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diff --git a/TargetModel/TargetIF.py b/TargetModel/TargetIF.py
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+++ b/TargetModel/TargetIF.py
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+"""
+Date: 2022-04-13
+Author: [email protected]
+Desc: target model: Isolate Forest
+"""
+from sklearn.ensemble import IsolationForest
+import torch
+from sklearn.metrics import confusion_matrix
+import joblib
+from torch.utils.data import DataLoader
+import numpy as np
+import warnings
+warnings.filterwarnings("ignore")
+
+class TargetIF():
+    """
+    """
+    def __init__(self, param):
+        # 正则化
+        self.clf = IsolationForest(
+            n_estimators=param['n_estimators'],
+            contamination=param['outliers_fraction1'],
+            n_jobs=-1
+        )
+
+    def train(self, dataloader):
+        X = []
+        y = []
+        for batch_x, batch_y in dataloader:
+            X += batch_x.data.numpy().tolist()
+            y += batch_y.data.numpy().tolist()
+        X = np.array(X)
+        y = np.array(y)
+        # print("X.shape:{}".format(X.size))
+        # print("y.shape:{}".format(y.size))
+        self.clf.fit(X, y)
+        # print("training score:{}".format(self.clf.score(X, y)))
+
+    def eval(self, dataloader):
+        X = []
+        y = []
+        for batch_x, batch_y in dataloader:
+            X += batch_x.data.numpy().tolist()
+            y += batch_y.data.numpy().tolist()
+        X = np.array(X)
+        y = np.array(y)
+        # print("X.shape:{}".format(X.size))
+        # print("y.shape:{}".format(y.size))
+        y_pred = self.clf.predict(X)
+        y_pred = y_pred.reshape(-1,1)
+        y_pred[y_pred == -1] = 0
+        y_pred[y_pred == 1] = 1
+        return y_pred, y
+
+    def save(self, filename):
+        joblib.dump(self.clf, filename)
+
+    def load(self, filename):
+        self.clf = joblib.load(filename)
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