<filename>src/xgb_model.py
import pandas as pd
import glob
from sklearn.metrics import roc_auc_score
import numpy as np
from functools import partial
from scipy.optimize import fmin
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler

import xgboost as xgb


def run_training(pred_df, fold):
    train_df = pred_df[pred_df.kfold != fold].reset_index(drop=True)
    valid_df = pred_df[pred_df.kfold == fold].reset_index(drop=True)
    xtrain = train_df[["lr_pred", "lr_cnt_pred", "rf_svd_pred"]].values
    xvalid = valid_df[["lr_pred", "lr_cnt_pred", "rf_svd_pred"]].values

    scl = StandardScaler()
    xtrain = scl.fit_transform(xtrain)
    xvalid = scl.transform(xvalid)

    opt = xgb.XGBClassifier(random_state=42, tree_method='gpu_hist')
    opt.fit(xtrain, train_df.sentiment.values)
    pred = opt.predict_proba(xvalid)[:, 1]
    auc = roc_auc_score(valid_df.sentiment.values, pred)
    valid_df.loc[:, 'xgb_pred'] = pred
    print(f'Fold : {fold}, AUC : {auc}')
    return valid_df


if __name__ == '__main__':
    files = glob.glob('../model_pred/*.csv')
    df = None
    for f in files:
       if df is None:
           df = pd.read_csv(f)
       else:
           temp_df = pd.read_csv(f)
           df = df.merge(temp_df, on="id", how="left")
    targets = df.sentiment.values
    pred_col = ["lr_pred", "lr_cnt_pred", "rf_svd_pred"]

    dfs = []
    for j in range(5):
        temp_df = run_training(df, j)
        dfs.append(temp_df)

    fin_valid_df = pd.concat(dfs)
    print(roc_auc_score(targets, fin_valid_df.xgb_pred.values))

<filename>src/xgb_model.py
import pandas as pd
import glob
from sklearn.metrics import roc_auc_score
import numpy as np
from functools import partial
from scipy.optimize import fmin
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler

import xgboost as xgb


def run_training(pred_df, fold):
    train_df = pred_df[pred_df.kfold != fold].reset_index(drop=True)
    valid_df = pred_df[pred_df.kfold == fold].reset_index(drop=True)
    xtrain = train_df[["lr_pred", "lr_cnt_pred", "rf_svd_pred"]].values
    xvalid = valid_df[["lr_pred", "lr_cnt_pred", "rf_svd_pred"]].values

    scl = StandardScaler()
    xtrain = scl.fit_transform(xtrain)
    xvalid = scl.transform(xvalid)

    opt = xgb.XGBClassifier(random_state=42, tree_method='gpu_hist')
    opt.fit(xtrain, train_df.sentiment.values)
    pred = opt.predict_proba(xvalid)[:, 1]
    auc = roc_auc_score(valid_df.sentiment.values, pred)
    valid_df.loc[:, 'xgb_pred'] = pred
    print(f'Fold : {fold}, AUC : {auc}')
    return valid_df


if __name__ == '__main__':
    files = glob.glob('../model_pred/*.csv')
    df = None
    for f in files:
       if df is None:
           df = pd.read_csv(f)
       else:
           temp_df = pd.read_csv(f)
           df = df.merge(temp_df, on="id", how="left")
    targets = df.sentiment.values
    pred_col = ["lr_pred", "lr_cnt_pred", "rf_svd_pred"]

    dfs = []
    for j in range(5):
        temp_df = run_training(df, j)
        dfs.append(temp_df)

    fin_valid_df = pd.concat(dfs)
    print(roc_auc_score(targets, fin_valid_df.xgb_pred.values))
