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| #create the explainer and shap values | |
| import pandas as pd | |
| import streamlit as st | |
| from st_aggrid import AgGrid, GridOptionsBuilder | |
| from st_aggrid.shared import GridUpdateMode | |
| import shap | |
| import streamlit as st | |
| import streamlit.components.v1 as components | |
| import pickle | |
| import xgboost | |
| import joblib | |
| def st_shap(plot, height=None): | |
| shap_html = f"<head>{shap.getjs()}</head><body>{plot.html()}</body>" | |
| components.html(shap_html, height=height) | |
| def scale_and_standardize(df): | |
| from sklearn.preprocessing import StandardScaler | |
| scaler = StandardScaler() | |
| scaler.fit(df) | |
| scaled_df = scaler.transform(df) | |
| return scaled_df | |
| def aggrid_interactive_table(df: pd.DataFrame): | |
| """Creates an st-aggrid interactive table based on a dataframe. | |
| Args: | |
| df (pd.DataFrame]): Source dataframe | |
| Returns: | |
| dict: The selected row | |
| """ | |
| options = GridOptionsBuilder.from_dataframe( | |
| df, enableRowGroup=True, enableValue=True, enablePivot=True | |
| ) | |
| options.configure_side_bar() | |
| options.configure_selection("single") | |
| selection = AgGrid( | |
| df, | |
| enable_enterprise_modules=True, | |
| gridOptions=options.build(), | |
| theme="light", | |
| update_mode=GridUpdateMode.MODEL_CHANGED, | |
| allow_unsafe_jscode=True, | |
| ) | |
| return selection | |
| st.title("Boba Leaderboard") | |
| #load joblib | |
| df_M_character = pickle.load(open('df_M_character.pickle', 'rb')) | |
| # explainer = pickle.load(open('explainer.pickle', 'rb')) | |
| # shap_values = pickle.load(open('shap_values.pickle', 'rb')) | |
| df_M_character_scale = scale_and_standardize(df_M_character) | |
| author_idx_lookup = dict([(name, idx) for idx, name in enumerate(df_M_character.index)]) | |
| ppl_labels = joblib.load('ppl_labels.joblib') | |
| # train XGBoost model | |
| X, y = pd.DataFrame( | |
| df_M_character_scale, | |
| columns=df_M_character.columns | |
| ), ppl_labels | |
| bst = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X, label=y), 100) | |
| # explain the model's predictions using SHAP values | |
| explainer = shap.TreeExplainer(bst) | |
| shap_values = explainer.shap_values(X) | |
| # shap.summary_plot(shap_values, X) | |
| X = pd.DataFrame( | |
| df_M_character_scale, | |
| columns=df_M_character.columns) | |
| df_leaderboard = pd.read_csv("leaderboard.csv") | |
| df_leaderboard.rename(columns = {'Unnamed: 0':'Rank', 'author':'member'}, inplace=True) | |
| selection = aggrid_interactive_table(df=df_leaderboard) | |
| if selection: | |
| try: | |
| auth = selection["selected_rows"][0]['member'] | |
| except: | |
| auth = df_leaderboard['member'].iloc[0] | |
| st.write(auth) | |
| author_idx = author_idx_lookup.get(auth) | |
| # visualize the first prediction's explaination with default colors | |
| st_shap(shap.force_plot(explainer.expected_value, shap_values[author_idx,:], X.iloc[author_idx,:])) | |