Prep for release v0.2.0 (#172)

Prep for release v0.2.0 ...

Co-authored-by: Amirhessam Tahmassebi <admin@slickml.com>

CHANGELOG.md

@@ -5,12 +5,20 @@
 - Please checkout [SlickML Official Releases](https://github.com/slickml/slick-ml/releases) for more details.
 
 
----
 ## 📍 Unreleased Version X.X.X - XXXX-XX-XX
+### 🛠 Fixed
+
+### 🔥 Added
+---
+## 📍 Version 0.2.0 - 2022-11-27
 
 ### 🛠 Fixed
+- [#170](https://github.com/slickml/slick-ml/pull/170) enabled more `flake8` plugins and fixed `poe check` command and `mypy` dependencies.
+- [#169](https://github.com/slickml/slick-ml/pull/169) refactored `XGBoostHyperOptimizer` class.
 
 ### 🔥 Added
+- [#171](https://github.com/slickml/slick-ml/pull/171) added `type-stubs` and rolled out type checking with `mypy` across library.
+
 ---
 
 ## 📍 Version 0.2.0-beta.2 - 2022-11-13

README.md

@@ -5,9 +5,9 @@
 [![codecov](https://codecov.io/gh/slickml/slick-ml/branch/master/graph/badge.svg?token=Z7XP51MB4K)](https://codecov.io/gh/slickml/slick-ml)
 ![dependencies](https://img.shields.io/librariesio/github/slickml/slick-ml)
 [![license](https://img.shields.io/github/license/slickml/slick-ml)](https://github.com/slickml/slick-ml/blob/master/LICENSE/)
-[![downloads](https://pepy.tech/badge/slickml)](https://pepy.tech/project/slickml)
 ![pypi_version](https://img.shields.io/pypi/v/slickml)
 ![python_version](https://img.shields.io/pypi/pyversions/slickml)
+[![downloads](https://pepy.tech/badge/slickml)](https://pepy.tech/project/slickml)
 [![slack_invite](https://badgen.net/badge/Join/SlickML%20Slack/purple?icon=slack)](https://www.slickml.com/slack-invite)
 ![twitter_url](https://img.shields.io/twitter/url?style=social&url=https%3A%2F%2Ftwitter.com%2FSlickML)
 
@@ -43,7 +43,9 @@ good portion of the tasks based on tabular data can be addressed via gradient bo
 linear models<sup>[1](https://arxiv.org/pdf/2207.08815.pdf)</sup>. SlickML provides Data Scientists
 with a toolbox to quickly prototype solutions for a given problem with minimal code while maximizing
 the amount of information that can be inferred. Additionally, the prototype solutions can be easily
-promoted and served in production with our recommended recipes. 
+promoted and served in production with our recommended recipes via various model serving frameworks
+including [ZenML](https://github.com/zenml-io/zenml), [BentoML](https://github.com/bentoml/BentoML),
+and [Prefect](https://github.com/PrefectHQ/prefect). More details coming soon 🤞 ...
 
 
 ## 📖 Documentation

examples/quick-starts/optimization/XGBoostBayesianOptimizer.ipynb

@@ -30,7 +30,7 @@
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "Loaded SlickML Version = 0.2.0b1\n"
+      "Loaded SlickML Version = 0.2.0\n"
      ]
     }
    ],
@@ -56,11 +56,10 @@
       " |  \n",
       " |  XGBoost Hyper-Parameters Tuner using Bayesian Optimization.\n",
       " |  \n",
-      " |  This is wrapper using Bayesian Optimization algorithm [bayesian-optimization]_to tune the\n",
+      " |  This is wrapper using Bayesian Optimization algorithm [bayesian-optimization]_ to tune the\n",
       " |  hyper-parameter of XGBoost [xgboost-api]_ using ``xgboost.cv()`` functionality with n-folds\n",
       " |  cross-validation iteratively. This feature can be used to find the set of optimized set of\n",
       " |  hyper-parameters for both classification and regression tasks.\n",
-      " |  the optimized set of parameters before training.\n",
       " |  \n",
       " |  Notes\n",
       " |  -----\n",
@@ -91,7 +90,7 @@
       " |  objective : str, optional\n",
       " |      Objective function depending on the task whether it is regression or classification. Possible\n",
       " |      objectives for classification ``\"binary:logistic\"`` and for regression ``\"reg:logistic\"``,\n",
-      " |      ``\"reg:squaredlogerror\"``, and ``\"reg:squaredlogerror\"``, by default \"binary:logistic\"\n",
+      " |      ``\"reg:squarederror\"``, and ``\"reg:squaredlogerror\"``, by default \"binary:logistic\"\n",
       " |  \n",
       " |  acquisition_criterion : str, optional\n",
       " |      Acquisition criterion method with possible options of ``\"ei\"`` (Expected Improvement),\n",
@@ -223,10 +222,10 @@
       " |      \n",
       " |      Parameters\n",
       " |      ----------\n",
-      " |      X_train : Union[pd.DataFrame, np.ndarray]\n",
+      " |      X : Union[pd.DataFrame, np.ndarray]\n",
       " |          Input data for training (features)\n",
       " |      \n",
-      " |      y_train : Union[List[float], np.ndarray, pd.Series]\n",
+      " |      y : Union[List[float], np.ndarray, pd.Series]\n",
       " |          Input ground truth for training (targets)\n",
       " |      \n",
       " |      Returns\n",
@@ -254,7 +253,7 @@
       " |      -------\n",
       " |      BayesianOptimization\n",
       " |  \n",
-      " |  get_params_bounds(self) -> Dict[str, Tuple[Union[int, float], Union[int, float]]]\n",
+      " |  get_params_bounds(self) -> Optional[Dict[str, Tuple[Union[int, float], Union[int, float]]]]\n",
       " |      Returns the hyper-parameters boundaries for the tuning process.\n",
       " |      \n",
       " |      Returns\n",
@@ -812,7 +811,7 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<bayes_opt.bayesian_optimization.BayesianOptimization at 0x12dd73c10>"
+       "<bayes_opt.bayesian_optimization.BayesianOptimization at 0x1319f69a0>"
       ]
      },
      "metadata": {},
@@ -1276,6 +1275,14 @@
     }
    ],
    "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Feel free to add your favorite `Example` via a `pull-request`.\n",
+    "### More details can be found in our [Contributing Document](https://github.com/slickml/slick-ml/blob/master/CONTRIBUTING.md)."
+   ],
+   "metadata": {}
   }
  ],
  "metadata": {