From 29c4b4ceefaf461c451a0b4baf290ee9f39587f3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ricardo=20Monta=C3=B1ana?= Date: Tue, 25 Oct 2022 11:36:04 +0200 Subject: [PATCH] Update E203 in main.yml Create tests --- .github/workflows/main.yml | 2 +- benchmark/Datasets.py | 44 ++-- benchmark/tests/.env.arff | 6 + benchmark/tests/Dataset_test.py | 7 + benchmark/tests/TestBase.py | 7 +- benchmark/tests/datasets/all.txt | 4 +- benchmark/tests/datasets/hayes-roth.arff | 305 +++++++++++++++++++++++ benchmark/tests/datasets/iris.arff | 225 +++++++++++++++++ benchmark/tests/datasets/wine.arff | 302 ++++++++++++++++++++++ 9 files changed, 878 insertions(+), 24 deletions(-) create mode 100644 benchmark/tests/.env.arff create mode 100755 benchmark/tests/datasets/hayes-roth.arff create mode 100755 benchmark/tests/datasets/iris.arff create mode 100755 benchmark/tests/datasets/wine.arff diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 6052c8f..91f8e1d 100644 --- a/.github/workflows/main.yml +++ b/.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 diff --git a/benchmark/Datasets.py b/benchmark/Datasets.py index 8fb77b3..e3735e7 100644 --- a/benchmark/Datasets.py +++ b/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: diff --git a/benchmark/tests/.env.arff b/benchmark/tests/.env.arff new file mode 100644 index 0000000..3cff1df --- /dev/null +++ b/benchmark/tests/.env.arff @@ -0,0 +1,6 @@ +score=accuracy +platform=MacBookpro16 +n_folds=5 +model=ODTE +stratified=0 +source_data=Arff diff --git a/benchmark/tests/Dataset_test.py b/benchmark/tests/Dataset_test.py index 4669922..ca28453 100644 --- a/benchmark/tests/Dataset_test.py +++ b/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) diff --git a/benchmark/tests/TestBase.py b/benchmark/tests/TestBase.py index af33d8a..e6b2de0 100644 --- a/benchmark/tests/TestBase.py +++ b/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) diff --git a/benchmark/tests/datasets/all.txt b/benchmark/tests/datasets/all.txt index 16d4d76..ddf732a 100644 --- a/benchmark/tests/datasets/all.txt +++ b/benchmark/tests/datasets/all.txt @@ -1,2 +1,2 @@ -iris -wine +iris,class +wine,class diff --git a/benchmark/tests/datasets/hayes-roth.arff b/benchmark/tests/datasets/hayes-roth.arff new file mode 100755 index 0000000..4f0bd17 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/benchmark/tests/datasets/iris.arff b/benchmark/tests/datasets/iris.arff new file mode 100755 index 0000000..780480c --- /dev/null +++ b/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 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+5.9,3.0,5.1,1.8,Iris-virginica +% +% +% diff --git a/benchmark/tests/datasets/wine.arff b/benchmark/tests/datasets/wine.arff new file mode 100755 index 0000000..7d61c79 --- /dev/null +++ b/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 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