无法使用线性回归预测值

Question

那里

我正在学习Coursera的IBM数据科学课程，并试图创建一些片段进行实践。我创建了以下code：

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split

# Import and format the dataframes
ibov = pd.read_csv('https://raw.githubusercontent.com/thiagobodruk/datasets/master/ibov.csv')
ifix = pd.read_csv('https://raw.githubusercontent.com/thiagobodruk/datasets/master/ifix.csv')
ibov['DATA'] = pd.to_datetime(ibov['DATA'], format='%d/%m/%Y')
ifix['DATA'] = pd.to_datetime(ifix['DATA'], format='%d/%m/%Y')
ifix = ifix.sort_values(by='DATA', ascending=False)
ibov = ibov.sort_values(by='DATA', ascending=False)
ibov = ibov[['DATA','FECHAMENTO']]
ibov.rename(columns={'FECHAMENTO':'IBOV'}, inplace=True)
ifix = ifix[['DATA','FECHAMENTO']]
ifix.rename(columns={'FECHAMENTO':'IFIX'}, inplace=True)

# Merge datasets 
df_idx = ibov.merge( ifix, how='left', on='DATA')
df_idx.set_index('DATA', inplace=True)
df_idx.head()

# Split training and testing samples
x_train, x_test, y_train, y_test = train_test_split(df_idx['IBOV'], df_idx['IFIX'], test_size=0.2)

# Convert the samples to Numpy arrays
regr = linear_model.LinearRegression()
x_train = np.array([x_train])
y_train = np.array([y_train])
x_test = np.array([x_test])
y_test = np.array([y_test])

# Plot the result
regr.fit(x_train, y_train)
y_pred = regr.predict(y_train)
plt.scatter(x_train, y_train)
plt.plot(x_test, y_pred, color='blue', linewidth=3) # This line produces no result

我在train_test_split()方法返回的输出值方面遇到了一些问题。所以我把它们转换成Numpy数组，然后我的代码工作了。我可以正常绘制散点图，但我不能绘制预测线

在myIBM Data Cloud Notebook上运行此代码会产生以下警告：

/opt/conda/envs/Python36/lib/python3.6/site-packages/matplotlib/axes/_base.py:380: MatplotlibDeprecationWarning: cycling among columns of inputs with non-matching shapes is deprecated. cbook.warn_deprecated("2.2", "cycling among columns of inputs "

我在谷歌和StackOverflow上搜索过，但我不知道哪里错了

我会感谢你的帮助。提前谢谢

Answer 1

代码中有几个问题，比如y_pred = regr.predict(y_train)和划线方式

以下代码段应为您提供正确的方向：

# Split training and testing samples
x_train, x_test, y_train, y_test = train_test_split(df_idx['IBOV'], df_idx['IFIX'], test_size=0.2)

# Convert the samples to Numpy arrays
regr = linear_model.LinearRegression()
x_train = x_train.values
y_train = y_train.values
x_test = x_test.values
y_test = y_test.values

# Plot the result
plt.scatter(x_train, y_train)

regr.fit(x_train.reshape(-1,1), y_train)
idx = np.argsort(x_train)
y_pred = regr.predict(x_train[idx].reshape(-1,1))
plt.plot(x_train[idx], y_pred, color='blue', linewidth=3);

要对已拟合模型的测试子集执行相同操作：

# Plot the result
plt.scatter(x_test, y_test)
idx = np.argsort(x_test)
y_pred = regr.predict(x_test[idx].reshape(-1,1))
plt.plot(x_test[idx], y_pred, color='blue', linewidth=3);

如果您有任何问题，请随时提问