Merge pull request #145 from yg79/feature/v1.2.0

Feature/v1.2.0

Author: yg79

README.md

@@ -19,6 +19,11 @@ It provides several types of visualization that display explicit labels that eve
 Data Scientists can more easily understand their models and share their results. End users can understand the decision proposed by a model using a summary of the most influential criteria.
 
 - Readthedocs: [![documentation badge](https://readthedocs.org/projects/shapash/badge/?version=latest)](https://shapash.readthedocs.io/en/latest/)
+- Medium: [Towards AI](https://pub.towardsai.net/shapash-making-ml-models-understandable-by-everyone-8f96ad469eb3)
+- Presentation video for french speakers:
+<p align="center">
+  <a href="https://www.youtube.com/watch?v=r1R_A9B9apk" target="_blank"><img src="https://raw.githubusercontent.com/MAIF/shapash/master/docs/_static/webinar_screenshot.png" width="550" alt="French Webinar"/></a>
+</p>
 
 <br/> <br/>
 

docs/tutorials/tutorial01-Shapash-Overview-Launch-WebApp.rst

@@ -105,10 +105,6 @@ Model Fitting
 
     regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)
 
-.. code:: ipython
-
-    y_pred = pd.DataFrame(regressor.predict(Xtest),columns=['pred'],index=Xtest.index)
-
 Understanding my model with shapash
 -----------------------------------
 
@@ -128,8 +124,7 @@ Declare and Compile SmartExplainer
     xpl.compile(
         x=Xtest,
         model=regressor,
-        preprocessing=encoder, # Optional: compile step can use inverse_transform method
-        y_pred=y_pred # Optional
+        preprocessing=encoder # Optional: compile step can use inverse_transform method
     )
 
 

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.1.0
+current_version = 1.2.0
 commit = True
 tag = True
 

setup.py

@@ -30,7 +30,7 @@
 
 setup(
     name="shapash", # Replace with your own username
-    version="1.1.0",
+    version="1.2.0",
     python_requires='>3.5, < 3.9',
     url='https://github.com/MAIF/shapash',
     author="Yann Golhen, Sebastien Bidault, Yann Lagre, Maxime Gendre",

shapash/__init__.py

@@ -3,4 +3,4 @@
 
 __author__ = """Yann Golhen, Yann Lagré, Sebastien Bidault, Maxime Gendre, Thomas Bouche, Johann Martin"""
 __email__ = '[email protected], [email protected], [email protected], [email protected]'
-__version__ = '1.1.0'
+__version__ = '1.2.0'

tutorial/tutorial01-Shapash-Overview-Launch-WebApp.ipynb

@@ -389,15 +389,6 @@
     "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = pd.DataFrame(regressor.predict(Xtest),columns=['pred'],index=Xtest.index)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -447,8 +438,7 @@
     "xpl.compile(\n",
     "    x=Xtest,\n",
     "    model=regressor,\n",
-    "    preprocessing=encoder, # Optional: compile step can use inverse_transform method\n",
-    "    y_pred=y_pred # Optional\n",
+    "    preprocessing=encoder # Optional: compile step can use inverse_transform method\n",
     ")"
    ]
   },
@@ -699,4 +689,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}