Release v0.0.4 (#34)

* fix the longstanding `plotting.dendrogram` display bug

* incorporate `learning` into the `Dendrogram.score_at_point` method in `selection`

* added unit tests for `selection` (coverage is roughly 86%)

* landing page for docs is `index.html`

* modify the documentation structure

* update the package metadata

henchman/__init__.py

@@ -11,4 +11,4 @@
 
 __author__ = '''Feature Labs Team'''
 __email__ = 'team@featurelabs.com'
-__version__ = '0.0.3'
+__version__ = '0.0.4'

meta.yaml

@@ -1,6 +1,6 @@
 package:
   name: henchman
-  version: "0.0.3"
+  version: "0.0.4"
 
 source:
   path: ../henchman

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.0.3
+current_version = 0.0.4
 commit = True
 tag = True
 

setup.py

@@ -49,6 +49,6 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://github.com/featurelabs/henchman',
-    version='0.0.3.1',
+    version='0.0.4',
     zip_safe=False,
 )

tests/test_selection.py

@@ -12,12 +12,14 @@
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import accuracy_score
 
+
 @pytest.fixture(scope="module")
 def Xy():
     X = pd.read_csv('./tests/sample_data/sample_fm_enc.csv')
     y = X.pop('label')
     return X, y
 
+
 @pytest.fixture(scope="module")
 def fit_dend(Xy):
     X, y = Xy
@@ -43,25 +45,32 @@ def test_dend_fit(fit_dend):
     assert selector.edges is not None
     assert selector.graphs is not None
 
+
 def test_dend_set_params(fit_dend):
     threshlist = fit_dend.threshlist
     fit_dend.set_params(threshlist=None)
 
-    assert fit_dend.threshlist == None
+    assert fit_dend.threshlist is None
     fit_dend.threshlist = threshlist
 
+
 def test_dend_features_at_step(fit_dend):
     assert len(fit_dend.features_at_step(48)) == 79
 
+
 def test_dend_find_set_of_size(fit_dend, capsys):
     assert fit_dend.find_set_of_size(80) == 6
-
+
+
 def test_dend_score_at_point(Xy, fit_dend):
     X, y = Xy
-    scores, fit_model = fit_dend.score_at_point(X, y, RandomForestClassifier(random_state=0), accuracy_score, 2)
+    scores, fit_model = fit_dend.score_at_point(X, y,
+                                                RandomForestClassifier(random_state=0),
+                                                accuracy_score, 2)
     assert len(scores) == 1
     assert scores[0] - .866666 < .00001
 
+
 def test_dend_shuffle_all(fit_dend):
     keys_1 = set(fit_dend.graphs[1].keys())
     fit_dend.shuffle_all_representatives()
@@ -72,42 +81,46 @@ def test_dend_shuffle_all(fit_dend):
 def test_dend_shuffle_score_at_point(Xy, fit_dend):
     X, y = Xy
     keys_1 = set(fit_dend.graphs[1].keys())
-    scores, _ = fit_dend.shuffle_score_at_point(X, y, RandomForestClassifier(), 
-                                                    accuracy_score, 2, 2)
+    scores, _ = fit_dend.shuffle_score_at_point(X, y, RandomForestClassifier(),
+                                                accuracy_score, 2, 2)
     assert set(fit_dend.graphs[1].keys()) != keys_1
     assert len(scores) == 2
 
+
 def test_dend_transform(Xy, fit_dend, capsys):
     X, y = Xy
     X_new_1 = fit_dend.transform(X, 99)
     out1, _ = capsys.readouterr()
     X_new_2 = fit_dend.transform(X, 50)
     out2, _ = capsys.readouterr()
-    
+
     assert X_new_1.shape[1] == int(out1[10:12])
     assert X_new_2.shape[1] == int(out2[-3:-1])
 
+
 def test_dend_plot(fit_dend):
     show(dendrogram(fit_dend), static=True)
     show(dendrogram(fit_dend))
 
+
 def test_build_edges(capsys):
     fake_sel = selection.Dendrogram()
     fake_sel.adj = np.asarray(range(501))
     fake_sel._build_edges(None)
 
     output, _ = capsys.readouterr()
     split_output = output.split('\n')
-    
+
     real_line_1 = 'Calculating more than 500 graphs'
     real_line_2 = 'You can pass max_threshes as a kwarg to Dendrogram'
     assert split_output[0] == real_line_1
     assert split_output[1] == real_line_2
 
+
 def test_build_graphs_exit():
     fake_sel = selection.Dendrogram()
     fake_sel.threshlist = [1, 2]
-    fake_sel.edges = [[(0, 1)], [(1, 2), (0, 1)]] 
+    fake_sel.edges = [[(0, 1)], [(1, 2), (0, 1)]]
     fake_sel.graphs = [{0: {0, 1}, 2: {2}}, {0: {0, 1, 2}}]
     fake_sel._build_graphs()
 

tox.ini

@@ -1,5 +1,5 @@
 [tox]
-envlist = py27, py36, py37, flake8
+envlist = flake8, py27, py36, py37
 
 [travis]
 python =
@@ -10,7 +10,9 @@ python =
 [testenv:flake8]
 basepython = python
 deps = flake8
-commands = flake8 henchman
+commands = 
+    flake8 henchman
+    flake8 tests
 
 [testenv]
 setenv =

upload.sh

@@ -2,6 +2,7 @@
 
 versions="linux-64 win-64 osx-64"
 make clean
+conda build .
 python setup.py sdist
 python setup.py bdist_wheel
 twine upload dist/* -r pypi