Hugging Face's logo

Datasets:
mstz
/
monks

Tasks:
Tabular Classification
Languages:
English
Size:
n<1K
Tags:
student performance
tabular_classification
UCI
License:
Dataset card Files
xet
Community
mstz commited on
Commit
ab3049c
·
1 Parent(s): 4f9f9f2

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Files changed (9) hide show
  1. README.md +23 -1
  2. monks-1.test +432 -0
  3. monks-1.train +124 -0
  4. monks-2.test +432 -0
  5. monks-2.train +169 -0
  6. monks-3.test +432 -0
  7. monks-3.train +122 -0
  8. monks.names +78 -0
  9. monks.py +163 -0
README.md CHANGED
@@ -1,3 +1,25 @@
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  ---
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- license: cc-by-4.0
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - en
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+ tags:
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+ - student performance
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+ - tabular_classification
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+ pretty_name: Monk
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+ size_categories:
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+ - 1<n<100
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+ task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
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+ - tabular-classification
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+ configs:
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+ - monks1
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+ - monks2
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+ - monks3
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  ---
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+ # Monks
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+ The [Monk dataset](https://archive-beta.ics.uci.edu/dataset/70/monk+s+problems) from UCI.
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+
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+ # Configurations and tasks
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+ | **Configuration** | **Task** |
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+ |-------------------|---------------------------|
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+ | monks1 | Binary classification |
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+ | monks2 | Binary classification |
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+ | monks3 | Binary classification |
monks-1.test ADDED
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monks-2.test ADDED
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monks-2.train ADDED
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monks-3.test ADDED
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274
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275
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279
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292
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295
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296
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297
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+ 0 3 2 1 3 4 2 data_360
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+ 1 3 2 2 1 1 1 data_361
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+ 1 3 2 2 1 1 2 data_362
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+ 1 3 2 2 1 2 1 data_363
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+ 0 3 3 1 1 4 2 data_392
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+ 0 3 3 1 3 1 2 data_402
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+ 0 3 3 1 3 4 2 data_408
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+ 0 3 3 2 1 1 1 data_409
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+ 0 3 3 2 1 1 2 data_410
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+ 0 3 3 2 1 2 1 data_411
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+ 0 3 3 2 1 2 2 data_412
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+ 1 3 3 2 1 3 1 data_413
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+ 0 3 3 2 1 4 1 data_415
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+ 0 3 3 2 2 4 1 data_423
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+ 0 3 3 2 2 4 2 data_424
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+ 0 3 3 2 3 1 1 data_425
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+ 0 3 3 2 3 1 2 data_426
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+ 0 3 3 2 3 2 1 data_427
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+ 0 3 3 2 3 2 2 data_428
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+ 0 3 3 2 3 3 1 data_429
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+ 0 3 3 2 3 3 2 data_430
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+ 0 3 3 2 3 4 1 data_431
432
+ 0 3 3 2 3 4 2 data_432
monks-3.train ADDED
@@ -0,0 +1,122 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 1 1 1 1 1 1 2 data_2
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+ 1 1 1 1 2 2 2 data_12
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+ 1 1 1 2 1 2 2 data_28
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+ 0 1 1 2 1 4 2 data_32
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+ 1 1 1 2 2 2 2 data_36
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+ 0 1 1 2 2 4 1 data_39
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+ 0 1 1 2 2 4 2 data_40
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+ 1 1 1 2 3 1 1 data_41
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+ 1 1 1 2 3 1 2 data_42
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+ 1 1 1 2 3 3 1 data_45
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+ 1 1 1 2 3 3 2 data_46
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+ 1 1 2 1 1 3 1 data_53
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+ 1 1 2 1 2 2 1 data_59
20
+ 1 1 2 1 2 2 2 data_60
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+ 0 1 2 1 2 3 1 data_61
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+ 1 1 2 1 3 1 1 data_65
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+ 1 1 2 1 3 1 2 data_66
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+ 1 1 2 1 3 2 1 data_67
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+ 1 1 2 1 3 2 2 data_68
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+ 1 1 2 1 3 3 2 data_70
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+ 0 1 2 1 3 4 1 data_71
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+ 1 1 2 2 1 3 1 data_77
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+ 0 1 2 2 1 4 2 data_80
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+ 1 1 2 2 2 1 1 data_81
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+ 1 1 2 2 2 2 1 data_83
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+ 1 1 2 2 2 2 2 data_84
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+ 1 1 2 2 3 1 1 data_89
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+ 1 1 2 2 3 2 1 data_91
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+ 1 1 2 2 3 2 2 data_92
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+ 0 1 3 1 1 2 1 data_99
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+ 0 1 3 1 1 4 1 data_103
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+ 0 1 3 1 2 3 2 data_110
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+ 0 1 3 1 2 4 1 data_111
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+ 0 1 3 1 3 1 1 data_113
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+ 0 1 3 1 3 3 1 data_117
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+ 0 1 3 2 1 1 1 data_121
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+ 0 1 3 2 1 1 2 data_122
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+ 0 1 3 2 1 2 1 data_123
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+ 0 1 3 2 1 4 2 data_128
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+ 0 1 3 2 2 3 2 data_134
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+ 0 1 3 2 2 4 2 data_136
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+ 1 2 1 1 1 1 1 data_145
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+ 1 2 1 1 1 1 2 data_146
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+ 1 2 1 1 2 1 1 data_153
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+ 1 2 2 2 1 2 2 data_220
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+ 1 2 2 2 2 3 2 data_230
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+ 0 2 2 2 3 4 1 data_239
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+ 0 2 3 1 2 2 1 data_251
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+ 0 2 3 1 2 2 2 data_252
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+ 0 2 3 1 2 3 2 data_254
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+ 0 2 3 1 3 3 1 data_261
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+ 0 2 3 2 1 1 2 data_266
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+ 0 2 3 2 1 2 2 data_268
83
+ 0 2 3 2 1 4 1 data_271
84
+ 0 2 3 2 2 3 1 data_277
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+ 0 2 3 2 2 4 2 data_280
86
+ 0 2 3 2 3 1 1 data_281
87
+ 0 2 3 2 3 2 1 data_283
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+ 0 2 3 2 3 4 2 data_288
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+ 1 3 1 1 1 1 1 data_289
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+ 1 3 1 1 1 2 1 data_291
91
+ 1 3 1 1 1 3 1 data_293
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+ 0 3 1 1 2 4 2 data_304
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+ 1 3 1 1 3 1 2 data_306
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+ 0 3 1 1 3 4 2 data_312
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+ 1 3 1 2 1 2 1 data_315
96
+ 1 3 1 2 2 3 2 data_326
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+ 0 3 1 2 2 4 2 data_328
98
+ 1 3 1 2 3 1 1 data_329
99
+ 1 3 2 1 1 2 2 data_340
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+ 0 3 2 1 1 4 1 data_343
101
+ 1 3 2 1 2 3 1 data_349
102
+ 1 3 2 1 3 1 2 data_354
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+ 1 3 2 2 1 2 2 data_364
104
+ 1 3 2 2 1 3 2 data_366
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+ 1 3 2 2 2 1 2 data_370
106
+ 1 3 2 2 3 1 1 data_377
107
+ 1 3 2 2 3 3 2 data_382
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+ 0 3 2 2 3 4 1 data_383
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+ 1 3 3 1 1 3 2 data_390
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+ 1 3 3 1 1 4 1 data_391
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+ 0 3 3 1 2 4 2 data_400
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+ 0 3 3 1 3 1 1 data_401
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+ 0 3 3 1 3 2 1 data_403
114
+ 0 3 3 1 3 2 2 data_404
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+ 0 3 3 1 3 4 1 data_407
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+ 0 3 3 2 1 1 1 data_409
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+ 0 3 3 2 1 1 2 data_410
118
+ 0 3 3 2 2 2 2 data_420
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+ 0 3 3 2 2 3 2 data_422
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+ 0 3 3 2 3 1 1 data_425
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+ 0 3 3 2 3 3 2 data_430
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+ 0 3 3 2 3 4 2 data_432
monks.names ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ 1. Title: The Monk's Problems
3
+
4
+ 2. Sources:
5
+ (a) Donor: Sebastian Thrun
6
+ School of Computer Science
7
+ Carnegie Mellon University
8
+ Pittsburgh, PA 15213, USA
9
+
10
+ E-mail: [email protected]
11
+
12
+ (b) Date: October 1992
13
+
14
+ 3. Past Usage:
15
+
16
+ - See File: thrun.comparison.ps.Z
17
+
18
+ - Wnek, J., "Hypothesis-driven Constructive Induction," PhD dissertation,
19
+ School of Information Technology and Engineering, Reports of Machine
20
+ Learning and Inference Laboratory, MLI 93-2, Center for Artificial
21
+ Intelligence, George Mason University, March 1993.
22
+
23
+ - Wnek, J. and Michalski, R.S., "Comparing Symbolic and
24
+ Subsymbolic Learning: Three Studies," in Machine Learning: A
25
+ Multistrategy Approach, Vol. 4., R.S. Michalski and G. Tecuci (Eds.),
26
+ Morgan Kaufmann, San Mateo, CA, 1993.
27
+
28
+ 4. Relevant Information:
29
+
30
+ The MONK's problem were the basis of a first international comparison
31
+ of learning algorithms. The result of this comparison is summarized in
32
+ "The MONK's Problems - A Performance Comparison of Different Learning
33
+ algorithms" by S.B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B.
34
+ Cestnik, J. Cheng, K. De Jong, S. Dzeroski, S.E. Fahlman, D. Fisher,
35
+ R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R.S.
36
+ Michalski, T. Mitchell, P. Pachowicz, Y. Reich H. Vafaie, W. Van de
37
+ Welde, W. Wenzel, J. Wnek, and J. Zhang has been published as
38
+ Technical Report CS-CMU-91-197, Carnegie Mellon University in Dec.
39
+ 1991.
40
+
41
+ One significant characteristic of this comparison is that it was
42
+ performed by a collection of researchers, each of whom was an advocate
43
+ of the technique they tested (often they were the creators of the
44
+ various methods). In this sense, the results are less biased than in
45
+ comparisons performed by a single person advocating a specific
46
+ learning method, and more accurately reflect the generalization
47
+ behavior of the learning techniques as applied by knowledgeable users.
48
+
49
+ There are three MONK's problems. The domains for all MONK's problems
50
+ are the same (described below). One of the MONK's problems has noise
51
+ added. For each problem, the domain has been partitioned into a train
52
+ and test set.
53
+
54
+ 5. Number of Instances: 432
55
+
56
+ 6. Number of Attributes: 8 (including class attribute)
57
+
58
+ 7. Attribute information:
59
+ 1. class: 0, 1
60
+ 2. a1: 1, 2, 3
61
+ 3. a2: 1, 2, 3
62
+ 4. a3: 1, 2
63
+ 5. a4: 1, 2, 3
64
+ 6. a5: 1, 2, 3, 4
65
+ 7. a6: 1, 2
66
+ 8. Id: (A unique symbol for each instance)
67
+
68
+ 8. Missing Attribute Values: None
69
+
70
+ 9. Target Concepts associated to the MONK's problem:
71
+
72
+ MONK-1: (a1 = a2) or (a5 = 1)
73
+
74
+ MONK-2: EXACTLY TWO of {a1 = 1, a2 = 1, a3 = 1, a4 = 1, a5 = 1, a6 = 1}
75
+
76
+ MONK-3: (a5 = 3 and a4 = 1) or (a5 /= 4 and a2 /= 3)
77
+ (5% class noise added to the training set)
78
+
monks.py ADDED
@@ -0,0 +1,163 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Monk Dataset"""
2
+
3
+ from typing import List
4
+ from functools import partial
5
+
6
+ import datasets
7
+
8
+ import pandas
9
+
10
+
11
+ VERSION = datasets.Version("1.0.0")
12
+ _ORIGINAL_FEATURE_NAMES = [
13
+ "is_monk",
14
+ "head_shape",
15
+ "body_shape",
16
+ "is_smiling",
17
+ "holding",
18
+ "jacket_color",
19
+ "has_tie",
20
+ "ID"
21
+ ]
22
+ _BASE_FEATURE_NAMES = [
23
+ "head_shape",
24
+ "body_shape",
25
+ "is_smiling",
26
+ "holding",
27
+ "jacket_color",
28
+ "has_tie",
29
+ "is_monk"
30
+ ]
31
+
32
+ _ENCODING_DICS = {
33
+ "head_shape": {
34
+ 0: "round",
35
+ 1: "square",
36
+ 2: "octagon",
37
+ },
38
+ "body_shape": {
39
+ 0: "round",
40
+ 1: "square",
41
+ 2: "octagon",
42
+ },
43
+ "holding": {
44
+ 0: "sword",
45
+ 1: "baloon",
46
+ 2: "flag",
47
+ },
48
+ "jacket_color": {
49
+ 0: "red",
50
+ 1: "yellow",
51
+ 2: "green",
52
+ 3: "blue"
53
+ },
54
+ "is_smiling": {
55
+ 1: True,
56
+ 0: False
57
+ },
58
+ "has_tie": {
59
+ 1: True,
60
+ 0: False
61
+ }
62
+ }
63
+
64
+ DESCRIPTION = "Monk quality dataset."
65
+ _HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/70/monk+s+problems"
66
+ _URLS = ("https://archive-beta.ics.uci.edu/dataset/70/monk+s+problems")
67
+ _CITATION = """
68
+ @misc{misc_monk's_problems_70,
69
+ author = {Wnek,J.},
70
+ title = {{MONK's Problems}},
71
+ year = {1992},
72
+ howpublished = {UCI Machine Learning Repository},
73
+ note = {{DOI}: \\url{10.24432/C5R30R}}
74
+ }"""
75
+
76
+ # Dataset info
77
+ urls_per_split = {
78
+ "monks1": {
79
+ "train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-1.train",
80
+ "test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-1.test"
81
+ },
82
+ "monks2": {
83
+ "train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-2.train",
84
+ "test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-2.test"
85
+ },
86
+ "monks3": {
87
+ "train": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-3.train",
88
+ "test": "https://huggingface.co/datasets/mstz/monks/raw/main/monks-3.test"
89
+ }
90
+ }
91
+ features_types_per_config = {
92
+ "monks1": {
93
+ "head_shape": datasets.Value("string"),
94
+ "body_shape": datasets.Value("string"),
95
+ "is_smiling": datasets.Value("bool"),
96
+ "holding": datasets.Value("string"),
97
+ "jacket_color": datasets.Value("string"),
98
+ "has_tie": datasets.Value("bool"),
99
+ "is_monk": datasets.ClassLabel(num_classes=2)
100
+ }
101
+
102
+ }
103
+ features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
104
+
105
+
106
+ class MonkConfig(datasets.BuilderConfig):
107
+ def __init__(self, **kwargs):
108
+ super(MonkConfig, self).__init__(version=VERSION, **kwargs)
109
+ self.features = features_per_config[kwargs["name"]]
110
+
111
+
112
+ class Monk(datasets.GeneratorBasedBuilder):
113
+ # dataset versions
114
+ DEFAULT_CONFIG = "monks1"
115
+ BUILDER_CONFIGS = [
116
+ MonkConfig(name="monks1",
117
+ description="Monk 1 problem."),
118
+ MonkConfig(name="monks2",
119
+ description="Monk 2 problem."),
120
+ MonkConfig(name="monks3",
121
+ description="Monk 3 problem.")
122
+ ]
123
+
124
+
125
+ def _info(self):
126
+ info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
127
+ features=features_per_config[self.config.name])
128
+
129
+ return info
130
+
131
+ def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
132
+ downloads = dl_manager.download_and_extract(urls_per_split)
133
+
134
+ return [
135
+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}),
136
+ ]
137
+
138
+ def _generate_examples(self, filepath: str):
139
+ data = pandas.read_csv(filepath, header=None, sep=" ")
140
+ data = self.preprocess(data, config=self.config.name)
141
+ print(data.head())
142
+
143
+ for row_id, row in data.iterrows():
144
+ data_row = dict(row)
145
+
146
+ yield row_id, data_row
147
+
148
+ def preprocess(self, data: pandas.DataFrame, config: str = "monks1") -> pandas.DataFrame:
149
+ data.columns = _BASE_FEATURE_NAMES
150
+ data.drop("ID", axis="columns", inplace=True)
151
+ data.loc[:, "has_tie"] = data.has_tie.apply(bool)
152
+ data.loc[:, "is_smiling"] = data.is_smiling.apply(bool)
153
+
154
+ for feature in _ENCODING_DICS:
155
+ encoding_function = partial(self.encode, feature)
156
+ data.loc[:, feature] = data[feature].apply(encoding_function)
157
+
158
+ return data[list(features_types_per_config[config].keys())]
159
+
160
+ def encode(self, feature, value):
161
+ if feature in _ENCODING_DICS:
162
+ return _ENCODING_DICS[feature][value]
163
+ raise ValueError(f"Unknown feature: {feature}")