AttributeError-即使似乎没有属性错误

Question

I am currently learning how to python for Machine Learning. While I am progressing, the interpreter had detected a AttributeError but I do not see any problem. Can someone help to fix this error?

My Code:

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()

Error:

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

The issue is that last_date is a pandas Timestamp object, not a python datetime object. It does have a function like datetime.timetuple() , though. Try this:

Assuming last_date is in UTC, use this:

import calendar

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

If last_date is in your local timezone, use this:

import time

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