python - 如何针对数据集的所有特征绘制预测值

Question

我想可视化波士顿数据集的数据点，并绘制线性回归平面。但是，我收到一个值错误。我正在使用 colab。以下是我运行的代码。

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

data1 = load_boston()
df1 = pd.DataFrame(data1.data)
df1.columns = data1.feature_names
df1['price']=data1.target

X = df1.iloc[:,0:13]
Y = df1.iloc[:,13]

xtrain,xtest,ytrain,ytest = train_test_split(X,Y,test_size = 0.2,random_state =0)

lin_reg = LinearRegression()
lin_reg.fit(xtrain,ytrain)
ypredict = lin_reg.predict(xtest)

plt.scatter(xtrain, ytrain, color = 'red')
plt.plot(xtrain, lin_reg.predict(xtrain), color = 'blue')

我在最后两行中遇到错误。

ValueError                                Traceback (most recent call last)

<ipython-input-54-4f89e0554222> in <module>()
----> 1 plt.scatter(xtrain, ytrain, color = 'red')
      2 plt.plot(xtrain, lin_reg.predict(xtrain), color = 'blue')

3 frames

/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py in scatter(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)
   4389         y = np.ma.ravel(y)
   4390         if x.size != y.size:
-> 4391             raise ValueError("x and y must be the same size")
   4392 
   4393         if s is None:

ValueError: x and y must be the same size

我知道 X 有 13 列，而 Y 有 1 列。这就是错误显示的原因。但我不知道如何纠正它。

有人可以帮忙吗？

标签： pythonpandasmatplotlibscikit-learnlinear-regression

Answer 1

• 每个要素都必须单独绘制。
• 请记住，这'price'是目标，因变量，这lin_reg.predict(xtrain)是来自训练数据的预测价格。

# predicted price from xtrain
ypred_train = lin_reg.predict(xtrain)

# create the figure
fig, axes = plt.subplots(ncols=4, nrows=4, figsize=(20, 20))

# flatten the axes to make it easier to index
axes = axes.flatten()

# iterate through the column values, and use i to index the axes
for i, v in enumerate(xtrain.columns):
    
    # seclect the column to be plotted
    data = xtrain[v]
    
    # plot the actual price against the features
    axes[i].scatter(x=data, y=ytrain, s=35, ec='white', label='actual')
    
    # plot predicted prices against the features
    axes[i].scatter(x=data, y=ypred_train, c='pink', s=20, ec='white', alpha=0.5, label='predicted')

    # set the title and ylabel
    axes[i].set(title=f'Feature: {v}', ylabel='price')

# set a single legend
axes[12].legend(title='Price', bbox_to_anchor=(1.05, 1), loc='upper left')

# delete the last 3 unused axes
for v in range(13, 16):
    fig.delaxes(axes[v])

• 如果你把所有的东西都画成一个图，那会很拥挤而且毫无用处

• 您还可以通过从宽格式 seaborn.relplot融合到长格式来绘制所有数据。df1
  • 但是，将预测值添加到图形级别图的顶部更加困难。

import seaborn as sns

dfm = df1.melt(id_vars='price', value_vars=df1.columns[:-1], var_name='Feature')

p = sns.relplot(kind='scatter', data=dfm, x='value', y='price', height=3,
                col='Feature', col_wrap=4, facet_kws={'sharex': False})