Adding functions

src/main.py

@@ -3,56 +3,94 @@ import numpy as np
 import pandas as pd 
 import matplotlib.pyplot as plt 
 import sklearn as sk
-
+import os
-# 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
 
+
+# Classification 
+
+
+def printPredictedValues(ypredit,ytest):
+    for i in range(0,len(ypredit)):
+        print("✅ Prédit/Réel: ",ypredit[i],ytest[i]) if ypredit[i]==ytest[i] else print("🔴 Prédit/Réel: ",ypredit[i], ytest[i])
+
+def printStatValues(ypredit,ytest):
+    galaxyStats = 0 
+    starStats = 0 
+    QSOStats = 0 
+    N = len(ypredit)
+    NF = 0
+    for i in range(0,N):
+        if ypredit[i] != ytest[i]:
+            NF +=1
+            if ypredit[i] == "GALAXY":
+                galaxyStats+=1
+            elif ypredit[i] == "QSO":
+                QSOStats+=1 
+            elif ypredit[i]=="STAR":
+                starStats+=1 
+    print("Répartition des prédiction fausses : ")
+    print("Galaxy : ",(galaxyStats*100/NF),"%","Star :",(starStats*100/NF),"%","QSO : ",(QSOStats*100/NF),"%")
+
 #charger les données
-df=pd.read_csv('../data.csv')
 
-# Clear datas
+
+def training(df):
 # 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)
+    x = df.drop(['obj_ID','field_ID','run_ID','rerun_ID','cam_col','plate','MJD','fiber_ID','class'],axis=1)
-y = df['class'].values
+    y = df['class'].values
-
 
-x.hist()
+    print(" Rentre un chiffre:\n\n1 - KNN\n2 - Tree\n3- RandomForestClassifier")
-plt.show()
+    res = int(input())
     
-print(" Rentre un chiffre:\n\n1 - KNN\n2 - Tree\n3- RandomForestClassifier")
+    if(res == 1):
-res = int(input())
-if(res == 1):
         model = KNeighborsClassifier()
-elif(res == 2):
+    elif(res == 2):
         model = DecisionTreeClassifier(random_state=0, max_depth=20)
-elif(res == 3):
+    elif(res == 3):
         model = RandomForestClassifier(n_estimators=100 ,criterion='entropy')
-else:
+    else:
-    raise Exception('RENTRE LE BON NOMBRE GROS CON')
+        raise Exception('Mauvaise saisie')
-
-
-
     
-Xtrain, Xtest, ytrain, ytest = train_test_split(x, y,test_size=0.25, random_state=0)
+    Xtrain, Xtest, ytrain, ytest = train_test_split(x, y,test_size=0.25, random_state=0)
+    Xtrain = Xtrain.values
+    Xtest = Xtest.values
 
-Xtrain = Xtrain.values
+    if len(Xtrain.shape) < 2:
-Xtest = Xtest.values
-
-if len(Xtrain.shape) < 2:
         Xtrain = Xtrain.reshape(-1, 1)
-    
+    if len(Xtest.shape) < 2:
-if len(Xtest.shape) < 2:
         Xtest = Xtest.reshape(-1, 1)
-model.fit(Xtrain,ytrain)
+    # Model training
+    model.fit(Xtrain,ytrain)
+    
+    ypredit = model.predict(Xtest)
+    os.system("clear")
+    while(res != 0):
+        print(" Rentre un chiffre:\n\n1 - Stats %\n2 - Stats raw\n3 - accuracy_score")
+        print("0 - QUIT")
+        res = int(input())
+        if(res == 1):
+            os.system("clear")
+            printStatValues(ypredit,ytest)
+        elif(res == 2):
+            os.system("clear")
+            printPredictedValues(ypredit,ytest)
+        elif res == 3:
+            os.system("clear")
+            print(accuracy_score(ytest, ypredit))
+        elif res == 0:
+            break
+        else:
+            raise Exception('Mauvaise saisie')
+
+def clearData(df):
+    res = df["class"].value_counts()
     
-ypredit = model.predict(Xtest)
+
-# print(ypredit)
+df=pd.read_csv('../data.csv')
-print(accuracy_score(ytest, ypredit))
+clearData(df)