def shapley_kernel_wrapper(model, trainx, testx, config):
""" This function is called by the prediction algorithms (ml_functions)
to compute the Shapley values. Note that the decision tree based models
such as random forest do provide faster and exact (non-approximated)
Shapley values with the TreeShapExplainer"""
if config.exp_shap_background >= trainx.shape[0]:
background = trainx
else:
background = shap.kmeans(trainx, config.exp_shap_background)
# random sample of background values
# ixx = np.random.choice(trainx.shape[0], config.exp_shap_background, replace=False)
# background = trainx[ixx, :]
# print(background.shape)
explainer = shap.KernelExplainer(model.predict_proba, background)
if isinstance(
model, LogisticRegression
): # one background instance is enough if we use a linear model
background = shap.kmeans(trainx, 1)
backup_base = explainer.fnull
explainer = shap.KernelExplainer(model.predict_proba, background)
explainer.fnull = backup_base
fnull_save = explainer.fnull[1]
out = [
explainer.shap_values(testx[i, :], l1_reg=0.0)[1]
for i in np.arange(len(testx))
]
return np.vstack(out)
def get_shap_kernel(estimator: object, X_train):
"""compute the shap value importance for non-tree based model
Args:
estimator (a none tree based sklearn estimator): a sklearn non tree based estimator
x_train ((pd.DataFrame, np.ndarray),): X training data
x_test ((pd.DataFrame, np.ndarray),): X testing data
Returns:
shap plot
"""
warnings.filterwarnings("ignore")
# because the kernel explainer for non-tree based model extremly slower
# so we must use kmeans to extract mainly information from x_train
# to speed up the calculation
if X_train.shape[1] > 3:
x_train_summary = shap.kmeans(X_train, 3)
else:
x_train_summary = shap.kmeans(X_train, X_train.shape[1])
explainer = shap.KernelExplainer(estimator.predict, x_train_summary)
size = len(X_train)
if size < 50:
size = size
elif size * 0.2 > 50:
size = 50
else:
size = int(size * 0.2)
sample_values = shap.sample(X_train, size)
shap_values = explainer.shap_values(sample_values,
lr_reg='num_features(10)')
return explainer, shap_values, sample_values
def explain_dnns(datasource, estimator, shap_dataset, plot_type, result_table,
feature_column_names, is_pai, pai_table, hdfs_namenode_addr,
hive_location, hdfs_user, hdfs_pass):
def predict(d):
def input_fn():
return tf.data.Dataset.from_tensor_slices(
dict(pd.DataFrame(d,
columns=shap_dataset.columns))).batch(1000)
return np.array(
[p['probabilities'][-1] for p in estimator.predict(input_fn)])
if len(shap_dataset) > 100:
# Reduce to 16 weighted samples to speed up
shap_dataset_summary = shap.kmeans(shap_dataset, 16)
else:
shap_dataset_summary = shap_dataset
shap_values = shap.KernelExplainer(
predict, shap_dataset_summary).shap_values(shap_dataset, l1_reg="aic")
if result_table != "":
if is_pai:
write_shap_values(shap_values, "pai_maxcompute", None,
result_table, feature_column_names,
hdfs_namenode_addr, hive_location, hdfs_user,
hdfs_pass)
else:
conn = connect_with_data_source(datasource)
write_shap_values(shap_values, conn.driver, conn, result_table,
feature_column_names, hdfs_namenode_addr,
hive_location, hdfs_user, hdfs_pass)
else:
explainer.plot_and_save(lambda: shap.summary_plot(
shap_values, shap_dataset, show=False, plot_type=plot_type))
def init_shap(self):
"""
Initialize shap. Calculate shap values. This operation is time consuming.
:return: void (Sets the value of the shap_values variable)
"""
import shap
if not self.shap_values:
log.info("Initializing Shap - calculating shap values."
" This operation is time-consuming so please be patient.")
logger = log.getLogger('shap')
logger.setLevel(log.WARN)
shap_kernel_explainer = shap.KernelExplainer(
self.model[1].predict_proba, shap.kmeans(self.X_test_ohe, 1))
shap_values = shap_kernel_explainer.shap_values(self.X_test_ohe)
# from util.commons import RANDOM_NUMBER
# shap_values = shap_kernel_explainer.shap_values(self.X_test_ohe.sample(66, random_state=RANDOM_NUMBER))
self.shap_kernel_explainer = shap_kernel_explainer
self.shap_values = shap_values
else:
log.info("Shap is already initialized.")
def _summarize_data(X, k=10, to_round_values=True):
"""Summarize a dataset.
For dense dataset, use k mean samples weighted by the number of data points they
each represent.
For sparse dataset, use a sparse row for the background with calculated
median for dense columns.
:param X: Matrix of data samples to summarize (# samples x # features).
:type X: numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix
:param k: Number of cluster centroids to use for approximation.
:type k: int
:param to_round_values: When using kmeans, for each element of every cluster centroid to match the nearest value
from X in the corresponding dimension. This ensures discrete features
always get a valid value. Ignored for sparse data sample.
:type to_round_values: bool
:return: DenseData or SparseData object.
:rtype: iml.datatypes.DenseData or iml.datatypes.SparseData
"""
is_sparse = issparse(X)
if not isinstance(X, DenseData):
if is_sparse:
module_logger.debug('Creating sparse data summary as csr matrix')
# calculate median of sparse background data
median_dense = csc_median_axis_0(X.tocsc())
return csr_matrix(median_dense)
elif len(X) > 10 * k:
module_logger.debug('Create dense data summary with k-means')
# use kmeans to summarize the examples for initialization
# if there are more than 10 x k of them
return shap.kmeans(X, k, to_round_values)
return X
def get_shap_values():
global data
model = []
model_params = []
xx = []
y = []
y_train = []
x_train = []
for d in data:
model.append(d['model'])
model_params.append(d['model_params'])
xx.append(d['x'])
y.append(d['y'])
y_train.append(d['y_train'])
x_train.append(d['x_train'])
all_shap = []
shap_names = get_shap_names()
for l, g, g_params, x, bd in zip(y, model, model_params, xx, x_train):
r_model = reg_model(g, g_params)
k_meaned = shap.kmeans(bd, 20)
shap_values = shap.KernelExplainer(r_model, k_meaned).shap_values(x)
all_shap.extend(shap_values)
all_shap = np.array(all_shap)
all_x = np.concatenate(xx)
shap.summary_plot(all_shap,
features=all_x,
feature_names=shap_names,
max_display=8)
print('nice')
def test_log_explanation_with_small_features():
"""
Verifies that `log_explanation` does not fail even when `features` has less records than
`_MAXIMUM_BACKGROUND_DATA_SIZE`.
"""
num_rows = 50
assert num_rows < mlflow.shap._MAXIMUM_BACKGROUND_DATA_SIZE
X, y = get_boston()
X, y = X.iloc[:num_rows], y[:num_rows]
model = RandomForestRegressor()
model.fit(X, y)
with mlflow.start_run() as run:
explanation_uri = mlflow.shap.log_explanation(model.predict, X)
artifact_path = "model_explanations_shap"
artifacts = set(yield_artifacts(run.info.run_id))
assert explanation_uri == os.path.join(run.info.artifact_uri, artifact_path)
assert artifacts == {
os.path.join(artifact_path, "base_values.npy"),
os.path.join(artifact_path, "shap_values.npy"),
os.path.join(artifact_path, "summary_bar_plot.png"),
}
explainer = shap.KernelExplainer(model.predict, shap.kmeans(X, num_rows))
shap_values_expected = explainer.shap_values(X)
base_values = np.load(os.path.join(explanation_uri, "base_values.npy"))
shap_values = np.load(os.path.join(explanation_uri, "shap_values.npy"))
np.testing.assert_array_equal(base_values, explainer.expected_value)
np.testing.assert_array_equal(shap_values, shap_values_expected)
def explain_model(model, nlp_args, X_train, X_test, y_train, y_test):
model.fit(X_train, y_train)
# we use the first 100 training examples as our background dataset to integrate over
if pipe.named_steps['clf'].__class__.__name__ == "LogisticRegression":
explainer = shap.LinearExplainer(
model.named_steps['clf'],
model.named_steps['vectorizer'].transform(
model.named_steps['preprocess'].transform(X_train)))
shap_values = explainer.shap_values(
model.named_steps['vectorizer'].transform(
model.named_steps['preprocess'].transform(X_test)).toarray())
elif pipe.named_steps[
'clf'].__class__.__name__ == "RandomForestClassifier":
explainer = shap.TreeExplainer(model.named_steps['clf'])
shap_values = explainer.shap_values(
model.named_steps['vectorizer'].transform(
model.named_steps['preprocess'].transform(X_test)).toarray())
else:
X_train_summary = shap.kmeans(
model.named_steps['vectorizer'].transform(
model.named_steps['preprocess'].transform(X_train)).toarray(),
10)
explainer = shap.KernelExplainer(
model.named_steps['clf'].predict_proba, X_train_summary)
shap_values = explainer.shap_values(
model.named_steps['vectorizer'].transform(
model.named_steps['preprocess'].transform(X_test)).toarray())
return explainer, shap_values
def script(num_models, k, scoring, num_clust):
X = in_data.X
y = in_data.Y
best_params = {'hidden_layer_sizes': (28, 28), 'solver': 'adam'}
print(in_data.X.shape)
print(best_params)
results = {}
scores = {}
models = {}
for m in range(num_models):
model = MLPRegressor(**best_params)
kfold = model_selection.KFold(n_splits=k)
cv_results = model_selection.cross_validate(model,
X,
y,
cv=kfold,
scoring=scoring)
print(cv_results)
score = sum(cv_results['test_r2']) / len(cv_results['test_r2'])
results[m] = cv_results
scores[m] = score
models[m] = model
print(m, max(scores.values()), score)
print(max(scores, key=scores.get), scores, results[max(scores,
key=scores.get)])
best_model = models[max(scores, key=scores.get)]
feat_names = [x.name for x in in_data.domain.attributes]
#new_feat_names = [x.split('=')[-1] for x in feat_names]
print(feat_names)
shap.initjs()
best_model.fit(X, y)
Xsum = shap.kmeans(X, num_clust) if num_clust is not None else X
explainer = shap.KernelExplainer(best_model.predict, Xsum.data)
shap_values = explainer.shap_values(X)
#feat_names = ['{0}: {1}'.format(x[0], x[1]) for x in list(zip(feat_names, np.around(np.mean(np.absolute(shap_values), axis=0), 3)))]
#id = len(Window.get_all_windows())
#summary_plot(shap_values, features=X, feature_names=feat_names, class_names=class_names, plot_type='bar', color='black', id=id)
#summary_plot(shap_values, features=X, feature_names=feat_names, class_names=class_names, plot_type='dot', title=title, id=id)
d = Orange.data.Domain(
[Orange.data.ContinuousVariable(f) for f in feat_names] +
[Orange.data.ContinuousVariable('S_' + f) for f in feat_names],
class_vars=in_data.domain.class_vars,
metas=in_data.domain.metas)
xs = np.concatenate((in_data.X, shap_values), axis=1)
ys = in_data.Y
metas = in_data.metas
out_data = Orange.data.Table.from_numpy(d, xs, Y=ys, metas=metas)
return out_data, None, None, shap_values
def run_callback(self):
self.model_idx = self.model_names.index(self.model_select.value)
self.kfold = int(self.kfold_select.value)
# set all the parameters of the new kfold
self.geos_train, self.X_train_df, self.y_train, self.train_probas, \
self.geos_test, self.X_test_df, self.y_test, self.test_probas, \
self.models, self.model_names, self.auc_scores = self._extract_model_results(self.full_results, self.model_idx,
self.kfold)
self.num_kfold = len(self.full_results[0]['train_idx'])
self.features_df = pd.concat([self.X_train_df, self.X_test_df])
self.features_kmeans = shap.kmeans(self.features_df, 10)
self.all_probas = np.concatenate([self.train_probas[self.model_idx], self.test_probas[self.model_idx]])
self.all_probas_df = pd.Series(data=self.all_probas, index=self.features_df.index)
# create new precision recall curve
test_probas_df = pd.DataFrame(
{model_name: test_proba for model_name, test_proba in zip(self.model_names, self.test_probas)})
pr_curve = bokeh_multiple_pr_curves(test_probas_df, self.y_test.values)
self.left_column.children[0] = pr_curve
# create new folium map
print(f"Choose model: {self.model_select.value}\t kfold: {self.kfold}")
new_folium = bokeh_scored_polygons_folium([self.all_probas_df],
[True], train_geos=self.geos_train,
test_geos=self.geos_test, start_zoom=self.folium_zoom,
start_location=self.start_location, file_name=self.folium_name,
width=700, lonlat_text_inputs=self.lonlat_text_inputs)
self.folium_column.children[-2] = new_folium
self.sample_feature_importance_update()
def explain(self):
shap.initjs()
X_train_summary = shap.kmeans(self.X_train, self.n_cluster)
rf_explainer = shap.KernelExplainer(self.clf.predict, X_train_summary)
shap_values_RF_test = rf_explainer.shap_values(self.X_test)
self.shap = shap_values_RF_test
return shap_values_RF_test
def SHAP_values(model, X_train):
k_sample = shap.kmeans(X_train, 5)
explainer = shap.KernelExplainer(model.predict, k_sample)
shap_values = explainer.shap_values(X_train)
shap.summary_plot(shap_values, X_train, plot_type='bar')
plt.show()
def calculate_deepshap_values(model, training_data, test_data, K=None):
"""Calculate deepSHAP values for the given model.
Args:
model: trained deep model
training_data: data of shape (num_samples, num_features) on which model was trained
test_data: data of shape (num_samples, num_features) on which model was tested
K: number of means to use for K-means summarization of training_data (used for
calculating feature importances in SHAP values); if None, then all the
training data are used for this calculation (potentially expensive)
Returns:
A tuple consisting of 1. the explainer's expected values and 2. the SHAP values.
"""
if K:
kmeans_summary = shap.kmeans(training_data, K)
print("Finished k-means summarization!")
explainer = shap.DeepExplainer(model, kmeans_summary, link="logit")
else:
explainer = shap.DeepExplainer(model, training_data, link="logit")
shap_values = explainer.shap_values(test_data)
return explainer.expected_value, shap_values
def __init__(self, task: TaskHandler, results_dir=BUILDING_RESULTS_DIR):
self.task = task
self.main_panel = column()
self.folium_name = task.__str__()
# unpack building experiment results
self.kfold = 0
self.model_idx = 0
# self.full_results = load_experiment_results(results_dir)['model_results']
self.full_results = load_separate_model_results(results_dir)['model_results']
self.model_results = self.full_results[self.model_idx]
self.geos_train, self.X_train_df, self.y_train, self.train_probas, \
self.geos_test, self.X_test_df, self.y_test, self.test_probas, \
self.models, self.model_names, self.auc_scores = self._extract_model_results(self.full_results, self.model_idx,
self.kfold)
self.num_kfold = len(self.full_results[0]['train_idx'])
self.features_df = pd.concat([self.X_train_df, self.X_test_df])
self.features_kmeans = shap.kmeans(self.features_df, 10)
self.all_probas = np.concatenate([self.train_probas[self.model_idx], self.test_probas[self.model_idx]])
self.all_probas_df = pd.Series(data=self.all_probas, index=self.features_df.index)
self.mean_auc = self.mean_auc_panel(self.model_names, self.auc_scores)
plot = self.bokeh_plot()
self.main_panel.children.append(plot)
def run(self, X_train, nsamples=1000):
X_train_summary = shap.kmeans(X_train, 10)
self.sensor_names = X_train.columns
self.explainer = shap.KernelExplainer(self.predict_LR,
X_train_summary,
l1_reg="auto")
self.shap_values = np.array(
self.explainer.shap_values(X_test, nsamples=nsamples)) #[0]
def get_shap_mean_values(model, df, features):
explainer = shap.KernelExplainer(model=model.predict,
data=shap.kmeans(df[features], 10))
shap_values = explainer.shap_values(df[FEATURES].sample(10),
l1_reg='aic',
silent=True)
mean_shap_values = shap_values.mean(axis=0)
return mean_shap_values
def get_shap_feature_importance(self, base_model, X_test, X_train):
try:
import shap
except ImportError:
raise ImportError('You must have shap installed to use shap')
if self.flags['tree'] or self.flags['linear']:
if self.flags['linear']:
fp = self.shap_params.linear_feature_perturbation
n = self.shap_params.linear_nsamples
explainer = shap.LinearExplainer(base_model, X_train,
nsamples=n,
feature_perturbation=fp)
shap_values = explainer.shap_values(X_test)
elif self.flags['tree']:
tmo = self.shap_params.tree_model_output
tfp = self.shap_params.tree_feature_perturbation
explainer =\
shap.TreeExplainer(
base_model, X_train,
model_output=tmo,
feature_perturbation=tfp)
ttl = self.shap_params.tree_tree_limit
shap_values =\
explainer.shap_values(X_test,
tree_limit=ttl)
# Kernel
else:
nkmean = self.shap_params.kernel_nkmean
if nkmean is not None:
X_train_summary = shap.kmeans(X_train, nkmean)
else:
X_train_summary = X_train
explainer =\
self.get_kernel_explainer(base_model, X_train_summary,
self.shap_params.kernel_link)
klr = self.shap_params.kernel_l1_reg
kns = self.shap_params.kernel_nsamples
shap_values =\
explainer.shap_values(np.array(X_test),
l1_reg=klr,
n_samples=kns)
return self.proc_shap_vals(shap_values)
def regressor():
X, y = get_boston()
model = RandomForestRegressor()
model.fit(X, y)
explainer = shap.KernelExplainer(model.predict, shap.kmeans(X, 100))
shap_values = explainer.shap_values(X)
return ModelWithExplanation(model, X, shap_values, explainer.expected_value)
def classifier():
X, y = get_iris()
model = RandomForestClassifier()
model.fit(X, y)
explainer = shap.KernelExplainer(model.predict_proba, shap.kmeans(X, 100))
shap_values = explainer.shap_values(X)
return ModelWithExplanation(model, X, shap_values, explainer.expected_value)
def main(toolName, datasetName):
tool = getToolObject(toolName)
#load the dataset [used as data in shapley plot]
sequences = getDataset(datasetName, toolName)
print("Loaded the dataset " + datasetName + " of " + str(len(sequences)) +
" data points.")
#Calculate the features for the dataset
print(
"Calculating features for the dataset. [Might take a while for wu-crispr]"
)
feature_set = []
cnt = 1
for seq in sequences:
features = tool.getFeatures(seq)
feature_set.append(features)
if toolName == 'wu-crispr' and cnt % 100 == 0: #inform on progress [wu-crispr takes a while]
print("-- Calculated features for " + str(cnt))
cnt = cnt + 1
print("Calculated the features for this dataset.")
#Get feature names [for printing in the shapley plot]
feature_names = tool.loadFeatureNames()
print("Loaded the names of all " + str(len(feature_names)) + " features.")
#Put together features with names in one dataframe
dataset_df = pd.DataFrame(np.array(feature_set), columns=feature_names)
#training set (must be loaded before model)
train_df = tool.loadTrainingSet()
print("Loaded the training set used for tool " + toolName + ", size: " +
str(train_df.shape) + ".")
#load model of the tool
model = tool.loadModel()
print("Loaded the model for tool " + toolName + ".")
#summarize training set and subsample test set
summary_train_df = shap.kmeans(train_df, 2)
dataset_sub_df = dataset_df # optional to speed up things can use dataset_df.sample(400)
#compute and plot shapley values
shap_explainer = shap.KernelExplainer(model.predict, summary_train_df)
print(dataset_sub_df)
shap_values = shap_explainer.shap_values(dataset_sub_df)
#save the values
with open("../results/SHAP-" + toolName + "-" + datasetName, 'wb') as file:
pickle.dump(shap_values, file) #the SHAP values
pickle.dump(dataset_sub_df, file) #the data used
print("Computed and saved SHAP values.")
#ploatting
shap.summary_plot(shap_values, dataset_sub_df)
def _kmeans(self, means: int) -> DenseData:
"""
Wrapper to cache kmeans results for repeated runs
:param means: amount of centers for kmeans
:return: kmeaned background data for shap (either from cache or newly counted)
"""
if means in self.sampled_background.keys():
return self.sampled_background[means]
self.sampled_background[means] = kmeans(self.X_train, means)
return self.sampled_background[means]
def build_shap_explainer(model, dataset):
# --- Summarizing the dataset ---
# Can be fine tuned further (Kmeans, etc.)
# summ = np.median(dataset, axis=0).reshape((1,dataset.shape[1]))
summ = shap.kmeans(dataset, 100)
print("Summary data for SHAP:", summ)
explainer = shap.KernelExplainer(model.run_model_data_prob,
summ,
link="identity")
print("Expected value for SHAP:", explainer.expected_value[1])
return explainer
def run_shap(features_csv, labels_csv, output_file):
# Import dfs
labels = pd.read_csv(os.path.join(os.getcwd(), labels_csv))
imp_feat = pd.read_csv(os.path.join(os.getcwd(), features_csv))
# set label index
labels.set_index('respondent_id', inplace=True)
# IMPUTED
imp_feat.set_index('Unnamed: 0', inplace=True)
imp_feat.sort_index(inplace=True)
# merge_df options
merged_df = imp_feat.join(labels)
# merged_df = imp_feat_small.join(labels)
df_h1n1 = merged_df.reset_index(drop=True).drop(['seasonal_vaccine'],
axis=1)
print(df_h1n1.shape)
X = df_h1n1.iloc[:, :-1]
y = df_h1n1.iloc[:, -1]
X_train, X_val, y_train, y_val = train_test_split(X,
y,
test_size=0.1,
stratify=y,
random_state=42)
# get feature names
feature_names = list(X_train)
# check shape
print(X.shape)
print(X_train.shape)
# IMPUTED Scaling and
X_train = StandardScaler().fit_transform(X_train)
print(X_train.shape)
X_val = StandardScaler().fit_transform(X_val)
clf = SVC(kernel='rbf', probability=True).fit(X_train, y_train)
X_train_summary = shap.kmeans(X_train, 10)
explainer = shap.KernelExplainer(clf.predict_proba, X_train_summary)
shap_values_train = explainer.shap_values(X_train)
shap_values_test = explainer.shap_values(X_val)
df_SVC = pd.DataFrame(shap_values_train[0].mean(0),
index=X.columns,
columns=['SVC']).sort_values('SVC', ascending=False)
df_SVC.to_csv(output_file)
def run_shap(results, X_test, new_path, hp):
for key, item in results.items():
model = item[0]
# X_test = X_test.iloc[:10, :]
k_X = shap.kmeans(X_test, 5)
if key == 'MLP':
explainer = shap.KernelExplainer(model.model.predict_classes, k_X)
else:
explainer = shap.KernelExplainer(model.model.predict, k_X)
shap_values = explainer.shap_values(X_test)
f = plt.figure()
shap.summary_plot(shap_values, X_test, show=False, plot_type='bar')
f.savefig(new_path + "/" + hp + '-' + key + "-summary_plot.pdf", bbox_inches='tight', dpi=600)
def _get_kernel_explainer(predict_func, bkgrd_data, kmeans_size=10):
if predict_func is None:
raise ValueError(
"No target to compute shap values. Expected either model or predict_func"
)
# rather than use the whole training set to estimate expected values,
# summarize with a set of weighted kmeans, each weighted by
# the number of points they represent.
if kmeans_size is None:
x_bkgrd_summary = bkgrd_data
else:
x_bkgrd_summary = shap.kmeans(bkgrd_data, kmeans_size)
return shap.KernelExplainer(predict_func, x_bkgrd_summary)
def calculate_shap_values(model, train_data, shap_data, model_type="kernel"):
# use faster tree explainer for tree based models
if model_type == "tree":
explainer = shap.TreeExplainer(model)
else:
if len(train_data) > 100:
explainer = shap.KernelExplainer(model,
shap.kmeans(train_data, 100))
else:
explainer = shap.KernelExplainer(model, train_data)
shap_values = explainer.shap_values(shap_data)
return explainer, shap_values
def kernel(self, print_result=False):
result = {}
result['name'] = self.name
result['shap_method'] = 'kernel'
X_train_summary = shap.kmeans(self.X_train, 50)
explainer = shap.KernelExplainer(model=self.model.predict_proba,
data=X_train_summary)
shap_values = explainer.shap_values(self.X_test)
if isinstance(shap_values, list):
shap_values = shap_values[1]
return self._run(shap_values, result, print_result)
def explain_dnns(datasource, estimator, shap_dataset, plot_type, result_table,
feature_column_names, is_pai, pai_table, hdfs_namenode_addr,
hive_location, hdfs_user, hdfs_pass, oss_dest, oss_ak, oss_sk,
oss_endpoint, oss_bucket_name):
def predict(d):
if len(d) == 1:
# This is to make sure the progress bar of SHAP display properly:
# 1. The newline makes the progress bar string captured in pipe
# 2. The ASCII control code moves cursor up twice for alignment
print("\033[A" * 2)
def input_fn():
return tf.data.Dataset.from_tensor_slices(
dict(pd.DataFrame(d,
columns=shap_dataset.columns))).batch(1000)
if plot_type == 'bar':
predictions = [
p['logits'] if 'logits' in p else p['predictions']
for p in estimator.predict(input_fn)
]
else:
predictions = [
p['logits'][-1] if 'logits' in p else p['predictions'][-1]
for p in estimator.predict(input_fn)
]
return np.array(predictions)
if len(shap_dataset) > 100:
# Reduce to 16 weighted samples to speed up
shap_dataset_summary = shap.kmeans(shap_dataset, 16)
else:
shap_dataset_summary = shap_dataset
shap_values = shap.KernelExplainer(
predict, shap_dataset_summary).shap_values(shap_dataset, l1_reg="aic")
if result_table != "":
if is_pai:
write_shap_values(shap_values, "pai_maxcompute", None,
result_table, feature_column_names,
hdfs_namenode_addr, hive_location, hdfs_user,
hdfs_pass)
else:
conn = connect_with_data_source(datasource)
write_shap_values(shap_values, conn.driver, conn, result_table,
feature_column_names, hdfs_namenode_addr,
hive_location, hdfs_user, hdfs_pass)
explainer.plot_and_save(
lambda: shap.summary_plot(
shap_values, shap_dataset, show=False, plot_type=plot_type),
is_pai, oss_dest, oss_ak, oss_sk, oss_endpoint, oss_bucket_name)
def get_model_explanations(model, train_data, test_data, background_samples=10, nsamples='auto', num_features=50):
"""runs KernelSHAP on model and returns subjectwise model explanations
model : trained sklearn estimator
train_data : n x p array of training data used in model training
test_data : n x p array of test data
background_samples : number of kmeans clusters used to summarise training data, fewer=faster
nsamples : number of times to reevaluate model to estimate Shapley values, more=lower variance
num_features : number of features to include in local model
"""
explainer = shap.KernelExplainer(model.predict, shap.kmeans(train_data, background_samples), link='identity')
explanations = explainer.shap_values(test_data, nsamples=nsamples, l1_reg='num_features({:})'.format(num_features))
return explanations
def show_federate_shap_on_each_client(self):
logging.info("################load data for shap")
client = self.client_list[0]
fed_pos = 0 # 联邦特征的起始下标
for client_idx in range(self.args.client_num_in_total):
if self.test_data_local_dict[client_idx] is None:
continue
client.update_local_dataset(
0, self.train_data_local_dict[client_idx],
self.test_data_local_dict[client_idx],
self.train_data_local_num_dict[client_idx])
train_X, train_y, test_X = client.get_all_X()
# federate shapley
train_X_all_pd = pd.DataFrame(train_X.numpy())
f_knn = lambda x: self.model_trainer.model.forward(x)
med = train_X_all_pd.median().values.reshape(
(1, train_X_all_pd.shape[1]))
feature_num = len(self.feature_name) - 1 # 特征个数
fs = FederateShap()
# Aggregated and average federated shap
data = shap.kmeans(train_X_all_pd, 20)
step = 3
shap_values_whole = []
cols_federated = self.feature_name[:-1]
cols_federated[fed_pos] = 'Federated'
del cols_federated[fed_pos + 1:fed_pos + step]
for x in data.data:
phi = fs.kernel_shap_federated_with_step(
f_knn, x, med, feature_num, fed_pos, step)
base_value = phi[-1]
shap_values = phi[:-1]
shap_values_whole.append(list(shap_values))
shap_values_whole = np.array(shap_values_whole)
shap_values_whole_mean = np.mean(shap_values_whole,
axis=0).transpose()
# 绘制联邦特征shapley值的图
shap.summary_plot(shap_values_whole_mean,
feature_names=cols_federated,
sort=False)
shap.summary_plot(shap_values_whole_mean,
feature_names=cols_federated,
sort=False,
plot_type="bar")
fed_pos += step
