Renaming MLChecks to deepchecks (deepchecks#133)

* Renaming MLChecks to deepchecks

* linting

* linting

CONTRIBUTING.md

@@ -1,10 +1,10 @@
 # Contributing Guidelines
 
 ## Pull Request Checklist
-- Read the [contributing guidelines](https://github.com/deepchecks/MLChecks/blob/master/CONTRIBUTING.md).
-- Check if your changes are consistent with the [guidelines](https://github.com/deepchecks/MLChecks/blob/master/CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution).
-- Changes are consistent with the [coding style](https://github.com/deepchecks/MLChecks/blob/master/CONTRIBUTING.md#coding-style)
-- Run the [unit tests](https://github.com/deepchecks/MLChecks/blob/master/CONTRIBUTING.md#running-unit-tests).
+- Read the [contributing guidelines](https://github.com/deepchecks/deepchecks/blob/master/CONTRIBUTING.md).
+- Check if your changes are consistent with the [guidelines](https://github.com/deepchecks/deepchecks/blob/master/CONTRIBUTING.md#general-guidelines-and-philosophy-for-contribution).
+- Changes are consistent with the [coding style](https://github.com/deepchecks/deepchecks/blob/master/CONTRIBUTING.md#coding-style)
+- Run the [unit tests](https://github.com/deepchecks/deepchecks/blob/master/CONTRIBUTING.md#running-unit-tests).
 
 
 

LICENSE

@@ -1,8 +1,8 @@
 ================================
- MLChecks licensing terms
+ Deepchecks licensing terms
 ================================
 
-MLChecks is licensed under the terms of the  GNU AFFERO GENERAL PUBLIC LICENSE Version 3
+Deepchecks is licensed under the terms of the  GNU AFFERO GENERAL PUBLIC LICENSE Version 3
 as published by the Free Software Foundation; 
 
 --------------------------------

README.md

@@ -1,18 +1,18 @@
 
-# MLChecks
+# Deepchecks
 
-![pyVersions](https://img.shields.io/pypi/pyversions/mlchecks)
-![pkgVersion](https://img.shields.io/pypi/v/mlchecks)
-![build](https://github.com/deepchecks/mlchecks/actions/workflows/build.yml/badge.svg)
-![coverage](https://deepchecks-public.s3.eu-west-1.amazonaws.com/mlchecks/coverage.svg)
-![pylint](https://deepchecks-public.s3.eu-west-1.amazonaws.com/mlchecks/pylint.svg)
+![pyVersions](https://img.shields.io/pypi/pyversions/deepchecks)
+![pkgVersion](https://img.shields.io/pypi/v/deepchecks)
+![build](https://github.com/deepchecks/deepchecks/actions/workflows/build.yml/badge.svg)
+![coverage](https://deepchecks-public.s3.eu-west-1.amazonaws.com/deepchecks/coverage.svg)
+![pylint](https://deepchecks-public.s3.eu-west-1.amazonaws.com/deepchecks/pylint.svg)
 
-MLChecks is a Python package, for quickly and efficiently validating many aspects of your trained machine learning models. These include model performance related issues, machine learning methodology best-practices, model and data integrity.
+Deepchecks is a Python package, for quickly and efficiently validating many aspects of your trained machine learning models. These include model performance related issues, machine learning methodology best-practices, model and data integrity.
 
 ## 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 MLChecks package, covering all kinds of common issues, such as:
+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:
 
 -   PerformanceOverfit
 
@@ -28,22 +28,22 @@ Each check enables you to inspect a specific aspect of your data and models. The
 Each check displays a visual result and returns a custom result value that can be used to validate the expected check results by setting conditions upon them.
 
 #### Suite
-An ordered collection of checks. [Here](./mlchecks/suites) you can find the existing suites and a code example for how to add your own custom suite. You can edit the preconfigured suites or build a suite of your own with a collection of checks and result conditions.
+An ordered collection of checks. [Here](deepchecks/suites) you can find the existing suites and a code example for how to add your own custom suite. You can edit the preconfigured suites or build a suite of your own with a collection of checks and result conditions.
 
 
 ## Installation
 
 #### Using pip with package wheel file
 From the directory in which you have the wheel file, run:
 ```bash
-pip3 install MLChecks-latest.whl #--user
+pip3 install deepchecks-latest.whl #--user
 ```
 
 #### From source
 First clone the repository and then install the package from inside the repository's directory:
 ```bash
-git clone https://github.com/deepchecks/MLChecks.git
-cd MLChecks
+git clone https://github.com/deepchecks/deepchecks.git
+cd deepchecks
 python setup.py install # --user
 ```
 
@@ -64,8 +64,9 @@ Additionally, many of the checks and some of the suites need only a subset of th
 
 ### Running a Check
 For running a specific check on your dataframe, all you need to do is:
+
 ```python
-from mlchecks.checks import *
+from deepchecks.checks import *
 import pandas as pd
 
 df_to_check = pd.read_csv('data_to_validate.csv')
@@ -124,19 +125,20 @@ df_train = iris_df[:train_len]
 df_val = iris_df[train_len:]
 ```
 To run an existing suite all you need to do is import the suite and run it -
+
 ```python
-from mlchecks.suites import IntegrityCheckSuite
+from deepchecks.suites import IntegrityCheckSuite
 IntegrityCheckSuite.run(train_dataset=df_train, validation_dataset=df_val, check_datasets_policy='both')
 ```
 Which will result in printing the outputs of all of the checks that are in that suite.
 
 ### Example Notebooks
 For full usage examples, check out: 
-- [**MLChecks Quick Start Notebook**](./notebooks/examples/models/Iris%20Dataset%20CheckSuite%20Example.ipynb) - for a simple example notebook for working with checks and suites.
+- [**deepchecks Quick Start Notebook**](./notebooks/examples/models/Iris%20Dataset%20CheckSuite%20Example.ipynb) - for a simple example notebook for working with checks and suites.
 - [**Example Checks Output Notebooks**](./notebooks) - to see all of the existing checks and their usage examples.
 
 ## Communication
 - Join our [Slack Community](https://join.slack.com/t/mlcheckscommunity/shared_invite/zt-y28sjt1v-PBT50S3uoyWui_Deg5L_jg) to connect with the maintainers and follow users and interesting discussions
-- Post a [Github Issue](https://github.com/deepchecks/MLChecks/issues) to suggest improvements, open an issue, or share feedback.
+- Post a [Github Issue](https://github.com/deepchecks/deepchecks/issues) to suggest improvements, open an issue, or share feedback.
 
-[comment]: <> "- Send us an [email](mailto:info@mlchecks.com) at info@mlchecks.com"
+[comment]: <> "- Send us an [email](mailto:info@deepchecks.com) at info@deepchecks.com"

mlchecks/__init__.py → deepchecks/__init__.py

@@ -1,4 +1,4 @@
-"""Top module for MLChecks library."""
+"""Top module for deepchecks library."""
 from .base import *
 import matplotlib
 import matplotlib.pyplot as plt

mlchecks/base/__init__.py → deepchecks/base/__init__.py

@@ -1,6 +1,6 @@
 """Module for base classes.
 
-Import objects to be available in parent mlchecks module.
+Import objects to be available in parent deepchecks module.
 """
 from .dataset import *
 from .check import *

mlchecks/base/check.py → deepchecks/base/check.py

@@ -12,8 +12,8 @@
 from IPython.core.display import display_html
 from matplotlib import pyplot as plt
 
-from mlchecks.string_utils import split_camel_case
-from mlchecks.utils import MLChecksValueError
+from deepchecks.string_utils import split_camel_case
+from deepchecks.utils import DeepchecksValueError
 
 
 class Condition:
@@ -25,10 +25,10 @@ class Condition:
 
     def __init__(self, name: str, function: Callable, params: Dict):
         if not isinstance(function, Callable):
-            raise MLChecksValueError(f'Condition must be a function `(Any) -> Union[ConditionResult, bool]`, '
+            raise DeepchecksValueError(f'Condition must be a function `(Any) -> Union[ConditionResult, bool]`, '
                                      f'but got: {type(function).__name__}')
         if not isinstance(name, str):
-            raise MLChecksValueError(f'Condition name must be of type str but got: {type(name).__name__}')
+            raise DeepchecksValueError(f'Condition name must be of type str but got: {type(name).__name__}')
         self.name = name
         self.function = function
         self.params = params
@@ -133,7 +133,7 @@ def __init__(self, value, header: str = None, check=None, display: Any = None):
 
         for item in self.display:
             if not isinstance(item, (str, pd.DataFrame, Callable)):
-                raise MLChecksValueError(f'Can\'t display item of type: {type(item)}')
+                raise DeepchecksValueError(f'Can\'t display item of type: {type(item)}')
 
     def _ipython_display_(self):
         if self.header:
@@ -210,7 +210,7 @@ def conditions_decision(self, result: CheckResult) -> List[ConditionResult]:
             if isinstance(output, bool):
                 output = ConditionResult(output)
             elif not isinstance(output, ConditionResult):
-                raise MLChecksValueError(f'Invalid return type from condition {condition.name}, got: {type(output)}')
+                raise DeepchecksValueError(f'Invalid return type from condition {condition.name}, got: {type(output)}')
             output.set_name(condition.name)
             results.append(output)
         return results
@@ -267,7 +267,7 @@ def remove_condition(self, index: int):
             index (int): index of condtion to remove
         """
         if index not in self._conditions:
-            raise MLChecksValueError(f'Index {index} of conditions does not exists')
+            raise DeepchecksValueError(f'Index {index} of conditions does not exists')
         self._conditions.pop(index)
 
 

mlchecks/base/dataframe_utils.py → deepchecks/base/dataframe_utils.py

@@ -3,7 +3,7 @@
 
 import pandas as pd
 
-from mlchecks.utils import MLChecksValueError
+from deepchecks.utils import DeepchecksValueError
 
 
 __all__ = ['validate_columns_exist', 'filter_columns_with_validation']
@@ -17,21 +17,21 @@ def validate_columns_exist(df: pd.DataFrame, columns: Union[None, str, List[str]
         columns (Union[None, str, List[str]]): Column names to check
 
     Raise:
-        MLChecksValueError: In case one of columns given don't exists raise error
+        DeepchecksValueError: In case one of columns given don't exists raise error
     """
     if columns is None:
-        raise MLChecksValueError('Got empty columns')
+        raise DeepchecksValueError('Got empty columns')
     if isinstance(columns, str):
         columns = [columns]
     elif isinstance(columns, List):
         if any((not isinstance(s, str) for s in columns)):
-            raise MLChecksValueError(f'Columns must be of type str: {", ".join(columns)}')
+            raise DeepchecksValueError(f'Columns must be of type str: {", ".join(columns)}')
     else:
-        raise MLChecksValueError(f'Columns must be of types `str` or `List[str]`, but got {type(columns).__name__}')
+        raise DeepchecksValueError(f'Columns must be of types `str` or `List[str]`, but got {type(columns).__name__}')
     # Check columns exists
     non_exists = set(columns) - set(df.columns)
     if non_exists:
-        raise MLChecksValueError(f'Given columns do not exist in dataset: {", ".join(non_exists)}')
+        raise DeepchecksValueError(f'Given columns do not exist in dataset: {", ".join(non_exists)}')
 
 
 def filter_columns_with_validation(df: pd.DataFrame, columns: Union[str, List[str], None] = None,
@@ -43,10 +43,11 @@ def filter_columns_with_validation(df: pd.DataFrame, columns: Union[str, List[st
         columns (Union[str, List[str], None]): Column names to keep.
         ignore_columns (Union[str, List[str], None]): Column names to drop.
     Raise:
-        MLChecksValueError: In case one of columns given don't exists raise error
+        DeepchecksValueError: In case one of columns given don't exists raise error
     """
     if columns and ignore_columns:
-        raise MLChecksValueError('Cannot receive both parameters "columns" and "ignore", only one must be used at most')
+        raise DeepchecksValueError('Cannot receive both parameters "columns" and "ignore", '
+                                   'only one must be used at most')
     elif columns:
         validate_columns_exist(df, columns)
         return df[columns]