Question

我使用iloc传递行索引并使用n指定位置。相反，如何修改代码以从class_zero传递行，并打印每个预测。

import numpy as np
import pandas as pd
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier


X, y = make_classification(n_samples=1000,
                           n_features=6,
                           n_informative=3,
                           n_classes=2,
                           random_state=0,
                           shuffle=False)

# Creating a dataFrame
df = pd.DataFrame({'Feature 1':X[:,0],
                                  'Feature 2':X[:,1],
                                  'Feature 3':X[:,2],
                                  'Feature 4':X[:,3],
                                  'Feature 5':X[:,4],
                                  'Feature 6':X[:,5],
                                  'Class':y})

y_train = df['Class']
X_train = df.drop('Class', axis=1)
class_zero = df.loc[df['Class'] == 0]

n = 5  #instead of specifying 5 which is where class_zero = 0, I want to pass directly the class_zero from the list I created
#and print for each one

rf = RandomForestClassifier()
rf.fit(X_train, y_train)
instances = X_train.iloc[n].values.reshape(1, -1)

predictValue = rf.predict(instances)
actualValue = y_train.iloc[n]

print('##')
print(n)
print(predictValue)
print(actualValue)
print('##')

Answer 1

您可以使用class == 0的行索引作为iloc()

中的列表

像这样更改class_zero：

class_zero = df.index[df['Class'] == 0].tolist()

你正在重塑这个错误。保持这样：

instances = X_train.iloc[class_zero].values

编辑评论：

for n in class_zero:
    instances = X_train.iloc[n].values.reshape(1,-1)

    predictValue = rf.predict(instances)
    actualValue = y_train.iloc[n]

    print('##')
    print(n)
    print(predictValue)
    print(actualValue)
    print('##')

传递数据帧中的单行以使用循环进行预测

1 个答案: