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def modelfit(alg, dtrain, predictors,useTrainCV=True, cv_folds=5, early_stopping_rounds=50): | |
if useTrainCV: | |
xgb_param = alg.get_xgb_params() | |
xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values) | |
cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds, | |
metrics='auc', early_stopping_rounds=early_stopping_rounds, show_progress=False) | |
alg.set_params(n_estimators=cvresult.shape[0]) | |
#Fit the algorithm on the data | |
alg.fit(dtrain[predictors], dtrain['Disbursed'],eval_metric='auc') | |
#Predict training set: | |
dtrain_predictions = alg.predict(dtrain[predictors]) | |
dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1] | |
#Print model report: | |
print "\nModel Report" | |
print "Accuracy : %.4g" % metrics.accuracy_score(dtrain['Disbursed'].values, dtrain_predictions) | |
print "AUC Score (Train): %f" % metrics.roc_auc_score(dtrain['Disbursed'], dtrain_predprob) | |
feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False) | |
feat_imp.plot(kind='bar', title='Feature Importances') | |
plt.ylabel('Feature Importance Score') | |
#Choose all predictors except target & IDcols | |
predictors = [x for x in train.columns if x not in [target, IDcol]] | |
xgb1 = XGBClassifier( | |
learning_rate =0.1, | |
n_estimators=1000, | |
max_depth=5, | |
min_child_weight=1, | |
gamma=0, | |
subsample=0.8, | |
colsample_bytree=0.8, | |
objective= 'binary:logistic', | |
nthread=4, | |
scale_pos_weight=1, | |
seed=27 | |
) | |
modelfit(xgb1, train[:10000], predictors) |
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