diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 2ebca5e4..b569f1cd 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -3,11 +3,7 @@ name: tests -on: - push: - branches: [ master ] - pull_request: - branches: [ master ] +on: [push, pull_request] jobs: build: @@ -15,7 +11,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.7, 3.8] + python-version: [3.7, 3.8.5] steps: - uses: actions/checkout@v2 @@ -26,7 +22,7 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - pip install . + pip install . --no-cache-dir - name: Test with pytest run: | pytest diff --git a/tests/brl_test.py b/tests/brl_test.py index 4a573086..d9c79637 100644 --- a/tests/brl_test.py +++ b/tests/brl_test.py @@ -1,6 +1,8 @@ import unittest +from urllib.request import urlretrieve import numpy as np +from scipy.io.arff import loadarff from sklearn.datasets import fetch_openml from sklearn.model_selection import train_test_split @@ -30,9 +32,11 @@ def test_integration_fitting(self): "Triceps skin fold thickness(mm)", "2-Hour serum insulin (mu U/ml)", "Body mass index", "Diabetes pedigree function", "Age (years)"] - data = fetch_openml("diabetes") # get dataset - X = data.data - y = (data.target == 'tested_positive').astype(np.int) # labels 0-1 + + data = loadarff("tests/test_data/diabetes.arff") + data_np = np.array(list(map(lambda x: np.array(list(x)), data[0]))) + X, y_text = data_np[:, :-1].astype('float32'), data_np[:, -1].astype('str') + y = (y_text == 'tested_positive').astype(np.int) # labels 0-1 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.75) # split diff --git a/tests/test_data/diabetes.arff b/tests/test_data/diabetes.arff new file mode 100644 index 00000000..a33cbd81 --- /dev/null +++ b/tests/test_data/diabetes.arff @@ -0,0 +1,863 @@ +% 1. Title: Pima Indians Diabetes Database +% +% 2. Sources: +% (a) Original owners: National Institute of Diabetes and Digestive and +% Kidney Diseases +% (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu) +% Research Center, RMI Group Leader +% Applied Physics Laboratory +% The Johns Hopkins University +% Johns Hopkins Road +% Laurel, MD 20707 +% (301) 953-6231 +% (c) Date received: 9 May 1990 +% +% 3. Past Usage: +% 1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \& +% Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast +% the onset of diabetes mellitus. In {\it Proceedings of the Symposium +% on Computer Applications and Medical Care} (pp. 261--265). IEEE +% Computer Society Press. +% +% The diagnostic, binary-valued variable investigated is whether the +% patient shows signs of diabetes according to World Health Organization +% criteria (i.e., if the 2 hour post-load plasma glucose was at least +% 200 mg/dl at any survey examination or if found during routine medical +% care). The population lives near Phoenix, Arizona, USA. +% +% Results: Their ADAP algorithm makes a real-valued prediction between +% 0 and 1. This was transformed into a binary decision using a cutoff of +% 0.448. Using 576 training instances, the sensitivity and specificity +% of their algorithm was 76% on the remaining 192 instances. +% +% 4. Relevant Information: +% Several constraints were placed on the selection of these instances from +% a larger database. In particular, all patients here are females at +% least 21 years old of Pima Indian heritage. ADAP is an adaptive learning +% routine that generates and executes digital analogs of perceptron-like +% devices. It is a unique algorithm; see the paper for details. +% +% 5. Number of Instances: 768 +% +% 6. Number of Attributes: 8 plus class +% +% 7. For Each Attribute: (all numeric-valued) +% 1. Number of times pregnant +% 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test +% 3. Diastolic blood pressure (mm Hg) +% 4. Triceps skin fold thickness (mm) +% 5. 2-Hour serum insulin (mu U/ml) +% 6. Body mass index (weight in kg/(height in m)^2) +% 7. Diabetes pedigree function +% 8. Age (years) +% 9. Class variable (0 or 1) +% +% 8. Missing Attribute Values: None +% +% 9. Class Distribution: (class value 1 is interpreted as "tested positive for +% diabetes") +% +% Class Value Number of instances +% 0 500 +% 1 268 +% +% 10. Brief statistical analysis: +% +% Attribute number: Mean: Standard Deviation: +% 1. 3.8 3.4 +% 2. 120.9 32.0 +% 3. 69.1 19.4 +% 4. 20.5 16.0 +% 5. 79.8 115.2 +% 6. 32.0 7.9 +% 7. 0.5 0.3 +% 8. 33.2 11.8 +% +% +% +% +% +% +% Relabeled values in attribute 'class' +% From: 0 To: tested_negative +% From: 1 To: tested_positive +% +@relation pima_diabetes +@attribute 'preg' real +@attribute 'plas' real +@attribute 'pres' real +@attribute 'skin' real +@attribute 'insu' real +@attribute 'mass' real +@attribute 'pedi' real +@attribute 'age' real +@attribute 'class' { tested_negative, tested_positive} +@data +6,148,72,35,0,33.6,0.627,50,tested_positive +1,85,66,29,0,26.6,0.351,31,tested_negative +8,183,64,0,0,23.3,0.672,32,tested_positive +1,89,66,23,94,28.1,0.167,21,tested_negative +0,137,40,35,168,43.1,2.288,33,tested_positive +5,116,74,0,0,25.6,0.201,30,tested_negative +3,78,50,32,88,31,0.248,26,tested_positive +10,115,0,0,0,35.3,0.134,29,tested_negative 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