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c7f2032dd7
* Add user-generated annotations tests to the server Partially completes https://github.com/chanzuckerberg/cellxgene/issues/969 * Auto-format python code * @skip_if: passing lambdas > than property strings * Respond to feedback from @bkmartinjr
83 lines
3.6 KiB
Python
83 lines
3.6 KiB
Python
import unittest
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import pandas as pd
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import numpy as np
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from scipy import sparse
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import server.test.decode_fbs as decode_fbs
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from server.data_common.fbs.matrix import encode_matrix_fbs, decode_matrix_fbs
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class FbsTests(unittest.TestCase):
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"""Test Case for Matrix FBS data encode/decode """
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def test_encode_boundary(self):
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""" test various boundary checks """
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# row indexing is unsupported
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with self.assertRaises(ValueError):
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encode_matrix_fbs(matrix=pd.DataFrame(), row_idx=[])
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# matrix must be 2D
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with self.assertRaises(ValueError):
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encode_matrix_fbs(matrix=np.zeros((3, 2, 1)))
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with self.assertRaises(ValueError):
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encode_matrix_fbs(matrix=np.ones((10,)))
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def fbs_checks(self, fbs, dims, expected_types, expected_column_idx):
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d = decode_fbs.decode_matrix_FBS(fbs)
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self.assertEqual(d["n_rows"], dims[0])
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self.assertEqual(d["n_cols"], dims[1])
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self.assertIsNone(d["row_idx"])
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self.assertEqual(len(d["columns"]), dims[1])
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for i in range(0, len(d["columns"])):
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self.assertEqual(len(d["columns"][i]), dims[0])
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self.assertIsInstance(d["columns"][i], expected_types[i][0])
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if expected_types[i][1] is not None:
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self.assertEqual(d["columns"][i].dtype, expected_types[i][1])
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if expected_column_idx is not None:
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self.assertSetEqual(set(expected_column_idx), set(d["col_idx"]))
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def test_encode_DataFrame(self):
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df = pd.DataFrame(
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data={
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"a": np.zeros((10,), dtype=np.float32),
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"b": np.ones((10,), dtype=np.int64),
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"c": np.array([i for i in range(0, 10)], dtype=np.uint16),
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"d": pd.Series(["x", "y", "z", "x", "y", "z", "a", "x", "y", "z"], dtype="category"),
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}
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)
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expected_types = ((np.ndarray, np.float32), (np.ndarray, np.int32), (np.ndarray, np.uint32), (list, None))
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fbs = encode_matrix_fbs(matrix=df, row_idx=None, col_idx=df.columns)
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self.fbs_checks(fbs, (10, 4), expected_types, ["a", "b", "c", "d"])
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def test_encode_ndarray(self):
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arr = np.zeros((3, 2), dtype=np.float32)
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expected_types = ((np.ndarray, np.float32), (np.ndarray, np.float32), (np.ndarray, np.float32))
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fbs = encode_matrix_fbs(matrix=arr, row_idx=None, col_idx=None)
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self.fbs_checks(fbs, (3, 2), expected_types, None)
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def test_encode_sparse(self):
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csc = sparse.csc_matrix(np.array([[0, 1, 2], [3, 0, 4]]))
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expected_types = ((np.ndarray, np.int32), (np.ndarray, np.int32), (np.ndarray, np.int32))
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fbs = encode_matrix_fbs(matrix=csc, row_idx=None, col_idx=None)
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self.fbs_checks(fbs, (2, 3), expected_types, None)
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def test_roundtrip(self):
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dfSrc = pd.DataFrame(
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data={
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"a": np.zeros((10,), dtype=np.float32),
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"b": np.ones((10,), dtype=np.int64),
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"c": np.array([i for i in range(0, 10)], dtype=np.uint16),
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"d": pd.Series(["x", "y", "z", "x", "y", "z", "a", "x", "y", "z"], dtype="category"),
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}
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)
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dfDst = decode_matrix_fbs(encode_matrix_fbs(matrix=dfSrc, col_idx=dfSrc.columns))
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self.assertEqual(dfSrc.shape, dfDst.shape)
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self.assertEqual(set(dfSrc.columns), set(dfDst.columns))
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for c in dfSrc.columns:
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self.assertTrue(c in dfDst.columns)
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if isinstance(dfSrc[c], pd.Series):
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self.assertTrue(np.all(dfSrc[c] == dfDst[c]))
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else:
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self.assertEqual(dfSrc[c], dfDst[c])
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