python - AttributeError - 即使似乎没有属性错误

Question

我目前正在学习如何使用 python 进行机器学习。当我进行操作时，解释器检测到了 AttributeError，但我没有看到任何问题。有人可以帮忙修复这个错误吗？

我的代码:

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

style.use('ggplot')

quandl.ApiConfig.api_key = ''

df = quandl.get('EOD/V', api_key = '')
df = df[['Adj_Open','Adj_High','Adj_Low','Adj_Close','Adj_Volume',]]
df['ML_PCT'] = (df['Adj_High'] - df['Adj_Close']) / df['Adj_Close'] * 100.0
df['PCT_change'] = (df['Adj_Close'] - df['Adj_Open']) / df['Adj_Open'] * 100.0

df = df[['Adj_Close', 'ML_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 = 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)
print(confidence)

X_lately = X[-forecast_out:]
forecast_set = clf.predict(X_lately)
print(forecast_set, confidence, forecast_out)

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()

错误:

C:\Python27\lib\site-packages\sklearn\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)
0.989124557421
(array([ 94.46383723,  93.27713267,  93.15533011,  93.89038799,
        94.71390166,  95.29332756,  96.23047821,  96.51527839,
        96.17180986,  96.17575181,  96.68721678,  96.85114045,
        97.57455941,  97.98680762,  97.32961443,  97.55881174,
        97.54090546,  96.17175855,  94.95430597,  96.49002102,
        96.82364097,  95.63098589,  95.61236103,  96.24114818])Traceback (most recent call last):, 0.98912455742140903, 24)

  File "C:\Users\qasim\Documents\python_machine_learning\regression.py", line 47, in <module>
    last_unix = last_date.timestamp()
AttributeError: 'Timestamp' object has no attribute 'timestamp'
[Finished in 36.6s]

Answer 1

问题在于，last_date 是 pandas Timestamp 对象，而不是 python datetime 对象。不过，它确实有一个类似 datetime.timetuple() 的函数。试试这个:

假设 last_date 采用 UTC 格式，请使用:

import calendar

...
last_date = df.iloc[-1].name
last_unix = calendar.timegm(last_date.timetuple())

如果last_date位于您本地的时区，请使用:

import time

...
last_date = df.iloc[-1].name
last_unix = time.mktime(last_date.timetuple())