| import streamlit as st |
| import pandas as pd |
| import numpy as np |
| from oocsi_source import OOCSI |
| from uuid import uuid4 |
| from streamlit_extras.switch_page_button import switch_page |
| import random |
| import shap |
| import xgboost as xgb |
| import matplotlib.pyplot as plt |
| import streamlit.components.v1 as components |
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| |
| if 'profile4' not in st.session_state: |
| st.session_state.pages.remove("visualMap") |
| st.session_state.profile4= 'deleted' |
| if (len(st.session_state.pages)>0): |
| st.session_state.nextPage4 = random.randint(0, len(st.session_state.pages)-1) |
| st.session_state.lastQuestion= 'no' |
| else: |
| st.session_state.lastQuestion= 'yes' |
|
|
|
|
| if 'index4' not in st.session_state: |
| st.session_state.index4= 0 |
|
|
| if 'profileIndex' not in st.session_state: |
| st.session_state.profileIndex= st.session_state.profileIndices[st.session_state.index4] |
| |
| |
|
|
| header1, header2, header3 = st.columns([1,2,1]) |
| characteristics1, characteristics2, characteristics3 = st.columns([1,2,1]) |
| prediction1, prediction2, prediction3 =st.columns([1,2,1]) |
| explanationheader1,explanationheader2, explanationheader3 = st.columns([1,2,1]) |
| explanation1, explanation2, explanation3 = st.columns([1,6,1]) |
| footer1, footer2, footer3 =st.columns([1,2,1]) |
| evaluation1, evaluation2, evaluation3 = st.columns([1,2,1]) |
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|
| name= st.session_state.X_test_names.loc[st.session_state.profileIndex, "Name"] |
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|
| @st.cache_resource |
| def trainModel(X_train,Y_train): |
| model = xgb.XGBClassifier().fit(X_train, Y_train) |
| return model |
|
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|
| @st.cache_resource |
| def getSHAPvalues(_model,X_train, Y_train, X_test): |
| |
| explainer = shap.Explainer(_model, X_test) |
| shap_values = explainer(X_test) |
| return shap_values |
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|
| def shapPlot(X_test, _shap_values): |
| return shap.plots.waterfall(shap_values[st.session_state.profileIndex]) |
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|
| with header2: |
| st.header('Visual Method for XAI') |
| st.markdown('''In this part, a new method of Explainability was implemented using more visual techniques for communicating of the model |
| predictions and the features influence. The values showed when clicked on each feature (title, Age, deck, ...) were obtained using the SHAP algorithm. |
| Let yourself play with it and tell us how easy it was to understand the model prediciton and the influence of the features! |
| |
| ''') |
| st.markdown("Click on the image to see how each attribute contributed and hover over them to see the SHAP values") |
| |
| st.write("For debugging:") |
| st.write(st.session_state.participantID) |
| XGBmodel= trainModel(st.session_state.X_train, st.session_state.Y_train) |
| |
| with characteristics2: |
| |
| data = st.session_state.X_test.iloc[st.session_state.profileIndex].values.reshape(1, -1) |
| |
| df = pd.DataFrame(data, columns=st.session_state.X_test.columns) |
| |
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|
| with prediction2: |
| st.subheader("Prediction") |
| prediction = XGBmodel.predict(st.session_state.X_test.iloc[st.session_state.profileIndex].values.reshape(1, -1)) |
| probability = XGBmodel.predict_proba(st.session_state.X_test.iloc[st.session_state.profileIndex].values.reshape(1, -1)) |
| if prediction == 0: |
| prob = round((probability[0][0]*100),2) |
| st.markdown("The model predicts with {}% probability that {} will :red[**not survive**]".format(prob, name) ) |
| else: |
| prob = round((probability[0][1]*100),2) |
| st.markdown("The model predicts with {}% probability that {} will :green[**survive**]".format(prob, name) ) |
| |
| |
| |
| |
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|
| with explanation2: |
| components.iframe("https://observablehq.com/embed/d177ef99668b6553@1065?cells=viewof+button%2Cchart2", scrolling=False, height=683) |
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|
| with footer2: |
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|
| def is_user_active(): |
| if 'user_active4' in st.session_state.keys() and st.session_state['user_active4']: |
| return True |
| else: |
| return False |
| |
| if is_user_active(): |
| with st.form("my_form4", clear_on_submit=True): |
| st.subheader("Evaluation") |
| st.write("These questions only ask for your opinion about this specific explanation") |
| q1 = st.select_slider( |
| '**1**- From the explanation, I **understand** how the algorithm works:', |
| options=['totally disagree', 'disagree', 'neutral' , 'agree', 'totally agree']) |
|
|
| q2 = st.select_slider( |
| '**2**- This explanation of how the algorithm works is **satisfying**:', |
| options=['totally disagree', 'disagree', 'neutral' , 'agree', 'totally agree']) |
|
|
| q3 = st.select_slider( |
| '**3**- This explanation of how the algorithm works has **sufficient detail**:', |
| options=['totally disagree', 'disagree', 'neutral' , 'agree', 'totally agree']) |
|
|
| q4 = st.select_slider( |
| '**4**- This explanation of how the algorithm works seems **complete**:', |
| options=['totally disagree', 'disagree', 'neutral' , 'agree', 'totally agree']) |
|
|
| q5 = st.select_slider( |
| '**5**- This explanation of how the algorithm works **tells me how to use it**:', |
| options=['totally disagree', 'disagree', 'neutral' , 'agree', 'totally agree']) |
|
|
| q6 = st.select_slider( |
| '**6**- This explanation of how the algorithm works is **useful to my goals**:', |
| options=['totally disagree', 'disagree', 'neutral' , 'agree', 'totally agree']) |
|
|
| q7 = st.select_slider( |
| '**7**- This explanation of the algorithm shows me how **accurate** the algorithm is:', |
| options=['totally disagree', 'disagree', 'neutral' , 'agree', 'totally agree']) |
|
|
| q8 = st.select_slider( |
| '**8**- This explanation lets me judge when I should **trust and not trust** the algorithm:', |
| options=['totally disagree', 'disagree', 'neutral' , 'agree', 'totally agree']) |
|
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| |
| submitted = st.form_submit_button("Submit") |
| if submitted: |
| |
| st.session_state.oocsi.send('EngD_HAII', { |
| 'participant_ID': st.session_state.participantID, |
| 'type of explanation': 'visualmap', |
| 'q1': q1, |
| 'q2': q2, |
| 'q3': q3, |
| 'q4': q4, |
| 'q5': q5, |
| 'q6': q6, |
| 'q7': q7, |
| 'q8': q8, |
| |
| }) |
| if (st.session_state.lastQuestion =='yes'): |
| switch_page('finalPage') |
| else: |
| st.session_state.profileIndex =st.session_state.profileIndices[0] |
| switch_page(st.session_state.pages[st.session_state.nextPage4]) |
| else: |
| if st.button('Continue to evaluation'): |
| st.session_state['user_active4']=True |
| st.experimental_rerun() |
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