python - 如何修复 '' 发现输入变量的样本数量不一致 : [219, 247 ]''

Question

正如标题所说，当运行以下代码时，我遇到了麻烦 发现输入变量的样本数量不一致:[219, 247]，我已经读到问题应该出在为 X 和 y 设置的 np.array 上，但是我无法解决这个问题，因为每个日期都有一个价格，所以我不明白为什么会发生这种情况，任何帮助将不胜感激，谢谢!

import pandas as pd
import quandl, math, datetime
import numpy as np
from sklearn import preprocessing, svm, model_selection
from sklearn.linear_model import LinearRegression
import matplotlib as plt
from matplotlib import style

style.use('ggplot')


df = quandl.get("NASDAQOMX/XNDXT25NNR", authtoken='myapikey')   
df = df[['Index Value','High','Low','Total Market Value']]
df['HL_PCT'] = (df['High'] - df['Low']) / df['Index Value'] * 100.0
df = df[['Low','High','HL_PCT']]

forecast_col = 'High'
df.fillna(-99999, inplace=True)

forecast_out = int(math.ceil(0.1*len(df)))

df['label'] = df[forecast_col].shift(-forecast_out)
df.dropna(inplace= True)

X = np.array(df.drop(['label'],1))

X = preprocessing.scale(X)

X_lately = X[-forecast_out:]

X = X[:-forecast_out]

y=np.array(df['label'])
#X= X[:-forecast_out+1]
df.dropna(inplace=True)
y= np.array(df['label'])

X_train, X_test, y_train, y_test= model_selection.train_test_split(X, 
y,test_size=0.2)

clf= LinearRegression(n_jobs=-1)
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
forecast_set= clf.predict(X_lately)
print(forecast_set, accuracy, forecast_out)
df['Forecast'] = np.nan

last_data= df.iloc[-1].name
last_unix= last_date.timestamp()
one_day=86400 
next_unix= last_unix + one_day

for i in forecast_set:
    next_date= datetime.datetime.fromtimestamp(next_unix)
    next_unix += one_day
    df.loc[next_date]= [np.nan for _ in range(len(df.columns) -1)] + 
[i]

df['High'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

预期结果应该是该股票的 future 价格预测图，但除此之外，它还会抛出错误“发现样本数量不一致的输入变量:[219, 247]”。

Answer 1

您的问题在于从代码中提取的这两行:

X = X[:-forecast_out]
y= np.array(df['label'])

您正在对 X 进行子集化，但将 y 保留为“原样”。

您可以通过以下方式检查形状是否确实不同:

X.shape, y.shape

将最后一行更改为:

y= np.array(df[:-forecast_out]['label'])

你很好。

还要注意，不要使用这些重复的行:

y=np.array(df['label'])
#X= X[:-forecast_out+1]
df.dropna(inplace=True) # there is no na at this point
y= np.array(df['label'])

以下行(解决您的问题)就足够了:

y= np.array(df[:-forecast_out]['label'])