Sklearn中的3d阵列错误

Question

所以我试图获取有关股票的数据，收盘价格和移动平均线50,100,200。我得到了另一个数组，然后是买卖的标签。它与所有其他数组一起在一个数据框上得出结论。但问题是，当我尝试训练分类器，它给了我一个错误：

ValueError: Found array with dim 3. Estimator expected <= 2. 

When I concatenate the array, it gives me an error, ValueError: Unknown label type: array([[7.87401353,]])在它 更值这是我的代码：

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) 
close_buy = df[['Close']+['B/S']] 
closing = df[['Close']].as_matrix() 
buy_sell = df[['B/S']] 


close_buy = pd.DataFrame.dropna(close_buy, 0, 'any') 
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') 

close_buy = (df.loc['2013-02-15':'2016-05-21']).as_matrix() 
ma_50 = (df.loc['2013-02-15':'2016-05-21']).as_matrix() 
ma_100 = (df.loc['2013-02-15':'2016-05-21']).as_matrix() 
ma_200 = (df.loc['2013-02-15':'2016-05-21']).as_matrix() 
buy_sell = (df.loc['2013-02-15':'2016-05-21']).as_matrix() # Fixed 

list(close_buy) 

clf = tree.DecisionTreeClassifier() 
X = list([close_buy,ma_50,ma_100,ma_200]) 
y = [buy_sell] 

Answer 1

的问题是，你是创建一个变量X，它是一个二维数组列表。这自动意味着第三个维度。

# offending line 
X = list([close_buy,ma_50,ma_100,ma_200]) 

这需要连接以维持2个维度。

# corrected 
X = np.concatenate([close_buy,ma_50,ma_100,ma_200], axis=1) 

而且，我怀疑，一旦这个问题得到解决，你就会有另一种搭配：

y = [buy_sell] 

没有理由在[]到这个包起来。这会导致相同的三维问题。只是把这个：

y = buy_sell