def test_explain_linear_dense():
clf = LogisticRegression(random_state=42)
data = [{
'day': 'mon',
'moon': 'full'
}, {
'day': 'tue',
'moon': 'rising'
}, {
'day': 'tue',
'moon': 'rising'
}, {
'day': 'mon',
'moon': 'rising'
}]
vec = DictVectorizer(sparse=False)
X = vec.fit_transform(data)
clf.fit(X, [0, 1, 1, 0])
test_day = {'day': 'tue', 'moon': 'full'}
target_names = ['sunny', 'shady']
res1 = explain_prediction(clf,
test_day,
vec=vec,
target_names=target_names)
expl_text, expl_html = format_as_all(res1, clf)
assert 'day=tue' in expl_text
assert 'day=tue' in expl_html
[test_day_vec] = vec.transform(test_day)
res2 = explain_prediction(clf,
test_day_vec,
target_names=target_names,
vectorized=True,
feature_names=vec.get_feature_names())
assert res1 == res2
def explainELI5(rows, name, classes, XY):
import eli5
import eli5.sklearn
from eli5 import show_weights
from eli5.sklearn import PermutationImportance
from eli5.sklearn import explain_prediction_linear_classifier
from eli5 import show_prediction
cols = XY[2]
clf = loadModel(name+"-"+classes)
ExplainPath_Specific = ExplainPath + name + "_" + classes
weights = eli5.explain_weights(clf, feature_names=cols, top=(len(cols)+1))
predictionA = eli5.explain_prediction(clf, XY[0][0], feature_names=cols, top=(len(cols)+1))
predictionB = eli5.explain_prediction(clf, XY[0][1], feature_names=cols, top=(len(cols)+1))
html = eli5.format_as_html(weights)
with open(uniquify(ExplainPath_Specific + "/" + classes +"_ELI5_WEIGHTS.html"), 'w') as f:
f.write(html)
f.close()
html = eli5.format_as_html(predictionA)
with open(uniquify(ExplainPath_Specific + "/" + classes +"_ELI5_PREDICTION_A.html"), 'w') as f:
f.write(html)
f.close()
html = eli5.format_as_html(predictionB)
with open(uniquify(ExplainPath_Specific + "/" + classes +"_ELI5_PREDICTION_B.html"), 'w') as f:
f.write(html)
f.close()
def assert_binary_linear_classifier_explained(newsgroups_train_binary, clf,
explain_prediction):
docs, y, target_names = newsgroups_train_binary
vec = TfidfVectorizer()
X = vec.fit_transform(docs)
clf.fit(X, y)
assert y[2] == 1
cg_document = docs[2]
res = explain_prediction(clf, cg_document, vec=vec,
target_names=target_names, top=20)
expl_text, expl_html = format_as_all(res, clf)
for expl in [expl_text, expl_html]:
assert 'software' in expl or 'thanks' in expl
assert target_names[1] in expl
assert y[15] == 0
atheism_document = docs[15]
res = explain_prediction(clf, atheism_document, vec=vec,
target_names=target_names, top=20)
expl_text, expl_html = format_as_all(res, clf)
for expl in [expl_text, expl_html]:
assert 'god' in expl
assert target_names[0] in expl
assert_correct_class_explained_binary(clf, X[::10])
def test_explain_prediction_unsupported():
clf = BaseEstimator()
doc = 'doc'
res = explain_prediction(clf, doc)
assert 'BaseEstimator' in res.error
with pytest.raises(TypeError):
explain_prediction(clf, doc, unknown_argument=True)
def test_explain_hashing_vectorizer(newsgroups_train_binary):
# test that we can pass InvertableHashingVectorizer explicitly
vec = HashingVectorizer(n_features=1000)
ivec = InvertableHashingVectorizer(vec)
clf = LogisticRegression(random_state=42)
docs, y, target_names = newsgroups_train_binary
ivec.fit([docs[0]])
X = vec.fit_transform(docs)
clf.fit(X, y)
get_res = lambda **kwargs: explain_prediction(
clf, docs[0], vec=ivec, target_names=target_names, top=20, **kwargs)
res = get_res()
check_explain_linear_binary(res, clf)
assert res == get_res()
res_vectorized = explain_prediction(clf,
vec.transform([docs[0]])[0],
vec=ivec,
target_names=target_names,
top=20,
vectorized=True)
pprint(res_vectorized)
assert res_vectorized == _without_weighted_spans(res)
assert res == get_res(feature_names=ivec.get_feature_names(
always_signed=False))
def test_explain_prediction_booster_multitarget(newsgroups_train):
docs, ys, target_names = newsgroups_train
vec = CountVectorizer(stop_words='english', dtype=np.float64)
xs = vec.fit_transform(docs)
clf = lightgbm.train(params={
'objective': 'multiclass',
'verbose_eval': -1,
'max_depth': 2,
'n_estimators': 100,
'min_child_samples': 1,
'min_child_weight': 1,
'num_class': len(target_names)
},
train_set=lightgbm.Dataset(xs.toarray(), label=ys))
doc = 'computer graphics in space: a new religion'
res = explain_prediction(clf, doc, vec=vec, target_names=target_names)
format_as_all(res, clf)
check_targets_scores(res)
graphics_weights = res.targets[1].feature_weights
assert 'computer' in get_all_features(graphics_weights.pos)
religion_weights = res.targets[3].feature_weights
assert 'religion' in get_all_features(religion_weights.pos)
top_target_res = explain_prediction(clf, doc, vec=vec, top_targets=2)
assert len(top_target_res.targets) == 2
assert sorted(t.proba for t in top_target_res.targets) == sorted(
t.proba for t in res.targets)[-2:]
def assert_linear_regression_explained(boston_train, reg, explain_prediction):
X, y, feature_names = boston_train
reg.fit(X, y)
res = explain_prediction(reg, X[0])
expl_text, expl_html = format_as_all(res, reg)
assert len(res.targets) == 1
target = res.targets[0]
assert target.target == 'y'
pos, neg = (get_all_features(target.feature_weights.pos),
get_all_features(target.feature_weights.neg))
assert 'x11' in pos or 'x11' in neg
if has_intercept(reg):
assert '<BIAS>' in pos or '<BIAS>' in neg
assert '<BIAS>' in expl_text
assert '<BIAS>' in expl_html
else:
assert '<BIAS>' not in pos and '<BIAS>' not in neg
assert '<BIAS>' not in expl_text
assert 'BIAS' not in expl_html
for expl in [expl_text, expl_html]:
assert 'x11' in expl
assert '(score' in expl
assert "'y'" in expl_text
assert '<b>y</b>' in strip_blanks(expl_html)
assert res == explain_prediction(reg, X[0])
def assert_multitarget_linear_regression_explained(reg, explain_prediction):
X, y = make_regression(n_samples=100, n_targets=3, n_features=10,
random_state=42)
reg.fit(X, y)
res = explain_prediction(reg, X[0])
expl_text, expl_html = format_as_all(res, reg)
assert len(res.targets) == 3
target = res.targets[1]
assert target.target == 'y1'
pos, neg = (get_all_features(target.feature_weights.pos),
get_all_features(target.feature_weights.neg))
assert 'x8' in pos or 'x8' in neg
if has_intercept(reg):
assert '<BIAS>' in pos or '<BIAS>' in neg
assert 'x8' in expl_text
if has_intercept(reg):
assert '<BIAS>' in expl_text
assert "'y2'" in expl_text
assert res == explain_prediction(reg, X[0])
check_targets_scores(res)
top_targets_res = explain_prediction(reg, X[0], top_targets=1)
assert len(top_targets_res.targets) == 1
def assert_linear_regression_explained(boston_train,
reg,
explain_prediction,
atol=1e-8,
reg_has_intercept=None):
X, y, feature_names = boston_train
reg.fit(X, y)
res = explain_prediction(reg, X[0], feature_names=feature_names)
expl_text, expl_html = expls = format_as_all(res, reg)
assert len(res.targets) == 1
target = res.targets[0]
assert target.target == 'y'
get_pos_neg_features = lambda fw: (
get_all_features(fw.pos, with_weights=True),
get_all_features(fw.neg, with_weights=True))
pos, neg = get_pos_neg_features(target.feature_weights)
assert 'LSTAT' in pos or 'LSTAT' in neg
if reg_has_intercept is None:
reg_has_intercept = has_intercept(reg)
if reg_has_intercept:
assert '<BIAS>' in pos or '<BIAS>' in neg
assert '<BIAS>' in expl_text
assert '<BIAS>' in expl_html
else:
assert '<BIAS>' not in pos and '<BIAS>' not in neg
assert '<BIAS>' not in expl_text
assert 'BIAS' not in expl_html
for expl in [expl_text, expl_html]:
assert 'LSTAT' in expl
assert '(score' in expl
assert "'y'" in expl_text
assert '<b>y</b>' in strip_blanks(expl_html)
for expl in expls:
assert_feature_values_present(expl, feature_names, X[0])
assert res == explain_prediction(reg, X[0], feature_names=feature_names)
check_targets_scores(res, atol=atol)
flt_res = explain_prediction(
reg,
X[0],
feature_names=feature_names,
feature_filter=lambda name, v: name != 'LSTAT')
format_as_all(flt_res, reg)
flt_target = flt_res.targets[0]
flt_pos, flt_neg = get_pos_neg_features(flt_target.feature_weights)
assert 'LSTAT' not in flt_pos and 'LSTAT' not in flt_neg
flt_all = dict(flt_pos, **flt_neg)
expected = dict(pos, **neg)
expected.pop('LSTAT')
assert flt_all == expected
def test_unsupported():
vec = CountVectorizer()
clf = BaseEstimator()
doc = 'doc'
res = explain_prediction(clf, doc, vec=vec)
assert 'BaseEstimator' in res.error
for expl in format_as_all(res, clf):
assert 'Error' in expl
assert 'BaseEstimator' in expl
with pytest.raises(TypeError):
explain_prediction(clf, doc, unknown_argument=True)
def test_explain_tree_regressor_multitarget(reg):
X, y = make_regression(n_samples=100, n_targets=3, n_features=10,
random_state=42)
reg.fit(X, y)
res = explain_prediction(reg, X[0])
for expl in format_as_all(res, reg):
for target in ['y0', 'y1', 'y2']:
assert target in expl
assert 'BIAS' in expl
assert any('x%d' % i in expl for i in range(10))
check_targets_scores(res)
top_targets_res = explain_prediction(reg, X[0], top_targets=1)
assert len(top_targets_res.targets) == 1
def test_explain_regression_hashing_vectorizer(newsgroups_train_binary):
docs, y, target_names = newsgroups_train_binary
vec = HashingVectorizer(norm=None)
clf = LinearRegression()
clf.fit(vec.fit_transform(docs), y)
# Setting large "top" in order to compare it with CountVectorizer below
# (due to small differences in the coefficients they might have cutoffs
# at different points).
res = explain_prediction(clf,
docs[0],
vec=vec,
target_names=[target_names[1]],
top=1000)
expl, _ = format_as_all(res, clf)
assert len(res.targets) == 1
e = res.targets[0]
assert e.target == 'comp.graphics'
neg = get_all_features(e.feature_weights.neg)
assert 'objective' in neg
assert 'that' in neg
assert 'comp.graphics' in expl
assert 'objective' in expl
assert 'that' in expl
# HashingVectorizer with norm=None is "the same" as CountVectorizer,
# so we can compare it and check that explanation is almost the same.
count_vec = CountVectorizer()
count_clf = LinearRegression()
count_clf.fit(count_vec.fit_transform(docs), y)
count_res = explain_prediction(count_clf,
docs[0],
vec=count_vec,
target_names=[target_names[1]],
top=1000)
pprint(count_res)
count_expl, _ = format_as_all(count_res, count_clf)
print(count_expl)
for key in ['pos', 'neg']:
values, count_values = [
sorted(get_names_coefs(getattr(r.targets[0].feature_weights, key)))
for r in [res, count_res]
]
assert len(values) == len(count_values)
for (name, coef), (count_name,
count_coef) in zip(values, count_values):
assert name == count_name
assert abs(coef - count_coef) < 0.05
def test_explain_tree_clf_multiclass(clf, iris_train):
X, y, feature_names, target_names = iris_train
clf.fit(X, y)
res = explain_prediction(
clf, X[0], target_names=target_names, feature_names=feature_names)
for expl in format_as_all(res, clf):
for target in target_names:
assert target in expl
assert 'BIAS' in expl
assert any(f in expl for f in feature_names)
assert_feature_values_present(expl, feature_names, X[0])
check_targets_scores(res)
top_targets_res = explain_prediction(clf, X[0], top_targets=1)
assert len(top_targets_res.targets) == 1
def assert_tree_explain_prediction_single_target(clf, X, feature_names):
get_res = lambda _x, **kwargs: explain_prediction(
clf, _x, feature_names=feature_names, **kwargs)
res = get_res(X[0])
for expl in format_as_all(res, clf):
assert_feature_values_present(expl, feature_names, X[0])
checked_flt = False
all_expls = []
for x in X[:5]:
res = get_res(x)
text_expl = format_as_text(res, show=fields.WEIGHTS)
print(text_expl)
assert '<BIAS>' in text_expl
check_targets_scores(res)
all_expls.append(text_expl)
get_all = lambda fw: get_all_features(fw.pos) | get_all_features(fw.neg
)
all_features = get_all(res.targets[0].feature_weights)
if len(all_features) > 1:
f = list(all_features - {'<BIAS>'})[0]
flt_res = get_res(x, feature_filter=lambda name, _: name != f)
flt_features = get_all(flt_res.targets[0].feature_weights)
assert flt_features == (all_features - {f})
checked_flt = True
assert checked_flt
assert any(f in ''.join(all_expls) for f in feature_names)
def explain(model_path):
wordseg = Wordsegmenter("./bin/pyseg.so", "./bin/qsegconf.ini")
sent_word2vec_path = "./data/word2vec.query.bin"
sent_vocab_path = "./data/word2vec.query.vocab"
sent_model_path = "./data/sif.model"
sent_word2vec = KeyedVectors.load_word2vec_format(sent_word2vec_path,
binary=True)
sent_vocab_dict = load_vocab(sent_vocab_path)
sent_model = joblib.load(sent_model_path)
tfidf_count_hash_vectorModels = VectorModels()
ner_dict_path = "./data/ner.dict"
syn_dict_path = "./data/syn.dict"
ner_dict, syn_dict = load_ner_dict(ner_dict_path, syn_dict_path)
model = joblib.load(model_path)
pd.set_option('display.max_rows', None)
explain = eli5.explain_weights(model, top=None)
explain = eli5.format_as_text(explain)
print explain
feature_names = []
column_names = ["qid", "ql", "qr"]
#reader = pd.read_csv(in_path, sep="\t", dtype="str", names=column_names, chunksize=100)
reader = pd.read_csv(sys.stdin,
sep="\t",
dtype="str",
names=column_names,
chunksize=1)
first_chunk = True
feature_extractor = lambda row: extract_features(
wordseg, row["ql"], row["qr"], tfidf_count_hash_vectorModels,
sent_word2vec, sent_vocab_dict, sent_model, ner_dict, syn_dict)
for data in reader:
_ = data.fillna("", inplace=True)
X = data[["ql", "qr"]].apply(feature_extractor, axis=1)
X_features = X.apply(pd.Series)
feature_names = X_features.columns.values.tolist()
X_features = X_features[feature_names]
y_preds = model.predict_proba(X_features,
ntree_limit=model.best_ntree_limit)
y_preds = map(lambda o: o[1], y_preds)
data = pd.concat([data, X_features], axis=1)
data = data.assign(predict=y_preds)
#if first_chunk:
# data.to_csv(in_path + ".predict", header=True, sep="\t", mode="w")
# first_chunk = False
#else:
# data.to_csv(in_path + ".predict", header=False, sep="\t", mode="a")
data.to_csv(sys.stdout, header=False, sep="\t")
explain = eli5.explain_prediction(model, X_features.iloc[0])
explain = eli5.format_as_text(explain)
print explain
print X_features.iloc[0]
def test_explain_linear_classifiers_unsupported_kernels(
clf, newsgroups_train_binary):
docs, y, target_names = newsgroups_train_binary
vec = TfidfVectorizer()
clf.fit(vec.fit_transform(docs), y)
res = explain_prediction(clf, docs[0], vec=vec)
assert 'supported' in res.error
def explore_final_model():
#https://github.com/gameofdimension/xgboost_explainer/blob/master/xgboost_explainer_demo.ipynb
nr_labels = len(y)
value_counts = y.value_counts()
perc_per_label = {k:round(100 * v/float(nr_labels),2) for k,v in value_counts.items()}
print('value counts:', y.value_counts())
print('perc per label:', perc_per_label)
model = pickle.load(open(filename_model, "rb"))
model_feature_names = model.attr('feature_names').split('|')
index_to_class = json.loads(model.attr('index_to_class'))
print(index_to_class)
classes = [index_to_class[k] for k in sorted(index_to_class.keys())]
print(classes)
print('eli5 explain weights (gain):\n',eli5.format_as_text(eli5.explain_weights(model, top=10))) #gain
df_test = pd.read_json(open(test_filename, "r"))
df_test = df_test.head(5)
feature_extractor = FeatureExtractor(df_test)
X_test, X_test_featurenames = feature_extractor.get_features_pred_instances(df_test, model_feature_names)
print(X)
print(set(X.dtypes))
# print(X.iloc[0])
print(eli5.format_as_text(eli5.explain_prediction(model, X_test.head(1), target_names = classes, top = 10, feature_names = X_test_featurenames)))
def print_eli5(click_data, category):
pred = pd.read_csv(category + '_xy.csv')
model = joblib.load(category + ".h5")
pred = pred.loc[(pred['grid_x'] == click_data['points'][0]['lon']) &
(pred['grid_y'] == click_data['points'][0]['lat']), :]
pred_sqr = pred['eurogrid_0250_1'].values[0]
dane_model = df.loc[df['eurogrid_0250_1'] == pred_sqr, :]
dict_ = eli5.format_as_dataframe(eli5.explain_weights(model))
cols = dict_['feature'].values
maping = {}
for i in range(len(cols)):
maping['x' + str(i)] = cols[i]
# print(dane_model.columns)
expl = dane_model.loc[:, cols]
# print(expl.head())
all_cols = itertools.permutations(cols)
for cols in all_cols:
try:
expl = expl.loc[:, list(cols)]
expl = eli5.formatters.format_as_dataframe(
eli5.explain_prediction(model, expl))
break
except:
continue
expl['feature'] = expl['feature'].apply(lambda x: map_x(x, maping))
return generate_table(expl)
def result():
tweet = str(request.data)
explain = explain_prediction(gnb,
tweet,
vec=tfid,
target_names=['known weird', 'less weird'])
return str(format_as_text(explain))
def assert_multiclass_linear_classifier_explained(newsgroups_train, clf,
explain_prediction):
docs, y, target_names = newsgroups_train
vec = TfidfVectorizer()
X = vec.fit_transform(docs)
clf.fit(X, y)
get_res = lambda: explain_prediction(
clf, docs[0], vec=vec, target_names=target_names, top=20)
res = get_res()
pprint(res)
expl_text, expl_html = format_as_all(res, clf)
for e in res.targets:
if e.target != 'comp.graphics':
continue
pos = get_all_features(e.feature_weights.pos)
assert 'file' in pos
for expl in [expl_text, expl_html]:
for label in target_names:
assert str(label) in expl
assert 'file' in expl
assert res == get_res()
def test_explain_one_class_svm():
X = np.array([[0, 0], [0, 1], [5, 3], [93, 94], [90, 91]])
clf = OneClassSVM(kernel='linear', random_state=42).fit(X)
res = explain_prediction(clf, X[0])
assert res.targets[0].score < 0
for expl in format_as_all(res, clf):
assert 'BIAS' in expl
assert 'x0' not in expl
assert 'x1' not in expl
res = explain_prediction(clf, X[4])
assert res.targets[0].score > 0
for expl in format_as_all(res, clf):
assert 'BIAS' in expl
assert 'x0' in expl
assert 'x1' in expl
def test_explain_prediction_clf_binary(newsgroups_train_binary_big):
docs, ys, target_names = newsgroups_train_binary_big
vec = CountVectorizer(stop_words='english', dtype=np.float64)
clf = LGBMClassifier(n_estimators=100,
max_depth=2,
min_child_samples=1,
min_child_weight=1)
xs = vec.fit_transform(docs)
clf.fit(xs, ys)
get_res = lambda **kwargs: explain_prediction(
clf,
'computer graphics in space: a sign of atheism',
vec=vec,
target_names=target_names,
**kwargs)
res = get_res()
for expl in format_as_all(res, clf, show_feature_values=True):
assert 'graphics' in expl
check_targets_scores(res)
weights = res.targets[0].feature_weights
pos_features = get_all_features(weights.pos)
neg_features = get_all_features(weights.neg)
assert 'graphics' in pos_features
assert 'computer' in pos_features
assert 'atheism' in neg_features
flt_res = get_res(feature_re='gra')
flt_pos_features = get_all_features(flt_res.targets[0].feature_weights.pos)
assert 'graphics' in flt_pos_features
assert 'computer' not in flt_pos_features
def fi_eli(self, instance, prediction, model):
"""
Parameters
----------
instance: array
The instance which prediction's interpretation(s) Altruist will investigate.
prediction: None or any
it is not used in this function, but kept for consistency
model:
The machine learning model made the prediction
Returns
-------
list
The feature importances provided by Eli5
"""
fn = [i for i in range(len(instance))]
temp = format_as_dataframe(explain_prediction(model, instance,top=None))
temp.drop(['target', 'value'],axis=1,inplace=True)
temp = temp[temp.feature != '<BIAS>']
def remove_x(x):
return int(x.replace('x',''))
temp['feature'] = temp['feature'].apply(remove_x)
zero = [j for j in fn if j not in temp['feature'].values]
for z in zero:
temp = temp.append({'feature':z,'weight':0}, ignore_index=True)
temp = temp.sort_values(by=['feature'])
return temp.values[:,1]
def explain_pred(input_data, model):
y_preds = []
y_probs = []
encoded_htmls = []
for i in input_data:
expl = eli5.explain_prediction(
model.steps[-1][1],
i,
model.steps[0][1],
target_names=['Compliant', 'Not Compliant'],
top=10)
html_explanation = format_as_html(expl,
force_weights=False,
show_feature_values=True).replace(
"\n", "").strip()
encoded_html = base64.b64encode(
bytes(html_explanation, encoding='utf-8'))
encoded_htmls.append(encoded_html)
expl_dict = format_as_dict(expl)
targets = expl_dict['targets'][0]
target = targets['target']
y_pred = 1 if target.startswith('N') else 0
y_prob = targets['proba']
if len(i.split()) < 3:
# one or two words can't be non-compliant
y_pred = 0
y_prob = 1.0
y_prob = f'{round(y_prob, 3) * 100}%'
y_preds.append(y_pred)
y_probs.append(y_prob)
inferences = np.column_stack((y_probs, y_preds, encoded_htmls))
return inferences
def test_explain_tree_classifier_text(clf, newsgroups_train_big):
docs, y, target_names = newsgroups_train_big
vec = CountVectorizer(binary=True, stop_words='english')
X = vec.fit_transform(docs)
clf.fit(X, y)
res = explain_prediction(clf, docs[0], vec=vec, target_names=target_names)
check_targets_scores(res)
format_as_all(res, clf)
def test_unsupported():
vec = CountVectorizer()
clf = BaseEstimator()
res = explain_prediction(clf, 'hello, world', vec=vec)
assert 'BaseEstimator' in res.error
for expl in format_as_all(res, clf):
assert 'Error' in expl
assert 'BaseEstimator' in expl
def explain_prediction_df(estimator, doc, **kwargs):
# type: (...) -> pd.DataFrame
""" Explain prediction and export explanation to ``pandas.DataFrame``
All keyword arguments are passed to :func:`eli5.explain_prediction`.
Weights of all features are exported by default.
"""
kwargs = _set_defaults(kwargs)
return format_as_dataframe(
eli5.explain_prediction(estimator, doc, **kwargs))
def test_explain_prediction_single_leaf_tree(iris_train):
X, y, feature_names, target_names = iris_train
clf = LGBMClassifier(n_estimators=100)
clf.fit(X, y)
# at least one of the trees has only a single leaf
res = explain_prediction(clf, X[0], target_names=target_names)
format_as_all(res, clf)
check_targets_scores(res)
def explain_score(lead_no):
nb_feat = 20
lead_no = int(lead_no)
get = test[test[ID]==lead_no].reset_index(drop=True)
exps = eli5.explain_prediction(xgb_clf, get[xgb_clf.booster().feature_names].iloc[0], top=nb_feat)
score_explain = eli5.format_as_html(exps, show=('targets', 'feature_importances'), show_feature_values=True)
target_flag = get[TARGET].values[0]
proba_target= get[TARGET_PROBA].values[0]
proba_target = round(proba_target, 3)
return render_template('explain.html', lead_no=lead_no, score_explain=score_explain, target_flag=target_flag, proba_target=proba_target, nb_feat=nb_feat)
def test_explain_prediction_pandas(reg, boston_train):
pd = pytest.importorskip('pandas')
X, y, feature_names = boston_train
df = pd.DataFrame(X, columns=feature_names)
reg.fit(df, y)
res = explain_prediction(reg, df.iloc[0])
for expl in format_as_all(res, reg):
assert 'PTRATIO' in expl
if has_intercept(reg):
assert 'BIAS' in expl
