option to save as md files - cml integration

Background cml is a ci tool which for our purposes sends md files as comments in PRs. (its features as a runner is irrelevant).

Is your feature request related to a problem? Please describe. We can currently save checks and suites as html, we need to add an option to save as md files too. as for now we can send just the condition table easily but it is not really helpful.

Implementation Details Mostly we need to remove any style blocks in the html and save the plots as an image (this part might make us use some git giles api).
feature

opened by JKL98ISR 23

[issue-1077] Display logic refactoring

resolves #1077 resolves #627

As was said in the issue description currently, the code responsible for the check&suite displays is not clean and very hard to maintain. From my point of view in order to clean it up we need to do next things:

Decouple display logic from CheckResult and SuiteResult
Decouple different display approaches from each other (html, widget)

I suggest the next set of the abstractions in order to achieve this


# Serializer - class that implements serialization logic for the value of type `T` into some particular format 

class Serializer(t.Protocol[T]):
    value: T
    def __init__(self, value: T, **kwargs):
        self.value = value

class HtmlSerializer(Serializer[T]):
    def serialize(self, **kwargs) -> str:
        ...

class JsonSerializer(Serializer[T]):
    def serialize(self, **kwargs) -> t.Dict[t.Any, t.Any]:
        ...

class WidgetSerializer(Serializer[T]):
    def serialize(self, **kwargs) -> Widget:
        ...

# ============

# Presentation - a facade class for a set of serializers, it should not contain 
# implementation of a serialization logic, its main task is to instantiate instances 
# of serializers and to provide an interface to them. 

class Presentation(t.Protocol[T]):
    value: T

    def __init__(self, value: T, **kwargs):
        self.value = value

    def to_json(self, **kwargs) -> t.Dict[t.Any, t.Any]:
        raise NotImplementedError()

    def to_html(self, **kwargs) -> str:
        raise NotImplementedError()

    def to_widget(self, **kwargs) -> Widget:
        raise NotImplementedError()

# ============

# It is also totally logical to add a `Deserializer` type that will contain 
# logic opposite to the logic  `Serializer` but currently I am not sure about this idea

For more details take a look at the code.

Note: currently this PR is in the draft state but it already contains basic logic for CheckResult and SuiteResult

feature

opened by yromanyshyn 16

Cml markdown
Reference Issues/PRs

Resolve #1290

What does this implement/fix? Explain your changes.

This PR implements saving a report as a GitHub- or GitLab- compliant markdown file for ease of use in CI Pipelines. The use case is to use the command:

cml comment create report_made_by_deepchecks.md --publish --publish-native

And have a nice summary being posted in a PR made by the data scientist that shows a summary table of the checks' conditions with an attachment to the full html report (and only this attachment includes nice plotly visualizations).

Any other comments?

As of the opening of this PR, the commit dd20560 is meant to kickstart the discussion around the code. Namely,

whether I should write a specific Serializer for this feature

tips for writing the test of this feature is greatly appreciated.

TODO

[ ] ~~zip suite results .html for GitHub, as GitHub does not accept html file attachments.~~

CML is supposed to handle that.

[x] write documentation under "Integrations" in a new CML page (under docs/source/user-guide/integrations)

[x] make pylint

feature
opened by bgalvao 13
[issue-1365] plotly figures width/height
resolves #1365

I have experimented with height/width options, and noticed that:

yup, it is Indeed responsive to the window/screen size

it looks like by default (without explicitly provided width/height) it uses something like - width: 100%; height: max-content;. Added screenshots below to illustrate this.

IMO, in some cases more compact figure (with explicit width/height) could look better/ It all depends on the situation

also, should I attach screenshots of the affected checks?

-- with width=800, height=500 --

-- without explicit width/height --
opened by yromanyshyn 13
[BUG] Some of the links in README file not working or redirecting properly
Describe the bug Some of the links in the README file not working or redirecting properly.

Below link does not redirect to the desired destination:

Below link does not work:

Expected behavior

Link should have redirected to the When Should You Use Deepchecks? section of the Getting Started page of deepchecks documentation. Instead it just redirects to the top of the Getting Started page.

Link should redirect to either Which Types of Checks Exist? section of the Getting Started page of deep checks documentation or the README should be changed to point to Tabular Checks and Vision Checks page of deepchecks documentation.

documentation bug
opened by kishore-s-15 11
[MLC-153] new dataset column type
There are two things that still need to be done (TODO):

We need to verify all checks, that there is no function/method that tries to perform string operation over a column name without first checking that it is actually a string. Probably it is a good idea to add a test case to those checks for which this is actual.

A lot of routines in our code-base use the next dtype for the columns list argument - Union[Hashable, Iterable[Hashable]]. For example LabelAmbiguity check.

It raises a question - What if the provided value is hashable but also iterable? like tuple for example I think it is better to change those routines signature just to Iterable[Hashable]

@noamzbr @matanper @ItayGabbay what do you think?
opened by yromanyshyn 11
Implement PerformanceDisparityReport
Reference Issues/PRs

Implements PerformanceDisparityReport (see #2208).

Any other comments?

See https://github.com/OlivierBinette/PerformanceDisparityReport-test/blob/main/test_plots.ipynb for plot tests

A few potential features have not yet been implemented:

Alternative choice of baseline score (best possible score or best subgroup score).

Alternative ranking by relative score difference

Allowing lists of protected_feature and control_feature

Allowing multiple scorers (there could be a tab for each scorer).

Note: I wasn't able to run make test or make tox locally (I get the error "Could not find a version that satisfies the requirement torch==1.10.2+cpu"). I'm opening the PR to trigger CI/CD and there will probable be a few issues to fix with the tests.
feature
opened by OlivierBinette 10
fix(docs): Minor grammar suggestions

Reference Issues/PRs

N/A

What does this implement/fix? Explain your changes.

Makes some suggestions to improve grammar in the documentation

Any other comments?

I think Deepchecks is super cool and am just trying to help, even if just a wee little bit😁

Thanks for contributing a pull request! Please ensure you have taken a look at the contribution guidelines: https://github.com/deepchecks/deepchecks/blob/main/CONTRIBUTING.rst
documentation feature

opened by jhwohlgemuth 10
QST: Question regarding documentation
Research

[X] I have searched the [deepchecks] tag on StackOverflow for similar questions.

[X] I have asked my usage related question on StackOverflow.

Link to question on StackOverflow

https://docs.deepchecks.com/en/stable/user-guide/when_should_you_use.html#when-should-you-use-new-data

Question about deepchecks

Hi, Based on your documentation page => https://docs.deepchecks.com/en/stable/user-guide/when_should_you_use.html#when-should-you-use-new-data

There are 4 phases : 1)New Data: Single Dataset Validation -> For these purposes you can use the single_dataset_integrity() 2)After Splitting the Data: Train-Test Validation -> For these purposes you can use the -> train_test_validation() 3)After Training a Model: Analysis & Validation ->model_evaluation() 4)General Overview: Full Suite -> full_suite()

The question where can I see the examples of using this 4 options ?

Thanks, Boris
question
opened by borisRa 10
[BUG] Format precentage in new category output

Describe the bug

See "percent of new category in sample"

To Reproduce Use to following dataset: https://github.com/AllonHammer/CPI_HRNN/blob/master/resources/cpi_us_dataset.csv Dataset definition: ds = Dataset(df, datetime_name='Date', label='Price', cat_features=['Category_id', 'Category', 'Indent', 'Parent', 'Parent_ID']) Split first 40000 samples to be train, and the rest to be test. And run the relevant checks (or the train-test-validation suite)
bug

opened by shir22 9
[BUG] fix progress bar / remove it

Describe the bug

To reproduce: run the following `from deepchecks.vision.datasets.detection import mask from deepchecks.vision.suites import data_integrity, train_test_validation, model_evaluation, full_suite from deepchecks.vision import VisionData

train_data = mask.load_dataset(object_type="VisionData", shuffle=False) test_data = mask.load_dataset(object_type="VisionData", shuffle=False)

N_SAMPLES = 1000 suite = model_evaluation(n_samples = N_SAMPLES) suite.run(train_dataset=train_data,test_dataset=test_data,max_samples=N_SAMPLES) `
bug vision needs triage

opened by Nadav-Barak 0
[FEAT] seperate conftest of vision, tabular, nlp

Currently all tests use all the conftests, which is a. un efficient b. causes error in some cases - for example we cannot test python 3.6 only on the tabular sub package c. ugly

This is also partly related to https://github.com/deepchecks/deepchecks/issues/2240
refactoring needs triage

opened by Nadav-Barak 0
Separate vision and tabular module to namespaced packages
So, we don't support vision on Python 3.6?

That's a good reason to separate the package into namespaced packages.

Originally posted by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/2234#pullrequestreview-1233539158
breaking-backwards-compatabillity feature
opened by ItayGabbay 0
Multivariate drift does not show drift bar if feature importance was not calculated

In multivariate drift (in all submodules), if feature importance was not calculated, the individual graphs should not be shown. However, the drift bar graph above them should show and now it doesn't.
bug

opened by nirhutnik 0
[FEAT] Control charts for temporal datasets
Is your feature request related to a problem? Please describe.

I'd like to be able to validate future data based on what old data looked like, and flag outlying values. The goal is to catch errors and process failures, not monitor slow drift.

Examples:

Suppose I have a list of historical performance of a model. I'd like to check if this year's model performance is in the same range as before or if something has changed. I don't care much about gradual changes to the performance. Rather, I want to detect sudden changes that might indicate a bug or major failure.

Suppose I have a large dataset that is updated in batches (of size 1+) over time. At each new batch, I'd like to validate that the data falls within the range expected from past data. Again, the goal is to catch bugs, errors, and invalid data, not to detect slow drift over time.

Describe the solution you'd like

I'd like to use control charts to do this kind of monitoring. Learn a model from past data, get what you think new data should look like, and then compare the new data to the expectation.

The implementation could look like this:

check = ControlChart("performance_score", timedate="timedate_column", check_range="latest") check.add_condition_statistical_control(confidence=0.99) # Only flag data falling outside of the 99% confidence interval. check.add_condition_limits(range=(0.8, 0.9)) # Check that latest performance is between 0.8 and 0.9. check.run(dataset) # Check that the latest performance score is as expected.

The name "control chart" could be replaced by "process monitoring" or something more explicit.

Describe alternatives you've considered

Some of this is already done in train/test validation checks and there's already an outlier detection check. However, the goal is to detect drift rather than to check for errors given incremental changes.

I think that some form of control charts or incremental outlier detection is necessary for Deepchecks to be used as part of a monitoring pipeline.
needs triage
opened by OlivierBinette 0

Releases(0.10.0)

0.10.0(Dec 11, 2022)
What's Changed

Fix columns info data integrity call to return all columns by adding None to 'column_importance_sorter_dict' by @alphabetagamer in https://github.com/deepchecks/deepchecks/pull/2120

1945 additional property texture level by @TheSolY in https://github.com/deepchecks/deepchecks/pull/2128

Enable getting model predictions using only the proba and the model_classes by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2126

2095 deepcheck vision metrics access by @TheSolY in https://github.com/deepchecks/deepchecks/pull/2127

docs: add alphabetagamer as a contributor for code, and bug by @allcontributors in https://github.com/deepchecks/deepchecks/pull/2129

image properties linton by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/2123

1721 update cv quickstarts remove reference to full suite by @TheSolY in https://github.com/deepchecks/deepchecks/pull/2132

Add mask dataset by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2130

change time_index to day_index by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2136

Fix the x zoom in edge cases of drift by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2138

[Fix: 2110] Pass feature_importance_timeout through to feature importance by @IngoStatworx in https://github.com/deepchecks/deepchecks/pull/2121

docs: add IngoStatworx as a contributor for code, and bug by @allcontributors in https://github.com/deepchecks/deepchecks/pull/2140

Add an util to display the wolf husky dataset by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2139

Ignore nans completely in new category check by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2143

Fix _do_static_prop by @matanper in https://github.com/deepchecks/deepchecks/pull/2145

Fix warning in task_inference.py by @matanper in https://github.com/deepchecks/deepchecks/pull/2149

Greater is better by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2155

Bug fix ROC report + some code cleanup by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/2154

docstring rule all by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/2157

Remove reduce from TrainTestPerformance by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2156

1780 vision weak segment performance by @TheSolY in https://github.com/deepchecks/deepchecks/pull/2148

Add return statement to the validate_format function by @osw282 in https://github.com/deepchecks/deepchecks/pull/2133

docs: add osw282 as a contributor for code by @allcontributors in https://github.com/deepchecks/deepchecks/pull/2162

Make CI pipeline run also on major feature branches by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2167

Fix debugpy license check by @matanper in https://github.com/deepchecks/deepchecks/pull/2180

Cml markdown by @bgalvao in https://github.com/deepchecks/deepchecks/pull/2147

fixing test-windows by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/2178

Fix cat segmentation by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2163

Update all contributors badge by @matanper in https://github.com/deepchecks/deepchecks/pull/2183

docs: add bgalvao as a contributor for code, and doc by @allcontributors in https://github.com/deepchecks/deepchecks/pull/2182

Created junit serialization capability by @rcwoolston in https://github.com/deepchecks/deepchecks/pull/2159

docs: add rcwoolston as a contributor for code, doc, and ideas by @allcontributors in https://github.com/deepchecks/deepchecks/pull/2164

[DOCS] fix readme and update badge template by @shir22 in https://github.com/deepchecks/deepchecks/pull/2187

Fix tests runner by @matanper in https://github.com/deepchecks/deepchecks/pull/2188

Minor import and typo fixes by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/2191

feature_aggregation_method_argument is shared, so it shouldn't say drift by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2186

check config update by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/2192

add_include_defaults_to_other_checks by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/2194

New Contributors

@alphabetagamer made their first contribution in https://github.com/deepchecks/deepchecks/pull/2120

@IngoStatworx made their first contribution in https://github.com/deepchecks/deepchecks/pull/2121

@osw282 made their first contribution in https://github.com/deepchecks/deepchecks/pull/2133

@bgalvao made their first contribution in https://github.com/deepchecks/deepchecks/pull/2147

@rcwoolston made their first contribution in https://github.com/deepchecks/deepchecks/pull/2159

Full Changelog: https://github.com/deepchecks/deepchecks/compare/0.9.2...0.10.0
Source code(tar.gz)
Source code(zip)
deepchecks-0.10.0-py3-none-any.whl(3.41 MB)
deepchecks-0.10.0.tar.gz(3.22 MB)
LICENSE(34.03 KB)
0.9.2(Oct 31, 2022)
Commits

e275a0f: 1743 feat pass scorer list to model evaluation suite (#2101) (Sol) #2101

79cbbf1: Plotly relax requirements (#2113) (JKL98ISR) #2113

d6e290c: 1852 single dataset static predictions (#2111) (Sol) #2111

1b5bac9: regression_error_distribution makeover (#2112) (Nadav Barak) #2112

49ffc01: Fix scorer per class bug (#2107) (matanper) #2107

e16e357: Merge branch 'main' of https://github.com/deepchecks/deepchecks into 0.9.x (JKL98ISR)

9b3abe9: bump (JKL98ISR)

Source code(tar.gz)
Source code(zip)
deepchecks-0.9.2-py3-none-any.whl(3.39 MB)
deepchecks-0.9.2.tar.gz(3.21 MB)
LICENSE(34.03 KB)
0.9.1(Oct 25, 2022)
Bug Fixes

docs: Minor grammar suggestions in when_should_you_use (#2012) #2012 (Jason Wohlgemuth)

Documentation

add jhwohlgemuth as a contributor for doc (#2099) #2099 (allcontributors[bot])

Commits

98925d9: 0.9 hf (#2025) (Itay Gabbay) #2025

e1954e7: [DOCS] Added documentation for tabular Model Evaluation checks (#1999) (DanBasson) #1999

f07835a: [DOCS] Added documentation for tabular Model Evaluation checks (#1999) (DanBasson)

93f4be1: [FEAT] new class imbalance check (#2015) (DanBasson) #2015

30b93e3: Static properties enum fix (#2032) (Sol) #2032

1e5057b: ability to pass classes argument + auto infer to context (#2026) (Nadav-Barak) #2026

72d73e4: [DOCS] Test for adding "all-contributors" to readme (#2035) (shir22) #2035

fe53b50: [DOCS] all-contributors with first contributor (#2036) (allcontributors[bot]) #2036

df89962: [DOCS] README - add one line of maintainers to all-contributors [skip ci] (#2040) (allcontributors[bot]) #2040

7b379e6: [DOCS] Omit double sentence in property label correlation check examples (#2033) (shir22) #2033

90426dd: [DOCS] add second line of all-contributors [skip ci] (#2045) (allcontributors[bot]) #2045

ea612a8: deepchecks scorer 2.0 (#2050) (Nadav-Barak) #2050

e07a56a: Add favicon to suite html output (#2049) (Noam Bressler) #2049

ea1e839: Fix certifi (Noam Bressler)

b7b99f5: Add additional maintainers to readme with all-contributors (#2060) (allcontributors[bot]) #2060

377dbee: Fix add_condition_train_test_relative_degradation_less_than for Regression (#2058) (Noam Bressler) #2058

3b7fe83: [DOCS] Add contributors to readme with all-contributors, #1 out of 2 (allcontributors[bot]) #2071

23a7258: image property drift static properties fix + properties conf fix (#2059) (JKL98ISR) #2059

74131db: [DOCS] Add contributors to readme with all-contributors, #2 out of 2 (#2072) (allcontributors[bot]) #2072

9f24c20: Fix typo in makefile (#2082) (Ikko Ashimine) #2082

989eddb: Fix tabular class imbalance docs (#2085) (DanBasson) #2085

69fe8d1: [DOC] add eltociear as a contributor for doc (#2086) (allcontributors[bot]) #2086

4603318: 1984 detection scorers averaging (#2079) (Sol) #2079

66fca52: [DOCS] Update readme to markdown [skip ci] (#2088) (shir22) #2088

5a724e4: Make deepchecks work with classification tasks when no proba is given (#2081) (Noam Bressler) #2081

86ac003: calc_default_image_properties (#2087) (Sol) #2087

5ec7a99: Small changes in Class Imbalance texts (#2089) (Nir Hutnik) #2089

4a55424: 2027 single dataset check select dataset (#2091) (Sol) #2091

0ac815b: Remove warning (#2094) (Nadav-Barak) #2094

210e774: [DOCS] Update .all-contributorsrc to readme.md (#2098) (shir22) #2098

f1cd58f: iou range parameter in mean average precision (#2096) (Sol) #2096

b7d5d1b: Nb/feat/airbnb dataset (#2100) (Nadav Barak) #2100

00313ec: 1867 doc for static properties (#2097) (Sol) #2097

e40c536: adult dataset no split fixed issue with the label (#2102) (Sol) #2102

0450488: Fix len() on None in single dataset performance (#2103) (matanper) #2103

80e3659: updated warning comment (#2104) (Nadav Barak) #2104

1b91244: Merge branch 'main' of https://github.com/deepchecks/MLChecks into 0.9.x (Itay Gabbay)

5d5c35c: Bumping to 0.9.1 (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.9.1-py3-none-any.whl(3.39 MB)
deepchecks-0.9.1.tar.gz(3.20 MB)
LICENSE(34.03 KB)
0.9.0(Sep 19, 2022)
What's Changed

remove 'actual' kwarg from testing by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/1891

fix feature order in TrainTestFeatureDrift if a column is selected by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/1890

Add semantic segmentation by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1883

Fixing feat imp to skip running feature importance with static predictions by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1896

add details for columns_info check by @DanBasson in https://github.com/deepchecks/deepchecks/pull/1894

[Issue-1739] Suite/check instances serialization/deserialization to/from config by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/1885

l2 combination feature drift by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1898

[Issue-1846] fixed CheckResult.priority property by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/1915

Nb/bug/small fixes by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1916

bug fixed by @TheSolY in https://github.com/deepchecks/deepchecks/pull/1911

Documentation/static predictions vision by @TheSolY in https://github.com/deepchecks/deepchecks/pull/1910

Vision Properties v2 by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/1919

Remove percent change from display of label correlation checks by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1924

Add option to sort columns in drift according to drift + importance by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1922

Fixed truncate_zero_percent by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1925

1608 property label correlation check by @TheSolY in https://github.com/deepchecks/deepchecks/pull/1897

Categorical drift methods update by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1923

[issue-1917] result.show_in_window fix by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/1926

[DOC] Consistent structure for tabular and vision checks #1893 by @DanBasson in https://github.com/deepchecks/deepchecks/pull/1921

[FEAT] Add package installation explanation to vision quickstarts by @kishore-s-15 in https://github.com/deepchecks/deepchecks/pull/1886

Adding concurrency to CI jobs by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1935

AveragePrecisionRecall._evaluate_image: Don't recompute on each min_iou. by @daavoo in https://github.com/deepchecks/deepchecks/pull/1933

run time improvements by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1936

1863 resolve two ways of passing n samples to vision suites checks by @TheSolY in https://github.com/deepchecks/deepchecks/pull/1937

fixing md5 coco by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1954

[DOCS] Many docs fixes (links, various warnings, and content updates) by @shir22 in https://github.com/deepchecks/deepchecks/pull/1956

make unused feature a single dataset check by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/1950

Reduce mixin v2 by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1943

Dataset name by @TheSolY in https://github.com/deepchecks/deepchecks/pull/1946

Changed gower distance to use actual given samples instead of only indices. by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1955

minor improvement in cramers-v runtime by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1966

added vision suite static properties kwarg by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/1963

positive_regression_scorers by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1969

[issue-1917][v2] display_in_window fix by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/1952

Fixed tqdm version for correct license by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1985

Small scorer update by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/1977

top_5 aggregation method + bug fix in percent_of_nulls.py by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1987

Add roc_auc_per_class by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1989

Fixed dependencies check by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1996

feature: Task type warning for multiclass label containing integer by @DanBasson in https://github.com/deepchecks/deepchecks/pull/1953

Deprecate old modules by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1995

Changed owners: by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1998

Nb/feat/reduce performance by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/2001

use deepchecks metrics for precision recall f1 by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2006

Added duplicate column validation by @kishore-s-gitaa in https://github.com/deepchecks/deepchecks/pull/1994

Remove the ever-present warnings about direct imports from deepchecks by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2007

Added env var FAIL_ON_DEEPCHECKS_DEPRECATION_WARNINGS by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/2008

set_verbosity ERROR will ignore also regular warnings by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2009

Segmentation docs and tests 1888 by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1991

Add code to load the airbnb dataset by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2014

Feat/added deprecation warning to whole dataset drift check by @kishore-s-15 in https://github.com/deepchecks/deepchecks/pull/2011

Add n_samples to all tabular checks (except dataset size check) by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2019

Add all sklearn scorers by @noamzbr in https://github.com/deepchecks/deepchecks/pull/2020

New Contributors

@daavoo made their first contribution in https://github.com/deepchecks/deepchecks/pull/1933

@kishore-s-gitaa made their first contribution in https://github.com/deepchecks/deepchecks/pull/1994

Full Changelog: https://github.com/deepchecks/deepchecks/compare/0.8.3...0.9.0
Source code(tar.gz)
Source code(zip)
deepchecks-0.9.0-py3-none-any.whl(3.38 MB)
deepchecks-0.9.0.tar.gz(3.20 MB)
LICENSE(34.03 KB)
0.8.3(Aug 23, 2022)
Commits

51e7076: [HOTFIX] fixing API reference link (shir22)

c7b351d: Fixing API reference (Itay Gabbay)

860754f: Update plot_create_a_custom_check.py (shir22)

ce69bb8: Fixing ipywidgets version issue (Itay Gabbay)

2e5ae97: 0.8.3 bump 🚀 (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.8.3-py3-none-any.whl(3.36 MB)
deepchecks-0.8.3.tar.gz(3.19 MB)
LICENSE(34.03 KB)
0.8.2(Aug 8, 2022)
What's Changed

fixing double output in docs by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1733

Suite/check conf serialize by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/1736

fixing pps docs by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1729

Image property outliers display when there are no outliers by @TheSolY in https://github.com/deepchecks/deepchecks/pull/1738

handle single value columns gower dist by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1741

Fixing a deprecated call in the create a custom suite tutorial by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1744

Separate gower distance function by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1746

[issue-1660] dataframe index display fix by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/1740

[Issue 1654] added "connected" parameter to the "save_as_html" method by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/1735

Scorers string by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/1745

Nb/feat/new metrics by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1751

Nb/feat/performance report update by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1755

fixed edge cases with weird data + tested by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/1762

Fixed docs links in data classes by @kishore-s-15 in https://github.com/deepchecks/deepchecks/pull/1763

Fixed README links by @kishore-s-15 in https://github.com/deepchecks/deepchecks/pull/1765

Nb/feat/drift scores by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1773

Adding the run function of the suite to the suite api docs by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1779

[Monitoring][issue-52] Added PercentOfNull check / added ReduceMixin to checks by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/1758

Update README.rst by @shir22 in https://github.com/deepchecks/deepchecks/pull/1784

[Issues: 1715, 1730] added 'read more' link to the check short description / fixed 'display_in_gui' func by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/1771

1637 SingleDatasetPerformance update to support multiple scorers by @TheSolY in https://github.com/deepchecks/deepchecks/pull/1778

tabular single dataset performance by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1788

[Issue-1646] renamed 'FeatureLabelCorrelationChange' check by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/1787

Adapting tabular SimpleModelComparison API by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1785

Adding methods and attributes to API ref classes by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1793

Nb/feat/update custom check guide by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1792

SingleDatasetPerformance Minor adjustment to the scorers signature and display by @TheSolY in https://github.com/deepchecks/deepchecks/pull/1791

fix_static_model_bug by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/1797

updated unused features plot example by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1805

updated classification_dataset_from_directory by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1803

small fixes to drift and gower distance by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1794

Add indices to conflicting labels display by @matanper in https://github.com/deepchecks/deepchecks/pull/1811

[DOCS] Guide for Viewing Results (show) and for Exporting them (json & html) by @shir22 in https://github.com/deepchecks/deepchecks/pull/1812

add model_evaluation_gif in README by @shir22 in https://github.com/deepchecks/deepchecks/pull/1817

Removing Similar image leakage from the suites because of performance issues by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1806

fixed license dependency by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1826

bugfix - label in evaluation suite example inferred incorrectly by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1818

Added text for drift differentiation by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1815

FI new function for users by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1822

run checks without labels if they are not needed by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1834

Add probability drift on proba by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1809

Nb/doc/small fixes by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1837

Fix issues in date overlap and duplicate checks by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1839

Fixing dependencies by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1844

Added flag for users to not ignore nulls in categorical columns by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1843

Shir was right, I was wrong by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1840

Unify supported models and static predictions by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1835

Update plot_create_a_custom_suite.py by @cemalgurpinar in https://github.com/deepchecks/deepchecks/pull/1853

[Doc] Documentation contains a typo in "Using Deepchecks Vision With a Few Lines of Code" tutorial. by @cemalgurpinar in https://github.com/deepchecks/deepchecks/pull/1855

[suggestion] small fixes to the result display guide by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/1827

1770 seperate quickstarts in user guide by @kishore-s-15 in https://github.com/deepchecks/deepchecks/pull/1798

Nb/bug/drift na by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1851

Fix the deprecated import warning by importing directly from source by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1857

Correctly count the ratio of new categories by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1860

Add FeatureFeatureCorrelation to suite and fix python 3.6 bug by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1866

aggregation methods for object detection by @Nadav-Barak in https://github.com/deepchecks/deepchecks/pull/1870

Adding a section for evaluating a suite in pytest by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1873

Added max_num_categories_for_display and show_categories_by to ImageDatasetDrift by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/1876

Metrics Guide by @TheSolY in https://github.com/deepchecks/deepchecks/pull/1799

Fixing bugs in the docs by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1881

Fix links by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1877

Fix issues with image docs by @noamzbr in https://github.com/deepchecks/deepchecks/pull/1879

Adding warning if the user is not on the latest version by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/1882

1706 static properties by @TheSolY in https://github.com/deepchecks/deepchecks/pull/1864

New Contributors

@kishore-s-15 made their first contribution in https://github.com/deepchecks/deepchecks/pull/1763

@cemalgurpinar made their first contribution in https://github.com/deepchecks/deepchecks/pull/1853

Full Changelog: https://github.com/deepchecks/deepchecks/compare/0.8.1...0.8.2
Source code(tar.gz)
Source code(zip)
deepchecks-0.8.2-py3-none-any.whl(3.36 MB)
deepchecks-0.8.2.tar.gz(3.19 MB)
LICENSE(34.03 KB)
0.8.1(Jul 7, 2022)
Commits

379db4b: Kaggle display fix (#1659) (Itay Gabbay) #1659

902ee4b: Static pred vision to use dicts (#1653) (JKL98ISR) #1653

e107fd4: Adding 0.8 release (Itay Gabbay)

5330f17: [Issue-1545] progress bar text (#1658) (Yurii Romanyshyn) #1658

4478be7: [Issue-1613] cv2 import fix (#1657) (Yurii Romanyshyn) #1657

06bef87: 1586 add condition to image dataset drift (#1665) (Sol) #1665

78593c4: Add tabular benchmark tests (#1668) (matanper) #1668

a738df2: Update pr.yaml (#1663) (Nir Hutnik) #1663

c4e84b7: Adding benchmarks to CI (#1664) (Itay Gabbay) #1664

cb88549: Add new custom check templates (#1633) (matanper) #1633

37fb754: [Issue-1561] features_importance parameter deprecation (#1667) (Yurii Romanyshyn) #1667

d14b56f: 1585 rename identifier leakage (#1670) (Sol) #1670

4c3b023: Weak segments performance (#1671) (Nadav-Barak) #1671

19c387b: Update docs index.rst (#1673) (matanper) #1673

8601969: Display flag (#1662) (JKL98ISR) #1662

5c28d02: [DOCS] Update how to use package in IDE (#1682) (Sol) #1682

1ca38ad: Fix nullable int bug (#1676) (JKL98ISR) #1676

fbfcaa0: added "result =" to all of the checks documentation + required adjustments (#1684) (Sol) #1684

d1ac880: [Issue-1542] MixedNull display table order (#1679) (Yurii Romanyshyn) #1679

236b16b: Deprecating the DominantFrequencyChange check (#1685) (Itay Gabbay) #1685

f2ef42b: [issue-1674] mixed nulls bug fix (#1677) (Yurii Romanyshyn) #1677

7cf93ae: [Issue-1560] fix effect of display control parameters on CheckResult.value (#1680) (Yurii Romanyshyn) #1680

7bd8d22: display map widget fix (#1696) (Yurii Romanyshyn) #1696

b0bf20b: [issue-1514] conflicting labels check fix (#1694) (Yurii Romanyshyn) #1694

482664f: Shared docstrings (#1669) (Yurii Romanyshyn) #1669

bac1be9: Vision benchmark (#1681) (JKL98ISR) #1681

d57bb29: Suite api docs (#1693) (Itay Gabbay) #1693

ac32522: New vis metrics (#1698) (JKL98ISR) #1698

d211cf1: custom vision scorer (#1704) (JKL98ISR) #1704

fc70ca5: Nb/feat/weak segments performance (#1678) (Nadav-Barak) #1678

a2d8231: Class performance / performance report condition details upgrade (#1720) (JKL98ISR) #1720

54ca182: Ignore NaNs in FeatureFeatureCorrelation (#1718) (Sol) #1718

cd967d3: [suggestion] html progress bar (#1717) (Yurii Romanyshyn) #1717

8f07ffb: Update label-new-issue.yml (#1727) (Nir Hutnik) #1727

6a33c73: deprecation warnings (#1732) (Nadav-Barak) #1732

eef382e: Quick start model evaluation + new dataset (#1726) (Nadav-Barak) #1726

524595f: Merge branch 'main' of https://github.com/deepchecks/MLChecks into 0.8.x (Itay Gabbay)

b0f3fc2: 0.8.1 version bump (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.8.1-py3-none-any.whl(482.08 KB)
deepchecks-0.8.1.tar.gz(323.14 KB)
LICENSE(34.03 KB)
0.8.0(Jun 19, 2022)
Commits

b92ae5c: Update dataset exceptions (#1520) (matanper) #1520

7a0b2d3: Mixed data types examples (#1528) (JKL98ISR) #1528

541b119: Fixing doc copyubtton (Itay Gabbay)

22f59bd: Enable validation of empty predictions (#1530) (Noam Bressler) #1530

b4b620c: [issue-1448] IsSingleValue check - ignore nan (#1522) (Yurii Romanyshyn) #1522

d3c51ac: Deprecation error 1536 (#1548) (Nir Hutnik) #1548

b07b308: Update switcher.json (Itay Gabbay)

1cb5be3: reduce_image_size (#1551) (JKL98ISR) #1551

d0a49c4: added reduce mixin to performance report (#1550) (JKL98ISR) #1550

8f0f6e2: 1433 feat support scikit learn 1.1.0 (#1554) (matanper) #1554

1343ac8: Vision conditions "more details" update (#1546) (matanper) #1546

0cb97cb: Fixing API reference (#1557) (Itay Gabbay) #1557

defcbeb: static_model_suites_fix (#1558) (JKL98ISR) #1558

5387e3a: [Issue 1445] properties output type (#1547) (Yurii Romanyshyn) #1547

f71ec33: sticking pylint to 2.13.5 (Itay Gabbay)

b461d40: Fixing dependencies license check (Itay Gabbay)

83719b1: Create label-new-issue.yml (Itay Gabbay)

c5f15bb: Update label-new-issue.yml (Itay Gabbay)

026780a: Update label-new-issue.yml (Itay Gabbay)

8fa20cc: Centralize Requirements dir and file to variable (#1563) (DanArlowski) #1563

0617ca3: [Issue-1443] tabular Dataset repr (#1549) (Yurii Romanyshyn) #1549

659b6d1: Performance report: add reduce parameter (#1559) (JKL98ISR) #1559

26d7a7b: Fixed bug in cramers V for single value (#1570) (Nir Hutnik) #1570

456a6b0: Add display in python gui (#1568) (matanper) #1568

3f2e686: Nb/infa/pylint violations fix (#1579) (Nadav-Barak) #1579

1e77878: improved condition argument for feature drift (#1578) (Nadav-Barak) #1578

d36be62: base form operates only if at least one alphanumeric char exist (#1581) (Nadav-Barak) #1581

677f6f5: Refactor all conditions names (#1583) (matanper) #1583

34adcfe: Lending club dataset (#1588) (Nadav-Barak) #1588

07b1f03: Bugfix/seg infinite loop (#1590) (Noam Bressler) #1590

2e40cca: adding lending club dataset to init (#1595) (shir22) #1595

8b33000: Add passed function to suite result (#1594) (matanper) #1594

de91c9b: [DOCS] train test validation quickstart (#1531) (shir22) #1531

8aae4ef: static_model_tab_docs (#1562) (JKL98ISR) #1562

Fix display bug on numeric column names (#1615) #1615 (matanper)

Replace dataset type with dataset kind (#1616) #1616 (matanper)

f827333: Fixed broken links (#1623) (Nadav-Barak)

77823e4: standardize the sampling footnote (#1621) (matanper) #1621

918cc5e: added logger util (#1610) (JKL98ISR) #1610

29e21a3: Improved categorical feature inference + label column description (#1612) (Nadav-Barak) #1612

9dbb763: Fix bug in plots caused by new albumentations version (#1636) (matanper) #1636

19fec99: no_lru (#1631) (JKL98ISR) #1631

eda1a42: fixing databricks tutorial (#1644) (Itay Gabbay) #1644

d06cb62: Update Readme (#1639) (shir22) #1639

3f899a1: Docs Welcome Page Update (#1641) (shir22) #1641

3d532f7: Update city_arrogance test dataset (#1624) (matanper) #1624

a4c07dc: [Issue-1501] new suite output design (#1627) (Yurii Romanyshyn) #1627

01d0c0d: skip timeout/calculation msg in our fi calculations (#1645) (JKL98ISR) #1645

f411999: Remove upper level titles from the image dataset drift guide (#1648) (Noam Bressler) #1648

3f9627b: [Issue-1574] new display item type - DisplayMap (#1638) (Yurii Romanyshyn) #1638

aed8c9c: Add new check - correlation between features (Sol) #1606

bd550b9: Fixed bug (#1651) (Nir Hutnik) #1651

a6894a3: fixed link in getting started (#1655) (shir22) #1655

d9d9a19: [suggestion] new suite/check result display api (#1611) (Yurii Romanyshyn) #1611

7761cb7: Version bump (Itay Gabbay)

9593171: docs 0.8 bump (Itay Gabbay)

914385e: Kaggle display fix (#1659) (Itay Gabbay)

baea2cb: Fix (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.8.0-py3-none-any.whl(455.83 KB)
deepchecks-0.8.0.tar.gz(307.18 KB)
LICENSE(34.03 KB)
0.7.2(Jun 7, 2022)
Commits

5902a5e: Fixing doc copyubtton (Itay Gabbay)

ede8fbe: Fixed bug in cramers V for single value (#1570) (Nir Hutnik)

ba81d45: Version bump (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.7.2-py3-none-any.whl(432.87 KB)
deepchecks-0.7.2.tar.gz(290.49 KB)
LICENSE(34.03 KB)
0.7.1(May 31, 2022)
Commits

67ee47c: Deprecation error 1536 (#1548) (Nir Hutnik)

5107884: Enable validation of empty predictions (#1530) (Noam Bressler)

f9372a1: bump 0.7.1 (Matan Perlmutter)

Source code(tar.gz)
Source code(zip)
deepchecks-0.7.1-py3-none-any.whl(432.82 KB)
deepchecks-0.7.1.tar.gz(290.45 KB)
LICENSE(34.03 KB)
0.7.0(May 29, 2022)
Commits

84a7e2c: Fix string mismatch performance (#1432) (matanper) #1432

6059c0a: Create about.RST (#1434) (Itay Gabbay) #1434

2c6acd6: Rename ABOUT.rst (shir22) #1435

cfeaf97: Update about (#1436) (shir22) #1436

9ae6f18: Airflow integration (#1412) (Itay Gabbay) #1412

9fcb58e: Update checks_and_conditions image in docs (#1441) (shir22) #1441

b738310: Add datasets tests (#1439) (matanper) #1439

5cc5db6: Doc mainpage redirect (#1450) (Itay Gabbay) #1450

747a4c0: Catch warnings in detection metric (#1451) (matanper) #1451

d80f851: Cramers v (#1446) (JKL98ISR) #1446

fca4e27: better format validation for detection data class (#1428) (Nadav-Barak) #1428

14abff1: Segment Performance: fix figure and unnecessary feature importance usage (#1456) (matanper) #1456

27e3e05: Check/Suite Result repr_html fix (#1458) (Yurii Romanyshyn) #1458

98adbfa: Docs get started update (#1466) (shir22) #1466

05e8013: Hide checks without display and remove links (#1470) (matanper) #1470

4bd7fe0: Replace logger.warnings with warnings.warn (#1460) (matanper) #1460

8a325c9: Restructure of the tabular submodule (#1469) (Itay Gabbay) #1469

524ac70: 1409 feat single dataset performance check (#1442) (Sol) #1442

6a5bade: Fix grouping nan types in MixedNulls for pandas < 1.4 (#1473) (matanper) #1473

af2f9d7: Vision new structure (#1481) (Itay Gabbay) #1481

f945c0d: [issue-1340][BUGFIX] fixed TrainTestSamplesMix fillna issue (#1486) (Yurii Romanyshyn) #1486

4871b77: Images merge (#1488) (Itay Gabbay) #1488

ae2c3c4: [Docs] Avocado Sales - Integrity Suite Quickstart (#1474) (shir22) #1474

f374742: Fixes for running prediction drift & segment performance on various datasets (Noam Bressler) #1482

0a7d474: Add details to conditions for tabular model evaluation (#1489) (matanper) #1489

3942d0e: Improve sfc 1399 (#1478) (Nir Hutnik) #1478

ab84485: [Issue 1410] Progress Bar fix (#1471) (Yurii Romanyshyn) #1471

ddc6c0e: Update conditions details for tabular integrity checks (#1485) (matanper) #1485

6861918: import order fix (#1492) (Yurii Romanyshyn) #1492

0679a72: Fixing copybutton (#1494) (Itay Gabbay) #1494

e8e85fc: [issue-1467] suite widgets serializer fix (#1493) (Yurii Romanyshyn) #1493

712f824: Nb/feat/correlation methods (#1484) (Nadav-Barak) #1484

edfecb4: Add the hugging face transformer example (#1477) (Noam Bressler) #1477

7d693ba: Tabular train-test checks conditions details (#1495) (matanper) #1495

2909a99: Fix H2O subsection title (Noam Bressler) #1499

d9029eb: Fixing avocado (#1502) (Itay Gabbay) #1502

067170e: Rename single feature contribution to feature label correlation 1399 (#1498) (Nir Hutnik) #1498

95d8481: bug fix in inf calculations gower method (#1505) (Nadav-Barak) #1505

8be63cc: Warn if cuda present but not used (Itay Gabbay) #1506

fecc341: Static model (#1491) (JKL98ISR) #1491

826ae3a: Mixed nulls bug (#1507) (matanper) #1507

48ecb47: fix links to tabular and vision checks (#1504) (shir22) #1504

0959405: [Issue-1365][V2] Plotly figures width&height (#1483) (Yurii Romanyshyn) #1483

ac6412d: [Refactoring] add wandb context manager (Yurii Romanyshyn) #1496

9659b7f: Added kwargs for tabular suite + fixed missing kwargs in checks (#1515) (JKL98ISR) #1515

f1ddff5: [issue-1516] fixed suite output display order (#1521) (Yurii Romanyshyn) #1521

eeadd80: remove DominantFrequencyChange from default suite (#1519) (Noam Bressler) #1519

bac80e5: Fixing right nav scroll (#1526) (Itay Gabbay) #1526

40df64d: Fixing the release pipeline (#1529) (Itay Gabbay) #1529

e87b2cd: Drift guide 1066 (#1447) (Nir Hutnik) #1447

f76ba17: Static vision model (#1525) (JKL98ISR) #1525

2bcf0e5: Adding device to missing tests (#1524) (Itay Gabbay) #1524

b4bd746: 0.7.x (JKL98ISR)

Source code(tar.gz)
Source code(zip)
deepchecks-0.7.0-py3-none-any.whl(432.03 KB)
deepchecks-0.7.0.tar.gz(289.77 KB)
LICENSE(34.03 KB)
0.6.4(May 12, 2022)
Commits

4065254: Simple model global metric (#1352) (Itay Gabbay) #1352

c7043bb: Nb/documentation and autocasting pixels (#1345) (Nadav-Barak) #1345

040b8ff: Modify import order (#1353) (Itay Gabbay) #1353

310ae95: Fix adult dataset fitted model (#1354) (matanper) #1354

351f118: Sklearn 0.23.2 support (#1333) (JKL98ISR) #1333

3e19f63: Explaining boosting overfit notebook (#1350) (Itay Gabbay) #1350

1806a5c: Rename check label ambiguity (#1359) (matanper) #1359

f606ff0: Fixing tutorials and checks examples (#1362) (Itay Gabbay) #1362

bc9dbeb: adding submodule to each doc page (#1366) (Itay Gabbay) #1366

d4d7ec9: Update plot model error analysis (#1361) (matanper) #1361

b708e14: Torch transformers support (#1357) (JKL98ISR) #1357

3f09614: Added margin_quantile_filter to EMD drift score (#1368) (Nir Hutnik) #1368

3528633: [issue-892] modified VisionData ctor (#1356) (Yurii Romanyshyn) #1356

ad112a3: [issue-811] TrainTestFeatureDrift - fi order bug (#1372) (Yurii Romanyshyn) #1372

fc35b1e: Update mixed data types plot (#1363) (matanper) #1363

59c42ac: [issue-1248] features inference for passed into check dataframes (#1355) (Yurii Romanyshyn) #1355

3852c62: redo_plotly_ver (#1373) (JKL98ISR) #1373

6114d8e: Fix minor link issue & cast to float in log loss (#1374) (Noam Bressler) #1374

15c6f37: Nb/vision/new label (#1369) (Nadav-Barak) #1369

a5fcf3e: [issue-1077] Display logic refactoring (#1279) (Yurii Romanyshyn) #1279

ce9d4b2: Add mean and median to dist plots (#1376) (matanper) #1376

6876380: Serialization subpackage fix - added init.py module (#1389) (Yurii Romanyshyn) #1389

6de5e8c: [issue-1370] fixed coco labels map (#1380) (Yurii Romanyshyn) #1380

6929f74: Fix emd drift 1276 revised (#1390) (Nir Hutnik) #1390

e3fc8ce: Nb/vision/map output + doc (#1388) (Nadav-Barak) #1388

d2d2992: Confusion matrix normalized display (#1377) (JKL98ISR) #1377

9133ae6: Added feature_importance.rst (#1391) (Nir Hutnik) #1391

11b9e52: Adding spark databricks integration (#1395) (Itay Gabbay) #1395

01ec9ff: Fixing examples README.rst, removing OutlierSampleDetection from suite (#1405) (Itay Gabbay) #1405

e8205cf: From json (#1402) (JKL98ISR) #1402

8454128: Fixing validations on GPU (#1396) (Itay Gabbay) #1396

c6b64d2: hot-fix for the SuiteResult.save_as_html (#1413) (Yurii Romanyshyn) #1413

edf9ce2: Adding pytest integration (#1403) (Itay Gabbay) #1403

46ad6e6: Run the phishing integrity suite on a Dataset instead of a DataFrame. (#1417) (Noam Bressler) #1417

d8b2885: Minor fixes to the vision documentation: task_type clarification and typo in DetectionData (#1422) (Sol) #1422

e09ddd5: Removing the extra display info when nothing found (#1423) (matanper) #1423

fb884cc: Docs improve (#1424) (Itay Gabbay) #1424

969be4d: Add example for using H2O with deepchecks (#1400) (Noam Bressler) #1400

f4d1e6d: Nb/tabular/increase speed outlier detection + timeout (#1414) (Nadav-Barak) #1414

56c77c0: [BUGFIX] Serialization logic - additional fixes. (#1420) (Yurii Romanyshyn) #1420

0b05fca: Single feature fixes (#1419) (JKL98ISR) #1419

6ba8629: Fixes to rst files in integration examples. (Noam Bressler) #1426

0fe0db3: new png (#1430) (shir22) #1430

c3df4f2: Add note to string mismatch checks (#1429) (Noam Bressler) #1429

c4a2deb: Merge branch 'main' of https://github.com/deepchecks/MLChecks into 0.6.x (Itay Gabbay)

8fd0895: 0.6.4 version bump 🚀 (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.6.4-py3-none-any.whl(412.20 KB)
deepchecks-0.6.4.tar.gz(271.79 KB)
LICENSE(34.03 KB)
0.6.3(Apr 28, 2022)
Commits

81e5b08: taking out the OutlierSampleDetection from the full_suite (Itay Gabbay)

d6c0ce1: 0.6.3 bump (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.6.3-py3-none-any.whl(360.18 KB)
deepchecks-0.6.3.tar.gz(241.90 KB)
LICENSE(34.03 KB)
0.6.2(Apr 28, 2022)
Commits

8a1b87c: Remove difference from heatmap_comparison.py (#1269) (matanper) #1269

c7d1390: 1168 feat single dataset outlier detection (#1237) (Nadav-Barak) #1237

00fceed: improve performance_report notebook (#1271) (DBI) #1271

eb13d00: Get exception traceback in suite easily (#1270) (DBI) #1270

d02251d: Manually set iris label type (#1275) (matanper) #1275

f0aa52e: Check to html (#1277) (JKL98ISR) #1277

16a9014: Link examples with apis (#1264) (Shiv Shankar Dayal) #1264

442b87c: Prevent empty datasets, as that can create bugs down the line (#1273) (Noam Bressler) #1273

ff79ce0: fix over 255 bug in whole dataset drift (#1278) (DBI) #1278

a43a41a: Add similar image leakage to default suite (#1241) (Noam Bressler) #1241

a5aee41: Segment Performance display fix (#1282) (matanper) #1282

95fb347: make show function work on sphinx + fixed double output (#1281) (JKL98ISR) #1281

6c12a8e: Add option to pass FI in tabular to single run (#1285) (matanper) #1285

00843e9: Update warning on categorical infer (#1293) (matanper) #1293

4fd87f9: fix_check_html_style (#1291) (JKL98ISR) #1291

718b7b9: dissable tqdm globaly in tests (#1289) (DBI) #1289

06fb730: Changed calculation of similar image leakage to only take one similar image per test image (#1297) (Nir Hutnik) #1297

0e0827c: New categorical drift method 1138 (#1288) (Nir Hutnik) #1288

60ee6b5: Improve dominant frequency change example (#1296) (Itay Gabbay) #1296

b5409ad: Adding examples validation (#1298) (Itay Gabbay) #1298

80f1b37: v0 (#1304) (JKL98ISR) #1304

d026204: fix inconsistent fi behavior (#1306) (JKL98ISR) #1306

e7841a2: Dc 1238 found nothing (#1316) (DBI) #1316

30dc402: Add test for depr warn (#1318) (Nir Hutnik) #1318

6474add: Tutorial custom check quickfixes (#1321) (Levi Bard) #1321

6b68aaa: Update unused features plot (#1317) (matanper) #1317

0e8bb3d: Fi pipeline to always use permutation (#1315) (JKL98ISR) #1315

bac0043: Simple classification data example using EuroSAT to find Simple Feature Contribution issue (#1327) (Noam Bressler) #1327

79d705a: Condition image property fix (#1329) (JKL98ISR) #1329

54e052f: fix_cpu_detach (#1334) (JKL98ISR) #1334

161059c: Update read more generation and add test (#1326) (matanper) #1326

93046ae: Various single feature contribution fixes (#1324) (Noam Bressler) #1324

fc2ffee: Similar image small fixes in docs (#1331) (Nir Hutnik) #1331

6359c7a: Update train test samples mix docs (#1325) (matanper) #1325

c700e58: Update single feature contribution plot (#1337) (matanper) #1337

f442813: Examples restructure (#1335) (Itay Gabbay) #1335

e679b25: image_segment_mem_fix (#1341) (JKL98ISR) #1341

df80eb1: Merge branch 'main' of https://github.com/deepchecks/MLChecks into 0.6.x (Itay Gabbay)

aba238d: 0.6.2 bump (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.6.2-py3-none-any.whl(360.19 KB)
deepchecks-0.6.2.tar.gz(241.85 KB)
LICENSE(34.03 KB)
0.6.1(Apr 12, 2022)
Commits

fd8671a: adding 0.6 (Itay Gabbay)

ddf6d57: Using abstraction for vision average precision (#1160) (matanper) #1160

3fe0c83: added texlive-latex-extra for missing style files (#1176) (Shiv Shankar Dayal) #1176

c5e78f3: fix image array dtype (#1178) (matanper) #1178

a93c326: custom metrics reuse bug (#1180) (matanper) #1180

c824e77: Fix ModelErrorAnalysis task type error (#1190) (matanper) #1190

e229d89: fi_random_state_fix (#1182) (JKL98ISR) #1182

000fd77: Update robustness test (#1192) (matanper) #1192

826fef9: Add Similar image leakage check (#1135) (Noam Bressler) #1135

fc2a52b: fix_train_yest_links (#1181) (JKL98ISR) #1181

22d4a72: fixed tooltips and getting started content on sidebar (#1185) (Shiv Shankar Dayal) #1185

0e7ec77: fix_tensor_gpu (#1197) (JKL98ISR) #1197

0514348: change image property drift to raise error instead of returning None (#1193) (JKL98ISR) #1193

c4996e5: add_condition_type and test suite condition category results (#1194) (JKL98ISR) #1194

28c72d5: Add sampling to checks and suite run (#1195) (matanper) #1195

8936eac: Update tabular drift checks to raise correct error if there are no features defined (#1205) (matanper) #1205

c8a9949: v1 (#1207) (JKL98ISR) #1207

9630d75: Fix the logic finding the weak segment in categorical features. (#1209) (Noam Bressler) #1209

00aa059: Fixing performance bugs (#1212) (Itay Gabbay) #1212

6793e13: apply device to validators (#1216) (Noam Bressler) #1216

2419033: 1141 bug cv unclear if the prediction was correct in classification data validation (#1214) (matanper) #1214

b6824ff: Sending anonymous analytics (#1204) (Itay Gabbay) #1204

904e010: Fix read more (#1217) (matanper) #1217

bad6c69: Fixing wandb example redirect(#1224) (Itay Gabbay) #1224

576efbd: Fixing wandb example redirect(#1224) (Itay Gabbay)

6b5ec9c: fix (Itay Gabbay)

94c93eb: wandb train/test fix plus better value formatting (#1213) (JKL98ISR) #1213

4fa9ae0: Adding examples to conditions (#1226) (Itay Gabbay) #1226

d9c1c29: Disable CI telemetry (#1227) (Itay Gabbay) #1227

5c1dcac: Fix drift plots for few unique numerical values (#1199) (matanper) #1199

a64bb51: fixed simple model new classes in test behavior (#1230) (JKL98ISR) #1230

817d4bf: 1140 bug cv add label map to conditions (#1228) (matanper) #1228

c9ce8b9: Update README to accommodate beta release of vision package (#1231) (shir22) #1231

8108684: reduce runtime of jaccard_iou by more than 50 percent (#1223) (DBI) #1223

6fa04e6: Upgrade gpu torch vision (#1234) (Itay Gabbay) #1234

7a0b9de: fix suite progress bar in single checks (#1232) (JKL98ISR) #1232

0fc6e9c: RobustnessReport & ClassPerformance work on custom task (#1235) (matanper) #1235

1f10d97: Add tests for docs plots on GPU (#1229) (matanper) #1229

e49958b: Add progress bars to single checks runs (#1236) (matanper) #1236

3b74680: Fix model error plot (#1239) (matanper) #1239

0475894: Align the label index in LABEL_MAP to match cls_id (#1244) (Noam Bressler) #1244

6de3e96: Update outliers & robustness image grid (#1245) (matanper) #1245

195c7fd: Model error analysis gallery (#1225) (Itay Gabbay) #1225

fd0e0bb: mixed nulls on non string columns (#1211) (DBI) #1211

b4f1227: Update width: inherit to fill-available (#1249) (matanper) #1249

b09450b: Update string mismatch docs (#1247) (matanper) #1247

d6ce78f: allow checks to return CheckFailure (#1252) (DBI) #1252

b8898ef: Image segment graph improvements (#1251) (JKL98ISR) #1251

85ad712: Merge from main (Itay Gabbay)

86b67f8: versio bump (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.6.1-py3-none-any.whl(347.70 KB)
deepchecks-0.6.1.tar.gz(233.79 KB)
LICENSE(34.03 KB)
0.6.0(Apr 1, 2022)
Commits

0bbcfed: New version release (github-actions[bot])

2b017f3: Move responsibilities from context to dataset (#767) (matanper) #767

9a54486: [issue-650] functionality split (#751) (Yurii Romanyshyn) #751

3defd1b: small fix (#770) (Yurii Romanyshyn) #770

65739b7: links-checks github action fix (#775) (Yurii Romanyshyn) #775

08a350f: added_series_predict_support (#771) (JKL98ISR) #771

2543d85: updated makefile (#780) (Yurii Romanyshyn) #780

08b9d75: Adding base vision package (#773) (Itay Gabbay) #773

0fdb33d: Add links to suite in where to use docs (#783) (matanper) #783

39bfef7: Making the notebooks gen multi-procces (#785) (Itay Gabbay) #785

e6e36c3: Vision docs placeholder (#788) (Itay Gabbay) #788

142524d: Fixing rtd build (#790) (Itay Gabbay) #790

0b2886f: Add sample data loader & sample predictions and label caching (#786) (matanper) #786

7995a74: Fix pandas warning (#792) (JKL98ISR) #792

e2ba947: [issue-781] moved checks base classes to core (#789) (Yurii Romanyshyn) #789

e739cc5: Fixing nb generation (#795) (Itay Gabbay) #795

c2bca9a: [issue-757] updated MNIST dataset api (#798) (Yurii Romanyshyn) #798

8eb0187: Adding 2022 to license (#802) (Itay Gabbay) #802

d4d33a6: Label drift fixes (#797) (Nir Hutnik) #797

7c9ae4e: Adding labels and detection encoder classes (#800) (Itay Gabbay) #800

3d84a3e: fix_widget_plotly_bug (#807) (JKL98ISR) #807

0abec77: New version release (github-actions[bot])

bcd6de2: Updated Readme (#814) (shir22) #814

0edb5b5: rename VisionDataset to VisionData (#820) (Noam Bressler) #820

4bcd1d7: [DOCS] Updated main page and when should you use page (shir22) #821

ec71505: add DataFormatter (#812) (Noam Bressler) #812

66b7f28: Fixing label drift in vision submodule (#823) (Itay Gabbay) #823

b3bf954: rename to ImageFormatter and fix bug in VisionData init (#824) (Noam Bressler) #824

d546f38: [issue-799] coco split into train/test; updated coco dataset module api (#805) (Yurii Romanyshyn) #805

f9b1ef6: change checks to init - update - compute structure (#826) (Noam Bressler) #826

ec307ea: Fixing CI (#838) (Itay Gabbay) #838

0833fd8: Label drift fixes (#808) (Nir Hutnik) #808

11230f6: Label drift refactor (#843) (Nir Hutnik) #843

0a68f7f: Adding performance report nb (#844) (Itay Gabbay) #844

8e852a5: Update README.rst (#849) (JKL98ISR) #849

f911fe8: Added typehints where they were missing in CheckResult's methods. Also slightly improved english in docs for those functions (#848) (RonItay) #848

77735cd: Formatter docs (#837) (Itay Gabbay) #837

b2f0c20: [issue-787] added device parameter to the vision checks context (#832) (Yurii Romanyshyn) #832

1f9f6b4: Added properties in image formatter (#855) (Nir Hutnik) #855

29302fa: Vision quickstart notebook (#853) (Noam Bressler) #853

de7bab1: Robustness Report (#801) (matanper) #801

2ace56c: Image properties (#858) (Nir Hutnik) #858

aaa22c7: suite fixes (#857) (Yurii Romanyshyn) #857

595b58d: Vision docs (#860) (Itay Gabbay) #860

ba13a32: add export to html to quickstart (#865) (Noam Bressler) #865

754b25a: Adding class display limit in ClassPerformance check (#866) (Itay Gabbay) #866

c7ccd5b: Add title to drift plot (#867) (Noam Bressler) #867

eb5ecd8: Improved metric (#825) (JKL98ISR) #825

69a0f65: Added imgaug-equivalent augmentations. (#869) (Nir Ben-Zvi) #869

1e62372: add wrap_run to ModelComparisonCheck (#868) (Noam Bressler) #868

fee9fd5: remove warnings from main (#870) (JKL98ISR) #870

44e2551: Modifying make notebook (#873) (Itay Gabbay) #873

0ffe328: Add recall to default metrics (#872) (JKL98ISR) #872

fbd8e46: Fixing deployment (#861) (Itay Gabbay) #861

8dcb676: Vision domain classifier issue 841 (#879) (Nir Hutnik) #879

988eed7: MeanAveragePrecisionReport check (#880) (JKL98ISR) #880

d25643e: Formatters validation (#875) (matanper) #875

dccb198: v1 (#885) (JKL98ISR) #885

28e5eb5: Fix version read in init (#891) (matanper) #891

45e379a: fix_cells (JKL98ISR)

3f98ce3: Revert "fix_cells" (JKL98ISR)

148e0df: Adding label_map to dataset (#877) (Itay Gabbay) #877

d31a0cb: Vision Suite refactor (#890) (JKL98ISR) #890

547b225: Fixed (maybe?) the tests (#893) (Nir Hutnik) #893

ec6122b: Make image drift tests consistent (#902) (matanper) #902

a9595a4: fixing docs (#898) (Itay Gabbay) #898

1220e7f: Vision model comparison (#897) (Itay Gabbay) #897

61b30a0: Save mnist model for tests - Fix robustness test (#905) (matanper) #905

b68f44e: Set seed in vision context (#904) (matanper) #904

5c3f663: [Issue 834] label formatters (#882) (Yurii Romanyshyn) #882

12f5e34: Changing alternative_metrics to Dict (#907) (Itay Gabbay) #907

9ec023e: set default prediction formatters per task type (#909) (Noam Bressler) #909

e196de6: The notebook is back (#900) (Nir Hutnik) #900

580a12c: Robustness + VisionData: copy generator when copying dataset (#924) (matanper) #924

b801321: Update robustness report notebook (#913) (matanper) #913

f0e45e8: Remove state copying and setting seed in robustness_report (#926) (matanper) #926

fc854e0: Gpu fixes (#912) (Itay Gabbay) #912

2cecf54: Heatmap comp (#901) (Noam Bressler) #901

b7fa71c: add confusion matrix (#828) (DBI) #828

d347792: [Issue 755] Property Drift Check (#908) (Yurii Romanyshyn) #908

c89c4c3: version bump (#927) (Itay Gabbay) #927

e3336a0: Add new checks to vision full suite (Noam Bressler) #929

3ab5dbd: Wandb integration (#906) (JKL98ISR) #906

e7f6149: Fix bugs when task_type is None and remove print (#937) (Noam Bressler) #937

0e0fba2: Adding gpu tests (#939) (Itay Gabbay) #939

f767889: Adding explanations to label drift notebook. (#936) (Itay Gabbay) #936

55b7b85: Add segment performance check (#935) (matanper) #935

5109c78: Fixing quickstart (#953) (Itay Gabbay) #953

f79caa3: Add latest version check to package init (#956) (matanper) #956

e9d89a5: Refactor label drift (#958) (Noam Bressler) #958

faa527c: New version release (github-actions[bot])

f8e6adb: Add DEEPCHECKS_DISABLE_LATEST to build (#962) (matanper) #962

c35bac6: Prediction drift check (#963) (Noam Bressler) #963

20e08a4: Add prediction and label drift conditions (#968) (Noam Bressler) #968

Fix sphinx errors (#970) #970 (Shiv Shankar Dayal)

f5ec928: Update robustness report display (#969) (matanper) #969

5d8cd2f: Update drift plot title (#971) (matanper) #971

50e2859: Fix order of results in vision suite (#977) (matanper) #977

7cc184f: Split VisionData and remove Formatters (#966) (JKL98ISR) #966

d676fde: Fix inverted confusion matrix axis (#980) (matanper) #980

0d48726: Fix vision suite run (#982) (matanper) #982

2e63c00: Fixing heatmap_comparison.py (#981) (Itay Gabbay) #981

44ef310: Import warnings fix/ import backward compatibility (#972) (Yurii Romanyshyn) #972

38e185a: Confusion matrix - detection classes ids bug (#983) (matanper) #983

2c3abe0: Update validate image data error message to be clearer and include range (#986) (matanper) #986

f482107: Add progress bars to vision suite run (#984) (matanper) #984

f0dd066: Split many files in core and base (#987) (JKL98ISR) #987

599c014: Make sure cv2 IO is unit8 (#992) (Noam Bressler) #992

f358feb: Dc 941 test load dataset use downloaded dataset (#978) (DBI) #978

c9e56cb: [Issue 922] add Batch type (#994) (Yurii Romanyshyn) #994

c26bb69: Updating Main Readme Visuals (#1002) (shir22) #1002

b513428: Simple feature contribution issue 842 (#989) (Nir Hutnik) #989

4c01c1f: 0.6.0.dev0 (#1001) (Itay Gabbay) #1001

9ba1cc8: fix errors in classification tutorial (#1003) (Noam Bressler) #1003

42b33d0: Some condition changes (#1007) (JKL98ISR) #1007

daf7456: label and predictions should be tensors (#999) (Noam Bressler) #999

4e19795: Add sample option to vision (#991) (matanper) #991

d33cc5f: Fixing RTD badge (#1008) (Itay Gabbay) #1008

6fafb35: vision model error analysis (#964) (DBI) #964

aebd6cc: Refactor image & label & prediction properties (#1013) (matanper) #1013

8ddc5fc: Heatmap per class fix (#1017) (JKL98ISR) #1017

04913ac: Add map of class to list of dataset index (#1024) (matanper) #1024

7e313a4: refactor _evaluate_image to improve performance (#1027) (DBI) #1027

ac91e11: Robustness images sampled using classes_indices (#1028) (matanper) #1028

3d5e760: [Issue 985] Confusion matrix report - unseen classes bug fix (#1029) (Yurii Romanyshyn) #1029

7a07749: deepchecks label to wandb runs (#1035) (JKL98ISR) #1035

26ec933: Remove auto cache calc on classes_indices (#1033) (matanper) #1033

78b2a49: Docs restructure (#1032) (Itay Gabbay) #1032

2aa48a7: Add "other" task type (#1037) (matanper) #1037

4dc894d: Update Readme.rst (#1044) (Julien Schuermans) #1044

66a6514: Class performance alt metrics fix (#1036) (JKL98ISR) #1036

5a5183d: Add properties user guide (#1026) (matanper) #1026

ddb23ad: Raise an error if model is in training mode when passed to check (#1050) (Noam Bressler) #1050

3438789: Visiondata docs (#1038) (Itay Gabbay) #1038

df3207b: Revert "Visiondata docs (#1038)" (#1063) (matanper) #1063

3b48e2c: Adding mocks to yolo and mnist (#1046) (Itay Gabbay) #1046

5098293: [Issue-990] "numpy_to_html_image" improvement (#1043) (Yurii Romanyshyn) #1043

14916c9: Custom vision check (#1047) (Noam Bressler) #1047

5167de4: Automatic deployment of docs to github pages (#1060) (Itay Gabbay) #1060

279b0a2: Fixing refs bug (Itay Gabbay)

d87a63d: [issue-1005] SimpleClasifficationDataset (#1045) (Yurii Romanyshyn) #1045

145bb7a: Visiondata docs (#1065) (Itay Gabbay) #1065

44c54bc: Fixing deprecation warning while importing deepchecks (#1068) (Itay Gabbay) #1068

e9e42a2: Adding version selector to the documentation (#1071) (Itay Gabbay) #1071

dc4e007: Add classes_to_display to property drift (#1070) (Noam Bressler) #1070

1e065a3: add test to vision base (#1049) (DBI) #1049

1919d1b: some_visual_improvments (#1059) (JKL98ISR) #1059

e9f8fcb: Add custom task tutorial (#1057) (matanper) #1057

a65952e: Check display validation (#1034) (JKL98ISR) #1034

3981b95: change x axis name to column name instead of value in drift graphs (#1073) (DBI) #1073

39ace96: Add a classes_to_display optional list parameter to Simple Feature Contribution (Nir Hutnik) #1072

58c421c: docs version selector fix (#1075) (Itay Gabbay) #1075

Add assert in vision objects to Batch (#1078) #1078 (matanper)

327cc01: Implement sphinx gallery (#1041) (Shiv Shankar Dayal) #1041

d1e74e5: updating docs main page for vision (#1079) (shir22) #1079

8802bcd: Deleting notebooks (#1088) (Itay Gabbay) #1088

a559684: [Issue-667] added **kwargs to all checks init (#1082) (Yurii Romanyshyn) #1082

191ae90: fix display bug (#1092) (JKL98ISR) #1092

a68b10a: Refactor for labels & image outlier (#1091) (matanper) #1091

9382cb4: update docs main page (#1090) (shir22) #1090

1a29cf2: adding code owners (#1094) (Itay Gabbay) #1094

e95572e: add test to conditions in class performance check (#1087) (DBI) #1087

2df9203: add example of limiting classes to property drift display (#1098) (Noam Bressler) #1098

6054e6a: Rst to gallery (#1099) (Itay Gabbay) #1099

f409302: Json cond fix + condition refactor (#1100) (JKL98ISR) #1100

88aa120: fix_quick_start_example (#1101) (JKL98ISR) #1101

b2b149f: Adding condition to CV simple model comparison (#1089) (Itay Gabbay) #1089

d748d30: cond_order_fix (#1106) (JKL98ISR) #1106

9a43ef6: Fix Model Error Analysis visualization and log_loss computation (JKL98ISR) #1085

14578ee: remove_accidental_code (#1111) (JKL98ISR) #1111

fdb0612: fix docstring (#1093) (DBI) #1093

c254ff2: fixed TOC, thumbnails, plots and search (#1110) (Shiv Shankar Dayal) #1110

eff50be: improve colors (#1117) (DBI) #1117

739f042: Adding the adult dataset to deepchecks.datasets (#1112) (Itay Gabbay) #1112

77c9ec1: updated setup.py (#1076) (Yurii Romanyshyn) #1076

1e6f8b4: fix read more url (#1103) (matanper) #1103

45b7735: Adding citation (#1121) (Itay Gabbay) #1121

9bd9f2b: version selector is now working (#1113) (Shiv Shankar Dayal) #1113

6cc2d25: Add a tutorial for using simple classification data to run a train test distribution suite(#1119) (Noam Bressler) #1119

2e630e8: Image property outlier (#1102) (matanper) #1102

535d0cb: make numerical type explicit + other edge case fixes (#1114) (JKL98ISR) #1114

0e7b25a: fixing search (Itay Gabbay)

ef7ffa3: add prediction drift to tabular (#1105) (DBI) #1105

83e991f: fixed broken links (#1125) (Shiv Shankar Dayal) #1125

27a18f5: [Issue-721][tabular] IdentifierLeakage fix (#1104) (Yurii Romanyshyn) #1104

4e5f63e: fixing search (#1126) (Itay Gabbay) #1126

2178e4c: Fix bad model metrics/bbox properties (#1128) (JKL98ISR) #1128

7b59f08: tabular unitests for more model types (#1131) (JKL98ISR) #1131

2aea59b: Label property outlier (#1132) (matanper) #1132

05c06bc: Fix outliers with none properties (#1136) (matanper) #1136

08b936e: remove trust score (#1146) (Noam Bressler) #1146

8ff977c: Heatmap Comparison fix to work on custom task (#1134) (matanper) #1134

dab4a3f: Sending version (#1137) (Itay Gabbay) #1137

eeb7ac5: fix_typo_context (#1149) (JKL98ISR) #1149

983a064: Remove the word alternative from properties params (#1150) (matanper) #1150

65b81ad: Fix problems in old torch version (#1152) (JKL98ISR) #1152

b3143b6: Add device to validate formatters (#1153) (matanper) #1153

a5a1bb4: Add new integrity suite and kwargs to vision suites (#1155) (matanper) #1155

d3606e8: Add README for Swindler Detection (#1158) (shir22) #1158

adde700: Fix incorrect exceptions (#1157) (matanper) #1157

01a3462: check ordinal encoder kwargs (#1154) (JKL98ISR) #1154

b545c21: Docs get started (#1156) (shir22) #1156

20bd3e0: Revert "Add README for Swindler Detection (#1158)" (Itay Gabbay)

eeb3274: adding 0.6 (Itay Gabbay)

c90fb78: adding 0.6 (Itay Gabbay)

9c3eb17: adding 0.6 (Itay Gabbay)

e8c4699: adding 0.6 (Itay Gabbay)

4e5f510: adding 0.6 (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.6.0-py3-none-any.whl(335.18 KB)
deepchecks-0.6.0.tar.gz(226.08 KB)
LICENSE(34.03 KB)
0.5.0(Mar 3, 2022)
Main Features

Introducing Weights and Biases integration

Full Changelog: https://github.com/deepchecks/deepchecks/compare/0.4.2...0.5.0
Source code(tar.gz)
Source code(zip)
deepchecks-0.5.0-py3-none-any.whl(211.78 KB)
deepchecks-0.5.0.tar.gz(145.00 KB)
LICENSE(34.03 KB)
0.5.0.dev2(Feb 23, 2022)
What's Changed

Move responsibilities from context to dataset by @matanper in https://github.com/deepchecks/deepchecks/pull/767

[issue-650] functionality split by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/751

functionality-split additional fix by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/770

links-checks github action fix by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/775

added tests for single feature data + improved tests by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/771

updated makefile by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/780

Adding base vision package by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/773

Add links to suite by @matanper in https://github.com/deepchecks/deepchecks/pull/783

Making the notebooks gen multi-procces by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/785

Vision docs placeholder by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/788

Fixing rtd build by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/790

Add sample data loader & sample predictions and label caching by @matanper in https://github.com/deepchecks/deepchecks/pull/786

Fix pandas warning by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/792

[issue-781] moved checks base classes to core by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/789

Fixing nb generation by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/795

[issue-757] updated MNIST dataset api by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/798

Adding 2022 to license by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/802

Label drift fixes by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/797

Adding labels and detection encoder classes by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/800

Fix widget plotly bug by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/807

📓 Updating Readme by @shir22 in https://github.com/deepchecks/deepchecks/pull/814

rename VisionDataset to VisionData by @noamzbr in https://github.com/deepchecks/deepchecks/pull/820

[DOCS] Updated main page and when should you use page by @shir22 in https://github.com/deepchecks/deepchecks/pull/821

add DataFormatter by @noamzbr in https://github.com/deepchecks/deepchecks/pull/812

Fixing label drift in vision submodule by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/823

rename to ImageFormatter and fix bug in VisionData init by @noamzbr in https://github.com/deepchecks/deepchecks/pull/824

[issue-799] coco split into train/test; updated coco dataset module api by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/805

change checks to init - update - compute structure by @noamzbr in https://github.com/deepchecks/deepchecks/pull/826

Fixing CI by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/838

Label drift fixes by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/808

Label drift refactor by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/843

Adding performance report nb by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/844

fix README.rst typo train-test by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/849

Added typehints where they were missing in CheckResult's methods. by @RonItay in https://github.com/deepchecks/deepchecks/pull/848

Formatter docs by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/837

[issue-787] added device parameter to the vision checks context by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/832

Added properties in image formatter by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/855

Vision quickstart notebook by @noamzbr in https://github.com/deepchecks/deepchecks/pull/853

Robustness Report by @matanper in https://github.com/deepchecks/deepchecks/pull/801

Image properties by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/858

suite fixes by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/857

Vision docs by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/860

add export to html to quickstart by @noamzbr in https://github.com/deepchecks/deepchecks/pull/865

Adding class display limit in ClassPerformance check by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/866

Add title to drift plot by @noamzbr in https://github.com/deepchecks/deepchecks/pull/867

Improved metric by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/825

Added imgaug-equivalent augmentations. by @nirbenz in https://github.com/deepchecks/deepchecks/pull/869

add wrap_run to ModelComparisonCheck by @noamzbr in https://github.com/deepchecks/deepchecks/pull/868

remove warnings by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/870

Modifying make notebook by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/873

Add recall to default metrics by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/872

Fixing deployment by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/861

Vision domain classifier issue 841 by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/879

MeanAveragePrecisionReport check by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/880

Formatters validation by @matanper in https://github.com/deepchecks/deepchecks/pull/875

Mean Average Recall Report check by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/885

Fix version read in init by @matanper in https://github.com/deepchecks/deepchecks/pull/891

Adding label_map to dataset by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/877

Vision Suite refactor by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/890

Fixed (maybe?) the tests by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/893

Make image drift tests consistent by @matanper in https://github.com/deepchecks/deepchecks/pull/902

fixing docs by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/898

Vision model comparison by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/897

Save mnist model for tests by @matanper in https://github.com/deepchecks/deepchecks/pull/905

Set seed in vision context and fix YOLO tests by @matanper in https://github.com/deepchecks/deepchecks/pull/904

[Issue 834] label formatters by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/882

Changing alternative_metrics to Dict by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/907

set default prediction formatters by @noamzbr in https://github.com/deepchecks/deepchecks/pull/909

The notebook is back by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/900

Robustness + VisionData: copy generator when copying dataset by @matanper in https://github.com/deepchecks/deepchecks/pull/924

Update robustness report notebook by @matanper in https://github.com/deepchecks/deepchecks/pull/913

Remove state copying and setting seed in robustness_report by @matanper in https://github.com/deepchecks/deepchecks/pull/926

Gpu fixes by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/912

Heatmap comp by @noamzbr in https://github.com/deepchecks/deepchecks/pull/901

add confusion matrix by @benisraeldan in https://github.com/deepchecks/deepchecks/pull/828

[Issue 755] Property Drift Check by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/908

New Contributors

@RonItay made their first contribution in https://github.com/deepchecks/deepchecks/pull/848

@nirbenz made their first contribution in https://github.com/deepchecks/deepchecks/pull/869

Full Changelog: https://github.com/deepchecks/deepchecks/compare/0.4.0...0.5.0.dev2
Source code(tar.gz)
Source code(zip)
0.5.0.dev0(Feb 14, 2022)
What's Changed

Move responsibilities from context to dataset by @matanper in https://github.com/deepchecks/deepchecks/pull/767

[issue-650] functionality split by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/751

functionality-split additional fix by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/770

links-checks github action fix by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/775

added tests for single feature data + improved tests by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/771

updated makefile by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/780

Adding base vision package by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/773

Add links to suite by @matanper in https://github.com/deepchecks/deepchecks/pull/783

Making the notebooks gen multi-procces by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/785

Vision docs placeholder by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/788

Fixing rtd build by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/790

Add sample data loader & sample predictions and label caching by @matanper in https://github.com/deepchecks/deepchecks/pull/786

Fix pandas warning by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/792

[issue-781] moved checks base classes to core by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/789

Fixing nb generation by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/795

[issue-757] updated MNIST dataset api by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/798

Adding 2022 to license by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/802

Label drift fixes by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/797

Adding labels and detection encoder classes by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/800

Fix widget plotly bug by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/807

📓 Updating Readme by @shir22 in https://github.com/deepchecks/deepchecks/pull/814

rename VisionDataset to VisionData by @noamzbr in https://github.com/deepchecks/deepchecks/pull/820

[DOCS] Updated main page and when should you use page by @shir22 in https://github.com/deepchecks/deepchecks/pull/821

add DataFormatter by @noamzbr in https://github.com/deepchecks/deepchecks/pull/812

Fixing label drift in vision submodule by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/823

rename to ImageFormatter and fix bug in VisionData init by @noamzbr in https://github.com/deepchecks/deepchecks/pull/824

[issue-799] coco split into train/test; updated coco dataset module api by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/805

change checks to init - update - compute structure by @noamzbr in https://github.com/deepchecks/deepchecks/pull/826

Fixing CI by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/838

Label drift fixes by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/808

Label drift refactor by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/843

Adding performance report nb by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/844

fix README.rst typo train-test by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/849

Added typehints where they were missing in CheckResult's methods. by @RonItay in https://github.com/deepchecks/deepchecks/pull/848

Formatter docs by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/837

[issue-787] added device parameter to the vision checks context by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/832

Added properties in image formatter by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/855

Vision quickstart notebook by @noamzbr in https://github.com/deepchecks/deepchecks/pull/853

Robustness Report by @matanper in https://github.com/deepchecks/deepchecks/pull/801

Image properties by @nirhutnik in https://github.com/deepchecks/deepchecks/pull/858

suite fixes by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/857

Vision docs by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/860

New Contributors

@RonItay made their first contribution in https://github.com/deepchecks/deepchecks/pull/848

Full Changelog: https://github.com/deepchecks/deepchecks/compare/0.4.0...0.5.0.dev0
Source code(tar.gz)
Source code(zip)
0.4.2(Feb 4, 2022)
What's Changed

Fix widget plotly bug (#807) by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/809

Docs 0.4.x fixes by @shir22 in https://github.com/deepchecks/deepchecks/pull/817

Full Changelog: https://github.com/deepchecks/deepchecks/compare/0.4.1...0.4.2
Source code(tar.gz)
Source code(zip)
deepchecks-0.4.2-py3-none-any.whl(209.00 KB)
deepchecks-0.4.2.tar.gz(142.74 KB)
LICENSE(34.03 KB)
0.4.1(Jan 26, 2022)
Commits

b8555e8: Fixing import issues (Itay Gabbay)

0764ef1: 0.4.1 (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.4.1-py3-none-any.whl(208.76 KB)
deepchecks-0.4.1.tar.gz(142.56 KB)
LICENSE(34.03 KB)
0.4.0(Jan 26, 2022)
Deepchecks Version 0.4.0

What's Changed

Update index.rst by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/699

Adding notebooks by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/702

Build model if loading causes errors by @benisraeldan in https://github.com/deepchecks/deepchecks/pull/703

merge kaggle widgets hotfix to main by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/709

update suite exceptions and links by @matanper in https://github.com/deepchecks/deepchecks/pull/706

Colab prettier suite view by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/717

Check display read more by @matanper in https://github.com/deepchecks/deepchecks/pull/715

[Issue-571] make docs errors/warnings fixes by @yromanyshyn in https://github.com/deepchecks/deepchecks/pull/725

make conda build trigger by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/726

Add check context by @matanper in https://github.com/deepchecks/deepchecks/pull/700

merge html resources fix from 0.3.2 (#728) by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/733

update readme plot by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/720

Added validations to numpydoc docstrings ✅ by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/738

Update docstrings in default_suites.py by @matanper in https://github.com/deepchecks/deepchecks/pull/718

explicitly set default timeout for fi calculation by @noamzbr in https://github.com/deepchecks/deepchecks/pull/739

Rename scorers related parameters by @matanper in https://github.com/deepchecks/deepchecks/pull/734

Removed generated html files generated from notebook by @matanper in https://github.com/deepchecks/deepchecks/pull/741

Warn user when trying to show result in a non-interactive shell by @matanper in https://github.com/deepchecks/deepchecks/pull/745

add check display tests by @benisraeldan in https://github.com/deepchecks/deepchecks/pull/735

Add more warning for the user when calculating FI by @matanper in https://github.com/deepchecks/deepchecks/pull/749

Add FI permutation options to UnusedFeatures by @matanper in https://github.com/deepchecks/deepchecks/pull/740

label amibuity notebook improvemnts by @benisraeldan in https://github.com/deepchecks/deepchecks/pull/760

add conda recipe for ipython by @benisraeldan in https://github.com/deepchecks/deepchecks/pull/764

0.4.0 version bump 🚀 by @ItayGabbay in https://github.com/deepchecks/deepchecks/pull/766

Full Changelog: https://github.com/deepchecks/deepchecks/compare/0.3.0...0.4.0
Source code(tar.gz)
Source code(zip)
deepchecks-0.4.0-py3-none-any.whl(208.74 KB)
deepchecks-0.4.0.tar.gz(142.55 KB)
LICENSE(34.03 KB)
0.3.2(Jan 24, 2022)
Commits

4c2752b: include_resources (#728) (JKL98ISR) #728

Source code(tar.gz)
Source code(zip)
deepchecks-0.3.2-py3-none-any.whl(206.75 KB)
deepchecks-0.3.2.tar.gz(141.50 KB)
LICENSE(34.03 KB)
0.3.1(Jan 20, 2022)
What's Changed

Kaggle patch for plotly widgets by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/707

add kaggle to allowed words + version bump by @JKL98ISR in https://github.com/deepchecks/deepchecks/pull/708

Full Changelog: https://github.com/deepchecks/deepchecks/compare/0.3.0...0.3.1
Source code(tar.gz)
Source code(zip)
deepchecks-0.3.1-py3-none-any.whl(204.70 KB)
deepchecks-0.3.1.tar.gz(139.63 KB)
LICENSE(34.03 KB)
0.3.0(Jan 19, 2022)
Deepchecks Version 0.3.0

Supported Environments

Support running from non-jupyter environments - Suite outputs can be exported to html, and Check or Suite outputs can be exported to JSON for serialization. HTML export: #638, JSON export: #665

Added support also for non-sklearn models. Supporting any model object that implements the predict method (and also predict_proba for classification), see supported models #594

Fixed dependency warning when installing on google colab #577

Fixed links from inside suite and check output to open in a new tab

Display and Output Improvements

Output can be exported to html #638

Suite output is now in a widget, enabling to browse quickly between checks, with and without conditions or display #610

Distribution graphs smoothened, graphs looks better also when feature has outlier values #664

Improved error handling. Checks that didn’t run due to irrelevant input (e.g. no index column) are no longer displayed as errors. #640

Docs

Improved documentation for many check examples:

Trust Score

Train Test Feature Drift

Whole Dataset Drift

Simple Model Comparison

Single Feature Contribution

Data Duplicates

Added documentation for export types:

To HTML and To JSON

New Contributors

@idow09 made their first contribution 🥳

@BioGeek made their first contribution 🥳

@shaypal5 made their first contribution 🥳

Maintainers

@matanper , @ItayGabbay , @yromanyshyn , @JKL98ISR , @benisraeldan , @deepchecks-bot , @shir22 , @nirhutnik , @noamzbr
Source code(tar.gz)
Source code(zip)
deepchecks-0.3.0-py3-none-any.whl(204.63 KB)
deepchecks-0.3.0.tar.gz(139.59 KB)
LICENSE(34.03 KB)
0.2.1(Jan 7, 2022)
Commits

d8b1d43: New version release (github-actions[bot])

1ad95b1: Update README.rst (#513) (shir22) #513

69100ea: Add examples folder and routing from readme (#514) (shir22) #514

d96117b: changing gh labels (#515) (Itay Gabbay) #515

aee01a6: Update README.rst (#516) (shir22) #516

233fbce: Update performance.rst (#519) (shir22) #519

4c7a4c1: Update display_suite.py (#518) (shir22) #518

214213a: Documentation and Readme (#521) (shir22) #521

c919dd5: Merge branch 'main' of https://github.com/deepchecks/MLChecks into 0.2.x (Itay Gabbay)

48fd68c: docs fixes (Itay Gabbay)

0cfa6f3: Fix PerfectModel when data have nulls in label (#526) (matanper) #526

2051d65: - Adding iris to the datasets section (#530) (Itay Gabbay) #530

dfa08fa: Update the Dataset Object Documentation (#500) (DBI)

9a0e202: version bump (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.2.1-py3-none-any.whl(191.64 KB)
deepchecks-0.2.1.tar.gz(129.63 KB)
LICENSE(34.03 KB)
0.2.0(Jan 5, 2022)
Commits

0df2f8e: Performance report degradation fix (#444) (JKL98ISR) #444

2a243b4: Change binary scorers to also return per class (#470) (matanper) #470

fb94d10: N top show how many (#469) (JKL98ISR) #469

84ee22f: remove_zero_warn (#476) (JKL98ISR) #476

b52add9: no_links_collab (#478) (JKL98ISR) #478

5ec8829: [Issue-439, addition] SimpleModelComparison: added 'Number of samples' column to the result df (#473) (Yurii Romanyshyn) #473

f368333: pipeline model can also no be of type BaseEstimator (#468) (DBI) #468

b3e73d2: added favicons to the docs (#481) (Yurii Romanyshyn) #481

33b4820: [issue-409] added datetime formating function to utils (#464) (Yurii Romanyshyn) #464

31018fa: nothing to show on when pps is 0 on all fields (#480) (DBI) #480

ca7866b: Small typos fixes (#487) (nirhutnik) #487

18436ed: Moving examples into docs (#495) (Itay Gabbay) #495

9b3b0b8: [Automatic] Examples Update (#496) (deepchecks-bot) #496

556c01a: Docs and readme fixes (#497) (Itay Gabbay) #497

9d29f0e: [issue-431] modified suite output text (#499) (Yurii Romanyshyn) #499

f834aee: use_undeprecated_tqdm (#498) (JKL98ISR) #498

421d332: fix errors related to string labels (#501) (JKL98ISR) #501

a54f6fe: Change condition of simple model check to use gain (#488) (matanper) #488

a33f540: Remove default from metrics (#507) (JKL98ISR) #507

682c4cf: add dataset script and notebook for phishing url example (#485) (Noam Bressler) #485

1112b97: Fix string labels in performance report / simple model comparison (#504) (JKL98ISR) #504

de8742f: dataset mutable properties fix (#509) (Yurii Romanyshyn) #509

a192c42: [Automatic] Examples Update (#510) (deepchecks-bot) #510

44d69b9: Update README.rst (#511) (shir22) #511

8076354: Limit permutation time (#512) (Noam Bressler) #512

f9e8484: 0.2.0 version bump (Itay Gabbay)

Source code(tar.gz)
Source code(zip)
deepchecks-0.2.0-py3-none-any.whl(189.64 KB)
deepchecks-0.2.0.tar.gz(128.65 KB)
LICENSE(34.03 KB)
0.1.3(Jan 4, 2022)
Commits

d332548: Fix x axis to show categorical correct (#413) (matanper) #413

Fix legend position (#414) #414 (matanper)

76f256e: [Automatic] Examples Update (#427) (deepchecks-bot) #427

fbe6da3: Fix code example of scorer note docstring (#426) (matanper) #426

44b539a: Update README.md (#442) (shir22) #442

aa87c08: Roc plotly (#415) (JKL98ISR) #415

4d22f3a: Changing tag in GA (#450) (Itay Gabbay) #450

b0c3576: Fix trying to calculate features importance on none model (#440) (matanper) #440

6c61816: Add to suite output the checks that haven't run because of missing parameters (#436) (matanper) #436

ed5fdf1: catch condition errors (#449) (JKL98ISR) #449

6e93077: Issue template (#448) (Itay Gabbay) #448

00d6726: Update config.yml (Itay Gabbay)

cc315fd: test (#445) (nirhutnik) #445

07f4624: move to plotly (#455) (DBI) #455

b8fc116: fix - validate_fitting failing when data contains only one label (Noam Bressler) #454

f3dd910: Changing README files from markdown to RST (#459) (Itay Gabbay) #459

63af15f: Fix overlap of titles in drift plots (#462) (DBI) #462

c917845: re-adding widget progressbar (#460) (JKL98ISR) #460

b95dc48: remove label_name from dataset constructor (#456) (matanper) #456

50a49e3: Updating long description (#463) (Itay Gabbay) #463

09e6fba: [issue-439] added 'Number of samples' column to the result dataframe; (#461) (Yurii Romanyshyn) #461

375ae16: Create pull_request_template.md (#465) (Itay Gabbay) #465

6080e21: Updating long description (#471) (Itay Gabbay) #471

Source code(tar.gz)
Source code(zip)
deepchecks-0.1.3-py3-none-any.whl(182.61 KB)
deepchecks-0.1.3.tar.gz(124.58 KB)
LICENSE(34.03 KB)
0.1.1(Jan 2, 2022)
Commits

551e30b: remove default class imbalance condition (#412) (Noam Bressler) #412

Source code(tar.gz)
Source code(zip)
deepchecks-0.1.1-py3-none-any.whl(181.50 KB)
deepchecks-0.1.1.tar.gz(122.60 KB)
LICENSE(34.03 KB)
0.1.0(Jan 2, 2022)
Commits

d32acfd: Mlc 456 unused feature bug (#379) (nirhutnik) #379

7c2d7a6: Label + prediction distribution (#380) (nirhutnik) #380

b92efaf: Mlc 185 drift for label and pred (#388) (nirhutnik) #388

e531c07: Make trust score plot cut off upper and lower 5% of data for display (#391) (Noam Bressler) #391

b994b21: Fixing conda packaging (#389) (Itay Gabbay) #389

839f63a: Mlc 185 drift for label and pred (#396) (nirhutnik) #396

7fc29b5: Simple model classes fix (#395) (matanper) #395

eb1300d: Fix confusion matrix heatmap (#398) (matanper) #398

ceee746: Add explanation about scorers to simple model check (#387) (matanper) #387

c3995e7: assume task type from model or label (#397) (DBI) #397

700598c: Mlc 292 model error analysis (#378) (Noam Bressler) #378

e537e01: Temporary fix for groupby on category (#399) (matanper) #399

a18fc89: Merge performance report, Class Performance Imbalance and Train test Overfit into one check (#364) (JKL98ISR) #364

5b8e1bc: drop nan before gausian_kde (#402) (DBI) #402

eb7e6bb: only one performance report (#406) (Noam Bressler) #406

df7ad4e: Fix model info to work with pipeline (#405) (matanper) #405

652a1f4: Updated readme, user guide and how-to-guides (#340) (shir22) #340

f6b70e0: v0 (#410) (JKL98ISR) #410

9279b06: [Automatic] Examples Update (#411) (deepchecks-bot) #411

Source code(tar.gz)
Source code(zip)
deepchecks-0.1.0-py3-none-any.whl(181.52 KB)
deepchecks-0.1.0.tar.gz(122.71 KB)
LICENSE(34.03 KB)

Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort

Related tags

Overview

Test Suites for Validating ML Models & Data

Installation

Using pip

Using conda

Try it Out!

Usage Examples

Running a Suite

Single Dataset Integrity Suite

Conditions Summary

Running a Check

Train Test Drift

Key Concepts

Check

Condition

Suite

What Do You Need in Order to Start Validating?

Environment

Data / Model

Documentation

Community

Comments

Describe the solution you'd like

Describe alternatives you've considered

Additional context

What could this look like?

How can we visualize performance at the intersection of many subgroups?

What conditions could be added?

Why only allow for a single control feature?

What else could be considered?

Reference Issues/PRs

What does this implement/fix? Explain your changes.

Any other comments?

TODO

Reference Issues/PRs

Any other comments?

Reference Issues/PRs

What does this implement/fix? Explain your changes.

Any other comments?

Research

Link to question on StackOverflow

Question about deepchecks

Is your feature request related to a problem? Please describe.

Describe the solution you'd like

Describe alternatives you've considered

Releases(0.10.0)

0.10.0(Dec 11, 2022)

What's Changed

New Contributors

0.9.2(Oct 31, 2022)

Commits

0.9.1(Oct 25, 2022)

Bug Fixes

Documentation

Commits

0.9.0(Sep 19, 2022)

What's Changed

New Contributors

0.8.3(Aug 23, 2022)

Commits

0.8.2(Aug 8, 2022)

What's Changed

New Contributors

0.8.1(Jul 7, 2022)

Commits

0.8.0(Jun 19, 2022)

Commits

0.7.2(Jun 7, 2022)

Commits

0.7.1(May 31, 2022)

Commits

0.7.0(May 29, 2022)

Commits

0.6.4(May 12, 2022)

Commits

0.6.3(Apr 28, 2022)

Commits

0.6.2(Apr 28, 2022)