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通过循环从数据帧传递单行以进行预测

scikit-learn machine-learning numpy pandas python

1

user9238790 · 技术社区 · 8 年前

我使用传递行索引 iloc 并使用指定位置 n . 相反,如何修改代码以从 class_zero ,并打印它的每个预测。

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('##')

1 回复 | 直到 8 年前

1

Vivek Kumar 8 年前

可以将class==0的行索引用作中的列表 iloc()

更改class\u zero如下:

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

而你做的重塑是错误的。保持这样:

instances = X_train.iloc[class_zero].values

编辑以供评论:

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('##')