Question

我一直尝试通过panda从yahoo finance导入数据，然后通过.as_matrix（）将其转换为数组，然后当我将数据输入到classifer进行训练时，它给了我一个错误。

ValueError: Found array with dim 4. Estimator expected <= 2.

以下是我的代码：

from sklearn import tree
import pandas as pd
import pandas_datareader.data as web

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

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

clf.fit(x,y)

Answer 1

我发现了一些需要修复的错误/事情。

缺少parantheses buy_sell = (df.loc['2013-02-15':'2016-05-21']).as_matrix
[[close_buy,ma_50,ma_100,ma_200]]可以为您提供4个维度。相反，我使用np.concatenate来获取数组列表，并将它们以长度方式或宽度方式相互附加。参数axis=1指定宽度。这样做是使x成为822 x 28矩阵的822个28个特征的观测值。如果这不是你想要的，那么显然我没有达到标准。但这些维度与您的y。

相反：

from sklearn import tree
import pandas as pd
import pandas_datareader.data as web

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

print(ma_100)
clf = tree.DecisionTreeClassifier()
x = np.concatenate([close_buy,ma_50,ma_100,ma_200], axis=1)  # Fixed
y = buy_sell  # Brackets not necessary... I don't think

clf.fit(x,y)

这适合我：

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            random_state=None, splitter='best')

Sklearn Error，4 dim的数组。估计量＆lt; = 2

1 个答案: