Update E203 in main.yml

Create tests

.github/workflows/main.yml

@@ -46,7 +46,7 @@ jobs:
       - name: Lint
         run: |
           black --check --diff benchmark
-          flake8 --count benchmark
+          flake8 --count benchmark --ignore=E203
       - name: Tests
         run: |
           coverage run -m unittest -v benchmark.tests

benchmark/Datasets.py

@@ -80,7 +80,7 @@ class DatasetsSurcov:
 
 class Datasets:
     def __init__(self, dataset_name=None):
-        default_class = "class"
+
         envData = EnvData.load()
         class_name = getattr(
             __import__(__name__),
@@ -88,24 +88,32 @@ class Datasets:
         )
         self.dataset = class_name()
         self.class_names = []
-        if dataset_name is None:
-            file_name = os.path.join(self.dataset.folder(), Files.index)
-            with open(file_name) as f:
-                self.data_sets = f.read().splitlines()
-                self.class_names = [default_class] * len(self.data_sets)
-            if "," in self.data_sets[0]:
-                result = []
-                class_names = []
-                for data in self.data_sets:
-                    name, class_name = data.split(",")
-                    result.append(name)
-                    class_names.append(class_name)
-                self.data_sets = result
-                self.class_names = class_names
-
-        else:
+        self.load_names()
+        if dataset_name is not None:
+            try:
+                class_name = self.class_names[
+                    self.data_sets.index(dataset_name)
+                ]
+                self.class_names = [class_name]
+            except ValueError:
+                raise ValueError(f"Unknown dataset: {dataset_name}")
             self.data_sets = [dataset_name]
-            self.class_names = [default_class]
+
+    def load_names(self):
+        file_name = os.path.join(self.dataset.folder(), Files.index)
+        default_class = "class"
+        with open(file_name) as f:
+            self.data_sets = f.read().splitlines()
+            self.class_names = [default_class] * len(self.data_sets)
+        if "," in self.data_sets[0]:
+            result = []
+            class_names = []
+            for data in self.data_sets:
+                name, class_name = data.split(",")
+                result.append(name)
+                class_names.append(class_name)
+            self.data_sets = result
+            self.class_names = class_names
 
     def load(self, name):
         try:

benchmark/tests/.env.arff

@@ -0,0 +1,6 @@
+score=accuracy
+platform=MacBookpro16
+n_folds=5
+model=ODTE
+stratified=0
+source_data=Arff

benchmark/tests/Dataset_test.py

@@ -29,6 +29,7 @@ class DatasetTest(TestBase):
         test = {
             ".env.dist": ["balance-scale", "balloons"],
             ".env.surcov": ["iris", "wine"],
+            ".env.arff": ["iris", "wine"],
         }
         for key, value in test.items():
             self.set_env(key)
@@ -52,6 +53,11 @@ class DatasetTest(TestBase):
         self.assertSequenceEqual(X.shape, (625, 4))
         self.assertSequenceEqual(y.shape, (625,))
 
+    def test_create_with_unknown_dataset(self):
+        with self.assertRaises(ValueError) as msg:
+            Datasets("unknown")
+        self.assertEqual(str(msg.exception), "Unknown dataset: unknown")
+
     def test_load_unknown_dataset(self):
         dt = Datasets()
         with self.assertRaises(ValueError) as msg:
@@ -62,6 +68,7 @@ class DatasetTest(TestBase):
         test = {
             ".env.dist": "balloons",
             ".env.surcov": "wine",
+            ".env.arff": "iris",
         }
         for key, value in test.items():
             self.set_env(key)

benchmark/tests/TestBase.py

@@ -14,6 +14,7 @@ class TestBase(unittest.TestCase):
         os.chdir(os.path.dirname(os.path.abspath(__file__)))
         self.test_files = "test_files"
         self.output = "sys.stdout"
+        self.ext = ".test"
         super().__init__(*args, **kwargs)
 
     def remove_files(self, files, folder):
@@ -31,7 +32,7 @@ class TestBase(unittest.TestCase):
                         print(f'{row};{col};"{value}"', file=f)
 
     def check_excel_sheet(self, sheet, file_name):
-        file_name += ".test"
+        file_name += self.ext
         with open(os.path.join(self.test_files, file_name), "r") as f:
             expected = csv.reader(f, delimiter=";")
             for row, col, value in expected:
@@ -45,7 +46,7 @@ class TestBase(unittest.TestCase):
                 self.assertEqual(sheet.cell(int(row), int(col)).value, value)
 
     def check_output_file(self, output, file_name):
-        file_name += ".test"
+        file_name += self.ext
         with open(os.path.join(self.test_files, file_name)) as f:
             expected = f.read()
         self.assertEqual(output.getvalue(), expected)
@@ -58,7 +59,7 @@ class TestBase(unittest.TestCase):
     def check_file_file(self, computed_file, expected_file):
         with open(computed_file) as f:
             computed = f.read()
-        expected_file += ".test"
+        expected_file += self.ext
         with open(os.path.join(self.test_files, expected_file)) as f:
             expected = f.read()
         self.assertEqual(computed, expected)

benchmark/tests/datasets/all.txt

@@ -1,2 +1,2 @@
-iris
-wine
+iris,class
+wine,class

benchmark/tests/datasets/hayes-roth.arff

@@ -0,0 +1,305 @@
+% 1. Title: Hayes-Roth & Hayes-Roth (1977) Database
+% 
+% 2. Source Information:
+%    (a) Creators: Barbara and Frederick Hayes-Roth
+%    (b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779   
+%    (c) Date: March, 1989
+% 
+% 3. Past Usage:
+%     1. Hayes-Roth, B., & Hayes-Roth, F. (1977).  Concept learning and the
+%        recognition and classification of exemplars.  Journal of Verbal Learning
+%        and Verbal Behavior, 16, 321-338.
+%        -- Results: 
+%           -- Human subjects classification and recognition performance:
+% 	       1. decreases with distance from the prototype,
+% 	       2. is better on unseen prototypes than old instances, and
+% 	       3. improves with presentation frequency during learning.
+%     2. Anderson, J.R., & Kline, P.J. (1979).  A learning system and its 
+%        psychological implications.  In Proceedings of the Sixth International
+%        Joint Conference on Artificial Intelligence (pp. 16-21).  Tokyo, Japan:
+%        Morgan Kaufmann.
+%        -- Partitioned the results into 4 classes:
+% 	    1. prototypes
+% 	    2. near-prototypes with high presentation frequency during learning
+% 	    3. near-prototypes with low presentation frequency during learning
+% 	    4. instances that are far from protoypes
+%        -- Described evidence that ACT's classification confidence and
+%           recognition behaviors closely simulated human subjects' behaviors.
+%     3. Aha, D.W. (1989).  Incremental learning of independent, overlapping, and
+%        graded concept descriptions with an instance-based process framework.
+%        Manuscript submitted for publication.
+%        -- Used same partition as Anderson & Kline
+%        -- Described evidence that Bloom's classification confidence behavior
+% 	  is similar to the human subjects' behavior.  Bloom fitted the data
+% 	  more closely than did ACT. 
+% 
+% 4. Relevant Information:
+%      This database contains 5 numeric-valued attributes.  Only a subset of
+%      3 are used during testing (the latter 3).  Furthermore, only 2 of the
+%      3 concepts are "used" during testing (i.e., those with the prototypes
+%      000 and 111).  I've mapped all values to their zero-indexing equivalents.
+% 
+%      Some instances could be placed in either category 0 or 1.  I've followed
+%      the authors' suggestion, placing them in each category with equal
+%      probability.
+% 
+%      I've replaced the actual values of the attributes (i.e., hobby has values
+%      chess, sports and stamps) with numeric values.  I think this is how 
+%      the authors' did this when testing the categorization models described
+%      in the paper.  I find this unfair.  While the subjects were able to bring
+%      background knowledge to bear on the attribute values and their
+%      relationships, the algorithms were provided with no such knowledge.  I'm
+%      uncertain whether the 2 distractor attributes (name and hobby) are
+%      presented to the authors' algorithms during testing.  However, it is clear
+%      that only the age, educational status, and marital status attributes are
+%      given during the human subjects' transfer tests.  
+%     
+% 5. Number of Instances: 132 training instances, 28 test instances
+% 
+% 6. Number of Attributes: 5 plus the class membership attribute.  3 concepts.
+% 
+% 7. Attribute Information:
+%       -- 1. name: distinct for each instance and represented numerically
+%       -- 2. hobby: nominal values ranging between 1 and 3
+%       -- 3. age: nominal values ranging between 1 and 4
+%       -- 4. educational level: nominal values ranging between 1 and 4
+%       -- 5. marital status: nominal values ranging between 1 and 4
+%       -- 6. class: nominal value between 1 and 3
+% 
+% 9. Missing Attribute Values: none
+% 
+% 10. Class Distribution: see below
+% 
+% 11. Detailed description of the experiment:
+%   1. 3 categories (1, 2, and neither -- which I call 3)
+%      -- some of the instances could be classified in either class 1 or 2, and
+%         they have been evenly distributed between the two classes
+%   2. 5 Attributes
+%      -- A. name (a randomly-generated number between 1 and 132)
+%      -- B. hobby (a randomly-generated number between 1 and 3)
+%      -- C. age (a number between 1 and 4)
+%      -- D. education level (a number between 1 and 4)
+%      -- E. marital status (a number between 1 and 4)
+%   3. Classification: 
+%      -- only attributes C-E are diagnostic; values for A and B are ignored
+%      -- Class Neither: if a 4 occurs for any attribute C-E
+%      -- Class 1: Otherwise, if (# of 1's)>(# of 2's) for attributes C-E
+%      -- Class 2: Otherwise, if (# of 2's)>(# of 1's) for attributes C-E
+%      -- Either 1 or 2: Otherwise, if (# of 2's)=(# of 1's) for attributes C-E
+%   4. Prototypes:
+%      -- Class 1: 111
+%      -- Class 2: 222
+%      -- Class Either: 333
+%      -- Class Neither: 444  
+%   5. Number of training instances: 132
+%      -- Each instance presented 0, 1, or 10 times
+%      -- None of the prototypes seen during training
+%      -- 3 instances from each of categories 1, 2, and either are repeated 
+%         10 times each
+%      -- 3 additional instances from the Either category are shown during
+%         learning
+%   5. Number of test instances: 28
+%      -- All 9 class 1
+%      -- All 9 class 2
+%      -- All 6 class Either
+%      -- All 4 prototypes
+%      --------------------
+%      --    28 total
+% 
+% Observations of interest:
+%   1. Relative classification confidence of 
+%      -- prototypes for classes 1 and 2 (2 instances)
+%         (Anderson calls these Class 1 instances)
+%      -- instances of class 1 with frequency 10 during training and
+%         instances of class 2 with frequency 10 during training that
+%         are 1 value away from their respective prototypes (6 instances)
+%         (Anderson calls these Class 2 instances)
+%      -- instances of class 1 with frequency 1 during training and 
+%         instances of class 2 with frequency 1 during training that
+%         are 1 value away from their respective prototypes (6 instances)
+%         (Anderson calls these Class 3 instances)
+%      -- instances of class 1 with frequency 1 during training and 
+%         instances of class 2 with frequency 1 during training that
+%         are 2 values away from their respective prototypes (6 instances)
+%         (Anderson calls these Class 4 instances)
+%  2. Relative classification recognition of them also
+% 
+% Some Expected results:
+%    Both frequency and distance from prototype will effect the classification
+%    accuracy of instances.  Greater the frequency, higher the classification
+%    confidence.  Closer to prototype, higher the classification confidence.
+%
+% Information about the dataset
+% CLASSTYPE: nominal
+% CLASSINDEX: last
+%
+
+@relation hayes-roth
+
+@attribute hobby INTEGER
+@attribute age INTEGER
+@attribute educational_level INTEGER
+@attribute marital_status INTEGER
+@attribute class {1,2,3,4}
+
+@data
+2,1,1,2,1
+2,1,3,2,2
+3,1,4,1,3
+2,4,2,2,3
+1,1,3,4,3
+1,1,3,2,2
+3,1,3,2,2
+3,4,2,4,3
+2,2,1,1,1
+3,2,1,1,1
+1,2,1,1,1
+2,2,3,4,3
+1,1,2,1,1
+2,1,2,2,2
+2,4,1,4,3
+1,1,3,3,1
+3,2,1,2,2
+1,2,1,1,1
+3,3,2,1,1
+3,1,3,2,1
+1,2,2,1,2
+3,2,1,3,1
+2,1,2,1,1
+3,2,1,3,1
+2,3,2,1,1
+3,2,2,1,2
+3,2,1,3,2
+2,1,2,2,2
+1,1,3,2,1
+3,2,1,1,1
+1,4,1,1,3
+2,2,1,3,1
+1,2,1,3,2
+1,1,1,2,1
+2,4,3,1,3
+3,1,2,2,2
+1,1,2,2,2
+3,2,2,1,2
+1,2,1,2,2
+3,4,3,2,3
+2,2,2,1,2
+2,2,1,2,2
+3,2,1,3,2
+3,2,1,1,1
+3,1,2,1,1
+1,2,1,3,2
+2,1,1,2,1
+1,1,1,2,1
+1,2,2,3,2
+3,3,1,1,1
+3,3,3,1,1
+3,2,1,2,2
+3,2,1,2,2
+3,1,2,1,1
+1,1,1,2,1
+2,1,3,2,1
+2,2,2,1,2
+2,1,2,1,1
+2,2,1,3,1
+2,1,2,2,2
+1,2,4,2,3
+2,2,1,2,2
+1,1,2,4,3
+1,3,2,1,1
+2,4,4,2,3
+2,3,2,1,1
+3,1,2,2,2
+1,1,2,2,2
+1,3,2,4,3
+1,1,2,2,2
+3,1,4,2,3
+2,1,3,2,2
+1,1,3,2,2
+3,1,3,2,1
+1,2,4,4,3
+1,4,2,1,3
+2,1,2,1,1
+3,4,1,2,3
+2,2,1,1,1
+1,1,2,1,1
+2,2,4,3,3
+3,1,2,2,2
+1,1,3,2,1
+1,2,1,3,1
+1,4,4,1,3
+3,3,3,2,2
+2,2,1,3,2
+3,3,2,1,2
+1,1,1,3,1
+2,2,1,2,2
+2,2,2,1,2
+2,3,2,3,2
+1,3,2,1,2
+2,2,1,2,2
+1,1,1,2,1
+3,2,2,1,2
+3,2,1,1,1
+1,1,2,1,1
+3,1,4,4,3
+3,3,2,1,2
+2,3,2,1,2
+2,1,3,1,1
+1,2,1,2,2
+3,1,1,2,1
+2,2,4,1,3
+1,2,2,1,2
+2,3,2,1,2
+2,2,1,4,3
+1,4,2,3,3
+2,2,1,1,1
+1,2,1,1,1
+2,2,3,2,2
+1,3,2,1,1
+3,1,2,1,1
+3,1,1,2,1
+3,3,1,4,3
+2,3,4,1,3
+1,2,3,3,2
+3,3,2,2,2
+3,3,4,2,3
+1,2,2,1,2
+2,1,1,4,3
+3,1,2,2,2
+3,2,2,4,3
+2,3,1,3,1
+2,1,1,2,1
+3,4,1,3,3
+1,1,4,3,3
+2,1,2,1,1
+1,2,1,2,2
+1,2,2,1,2
+3,1,1,2,1
+1,1,1,2,1
+1,1,2,1,1
+1,2,1,1,1
+1,1,1,3,1
+1,1,3,1,1
+1,3,1,1,1
+1,1,3,3,1
+1,3,1,3,1
+1,3,3,1,1
+1,2,2,1,2
+1,2,1,2,2
+1,1,2,2,2
+1,2,2,3,2
+1,2,3,2,2
+1,3,2,2,2
+1,2,3,3,2
+1,3,2,3,2
+1,3,3,2,2
+1,1,3,2,1
+1,3,2,1,2
+1,2,1,3,1
+1,2,3,1,2
+1,1,2,3,1
+1,3,1,2,2
+1,1,1,1,1
+1,2,2,2,2
+1,3,3,3,1
+1,4,4,4,3

benchmark/tests/datasets/iris.arff

@@ -0,0 +1,225 @@
+% 1. Title: Iris Plants Database
+% 
+% 2. Sources:
+%      (a) Creator: R.A. Fisher
+%      (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
+%      (c) Date: July, 1988
+% 
+% 3. Past Usage:
+%    - Publications: too many to mention!!!  Here are a few.
+%    1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
+%       Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
+%       to Mathematical Statistics" (John Wiley, NY, 1950).
+%    2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
+%       (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
+%    3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
+%       Structure and Classification Rule for Recognition in Partially Exposed
+%       Environments".  IEEE Transactions on Pattern Analysis and Machine
+%       Intelligence, Vol. PAMI-2, No. 1, 67-71.
+%       -- Results:
+%          -- very low misclassification rates (0% for the setosa class)
+%    4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE 
+%       Transactions on Information Theory, May 1972, 431-433.
+%       -- Results:
+%          -- very low misclassification rates again
+%    5. See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II
+%       conceptual clustering system finds 3 classes in the data.
+% 
+% 4. Relevant Information:
+%    --- This is perhaps the best known database to be found in the pattern
+%        recognition literature.  Fisher's paper is a classic in the field
+%        and is referenced frequently to this day.  (See Duda & Hart, for
+%        example.)  The data set contains 3 classes of 50 instances each,
+%        where each class refers to a type of iris plant.  One class is
+%        linearly separable from the other 2; the latter are NOT linearly
+%        separable from each other.
+%    --- Predicted attribute: class of iris plant.
+%    --- This is an exceedingly simple domain.
+% 
+% 5. Number of Instances: 150 (50 in each of three classes)
+% 
+% 6. Number of Attributes: 4 numeric, predictive attributes and the class
+% 
+% 7. Attribute Information:
+%    1. sepal length in cm
+%    2. sepal width in cm
+%    3. petal length in cm
+%    4. petal width in cm
+%    5. class: 
+%       -- Iris Setosa
+%       -- Iris Versicolour
+%       -- Iris Virginica
+% 
+% 8. Missing Attribute Values: None
+% 
+% Summary Statistics:
+%  	           Min  Max   Mean    SD   Class Correlation
+%    sepal length: 4.3  7.9   5.84  0.83    0.7826   
+%     sepal width: 2.0  4.4   3.05  0.43   -0.4194
+%    petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)
+%     petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)
+% 
+% 9. Class Distribution: 33.3% for each of 3 classes.
+
+@RELATION iris
+
+@ATTRIBUTE sepallength	REAL
+@ATTRIBUTE sepalwidth 	REAL
+@ATTRIBUTE petallength 	REAL
+@ATTRIBUTE petalwidth	REAL
+@ATTRIBUTE class 	{Iris-setosa,Iris-versicolor,Iris-virginica}
+
+@DATA
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+%
+%
+%

benchmark/tests/datasets/wine.arff

@@ -0,0 +1,302 @@
+% 1. Title of Database: Wine recognition data
+% 	Updated Sept 21, 1998 by C.Blake : Added attribute information
+% 
+% 2. Sources:
+%    (a) Forina, M. et al, PARVUS - An Extendible Package for Data
+%        Exploration, Classification and Correlation. Institute of Pharmaceutical
+%        and Food Analysis and Technologies, Via Brigata Salerno, 
+%        16147 Genoa, Italy.
+% 
+%    (b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au
+%    (c) July 1991
+% 3. Past Usage:
+% 
+%    (1)
+%    S. Aeberhard, D. Coomans and O. de Vel,
+%    Comparison of Classifiers in High Dimensional Settings,
+%    Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
+%    Mathematics and Statistics, James Cook University of North Queensland.
+%    (Also submitted to Technometrics).
+% 
+%    The data was used with many others for comparing various 
+%    classifiers. The classes are separable, though only RDA 
+%    has achieved 100% correct classification.
+%    (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
+%    (All results using the leave-one-out technique)
+% 
+%    In a classification context, this is a well posed problem 
+%    with "well behaved" class structures. A good data set 
+%    for first testing of a new classifier, but not very 
+%    challenging.
+% 
+%    (2) 
+%    S. Aeberhard, D. Coomans and O. de Vel,
+%    "THE CLASSIFICATION PERFORMANCE OF RDA"
+%    Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
+%    Mathematics and Statistics, James Cook University of North Queensland.
+%    (Also submitted to Journal of Chemometrics).
+% 
+%    Here, the data was used to illustrate the superior performance of
+%    the use of a new appreciation function with RDA. 
+% 
+% 4. Relevant Information:
+% 
+%    -- These data are the results of a chemical analysis of
+%       wines grown in the same region in Italy but derived from three
+%       different cultivars.
+%       The analysis determined the quantities of 13 constituents
+%       found in each of the three types of wines. 
+% 
+%    -- I think that the initial data set had around 30 variables, but 
+%       for some reason I only have the 13 dimensional version. 
+%       I had a list of what the 30 or so variables were, but a.) 
+%       I lost it, and b.), I would not know which 13 variables
+%       are included in the set.
+% 
+%    -- The attributes are (dontated by Riccardo Leardi, 
+% 	riclea@anchem.unige.it )
+%  	1) Alcohol
+%  	2) Malic acid
+%  	3) Ash
+% 	4) Alcalinity of ash  
+%  	5) Magnesium
+% 	6) Total phenols
+%  	7) Flavanoids
+%  	8) Nonflavanoid phenols
+%  	9) Proanthocyanins
+% 	10)Color intensity
+%  	11)Hue
+%  	12)OD280/OD315 of diluted wines
+%  	13)Proline            
+% 
+% 5. Number of Instances
+% 
+%       	class 1 59
+% 	class 2 71
+% 	class 3 48
+% 
+% 6. Number of Attributes 
+% 	
+% 	13
+% 
+% 7. For Each Attribute:
+% 
+% 	All attributes are continuous
+% 	
+% 	No statistics available, but suggest to standardise
+% 	variables for certain uses (e.g. for us with classifiers
+% 	which are NOT scale invariant)
+% 
+% 	NOTE: 1st attribute is class identifier (1-3)
+% 
+% 8. Missing Attribute Values:
+% 
+% 	None
+% 
+% 9. Class Distribution: number of instances per class
+% 
+%       	class 1 59
+% 	class 2 71
+% 	class 3 48
+%
+% Information about the dataset
+% CLASSTYPE: nominal
+% CLASSINDEX: first
+%
+
+@relation wine
+
+@attribute class {1,2,3}
+@attribute Alcohol REAL
+@attribute Malic_acid REAL
+@attribute Ash REAL
+@attribute Alcalinity_of_ash REAL
+@attribute Magnesium INTEGER
+@attribute Total_phenols REAL
+@attribute Flavanoids REAL
+@attribute Nonflavanoid_phenols REAL
+@attribute Proanthocyanins REAL
+@attribute Color_intensity REAL
+@attribute Hue REAL
+@attribute OD280/OD315_of_diluted_wines REAL
+@attribute Proline INTEGER
+
+@data
+1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
+1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
+1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
+1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
+1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
+1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
+1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
+1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
+1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
+1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
+1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
+1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
+1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
+1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
+1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
+1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
+1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
+1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
+1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
+1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
+1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
+1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
+1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
+1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
+1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
+1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
+1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
+1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
+1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
+1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
+1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
+1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
+1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
+1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
+1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
+1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
+1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
+1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
+1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
+1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
+1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
+1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
+1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
+1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
+1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
+1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
+1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
+1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
+1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
+1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
+1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
+1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
+1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
+1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
+1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
+1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
+1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
+1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
+1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
+2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
+2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
+2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
+2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
+2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
+2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
+2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
+2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
+2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
+2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
+2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
+2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
+2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
+2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
+2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
+2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
+2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
+2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
+2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
+2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
+2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
+2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
+2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
+2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
+2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
+2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
+2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
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