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| from joblib import load | |
| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import shap | |
| import matplotlib.pyplot as plt | |
| from features import extract_basic_aggregations | |
| from os import listdir | |
| from os.path import join, isfile | |
| examples_path = './csv_examples/' | |
| examples = [[join(examples_path, f), 'A'] for f in listdir(examples_path)] | |
| model = load('xgb_cpu.joblib') | |
| explainer = shap.TreeExplainer(model) | |
| products = {'A': 0, | |
| 'B': 1, | |
| 'C': 2, | |
| 'D': 3, | |
| 'E': 4 | |
| } | |
| def score_client(card_transactions_file, product: str): | |
| df = pd.read_csv(card_transactions_file) | |
| assert product in products | |
| features = extract_basic_aggregations(df, cat_columns=['mcc_category', 'day_of_week', 'operation_type']) | |
| features = features.reindex(columns=model.feature_names_in_, fill_value=0) | |
| features['product'] = products[product] | |
| default_proba = model.predict_proba(features)[0][0] | |
| shap_values = explainer.shap_values(features) | |
| shap.plots.waterfall(explainer(features)[0], max_display=14, show=False) | |
| plt.tight_layout() | |
| shap_fig = plt.gcf() | |
| plt.close() | |
| return default_proba, shap_fig | |
| title = "Credit score demo" | |
| description = "This demo allows to evaluate credit score solely based on card transaction history. \ | |
| You can upload your own transaction history .csv file or use transactions from the examples. \ | |
| After that, please specify a credit product of interest. When the evaluation is done, you can examine an importances plot that may explain the result." | |
| inputs = [gr.File(), gr.Dropdown(choices=list(products.keys()), value=list(products.keys())[0])] | |
| outputs = [gr.Textbox(label='Your credit score (the more, the better)', interactive=False), | |
| gr.Plot(label='SHAP') | |
| ] | |
| demo = gr.Interface( | |
| fn=score_client, | |
| inputs=inputs, | |
| outputs=outputs, | |
| allow_flagging='never', | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| ) | |
| demo.launch() | |