Note
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shap
import os
from shap import Explanation
import shap
import pandas as pd
from ai4water import Model
import matplotlib.pyplot as plt
from ai4water.postprocessing import ShapExplainer
shap.__version__
from sul1_utils import get_fitted_model, sul1_data
model = get_fitted_model(Model)
x, y, input_features, output_features = sul1_data()
********** Removing Examples with nan in labels **********
***** Training *****
input_x shape: (374, 6)
target shape: (374, 1)
class MyShapExplainer(ShapExplainer):
def summary_plot(
self,
plot_type: str = None,
name: str = "summary_plot",
**kwargs
):
"""
Plots the `summary <https://shap-lrjball.readthedocs.io/en/latest/generated/shap.summary_plot.html#shap.summary_plot>`_
plot of SHAP package.
Arguments:
plot_type : str,
either "bar", or "violen" or "dot"
name:
name of saved file
kwargs:
any keyword arguments to shap.summary_plot
"""
def _summary_plot(_shap_val, _data, _features, _name):
plt.close('all')
shap.summary_plot(_shap_val, _data, show=False, plot_type=plot_type,
feature_names=_features,
**kwargs)
# if self.save:
# plt.savefig(os.path.join(self.path, _name + " _bar"), dpi=300,
# bbox_inches="tight")
# if self.show:
# plt.show()
return
shap_vals = self.shap_values
if isinstance(shap_vals, list) and len(shap_vals) == 1:
shap_vals = shap_vals[0]
data = self.data
if self.single_source:
if data.ndim == 3:
assert shap_vals.ndim == 3
for lookback in range(data.shape[1]):
_summary_plot(shap_vals[:, lookback],
_data=data[:, lookback],
_features=self.features,
_name=f"{name}_{lookback}")
else:
_summary_plot(_shap_val=shap_vals, _data=data,
_features=self.features, _name=name)
else:
# data is a list of data sources
for idx, _data in enumerate(data):
if _data.ndim == 3:
for lb in range(_data.shape[1]):
_summary_plot(_shap_val=shap_vals[idx][:, lb],
_data=_data[:, lb],
_features=self.features[idx],
_name=f"{name}_{idx}_{lb}")
else:
_summary_plot(_shap_val=shap_vals[idx], _data=_data,
_features=self.features[idx], _name=f"{name}_{idx}")
return
def waterfall_plot_single_example(
self,
example_index: int,
name: str = "waterfall",
max_display: int = 10,
):
"""draws and saves waterfall_ plot
for one example.
The waterfall plots are based upon SHAP values and show the
contribution by each feature in model's prediction. It shows which
feature pushed the prediction in which direction. They answer the
question, why the ML model simply did not predict mean of training y
instead of what it predicted. The mean of training observations that
the ML model saw during training is called base value or expected value.
Arguments:
example_index : int
index of example to use
max_display : int
maximu features to display
name : str
name of plot
.. _waterfall:
https://shap.readthedocs.io/en/latest/generated/shap.plots.waterfall.html
"""
if self.explainer.__class__.__name__ in ["Deep", "Kernel"]:
shap_vals_as_exp = None
else:
shap_vals_as_exp = self.explainer(self.data)
shap_values = self.shap_values
if isinstance(shap_values, list) and len(shap_values) == 1:
shap_values = shap_values[0]
plt.close('all')
if shap_vals_as_exp is None:
features = self.features
if not self.data_is_2d:
features = self.unrolled_features
# waterfall plot expects first argument as Explaination class
# which must have at least these attributes (values, data, feature_names, base_values)
# https://github.com/slundberg/shap/issues/1420#issuecomment-715190610
if not self.data_is_2d: # if original data is 3d then we flat it into 1d array
values = shap_values[example_index].reshape(-1, )
data = self.data[example_index].reshape(-1, )
else:
values = shap_values[example_index]
data = self.data.iloc[example_index]
exp_value = self.explainer.expected_value
if self.explainer.__class__.__name__ in ["Kernel"]:
pass
else:
exp_value = exp_value[0]
e = Explanation(
values,
base_values=exp_value,
data=data,
feature_names=features
)
shap.plots.waterfall(e, show=False, max_display=max_display)
else:
shap.plots.waterfall(shap_vals_as_exp[example_index], show=False, max_display=max_display)
if self.save:
plt.savefig(os.path.join(self.path, f"{name}_{example_index}"),
dpi=300,
bbox_inches="tight")
if self.show:
plt.show()
return
explainer = MyShapExplainer(model=model,
data=x,
train_data=x,
feature_names=model.input_features,
#save=False
)
explainer.explainer
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<shap.explainers._kernel.Kernel object at 0x7fa22c60e9d0>
explainer.plot_shap_values()
explainer.waterfall_plot_single_example(27)
#%%
explainer.waterfall_plot_single_example(28)
#%%
explainer.waterfall_plot_single_example(29)
#%%
explainer.waterfall_plot_single_example(30)
#%%
explainer.waterfall_plot_single_example(31)
#%%
explainer.waterfall_plot_single_example(32)
#%%
explainer.waterfall_plot_single_example(33)
#explainer.beeswarm_plot()
#explainer.heatmap()
explainer.summary_plot(plot_type="dot")
explainer.summary_plot(plot_type="bar")
explainer.summary_plot(plot_type="violin")
explainer.dependence_plot_all_features()
# explainer.scatter_plot_all_features()
## Kernel Explainer
explainer = MyShapExplainer(model=model,
data=x,
train_data=x,
feature_names=model.input_features,
explainer="KernelExplainer",
#save=False
)
explainer.explainer
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<shap.explainers._kernel.Kernel object at 0x7fa22c5b34f0>
explainer.plot_shap_values()
#explainer.beeswarm_plot()
explainer.summary_plot(plot_type="bar")
explainer.summary_plot(plot_type="dot")
explainer.summary_plot(plot_type="violin")
#explainer.heatmap()
#explainer.waterfall_plot_single_example(27)
explainer.dependence_plot_all_features()
#explainer.scatter_plot_all_features()
Total running time of the script: ( 1 minutes 39.573 seconds)
