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this is my python code, it list out three problem 'E1123:Unexpected keyword argument 'n_folds' in constructor call, E1123:Unexpected keyword argument 'n' in constructor call, E1133:Non-iterable value k_fold is used in an iterating context' 

import math
import random
import sys
import warnings
from math import sqrt

import numpy as np
import scipy.spatial
import scipy.stats
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold

warnings.simplefilter("error")

users = 6040
items = 3952

def readingFile(filename):
f = open(filename,"r")
data = []
for row in f:
    r = row.split(',')
    e = [int(r[0]), int(r[1]), int(r[2])]
    data.append(e)
return data

def similarity_user(data):
print "Hello User"
user_similarity_cosine = np.zeros((users,users))
user_similarity_jaccard = np.zeros((users,users))
user_similarity_pearson = np.zeros((users,users))
for user1 in range(users):
    print user1
    for user2 in range(users):
        if np.count_nonzero(data[user1]) and np.count_nonzero(data[user2]):
            user_similarity_cosine[user1][user2] = 1-scipy.spatial.distance.cosine(data[user1],data[user2])
            user_similarity_jaccard[user1][user2] = 1-scipy.spatial.distance.jaccard(data[user1],data[user2])
            try:
                if not math.isnan(scipy.stats.pearsonr(data[user1],data[user2])[0]):
                    user_similarity_pearson[user1][user2] = scipy.stats.pearsonr(data[user1],data[user2])[0]
                else:
                    user_similarity_pearson[user1][user2] = 0
            except:
                user_similarity_pearson[user1][user2] = 0


return user_similarity_cosine, user_similarity_jaccard, user_similarity_pearson

def modelSelection(data):
k_fold = KFold(n=len(data), n_folds=10)

Mat = np.zeros((users,items))
for e in data:
    Mat[e[0]-1][e[1]-1] = e[2]

sim_user_cosine, sim_user_jaccard, sim_user_pearson = similarity_user(Mat)


'''sim_user_cosine = np.zeros((users,users))
sim_user_jaccard = np.zeros((users,users))
sim_user_pearson = np.zeros((users,users))

f_sim = open("sim_user_based.txt", "r")
for row in f_sim:
    r = row.strip().split(',')
    sim_user_cosine[int(r[0])][int(r[1])] = float(r[2])
    sim_user_jaccard[int(r[0])][int(r[1])] = float(r[3])
    sim_user_pearson[int(r[0])][int(r[1])] = float(r[4])
f_sim.close()'''

rmse_cosine = []
rmse_jaccard = []
rmse_pearson = []

for train_indices, test_indices in k_fold:
    train = [data[i] for i in train_indices]
    test = [data[i] for i in test_indices]

    M = np.zeros((users,items))

    for e in train:
        M[e[0]-1][e[1]-1] = e[2]

    true_rate = []
    pred_rate_cosine = []
    pred_rate_jaccard = []
    pred_rate_pearson = []

    for e in test:
        user = e[0]
        item = e[1]
        true_rate.append(e[2])

        pred_cosine = 3.0
        pred_jaccard = 3.0
        pred_pearson = 3.0

        #user-based
        if np.count_nonzero(M[user-1]):
            sim_cosine = sim_user_cosine[user-1]
            sim_jaccard = sim_user_jaccard[user-1]
            sim_pearson = sim_user_pearson[user-1]
            ind = (M[:,item-1] > 0)
            #ind[user-1] = False
            normal_cosine = np.sum(np.absolute(sim_cosine[ind]))
            normal_jaccard = np.sum(np.absolute(sim_jaccard[ind]))
            normal_pearson = np.sum(np.absolute(sim_pearson[ind]))
            if normal_cosine > 0:
                pred_cosine = np.dot(sim_cosine,M[:,item-1])/normal_cosine

            if normal_jaccard > 0:
                pred_jaccard = np.dot(sim_jaccard,M[:,item-1])/normal_jaccard

            if normal_pearson > 0:
                pred_pearson = np.dot(sim_pearson,M[:,item-1])/normal_pearson

        if pred_cosine < 0:
            pred_cosine = 0

        if pred_cosine > 5:
            pred_cosine = 5

        if pred_jaccard < 0:
            pred_jaccard = 0

        if pred_jaccard > 5:
            pred_jaccard = 5

        if pred_pearson < 0:
            pred_pearson = 0

        if pred_pearson > 5:
            pred_pearson = 5

        print str(user) + "\t" + str(item) + "\t" + str(e[2]) + "\t" + str(pred_cosine) + "\t" + str(pred_jaccard) + "\t" + str(pred_pearson)
        pred_rate_cosine.append(pred_cosine)
        pred_rate_jaccard.append(pred_jaccard)
        pred_rate_pearson.append(pred_pearson)

    rmse_cosine.append(sqrt(mean_squared_error(true_rate, pred_rate_cosine)))
    rmse_jaccard.append(sqrt(mean_squared_error(true_rate, pred_rate_jaccard)))
    rmse_pearson.append(sqrt(mean_squared_error(true_rate, pred_rate_pearson)))

    print str(sqrt(mean_squared_error(true_rate, pred_rate_cosine))) + "\t" + str(sqrt(mean_squared_error(true_rate, pred_rate_jaccard))) + "\t" + str(sqrt(mean_squared_error(true_rate, pred_rate_pearson)))
    #raw_input()

#print sum(rms) / float(len(rms))
rmse_cosine = sum(rmse_cosine) / float(len(rmse_cosine))
rmse_pearson = sum(rmse_pearson) / float(len(rmse_pearson))
rmse_jaccard = sum(rmse_jaccard) / float(len(rmse_jaccard))

print str(rmse_cosine) + "\t" + str(rmse_jaccard) + "\t" + str(rmse_pearson)

f_rmse = open("results/rmse_user.txt","w")
f_rmse.write(str(rmse_cosine) + "\t" + str(rmse_jaccard) + "\t" + str(rmse_pearson) + "\n")

rmse = [rmse_cosine, rmse_jaccard, rmse_pearson]
req_sim = rmse.index(min(rmse))

print req_sim
f_rmse.write(str(req_sim))
f_rmse.close()

if req_sim == 0:
    sim_mat_user = sim_user_cosine

if req_sim == 1:
    sim_mat_user = sim_user_jaccard

if req_sim == 2:
    sim_mat_user = sim_user_pearson

#predictRating(Mat, sim_mat_user)
return Mat, sim_mat_user


def predictRating(recommend_data):

M, sim_user = modelSelection(recommend_data)

#f = open("toBeRated.csv","r")
f = open(sys.argv[2],"r")
toBeRated = {"user":[], "item":[]}
for row in f:
    r = row.split(',')  
    toBeRated["item"].append(int(r[1]))
    toBeRated["user"].append(int(r[0]))

f.close()

pred_rate = []

#fw = open('result1.csv','w')
fw_w = open('results/result1.csv','w')

l = len(toBeRated["user"])
for e in range(l):
    user = toBeRated["user"][e]
    item = toBeRated["item"][e]

    pred = 3.0

    #user-based
    if np.count_nonzero(M[user-1]):
        sim = sim_user[user-1]
        ind = (M[:,item-1] > 0)
        #ind[user-1] = False
        normal = np.sum(np.absolute(sim[ind]))
        if normal > 0:
            pred = np.dot(sim,M[:,item-1])/normal

    if pred < 0:
        pred = 0

    if pred > 5:
        pred = 5

    pred_rate.append(pred)
    print (str(user) + "," + str(item) + "," + str(pred))
    #fw.write(str(user) + "," + str(item) + "," + str(pred) + "\n")
    fw_w.write(str(pred) + "\n")

#fw.close()
fw_w.close()

#recommend_data = readingFile("ratings.csv")
recommend_data = readingFile(sys.argv[1])
#crossValidation(recommend_data)
predictRating(recommend_data)

and afterwards it gives this error code 

'pydevd.main()
  File "C:\Users\Morakinyo\.vscode\extensions\ms-python.python-2018.3.1\pythonFiles\experimental\ptvsd\ptvsd\pydevd\pydevd.py", line 1628,
in main
    globals = debugger.run(setup['file'], None, None, is_module)
  File "C:\Users\Morakinyo\.vscode\extensions\ms-python.python-2018.3.1\pythonFiles\experimental\ptvsd\ptvsd\pydevd\pydevd.py", line 1035,
in run
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "c:\Users\Morakinyo\Documents\recommend\Coll\code\userBased.py", line 227, in <module>
    recommend_data = readingFile(sys.argv[1])
IndexError: list index out of range'

Please help im a novice in python programming lhanguage

Mortech
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  • This is a lot of code to expect volunteers on SO to read. Please try to cut it down to the minimal amount of code necessary to reproduce your error. Try to create a [mcve]. However, based on your error message, it seems like you didn't pass in a required positional argument when calling the program. See if [this post](https://stackoverflow.com/questions/4117530/sys-argv1-meaning-in-script) helps you. – pault May 01 '18 at 14:14
  • i did that for proper referencing – Mortech May 01 '18 at 14:15
  • Please indent / use proper spacing for your code. Also, the first error begins here: `k_fold = KFold(n=len(data), n_folds=10)`. Check the documentation for this `KFold` class and make sure that `n_folds` is an actual parameter. If it is, you might need to remove `n_folds` and just pass in `10`. – natn2323 May 01 '18 at 14:45
  • after editing this line 'def modelSelection(data):kf = KFold(n_splits=len(data)) kf.get_n_splits(10)' and this line 'for train_indices, test_indices in kf.split(10):' @musikreck – Mortech May 02 '18 at 09:51
  • python says 'globals = debugger.run(setup['file'], None, None, is_module) File "C:\Users\Morakinyo\.vscode\extensions\ms-python.python-2018.3.1\pythonFiles\experimental\ptvsd\ptvsd\pydevd\pydevd.py", line 1035, in run pydev_imports.execfile(file, globals, locals) # execute the script File "c:\Users\Morakinyo\Documents\recommend\Coll\code\userBased.py", line 228, in recommend_data = readingFile(sys.argv[1]) IndexError: list index out of range' @musikreck – Mortech May 02 '18 at 09:54
  • The `IndexError` means that there's no `sys.argv[1]` value, meaning that you're not passing anything in via the command line. It looks like you have a `ratings.csv` on line 226. What happens when you run it with that? – natn2323 May 02 '18 at 14:32

0 Answers0