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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
from IPython.display import display_html
import xgboost as xgb
import matplotlib.pyplot as plt
# st.markdown("""<style>
# .stSlider {
# padding-bottom: 20px;
# }
# </style> """,
# unsafe_allow_html=True)
#Delete this page from the array of pages to visit, this way it cannot be visited twice
if 'profile1' not in st.session_state:
st.session_state.pages.remove("SHAP")
st.session_state.profile1= 'deleted'
if (len(st.session_state.pages)>0):
st.session_state.nextPage1 = random.randint(0, len(st.session_state.pages)-1)
st.session_state.lastQuestion= 'no'
else:
st.session_state.lastQuestion= 'yes'
if 'index1' not in st.session_state:
st.session_state.index1= 0
if 'profileIndex' not in st.session_state:
st.session_state.profileIndex= st.session_state.profileIndices[st.session_state.index1]
header1, header2, header3 = st.columns([1,2,1])
characteristics1, characteristics2, characteristics3 = st.columns([1,2,1])
prediction1, prediction2, prediction3 =st.columns([1,2,1])
explanation1, explanation2, explanation3 = st.columns([1,5,1])
footer1, footer2, footer3 =st.columns([1,2,1])
evaluation1, evaluation2, evaluation3 = st.columns([1,2,1])
name= st.session_state.X_test_names.loc[st.session_state.profileIndex, "Name"]
@st.cache_resource
def trainModel(X_train,Y_train):
model = xgb.XGBClassifier().fit(X_train, Y_train)
return model
@st.cache_resource
def getSHAPvalues(_model,X_train, Y_train, X_test):
# compute SHAP values
explainer = shap.Explainer(_model, X_test)
shap_values = explainer(X_test)
return shap_values
def shapPlot(X_test, _shap_values):
return shap.plots.waterfall(shap_values[st.session_state.profileIndex])
with header2:
st.header('Explanation - SHAP Values')
st.markdown(''' The SHAP value algorithm (SHapley Additive exPlanations) is a way to reverse-engineer the output of any predictive machine learning model.
the technique helps to understand the decision took by a complex model. The classical models will typically answer the question 'how much' whereas the SHAP
model will focus on the 'why'.
Finally, the representation of the SHAP value will show how much each feature are contributing to the final prediction made by the model. For the Titanic dataset, each feature
will analyse each the contribution of each will be presented for different persons explaining the reason why this person survived the shipwreck or not.
''')
st.subheader(name, anchor='top')
# st.write("For debugging:")
# st.write(st.session_state.participantID)
XGBmodel= trainModel(st.session_state.X_train, st.session_state.Y_train)
with characteristics2:
# initialize list of lists
data = st.session_state.X_test.iloc[st.session_state.profileIndex].values.reshape(1, -1)
# Create the pandas DataFrame
df = pd.DataFrame(data, columns=st.session_state.X_test.columns)
st.dataframe(df)
with prediction2:
# st.header("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) )
with explanation2:
st.subheader("Explanation")
# with st.spinner("Please be patient, we are generating a new explanation"):
shap_values= getSHAPvalues(XGBmodel, st.session_state.X_train, st.session_state.Y_train, st.session_state.X_test)
st.set_option('deprecation.showPyplotGlobalUse', False)
fig = shap.plots.waterfall(shap_values[st.session_state.profileIndex])
st.pyplot(fig, bbox_inches='tight')
data_indices = pd.concat([d.reset_index(drop=True) for d in [st.session_state.ports_df, st.session_state.title_df, st.session_state.gender_df]], axis=1)
# st.dataframe(st.session_state.ports_df)
# st.dataframe(st.session_state.title_df)
# st.dataframe(st.session_state.gender_df)
st.dataframe(data_indices)
with footer2:
if (st.session_state.index1 < len(st.session_state.profileIndices)-1):
if st.button("New profile"):
st.session_state.index1 = st.session_state.index1+1
st.session_state.profileIndex = st.session_state.profileIndices[st.session_state.index1]
st.experimental_rerun()
else:
def is_user_active():
if 'user_active1' in st.session_state.keys() and st.session_state['user_active1']:
return True
else:
return False
if is_user_active():
# st.markdown("You have reached the end of the profiles")
# if st.button("Continue to evaluation"):
# st.write(" ")
with st.form("my_form1", 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'])
# Every form must have a submit button.
submitted = st.form_submit_button("Submit")
if submitted:
st.write("question 1", q1)
st.session_state.oocsi.send('EngD_HAII', {
'participant_ID': st.session_state.participantID,
'type of explanation': 'SHAP',
'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.nextPage1])
else:
if st.button('Continue to evaluation'):
st.session_state['user_active1']=True
st.experimental_rerun()
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