Sklearn 错误，具有 4 个暗淡的数组。估计器 <=2

Question

我一直在尝试通过 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分类器进行训练时，它给了我一个错误。

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

下面是我的代码：

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/问题。

1. 缺少括号buy_sell = (df.loc['2013-02-15':'2016-05-21']).as_matrix
2. [[close_buy,ma_50,ma_100,ma_200]] 为您提供 4 个维度。相反，我会使用np.concatenate，它接受一个数组列表并将它们附加到彼此的长度或宽度上。参数axis=1 指定宽度。这样做的目的是使x 成为一个 822 x 28 矩阵，包含 28 个特征的 822 个观察值。如果这不是你想要的，那么显然我没有达到目标。但这些尺寸与您的 y 一致。

改为：

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)

这是为我跑的：

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')