def main():
df = pd.read_excel('data/mr_vs_fr_30.xlsx')
df = df.sample(frac=1, random_state=seed)
df['text_lemmatized'] = df['text'].apply(morphText)
X_train, X_test, y_train, y_test = train_test_split(
df['text_lemmatized'], df['label'], test_size=0.3, random_state=42, stratify=df['label'])
flag_test = True
get_pipe(X_train, y_train, flag_test, X_test, y_test)
flag_test = False
pipe = get_pipe(df['text_lemmatized'], df['label'], flag_test)
k = 0
words = []
for index, row in df.iterrows():
te5 = TextExplainer(clf=DecisionTreeClassifier(max_depth=5), random_state=seed)
te5.fit(row['text_lemmatized'], pipe.predict_proba)
df_eli5_w = eli5.format_as_dataframe(te5.explain_weights())
print('class {}'.format('male' if row['label'] == 0 else 'woman'))
print('predict:')
print(df_eli5_w)
print(100*'*')
temp_m = ', '.join(df_eli5_w[df_eli5_w['weight'] > 0]['feature'].tolist())
if temp_m:
words.append(temp_m)
else:
words.append('')
k += 1
df['words'] = words
df.to_excel('mr_vs_fr_words_30.xlsx', index=False)
def test_text_explainer_char_based(token_pattern):
text = "Hello, world!"
predict_proba = substring_presence_predict_proba('lo')
te = TextExplainer(char_based=True, token_pattern=token_pattern)
te.fit(text, predict_proba)
print(te.metrics_)
assert te.metrics_['score'] > 0.95
assert te.metrics_['mean_KL_divergence'] < 0.1
res = te.explain_prediction()
format_as_all(res, te.clf_)
check_targets_scores(res)
assert res.targets[0].feature_weights.pos[0].feature == 'lo'
# another way to look at results (not that useful for char ngrams)
res = te.explain_weights()
assert res.targets[0].feature_weights.pos[0].feature == 'lo'
def test_text_explainer_custom_classifier():
text = "foo-bar baz egg-spam"
predict_proba = substring_presence_predict_proba('bar')
# use decision tree to explain the prediction
te = TextExplainer(clf=DecisionTreeClassifier(max_depth=2))
te.fit(text, predict_proba)
print(te.metrics_)
assert te.metrics_['score'] > 0.99
assert te.metrics_['mean_KL_divergence'] < 0.01
expl = te.explain_prediction()
format_as_all(expl, te.clf_)
# with explain_weights we can get a nice tree representation
expl = te.explain_weights()
print(expl.decision_tree.tree)
assert expl.decision_tree.tree.feature_name == "bar"
format_as_all(expl, te.clf_)