Question

我试图为机器学习模型编写分类器算法，但是它带有错误。有人可以帮忙吗？预先感谢

import pandas as pd
from sklearn.metrics import accuracy_score
from scipy.spatial import distance

def euc(a, b):
        return distance.euclidean(a,b)

class classifierKN():
    def fit(self, X_train, Y_train):
        self.X_train = X_train
        self.Y_train = Y_train
        
    def predict(self, X_test):
        predictions = []
        for row in X_test:
            label = self.closest(row)
            predictions.append(label)
        return predictions
    def closest(self, row):
        best_dist = euc(row, self.X_train[0])
        best_index = 0
        for i in range(1, len(self.X_train)):
            dist = euc(row, self.X_train[i])
            if dist < best_dist:
                best_dist = dist
                best_index = i
        return self.Y_train[best_index]

#Load the dataset 
diabetdata = pd.read_csv("diabetes.csv")

#set features and target
features = ["PlasmaGlucose", "DiastolicBloodPressure", "TricepsThickness", "SerumInsulin"]
X = diabetdata[features]
print("FEATURES: " , X.head())

Y = diabetdata.Diabetic
print("TARGET: " , Y.head())
print("")




from sklearn.model_selection import train_test_split  #No module named 'sklearn.cross_validation' so I replace it with model_selection
X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.3, random_state=0)



#predict 
model= classifierKN()
model.fit(X_train,Y_train)
predictKN = model.predict(X)
print ("Predict result with KNeighborsClassifier")
print(predictKN)

#accuracy
print("Accuracy")
print (accuracy_score(Y, predictKN))

结果

During handling of the above exception, another exception occurred:

Traceback (most recent call last):

  File "C:\Users\Vlad\Desktop\Machine learning\Machine Learning\coursework\test2.py", line 63, in <module>
    predictKN = model.predict(X)

  File "C:\Users\Vlad\Desktop\Machine learning\Machine Learning\coursework\test2.py", line 26, in predict
    label = self.closest(row)

  File "C:\Users\Vlad\Desktop\Machine learning\Machine Learning\coursework\test2.py", line 30, in closest
    best_dist = euc(row, self.X_train[0])

  File "E:\Anaconda\lib\site-packages\pandas\core\frame.py", line 2800, in __getitem__
    indexer = self.columns.get_loc(key)

  File "E:\Anaconda\lib\site-packages\pandas\core\indexes\base.py", line 2648, in get_loc
    return self._engine.get_loc(self._maybe_cast_indexer(key))

  File "pandas\_libs\index.pyx", line 111, in pandas._libs.index.IndexEngine.get_loc

  File "pandas\_libs\index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc

  File "pandas\_libs\hashtable_class_helper.pxi", line 1619, in pandas._libs.hashtable.PyObjectHashTable.get_item

  File "pandas\_libs\hashtable_class_helper.pxi", line 1627, in pandas._libs.hashtable.PyObjectHashTable.get_item

KeyError: 0

Answer 1

您的代码实际上同时存在多个问题，因此要理解它有点困难。您的问题似乎主要与您对pandas Dataframes / Series的理解有关，因为您显然正在尝试使用以下方法遍历Dataframe的行：

def predict(self, X_test):
        predictions = []
        for row in X_test:
            label = self.closest(row)
            predictions.append(label)
        return predictions

这不适用于熊猫。要实际遍历行的值，您将需要以下内容：

def predict(self, X_test):
        predictions = []
        for row in X_test.iterrows():
            label = self.closest(list(row[1]))
            predictions.append(label)
        return predictions

此函数实际上确实遍历数据框中的行，并将行的值提供给closest()函数。`

def closest(self, row):
        best_dist = euc(row, self.X_train[0])
        best_index = 0
        for i in range(1, len(self.X_train)):
            dist = euc(row, self.X_train[i])
            if dist < best_dist:
                best_dist = dist
                best_index = i
        return self.Y_train[best_index]

但是该功能不起作用，因为您基本上是尝试使用best_dist = euc(row, self.X_train[0])获取row [0]的值。这只是抛出一个keyError，因为X_train是一个Dataframe并且没有第0列（无论如何您都不想索引该列）。您想要的是默认的best_dist作为输入行与数据框中第一行之间的距离。这将与类似 best_dist = euc(row, self.X_train.iloc[0])。然后，您需要遍历X_train中的行（此处的函数与以前一样存在问题），因此需要将其更改为类似以下内容：

def closest(self, row):
    best_dist = euc(row, self.X_train.iloc[0])
    best_index = 0
    for i in range(1, len(self.X_train.index)):
        dist = euc(row, list(self.X_train.iloc[i]))
        if dist < best_dist:
            best_dist = dist
            best_index = i
    return self.Y_train.iloc[best_index]

这至少有效。无论它是否能为您提供所需的输出和/或是否足够准确，我都无法保证，但是它确实可以解决您的直接问题。

编写机器学习分类器算法

1 个答案: