Sklearn错误，数组具有4个暗角.估计器< = 2 [英] Sklearn Error, array with 4 dim. Estimator <=2

问题描述

我一直在尝试通过熊猫从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分类器中进行训练时，它给了我一个错误.

I been trying import data from yahoo finance via panda then convert it to arrays via .as_matrix(), then as i input the data into the classifer to train, it gives me an error.

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

这是我的代码:

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)

推荐答案

我发现了几个需要修复的错误/内容.

I found a couple of bugs/things needing fixing.

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]]是为您提供4个维度的要素.取而代之的是，我使用np.concatenate，它接受一个数组列表，并将它们彼此附加在长度方向或宽度方向.参数axis=1指定宽度方向.这是使x成为822 x 28矩阵，其中包含822个对28个特征的观察结果.如果这不是您想要的，那么显然我没有达到目标.但是这些尺寸符合您的y.

1. Missing parantheses buy_sell = (df.loc['2013-02-15':'2016-05-21']).as_matrix
2. [[close_buy,ma_50,ma_100,ma_200]] is what gives you your 4 dimensions. Instead, I'd use np.concatenate which takes a list of arrays and appends them to each other either length wise or width wise. the parameter axis=1 specifies width wise. What this does is make x an 822 x 28 matrix of 822 observations of 28 features. If this isn't what you were going for, then clearly I didn't hit the mark. But those dimensions line up with your 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)

这为我跑了

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')

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