Fix mmap settings used by joblib.Parallel

CODE_AUTHORS.rst

@@ -13,3 +13,4 @@ Where component authors are known, add them here.
 | Anibal Medina-Mardones, anibal.medinamardones@epfl.ch
 | Wojciech Reise, reisewojciech@gmail.com
 | Roman Yurchak, roman.yurchak@symerio.com
+| Nick Sale, nicholas.j.sale@gmail.com

gtda/diagrams/_metrics.py

@@ -75,7 +75,7 @@ def landscapes(diagrams, sampling, n_layers):
 
 
 def _heat(image, sampled_diag, sigma):
-    _sample_image(image, sampled_diag)  # modifies `heat` inplace
+    _sample_image(image, sampled_diag)
     image[:] = gaussian_filter(image, sigma, mode="reflect")
 
 
@@ -258,18 +258,19 @@ def _parallel_pairwise(X1, X2, metric, metric_params,
     if X2 is None:
         X2 = X1
 
+    n_columns = len(X2)
     distance_matrices = Parallel(n_jobs=n_jobs)(
         delayed(metric_func)(_subdiagrams(X1, [dim], remove_dim=True),
                              _subdiagrams(X2[s], [dim], remove_dim=True),
                              sampling=samplings[dim],
                              step_size=step_sizes[dim],
                              **effective_metric_params)
         for dim in homology_dimensions
-        for s in gen_even_slices(X2.shape[0], effective_n_jobs(n_jobs)))
+        for s in gen_even_slices(n_columns, effective_n_jobs(n_jobs)))
 
     distance_matrices = np.concatenate(distance_matrices, axis=1)
     distance_matrices = np.stack(
-        [distance_matrices[:, i * X2.shape[0]:(i + 1) * X2.shape[0]]
+        [distance_matrices[:, i * n_columns:(i + 1) * n_columns]
          for i in range(len(homology_dimensions))],
         axis=2)
     return distance_matrices
@@ -341,13 +342,13 @@ def _parallel_amplitude(X, metric, metric_params, homology_dimensions, n_jobs):
 
     amplitude_arrays = Parallel(n_jobs=n_jobs)(
         delayed(amplitude_func)(
-            _subdiagrams(X, [dim], remove_dim=True)[s],
+            _subdiagrams(X[s], [dim], remove_dim=True),
             sampling=samplings[dim], step_size=step_sizes[dim],
             **effective_metric_params)
         for dim in homology_dimensions
         for s in gen_even_slices(_num_samples(X), effective_n_jobs(n_jobs)))
 
-    amplitude_arrays = (np.concatenate(amplitude_arrays).reshape(
-        len(homology_dimensions), X.shape[0]).T)
+    amplitude_arrays = np.concatenate(amplitude_arrays).\
+        reshape(len(homology_dimensions), len(X)).T
 
     return amplitude_arrays

gtda/diagrams/_utils.py

@@ -29,6 +29,7 @@ def _sort(Xs):
 
 
 def _sample_image(image, sampled_diag):
+    # NOTE: Modifies `image` in-place
     unique, counts = np.unique(sampled_diag, axis=0, return_counts=True)
     unique = tuple(tuple(row) for row in unique.astype(np.int).T)
     image[unique] = counts

gtda/diagrams/features.py

@@ -113,10 +113,9 @@ def transform(self, X, y=None):
 
         with np.errstate(divide='ignore', invalid='ignore'):
             Xt = Parallel(n_jobs=self.n_jobs)(
-                delayed(self._persistence_entropy)(_subdiagrams(X, [dim])[s])
+                delayed(self._persistence_entropy)(_subdiagrams(X[s], [dim]))
                 for dim in self.homology_dimensions_
-                for s in gen_even_slices(
-                    X.shape[0], effective_n_jobs(self.n_jobs))
+                for s in gen_even_slices(len(X), effective_n_jobs(self.n_jobs))
             )
         Xt = np.concatenate(Xt).reshape(self._n_dimensions, X.shape[0]).T
         return Xt

gtda/diagrams/representations.py

@@ -142,13 +142,12 @@ def transform(self, X, y=None):
         X = check_diagrams(X)
 
         Xt = Parallel(n_jobs=self.n_jobs)(delayed(betti_curves)(
-                _subdiagrams(X, [dim], remove_dim=True)[s],
+                _subdiagrams(X[s], [dim], remove_dim=True),
                 self._samplings[dim])
             for dim in self.homology_dimensions_
-            for s in gen_even_slices(X.shape[0],
-                                     effective_n_jobs(self.n_jobs)))
+            for s in gen_even_slices(len(X), effective_n_jobs(self.n_jobs)))
         Xt = np.concatenate(Xt).\
-            reshape(self._n_dimensions, X.shape[0], -1).\
+            reshape(self._n_dimensions, len(X), -1).\
             transpose((1, 0, 2))
         return Xt
 
@@ -354,14 +353,13 @@ def transform(self, X, y=None):
         X = check_diagrams(X)
 
         Xt = Parallel(n_jobs=self.n_jobs)(delayed(landscapes)(
-                _subdiagrams(X, [dim], remove_dim=True)[s],
+                _subdiagrams(X[s], [dim], remove_dim=True),
                 self._samplings[dim],
                 self.n_layers)
             for dim in self.homology_dimensions_
-            for s in gen_even_slices(X.shape[0],
-                                     effective_n_jobs(self.n_jobs)))
-        Xt = np.concatenate(Xt).reshape(self._n_dimensions, X.shape[0],
-                                        self.n_layers, self.n_bins).\
+            for s in gen_even_slices(len(X), effective_n_jobs(self.n_jobs)))
+        Xt = np.concatenate(Xt).\
+            reshape(self._n_dimensions, len(X), self.n_layers, self.n_bins).\
             transpose((1, 0, 2, 3))
         return Xt
 
@@ -587,14 +585,13 @@ def transform(self, X, y=None):
         check_is_fitted(self)
         X = check_diagrams(X, copy=True)
 
-        Xt = Parallel(n_jobs=self.n_jobs)(delayed(
-            heats)(_subdiagrams(X, [dim], remove_dim=True)[s],
+        Xt = Parallel(n_jobs=self.n_jobs, mmap_mode='c')(delayed(
+            heats)(_subdiagrams(X[s], [dim], remove_dim=True),
                    self._samplings[dim], self._step_size[dim], self.sigma)
             for dim in self.homology_dimensions_
-            for s in gen_even_slices(X.shape[0],
-                                     effective_n_jobs(self.n_jobs)))
-        Xt = np.concatenate(Xt).reshape(self._n_dimensions, X.shape[0],
-                                        self.n_bins, self.n_bins).\
+            for s in gen_even_slices(len(X), effective_n_jobs(self.n_jobs)))
+        Xt = np.concatenate(Xt).\
+            reshape(self._n_dimensions, len(X), self.n_bins, self.n_bins).\
             transpose((1, 0, 2, 3))
         return Xt
 
@@ -792,19 +789,19 @@ def transform(self, X, y=None):
         check_is_fitted(self)
         X = check_diagrams(X, copy=True)
 
-        Xt = Parallel(n_jobs=self.n_jobs)(
-            delayed(persistence_images)(_subdiagrams(X, [dim],
-                                                     remove_dim=True)[s],
-                                        self._samplings[dim],
-                                        self._step_size[dim],
-                                        self.weights_[dim],
-                                        self.sigma)
+        Xt = Parallel(n_jobs=self.n_jobs, mmap_mode='c')(
+            delayed(persistence_images)(
+                _subdiagrams(X[s], [dim], remove_dim=True),
+                self._samplings[dim],
+                self._step_size[dim],
+                self.weights_[dim],
+                self.sigma
+            )
             for dim in self.homology_dimensions_
-            for s in gen_even_slices(X.shape[0],
-                                     effective_n_jobs(self.n_jobs))
+            for s in gen_even_slices(len(X), effective_n_jobs(self.n_jobs))
         )
-        Xt = np.concatenate(Xt).reshape(self._n_dimensions, X.shape[0],
-                                        self.n_bins, self.n_bins).\
+        Xt = np.concatenate(Xt).\
+            reshape(self._n_dimensions, len(X), self.n_bins, self.n_bins).\
             transpose((1, 0, 2, 3))
         return Xt
 
@@ -975,14 +972,13 @@ def transform(self, X, y=None):
         X = check_diagrams(X)
 
         Xt = (Parallel(n_jobs=self.n_jobs)
-              (delayed(silhouettes)(_subdiagrams(X, [dim], remove_dim=True)[s],
+              (delayed(silhouettes)(_subdiagrams(X[s], [dim], remove_dim=True),
                                     self._samplings[dim], power=self.power)
               for dim in self.homology_dimensions_
-              for s in gen_even_slices(X.shape[0],
-                                       effective_n_jobs(self.n_jobs))))
+              for s in gen_even_slices(len(X), effective_n_jobs(self.n_jobs))))
 
-        Xt = np.concatenate(Xt). \
-            reshape(self._n_dimensions, X.shape[0], -1). \
+        Xt = np.concatenate(Xt).\
+            reshape(self._n_dimensions, len(X), -1).\
             transpose((1, 0, 2))
         return Xt
 

gtda/diagrams/tests/test_features_representations.py

@@ -110,6 +110,22 @@ def test_pi_positive(pts):
     assert np.all(pi.fit_transform(diagrams) >= 0.)
 
 
+def test_large_pi_null_multithreaded():
+    """Test that pi is computed correctly when the input array
+    is at least 1MB and more than 1 process is used, triggering
+    joblib's use of memmaps"""
+    X = np.linspace(0, 100, 300000)
+    pi = PersistenceImage(sigma=1, n_bins=10, n_jobs=2)
+    diagrams = np.expand_dims(np.stack([X, X,
+                                        np.zeros((X.shape[0],),
+                                                 dtype=int)]).transpose(),
+                              axis=0)
+    diagrams = np.repeat(diagrams, 2, axis=0)
+    diagrams[1, :, 1] += 1
+
+    assert_almost_equal(pi.fit_transform(diagrams)[0], 0)
+
+
 def test_silhouette_transform():
     sht = Silhouette(n_bins=31, power=1.)
     X_sht_res = np.array([0., 0.05, 0.1, 0.15, 0.2, 0.25, 0.2, 0.15, 0.1,
@@ -234,3 +250,21 @@ def test_hk_with_diag_points(pts):
                                  for x_ in [x, x_with_diag_points]]
 
     assert_almost_equal(x_with_diag_points_t, x_t, decimal=13)
+
+
+def test_large_hk_shape_multithreaded():
+    """Test that HeatKernel returns something of the right shape when the input
+    array is at least 1MB and more than 1 process is used, triggering joblib's
+    use of memmaps"""
+    X = np.linspace(0, 100, 300000)
+    n_bins = 10
+    diagrams = np.expand_dims(np.stack([X, X,
+                                        np.zeros((X.shape[0],),
+                                                 dtype=int)]).transpose(),
+                              axis=0)
+
+    hk = HeatKernel(sigma=1, n_bins=n_bins, n_jobs=2)
+    num_dimensions = 1
+    x_t = hk.fit_transform(diagrams)
+
+    assert x_t.shape == (diagrams.shape[0], num_dimensions, n_bins, n_bins)

gtda/plotting/persistence_diagrams.py

@@ -20,7 +20,6 @@ def plot_diagram(diagram, homology_dimensions=None, **input_layout):
         homology dimensions which appear in `diagram` will be plotted.
 
     """
-    from ..diagrams._utils import _subdiagrams  # To avoid circular imports
 
     # TODO: increase the marker size
     if homology_dimensions is None:
@@ -76,8 +75,7 @@ def plot_diagram(diagram, homology_dimensions=None, **input_layout):
 
     for dim in homology_dimensions:
         name = f'H{int(dim)}' if dim != np.inf else 'Any homology dimension'
-        subdiagram = _subdiagrams(np.asarray([diagram]), [dim],
-                                  remove_dim=True)[0]
+        subdiagram = diagram[diagram[:, 2] == dim]
         diff = (subdiagram[:, 1] != subdiagram[:, 0])
         subdiagram = subdiagram[diff]
         fig.add_trace(gobj.Scatter(x=subdiagram[:, 0], y=subdiagram[:, 1],