Merge branch 'master' into time_series

.github/workflows/test.yml

@@ -1,6 +1,10 @@
 name: pytest on push
 
-on: [push, pull_request]
+on:
+  push:
+  pull_request:
+  schedule:
+    - cron: "0 5 * * *"
 
 jobs:
   # JOBS MUST START WITH test !!!!
@@ -24,6 +28,7 @@ jobs:
           if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
           if [ -f requirements-ts.txt ]; then pip install -r requirements-ts.txt; fi
           if [ -f requirements-optional.txt ]; then pip install -r requirements-optional.txt; fi
+          if [ -f requirements-test.txt ]; then pip install -r requirements-test.txt; fi
           pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0-py3-none-any.whl
       - name: Remove tests
         run: |
@@ -54,9 +59,10 @@ jobs:
           python -m pip install --upgrade pip
           python -m pip install -U pytest
           python -m pip install codecov
-          pip install -r requirements.txt
-          pip install -r requirements-ts.txt
-          pip install -r requirements-optional.txt
+          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+          if [ -f requirements-ts.txt ]; then pip install -r requirements-ts.txt; fi
+          if [ -f requirements-optional.txt ]; then pip install -r requirements-optional.txt; fi
+          if [ -f requirements-test.txt ]; then pip install -r requirements-test.txt; fi
           pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0-py3-none-any.whl
       - name: Remove tests
         run: |
@@ -158,6 +164,7 @@ jobs:
           if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
           if [ -f requirements-ts.txt ]; then pip install -r requirements-ts.txt; fi
           if [ -f requirements-optional.txt ]; then pip install -r requirements-optional.txt; fi
+          if [ -f requirements-test.txt ]; then pip install -r requirements-test.txt; fi
           pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0-py3-none-any.whl
       - name: Remove tests
         run: |
@@ -214,6 +221,7 @@ jobs:
           if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
           if [ -f requirements-ts.txt ]; then pip install -r requirements-ts.txt; fi
           if [ -f requirements-optional.txt ]; then pip install -r requirements-optional.txt; fi
+          if [ -f requirements-test.txt ]; then pip install -r requirements-test.txt; fi
           pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0-py3-none-any.whl
       - name: Remove tests
         run: |

CHANGELOG.md

@@ -2,11 +2,42 @@
 All notable changes to this project will be documented in this file.
 <br/><br/>
 
-#### Release: PyCaret 2.3.2 | Release Date: XX XX, 2021 (SEVERAL BUGS FIXED)
-- `gain` and `lift` plot fixed in `pycaret.classification` module. Previously `y_pred` was passed instead of `y_true`. 
-- The Kolmogorov-Smirnov (or KS) plot added in the `plot_model` function under `pycaret.classification` module.
-- Partial Dependence Plot is now added in the `interpret_model` function under `pycaret.classification` and `pycaret.regression`.
-- shap value force plot for LightGBM in the `interpret_model` function was displayed as multiclass plot. Bug fixed now.
+#### Release: PyCaret 2.3.4 | Release Date: September 23rd, 2021 (NEW FEATURES, BUG FIXES)
+- Added `get_leaderboard` function for classification and regression modules
+- It is now possible to specify the plot save path with the save argument of `plot_model` and `interpret_model` - thanks to @bhanuteja2001 (https://github.com/pycaret/pycaret/pull/1537)
+- Fixed `interpret_model` affecting `plot_model` behavior - thanks to @naujgf (https://github.com/pycaret/pycaret/pull/1600)
+- Fixed issues with conda builds - thanks to @melonhead901 (https://github.com/pycaret/pycaret/pull/1479)
+- Documentation improvements - thanks to @caron14 and @harsh204016 (https://github.com/pycaret/pycaret/pull/1499, https://github.com/pycaret/pycaret/pull/1502)
+- Fixed `blend_models` and `stack_models` throwing an exception when using custom estimators (https://github.com/pycaret/pycaret/pull/1500)
+- Fixed a "Target Missing" issue with "Remove Multicolinearity" option (https://github.com/pycaret/pycaret/pull/1508)
+- `errors="ignore"` parameter for `compare_models` now correctly ignores errors during full fit (https://github.com/pycaret/pycaret/pull/1510)
+- Fixed certain data types being incorrectly encoded as int64 during setup (https://github.com/pycaret/pycaret/pull/1515)
+- `Pinned numba<0.54` (https://github.com/pycaret/pycaret/pull/1530)
+<br/><br/><br/>
+
+#### Release: PyCaret 2.3.3 | Release Date: July 24th, 2021 (NEW FEATURES, BUG FIXES)
+- Fixed issues with `[full]` install by pinning `interpret<=0.2.4`
+- Added support for S3 folder path in `deploy_model()` with AWS
+- Enabled experimental Optuna `TPESampler` options to improve convergence (in `tune_model()`)
+<br/><br/><br/>
+
+#### Release: PyCaret 2.3.2 | Release Date: July 7th, 2021 (NEW FEATURES, BUG FIXES)
+- Implemented PDP, MSA and PFI plots in `interpret_model` - thanks to @IncubatorShokuhou (https://github.com/pycaret/pycaret/pull/1415)
+- Implemented  Kolmogorov-Smirnov (KS) plot in `plot_model` under `pycaret.classification` module
+- Fixed a typo "RVF" to "RBF" - thanks to @baturayo (https://github.com/pycaret/pycaret/pull/1220)
+- Readme & license updates and improvements
+- Fixed `remove_multicollinearity` considering categorical features
+- Fixed keyword issues with PyCaret's cuML wrappers
+- Improved performance of iterative imputation
+- Fixed `gain` and `lift` plots taking wrong arguments, creating misleading plots
+- `interpret_model` on LightGBM will now show a beeswarm plot
+- Multiple improvements to exception handling and documentation in `pycaret.persistence` (https://github.com/pycaret/pycaret/pull/1324)
+- `remove_perfect_collinearity` option will now be show in the `setup()` summary - thanks to @mjkanji (https://github.com/pycaret/pycaret/pull/1342)
+- Fixed `IterativeImputer` setting wrong float precision
+- Fixed custom grids in `tune_model` raising an exception when composed of lists
+- Improved documentation in `pycaret.clustering` - thanks to @susmitpy (https://github.com/pycaret/pycaret/pull/1372)
+- Added support for LightGBM CUDA version - thanks to @IncubatorShokuhou (https://github.com/pycaret/pycaret/pull/1396)
+- Exposed `address` in `get_data` for alternative data sources - thanks to @IncubatorShokuhou (https://github.com/pycaret/pycaret/pull/1416)
 <br/><br/><br/>
 
 #### Release: PyCaret 2.3.1 | Release Date: April 28, 2021 (SEVERAL BUGS FIXED)

MANIFEST.in

@@ -1 +1 @@
-include README.md LICENSE requirements.txt requirements-optional.txt
+include README.md LICENSE requirements.txt requirements-optional.txt requirements-test.txt

README.md

@@ -1,14 +1,20 @@
-![alt text](https://github.com/pycaret/pycaret/blob/master/pycaret2.3.png)
+![alt text](https://github.com/pycaret/pycaret/blob/master/pycaret_234.jfif)
 
 # PyCaret 2.3
-![Python](https://img.shields.io/badge/Python-3.6%20%7C%203.7%20%7C%203.8-blue) ![pytest on push](https://github.com/pycaret/pycaret/workflows/pytest%20on%20push/badge.svg) [![Documentation Status](https://readthedocs.org/projects/pip/badge/?version=stable)](http://pip.pypa.io/en/stable/?badge=stable) [![PyPI version](https://badge.fury.io/py/pycaret.svg)](https://badge.fury.io/py/pycaret) [![License](https://img.shields.io/pypi/l/ansicolortags.svg)](https://img.shields.io/pypi/l/ansicolortags.svg) [![Git count](http://hits.dwyl.com/pycaret/pycaret/pycaret.svg)](http://hits.dwyl.com/pycaret/pycaret/pycaret) [![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/pycaret/shared_invite/zt-qc4pfcw5-OLV3UFF~DzMHFyaJeFKw~A)
+[![Python](https://img.shields.io/badge/Python-3.6%20%7C%203.7%20%7C%203.8-blue)](https://badge.fury.io/py/pycaret) 
+![pytest on push](https://github.com/pycaret/pycaret/workflows/pytest%20on%20push/badge.svg) 
+[![Documentation Status](https://readthedocs.org/projects/pip/badge/?version=stable)](http://pip.pypa.io/en/stable/?badge=stable) 
+[![PyPI version](https://badge.fury.io/py/pycaret.svg)](https://badge.fury.io/py/pycaret) 
+[![License](https://img.shields.io/pypi/l/ansicolortags.svg)](https://img.shields.io/pypi/l/ansicolortags.svg) 
+<!-- [![Git count](http://hits.dwyl.com/pycaret/pycaret/pycaret.svg)](http://hits.dwyl.com/pycaret/pycaret/pycaret) -->
+[![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/pycaret/shared_invite/zt-row9phbm-BoJdEVPYnGf7_NxNBP307w)
 
 ## What is PyCaret?
 PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.
 
 In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and many more.
 
-The design and simplicity of PyCaret is inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. Seasoned data scientists are often difficult to find and expensive to hire but citizen data scientists can be an effective way to mitigate this gap and address data-related challenges in the business setting.
+The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. Seasoned data scientists are often difficult to find and expensive to hire but citizen data scientists can be an effective way to mitigate this gap and address data-related challenges in the business setting.
 
 PyCaret is a great library which not only simplifies the machine learning tasks for citizen data scientists but also helps new startups to reduce the cost of investing in a team of data scientists. Therefore, this library has not only helped the citizen data scientists but has also helped individuals who want to start exploring the field of data science, having no prior knowledge in this field. Iniitial idea of PyCaret was inspired by Caret library in R.
 
@@ -18,7 +24,7 @@ PyCaret is a great library which not only simplifies the machine learning tasks
 ![alt text](https://github.com/pycaret/pycaret/blob/master/pycaret2-features.png)
 
 ## Current Release
-PyCaret `2.3.1` is now available. See `2.3.1` release notes. The easiest way to install pycaret is using pip.
+PyCaret `2.3.4` is now available. See `2.3.4` release notes. The easiest way to install pycaret is using pip.
 
 ```python
 pip install pycaret
@@ -66,17 +72,17 @@ cuML >= 0.15 cannot be installed on Google Colab. Instead use blazingSQL (https:
 - Other Resources: https://github.com/pycaret/pycaret/tree/master/resources
 - Issue Logs: https://github.com/pycaret/pycaret/issues
 - Contribute: https://pycaret.readthedocs.io/en/latest/contribute.html
-- Join Slack Community: https://join.slack.com/t/pycaret/shared_invite/zt-qc4pfcw5-OLV3UFF~DzMHFyaJeFKw~A
+- Join Slack Community: https://join.slack.com/t/pycaret/shared_invite/zt-row9phbm-BoJdEVPYnGf7_NxNBP307w
 
 ## Who should use PyCaret?
 PyCaret is an open source library that anybody can use. In our view the ideal target audience of PyCaret is: <br />
 
 - Experienced Data Scientists who want to increase productivity.
 - Citizen Data Scientists who prefer a low code machine learning solution.
 - Data Science Students.
-- Data Science Professionals who wants to build rapid prototypes.
+- Data Science Professionals who want to build rapid prototypes.
 
-## Current Contributors
+## Contributors
 <a href="https://github.com/pycaret/pycaret/graphs/contributors">
   <img src="https://contributors-img.web.app/image?repo=pycaret/pycaret" />
 </a>