值错误:无法将大小为 821 的序列复制到维度为 7 的阵列轴

Question

所以我输入了测试数据，但是当我尝试用clf.predict（）测试它时，它只会给我一个错误。所以我希望它根据我给出的数据进行预测，即最后收盘价，即移动平均线。但是，每次我尝试某些事情时，它只会给我一个错误。还有比熊猫更好的方法吗？

from sklearn import tree
import pandas as pd
import pandas_datareader.data as web
import numpy as np
df = web.DataReader('goog', 'yahoo', start='2012-5-1', end='2016-5-20')
close_price = df[['Close']]
ma_50 = (pd.rolling_mean(close_price, window=50))
ma_100 = (pd.rolling_mean(close_price, window=100))
ma_200 = (pd.rolling_mean(close_price, window=200))
#adding buys and sell based on the values
df['B/S']= (df['Close'].diff() < 0).astype(int)
closing = (df[['Close']])
buy_sell = (df[['B/S']])

ma_50 = pd.DataFrame.dropna(ma_50, 0, 'any')
ma_100 = pd.DataFrame.dropna(ma_100, 0, 'any')
ma_200 = pd.DataFrame.dropna(ma_200, 0, 'any')
closing = (df.loc['2013-02-15':'2016-05-21'])
ma_50 = (df.loc['2013-02-15':'2016-05-21'])
ma_100 = (df.loc['2013-02-15':'2016-05-21'])
ma_200 = (df.loc['2013-02-15':'2016-05-21'])
buy_sell = (df.loc['2013-02-15':'2016-05-21'])  # Fixed
close = pd.DataFrame(closing)
ma50 = pd.DataFrame(ma_50)
ma100 = pd.DataFrame(ma_100)
ma200 = pd.DataFrame(ma_200)
buy_sell = pd.DataFrame(buy_sell)
clf = tree.DecisionTreeRegressor()
x = np.concatenate([close, ma50, ma100, ma200], axis=1)
y = buy_sell
clf.fit(x,y)
close_buy1 = close[:-1]
m5 = ma_50[:-1]
m10 = ma_100[:-1]
ma20 = ma_200[:-1]
b = np.concatenate([close_buy1, m5, m10, ma20], axis=1)
clf.predict([close_buy1, m5, m10, ma20])

这给出的错误是：

ValueError: cannot copy sequence with size 821 to array axis with dimension `7`

我试图做我知道的一切，但它真的没有成功。

Answer 1

我认为你不能通过列表来预测。

相反，您必须将功能数据帧/矩阵concat在一起：

In [11]: clf.predict(pd.concat([close_buy1, m5, m10, ma20], axis=1))
Out[11]:
array([[  7.87401353e+02,   7.93261381e+02,   7.87071324e+02, ...,
          5.48000000e+06,   3.96049623e+02,   0.00000000e+00],
       [  7.95991368e+02,   8.07001373e+02,   7.95281379e+02, ...,
          5.88550000e+06,   4.03022676e+02,   0.00000000e+00],
       [  8.05301357e+02,   8.08971379e+02,   7.91791350e+02, ...,
          5.54900000e+06,   3.95834832e+02,   1.00000000e+00],
       ...,
       [  7.15989990e+02,   7.21520020e+02,   7.04109985e+02, ...,
          1.99950000e+06,   7.06229980e+02,   1.00000000e+00],
       [  7.03669983e+02,   7.11599976e+02,   7.00630005e+02, ...,
          1.76340000e+06,   7.06630005e+02,   0.00000000e+00],
       [  7.02359985e+02,   7.06000000e+02,   6.96799988e+02, ...,
          1.65630000e+06,   7.00320007e+02,   1.00000000e+00]])