add rf baseline

result/random_forest.py

@@ -0,0 +1,27 @@
+import xgboost as xgb
+import numpy as np
+
+model_name = 'brits_i'
+
+impute = np.load('./{}_data.npy'.format(model_name)).reshape(-1, 48 * 35)
+label = np.load('./{}_label.npy'.format(model_name))
+
+data = np.nan_to_num(impute)
+
+n_train = 3000
+
+print(impute.shape)
+print(label.shape)
+
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import roc_auc_score
+
+auc = []
+
+for i in range(10):
+    model = RandomForestClassifier().fit(data[:n_train], label[:n_train])
+    pred = model.predict_proba(data[n_train:])
+
+    auc.append(roc_auc_score(label[n_train:].reshape(-1,), pred[:, 1].reshape(-1, )))
+
+print(np.mean(auc))