Wrong mapping of the raw IDs to the internal IDs

[benhaf]

Hi,

Description

The mapping of the raw IDs of the users to the internal IDs is not correct when the dataset contains more than 25000 rows. I tried to read the ratings from a file and from a dataframe, but it always gives a wrong mapping of the user IDs. I tested several datasets.
In the code below, after saving the training set with the internal IDs to SupriseTrainingSet.csv, I compare the file Train.txt to SupriseTrainingSet.csv.

Steps/Code to Reproduce

from surprise import Dataset, KNNBasic, Reader
import pandas as pd
import csv

train_file = files_dir + folder + "Train.txt"

reader = Reader(line_format="user item rating", sep="\t")

data = Dataset.load_from_file(train_file, reader=reader)

trainset = data.build_full_trainset() #creates the training set from the whole dataset

with open(files_dir + folder +"SupriseTrainingSet.csv", 'w', newline='') as file:
writer = csv.writer(file)
# write each row of data to the CSV file
for row in trainset.all_ratings():
writer.writerow(row)

algo = KNNBasic()
algo.fit(trainset)

Expected Results

####Original dataset
User Item Rating
1 225 2
1 154 5
1 73 3
1 43 4
1 199 4
1 34 2
1 227 4
1 94 2
1 74 1
1 76 4
1 181 5
1 105 2
1 253 5
1 200 3
1 61 4
1 93 5
1 272 3
1 53 3
1 174 5
1 193 4
1 161 4
1 129 5
1 195 5
1 9 5
1 156 4
1 262 3
1 99 3
1 21 1
1 35 1
1 123 4
1 104 1
1 148 2
1 184 4
1 249 4
1 54 3
1 66 4
1 107 4
1 8 1
1 145 2
1 102 2
1 134 4
1 125 3
1 165 5
1 49 3
1 114 5
1 32 5
1 252 2
1 209 4
1 153 3
1 26 3
1 137 5
1 133 4
1 217 3
1 245 2
1 24 3
2 286 4
2 292 4
2 313 5
2 272 5
2 290 3
2 10 2
2 312 3
2 280 3
2 281 3
2 14 4
2 296 3
2 1 4
2 279 4
3 332 1
3 339 3
3 350 3
3 319 2
3 352 2
3 260 4
3 336 1
3 348 4
3 345 3
3 271 3
3 346 5
4 327 5
4 357 4
4 329 5
4 288 4
4 300 5
5 457 1
5 2 3

####Internal IDs of surprise
User Item Rating
0 0 2
0 1 5
0 2 3
0 3 4
0 4 4
0 5 2
0 6 4
0 7 2
0 8 1
0 9 4
0 10 5
0 11 2
0 12 5
0 13 3
0 14 4
0 15 5
0 16 3
0 17 3
0 18 5
0 19 4
0 20 4
0 21 5
0 22 5
0 23 5
0 24 4
0 25 3
0 26 3
0 27 1
0 28 1
0 29 4
0 30 1
0 31 2
0 32 4
0 33 4
0 34 3
0 35 4
0 36 4
0 37 1
0 38 2
0 39 2
0 40 4
0 41 3
0 42 5
0 43 3
0 44 5
0 45 5
0 46 2
0 47 4
0 48 3
0 49 3
0 50 5
0 51 4
0 52 3
0 53 2
0 54 3
1 369 4
1 533 5
1 503 3
1 451 1
1 239 4
1 314 4
1 110 4
1 956 4
1 714 4
1 134 4
1 674 4
1 227 5
1 471 1
2 180 5
2 382 5
2 264 4
2 213 3
2 517 1
2 86 1
2 351 5
2 162 5
2 272 2
2 410 4
2 822 2
3 1328 1
3 401 5
3 807 3
3 84 3
3 1074 5
4 415 5
4 589 4

Actual Results

####Original dataset
User Item Rating
1 225 2
1 154 5
1 73 3
1 43 4
1 199 4
1 34 2
1 227 4
1 94 2
1 74 1
1 76 4
1 181 5
1 105 2
1 253 5
1 200 3
1 61 4
1 93 5
1 272 3
1 53 3
1 174 5
1 193 4
1 161 4
1 129 5
1 195 5
1 9 5
1 156 4
1 262 3
1 99 3
1 21 1
1 35 1
1 123 4
1 104 1
1 148 2
1 184 4
1 249 4
1 54 3
1 66 4
1 107 4
1 8 1
1 145 2
1 102 2
1 134 4
1 125 3
1 165 5
1 49 3
1 114 5
1 32 5
1 252 2
1 209 4
1 153 3
1 26 3
1 137 5
1 133 4
1 217 3
1 245 2
1 24 3
2 286 4
2 292 4
2 313 5
2 272 5
2 290 3
2 10 2
2 312 3
2 280 3
2 281 3
2 14 4
2 296 3
2 1 4
2 279 4
3 332 1
3 339 3
3 350 3
3 319 2
3 352 2
3 260 4
3 336 1
3 348 4
3 345 3
3 271 3
3 346 5
4 327 5
4 357 4
4 329 5
4 288 4
4 300 5
5 457 1
5 2 3

####Internal IDs of surprise
User Item Rating
0 0 2
0 1 5
0 2 3
0 3 4
0 4 4
0 5 2
0 6 4
0 7 2
0 8 1
0 9 4
0 10 5
0 11 2
0 12 5
0 13 3
0 14 4
0 15 5
0 16 3
0 17 3
0 18 5
0 19 4
0 20 4
0 21 5
0 22 5
0 23 5
0 24 4
0 25 3
0 26 3
0 27 1
0 28 1
0 29 4
0 30 1
0 31 2
0 32 4
0 33 4
0 34 3
0 35 4
0 36 4
0 37 1
0 38 2
0 39 2
0 40 4
0 41 3
0 42 5
0 43 3
0 44 5
0 45 5
0 46 2
0 47 4
0 48 3
0 49 3
0 50 5
0 51 4
0 52 3
0 53 2
0 54 3
0 369 4
0 533 5
0 503 3
0 451 1
0 239 4
0 314 4
0 110 4
0 956 4
0 714 4
0 134 4
0 674 4
0 227 5
0 471 1
0 180 5
0 382 5
0 264 4
0 213 3
0 517 1
0 86 1
0 351 5
0 162 5
0 272 2
0 410 4
0 822 2
0 1328 1
0 401 5
0 807 3
0 84 3
0 1074 5
0 415 5
0 589 4

Uploading results.xlsx…

Versions

Windows-10-10.0.22621-SP0
Python 3.8.3 (default, Jul 2 2020, 17:30:36) [MSC v.1916 64 bit (AMD64)]
surprise 1.1.3