ValueError：找到的数组带有0个样本（形状=（0，35）），而StandardScaler至少需要1个]]

Question

我收到此错误。我该怎么办？

这是代码。

from __future__ import division

import numpy as np 
from sklearn import svm, preprocessing
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
from collections import Counter
from matplotlib import style
import statistics
style.use('ggplot')

premium_baseline = 0.06

features = ['DE Ratio',
            'Trailing P/E',
            'Price/Sales',
            'Price/Book',
            'Profit Margin',
            'Operating Margin',
            'Return on Assets',
            'Return on Equity',
            'Revenue Per Share',
            'Market Cap',
            'Enterprise Value',
            'Forward P/E',
            'PEG Ratio',
            'Enterprise Value/Revenue',
            'Enterprise Value/EBITDA',
            'Revenue',
            'Gross Profit',
            'EBITDA',
            'Net Income Avl to Common ',
            'Diluted EPS',
            'Earnings Growth',
            'Revenue Growth',
            'Total Cash',
            'Total Cash Per Share',
            'Total Debt',
            'Current Ratio',
            'Book Value Per Share',
            'Cash Flow',
            'Beta',
            'Held by Insiders',
            'Held by Institutions',
            'Shares Short (as of',
            'Short Ratio',
            'Short % of Float',
            'Shares Short (prior ']

def Premium(stock, sp500):
    difference = stock - sp500

    if difference > premium_baseline:
        return 1
    else:
        return 0

def Build_Data_Set():

    data_df = pd.read_csv('key_stats_acc_perf_WITH_NA.csv')
    data_df = data_df.fillna(0)
    data_df = data_df.reindex(np.random.permutation(data_df.index))
    data_df['premium'] = list(map(Premium, data_df['stock_p_change'], data_df['sp500_p_change']))

    X = data_df[features].values
    sc_X = StandardScaler()
    X = sc_X.fit_transform(X)
    # y = data_df['Status'].replace('underperform',0).replace('outperform',1).values
    y = data_df['premium'].values

    return X, y, data_df, sc_X

# def Randomizing():
#   df = pd.DataFrame({'D1':range(5),"D2":range(5)})
#   print df
#   df2 = df.reindex(np.random.permutation(df.index))
#   print df2


def Analysis():

    X, y, data_df, sc_X = Build_Data_Set()

    test_size = 1000
    invest_amount = 10000
    total_invests = 0
    if_market = 0
    if_strat = 0

    X_train = X[:-test_size]
    X_test = X[-test_size:]
    y_train = y[:-test_size]
    y_test = y[-test_size:]
    # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)

    clf = svm.SVC(kernel = "linear", C = 1.0)
    clf.fit(X_train, y_train)

    y_pred = clf.predict(X_test)

    accuracy = np.mean(y_pred == y_test)
    print (accuracy)

    cm = confusion_matrix(y_test, y_pred)
    print (cm)

    for i in range(y_pred.shape[0]):
        if y_pred[i] == 1:
            invest_return = invest_amount * (1 + data_df.loc[data_df.index[-(i+1)],'stock_p_change'])
            market_return = invest_amount * (1 + data_df.loc[data_df.index[-(i+1)],'sp500_p_change'])
            total_invests += 1
            if_strat += invest_return
            if_market += market_return

    print ("Total Trades:", total_invests)
    print ("Total Return with ML strategy trading", '$'+str(if_strat))
    print ("Total Return with sp500 basic market", '$'+str(if_market) )

    premium = round(((if_strat - if_market)/ if_market) * 100.0,2)

    do_nothing = total_invests * invest_amount
    avg_strat = round(((if_strat - do_nothing)/do_nothing) * 100.0,2)
    avg_market = round(((if_market - do_nothing)/do_nothing) * 100.0,2)

    print ("Compared to sp500 basic market, we earn", str(premium)+"% more.")
    print ("Average ML strategy trading return:", str(avg_strat) +"%.")
    print ("Average sp500 investment return:", str(avg_market) + "%.")

    sample_data_df = pd.read_csv('forward_sample_WITH_NA.csv')
    sample_data_df = sample_data_df.replace({'N/A</span>': np.nan}, regex=True)
    sample_data_df = sample_data_df.fillna(0)

    X_sample = sample_data_df[features].values
    X_sample = sc_X.transform(X_sample)

    invest_list = []

    for i in range(len(X_sample)):
        pred = clf.predict([X_sample[i]])[0]
        if pred == 1:
            invest_list.append(sample_data_df['Ticker'][i])

    print (len(invest_list), 'out of', len(X_sample))

    return invest_list


final_list = []

loops = 8

for x in range(loops):
    stock_list = Analysis()
    for i in stock_list:
        final_list.append(i)
    print (15*"_")

x = Counter(final_list)
print (x)

print (15*"_")
for each in x:
    if x[each] > loops - (loops/2.0):
        print (each)


    # w = clf.coef_[0]
    # a = -w[0] / w[1]

    # xx = np.linspace(min(X[:,0]), max(X[:,0]))
    # yy = a * xx - clf.intercept_[0] / w[1]

    # h0 = plt.plot(xx, yy, 'k-', label = "non weighted")

    # plt.scat      ter(X[:,0], X[:,1], c = y)
    # plt.ylabel('Trailing P/E')
    # plt.xlabel('DE Ratio')
    # plt.legend()
    # plt.show()


# High recall (989/989+40) but low accuracy (989/(989+748))
# 0.57127312296
# [[ 61 748]
#  [ 40 989]]
告诉我该怎么办。我如何纠正此错误。

File "/home/shubh/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 586, in check_array
    context))
ValueError: Found array with 0 sample(s) (shape=(0, 35)) while a minimum of 1 is required by StandardScaler
。

我收到此错误。我该怎么办 ？这是代码。从__future__导入部门导入numpy从sklearn导入svm导入为np，从sklearn.metrics进行预处理。import confusion_matrix ...

Answer 1

似乎您需要正确转换X，然后再将其传递给StandardScaler对象的fit_transform方法（而不是X，请使用X [:, np.newaxis]）