Knn tree and randomforest train menu

src/main.py

@@ -0,0 +1,58 @@
+#!/usr/bin/python3
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt 
+import sklearn as sk
+
+# Classification 
+
+## KNN
+
+from sklearn.metrics import accuracy_score
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+
+#charger les données
+df=pd.read_csv('../data.csv')
+
+# Clear datas
+# alpha delta u g r i z redshift spec_OBJ_ID 
+# Y : class 
+x = df.drop(['obj_ID','field_ID','run_ID','rerun_ID','cam_col','plate','MJD','fiber_ID','class'],axis=1)
+y = df['class'].values
+
+
+x.hist()
+plt.show()
+
+print(" Rentre un chiffre:\n\n1 - KNN\n2 - Tree\n3- RandomForestClassifier")
+res = int(input())
+if(res == 1):
+    model = KNeighborsClassifier()
+elif(res == 2):
+    model = DecisionTreeClassifier(random_state=0, max_depth=20)
+elif(res == 3):
+    model = RandomForestClassifier(n_estimators=100 ,criterion='entropy')
+else:
+    raise Exception('RENTRE LE BON NOMBRE GROS CON')
+
+
+
+
+Xtrain, Xtest, ytrain, ytest = train_test_split(x, y,test_size=0.25, random_state=0)
+
+Xtrain = Xtrain.values
+Xtest = Xtest.values
+
+if len(Xtrain.shape) < 2:
+    Xtrain = Xtrain.reshape(-1, 1)
+    
+if len(Xtest.shape) < 2:
+    Xtest = Xtest.reshape(-1, 1)
+model.fit(Xtrain,ytrain)
+
+ypredit = model.predict(Xtest)
+# print(ypredit)
+print(accuracy_score(ytest, ypredit))