def show_local_explanations(cls, results, indexs=None):
"""Plot local explanations of results
:param results:
:param indexs: a list of frame indices, or None. If None, will take the
second frame.
:return: figures of local explanation plots
"""
# assert results are indeed generated by class
for result in results:
assert cls.get_explanations_key() in result.result_dict
N = len(results)
if indexs is None:
indexs = [1] # default: second frame
figss = []
for n in range(N):
exps = results[n][cls.get_explanations_key()]
asset = results[n].asset
exps2 = LocalExplainer.select_from_exps(exps, indexs)
ys_pred = results[n][cls.get_scores_key()][indexs]
N2 = LocalExplainer.assert_explanations(exps2)
assets2 = [asset for _ in range(N2)]
# LocalExplainer.print_explanations(exps2, assets=assets2, ys=None, ys_pred=ys_pred)
figs = LocalExplainer.plot_explanations(exps2, assets=assets2, ys=None, ys_pred=ys_pred)
figss.append(figs)
return figss
def explain_model_on_dataset(model, test_assets_selected_indexs,
test_dataset_filepath):
def print_assets(test_assets):
print '\n'.join(
map(
lambda (i, asset): "Asset {i}: {name}".format(
i=i, name=get_file_name_without_extension(asset.dis_path)),
enumerate(test_assets)))
test_dataset = import_python_file(test_dataset_filepath)
test_assets = read_dataset(test_dataset)
print_assets(test_assets)
print "Assets selected for local explanation: {}".format(
test_assets_selected_indexs)
result_store = FileSystemResultStore()
test_assets = [test_assets[i] for i in test_assets_selected_indexs]
test_fassembler = FeatureAssembler(
feature_dict=model.model_dict['feature_dict'],
feature_option_dict=None,
assets=test_assets,
logger=None,
fifo_mode=True,
delete_workdir=True,
result_store=result_store,
optional_dict=None,
optional_dict2=None,
parallelize=True,
)
test_fassembler.run()
test_feature_results = test_fassembler.results
test_xs = model.get_xs_from_results(test_feature_results)
test_ys = model.get_ys_from_results(test_feature_results)
test_ys_pred = model.predict(test_xs)
explainer = LocalExplainer(neighbor_samples=1000)
test_exps = explainer.explain(model, test_xs)
explainer.print_explanations(test_exps,
assets=test_assets,
ys=test_ys,
ys_pred=test_ys_pred)
explainer.plot_explanations(test_exps,
assets=test_assets,
ys=test_ys,
ys_pred=test_ys_pred)
plt.show()
def explain_model_on_dataset(model, test_assets_selected_indexs, test_dataset_filepath):
def print_assets(test_assets):
print "\n".join(
map(
lambda (i, asset): "Asset {i}: {name}".format(
i=i, name=get_file_name_without_extension(asset.dis_path)
),
enumerate(test_assets),
)
)
test_dataset = import_python_file(test_dataset_filepath)
test_assets = read_dataset(test_dataset)
print_assets(test_assets)
print "Assets selected for local explanation: {}".format(test_assets_selected_indexs)
result_store = FileSystemResultStore()
test_assets = [test_assets[i] for i in test_assets_selected_indexs]
test_fassembler = FeatureAssembler(
feature_dict=model.model_dict["feature_dict"],
feature_option_dict=None,
assets=test_assets,
logger=None,
fifo_mode=True,
delete_workdir=True,
result_store=result_store,
optional_dict=None,
optional_dict2=None,
parallelize=True,
)
test_fassembler.run()
test_feature_results = test_fassembler.results
test_xs = model.get_xs_from_results(test_feature_results)
test_ys = model.get_ys_from_results(test_feature_results)
test_ys_pred = model.predict(test_xs)
explainer = LocalExplainer(neighbor_samples=1000)
test_exps = explainer.explain(model, test_xs)
explainer.print_explanations(test_exps, assets=test_assets, ys=test_ys, ys_pred=test_ys_pred)
explainer.plot_explanations(test_exps, assets=test_assets, ys=test_ys, ys_pred=test_ys_pred)
plt.show()
def show_local_explanations(cls, results, indexs=None):
"""Plot local explanations of results
:param results:
:param indexs: a list of frame indices, or None. If None, will take the
second frame.
:return: figures of local explanation plots
"""
# assert results are indeed generated by class
for result in results:
assert cls.get_explanations_key() in result.result_dict
N = len(results)
if indexs is None:
indexs = [1] # default: second frame
figss = []
for n in range(N):
exps = results[n][cls.get_explanations_key()]
asset = results[n].asset
exps2 = LocalExplainer.select_from_exps(exps, indexs)
ys_pred = results[n][cls.get_scores_key()][indexs]
N2 = LocalExplainer.assert_explanations(exps2)
assets2 = [asset for _ in range(N2)]
# LocalExplainer.print_explanations(exps2, assets=assets2, ys=None, ys_pred=ys_pred)
figs = LocalExplainer.plot_explanations(exps2,
assets=assets2,
ys=None,
ys_pred=ys_pred)
figss.append(figs)
return figss
