def test_fit_regression_multiple_datavariables(self):
time = pd.date_range('2000-01-01', freq='24H', periods=7)
time2 = pd.date_range('2000-01-08', freq='24H', periods=1)
bar = xr.DataArray([2, 2, 2, 2, 3, 3, 3],
dims=["time"],
coords={'time': time})
foo = xr.DataArray([4, 4, 4, 4, 6, 6, 6],
dims=["time"],
coords={'time': time})
target = xr.DataArray([6, 6, 6, 6, 9, 9, 9],
dims=["time"],
coords={'time': time})
lin_reg = LinearRegression()
wrapper = SKLearnWrapper(module=lin_reg)
self.assertFalse("coef_" in lin_reg.__dir__())
wrapper.fit(bar=bar, foo=foo, target=target)
result = wrapper.transform(bar=xr.DataArray([2],
dims=["time"],
coords={'time': time2}),
foo=xr.DataArray([4],
dims=["time"],
coords={'time': time2}))
self.assertAlmostEqual(result["target"].values[0, 0], 6.0)
self.assertEqual(result["target"].shape, (1, 1))
def test_transform_multiple_output(self):
lin_reg = LinearRegression()
multi_regressor = MultiOutputRegressor(lin_reg)
wrapper = SKLearnWrapper(module=multi_regressor)
time = pd.date_range('2000-01-01', freq='24H', periods=5)
time2 = pd.date_range('2000-01-08', freq='24H', periods=1)
bar = xr.DataArray([1, 2, 3, 4, 5],
dims=["time"],
coords={'time': time})
foo = xr.DataArray([1], dims=["time"], coords={'time': time2})
target = xr.DataArray([2, 2, 2, 2, 2],
dims=["time"],
coords={'time': time})
target2 = xr.DataArray([3, 3, 3, 3, 3],
dims=["time"],
coords={'time': time})
wrapper.fit(bar=bar, target1=target, target2=target2)
result = wrapper.transform(bar=foo)
self.assertAlmostEqual(result["target1"].values[0], 2.0)
self.assertAlmostEqual(result["target2"].values[0], 3.0)
self.assertEqual(result["target1"].shape, (1, 1))
self.assertEqual(result["target2"].shape, (1, 1))
def test_fit_TransformerMixin(self):
scaler = StandardScaler()
wrapper = SKLearnWrapper(module=scaler)
self.assertFalse("mean_" in scaler.__dir__())
wrapper.fit(test=xr.DataArray([1, 2, 3, 4, 5]))
self.assertTrue("mean_" in scaler.__dir__())
self.assertIsNotNone(scaler.mean_)
def test_fit_RegressorMixin(self):
lin_reg = LinearRegression()
wrapper = SKLearnWrapper(module=lin_reg)
self.assertFalse("coef_" in lin_reg.__dir__())
wrapper.fit(test=xr.DataArray([1, 2, 3, 4, 5]),
target=xr.DataArray([2, 2, 2, 2, 2]))
self.assertTrue("coef_" in lin_reg.__dir__())
self.assertIsNotNone(lin_reg.coef_)
def test_transform_RegressorMixin(self):
svr = SVR()
wrapper = SKLearnWrapper(module=svr)
time = pd.date_range('2000-01-08', freq='24H', periods=1)
bar = xr.DataArray([1], dims=["time"], coords={'time': time})
wrapper.fit(test=xr.DataArray([1, 2, 3, 4, 5]),
target=xr.DataArray([2, 2, 2, 2, 2]))
result = wrapper.transform(bar=bar)
assert result["target"].values[0] == 2.0
self.assertEqual(result["target"].shape, (1, 1))
def test_fit_ClassifierMixin(self):
svc = SVC()
wrapper = SKLearnWrapper(module=svc)
time = pd.date_range('2000-01-01', freq='24H', periods=5)
time2 = pd.date_range('2000-01-08', freq='24H', periods=1)
bar = xr.DataArray([1, 2, 3, 4, 5],
dims=["time"],
coords={'time': time})
foo = xr.DataArray([1], dims=["time"], coords={'time': time2})
target = xr.DataArray([0, 0, 1, 1, 1],
dims=["time"],
coords={'time': time})
wrapper.fit(bar=bar, target=target)
result = wrapper.transform(bar=foo)
assert result["target"].values[0] == 0
self.assertEqual(result["target"].shape, (1, 1))
def test_DensityMixin(self):
gauss_density = GaussianMixture(n_components=2)
wrapper = SKLearnWrapper(module=gauss_density)
time = pd.date_range('2000-01-01', freq='24H', periods=10)
time2 = pd.date_range('2000-01-08', freq='24H', periods=1)
bar = xr.DataArray([2, 4, 5, 4, 3, 2, 1, 5, 5, 5],
dims=["time"],
coords={'time': time})
wrapper.fit(bar=bar)
bar1 = xr.DataArray([5], dims=["time"], coords={'time': time2})
bar2 = xr.DataArray([2], dims=["time"], coords={'time': time2})
result1 = wrapper.transform(bar=bar1)
result0 = wrapper.transform(bar=bar2)
assert result1.values[0] != result0.values[0]
self.assertEqual(result1.shape, (1, ))
self.assertEqual(result0.shape, (1, ))
def test_fit_ClusterMixin(self):
kmeans = KMeans(n_clusters=2)
wrapper = SKLearnWrapper(module=kmeans)
# self.assertFalse("coef_" in lin_reg.__dir__())
time = pd.date_range('2000-01-01', freq='24H', periods=10)
time2 = pd.date_range('2000-01-08', freq='24H', periods=1)
bar = xr.DataArray([2, 4, 5, 4, 2, 2, 1, 5, 5, 5],
dims=["time"],
coords={'time': time})
foo1 = xr.DataArray([5], dims=["time"], coords={'time': time2})
foo2 = xr.DataArray([2], dims=["time"], coords={'time': time2})
wrapper.fit(bar=bar)
result1 = wrapper.transform(foo=foo1)
result0 = wrapper.transform(foo=foo2)
# Assert that both tested datapoints are in different clusters
assert result1.values[0].argmax() != result0.values[0].argmax()
self.assertEqual(result1.shape, (1, 2))
self.assertEqual(result0.shape, (1, 2))