# importing libraries
import numpy as np
import pandas
import pandas as pd
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
#read data
dataset=pd.read_csv("Salary_Levels.csv")
#data frame
data=pd.DataFrame(dataset)
#independant and dependant
x=data["level_id"].astype(int)
y=data["Salary"].astype(int)
#ployfeatures
poly=PolynomialFeatures(degree=4)
x_poly=poly.fit_transform(x)
pilreg=LinearRegression()
pilreg.fit(x_poly,y)
pilreg.predict(poly.fit_transform([[10]]))
#plot
plt.scatter(x,y,color='r',s=5)
plt.plot(x,pilreg.predict(poly.fit_transform(x)),color='blue')
plt.show()
Traceback (most recent call last):
File "/Users/david/desktop/code/Python/PolyReg/main.py", line 23, in <module>
x_poly=poly.fit_transform(x)
File "/Users/david/Desktop/Code/Python/PolyReg/venv/desktop/code/Python/Python/lib/python2.7/site-packages/sklearn/base.py", line 464, in fit_transform
return self.fit(X, **fit_params).transform(X)
File "/Users/david/Desktop/Code/Python/PolyReg/venv/desktop/code/Python/Python/lib/python2.7/site-packages/sklearn/preprocessing/data.py", line 1460, in fit
n_samples, n_features = check_array(X, accept_sparse=True).shape
File "/Users/david/Desktop/Code/Python/PolyReg/venv/desktop/code/Python/Python/lib/python2.7/site-packages/sklearn/utils/validation.py", line 552, in check_array
"if it contains a single sample.".format(array))
ValueError: Expected 2D array, got 1D array instead:
array=[1 2 3 4 5 6].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
不知道為什么我收到錯誤訊息。有任何想法嗎？？？嘗試進行多項式回歸，但似乎是 x 值的問題。錯誤資訊和代碼如上。我嘗試重塑，但沒有奏效。
uj5u.com熱心網友回復：
fit_transform 需要 2D 輸入，您必須按要求提供它（即使第二維是 1）
我沒有您的資料示例，但是將您的列切片為資料框應該可以解決問題：
x=data[["level_id"]].astype(int)
uj5u.com熱心網友回復：
你真的很親近。問題是因為 fit_transform 函式需要一個 DataFrame，而不是一個系列。將第 18 行更改為x=pd.DataFrame(data["level_id"].astype(int))，您就可以開始了。
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標籤：Python 熊猫 麻木的 matplotlib