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asharma567 commited on
Commit
1c7e53a
•
1 Parent(s): 0a2155d

Add application file

Browse files
Files changed (9) hide show
  1. app.py +90 -2
  2. df_M_character.joblib +0 -0
  3. df_M_character.pickle +0 -0
  4. explainer.joblib +0 -0
  5. explainer.pickle +0 -0
  6. leaderboard.csv +0 -0
  7. requirements.txt +5 -0
  8. shap_values.joblib +0 -0
  9. shap_values.pickle +0 -0
app.py CHANGED
@@ -1,4 +1,92 @@
 
 
 
 
 
 
 
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  import streamlit as st
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- x = st.slider('Select a value')
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- st.write(x, 'squared is', x * x)
 
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+ #create the explainer and shap values
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+
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+ import pandas as pd
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+ import streamlit as st
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+ from st_aggrid import AgGrid, GridOptionsBuilder
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+ from st_aggrid.shared import GridUpdateMode
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+ import shap
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  import streamlit as st
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+ import streamlit.components.v1 as components
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+ import pickle
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+ import xgboost
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+
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+
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+
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+
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+ def st_shap(plot, height=None):
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+ shap_html = f"<head>{shap.getjs()}</head><body>{plot.html()}</body>"
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+ components.html(shap_html, height=height)
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+
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+
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+ def scale_and_standardize(df):
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+ from sklearn.preprocessing import StandardScaler
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+ scaler = StandardScaler()
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+ scaler.fit(df)
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+ scaled_df = scaler.transform(df)
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+ return scaled_df
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+
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+ def aggrid_interactive_table(df: pd.DataFrame):
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+ """Creates an st-aggrid interactive table based on a dataframe.
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+
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+ Args:
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+ df (pd.DataFrame]): Source dataframe
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+
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+ Returns:
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+ dict: The selected row
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+ """
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+ options = GridOptionsBuilder.from_dataframe(
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+ df, enableRowGroup=True, enableValue=True, enablePivot=True
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+ )
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+
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+ options.configure_side_bar()
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+
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+ options.configure_selection("single")
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+ selection = AgGrid(
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+ df,
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+ enable_enterprise_modules=True,
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+ gridOptions=options.build(),
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+ theme="light",
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+ update_mode=GridUpdateMode.MODEL_CHANGED,
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+ allow_unsafe_jscode=True,
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+ )
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+
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+ return selection
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+
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+ st.title("Boba Leaderboard")
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+
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+ #load joblib
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+ df_M_character = pickle.load(open('df_M_character.pickle', 'rb'))
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+ explainer = pickle.load(open('explainer.pickle', 'rb'))
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+ shap_values = pickle.load(open('shap_values.pickle', 'rb'))
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+ df_M_character_scale = scale_and_standardize(df_M_character)
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+ author_idx_lookup = dict([(name, idx) for idx, name in enumerate(df_M_character.index)])
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+
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+ X = pd.DataFrame(
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+ df_M_character_scale,
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+ columns=df_M_character.columns)
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+
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+
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+ df_leaderboard = pd.read_csv("leaderboard.csv")
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+ df_leaderboard.rename(columns = {'Unnamed: 0':'Rank', 'author':'member'}, inplace=True)
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+ selection = aggrid_interactive_table(df=df_leaderboard)
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+
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+
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+
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+
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+ if selection:
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+ try:
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+ auth = selection["selected_rows"][0]['member']
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+ except:
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+ auth = df_leaderboard['member'].iloc[0]
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+
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+ st.write(auth)
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+ author_idx = author_idx_lookup.get(auth)
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+ # visualize the first prediction's explaination with default colors
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+ st_shap(shap.force_plot(explainer.expected_value, shap_values[author_idx,:], X.iloc[author_idx,:]))
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+
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+
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+
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+
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+
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+
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df_M_character.joblib ADDED
Binary file (519 kB). View file
 
df_M_character.pickle ADDED
Binary file (486 kB). View file
 
explainer.joblib ADDED
Binary file (304 kB). View file
 
explainer.pickle ADDED
Binary file (297 kB). View file
 
leaderboard.csv ADDED
The diff for this file is too large to render. See raw diff
 
requirements.txt ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
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+ streamlit==1.12.2
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+ streamlit-aggrid==0.3.2
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+ shap==0.41.0
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+ pandas==1.4.3
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+ xgboost==1.6.1
shap_values.joblib ADDED
Binary file (227 kB). View file
 
shap_values.pickle ADDED
Binary file (227 kB). View file