Remove unused functions and improve testing of evaluate

gala/evaluate.py

@@ -42,79 +42,6 @@ def nzcol(mat, row_idx):
     return mat[row_idx].nonzero()[1]
 
 
-def sparse_min(mat, axis=None):
-    """Compute the minimum value in a sparse matrix (optionally over an axis).
-
-    This function mimics the numpy.min() API for sparse.CSC or CSR matrices.
-
-    Parameters
-    ----------
-    mat : a scipy.sparse csc or csr matrix
-        The matrix for which to compute the min.
-    axis : int in {0, 1}, optional
-        Compute the minimum over each column (`axis=0`) or over each row
-        (`axis=1`). By default, compute over entire matrix.
-
-    Returns
-    -------
-    mn : mat.dtype (if `axis=None`) or np.ndarray of shape (mat.shape[1-axis],)
-        The minimum value in the array or along an axis.
-    """
-    mn = - sparse_max(-mat, axis)
-    return mn
-
-
-def sparse_max(mat, axis=None):
-    """Compute the maximum value in a sparse matrix (optionally over an axis).
-
-    This function mimics the numpy.max() API for sparse.CSC or CSR matrices.
-
-    Parameters
-    ----------
-    mat : a scipy.sparse csc or csr matrix
-        The matrix for which to compute the max.
-    axis : int in {0, 1}, optional
-        Compute the maximum over each column (`axis=0`) or over each row
-        (`axis=1`). By default, compute over entire matrix.
-
-    Returns
-    -------
-    mx : mat.dtype (if `axis=None`) or np.ndarray of shape (mat.shape[1-axis],)
-        The maximum value in the array or along an axis.
-    """
-    if type(mat) == sparse.csr_matrix:
-        mat = mat.tocsc()
-    if axis is None:
-        mx = np.max(mat.data)
-    elif axis == 0:
-        mx = sparse_csr_row_max(mat.T)
-    elif axis == 1:
-        mx = sparse_csr_row_max(mat.tocsr())
-    else:
-        raise ValueError("Invalid axis %i for matrix (2 dimensional)." % axis)
-    return mx
-
-
-def sparse_csr_row_max(csr_mat):
-    """Compute maximum over each row of a CSR format sparse matrix.
-
-    Parameters
-    ----------
-    csr_mat : scipy.sparse.csr_matrix
-        The input matrix.
-
-    Returns
-    -------
-    mx : np.ndarray of shape `(mat.shape[0],)`
-        The maximum along every row.
-    """
-    ret = np.zeros(csr_mat.shape[0])
-    row_diff = np.diff(csr_mat.indptr)
-    ret[row_diff != 0] = np.maximum.reduceat(csr_mat.data,
-                                             csr_mat.indptr[:-1][row_diff > 0])
-    return ret
-
-
 def pixel_wise_boundary_precision_recall(pred, gt):
     """Evaluate voxel prediction accuracy against a ground truth.
 

tests/test_evaluate.py

@@ -13,3 +13,9 @@ def test_contingency_table():
                                 [0.   , 0.   , 0.375],
                                 [0.125, 0.   , 0.   ]]))
     assert ct.shape == ct0.shape
+
+
+def test_vi():
+    seg = np.array([1, 2, 3, 4])
+    gt = np.array([1, 1, 8, 8])
+    assert_equal(ev.vi(seg, gt), 1)