【问题标题】:I'm stuck on with an attribute error with SKlearn我被 SKlearn 的属性错误困住了
【发布时间】:2019-01-02 19:40:22
【问题描述】:

我正在重新访问我在今年早些时候做的机器学习教程,因为我有一台新笔记本电脑,它似乎引发了一些兼容性问题。我查看了其他几个 SO 答案,并部分根据最新版本的 SKlearn 中似乎是新名称要求来解决它。这是代码,当我做教程时运行良好

import quandl, math
import numpy as np
import pandas as pd
from sklearn import preprocessing, cross_validation, svm
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from matplotlib import style
import datetime

style.use('ggplot')

df = quandl.get("WIKI/GOOGL")
df = df[['Adj. Open',  'Adj. High',  'Adj. Low',  'Adj. Close', 'Adj. 
Volume']]
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Close'] * 100.0
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 
100.0

df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
forecast_col = 'Adj. Close'
df.fillna(value=-99999, inplace=True)
forecast_out = int(math.ceil(0.01 * len(df)))
df['label'] = df[forecast_col].shift(-forecast_out)

X = np.array(df.drop(['label'], 1))
X = preprocessing.scale(X)
X_lately = X[-forecast_out:]
X = X[:-forecast_out]

df.dropna(inplace=True)

y = np.array(df['label'])

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, 
test_size=0.2)
clf = LinearRegression(n_jobs=-1)
clf.fit(X_train, y_train)
confidence = clf.score(X_test, y_test)

forecast_set = clf.predict(X_lately)
df['Forecast'] = np.nan

last_date = df.iloc[-1].name
last_unix = last_date.timestamp()
one_day = 86400
next_unix = last_unix + one_day

for i in forecast_set:
    next_date = datetime.datetime.fromtimestamp(next_unix)
    next_unix += 86400
    df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)]+[i]

df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

如果您在 3.7 中运行此代码,您将收到一些与 SKlearn 相关的错误,我已经能够从关于 SO 的建议中解决这些错误,但是一旦我处理它们,我就会收到如下错误

H:\Documents\Python Scripts>py ML_tutorial_vid_5.1.py
Traceback (most recent call last):
  File "ML_tutorial_vid_5.1.py", line 34, in <module>
    X_train, X_test, y_train, y_test = cross_validate.train_test_split(X, y, 
test_size=0.2)
AttributeError: 'function' object has no attribute 'train_test_split'

感谢所有帮助。

【问题讨论】:

    标签: python machine-learning scikit-learn


    【解决方案1】:

    您收到此错误是因为 train_test_split 现在位于 sklearnmodel_selection 模块中。您可以通过here 看到更改日志。

    您现在可以像这样导入它。

    from sklearn.model_selection import train_test_split
    

    并像这样使用它

    X_train, X_test, y_train, y_test = train_test_split(X, y, 
    test_size=0.2)
    

    【讨论】:

    • 您先生/女士是绅士/女士和学者。谢谢。
    • 很高兴听到它有帮助
    • 我一直在阅读并理解它,所以你介意解释为什么预处理和交叉验证不像以前那样工作吗?我得到了基本相同的错误,但如果我能找出你展示的方法来修复它,那我就大错特错了。它是“来自 sklearn 导入预处理,cross_validation”,现在我不知道......:/
    • cross_validation 模块在 sklearn 的早期版本中已被弃用。您可以从导入行中删除 cross_validation 并使其成为 from sklearn import preprocessing
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