KNN implementation in Python

Question

I am trying to implement a simple KNN technique in Python where I am using minute by minute stock price data, and using my x variables as Open, Close and Volume data to predict the next minute Open price. My code is as below:-

import numpy as np
import pandas as pd
import scipy
import matplotlib.pyplot as plt
from pylab import rcParams
import urllib
import sklearn
from sklearn.neighbors import KNeighborsRegressor
from sklearn import neighbors
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
from sklearn import metrics 
from googlefinance.client import get_price_data, get_prices_data, get_prices_time_data
import copy

np.set_printoptions(precision = 4, suppress = True)
rcParams['figure.figsize']=7,4
plt.style.use('seaborn-whitegrid')




param = {'q':"DJUSBK", 'i':"60",'x':"INDEXDJX",'p':"1Y"} # Dow Joes Banks
djusbk = get_price_data(param)
ticker_list=['ASB','BXS','BAC','BOH','BKU'] # 5 stocks from the Dow Jones Bank Index
ticker_dict = {}
for i in ticker_list :
    param = {'q':i, 'i':"60",'x':"NYSE",'p':"1Y"}
    df = get_price_data(param)
    x=i
    ticker_dict[x] = df

asb = copy.deepcopy(ticker_dict['ASB'])
asb_prime = pd.DataFrame(asb['Open'])
asb_prime['Close'] = asb['Close']
asb_prime['Volume'] = asb['Volume']

asb_prime_copy = copy.deepcopy(asb_prime)


# Splitting your data into test and training data sets
X_prime = asb_prime_copy.ix[:,(0,1,2)].values
asb_open_next = pd.DataFrame(copy.deepcopy(asb['Open']))
asb_open_next.drop(asb_open_next.index[:1], inplace=True)

asb_prime_copy= asb_prime_copy[:-1]

X_prime = asb_prime_copy.ix[:,(0,1,2)].values
y = asb_open_next.ix[:,(0)].values

X = preprocessing.scale(X_prime)

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.5,random_state = 17)

#Building and Training Model  with Training Data
clf = neighbors.KNeighborsRegressor()
clf.fit(X_train,y_train)
print(clf)

# Evaluating the model's predictions against the test dataset
y_expect=y_test

y_pred= clf.predict(X_test)
print(metrics.classification_report(y_expect,y_pred))

At the very end I am getting en error. Not sure why? I am using Python 3.x

  File "C:\Users\gg\Anaconda3\lib\site-packages\sklearn\utils\multiclass.py", line 97, in unique_labels
    raise ValueError("Unknown label type: %s" % repr(ys))

ValueError: Unknown label type: (array([ 28.2  ,  28.375,  28.325, ...,  28.075,  28.275,  28.1  ]), array([ 28.23 ,  28.4  ,  28.32 , ...,  28.055,  28.28 ,  28.08 ]))

As suggested in the below answer KNeighborsClassifier() was updated with KNeighborsRegressor() and that had solved the previous issue

Possible duplicate of LogisticRegression: Unknown label type: 'continuous' using sklearn in python — Nouman Riaz Khan
– Nouman Riaz Khan, Commented Jun 25, 2018 at 12:55
You have accepted an answer; what exactly you mean by "previous issue"?? Is there any issue still pending?? Practically, accepted answer = case closed, except possibly for minor adjustments/clarifications... I strongly suggest you open a new question. — desertnaut
– desertnaut, Commented Jun 25, 2018 at 14:05

Jan K · Accepted Answer · 2018-06-25 12:59:04Z

5

You are dealing with a regression problem: predicting a price. So switching from KNeighborsClassifier to KNeighborsRegressor will solve this issue.

answered Jun 25, 2018 at 12:59

Jan K

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4 Comments

vicky113 Over a year ago

In that case how should I ammend this, the last line? I have updated the last 3 lines of the code

Jan K Over a year ago

You cannot generate a classification report for a regression problem

Marcus V. Over a year ago

Yes, you need to amend the last line, as classification metrics are not useful for a regression problem. Check what regression metrics suit your problem and either take one of the provided or calculate your own as needed.

desertnaut Over a year ago

@DebdiptaMajumdar updating the code after an answer has been provided (and accepted!) is not good practice, as it makes the answer seem irrelevant. You should either edit your post and indicate so, or open a new question...

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