def test_predictable_tsne_intercept_weights(self):
iris = datasets.load_iris()
X, y = iris.data[:20], iris.target[:20]
clr = PredictableTSNE(keep_tsne_outputs=True)
clr.fit(X, y, sample_weight=numpy.ones((X.shape[0], )))
acc = clr.transform(X)
self.assertGreater(clr.loss_, 0)
self.assertEqual(acc.shape, (X.shape[0], 2))
def test_predictable_tsne(self):
iris = datasets.load_iris()
X, y = iris.data[:20], iris.target[:20]
clr = PredictableTSNE(keep_tsne_outputs=True)
clr.fit(X, y)
pred = clr.transform(X)
self.assertIsInstance(clr.estimator_, MLPRegressor)
self.assertGreater(clr.loss_, 0)
self.assertNotEmpty(pred)
def test_predictable_tsne_knn(self):
iris = datasets.load_iris()
X, y = iris.data[:20], iris.target[:20]
clr = PredictableTSNE(estimator=KNeighborsRegressor(),
keep_tsne_outputs=True)
clr.fit(X, y)
pred = clr.transform(X)
self.assertTrue(hasattr(clr, "tsne_outputs_"))
self.assertIsInstance(clr.estimator_, KNeighborsRegressor)
self.assertEqual(pred.shape, (X.shape[0], 2))
def test_predictable_tsne_relevance(self):
state = RandomState(seed=0)
Xs = []
Ys = []
n = 20
for i in range(0, 5):
for j in range(0, 4):
x1 = state.rand(n) + i * 1.1
x2 = state.rand(n) + j * 1.1
Xs.append(numpy.vstack([x1, x2]).T)
cl = state.randint(0, 4)
Ys.extend([cl for i in range(n)])
X = numpy.vstack(Xs)
Y = numpy.array(Ys)
clk = PredictableTSNE(transformer=TSNE(n_components=3),
normalizer=StandardScaler(with_mean=False),
keep_tsne_outputs=True)
clk.fit(X, Y)
pred = clk.transform(X)
self.assertGreater(clk.loss_, 0)
self.assertEqual(pred.shape, (X.shape[0], 3))
def test_predictable_tsne_clone(self):
self.maxDiff = None
test_sklearn_clone(lambda: PredictableTSNE())
def test_predictable_tsne_pickle(self):
iris = datasets.load_iris()
X, y = iris.data[:20], iris.target[:20]
test_sklearn_pickle(lambda: PredictableTSNE(), X, y)
