From 7724fe73deb4862420e87dc3c1011ee153c27d99 Mon Sep 17 00:00:00 2001 From: James Fletcher <38212943+jmsfltchr@users.noreply.github.com> Date: Tue, 3 Dec 2019 21:44:35 +0000 Subject: [PATCH] Don't Plot Preexisting Graph Elements (#108) ## What is the goal of this PR? Update graph plots in line with the loss function being used (where preexisting graph elements are not penalised) ## What are the changes implemented in this PR? - Preexisting graph elements are only drawn for the ground truth plot - Use consistent colours and change only the opacity to indicate certainty - Simplify the plotting code --- .../{images => .images}/diagnosis_schema.png | Bin .../diagnosis/.images/graph_snippet.png | Bin 0 -> 158986 bytes .../examples/diagnosis/.images/learning.png | Bin 0 -> 66974 bytes .../{images => .images}/queried_subgraph.png | Bin kglib/kgcn/examples/diagnosis/README.md | 8 +-- .../diagnosis/images/graph_snippet.png | Bin 157847 -> 0 bytes .../examples/diagnosis/images/learning.png | Bin 62769 -> 0 bytes kglib/kgcn/plot/plotting.py | 66 ++++++++---------- kglib/kgcn/plot/plotting_test.py | 41 +++++++---- 9 files changed, 60 insertions(+), 55 deletions(-) rename kglib/kgcn/examples/diagnosis/{images => .images}/diagnosis_schema.png (100%) create mode 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d1daf876..96c0f95d 100644 --- a/kglib/kgcn/examples/diagnosis/README.md +++ b/kglib/kgcn/examples/diagnosis/README.md @@ -4,7 +4,7 @@ This example is entirely fabricated as a demonstration for how to construct a KG Studying the schema for this example, we have people who present symptoms, with some severity. Separately, we may know that certain symptoms can be caused by a disease. Lastly, people can be diagnosed with a disease. -![Diagnosis Schema](images/diagnosis_schema.png) +![Diagnosis Schema](.images/diagnosis_schema.png) ## Running the Example @@ -93,13 +93,13 @@ You will see plots of metrics for the training process (training iteration on th - The fraction of all graph elements predicted correctly across the dataset - The fraction of completely solved examples (subgraphs extracted from Grakn that are solved in full) -![learning metrics](images/learning.png) +![learning metrics](.images/learning.png) #### Visualise the Predictions We also receive a plot of some of the predictions made on the test set. -![predictions made on test set](images/graph_snippet.png) +![predictions made on test set](.images/graph_snippet.png) **Blue box:** Ground Truth @@ -159,7 +159,7 @@ A single subgraph is extracted from Grakn by making these queries and combining We can visualise such a subgraph by running these two queries one after the other in Grakn Workbase: -![queried subgraph](images/queried_subgraph.png) +![queried subgraph](.images/queried_subgraph.png) You can get the relevant version of Grakn Workbase from the Assets of the [latest Workbase release](https://github.com/graknlabs/workbase/releases/latest). diff --git a/kglib/kgcn/examples/diagnosis/images/graph_snippet.png b/kglib/kgcn/examples/diagnosis/images/graph_snippet.png deleted file mode 100644 index c6202f6812a7ffeb33d88da3e2ab414c7ec73b2c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 157847 zcmb@ubzD^I`!-5(&))l-KaQUz%q*T+Pv7@--`Dj_w6>-SE*2#g0s;c=Q&mM>1O(Iw1O&ug3^d@* zZK;F?@C(sHS4AG7a-4b(c!TMxYUqK0fN$~eAMvA1sXb8Om;G}CPXi5gaVr-mZVPJ{ zOB-&mlPgdf0YMTh4!m@-@wA`^J2^Ugh=ZjV|CA61UO(LCVWj_4#M42F(Lh6+UctrP zhF*wUkeioL8jGHuUeetfB(AHd{GX2l@1z**JUv~-d3bz$e7JoCxLw?BdHBS{#CUl5 zdHDIcfD&9DzRsQ&U@m75roSHY-{&aWcv!jHyL#HYIMY8o*TT}p%TtPx@u8ys{qH~R zbaMUQ6*+tSr&mD#c)%8}Jbc`|Jb(8gPkYe+H1GfRsfWM(^|3#_`l~WYU}(e@+-)p8 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plot_predictions(raw_graphs, test_values, num_processing_steps_ge, solution_ ground_truth_node_prob = target["nodes"][:, -1] ground_truth_edge_prob = target["edges"][:, -1] + non_preexist_node_mask = mask_preexists(target["nodes"]) + non_preexist_edge_mask = mask_preexists(target["edges"]) + # Ground truth. iax = j * (2 + num_steps_to_plot) + 1 ax = draw_subplot(graph, fig, pos, node_size, h, w, iax, ground_truth_node_prob, ground_truth_edge_prob, True) @@ -118,16 +121,16 @@ def plot_predictions(raw_graphs, test_values, num_processing_steps_ge, solution_ # Prediction. for k, outp in enumerate(output): iax = j * (2 + num_steps_to_plot) + 2 + k - node_prob = softmax_prob_last_dim(outp["nodes"]) - edge_prob = softmax_prob_last_dim(outp["edges"]) + node_prob = softmax_prob_last_dim(outp["nodes"]) * non_preexist_node_mask + edge_prob = softmax_prob_last_dim(outp["edges"]) * non_preexist_edge_mask ax = draw_subplot(graph, fig, pos, node_size, h, w, iax, node_prob, edge_prob, False) ax.set_title("Model-predicted\nStep {:02d} / {:02d}".format( step_indices[k] + 1, step_indices[-1] + 1)) # Class Winners # Displays whether the class represented by the last dimension was the winner - node_prob = last_dim_was_class_winner(output[-1]["nodes"]) - edge_prob = last_dim_was_class_winner(output[-1]["edges"]) + node_prob = last_dim_was_class_winner(output[-1]["nodes"]) * non_preexist_node_mask + edge_prob = last_dim_was_class_winner(output[-1]["edges"]) * non_preexist_edge_mask iax = j * (2 + num_steps_to_plot) + 2 + len(output) ax = draw_subplot(graph, fig, pos, node_size, h, w, iax, node_prob, edge_prob, False) @@ -146,6 +149,10 @@ def plot_predictions(raw_graphs, test_values, num_processing_steps_ge, solution_ plt.savefig(output_file, bbox_inches='tight') +def mask_preexists(arr): + return (arr[:, 0] == 0) * 1 + + def softmax_prob_last_dim(x): e = np.exp(x) return e[:, -1] / np.sum(e, axis=-1) @@ -155,52 +162,39 @@ def last_dim_was_class_winner(x): return (np.argmax(x, axis=-1) == 2) * 1 -def above_base(val, base=0.0): - return val * (1.0 - base) + base - - def element_color(gt_plot, probability, element_props): """ Determine the color values to use for a node/edge and its label gt plot: - blue for existing elements, green for those to infer, red and transparent for candidates (as there could be many) + blue for existing elements, green for those to infer, red candidates output plot: blue for existing elements, green for those to infer, red for candidates, all with transparency """ - existing = dict(input=1, solution=0) - to_infer = dict(input=0, solution=2) - candidate = dict(input=0, solution=1) + existing = 0 + candidate = 1 + to_infer = 2 + + solution = element_props.get('solution') - to_infer = all([element_props.get(key) == value for key, value in to_infer.items()]) - candidate = all([element_props.get(key) == value for key, value in candidate.items()]) - existing = all([element_props.get(key) == value for key, value in existing.items()]) + color_config = { + to_infer: {'color': [0.0, 1.0, 0.0], 'gt_opacity': 1.0}, + candidate: {'color': [1.0, 0.0, 0.0], 'gt_opacity': 1.0}, + existing: {'color': [0.0, 0.0, 1.0], 'gt_opacity': 0.2} + } - default_gt_label_color = np.array([0.0, 0.0, 0.0, 1.0]) - output_label_color = np.array([0.0, 0.0, 0.0, above_base(probability, base=0.0)]) + chosen_config = color_config[solution] if gt_plot: - if to_infer: - return dict(element=np.array([0.0, 1.0, 0.0, 1.0]), label=default_gt_label_color) - elif existing: - return dict(element=np.array([0.0, 0.0, 1.0, 1.0]), label=default_gt_label_color) - elif candidate: - return dict(element=np.array([1.0, 0.0, 0.0, 0.1]), label=np.array([0.0, 0.0, 0.0, 0.1])) - else: - raise ValueError('Node to colour did not fit any category') + opacity = chosen_config['gt_opacity'] else: - if to_infer: - return dict(element=np.array([0.0, above_base(probability), 0.0, above_base(probability, base=0.0)]), - label=output_label_color) - elif existing: - return dict(element=np.array([0.0, 0.0, above_base(probability), above_base(probability, base=0.0)]), - label=output_label_color) - elif candidate: - return dict(element=np.array([above_base(probability), 0.0, 0.0, above_base(probability, base=0.0)]), - label=output_label_color) - else: - raise ValueError('Node to colour did not fit any category') + opacity = probability + + label = np.array([0.0, 0.0, 0.0] + [opacity]) + color = np.array(chosen_config['color'] + [opacity]) + + return dict(element=color, label=label) def draw_subplot(graph, fig, pos, node_size, h, w, iax, node_prob, edge_prob, gt_plot): diff --git a/kglib/kgcn/plot/plotting_test.py b/kglib/kgcn/plot/plotting_test.py index 1f06c11c..d261f290 100644 --- a/kglib/kgcn/plot/plotting_test.py +++ b/kglib/kgcn/plot/plotting_test.py @@ -34,9 +34,9 @@ def test_plot_is_created(self): graph = nx.MultiDiGraph(name=0) - existing = dict(input=1, solution=0) - to_infer = dict(input=0, solution=2) - candidate = dict(input=0, solution=1) + existing = dict(solution=0) + to_infer = dict(solution=2) + candidate = dict(solution=1) # people graph.add_node(0, type='person', **existing) @@ -47,18 +47,29 @@ def test_plot_is_created(self): graph.add_edge(2, 0, type='parent', **to_infer) graph.add_edge(2, 1, type='child', **candidate) - graph_tuple = GraphsTuple(nodes=np.array([[1., 0., 0.], - [1., 1., 0.], - [1., 0., 1.]]), - edges=np.array([[1., 0., 0.], - [1., 1., 0.]]), - receivers=np.array([1, 2], dtype=np.int32), - senders=np.array([0, 1], dtype=np.int32), - globals=np.array([[0., 0., 0., 0., 0.]], dtype=np.float32), - n_node=np.array([3], dtype=np.int32), - n_edge=np.array([2], dtype=np.int32)) - - test_values = {"target": graph_tuple, "outputs": [graph_tuple for _ in range(6)]} + graph_tuple_target = GraphsTuple(nodes=np.array([[1., 0., 0.], + [0., 1., 0.], + [0., 0., 1.]]), + edges=np.array([[0., 0., 1.], + [0., 1., 0.]]), + receivers=np.array([1, 2], dtype=np.int32), + senders=np.array([0, 1], dtype=np.int32), + globals=np.array([[0., 0., 0., 0., 0.]], dtype=np.float32), + n_node=np.array([3], dtype=np.int32), + n_edge=np.array([2], dtype=np.int32)) + + graph_tuple_output = GraphsTuple(nodes=np.array([[1., 0., 0.], + [1., 1., 0.], + [1., 0., 1.]]), + edges=np.array([[1., 0., 0.], + [1., 1., 0.]]), + receivers=np.array([1, 2], dtype=np.int32), + senders=np.array([0, 1], dtype=np.int32), + globals=np.array([[0., 0., 0., 0., 0.]], dtype=np.float32), + n_node=np.array([3], dtype=np.int32), + n_edge=np.array([2], dtype=np.int32)) + + test_values = {"target": graph_tuple_target, "outputs": [graph_tuple_output for _ in range(6)]} filename = f'./graph_{datetime.datetime.now()}.png'