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Datasets:
mstz
/
spect

Tasks:
Tabular Classification
Languages:
English
Size:
n<1K
Tags:
spect
tabular_classification
binary_classification
UCI
License:
Dataset card Files Community
mstz commited on
Commit
b398694
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1 Parent(s): 095ce58

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  1. README.md +30 -1
  2. SPECT.names +89 -0
  3. SPECT.test +187 -0
  4. SPECT.train +80 -0
  5. SPECTF.names +109 -0
  6. SPECTF.test +187 -0
  7. SPECTF.train +80 -0
  8. spect.py +150 -0
README.md CHANGED
@@ -1,3 +1,32 @@
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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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+ - spect
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+ - tabular_classification
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+ - binary_classification
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+ pretty_name: Ozone
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+ size_categories:
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+ - 1K<n<10K
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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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+ - spect
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+ - spectf
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  ---
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+ # Ozone
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+ The [Ozone dataset](https://archive.ics.uci.edu/ml/datasets/Ozone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
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+
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+ # Configurations and tasks
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+ | **Configuration** | **Task** | **Description** |
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+ |-------------------|---------------------------|-------------------------|
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+ | spect | Binary classification | Is there an ozone layer?|
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+ | spectf | Binary classification | Is there an ozone layer?|
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+
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+
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+ # Usage
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+ ```python
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+ from datasets import load_dataset
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+
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+ dataset = load_dataset("mstz/spectf", "spect")["train"]
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+ ```
SPECT.names ADDED
@@ -0,0 +1,89 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ 1. Title of Database: SPECT heart data
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+
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+ 2. Sources:
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+ -- Original owners: Krzysztof J. Cios, Lukasz A. Kurgan
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+ University of Colorado at Denver, Denver, CO 80217, U.S.A.
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+ [email protected]
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+ Lucy S. Goodenday
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+ Medical College of Ohio, OH, U.S.A.
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+
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+ -- Donors: Lukasz A.Kurgan, Krzysztof J. Cios
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+ -- Date: 10/01/01
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+
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+ 3. Past Usage:
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+ 1. Kurgan, L.A., Cios, K.J., Tadeusiewicz, R., Ogiela, M. & Goodenday, L.S.
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+ "Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis"
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+ Artificial Intelligence in Medicine, vol. 23:2, pp 149-169, Oct 2001
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+
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+ Results: The CLIP3 machine learning algorithm achieved 84.0% accuracy
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+ References:
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+ Cios, K.J., Wedding, D.K. & Liu, N.
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+ CLIP3: cover learning using integer programming.
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+ Kybernetes, 26:4-5, pp 513-536, 1997
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+
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+ Cios, K.J. & Kurgan, L.
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+ Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms,
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+ In: Jain, L.C., and Kacprzyk, J. (Eds.)
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+ New Learning Paradigms in Soft Computing,
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+ Physica-Verlag (Springer), 2001
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+
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+ SPECT is a good data set for testing ML algorithms; it has 267 instances
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+ that are descibed by 23 binary attributes
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+
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+ Other results (in press):
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+ -- CLIP4 algorithm achieved 86.1% accuracy
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+ -- ensemble of CLIP4 classifiers achieved 90.4% accuracy
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+ -- Predicted attribute: OVERALL_DIAGNOSIS (binary)
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+
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+ 4. Relevant Information:
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+ The dataset describes diagnosing of cardiac Single Proton Emission Computed Tomography (SPECT) images.
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+ Each of the patients is classified into two categories: normal and abnormal.
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+ The database of 267 SPECT image sets (patients) was processed to extract features that summarize the original SPECT images.
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+ As a result, 44 continuous feature pattern was created for each patient.
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+ The pattern was further processed to obtain 22 binary feature patterns.
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+ The CLIP3 algorithm was used to generate classification rules from these patterns.
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+ The CLIP3 algorithm generated rules that were 84.0% accurate (as compared with cardilogists' diagnoses).
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+
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+ 5. Number of Instances: 267
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+ 6. Number of Attributes: 23 (22 binary + 1 binary class)
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+ 7. Attribute Information:
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+ 1. OVERALL_DIAGNOSIS: 0,1 (class attribute, binary)
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+ 2. F1: 0,1 (the partial diagnosis 1, binary)
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+ 3. F2: 0,1 (the partial diagnosis 2, binary)
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+ 4. F3: 0,1 (the partial diagnosis 3, binary)
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+ 5. F4: 0,1 (the partial diagnosis 4, binary)
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+ 6. F5: 0,1 (the partial diagnosis 5, binary)
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+ 7. F6: 0,1 (the partial diagnosis 6, binary)
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+ 8. F7: 0,1 (the partial diagnosis 7, binary)
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+ 9. F8: 0,1 (the partial diagnosis 8, binary)
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+ 10. F9: 0,1 (the partial diagnosis 9, binary)
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+ 11. F10: 0,1 (the partial diagnosis 10, binary)
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+ 12. F11: 0,1 (the partial diagnosis 11, binary)
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+ 13. F12: 0,1 (the partial diagnosis 12, binary)
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+ 14. F13: 0,1 (the partial diagnosis 13, binary)
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+ 15. F14: 0,1 (the partial diagnosis 14, binary)
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+ 16. F15: 0,1 (the partial diagnosis 15, binary)
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+ 17. F16: 0,1 (the partial diagnosis 16, binary)
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+ 18. F17: 0,1 (the partial diagnosis 17, binary)
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+ 19. F18: 0,1 (the partial diagnosis 18, binary)
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+ 20. F19: 0,1 (the partial diagnosis 19, binary)
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+ 21. F20: 0,1 (the partial diagnosis 20, binary)
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+ 22. F21: 0,1 (the partial diagnosis 21, binary)
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+ 23. F22: 0,1 (the partial diagnosis 22, binary)
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+ -- dataset is divided into:
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+ -- training data ("SPECT.train" 80 instances)
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+ -- testing data ("SPECT.test" 187 instances)
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+ 8. Missing Attribute Values: None
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+ 9. Class Distribution:
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+ -- entire data
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+ Class # examples
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+ 0 55
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+ 1 212
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+ -- training dataset
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+ Class # examples
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+ 0 40
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+ 1 40
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+ -- testing dataset
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+ Class # examples
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+ 0 15
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+ 1 172
SPECT.test ADDED
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187
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SPECT.train ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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80
+ 0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
SPECTF.names ADDED
@@ -0,0 +1,109 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 1. Title of Database: SPECTF heart data
2
+
3
+ 2. Sources:
4
+ -- Original owners: Krzysztof J. Cios, Lukasz A. Kurgan
5
+ University of Colorado at Denver, Denver, CO 80217, U.S.A.
6
+ [email protected]
7
+ Lucy S. Goodenday
8
+ Medical College of Ohio, OH, U.S.A.
9
+ -- Donors: Lukasz A.Kurgan, Krzysztof J. Cios
10
+ -- Date: 10/01/01
11
+
12
+ 3. Past Usage:
13
+ 1. Kurgan, L.A., Cios, K.J., Tadeusiewicz, R., Ogiela, M. & Goodenday, L.S.
14
+ "Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis"
15
+ Artificial Intelligence in Medicine, vol. 23:2, pp 149-169, Oct 2001
16
+
17
+ Results: The CLIP3 machine learning algorithm achieved 77.0% accuracy.
18
+ CLIP3 references:
19
+ Cios, K.J., Wedding, D.K. & Liu, N.
20
+ CLIP3: cover learning using integer programming.
21
+ Kybernetes, 26:4-5, pp 513-536, 1997
22
+
23
+ Cios K. J. & Kurgan L.
24
+ Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms,
25
+ In: Jain L.C., and Kacprzyk J. (Eds.)
26
+ New Learning Paradigms in Soft Computing,
27
+ Physica-Verlag (Springer), 2001
28
+
29
+
30
+ SPECTF is a good data set for testing ML algorithms; it has 267 instances that are descibed by 45 attributes.
31
+ Predicted attribute: OVERALL_DIAGNOSIS (binary)
32
+ NOTE: See the SPECT heart data for binary data for the same classification task.
33
+
34
+ 4. Relevant Information:
35
+ The dataset describes diagnosing of cardiac Single Proton Emission Computed Tomography (SPECT) images.
36
+ Each of the patients is classified into two categories: normal and abnormal.
37
+ The database of 267 SPECT image sets (patients) was processed to extract features that summarize the original SPECT images.
38
+ As a result, 44 continuous feature pattern was created for each patient.
39
+ The CLIP3 algorithm was used to generate classification rules from these patterns.
40
+ The CLIP3 algorithm generated rules that were 77.0% accurate (as compared with cardilogists' diagnoses).
41
+
42
+ 5. Number of Instances: 267
43
+ 6. Number of Attributes: 45 (44 continuous + 1 binary class)
44
+ 7. Attribute Information:
45
+ 1. OVERALL_DIAGNOSIS: 0,1 (class attribute, binary)
46
+ 2. F1R: continuous (count in ROI (region of interest) 1 in rest)
47
+ 3. F1S: continuous (count in ROI 1 in stress)
48
+ 4. F2R: continuous (count in ROI 2 in rest)
49
+ 5. F2S: continuous (count in ROI 2 in stress)
50
+ 6. F3R: continuous (count in ROI 3 in rest)
51
+ 7. F3S: continuous (count in ROI 3 in stress)
52
+ 8. F4R: continuous (count in ROI 4 in rest)
53
+ 9. F4S: continuous (count in ROI 4 in stress)
54
+ 10. F5R: continuous (count in ROI 5 in rest)
55
+ 11. F5S: continuous (count in ROI 5 in stress)
56
+ 12. F6R: continuous (count in ROI 6 in rest)
57
+ 13. F6S: continuous (count in ROI 6 in stress)
58
+ 14. F7R: continuous (count in ROI 7 in rest)
59
+ 15. F7S: continuous (count in ROI 7 in stress)
60
+ 16. F8R: continuous (count in ROI 8 in rest)
61
+ 17. F8S: continuous (count in ROI 8 in stress)
62
+ 18. F9R: continuous (count in ROI 9 in rest)
63
+ 19. F9S: continuous (count in ROI 9 in stress)
64
+ 20. F10R: continuous (count in ROI 10 in rest)
65
+ 21. F10S: continuous (count in ROI 10 in stress)
66
+ 22. F11R: continuous (count in ROI 11 in rest)
67
+ 23. F11S: continuous (count in ROI 11 in stress)
68
+ 24. F12R: continuous (count in ROI 12 in rest)
69
+ 25. F12S: continuous (count in ROI 12 in stress)
70
+ 26. F13R: continuous (count in ROI 13 in rest)
71
+ 27. F13S: continuous (count in ROI 13 in stress)
72
+ 28. F14R: continuous (count in ROI 14 in rest)
73
+ 29. F14S: continuous (count in ROI 14 in stress)
74
+ 30. F15R: continuous (count in ROI 15 in rest)
75
+ 31. F15S: continuous (count in ROI 15 in stress)
76
+ 32. F16R: continuous (count in ROI 16 in rest)
77
+ 33. F16S: continuous (count in ROI 16 in stress)
78
+ 34. F17R: continuous (count in ROI 17 in rest)
79
+ 35. F17S: continuous (count in ROI 17 in stress)
80
+ 36. F18R: continuous (count in ROI 18 in rest)
81
+ 37. F18S: continuous (count in ROI 18 in stress)
82
+ 38. F19R: continuous (count in ROI 19 in rest)
83
+ 39. F19S: continuous (count in ROI 19 in stress)
84
+ 40. F20R: continuous (count in ROI 20 in rest)
85
+ 41. F20S: continuous (count in ROI 20 in stress)
86
+ 42. F21R: continuous (count in ROI 21 in rest)
87
+ 43. F21S: continuous (count in ROI 21 in stress)
88
+ 44. F22R: continuous (count in ROI 22 in rest)
89
+ 45. F22S: continuous (count in ROI 22 in stress)
90
+ -- all continuous attributes have integer values from the 0 to 100
91
+ -- dataset is divided into:
92
+ -- training data ("SPECTF.train" 80 instances)
93
+ -- testing data ("SPECTF.test" 187 instances)
94
+ 8. Missing Attribute Values: None
95
+ 9. Class Distribution:
96
+ -- entire data
97
+ Class # examples
98
+ 0 55
99
+ 1 212
100
+ -- training dataset
101
+ Class # examples
102
+ 0 40
103
+ 1 40
104
+ -- testing dataset
105
+ Class # examples
106
+ 0 15
107
+ 1 172
108
+
109
+ NOTE: See the SPECT heart data for binary data for the same classification task.
SPECTF.test ADDED
@@ -0,0 +1,187 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 1, 67, 68, 73, 78, 65, 63, 67, 60, 63, 62, 71, 68, 76, 73, 59, 61, 62, 56, 74, 73, 78, 76, 79, 79, 70, 70, 68, 67, 65, 67, 76, 75, 63, 61, 61, 56, 76, 75, 74, 77, 76, 74, 59, 68
2
+ 1, 75, 74, 71, 71, 62, 58, 70, 64, 71, 68, 76, 68, 71, 71, 58, 58, 70, 69, 70, 72, 75, 73, 74, 72, 66, 60, 63, 66, 70, 64, 75, 70, 64, 62, 66, 62, 68, 69, 69, 66, 64, 58, 57, 52
3
+ 1, 83, 64, 66, 67, 67, 74, 74, 72, 64, 68, 75, 73, 78, 73, 72, 57, 71, 67, 73, 65, 78, 73, 76, 69, 63, 57, 63, 53, 67, 60, 77, 74, 69, 64, 67, 64, 69, 63, 68, 54, 65, 64, 43, 42
4
+ 1, 72, 66, 65, 65, 64, 61, 71, 78, 73, 69, 68, 65, 62, 65, 66, 66, 72, 74, 67, 61, 77, 71, 68, 65, 64, 60, 73, 69, 70, 69, 74, 72, 61, 63, 69, 68, 68, 63, 71, 72, 65, 63, 58, 60
5
+ 1, 62, 60, 69, 61, 63, 63, 70, 68, 70, 65, 77, 56, 71, 65, 69, 68, 74, 78, 77, 70, 80, 73, 79, 75, 76, 67, 74, 69, 66, 71, 70, 61, 54, 54, 66, 66, 58, 56, 72, 73, 71, 64, 49, 42
6
+ 1, 68, 63, 67, 67, 65, 72, 74, 72, 70, 71, 79, 71, 72, 67, 68, 69, 75, 79, 67, 65, 78, 69, 72, 67, 64, 59, 67, 65, 73, 70, 80, 69, 63, 61, 70, 70, 70, 67, 77, 71, 77, 72, 68, 59
7
+ 1, 80, 76, 77, 76, 67, 68, 71, 76, 69, 66, 76, 78, 61, 71, 58, 61, 61, 68, 78, 72, 72, 75, 61, 66, 58, 62, 69, 66, 55, 40, 70, 71, 67, 58, 57, 58, 62, 65, 59, 45, 53, 58, 54, 55
8
+ 1, 68, 63, 62, 58, 60, 57, 69, 78, 59, 53, 61, 58, 48, 50, 52, 50, 72, 70, 59, 59, 71, 77, 50, 49, 53, 44, 76, 74, 64, 66, 68, 63, 52, 52, 66, 67, 74, 70, 77, 74, 66, 60, 59, 56
9
+ 1, 77, 61, 71, 69, 70, 66, 57, 55, 67, 67, 79, 72, 79, 71, 64, 54, 62, 63, 39, 47, 75, 76, 67, 71, 63, 44, 22, 25, 21, 18, 72, 79, 57, 58, 48, 40, 41, 39, 19, 16, 68, 72, 56, 65
10
+ 1, 69, 68, 73, 74, 62, 67, 74, 73, 67, 65, 70, 75, 64, 68, 58, 57, 66, 70, 57, 63, 68, 71, 68, 69, 40, 38, 55, 59, 67, 67, 76, 78, 65, 67, 61, 65, 76, 79, 74, 73, 64, 56, 53, 45
11
+ 1, 65, 69, 70, 71, 56, 63, 63, 70, 44, 51, 78, 74, 48, 49, 49, 52, 58, 60, 49, 51, 80, 65, 51, 48, 58, 43, 64, 62, 71, 70, 78, 73, 71, 57, 71, 73, 68, 64, 62, 59, 59, 64, 53, 55
12
+ 1, 65, 63, 73, 75, 67, 61, 76, 65, 62, 62, 79, 75, 74, 70, 57, 52, 62, 62, 62, 63, 78, 72, 57, 50, 39, 36, 51, 51, 41, 43, 64, 70, 56, 54, 50, 46, 66, 67, 49, 43, 43, 43, 41, 43
13
+ 1, 71, 73, 65, 80, 62, 67, 65, 77, 66, 68, 72, 80, 71, 74, 58, 62, 67, 73, 73, 73, 83, 78, 66, 77, 60, 59, 61, 61, 53, 46, 73, 75, 67, 65, 62, 65, 62, 66, 62, 60, 61, 68, 43, 56
14
+ 1, 64, 75, 52, 46, 59, 54, 79, 78, 61, 68, 61, 71, 36, 28, 59, 58, 67, 74, 51, 55, 57, 78, 31, 32, 40, 43, 66, 78, 49, 49, 47, 39, 31, 27, 59, 75, 51, 73, 60, 56, 41, 21, 33, 22
15
+ 1, 62, 54, 65, 65, 24, 31, 79, 66, 53, 54, 76, 72, 47, 50, 30, 25, 73, 74, 62, 55, 76, 78, 56, 47, 27, 34, 56, 78, 68, 68, 66, 68, 60, 60, 66, 68, 55, 51, 68, 71, 31, 29, 27, 18
16
+ 1, 57, 43, 62, 50, 43, 57, 61, 44, 44, 24, 53, 36, 60, 69, 58, 61, 30, 32, 61, 63, 75, 72, 62, 71, 42, 44, 67, 74, 29, 19, 76, 77, 65, 66, 41, 41, 57, 46, 27, 12, 41, 52, 42, 49
17
+ 1, 67, 65, 61, 61, 60, 62, 75, 74, 68, 68, 72, 75, 70, 75, 63, 68, 72, 75, 64, 70, 76, 75, 70, 70, 67, 63, 66, 67, 69, 66, 77, 77, 64, 66, 68, 69, 70, 69, 75, 66, 71, 70, 57, 63
18
+ 1, 62, 54, 73, 68, 72, 71, 76, 76, 62, 52, 66, 58, 73, 65, 70, 70, 73, 73, 59, 52, 65, 59, 70, 55, 72, 65, 68, 70, 69, 66, 69, 66, 61, 51, 69, 72, 60, 54, 74, 68, 73, 67, 56, 52
19
+ 1, 67, 59, 54, 48, 63, 67, 77, 78, 71, 61, 57, 57, 59, 50, 72, 72, 77, 77, 76, 73, 85, 67, 58, 50, 39, 33, 54, 50, 57, 50, 45, 41, 38, 43, 76, 77, 80, 69, 61, 48, 63, 61, 37, 36
20
+ 1, 65, 56, 67, 58, 76, 79, 70, 71, 59, 50, 76, 61, 72, 64, 69, 73, 71, 68, 67, 50, 75, 65, 71, 62, 63, 70, 66, 55, 59, 52, 74, 60, 59, 58, 68, 63, 54, 38, 57, 52, 71, 74, 59, 65
21
+ 1, 71, 56, 75, 74, 61, 69, 73, 73, 65, 63, 71, 69, 70, 74, 61, 64, 70, 66, 70, 69, 76, 81, 62, 60, 39, 39, 72, 72, 58, 64, 71, 71, 56, 55, 62, 61, 70, 75, 57, 63, 41, 61, 34, 40
22
+ 1, 73, 59, 76, 61, 67, 52, 66, 46, 70, 59, 76, 72, 71, 75, 63, 33, 68, 49, 68, 49, 73, 66, 70, 58, 30, 14, 54, 41, 54, 41, 76, 66, 77, 57, 62, 26, 74, 57, 58, 47, 40, 9, 28, 19
23
+ 1, 77, 69, 77, 79, 60, 59, 65, 68, 57, 54, 76, 76, 60, 66, 39, 36, 63, 65, 54, 60, 75, 71, 59, 63, 43, 38, 64, 49, 53, 42, 67, 73, 62, 60, 55, 52, 59, 58, 55, 43, 41, 37, 39, 26
24
+ 1, 66, 65, 72, 74, 59, 61, 67, 65, 59, 58, 70, 72, 71, 67, 58, 52, 64, 63, 73, 71, 78, 59, 75, 72, 54, 45, 66, 64, 61, 60, 73, 74, 58, 57, 60, 59, 74, 71, 75, 74, 70, 66, 63, 61
25
+ 1, 69, 80, 67, 67, 62, 61, 65, 67, 62, 71, 77, 75, 72, 77, 56, 54, 65, 60, 57, 63, 80, 69, 71, 72, 51, 39, 57, 50, 64, 63, 75, 70, 59, 56, 60, 55, 65, 68, 66, 67, 67, 68, 54, 54
26
+ 1, 74, 67, 69, 75, 59, 57, 70, 68, 70, 62, 79, 76, 74, 70, 64, 56, 71, 73, 72, 64, 81, 72, 75, 66, 60, 57, 68, 62, 59, 38, 74, 66, 71, 60, 67, 59, 66, 58, 70, 50, 45, 31, 36, 35
27
+ 1, 66, 70, 78, 75, 69, 64, 70, 66, 66, 69, 75, 76, 66, 65, 54, 52, 61, 57, 68, 65, 79, 69, 67, 63, 51, 50, 54, 43, 41, 49, 71, 74, 67, 67, 54, 52, 76, 72, 65, 69, 69, 64, 58, 56
28
+ 1, 79, 58, 73, 78, 67, 65, 74, 71, 64, 54, 76, 68, 72, 69, 70, 64, 69, 64, 63, 53, 74, 64, 73, 67, 60, 57, 58, 51, 65, 68, 76, 75, 66, 63, 66, 62, 66, 58, 71, 67, 71, 68, 62, 60
29
+ 1, 61, 66, 64, 65, 62, 66, 74, 67, 60, 62, 65, 64, 69, 70, 70, 67, 75, 73, 69, 69, 76, 78, 77, 80, 76, 76, 69, 73, 65, 73, 69, 63, 53, 58, 68, 65, 62, 60, 73, 73, 67, 63, 58, 61
30
+ 1, 63, 69, 70, 72, 66, 70, 66, 72, 71, 67, 74, 77, 70, 71, 67, 69, 69, 68, 75, 73, 73, 77, 67, 66, 62, 59, 62, 58, 61, 65, 74, 75, 63, 67, 65, 67, 71, 71, 68, 70, 74, 72, 56, 60
31
+ 1, 73, 67, 74, 73, 79, 75, 76, 77, 61, 60, 70, 73, 76, 77, 71, 71, 65, 65, 69, 64, 75, 76, 78, 80, 72, 69, 61, 59, 71, 68, 76, 79, 68, 67, 71, 67, 63, 59, 68, 66, 74, 71, 59, 57
32
+ 1, 66, 68, 67, 72, 65, 71, 71, 71, 60, 67, 69, 78, 75, 72, 68, 66, 69, 68, 77, 77, 81, 80, 68, 69, 70, 65, 66, 65, 62, 66, 74, 76, 59, 62, 64, 63, 67, 75, 70, 75, 74, 76, 62, 65
33
+ 1, 55, 54, 71, 74, 21, 25, 60, 27, 69, 64, 74, 79, 64, 70, 58, 38, 72, 64, 63, 62, 77, 75, 61, 58, 42, 37, 64, 54, 20, 15, 66, 73, 66, 67, 43, 36, 77, 59, 24, 35, 18, 27, 29, 22
34
+ 1, 70, 68, 68, 70, 56, 54, 66, 67, 68, 70, 75, 73, 59, 59, 60, 62, 75, 71, 50, 45, 55, 55, 61, 61, 46, 49, 55, 52, 66, 72, 66, 65, 62, 65, 68, 70, 69, 69, 67, 70, 56, 57, 56, 52
35
+ 1, 71, 71, 77, 82, 64, 63, 73, 71, 66, 64, 72, 79, 66, 59, 57, 57, 70, 63, 71, 68, 78, 79, 67, 65, 63, 63, 70, 67, 68, 70, 75, 77, 60, 62, 70, 67, 77, 74, 71, 75, 72, 67, 62, 60
36
+ 1, 57, 44, 74, 68, 82, 76, 78, 62, 66, 67, 74, 72, 71, 70, 67, 69, 68, 68, 69, 74, 75, 71, 73, 71, 69, 65, 58, 63, 54, 63, 68, 74, 73, 76, 66, 65, 63, 66, 71, 71, 71, 68, 66, 60
37
+ 1, 72, 71, 70, 72, 63, 69, 71, 73, 71, 71, 79, 75, 71, 71, 65, 74, 68, 74, 71, 71, 70, 78, 62, 67, 62, 60, 59, 59, 69, 66, 75, 75, 57, 57, 66, 69, 68, 67, 69, 64, 58, 62, 52, 54
38
+ 1, 60, 58, 74, 68, 44, 48, 85, 60, 56, 48, 71, 70, 46, 43, 57, 43, 75, 60, 62, 44, 69, 52, 57, 42, 20, 23, 58, 60, 38, 41, 67, 70, 64, 68, 59, 47, 69, 55, 65, 55, 19, 47, 18, 17
39
+ 1, 33, 21, 38, 54, 60, 52, 41, 50, 26, 29, 58, 58, 76, 71, 59, 68, 17, 13, 75, 73, 73, 78, 65, 67, 62, 47, 59, 57, 23, 26, 73, 74, 52, 58, 40, 38, 49, 45, 11, 13, 62, 64, 46, 40
40
+ 1, 58, 70, 62, 73, 34, 41, 55, 54, 65, 77, 73, 78, 65, 65, 33, 48, 57, 64, 69, 67, 68, 64, 61, 58, 60, 59, 73, 70, 45, 40, 71, 74, 52, 56, 59, 62, 67, 75, 36, 39, 22, 19, 29, 24
41
+ 1, 58, 40, 57, 43, 74, 57, 78, 53, 68, 58, 65, 54, 75, 50, 59, 73, 80, 77, 60, 59, 61, 61, 55, 56, 48, 63, 75, 77, 13, 7, 47, 52, 54, 58, 51, 31, 53, 18, 17, 6, 15, 69, 30, 40
42
+ 1, 77, 77, 70, 71, 68, 66, 75, 75, 73, 72, 71, 77, 66, 65, 60, 58, 70, 70, 71, 74, 80, 79, 64, 64, 60, 55, 70, 68, 76, 74, 75, 75, 65, 65, 73, 73, 71, 70, 73, 73, 63, 62, 52, 52
43
+ 1, 65, 67, 67, 61, 55, 57, 66, 74, 64, 69, 61, 62, 71, 71, 53, 53, 64, 71, 65, 66, 71, 73, 65, 70, 47, 39, 80, 75, 67, 64, 72, 66, 53, 55, 63, 64, 60, 61, 74, 63, 59, 58, 50, 49
44
+ 1, 77, 59, 76, 78, 40, 34, 75, 63, 65, 26, 70, 28, 50, 46, 25, 66, 46, 52, 63, 63, 68, 56, 46, 47, 12, 6, 28, 23, 56, 48, 68, 55, 63, 54, 40, 20, 65, 68, 56, 57, 5, 10, 13, 10
45
+ 1, 64, 59, 76, 72, 55, 52, 63, 66, 68, 63, 75, 72, 70, 64, 60, 57, 75, 68, 62, 59, 69, 73, 74, 67, 49, 42, 66, 51, 58, 48, 75, 73, 76, 75, 64, 62, 73, 70, 72, 51, 71, 42, 61, 39
46
+ 1, 57, 63, 67, 82, 69, 67, 63, 70, 74, 64, 71, 71, 72, 73, 59, 54, 65, 63, 79, 62, 77, 69, 70, 63, 54, 50, 61, 53, 59, 60, 72, 74, 72, 59, 64, 54, 70, 65, 73, 70, 64, 58, 60, 57
47
+ 1, 58, 57, 74, 71, 59, 60, 54, 59, 33, 39, 65, 74, 71, 75, 53, 57, 47, 60, 43, 44, 75, 78, 54, 57, 56, 56, 57, 61, 60, 62, 77, 76, 59, 57, 53, 60, 53, 57, 61, 74, 77, 82, 66, 68
48
+ 1, 61, 66, 64, 74, 51, 55, 64, 63, 67, 67, 73, 76, 69, 67, 55, 53, 70, 67, 65, 61, 70, 66, 70, 62, 43, 35, 68, 65, 61, 56, 70, 73, 59, 62, 64, 62, 67, 68, 55, 61, 53, 42, 37, 28
49
+ 1, 67, 80, 73, 76, 74, 69, 78, 81, 67, 68, 72, 79, 74, 77, 65, 65, 64, 66, 71, 74, 79, 81, 68, 69, 59, 63, 62, 63, 67, 66, 73, 73, 61, 65, 65, 66, 65, 72, 69, 63, 70, 67, 58, 56
50
+ 1, 69, 55, 75, 73, 63, 64, 70, 66, 61, 75, 77, 75, 74, 76, 63, 61, 73, 75, 49, 67, 72, 69, 66, 74, 48, 48, 53, 66, 64, 68, 71, 71, 60, 64, 62, 68, 67, 74, 65, 70, 63, 64, 50, 52
51
+ 1, 77, 63, 68, 70, 66, 70, 80, 79, 60, 57, 75, 68, 63, 66, 63, 69, 69, 70, 36, 34, 48, 47, 72, 77, 62, 66, 62, 62, 57, 69, 73, 75, 62, 63, 69, 71, 56, 53, 74, 76, 72, 73, 64, 63
52
+ 1, 46, 47, 65, 68, 41, 48, 52, 45, 54, 47, 74, 72, 63, 63, 47, 45, 31, 36, 60, 65, 66, 79, 57, 60, 49, 52, 45, 45, 31, 36, 70, 71, 63, 72, 34, 34, 66, 78, 26, 35, 60, 43, 52, 51
53
+ 1, 67, 74, 73, 74, 54, 63, 58, 72, 59, 71, 75, 82, 59, 77, 38, 38, 56, 70, 60, 70, 70, 84, 63, 67, 33, 38, 57, 69, 62, 65, 71, 72, 54, 60, 57, 61, 53, 73, 58, 67, 49, 56, 27, 36
54
+ 1, 73, 70, 69, 75, 62, 62, 67, 70, 70, 76, 75, 74, 73, 77, 63, 58, 68, 71, 64, 66, 76, 74, 67, 69, 56, 44, 65, 65, 68, 69, 72, 71, 57, 61, 62, 65, 62, 66, 66, 62, 54, 56, 44, 35
55
+ 1, 68, 77, 67, 70, 64, 62, 79, 80, 64, 64, 76, 74, 65, 64, 64, 60, 62, 67, 74, 75, 82, 77, 75, 74, 72, 73, 72, 71, 67, 69, 75, 73, 64, 62, 63, 65, 68, 70, 77, 69, 72, 71, 65, 57
56
+ 1, 57, 70, 65, 71, 58, 69, 74, 81, 67, 72, 75, 78, 69, 71, 65, 63, 71, 75, 61, 68, 72, 78, 66, 72, 41, 38, 48, 48, 63, 68, 69, 77, 62, 67, 65, 69, 70, 74, 77, 81, 67, 69, 55, 60
57
+ 1, 47, 47, 48, 69, 80, 52, 52, 52, 58, 46, 45, 48, 42, 59, 40, 38, 62, 54, 63, 69, 64, 63, 79, 70, 33, 19, 49, 46, 44, 43, 49, 57, 43, 75, 49, 27, 36, 39, 47, 57, 14, 20, 21, 4
58
+ 1, 64, 75, 74, 73, 57, 61, 63, 70, 63, 72, 75, 81, 73, 77, 36, 38, 63, 73, 63, 65, 66, 63, 74, 77, 29, 28, 53, 52, 64, 68, 68, 73, 59, 61, 64, 66, 58, 69, 56, 73, 37, 38, 24, 18
59
+ 1, 58, 66, 63, 61, 66, 68, 75, 79, 61, 63, 57, 62, 66, 66, 59, 59, 75, 74, 62, 65, 78, 76, 72, 72, 60, 60, 64, 65, 64, 70, 68, 68, 59, 67, 67, 66, 58, 57, 58, 73, 48, 59, 39, 51
60
+ 1, 73, 71, 71, 68, 64, 60, 72, 76, 75, 70, 77, 76, 67, 67, 65, 59, 69, 69, 70, 69, 77, 73, 64, 58, 56, 51, 58, 57, 71, 68, 75, 77, 72, 68, 72, 70, 72, 69, 72, 64, 62, 61, 53, 53
61
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62
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148
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150
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151
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152
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154
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155
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156
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157
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158
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159
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160
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161
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162
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163
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164
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165
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166
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167
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168
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169
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170
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171
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178
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179
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180
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181
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182
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183
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184
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185
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186
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187
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SPECTF.train ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 1,59,52,70,67,73,66,72,61,58,52,72,71,70,77,66,65,67,55,61,57,68,66,72,74,63,64,56,54,67,54,76,74,65,67,66,56,62,56,72,62,74,74,64,67
2
+ 1,72,62,69,67,78,82,74,65,69,63,70,70,72,74,70,71,72,75,66,65,73,78,74,79,74,69,69,70,71,69,72,70,62,65,65,71,63,60,69,73,67,71,56,58
3
+ 1,71,62,70,64,67,64,79,65,70,69,72,71,68,65,61,61,73,71,75,74,80,74,54,47,53,37,77,68,72,59,72,68,60,60,73,70,66,65,64,55,61,41,51,46
4
+ 1,69,71,70,78,61,63,67,65,59,59,66,69,71,75,65,58,60,55,62,59,67,66,74,74,64,60,57,54,70,73,69,76,62,64,61,61,66,65,72,73,68,68,59,63
5
+ 1,70,66,61,66,61,58,69,69,72,68,62,71,71,71,63,59,74,75,70,69,83,77,73,70,41,37,39,40,58,46,75,73,65,66,67,69,70,66,70,64,60,55,49,41
6
+ 1,57,69,68,75,69,74,73,71,57,61,72,74,73,69,61,58,60,55,71,62,79,70,77,71,65,63,69,55,61,68,75,74,63,64,63,58,69,67,79,77,72,70,61,65
7
+ 1,69,66,62,75,67,71,72,76,69,70,66,69,71,80,66,64,71,77,65,61,72,67,71,69,65,57,69,65,68,65,76,73,63,64,69,70,72,72,69,68,70,73,63,59
8
+ 1,61,60,60,62,64,72,68,67,74,68,76,70,74,71,76,74,74,70,75,66,69,62,65,60,66,65,68,59,64,59,72,65,55,56,66,66,66,60,60,58,60,67,49,52
9
+ 1,65,62,67,68,65,67,71,71,64,56,73,72,68,69,56,57,67,62,74,66,80,76,80,78,53,47,48,36,68,65,74,73,60,60,67,63,74,63,77,79,68,70,59,56
10
+ 1,74,73,72,79,66,61,76,66,65,64,78,74,62,57,48,36,62,50,67,63,79,70,61,57,52,36,69,49,55,65,74,73,58,60,64,62,73,69,62,67,60,56,53,46
11
+ 1,70,69,60,62,58,60,71,77,69,69,73,68,68,70,69,65,76,75,63,64,67,74,56,60,54,44,68,69,68,68,74,73,61,59,68,67,64,68,64,76,64,61,54,49
12
+ 1,67,66,65,77,66,70,72,72,72,67,76,72,73,76,74,71,74,73,69,61,78,70,76,73,70,70,62,51,70,68,79,77,75,68,72,71,69,63,65,61,73,73,64,67
13
+ 1,76,69,78,73,68,67,75,70,77,70,79,73,79,75,74,71,76,68,81,79,77,78,75,76,66,76,65,67,67,57,68,75,50,62,62,59,47,49,75,65,74,70,51,48
14
+ 1,70,69,67,66,68,60,76,77,70,67,71,71,79,79,70,64,77,76,63,54,68,65,72,67,59,52,56,50,67,61,74,72,67,59,68,66,73,68,74,68,77,69,65,62
15
+ 1,78,73,68,74,68,69,63,74,68,67,73,73,66,71,64,67,66,68,61,66,75,71,60,62,64,66,65,67,66,62,74,75,61,61,63,66,68,65,71,62,69,67,61,59
16
+ 1,67,51,73,65,69,56,72,63,64,56,68,67,70,62,70,58,68,62,65,59,77,69,68,59,64,55,65,60,70,60,72,65,58,51,66,59,71,62,74,60,76,65,62,56
17
+ 1,70,54,66,66,76,46,74,58,68,52,81,58,67,58,68,32,73,59,76,51,82,57,76,54,58,30,69,41,59,59,67,73,62,55,60,55,65,56,65,44,73,36,51,28
18
+ 1,63,63,69,72,67,62,65,57,68,53,68,71,73,78,64,58,61,54,70,61,72,67,72,68,57,55,61,53,66,60,76,77,73,66,66,58,75,70,77,67,78,68,64,58
19
+ 1,62,56,66,57,74,75,68,59,65,59,74,66,71,65,67,69,66,66,71,72,80,74,71,63,62,72,62,66,66,61,73,68,59,59,63,62,73,73,76,67,77,71,62,58
20
+ 1,80,74,82,77,74,74,73,77,64,61,73,73,73,72,62,66,66,68,63,61,69,75,70,75,59,64,63,69,66,66,70,77,62,62,60,65,67,66,74,71,66,71,59,62
21
+ 1,63,58,66,55,56,58,69,74,44,48,63,60,76,67,73,73,58,66,68,63,72,74,70,72,77,75,70,71,71,67,75,73,60,59,71,70,65,62,70,69,71,70,58,61
22
+ 1,70,65,65,62,68,67,77,74,62,61,66,61,69,74,64,62,71,71,74,70,77,78,69,70,67,65,67,70,49,48,73,71,65,73,73,73,75,71,73,72,73,70,65,64
23
+ 1,61,63,58,62,56,60,67,75,61,57,64,71,56,59,66,62,73,76,71,75,83,84,69,71,69,69,69,71,49,43,59,64,56,61,64,72,70,73,69,70,65,68,65,62
24
+ 1,70,64,52,58,75,89,70,72,26,30,46,55,54,59,40,40,39,37,35,17,59,52,66,72,23,46,8,31,17,20,49,72,61,70,31,13,40,23,31,30,57,67,41,57
25
+ 1,75,71,54,51,53,50,68,69,46,55,11,12,43,48,61,60,73,77,46,45,68,59,65,73,54,60,55,66,54,41,53,52,58,63,72,65,33,23,64,54,36,46,45,52
26
+ 1,77,61,62,68,62,58,72,68,77,71,76,77,72,75,62,57,77,74,61,58,72,76,69,68,56,53,57,54,69,70,73,79,65,70,66,68,67,66,71,67,60,60,53,57
27
+ 1,75,72,75,79,72,68,79,77,69,66,73,77,67,73,57,58,69,69,67,65,77,68,69,65,58,54,68,60,67,66,75,78,63,66,68,64,72,69,73,61,52,44,34,37
28
+ 1,78,76,71,72,65,71,75,74,70,64,65,76,65,73,59,57,65,65,73,73,81,80,68,66,59,44,62,63,62,59,71,74,59,60,64,61,77,76,62,67,44,42,44,30
29
+ 1,69,68,75,74,78,72,75,72,61,57,72,71,75,72,70,68,68,62,62,66,67,67,74,78,64,68,62,62,64,63,75,77,66,67,69,65,62,63,64,59,74,75,63,67
30
+ 1,72,66,75,67,61,59,64,63,61,67,75,76,66,48,61,56,69,68,68,68,68,75,69,67,68,71,70,68,48,47,74,79,63,75,62,62,64,67,56,52,69,83,59,73
31
+ 1,64,64,70,75,70,71,74,71,59,60,62,68,70,66,69,72,69,69,61,63,56,60,62,66,69,71,62,63,67,65,62,58,52,51,67,66,61,56,64,65,71,73,57,63
32
+ 1,72,63,68,62,72,63,79,61,57,49,76,75,55,57,43,37,54,52,57,56,78,78,57,55,35,37,57,57,41,35,67,70,75,72,53,46,63,62,53,43,38,35,32,26
33
+ 1,79,78,66,63,69,62,78,70,72,71,73,78,75,65,68,62,76,71,68,68,72,71,52,48,23,26,66,59,66,66,72,74,56,58,67,63,66,69,70,74,34,33,11,12
34
+ 1,66,81,75,72,69,67,74,81,67,73,76,75,69,64,58,57,74,74,56,62,74,78,55,50,37,38,65,73,61,61,73,73,61,63,67,66,60,71,46,58,35,37,24,20
35
+ 1,65,66,71,72,67,75,76,83,70,74,70,76,70,68,63,71,69,76,72,73,76,80,69,68,57,59,59,56,62,68,75,73,65,61,69,73,66,70,63,65,65,67,53,42
36
+ 1,71,75,76,74,71,68,67,68,69,75,75,74,59,58,71,69,70,74,74,72,71,78,69,72,69,72,63,61,59,70,72,75,61,66,70,71,59,64,64,60,72,61,55,63
37
+ 1,70,66,66,68,71,69,64,61,68,67,50,53,73,71,73,63,71,73,80,81,82,82,67,71,52,47,67,64,66,67,66,75,58,62,65,65,71,67,70,71,67,64,52,53
38
+ 1,73,76,68,74,56,59,73,76,54,48,75,78,47,53,25,19,60,56,56,54,80,79,47,53,19,14,58,50,67,71,63,54,49,48,66,65,62,58,57,72,31,30,15,11
39
+ 1,68,76,79,78,63,73,68,78,64,71,73,77,67,71,58,57,61,63,52,64,64,74,53,72,36,44,52,54,49,56,73,81,65,80,53,60,63,70,58,64,52,57,49,50
40
+ 1,68,64,65,68,63,64,77,73,75,72,80,77,70,71,61,61,73,68,63,62,76,73,69,69,48,59,62,44,66,59,75,74,64,64,63,61,70,69,74,67,51,48,45,45
41
+ 0,62,67,64,70,59,58,67,74,60,66,68,68,73,71,60,63,64,74,64,65,74,77,69,73,59,58,58,67,65,69,78,76,61,62,64,67,72,74,71,71,71,69,66,61
42
+ 0,62,67,68,70,65,70,73,77,69,70,69,73,71,74,71,71,76,75,66,67,73,73,70,74,63,67,58,68,66,69,78,79,69,70,71,73,72,71,73,77,72,76,64,66
43
+ 0,59,68,69,67,69,59,78,73,66,65,77,73,74,66,66,55,71,66,69,68,75,73,80,79,69,65,69,66,68,65,75,71,59,61,65,64,73,71,81,75,74,65,69,66
44
+ 0,75,75,70,77,67,75,75,75,67,66,74,73,68,72,64,70,76,70,67,63,74,75,72,68,69,68,75,69,71,74,75,76,63,70,71,69,66,63,70,73,66,68,58,59
45
+ 0,77,79,79,77,74,76,76,81,65,68,66,66,74,73,72,68,67,73,63,62,72,67,76,69,68,64,64,61,69,68,73,75,70,66,64,70,70,70,73,76,79,73,65,63
46
+ 0,68,64,74,80,76,72,78,75,67,64,75,80,78,77,66,64,67,67,70,60,78,82,70,68,63,60,64,60,54,56,70,73,59,65,55,58,50,51,73,70,69,65,42,41
47
+ 0,76,73,74,76,60,69,76,76,68,69,78,79,57,62,69,69,67,66,73,69,80,81,58,68,75,69,73,70,58,65,79,76,74,71,66,64,65,62,78,68,75,68,62,60
48
+ 0,61,76,71,68,77,69,77,69,64,75,71,81,75,72,71,69,70,73,61,71,69,79,64,65,62,66,61,65,71,68,67,71,59,64,66,65,60,68,74,71,69,68,63,59
49
+ 0,67,65,77,74,67,66,67,70,65,64,75,78,66,74,62,60,65,65,73,72,75,76,74,81,66,65,65,63,63,67,76,80,63,64,63,64,73,72,76,75,72,74,65,64
50
+ 0,71,61,74,74,76,74,69,56,68,78,71,78,58,64,70,72,71,68,72,71,79,78,67,68,63,60,67,67,76,74,67,79,67,71,71,64,70,74,83,76,74,73,54,54
51
+ 0,64,70,71,69,72,70,75,78,61,66,69,68,68,70,71,70,75,76,73,72,80,78,79,81,74,70,72,79,73,75,77,73,65,64,72,72,59,62,71,74,68,67,58,57
52
+ 0,76,75,68,78,71,72,72,75,61,65,67,70,67,75,60,58,63,67,59,63,67,72,74,73,56,56,52,52,67,68,73,78,65,68,61,67,69,74,77,75,74,70,63,61
53
+ 0,74,73,72,75,63,62,67,67,73,74,75,79,70,71,64,67,65,69,79,78,81,80,71,73,60,62,69,67,69,69,75,75,66,67,67,66,71,73,66,69,62,65,55,56
54
+ 0,65,67,69,76,62,68,65,66,65,64,74,73,60,75,66,63,64,62,73,65,77,74,69,69,66,59,68,59,69,69,76,79,65,63,60,60,69,64,69,74,69,70,62,57
55
+ 0,59,75,70,76,62,70,65,74,65,67,75,76,70,73,63,61,74,67,78,69,75,73,70,68,67,64,79,68,70,75,76,77,59,63,72,69,64,64,65,72,61,61,51,55
56
+ 0,76,72,73,69,67,73,74,72,60,65,73,66,66,73,68,67,69,70,60,58,66,76,69,75,65,64,63,60,74,71,77,79,61,68,71,70,62,63,73,76,62,69,52,59
57
+ 0,71,75,78,78,68,67,75,72,67,68,72,75,74,74,67,66,66,67,66,66,78,80,73,75,67,72,67,67,67,65,77,78,61,64,63,66,51,57,77,67,78,76,60,59
58
+ 0,80,76,75,75,69,68,74,75,77,77,76,78,74,70,66,65,67,75,74,73,74,77,68,67,61,58,60,67,61,63,75,75,66,62,59,61,77,74,69,67,65,66,61,58
59
+ 0,68,70,66,72,63,71,77,82,61,63,61,62,61,65,65,62,72,77,69,73,72,78,74,77,69,69,77,74,64,63,66,70,58,60,65,69,75,77,77,77,69,77,65,64
60
+ 0,67,57,73,78,63,68,72,73,61,59,59,76,71,72,69,66,70,68,65,77,79,68,71,75,62,66,70,78,68,69,70,76,65,65,66,67,62,72,69,72,70,68,60,59
61
+ 0,72,74,67,69,69,70,74,81,66,70,73,78,69,80,65,65,70,77,69,70,73,79,64,68,56,58,67,64,68,68,69,74,62,67,66,70,73,77,74,77,71,72,63,65
62
+ 0,62,71,78,84,64,68,72,74,53,57,71,70,71,72,54,53,63,67,54,57,71,71,70,74,54,57,54,57,62,64,75,72,62,65,60,64,64,67,72,77,62,62,61,65
63
+ 0,69,78,74,76,70,67,69,73,68,75,75,76,71,77,58,61,66,70,67,72,76,72,56,62,56,61,57,62,67,73,76,74,58,63,64,68,66,76,70,72,64,68,60,56
64
+ 0,59,65,53,60,72,74,67,69,64,67,64,65,71,70,68,71,72,70,70,70,75,78,72,71,71,71,67,64,67,71,71,74,62,66,72,73,57,57,64,71,70,69,53,49
65
+ 0,66,67,63,70,69,70,73,72,61,62,68,68,70,71,71,67,71,69,65,65,76,78,71,70,65,64,68,71,70,71,73,74,58,63,68,71,70,72,77,79,79,79,66,66
66
+ 0,62,66,66,68,73,76,68,71,62,62,63,68,74,75,63,68,71,68,58,58,65,66,65,76,64,70,63,60,70,72,71,77,67,69,72,71,63,63,70,72,75,79,62,59
67
+ 0,71,71,69,71,65,65,76,73,67,66,69,79,76,76,63,62,74,71,58,66,76,78,78,74,68,66,69,68,68,68,71,74,59,57,65,66,73,71,78,74,68,70,57,55
68
+ 0,63,61,75,72,68,71,69,70,64,56,70,75,76,73,70,72,76,73,68,68,79,76,68,76,66,73,58,68,72,75,72,75,65,61,66,67,58,57,72,72,68,71,57,58
69
+ 0,74,81,80,78,70,69,74,77,69,71,73,76,68,68,62,61,68,68,67,70,74,80,68,74,57,62,57,65,61,65,71,76,63,65,67,67,70,74,63,72,68,70,61,64
70
+ 0,69,64,73,72,49,70,66,71,57,56,64,62,76,74,65,62,63,58,63,63,75,76,78,80,75,77,51,62,74,68,77,77,70,68,68,64,59,58,69,66,74,75,62,59
71
+ 0,70,64,68,67,76,68,76,69,67,64,69,65,62,65,70,67,74,68,65,65,74,75,64,69,63,63,64,64,56,61,62,68,66,66,62,58,57,48,75,64,79,74,59,58
72
+ 0,65,68,70,78,65,72,72,74,64,69,71,73,72,68,62,62,65,62,72,75,79,78,72,76,66,67,62,61,68,76,72,78,65,64,67,63,64,67,67,77,66,66,59,57
73
+ 0,64,53,74,70,65,63,70,70,57,57,64,64,73,74,65,59,65,63,63,62,72,73,79,76,75,68,60,58,69,66,72,76,64,65,63,65,63,65,75,80,74,67,71,67
74
+ 0,70,71,71,74,68,66,72,70,66,69,74,72,70,69,64,66,73,72,75,73,81,81,76,75,71,70,74,82,69,72,76,74,60,56,66,66,64,64,73,72,66,63,54,58
75
+ 0,72,70,75,80,73,70,76,73,66,56,72,70,73,75,67,65,69,69,73,72,81,77,80,79,67,64,64,66,69,68,70,75,63,59,66,63,74,77,81,78,79,75,65,66
76
+ 0,70,75,72,72,67,71,71,78,63,67,73,76,71,74,59,61,67,64,74,71,77,77,70,72,61,61,62,58,63,69,76,75,64,65,66,67,68,70,70,71,64,67,56,54
77
+ 0,59,57,67,71,66,68,68,70,56,62,77,61,67,71,75,71,67,64,62,54,64,75,71,72,76,79,75,70,71,77,71,69,56,54,62,64,56,53,71,68,64,63,56,56
78
+ 0,67,64,73,75,77,77,74,70,65,62,74,75,65,67,68,70,66,69,67,60,74,75,62,64,66,71,62,61,64,69,73,76,64,66,61,64,65,60,68,75,74,80,67,68
79
+ 0,68,65,72,72,47,74,76,74,67,66,71,69,69,67,63,64,68,68,70,74,77,77,73,60,49,48,42,69,70,69,76,79,63,66,64,69,71,73,73,75,68,56,58,44
80
+ 0,66,54,69,66,69,69,75,72,63,62,68,66,68,70,71,68,70,69,66,68,73,72,65,73,67,63,60,57,70,68,75,75,65,67,69,65,65,64,67,69,71,68,59,59
spect.py ADDED
@@ -0,0 +1,150 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Spect"""
2
+
3
+ from typing import List
4
+
5
+ import datasets
6
+
7
+ import pandas
8
+
9
+
10
+ VERSION = datasets.Version("1.0.0")
11
+
12
+ DESCRIPTION = "Spect dataset from the UCI ML repository."
13
+ _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Spect"
14
+ _URLS = ("https://archive.ics.uci.edu/ml/datasets/Spect")
15
+ _CITATION = """
16
+ @misc{misc_spect_heart_95,
17
+ author = {Cios,Krzysztof, Kurgan,Lukasz & Goodenday,Lucy},
18
+ title = {{SPECT Heart}},
19
+ year = {2001},
20
+ howpublished = {UCI Machine Learning Repository},
21
+ note = {{DOI}: \\url{10.24432/C5P304}}
22
+ }"""
23
+
24
+ # Dataset info
25
+ urls_per_split = {
26
+ "spect": {
27
+ "train": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECT.train",
28
+ "test": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECT.test"
29
+ },
30
+ "spectf": {
31
+ "train": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECTF.train",
32
+ "test": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECTF.test"
33
+ }
34
+ }
35
+ features_types_per_config = {
36
+ "spect": {
37
+ "feature_0": datasets.Value("bool"),
38
+ "feature_1": datasets.Value("bool"),
39
+ "feature_2": datasets.Value("bool"),
40
+ "feature_3": datasets.Value("bool"),
41
+ "feature_4": datasets.Value("bool"),
42
+ "feature_5": datasets.Value("bool"),
43
+ "feature_6": datasets.Value("bool"),
44
+ "feature_7": datasets.Value("bool"),
45
+ "feature_8": datasets.Value("bool"),
46
+ "feature_9": datasets.Value("bool"),
47
+ "feature_10": datasets.Value("bool"),
48
+ "feature_11": datasets.Value("bool"),
49
+ "feature_12": datasets.Value("bool"),
50
+ "feature_13": datasets.Value("bool"),
51
+ "feature_14": datasets.Value("bool"),
52
+ "feature_15": datasets.Value("bool"),
53
+ "feature_16": datasets.Value("bool"),
54
+ "feature_17": datasets.Value("bool"),
55
+ "feature_18": datasets.Value("bool"),
56
+ "feature_19": datasets.Value("bool"),
57
+ "feature_20": datasets.Value("bool"),
58
+ "feature_21": datasets.Value("bool"),
59
+ "is_emitted": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
60
+ },
61
+ "spectf": {
62
+ "F1R": datasets.Value("int8"),
63
+ "F1S": datasets.Value("int8"),
64
+ "F2R": datasets.Value("int8"),
65
+ "F2S": datasets.Value("int8"),
66
+ "F3R": datasets.Value("int8"),
67
+ "F3S": datasets.Value("int8"),
68
+ "F4R": datasets.Value("int8"),
69
+ "F4S": datasets.Value("int8"),
70
+ "F5R": datasets.Value("int8"),
71
+ "F5S": datasets.Value("int8"),
72
+ "F6R": datasets.Value("int8"),
73
+ "F6S": datasets.Value("int8"),
74
+ "F7R": datasets.Value("int8"),
75
+ "F7S": datasets.Value("int8"),
76
+ "F8R": datasets.Value("int8"),
77
+ "F8S": datasets.Value("int8"),
78
+ "F9R": datasets.Value("int8"),
79
+ "F9S": datasets.Value("int8"),
80
+ "F10R": datasets.Value("int8"),
81
+ "F10S": datasets.Value("int8"),
82
+ "F11R": datasets.Value("int8"),
83
+ "F11S": datasets.Value("int8"),
84
+ "F12R": datasets.Value("int8"),
85
+ "F12S": datasets.Value("int8"),
86
+ "F13R": datasets.Value("int8"),
87
+ "F13S": datasets.Value("int8"),
88
+ "F14R": datasets.Value("int8"),
89
+ "F14S": datasets.Value("int8"),
90
+ "F15R": datasets.Value("int8"),
91
+ "F15S": datasets.Value("int8"),
92
+ "F16R": datasets.Value("int8"),
93
+ "F16S": datasets.Value("int8"),
94
+ "F17R": datasets.Value("int8"),
95
+ "F17S": datasets.Value("int8"),
96
+ "F18R": datasets.Value("int8"),
97
+ "F18S": datasets.Value("int8"),
98
+ "F19R": datasets.Value("int8"),
99
+ "F19S": datasets.Value("int8"),
100
+ "F20R": datasets.Value("int8"),
101
+ "F20S": datasets.Value("int8"),
102
+ "F21R": datasets.Value("int8"),
103
+ "F21S": datasets.Value("int8"),
104
+ "F22R": datasets.Value("int8"),
105
+ "F22S": datasets.Value("int8"),
106
+ "is_emitted": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
107
+ },
108
+
109
+ }
110
+ features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
111
+
112
+
113
+ class SpectConfig(datasets.BuilderConfig):
114
+ def __init__(self, **kwargs):
115
+ super(SpectConfig, self).__init__(version=VERSION, **kwargs)
116
+ self.features = features_per_config[kwargs["name"]]
117
+
118
+
119
+ class Spect(datasets.GeneratorBasedBuilder):
120
+ # dataset versions
121
+ DEFAULT_CONFIG = "spect"
122
+ BUILDER_CONFIGS = [
123
+ SpectConfig(name="spect",
124
+ description="Spect for binary classification."),
125
+ SpectConfig(name="spectf",
126
+ description="Spectf for binary classification.")
127
+ ]
128
+
129
+
130
+ def _info(self):
131
+ info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
132
+ features=features_per_config[self.config.name])
133
+
134
+ return info
135
+
136
+ def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
137
+ downloads = dl_manager.download_and_extract(urls_per_split)
138
+
139
+ return [
140
+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads[self.config.name]["train"]})
141
+ ]
142
+
143
+ def _generate_examples(self, filepath: str):
144
+ data = pandas.read_csv(filepath, header=None)
145
+ data.columns = [list(features_types_per_config[self.config.name])]
146
+
147
+ for row_id, row in data.iterrows():
148
+ data_row = dict(row)
149
+
150
+ yield row_id, data_row