Commit
•
c4ccf2e
1
Parent(s):
996905d
Update app.py
Browse files
app.py
CHANGED
@@ -207,6 +207,8 @@ form_explainer.header("Explainer Settings")
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position_dep = form_explainer.checkbox("Check this if you want to take into account the position of a word in the interpretation", value = False)
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number_samples = form_explainer.number_input("Enter the number of explainer peterbuted samples, higher creates a better explanation but takes longer - you should most likely increase this", value = 200)
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char_based = form_explainer.checkbox("Check this if you want to use a character based explanier", value = False)
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form_explainer.form_submit_button("Submit")
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@@ -254,9 +256,9 @@ if task == "Classification":
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target_feature_names = text_clf['clf'].classes_
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#target_feature_names = pd.unique(df[labels_column_name])
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target_feature_names_list = list(target_feature_names)
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t_pred = te.explain_prediction(target_names = target_feature_names_list)
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else:
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t_pred = te.explain_prediction()
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html = format_as_html(t_pred)
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form_html = st.sidebar.form("html_size_form")
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position_dep = form_explainer.checkbox("Check this if you want to take into account the position of a word in the interpretation", value = False)
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number_samples = form_explainer.number_input("Enter the number of explainer peterbuted samples, higher creates a better explanation but takes longer - you should most likely increase this", value = 200)
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char_based = form_explainer.checkbox("Check this if you want to use a character based explanier", value = False)
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top_features = form_explainer.number_input("Enter the top number of features we want to show", value = 200)
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top_targets = form_explainer.number_input("Enter the top number of targets we want to show explanations of", value = 5)
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form_explainer.form_submit_button("Submit")
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target_feature_names = text_clf['clf'].classes_
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#target_feature_names = pd.unique(df[labels_column_name])
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target_feature_names_list = list(target_feature_names)
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t_pred = te.explain_prediction(target_names = target_feature_names_list, top = top_features, top_targets = top_targets)
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else:
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t_pred = te.explain_prediction(top = top_features, top_targets = top_targets)
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html = format_as_html(t_pred)
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form_html = st.sidebar.form("html_size_form")
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