def test_columns_groupby(self): columns = Columns(self.column_data, kdims=self.kdims, vdims=self.vdims) cols = self.kdims + self.vdims group1 = pd.DataFrame(self.column_data[:2], columns=cols) group2 = pd.DataFrame(self.column_data[2:], columns=cols) grouped = HoloMap({'M': Columns(group1, kdims=['Age'], vdims=self.vdims), 'F': Columns(group2, kdims=['Age'], vdims=self.vdims)}, kdims=['Gender']) self.assertEqual(columns.groupby(['Gender']), grouped)
class GridColumnsTest(HomogeneousColumnTypes, ComparisonTestCase): """ Test of the NdColumns interface (mostly for backwards compatibility) """ def setUp(self): self.restore_datatype = Columns.datatype Columns.datatype = ['grid'] self.data_instance_type = dict self.init_data() def init_data(self): self.xs = range(11) self.xs_2 = [el**2 for el in self.xs] self.y_ints = [i*2 for i in range(11)] self.columns_hm = Columns((self.xs, self.y_ints), kdims=['x'], vdims=['y']) def test_columns_array_init_hm(self): "Tests support for arrays (homogeneous)" exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Columns(np.column_stack([self.xs, self.xs_2]), kdims=['x'], vdims=['x2']) def test_columns_dataframe_init_hm(self): "Tests support for homogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Columns(pd.DataFrame({'x':self.xs, 'x2':self.xs_2}), kdims=['x'], vdims=['x2']) def test_columns_ndelement_init_hm(self): "Tests support for homogeneous NdElement (backwards compatibility)" exception = "None of the available storage backends "\ "were able to support the supplied data format." with self.assertRaisesRegexp(Exception, exception): Columns(NdElement(zip(self.xs, self.xs_2), kdims=['x'], vdims=['x2'])) def test_columns_2D_aggregate_partial_hm(self): array = np.random.rand(11, 11) columns = Columns({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=['x', 'y'], vdims=['z']) self.assertEqual(columns.aggregate(['x'], np.mean), Columns({'x':self.xs, 'z': np.mean(array, axis=1)}, kdims=['x'], vdims=['z'])) def test_columns_2D_reduce_hm(self): array = np.random.rand(11, 11) columns = Columns({'x':self.xs, 'y':self.y_ints, 'z': array}, kdims=['x', 'y'], vdims=['z']) self.assertEqual(np.array(columns.reduce(['x', 'y'], np.mean)), np.mean(array)) def test_columns_add_dimensions_value_hm(self): with self.assertRaisesRegexp(Exception, 'Cannot add key dimension to a dense representation.'): self.columns_hm.add_dimension('z', 1, 0) def test_columns_add_dimensions_values_hm(self): table = self.columns_hm.add_dimension('z', 1, range(1,12), vdim=True) self.assertEqual(table.vdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.array(list(range(1,12)))) def test_columns_sort_vdim_hm(self): exception = ('Compressed format cannot be sorted, either instantiate ' 'in the desired order or use the expanded format.') with self.assertRaisesRegexp(Exception, exception): self.columns_hm.sort('y') def test_columns_groupby(self): self.assertEqual(self.columns_hm.groupby('x').keys(), list(self.xs))
class HeterogeneousColumnTypes(HomogeneousColumnTypes): """ Tests for data formats that all columns to have varied types """ def init_data(self): self.kdims = ['Gender', 'Age'] self.vdims = ['Weight', 'Height'] self.gender, self.age = ['M','M','F'], [10,16,12] self.weight, self.height = [15,18,10], [0.8,0.6,0.8] self.table = Columns({'Gender':self.gender, 'Age':self.age, 'Weight':self.weight, 'Height':self.height}, kdims=self.kdims, vdims=self.vdims) super(HeterogeneousColumnTypes, self).init_data() self.ys = np.linspace(0, 1, 11) self.zs = np.sin(self.xs) self.columns_ht = Columns({'x':self.xs, 'y':self.ys}, kdims=['x'], vdims=['y']) # Test the constructor to be supported by all interfaces supporting # heterogeneous column types. def test_columns_ndelement_init_ht(self): "Tests support for heterogeneous NdElement (backwards compatibility)" columns = Columns(NdElement(zip(self.xs, self.ys), kdims=['x'], vdims=['y'])) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_dataframe_init_ht(self): "Tests support for heterogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") columns = Columns(pd.DataFrame({'x':self.xs, 'y':self.ys}), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) # Test literal formats def test_columns_uniq_dimvals_ht(self): self.assertEqual(self.table.dimension_values('Gender', unique=True), np.array(['M', 'F'])) def test_columns_implicit_indexing_init(self): columns = Columns(self.ys, kdims=['x'], vdims=['y']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_tuple_init(self): columns = Columns((self.xs, self.ys), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_simple_zip_init(self): columns = Columns(zip(self.xs, self.ys), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_zip_init(self): columns = Columns(zip(self.gender, self.age, self.weight, self.height), kdims=self.kdims, vdims=self.vdims) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_odict_init(self): columns = Columns(OrderedDict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_dict_init(self): columns = Columns(dict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) # Operations def test_columns_sort_vdim_ht(self): columns = Columns({'x':self.xs, 'y':-self.ys}, kdims=['x'], vdims=['y']) columns_sorted = Columns({'x':self.xs[::-1], 'y':-self.ys[::-1]}, kdims=['x'], vdims=['y']) self.assertEqual(columns.sort('y'), columns_sorted) def test_columns_sort_string_ht(self): columns_sorted = Columns({'Gender':['F','M','M'], 'Age':[12,10,16], 'Weight':[10,15,18], 'Height':[0.8,0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEqual(self.table.sort(), columns_sorted) def test_columns_sample_ht(self): samples = self.columns_ht.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 0.5, 1])) def test_columns_reduce_ht(self): reduced = Columns({'Age':self.age, 'Weight':self.weight, 'Height':self.height}, kdims=self.kdims[1:], vdims=self.vdims) self.assertEqual(self.table.reduce(['Gender'], np.mean), reduced) def test_columns_1D_reduce_ht(self): self.assertEqual(self.columns_ht.reduce('x', np.mean), np.float64(0.5)) def test_columns_2D_reduce_ht(self): reduced = Columns({'Weight':[14.333333333333334], 'Height':[0.73333333333333339]}, kdims=[], vdims=self.vdims) self.assertEqual(self.table.reduce(function=np.mean), reduced) def test_columns_2D_partial_reduce_ht(self): columns = Columns({'x':self.xs, 'y':self.ys, 'z':self.zs}, kdims=['x', 'y'], vdims=['z']) reduced = Columns({'x':self.xs, 'z':self.zs}, kdims=['x'], vdims=['z']) self.assertEqual(columns.reduce(['y'], np.mean), reduced) def test_column_aggregate_ht(self): aggregated = Columns({'Gender':['M','F'], 'Weight':[16.5,10], 'Height':[0.7,0.8]}, kdims=self.kdims[:1], vdims=self.vdims) self.compare_columns(self.table.aggregate(['Gender'], np.mean), aggregated) def test_columns_2D_aggregate_partial_ht(self): columns = Columns({'x':self.xs, 'y':self.ys, 'z':self.zs}, kdims=['x', 'y'], vdims=['z']) reduced = Columns({'x':self.xs, 'z':self.zs}, kdims=['x'], vdims=['z']) self.assertEqual(columns.aggregate(['x'], np.mean), reduced) def test_columns_groupby(self): group1 = {'Age':[10,16], 'Weight':[15,18], 'Height':[0.8,0.6]} group2 = {'Age':[12], 'Weight':[10], 'Height':[0.8]} with sorted_context(False): grouped = HoloMap([('M', Columns(group1, kdims=['Age'], vdims=self.vdims)), ('F', Columns(group2, kdims=['Age'], vdims=self.vdims))], kdims=['Gender']) self.assertEqual(self.table.groupby(['Gender']), grouped) def test_columns_add_dimensions_value_ht(self): table = self.columns_ht.add_dimension('z', 1, 0) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.zeros(len(table))) def test_columns_add_dimensions_values_ht(self): table = self.columns_ht.add_dimension('z', 1, range(1,12)) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.array(list(range(1,12)))) # Indexing def test_columns_index_row_gender_female(self): indexed = Columns({'Gender':['F'],'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) row = self.table['F',:] self.assertEquals(row, indexed) def test_columns_index_rows_gender_male(self): row = self.table['M',:] indexed = Columns({'Gender':['M','M'],'Age':[10,16], 'Weight':[15,18], 'Height':[0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_columns_index_row_age(self): indexed = Columns({'Gender':['F'],'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(self.table[:, 12], indexed) def test_columns_index_item_table(self): indexed = Columns({'Gender':['F'],'Age':[12], 'Weight':[10], 'Height':[0.8]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(self.table['F', 12], indexed) def test_columns_index_value1(self): self.assertEquals(self.table['F', 12, 'Weight'], 10) def test_columns_index_value2(self): self.assertEquals(self.table['F', 12, 'Height'], 0.8) def test_columns_index_column_ht(self): self.compare_arrays(self.columns_ht['y'], self.ys) def test_columns_boolean_index(self): row = self.table[np.array([True, True, False])] indexed = Columns({'Gender':['M','M'],'Age':[10,16], 'Weight':[15,18], 'Height':[0.8,0.6]}, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_columns_value_dim_index(self): row = self.table[:, :, 'Weight'] indexed = Columns({'Gender':['M','M','F'],'Age':[10,16, 12], 'Weight':[15,18, 10]}, kdims=self.kdims, vdims=self.vdims[:1]) self.assertEquals(row, indexed) def test_columns_value_dim_scalar_index(self): row = self.table['M', 10, 'Weight'] self.assertEquals(row, 15) # Casting def test_columns_array_ht(self): self.assertEqual(self.columns_ht.array(), np.column_stack([self.xs, self.ys]))
class HeterogeneousColumnTypes(HomogeneousColumnTypes): """ Tests for data formats that all columns to have varied types """ def init_data(self): self.kdims = ['Gender', 'Age'] self.vdims = ['Weight', 'Height'] self.gender, self.age = ['M', 'M', 'F'], [10, 16, 12] self.weight, self.height = [15, 18, 10], [0.8, 0.6, 0.8] self.table = Columns( { 'Gender': self.gender, 'Age': self.age, 'Weight': self.weight, 'Height': self.height }, kdims=self.kdims, vdims=self.vdims) super(HeterogeneousColumnTypes, self).init_data() self.ys = np.linspace(0, 1, 11) self.zs = np.sin(self.xs) self.columns_ht = Columns({ 'x': self.xs, 'y': self.ys }, kdims=['x'], vdims=['y']) # Test the constructor to be supported by all interfaces supporting # heterogeneous column types. def test_columns_ndelement_init_ht(self): "Tests support for heterogeneous NdElement (backwards compatibility)" columns = Columns( NdElement(zip(self.xs, self.ys), kdims=['x'], vdims=['y'])) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_dataframe_init_ht(self): "Tests support for heterogeneous DataFrames" if pd is None: raise SkipTest("Pandas not available") columns = Columns(pd.DataFrame({ 'x': self.xs, 'y': self.ys }), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) # Test literal formats def test_columns_implicit_indexing_init(self): columns = Columns(self.ys, kdims=['x'], vdims=['y']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_tuple_init(self): columns = Columns((self.xs, self.ys), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_simple_zip_init(self): columns = Columns(zip(self.xs, self.ys), kdims=['x'], vdims=['y']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_zip_init(self): columns = Columns(zip(self.gender, self.age, self.weight, self.height), kdims=self.kdims, vdims=self.vdims) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_odict_init(self): columns = Columns(OrderedDict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) def test_columns_dict_init(self): columns = Columns(dict(zip(self.xs, self.ys)), kdims=['A'], vdims=['B']) self.assertTrue(isinstance(columns.data, self.data_instance_type)) # Operations def test_columns_sort_vdim_ht(self): columns = Columns({ 'x': self.xs, 'y': -self.ys }, kdims=['x'], vdims=['y']) columns_sorted = Columns({ 'x': self.xs[::-1], 'y': -self.ys[::-1] }, kdims=['x'], vdims=['y']) self.assertEqual(columns.sort('y'), columns_sorted) def test_columns_sort_string_ht(self): columns_sorted = Columns( { 'Gender': ['F', 'M', 'M'], 'Age': [12, 10, 16], 'Weight': [10, 15, 18], 'Height': [0.8, 0.8, 0.6] }, kdims=self.kdims, vdims=self.vdims) self.assertEqual(self.table.sort(), columns_sorted) def test_columns_sample_ht(self): samples = self.columns_ht.sample([0, 5, 10]).dimension_values('y') self.assertEqual(samples, np.array([0, 0.5, 1])) def test_columns_reduce_ht(self): reduced = Columns( { 'Age': self.age, 'Weight': self.weight, 'Height': self.height }, kdims=self.kdims[1:], vdims=self.vdims) self.assertEqual(self.table.reduce(['Gender'], np.mean), reduced) def test_columns_1D_reduce_ht(self): self.assertEqual(self.columns_ht.reduce('x', np.mean), np.float64(0.5)) def test_columns_2D_reduce_ht(self): reduced = Columns( { 'Weight': [14.333333333333334], 'Height': [0.73333333333333339] }, kdims=[], vdims=self.vdims) self.assertEqual(self.table.reduce(function=np.mean), reduced) def test_columns_2D_partial_reduce_ht(self): columns = Columns({ 'x': self.xs, 'y': self.ys, 'z': self.zs }, kdims=['x', 'y'], vdims=['z']) reduced = Columns({ 'x': self.xs, 'z': self.zs }, kdims=['x'], vdims=['z']) self.assertEqual(columns.reduce(['y'], np.mean), reduced) def test_column_aggregate_ht(self): aggregated = Columns( { 'Gender': ['M', 'F'], 'Weight': [16.5, 10], 'Height': [0.7, 0.8] }, kdims=self.kdims[:1], vdims=self.vdims) self.compare_columns(self.table.aggregate(['Gender'], np.mean), aggregated) def test_columns_2D_aggregate_partial_ht(self): columns = Columns({ 'x': self.xs, 'y': self.ys, 'z': self.zs }, kdims=['x', 'y'], vdims=['z']) reduced = Columns({ 'x': self.xs, 'z': self.zs }, kdims=['x'], vdims=['z']) self.assertEqual(columns.aggregate(['x'], np.mean), reduced) def test_columns_groupby(self): group1 = {'Age': [10, 16], 'Weight': [15, 18], 'Height': [0.8, 0.6]} group2 = {'Age': [12], 'Weight': [10], 'Height': [0.8]} with sorted_context(False): grouped = HoloMap( [('M', Columns(group1, kdims=['Age'], vdims=self.vdims)), ('F', Columns(group2, kdims=['Age'], vdims=self.vdims))], kdims=['Gender']) self.assertEqual(self.table.groupby(['Gender']), grouped) def test_columns_add_dimensions_value_ht(self): table = self.columns_ht.add_dimension('z', 1, 0) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.zeros(len(table))) def test_columns_add_dimensions_values_ht(self): table = self.columns_ht.add_dimension('z', 1, range(1, 12)) self.assertEqual(table.kdims[1], 'z') self.compare_arrays(table.dimension_values('z'), np.array(list(range(1, 12)))) # Indexing def test_columns_index_row_gender_female(self): indexed = Columns( { 'Gender': ['F'], 'Age': [12], 'Weight': [10], 'Height': [0.8] }, kdims=self.kdims, vdims=self.vdims) row = self.table['F', :] self.assertEquals(row, indexed) def test_columns_index_rows_gender_male(self): row = self.table['M', :] indexed = Columns( { 'Gender': ['M', 'M'], 'Age': [10, 16], 'Weight': [15, 18], 'Height': [0.8, 0.6] }, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_columns_index_row_age(self): indexed = Columns( { 'Gender': ['F'], 'Age': [12], 'Weight': [10], 'Height': [0.8] }, kdims=self.kdims, vdims=self.vdims) self.assertEquals(self.table[:, 12], indexed) def test_columns_index_item_table(self): indexed = Columns( { 'Gender': ['F'], 'Age': [12], 'Weight': [10], 'Height': [0.8] }, kdims=self.kdims, vdims=self.vdims) self.assertEquals(self.table['F', 12], indexed) def test_columns_index_value1(self): self.assertEquals(self.table['F', 12, 'Weight'], 10) def test_columns_index_value2(self): self.assertEquals(self.table['F', 12, 'Height'], 0.8) def test_columns_index_column_ht(self): self.compare_arrays(self.columns_ht['y'], self.ys) def test_columns_boolean_index(self): row = self.table[np.array([True, True, False])] indexed = Columns( { 'Gender': ['M', 'M'], 'Age': [10, 16], 'Weight': [15, 18], 'Height': [0.8, 0.6] }, kdims=self.kdims, vdims=self.vdims) self.assertEquals(row, indexed) def test_columns_value_dim_index(self): row = self.table[:, :, 'Weight'] indexed = Columns( { 'Gender': ['M', 'M', 'F'], 'Age': [10, 16, 12], 'Weight': [15, 18, 10] }, kdims=self.kdims, vdims=self.vdims[:1]) self.assertEquals(row, indexed) def test_columns_value_dim_scalar_index(self): row = self.table['M', 10, 'Weight'] self.assertEquals(row, 15) # Casting def test_columns_array_ht(self): self.assertEqual(self.columns_ht.array(), np.column_stack([self.xs, self.ys]))