与knn的y轴不匹配的样本

Question

[我试图使自己的方法比基于虹膜数据集的教程更灵活地输入knn输入脚本，但我在将匹配的第二维添加到＃6中的numpy数组时遇到了麻烦（我认为），当我来时至＃11。配件。

文件“ G：\ PROGRAMMERING \ Anaconda \ lib \ site-packages \ sklearn \ utils \ validation.py”，行212，在check_consistent_length中“样本：％r”％[长度为l的int（l）]）ValueError：找到样本数量不一致的输入变量：[150，1]

x为（150,5），y为（150,1）。两者中的样本数均为150，但它们的字段数不同，这是问题所在，如果是，该如何解决？

#1. Loading the Pandas libraries as pd
import pandas as pd
import numpy as np

#2. Read data from the file 'custom.csv' placed in your code directory
data = pd.read_csv("custom.csv") 

#3. Preview the first 5 lines of the loaded data 
print(data.head())
print(type(data))

#4.Test the shape of the data
print(data.shape)
df = pd.DataFrame(data)
print(df)

#5. Convert non-numericals to numericals
print(df.dtypes)
# Any object should be converted to numerical
df['species'] = pd.Categorical(df['species'])
df['species'] = df.species.cat.codes
print("outcome:")
print(df.dtypes)

#6.Convert df to numpy.ndarray
np = df.to_numpy()
print(type(np)) #this should state <class 'numpy.ndarray'>
print(data.shape) 
print(np)
x = np.data
y = [df['species']]
print(y)

#K-nearest neighbor (find closest) - searach for the K nearest observations in the dataset
#The model calculates the distance to all, and selects the K nearest ones.
#8. Import the class you plan to use
from sklearn.neighbors import (KNeighborsClassifier)
#9. Pick a value for K
k = 2
#10. Instantiate the "estimator" (make an instance of the model)
knn  = KNeighborsClassifier(n_neighbors=k)
print(knn)
#11. fit the model with data/model training
knn.fit(x, y)
#12. Predict the response for a new observation
print(knn.predict([[3, 5, 4, 2]]))```

Answer 1

这是我使用scikit-learn KNeighborsClassifier拟合knn模型的方式：

import numpy as np    
import pandas as pd
from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier

df = datasets.load_iris()
X = pd.DataFrame(df.data)
y = df.target
knn = KNeighborsClassifier(n_neighbors = 2)
knn.fit(X,y)
print(knn.predict([[6, 3, 5, 2]]))
#prints output class [2]
print(knn.predict([[3, 5, 4, 2]]))
#prints output class [1]

不需要从DataFrame转换为numpy array，您可以直接在DataFrame上拟合模型，也可以在将DataFrame转换为numpy array时将其命名为np，即也用于在顶部numpy上导入import numpy as np