""" ===================== 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 tetx_utils import get_fitted_model, tetx_data # %% model = get_fitted_model(Model) # %% x, y, input_features, output_features = tetx_data() # %% class MyShapExplainer(ShapExplainer): def summary_plot( self, plot_type: str = None, name: str = "summary_plot", **kwargs ): """ Plots the `summary `_ 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 #%% 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() #%% md ## Kernel Explainer #%% explainer = MyShapExplainer(model=model, data=x, train_data=x, feature_names=model.input_features, explainer="KernelExplainer", #save=False ) explainer.explainer #%% 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()