Shapash with custom colors

With this tutorial you will understand how to manipulate colors with Shapash plots

Contents: - Build a Regressor - Compile Shapash SmartExplainer - Use palette_name parameter - Use colors_dict parameter - Change the colors after comiling the explainer

Data from Kaggle House Prices

import pandas as pd
from category_encoders import OrdinalEncoder
from lightgbm import LGBMRegressor
from sklearn.model_selection import train_test_split

Building Supervized Model

from shapash.data.data_loader import data_loading
house_df, house_dict = data_loading('house_prices')

y_df=house_df['SalePrice'].to_frame()
X_df=house_df[house_df.columns.difference(['SalePrice'])]

house_df.head()

	MSSubClass	MSZoning	LotArea	Street	LotShape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	...	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
Id
1	2-Story 1946 & Newer	Residential Low Density	8450	Paved	Regular	Near Flat/Level	All public Utilities (E,G,W,& S)	Inside lot	Gentle slope	College Creek	...	0	0	0	0	0	2	2008	Warranty Deed - Conventional	Normal Sale	208500
2	1-Story 1946 & Newer All Styles	Residential Low Density	9600	Paved	Regular	Near Flat/Level	All public Utilities (E,G,W,& S)	Frontage on 2 sides of property	Gentle slope	Veenker	...	0	0	0	0	0	5	2007	Warranty Deed - Conventional	Normal Sale	181500
3	2-Story 1946 & Newer	Residential Low Density	11250	Paved	Slightly irregular	Near Flat/Level	All public Utilities (E,G,W,& S)	Inside lot	Gentle slope	College Creek	...	0	0	0	0	0	9	2008	Warranty Deed - Conventional	Normal Sale	223500
4	2-Story 1945 & Older	Residential Low Density	9550	Paved	Slightly irregular	Near Flat/Level	All public Utilities (E,G,W,& S)	Corner lot	Gentle slope	Crawford	...	272	0	0	0	0	2	2006	Warranty Deed - Conventional	Abnormal Sale	140000
5	2-Story 1946 & Newer	Residential Low Density	14260	Paved	Slightly irregular	Near Flat/Level	All public Utilities (E,G,W,& S)	Frontage on 2 sides of property	Gentle slope	Northridge	...	0	0	0	0	0	12	2008	Warranty Deed - Conventional	Normal Sale	250000

5 rows × 73 columns

from category_encoders import OrdinalEncoder

categorical_features = [col for col in X_df.columns if X_df[col].dtype == 'object']

encoder = OrdinalEncoder(
    cols=categorical_features,
    handle_unknown='ignore',
    return_df=True).fit(X_df)

X_df=encoder.transform(X_df)

Xtrain, Xtest, ytrain, ytest = train_test_split(X_df, y_df, train_size=0.75, random_state=1)

regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)

y_pred = pd.DataFrame(regressor.predict(Xtest),columns=['pred'],index=Xtest.index)

Shapash with different colors

Option 1 : use palette_name parameter

from shapash import SmartExplainer

xpl = SmartExplainer(
    model=regressor,
    preprocessing=encoder, # Optional: compile step can use inverse_transform method
    features_dict=house_dict,
    palette_name='blues'  # Other available name : 'default'
)

xpl.compile(
    x=Xtest,
    y_pred=y_pred, # Optional
    y_target=ytest, # Optional: allows to display True Values vs Predicted Values
)

Backend: Shap TreeExplainer

xpl.plot.features_importance()

Option 2 : define user-specific colors with colors_dict parameter

The colors declared will replace the one in the palette used.

In the example below, we replace the colors used in the features importance bar plot:

# first, let's print the colors used in the previous explainer:
xpl.colors_dict['featureimp_bar']

{'1': 'rgba(0, 154, 203, 1)', '2': 'rgba(223, 103, 0, 0.8)'}

# Now we replace these colors using the colors_dict parameter
xpl2 = SmartExplainer(
    model=regressor,
    preprocessing=encoder,
    features_dict=house_dict,
    colors_dict=dict(
        featureimp_bar={
            '1': 'rgba(100, 120, 150, 1)',
            '2': 'rgba(120, 103, 50, 0.8)'
        },
        featureimp_line='rgba(150, 150, 54, 0.8)'
    )
)

xpl2.compile(x=Xtest, y_pred=y_pred)

Backend: Shap TreeExplainer

xpl2.plot.features_importance()

Option 3 : redefine colors after compiling shapash

xpl3 = SmartExplainer(
    model=regressor,
    preprocessing=encoder,
    features_dict=house_dict,
)

xpl3.compile(x=Xtest, y_pred=y_pred)

Backend: Shap TreeExplainer

xpl3.plot.features_importance()

xpl3.plot.contribution_plot('1stFlrSF')

• We redefine the colors with the ``blues`` palette and custom colors for the features importance plot

xpl3.define_style(
    palette_name='blues',
    colors_dict=dict(
        featureimp_bar={
            '1': 'rgba(100, 120, 150, 1)',
            '2': 'rgba(120, 103, 50, 0.8)'
        }
))

xpl3.plot.features_importance()

xpl3.plot.contribution_plot('1stFlrSF')