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import numpy as np |
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import pandas as pd |
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import joblib |
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import streamlit as st |
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from catboost import CatBoostClassifier |
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from xgboost import XGBClassifier |
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MODEL_DIR = "models" |
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THRESHOLD_S1 = 0.463 |
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@st.cache_resource |
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def load_models(): |
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model_s1 = CatBoostClassifier() |
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model_s1.load_model(f"{MODEL_DIR}/stage1_catboost.cbm") |
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model_s2 = XGBClassifier() |
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model_s2.load_model(f"{MODEL_DIR}/stage2_xgb.json") |
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encoder = joblib.load(f"{MODEL_DIR}/encoder_s2.pkl") |
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return model_s1, model_s2, encoder |
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def run_inference(X_emb, meta, threshold): |
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model_s1, model_s2, encoder = load_models() |
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df_meta = pd.DataFrame(meta) |
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probs_s1 = model_s1.predict_proba(X_emb)[:, 1] |
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mask = probs_s1 >= THRESHOLD_S1 |
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X_pass = X_emb[mask] |
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df_pass = df_meta.loc[mask].copy() |
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probs_s2 = model_s2.predict_proba(X_pass)[:, 1] |
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final_score = 0.4 * probs_s1[mask] + 0.6 * probs_s2 |
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df_pass["Final_Score"] = final_score |
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df_pass["Final_Prediction"] = (final_score >= threshold).astype(int) |
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return df_pass |
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