Merge pull request MLBazaar#78 from HDI-Project/6_add_unit_tests

Add unit tests and integrate with codecov

Author: csala

.travis.yml

@@ -6,12 +6,14 @@ python:
 
 # Command to install dependencies
 install:
-   - pip install -U tox-travis
+   - pip install -U tox-travis codecov
    - sudo apt-get install graphviz
 
 # Command to run tests
 script: tox
 
+after_success: codecov
+
 deploy:
 
   - provider: pages

Makefile

@@ -103,7 +103,7 @@ fix-lint: ## fix lint issues using autoflake, autopep8, and isort
 
 .PHONY: test
 test: ## run tests quickly with the default Python
-	python -m pytest
+	python -m pytest --cov=mlblocks
 
 .PHONY: test-all
 test-all: ## run tests on every Python version with tox

README.md

@@ -11,11 +11,14 @@ Pipelines and Primitives for Machine Learning and Data Science.
 
 [![PyPi][pypi-img]][pypi-url]
 [![Travis][travis-img]][travis-url]
+[![CodeCov][codecov-img]][codecov-url]
 
 [pypi-img]: https://img.shields.io/pypi/v/mlblocks.svg
 [pypi-url]: https://pypi.python.org/pypi/mlblocks
 [travis-img]: https://travis-ci.org/HDI-Project/MLBlocks.svg?branch=master
 [travis-url]: https://travis-ci.org/HDI-Project/MLBlocks
+[codecov-img]: https://codecov.io/gh/HDI-Project/MLBlocks/branch/master/graph/badge.svg
+[codecov-url]: https://codecov.io/gh/HDI-Project/MLBlocks
 
 MLBlocks is a simple framework for composing end-to-end tunable Machine Learning Pipelines by
 seamlessly combining tools from any python library with a simple, common and uniform interface.

mlblocks/mlblock.py

@@ -94,7 +94,7 @@ def _extract_params(self, kwargs, hyperparameters):
         fit_args = [arg['name'] for arg in self.fit_args]
         produce_args = [arg['name'] for arg in self.produce_args]
 
-        for name in kwargs.keys():
+        for name in list(kwargs.keys()):
             if name in fit_args:
                 fit_params[name] = kwargs.pop(name)
 
@@ -127,9 +127,9 @@ def __init__(self, name, **kwargs):
         self._class = bool(self.produce_method)
 
         hyperparameters = metadata.get('hyperparameters', dict())
-        init_params, fit_params, produce_params = self._extract_params(
-            kwargs, hyperparameters)
-        self._hyperparamters = init_params
+        init_params, fit_params, produce_params = self._extract_params(kwargs, hyperparameters)
+
+        self._hyperparameters = init_params
         self._fit_params = fit_params
         self._produce_params = produce_params
 
@@ -175,7 +175,7 @@ def get_hyperparameters(self):
                 the dictionary containing the hyperparameter values that the
                 MLBlock is currently using.
         """
-        return self._hyperparamters
+        return self._hyperparameters.copy()
 
     def set_hyperparameters(self, hyperparameters):
         """Set new hyperparameters.
@@ -190,10 +190,10 @@ def set_hyperparameters(self, hyperparameters):
                                     of the hyperparameters and as values
                                     the values to be used.
         """
-        self._hyperparamters.update(hyperparameters)
+        self._hyperparameters.update(hyperparameters)
 
         if self._class:
-            self.instance = self.primitive(**self._hyperparamters)
+            self.instance = self.primitive(**self._hyperparameters)
 
     def fit(self, **kwargs):
         """Call the fit method of the primitive.
@@ -239,5 +239,5 @@ def produce(self, **kwargs):
         if self._class:
             return getattr(self.instance, self.produce_method)(**produce_args)
 
-        produce_args.update(self._hyperparamters)
+        produce_args.update(self._hyperparameters)
         return self.primitive(**produce_args)

setup.cfg

@@ -45,4 +45,3 @@ collect_ignore = ['setup.py']
 
 [tool:pylint]
 good-names = X,y
-

setup.py

@@ -15,12 +15,13 @@
 
 
 install_requires = [
+    'mlprimitives>=0.1.3',
 ]
 
 
 tests_require = [
-    'mock>=2.0.0',
     'pytest>=3.4.2',
+    'pytest-cov>=2.6.0',
 ]
 
 
@@ -62,11 +63,6 @@
 ]
 
 
-demo_requires = [
-    'mlprimitives==0.1.1',
-]
-
-
 setup(
     author='MIT Data To AI Lab',
     author_email='[email protected]',
@@ -81,8 +77,7 @@
     ],
     description="Pipelines and primitives for machine learning and data science.",
     extras_require={
-        'demo': demo_requires,
-        'dev': demo_requires + development_requires + tests_require,
+        'dev': development_requires + tests_require,
         'test': tests_require,
     },
     include_package_data=True,

tests/test__primitives.py

@@ -0,0 +1,58 @@
+# -*- coding: utf-8 -*-
+
+from unittest import TestCase
+from unittest.mock import Mock
+
+from mlblocks import datasets
+
+
+class TestDataset(TestCase):
+
+    def setUp(self):
+        self.description = """Dataset Name.
+
+        Some extended description.
+        """
+        self.score = Mock()
+        self.score.return_value = 1.0
+
+        self.dataset = datasets.Dataset(
+            self.description, 'data', 'target', self.score,
+            shuffle=False, stratify=True, some='kwargs')
+
+    def test___init__(self):
+
+        assert self.dataset.name == 'Dataset Name.'
+        assert self.dataset.description == self.description
+        assert self.dataset.data == 'data'
+        assert self.dataset.target == 'target'
+        assert self.dataset._shuffle is False
+        assert self.dataset._stratify is True
+        assert self.dataset._score == self.score
+        assert self.dataset.some == 'kwargs'
+
+    def test_score(self):
+        returned = self.dataset.score('a', b='c')
+
+        assert returned == 1.0
+        self.score.assert_called_once_with('a', b='c')
+
+    def test___repr__(self):
+        repr_ = str(self.dataset)
+
+        assert repr_ == "Dataset Name."
+
+
+def test_dataset_describe(capsys):
+    """Tested here because fixtures are not supported in TestCases."""
+
+    description = """Dataset Name.
+
+    Some extended description.
+    """
+
+    dataset = datasets.Dataset(description, 'data', 'target', 'score')
+    dataset.describe()
+
+    captured = capsys.readouterr()
+    assert captured.out == description + '\n'

tests/test_datasets.py

@@ -0,0 +1,149 @@
+# -*- coding: utf-8 -*-
+
+from unittest import TestCase
+from unittest.mock import patch
+
+from mlblocks.mlblock import MLBlock, import_object
+
+# import pytest
+
+
+class DummyClass:
+    pass
+
+
+def test_import_object():
+    dummy_class = import_object(__name__ + '.DummyClass')
+
+    assert dummy_class is DummyClass
+
+
+class TestMLBlock(TestCase):
+
+    def test__extract_params(self):
+        pass
+
+    @patch('mlblocks.mlblock.MLBlock.set_hyperparameters')
+    @patch('mlblocks.mlblock.import_object')
+    @patch('mlblocks.mlblock.load_primitive')
+    def test___init__(self, load_primitive_mock, import_object_mock, set_hps_mock):
+        load_primitive_mock.return_value = {
+            'primitive': 'a_primitive_name',
+            'produce': {
+                'args': [
+                    {
+                        'name': 'argument'
+                    }
+                ],
+                'output': [
+                ]
+            }
+        }
+
+        mlblock = MLBlock('given_primitive_name', argument='value')
+
+        assert mlblock.name == 'given_primitive_name'
+        assert mlblock.primitive == import_object_mock.return_value
+        assert mlblock._fit == dict()
+        assert mlblock.fit_args == list()
+        assert mlblock.fit_method is None
+
+        produce = {
+            'args': [
+                {
+                    'name': 'argument'
+                }
+            ],
+            'output': [
+            ]
+        }
+        assert mlblock._produce == produce
+        assert mlblock.produce_args == produce['args']
+        assert mlblock.produce_output == produce['output']
+        assert mlblock.produce_method is None
+        assert mlblock._class is False
+
+        assert mlblock._hyperparameters == dict()
+        assert mlblock._fit_params == dict()
+        assert mlblock._produce_params == {'argument': 'value'}
+
+        assert mlblock._tunable == dict()
+
+        set_hps_mock.assert_called_once_with(dict())
+
+    @patch('mlblocks.mlblock.import_object')
+    @patch('mlblocks.mlblock.load_primitive')
+    def test___str__(self, load_primitive_mock, import_object_mock):
+        load_primitive_mock.return_value = {
+            'primitive': 'a_primitive_name',
+            'produce': {
+                'args': [],
+                'output': []
+            }
+        }
+
+        mlblock = MLBlock('given_primitive_name')
+
+        assert str(mlblock) == 'MLBlock - given_primitive_name'
+
+    @patch('mlblocks.mlblock.import_object')
+    @patch('mlblocks.mlblock.load_primitive')
+    def test_get_tunable_hyperparameters(self, load_primitive_mock, import_object_mock):
+        """get_tunable_hyperparameters has to return a copy of the _tunables attribute."""
+        load_primitive_mock.return_value = {
+            'primitive': 'a_primitive_name',
+            'produce': {
+                'args': [],
+                'output': []
+            }
+        }
+
+        mlblock = MLBlock('given_primitive_name')
+
+        tunable = dict()
+        mlblock._tunable = tunable
+
+        returned = mlblock.get_tunable_hyperparameters()
+
+        assert returned == tunable
+        assert returned is not tunable
+
+    @patch('mlblocks.mlblock.import_object')
+    @patch('mlblocks.mlblock.load_primitive')
+    def test_get_hyperparameters(self, load_primitive_mock, import_object_mock):
+        """get_hyperparameters has to return a copy of the _hyperparameters attribute."""
+        load_primitive_mock.return_value = {
+            'primitive': 'a_primitive_name',
+            'produce': {
+                'args': [],
+                'output': []
+            }
+        }
+
+        mlblock = MLBlock('given_primitive_name')
+
+        hyperparameters = dict()
+        mlblock._hyperparameters = hyperparameters
+
+        returned = mlblock.get_hyperparameters()
+
+        assert returned == hyperparameters
+        assert returned is not hyperparameters
+
+    def test_set_hyperparameters_function(self):
+        pass
+
+    def test_set_hyperparameters_class(self):
+        pass
+
+    def test_fit_no_fit(self):
+        pass
+
+    def test_fit(self):
+        pass
+
+    def test_produce_function(self):
+        pass
+
+    def test_produce_class(self):
+        pass