%matplotlib inline

import lightgbm as lgb
lgb.__version__

'3.1.1'

import plotly
plotly.__version__

'4.14.3'

import shap
shap.initjs()
shap.__version__

'0.38.1'

logit[source]

logit(p)

Compute log-odds of p

expit[source]

expit(a)

The reverse of logit

class Shap[source]

Shap(X, y, model, n_samples=1000)

Fit a model for X, y, then explain it using SHAP plots

X, y = shap.datasets.adult()
print(y)
X

[False False False ... False False  True]

	Age	Workclass	Education-Num	Marital Status	Occupation	Relationship	Race	Sex	Capital Gain	Capital Loss	Hours per week	Country
0	39.0	7	13.0	4	1	0	4	1	2174.0	0.0	40.0	39
1	50.0	6	13.0	2	4	4	4	1	0.0	0.0	13.0	39
2	38.0	4	9.0	0	6	0	4	1	0.0	0.0	40.0	39
3	53.0	4	7.0	2	6	4	2	1	0.0	0.0	40.0	39
4	28.0	4	13.0	2	10	5	2	0	0.0	0.0	40.0	5
...	...	...	...	...	...	...	...	...	...	...	...	...
32556	27.0	4	12.0	2	13	5	4	0	0.0	0.0	38.0	39
32557	40.0	4	9.0	2	7	4	4	1	0.0	0.0	40.0	39
32558	58.0	4	9.0	6	1	1	4	0	0.0	0.0	40.0	39
32559	22.0	4	9.0	4	1	3	4	1	0.0	0.0	20.0	39
32560	52.0	5	9.0	2	4	5	4	0	15024.0	0.0	40.0	39

32561 rows × 12 columns

from lightgbm.sklearn import LGBMClassifier
model = LGBMClassifier()

sh = Shap(X, y, model)

Shap.waterfall_plot[source]

Shap.waterfall_plot(id:int)

Display SHAP waterfall plot

sh.waterfall_plot(id=0)

import plotly.io as pio
print(pio.renderers.default)

plotly_mimetype+notebook

Shap.wf_plot[source]

Shap.wf_plot(id:int, title='', n_feature=0, n_pos=0, n_neg=0)

Display shap values in a waterfall plot.

Params:

- n_feature: Number of features with larger SHAP values to show in the plot.
- n_pos: Number of features with positive SHAP values to show in the plot.
- n_neg: Number of features with negative SHAP values to show in the plot.

sh.wf_plot(3)

sh.wf_plot(3, n_pos=3, n_neg=3, title="Features' impact")

sh.wf_plot(3, n_feature=8, n_pos=3, n_neg=3)

Shap.bar_plot[source]

Shap.bar_plot(id:int, figsize=None)

Display shap values in a bar plot

sh.bar_plot(id=0, figsize=(5,3))

<AxesSubplot:>

Shap.force_plot[source]

Shap.force_plot(n_plots=1)

Display a SHAP force plot

sh.force_plot()

Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.

sh.force_plot(1000)

Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.