Upload server2.py

server2.py

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+import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import joblib
+from sklearn.tree import DecisionTreeClassifier, XGBClassifier                   #using sklearn decisiontreeclassifier
+from sklearn.model_selection import train_test_split
+
+import os
+import shutil
+
+# Define the directory for FHE client/server files
+fhe_directory = '/tmp/fhe_client_server_files/'
+
+# Create the directory if it does not exist
+if not os.path.exists(fhe_directory):
+    os.makedirs(fhe_directory)
+else:
+    # If it exists, delete its contents
+    shutil.rmtree(fhe_directory)
+    os.makedirs(fhe_directory)
+
+data=pd.read_csv('data/heart.xls')
+
+
+data.info()   #checking the info
+
+data_corr=data.corr()
+
+plt.figure(figsize=(20,20))
+sns.heatmap(data=data_corr,annot=True)
+#Heatmap for data
+"""
+# Get the Data
+X_train, y_train, X_val, y_val = train_test_split()
+classifier = XGBClassifier()
+# Training the Model
+classifier = classifier.fit(X_train, y_train)
+# Trained Model Evaluation on Validation Dataset
+confidence = classifier.score(X_val, y_val)
+# Validation Data Prediction
+y_pred = classifier.predict(X_val)
+# Model Validation Accuracy
+accuracy = accuracy_score(y_val, y_pred)
+# Model Confusion Matrix
+conf_mat = confusion_matrix(y_val, y_pred)
+# Model Classification Report
+clf_report = classification_report(y_val, y_pred)
+# Model Cross Validation Score
+score = cross_val_score(classifier, X_val, y_val, cv=3)
+
+try:
+    # Load Trained Model
+    clf = load(str(self.model_save_path + saved_model_name + ".joblib"))
+except Exception as e:
+    print("Model not found...")
+
+if test_data is not None:
+    result = clf.predict(test_data)
+    print(result)
+else:
+    result = clf.predict(self.test_features)
+accuracy = accuracy_score(self.test_labels, result)
+clf_report = classification_report(self.test_labels, result)
+print(accuracy, clf_report)
+"""
+####################
+feature_value=np.array(data_corr['output'])
+for i in range(len(feature_value)):
+    if feature_value[i]<0:
+        feature_value[i]=-feature_value[i]
+
+print(feature_value)
+
+features_corr=pd.DataFrame(feature_value,index=data_corr['output'].index,columns=['correalation'])
+
+feature_sorted=features_corr.sort_values(by=['correalation'],ascending=False)
+
+feature_selected=feature_sorted.index
+
+feature_selected     #selected features which are very much correalated
+
+clean_data=data[feature_selected]
+
+#making input and output dataset
+X=clean_data.iloc[:,1:]
+Y=clean_data['output']
+
+x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.25,random_state=0)
+
+print(x_train.shape,y_train.shape,x_test.shape,y_test.shape)     #data is splited in traing and testing dataset
+
+# feature scaling
+from sklearn.preprocessing import StandardScaler
+sc=StandardScaler()
+x_train=sc.fit_transform(x_train)
+x_test=sc.transform(x_test)
+
+#training our model
+dt=XGBClassifier(criterion='entropy',max_depth=6)
+dt.fit(x_train,y_train)
+#dt.compile(x_trqin)
+
+#predicting the value on testing data
+y_pred=dt.predict(x_test)
+
+#ploting the data
+from sklearn.metrics import confusion_matrix
+conf_mat=confusion_matrix(y_test,y_pred)
+print(conf_mat)
+accuracy=dt.score(x_test,y_test)
+print("\nThe accuracy of decisiontreelassifier on Heart disease prediction dataset is "+str(round(accuracy*100,2))+"%")
+
+joblib.dump(dt, 'heart_disease_dt_model.pkl')
+
+from concrete.ml.sklearn import DecisionTreeClassifier as ConcreteDecisionTreeClassifier
+from concrete.ml.sklearn import XGBClassifier as ConcreteXGBClassifier
+
+fhe_compatible = ConcreteXGBClassifier.from_sklearn_model(dt, x_train, n_bits = 10) #de FHE
+fhe_compatible.compile(x_train)
+
+
+
+
+
+
+#### server
+from concrete.ml.deployment import FHEModelDev, FHEModelClient, FHEModelServer
+
+# Setup the development environment
+dev = FHEModelDev(path_dir=fhe_directory, model=fhe_compatible)
+dev.save()
+
+# Setup the server
+server = FHEModelServer(path_dir=fhe_directory)
+server.load()