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metalni

VI Prv Kolokvium - Classification Machine Learning

metalni
Jun 30th, 2021
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  1. from sklearn import GaussianNB, DecisionTreeClassifier, RandomForestClassifier
  2.  
  3. dataset = [
  4.     [14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.06, .28, 2.29, 5.64, 1.04, 3.92, 1065, 0],
  5.     [13.2, 1.78, 2.14, 11.2, 100, 2.65, 2.76, .26, 1.28, 4.38, 1.05, 3.4, 1050, 0],
  6.     [13.16, 2.36, 2.67, 18.6, 101, 2.8, 3.24, .3, 2.81, 5.68, 1.03, 3.17, 1185, 0],
  7.     [14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, .24, 2.18, 7.8, .86, 3.45, 1480, 0],
  8.     [13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, .39, 1.82, 4.32, 1.04, 2.93, 735, 0],
  9.     [14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, .34, 1.97, 6.75, 1.05, 2.85, 1450, 0],
  10.     [14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, .3, 1.98, 5.25, 1.02, 3.58, 1290, 0],
  11.     [14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, .31, 1.25, 5.05, 1.06, 3.58, 1295, 0],
  12.     [14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, .29, 1.98, 5.2, 1.08, 2.85, 1045, 0],
  13.     [13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, .22, 1.85, 7.22, 1.01, 3.55, 1045, 0],
  14.     [14.1, 2.16, 2.3, 18, 105, 2.95, 3.32, .22, 2.38, 5.75, 1.25, 3.17, 1510, 0],
  15.     [14.12, 1.48, 2.32, 16.8, 95, 2.2, 2.43, .26, 1.57, 5, 1.17, 2.82, 1280, 0],
  16.     [13.75, 1.73, 2.41, 16, 89, 2.6, 2.76, .29, 1.81, 5.6, 1.15, 2.9, 1320, 0],
  17.     [14.75, 1.73, 2.39, 11.4, 91, 3.1, 3.69, .43, 2.81, 5.4, 1.25, 2.73, 1150, 0],
  18.     [14.38, 1.87, 2.38, 12, 102, 3.3, 3.64, .29, 2.96, 7.5, 1.2, 3, 1547, 0],
  19.     [13.63, 1.81, 2.7, 17.2, 112, 2.85, 2.91, .3, 1.46, 7.3, 1.28, 2.88, 1310, 0],
  20.     [14.3, 1.92, 2.72, 20, 120, 2.8, 3.14, .33, 1.97, 6.2, 1.07, 2.65, 1280, 0],
  21.     [13.83, 1.57, 2.62, 20, 115, 2.95, 3.4, .4, 1.72, 6.6, 1.13, 2.57, 1130, 0],
  22.     [14.19, 1.59, 2.48, 16.5, 108, 3.3, 3.93, .32, 1.86, 8.7, 1.23, 2.82, 1680, 0],
  23.     [13.64, 3.1, 2.56, 15.2, 116, 2.7, 3.03, .17, 1.66, 5.1, .96, 3.36, 845, 0],
  24.     [12.37, .94, 1.36, 10.6, 88, 1.98, .57, .28, .42, 1.95, 1.05, 1.82, 520, 1],
  25.     [12.33, 1.1, 2.28, 16, 101, 2.05, 1.09, .63, .41, 3.27, 1.25, 1.67, 680, 1],
  26.     [12.64, 1.36, 2.02, 16.8, 100, 2.02, 1.41, .53, .62, 5.75, .98, 1.59, 450, 1],
  27.     [13.67, 1.25, 1.92, 18, 94, 2.1, 1.79, .32, .73, 3.8, 1.23, 2.46, 630, 1],
  28.     [12.37, 1.13, 2.16, 19, 87, 3.5, 3.1, .19, 1.87, 4.45, 1.22, 2.87, 420, 1],
  29.     [12.17, 1.45, 2.53, 19, 104, 1.89, 1.75, .45, 1.03, 2.95, 1.45, 2.23, 355, 1],
  30.     [12.37, 1.21, 2.56, 18.1, 98, 2.42, 2.65, .37, 2.08, 4.6, 1.19, 2.3, 678, 1],
  31.     [13.11, 1.01, 1.7, 15, 78, 2.98, 3.18, .26, 2.28, 5.3, 1.12, 3.18, 502, 1],
  32.     [12.37, 1.17, 1.92, 19.6, 78, 2.11, 2, .27, 1.04, 4.68, 1.12, 3.48, 510, 1],
  33.     [13.34, .94, 2.36, 17, 110, 2.53, 1.3, .55, .42, 3.17, 1.02, 1.93, 750, 1],
  34.     [12.21, 1.19, 1.75, 16.8, 151, 1.85, 1.28, .14, 2.5, 2.85, 1.28, 3.07, 718, 1],
  35.     [12.29, 1.61, 2.21, 20.4, 103, 1.1, 1.02, .37, 1.46, 3.05, .906, 1.82, 870, 1],
  36.     [13.86, 1.51, 2.67, 25, 86, 2.95, 2.86, .21, 1.87, 3.38, 1.36, 3.16, 410, 1],
  37.     [13.49, 1.66, 2.24, 24, 87, 1.88, 1.84, .27, 1.03, 3.74, .98, 2.78, 472, 1],
  38.     [12.99, 1.67, 2.6, 30, 139, 3.3, 2.89, .21, 1.96, 3.35, 1.31, 3.5, 985, 1],
  39.     [11.96, 1.09, 2.3, 21, 101, 3.38, 2.14, .13, 1.65, 3.21, .99, 3.13, 886, 1],
  40.     [11.66, 1.88, 1.92, 16, 97, 1.61, 1.57, .34, 1.15, 3.8, 1.23, 2.14, 428, 1],
  41.     [13.03, .9, 1.71, 16, 86, 1.95, 2.03, .24, 1.46, 4.6, 1.19, 2.48, 392, 1],
  42.     [11.84, 2.89, 2.23, 18, 112, 1.72, 1.32, .43, .95, 2.65, .96, 2.52, 500, 1],
  43.     [12.33, .99, 1.95, 14.8, 136, 1.9, 1.85, .35, 2.76, 3.4, 1.06, 2.31, 750, 1],
  44.     [12.7, 3.87, 2.4, 23, 101, 2.83, 2.55, .43, 1.95, 2.57, 1.19, 3.13, 463, 1],
  45.     [12, .92, 2, 19, 86, 2.42, 2.26, .3, 1.43, 2.5, 1.38, 3.12, 278, 1],
  46.     [12.72, 1.81, 2.2, 18.8, 86, 2.2, 2.53, .26, 1.77, 3.9, 1.16, 3.14, 714, 1],
  47.     [12.86, 1.35, 2.32, 18, 122, 1.51, 1.25, .21, .94, 4.1, .76, 1.29, 630, 2],
  48.     [12.88, 2.99, 2.4, 20, 104, 1.3, 1.22, .24, .83, 5.4, .74, 1.42, 530, 2],
  49.     [12.81, 2.31, 2.4, 24, 98, 1.15, 1.09, .27, .83, 5.7, .66, 1.36, 560, 2],
  50.     [12.7, 3.55, 2.36, 21.5, 106, 1.7, 1.2, .17, .84, 5, .78, 1.29, 600, 2],
  51.     [12.51, 1.24, 2.25, 17.5, 85, 2, .58, .6, 1.25, 5.45, .75, 1.51, 650, 2],
  52.     [12.6, 2.46, 2.2, 18.5, 94, 1.62, .66, .63, .94, 7.1, .73, 1.58, 695, 2],
  53.     [12.25, 4.72, 2.54, 21, 89, 1.38, .47, .53, .8, 3.85, .75, 1.27, 720, 2],
  54.     [12.53, 5.51, 2.64, 25, 96, 1.79, .6, .63, 1.1, 5, .82, 1.69, 515, 2],
  55.     [13.49, 3.59, 2.19, 19.5, 88, 1.62, .48, .58, .88, 5.7, .81, 1.82, 580, 2],
  56.     [12.84, 2.96, 2.61, 24, 101, 2.32, .6, .53, .81, 4.92, .89, 2.15, 590, 2],
  57.     [12.93, 2.81, 2.7, 21, 96, 1.54, .5, .53, .75, 4.6, .77, 2.31, 600, 2],
  58.     [13.36, 2.56, 2.35, 20, 89, 1.4, .5, .37, .64, 5.6, .7, 2.47, 780, 2],
  59.     [13.52, 3.17, 2.72, 23.5, 97, 1.55, .52, .5, .55, 4.35, .89, 2.06, 520, 2],
  60.     [13.62, 4.95, 2.35, 20, 92, 2, .8, .47, 1.02, 4.4, .91, 2.05, 550, 2],
  61.     [12.25, 3.88, 2.2, 18.5, 112, 1.38, .78, .29, 1.14, 8.21, .65, 2, 855, 2],
  62.     [13.16, 3.57, 2.15, 21, 102, 1.5, .55, .43, 1.3, 4, .6, 1.68, 830, 2],
  63.     [13.88, 5.04, 2.23, 20, 80, .98, .34, .4, .68, 4.9, .58, 1.33, 415, 2],
  64.     [12.87, 4.61, 2.48, 21.5, 86, 1.7, .65, .47, .86, 7.65, .54, 1.86, 625, 2],
  65.     [13.32, 3.24, 2.38, 21.5, 92, 1.93, .76, .45, 1.25, 8.42, .55, 1.62, 650, 2],
  66.     [13.08, 3.9, 2.36, 21.5, 113, 1.41, 1.39, .34, 1.14, 9.40, .57, 1.33, 550, 2],
  67.     [13.5, 3.12, 2.62, 24, 123, 1.4, 1.57, .22, 1.25, 8.60, .59, 1.3, 500, 2],
  68.     [12.79, 2.67, 2.48, 22, 112, 1.48, 1.36, .24, 1.26, 10.8, .48, 1.47, 480, 2],
  69.     [13.11, 1.9, 2.75, 25.5, 116, 2.2, 1.28, .26, 1.56, 7.1, .61, 1.33, 425, 2]
  70. ]
  71.  
  72. if __name__ == '__main__':
  73.    
  74.     # read input
  75.     x1 = float(input())
  76.     x2 = float(input())
  77.    
  78.     zero = [row for row in dataset if row[-1] == 0]
  79.     one = [row for row in dataset if row[-1] == 1]
  80.     two = [row for row in dataset if row[-1] == 2]
  81.    
  82.     # Test_Set
  83.     test_set = zero[int(len(zero) * x2):] + one[int(len(one) * x2):] + two[int(len(two) * x2):]
  84.     test_x = [row[:-1] for row in test_set]
  85.     test_y = [row[-1] for row in test_set]
  86.    
  87.     # Naive Bayes
  88.     train_nb = zero[:int(len(zero) * x1)] + one[:int(len(one) * x1)] + two[:int(len(two) * x1)]
  89.     train_nb_x = [row[:-1] for row in train_nb]
  90.     train_nb_y = [row[-1] for row in train_nb]
  91.    
  92.     clf_nb = GaussianNB()
  93.     clf_nb.fit(train_nb_x, train_nb_y)
  94.    
  95.     # DecisionTree
  96.     train_df = zero[int(len(zero) * x1) : int(len(zero) * x2)] + one[int(len(one) * x1) : int(len(one) * x2)] + two[int(len(two) * x1) : int(len(two) * x2)]
  97.     train_df_x = [row[:-1] for row in train_df]
  98.     train_df_y = [row[-1] for row in train_df]
  99.    
  100.     clf_df = DecisionTreeClassifier()
  101.     clf_df.fit(train_df_x, train_df_y)
  102.    
  103.     # RandomForest
  104.     train_rf = zero[:int(len(zero) * x2)] + one[:int(len(one) * x2)] + two[:int(len(two) * x2)]
  105.     train_rf_x = [row[:-1] for row in train_rf]
  106.     train_rf_y = [row[-1] for row in train_rf]
  107.    
  108.     clf_rf = RandomForestClassifier(n_estimators=3)
  109.     clf_rf.fit(train_rf_x, train_rf_y)
  110.    
  111.     # Accuracy calculation
  112.     acc = 0
  113.     for i in range(len(test_set)):
  114.         count = 0
  115.        
  116.         pred_nb = clf_nb.predict([test_x[i]])
  117.         pred_df = clf_df.predict([test_x[i]])
  118.         pred_rf = clf_rf.predict([test_x[i]])
  119.        
  120.         if pred_nb[0] == test_y[i]:
  121.             count += 1
  122.         if pred_df[0] == test_y[i]:
  123.             count += 1
  124.         if pred_rf[0] == test_y[i]:
  125.             count += 1
  126.        
  127.         if count >= 2:
  128.             acc += 1
  129.            
  130.     print(f"Tochnost: {acc / len(test_set)}")
  131.    
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