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Fix minimum birth bug in plot_diagram #449

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Aug 4, 2020
Merged

Fix minimum birth bug in plot_diagram #449

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8cea7e6
Fix bug introduced in 4bc90b2e88926dbf2b4da2a455e7480485303d64
ulupo Aug 4, 2020
cec4a5c
Clean/simplify plot_diagram further to be more ready for extended per…
ulupo Aug 4, 2020
1858e6f
Make`store_edge_elements` work with MapperPipeline.get_mapper_params …
ulupo Aug 4, 2020
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78 changes: 39 additions & 39 deletions gtda/mapper/pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,16 +8,16 @@
from .utils._list_feature_union import ListFeatureUnion
from .utils.pipeline import transformer_from_callable_on_rows, identity

global_pipeline_params = ('memory', 'verbose')
nodes_params = ('scaler', 'filter_func', 'cover')
clust_prepr_params = ('clustering_preprocessing',)
clust_params = ('clusterer', 'n_jobs',
'parallel_backend_prefer')
nerve_params = ('min_intersection',)
clust_prepr_params_prefix = 'pullback_cover__'
nodes_params_prefix = 'pullback_cover__map_and_cover__'
clust_params_prefix = 'clustering__'
nerve_params_prefix = 'nerve__'
global_pipeline_params = ("memory", "verbose")
nodes_params = ("scaler", "filter_func", "cover")
clust_prepr_params = ("clustering_preprocessing",)
clust_params = ("clusterer", "n_jobs",
"parallel_backend_prefer")
nerve_params = ("min_intersection", "store_edge_elements")
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This is important and was forgotten in #447.

clust_prepr_params_prefix = "pullback_cover__"
nodes_params_prefix = "pullback_cover__map_and_cover__"
clust_params_prefix = "clustering__"
nerve_params_prefix = "nerve__"


class MapperPipeline(Pipeline):
Expand All @@ -43,10 +43,10 @@ class MapperPipeline(Pipeline):
>>> pipe = make_mapper_pipeline(filter_func=filter_func,
... cover=cover,
... clusterer=clusterer)
>>> print(pipe.get_mapper_params()['clusterer__eps'])
>>> print(pipe.get_mapper_params()["clusterer__eps"])
0.5
>>> pipe.set_params(clusterer___eps=0.1)
>>> print(pipe.get_mapper_params()['clusterer__eps'])
>>> print(pipe.get_mapper_params()["clusterer__eps"])
0.1

See also
Expand Down Expand Up @@ -130,14 +130,14 @@ def _clean_dict_keys(kwargs, prefix):
key[len(prefix):]: kwargs[key]
for key in kwargs
if (key.startswith(prefix)
and not key.startswith(prefix + 'steps')
and not key.startswith(prefix + 'memory')
and not key.startswith(prefix + 'verbose')
and not key.startswith(prefix + 'transformer_list')
and not key.startswith(prefix + 'n_jobs')
and not key.startswith(prefix + 'transformer_weights')
and not key.startswith(prefix + 'map_and_cover'))
}
and not key.startswith(prefix + "steps")
and not key.startswith(prefix + "memory")
and not key.startswith(prefix + "verbose")
and not key.startswith(prefix + "transformer_list")
and not key.startswith(prefix + "n_jobs")
and not key.startswith(prefix + "transformer_weights")
and not key.startswith(prefix + "map_and_cover"))
}


def make_mapper_pipeline(scaler=None,
Expand All @@ -146,7 +146,7 @@ def make_mapper_pipeline(scaler=None,
clustering_preprocessing=None,
clusterer=None,
n_jobs=None,
parallel_backend_prefer='threads',
parallel_backend_prefer="threads",
graph_step=True,
min_intersection=1,
store_edge_elements=False,
Expand Down Expand Up @@ -199,12 +199,12 @@ def make_mapper_pipeline(scaler=None,
in a :obj:`joblib.parallel_backend` context. ``-1`` means using all
processors.

parallel_backend_prefer : ``'processes'`` | ``'threads'``, optional, \
default: ``'threads'``
parallel_backend_prefer : ``"processes"`` | ``"threads"``, optional, \
default: ``"threads"``
Soft hint for the default joblib backend to use in a joblib-parallel
application of the clustering step across pullback cover sets. To be
used in conjunction with `n_jobs`. The default process-based backend is
'loky' and the default thread-based backend is 'threading'. See [2]_.
"loky" and the default thread-based backend is "threading". See [2]_.

graph_step : bool, optional, default: ``True``
Whether the resulting pipeline should stop at the calculation of the
Expand Down Expand Up @@ -257,11 +257,11 @@ def make_mapper_pipeline(scaler=None,
>>> print(mapper.__class__)
<class 'gtda.mapper.pipeline.MapperPipeline'>
>>> mapper_params = mapper.get_mapper_params()
>>> print(mapper_params['filter_func'].__class__)
>>> print(mapper_params["filter_func"].__class__)
<class 'sklearn.decomposition._pca.PCA'>
>>> print(mapper_params['cover'].__class__)
>>> print(mapper_params["cover"].__class__)
<class 'gtda.mapper.cover.CubicalCover'>
>>> print(mapper_params['clusterer'].__class__)
>>> print(mapper_params["clusterer"].__class__)
<class 'sklearn.cluster._dbscan.DBSCAN'>
>>> X = np.random.random((10000, 4)) # 10000 points in 4-dimensional space
>>> mapper_graph = mapper.fit_transform(X) # Create the mapper graph
Expand All @@ -272,7 +272,7 @@ def make_mapper_pipeline(scaler=None,
['pullback_set_label', 'partial_cluster_label', 'node_elements']
>>> # Find which points belong to first node of graph
>>> node_id = 0
>>> node_elements = mapper_graph.vs['node_elements']
>>> node_elements = mapper_graph.vs["node_elements"]
>>> print(f"Node ID: {node_id}, Node elements: {node_elements[node_id]}, "
... f"Data points: {X[node_elements[node_id]")
Node Id: 0,
Expand Down Expand Up @@ -372,7 +372,7 @@ def make_mapper_pipeline(scaler=None,
if filter_func is None:
from sklearn.decomposition import PCA
_filter_func = PCA(n_components=2)
elif not hasattr(filter_func, 'fit_transform'):
elif not hasattr(filter_func, "fit_transform"):
_filter_func = transformer_from_callable_on_rows(filter_func)
else:
_filter_func = filter_func
Expand All @@ -395,26 +395,26 @@ def make_mapper_pipeline(scaler=None,
_clusterer = clusterer

map_and_cover = Pipeline(
steps=[('scaler', _scaler),
('filter_func', _filter_func),
('cover', _cover)],
steps=[("scaler", _scaler),
("filter_func", _filter_func),
("cover", _cover)],
verbose=verbose)

all_steps = [
('pullback_cover', ListFeatureUnion(
[('clustering_preprocessing', _clustering_preprocessing),
('map_and_cover', map_and_cover)])),
('clustering', ParallelClustering(
("pullback_cover", ListFeatureUnion(
[("clustering_preprocessing", _clustering_preprocessing),
("map_and_cover", map_and_cover)])),
("clustering", ParallelClustering(
clusterer=_clusterer,
n_jobs=n_jobs,
parallel_backend_prefer=parallel_backend_prefer))
]
]

if graph_step:
all_steps.append(
('nerve', Nerve(min_intersection=min_intersection,
("nerve", Nerve(min_intersection=min_intersection,
store_edge_elements=store_edge_elements))
)
)

mapper_pipeline = MapperPipeline(
steps=all_steps, memory=memory, verbose=verbose)
Expand Down
36 changes: 18 additions & 18 deletions gtda/plotting/persistence_diagrams.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,23 +37,26 @@ def plot_diagram(diagram, homology_dimensions=None, plotly_params=None):
homology_dimensions = np.unique(diagram[:, 2])

diagram_no_dims = diagram[:, :2]
max_birth, max_death = np.max(
np.where(np.isposinf(diagram_no_dims), -np.inf, diagram_no_dims),
axis=0
max_val_display = np.max(
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Taking the global maxima and minima makes the code ready for extended persistence.

np.where(np.isposinf(diagram_no_dims), -np.inf, diagram_no_dims)
)
min_birth, min_death = np.max(
np.where(np.isneginf(diagram_no_dims), np.inf, diagram_no_dims),
axis=0
min_val_display = np.min(
np.where(np.isneginf(diagram_no_dims), np.inf, diagram_no_dims)
)
parameter_range = max_val_display - min_val_display
extra_space = 0.02 * parameter_range
min_val_display -= extra_space
max_val_display += extra_space

fig = gobj.Figure()
fig.add_trace(gobj.Scatter(x=[100 * min(-np.abs(max_death), min_birth),
100 * max_death],
y=[100 * min(-np.abs(max_death), min_birth),
100 * max_death],
mode="lines",
line=dict(dash="dash", width=1, color="black"),
showlegend=False, hoverinfo="none"))
fig.add_trace(gobj.Scatter(
x=[min_val_display, max_val_display],
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@ulupo ulupo Aug 4, 2020 •
edited
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This leads to better behaviour when clicking "Autoscale" on the plotly HTML object in a notebook. Currently, the factor of 100 means that autoscaling leads to huge ranges.

y=[min_val_display, max_val_display],
mode="lines",
line={"dash": "dash", "width": 1, "color": "black"},
showlegend=False,
hoverinfo="none"
))

for dim in homology_dimensions:
name = f"H{int(dim)}" if dim != np.inf else "Any homology dimension"
Expand All @@ -63,17 +66,14 @@ def plot_diagram(diagram, homology_dimensions=None, plotly_params=None):
fig.add_trace(gobj.Scatter(x=subdiagram[:, 0], y=subdiagram[:, 1],
mode="markers", name=name))

parameter_range = max_death - min_birth
extra_space = 0.02 * parameter_range

fig.update_layout(
width=500,
height=500,
xaxis1={
"title": "Birth",
"side": "bottom",
"type": "linear",
"range": [min_birth - extra_space, max_death + extra_space],
"range": [min_val_display, max_val_display],
"autorange": False,
"ticks": "outside",
"showline": True,
Expand All @@ -88,7 +88,7 @@ def plot_diagram(diagram, homology_dimensions=None, plotly_params=None):
"title": "Death",
"side": "left",
"type": "linear",
"range": [min_birth - extra_space, max_death + extra_space],
"range": [min_val_display, max_val_display],
"autorange": False, "scaleanchor": "x", "scaleratio": 1,
"ticks": "outside",
"showline": True,
Expand Down
57 changes: 32 additions & 25 deletions gtda/plotting/point_clouds.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,7 +58,7 @@ def plot_point_cloud(point_cloud, dimension=None, plotly_params=None):
"zeroline": True,
"showexponent": "all",
"exponentformat": "e"
},
},
"yaxis1": {
"title": "1st",
"side": "left",
Expand All @@ -69,58 +69,65 @@ def plot_point_cloud(point_cloud, dimension=None, plotly_params=None):
"zeroline": True,
"showexponent": "all",
"exponentformat": "e"
},
},
"plot_bgcolor": "white"
}
}

fig = gobj.Figure(layout=layout)
fig.update_xaxes(zeroline=True, linewidth=1, linecolor='black',
fig.update_xaxes(zeroline=True, linewidth=1, linecolor="black",
mirror=False)
fig.update_yaxes(zeroline=True, linewidth=1, linecolor='black',
fig.update_yaxes(zeroline=True, linewidth=1, linecolor="black",
mirror=False)

fig.add_trace(gobj.Scatter(x=point_cloud[:, 0],
y=point_cloud[:, 1],
mode='markers',
marker=dict(size=4,
color=list(range(
point_cloud.shape[0])),
colorscale='Viridis',
opacity=0.8)))
fig.add_trace(gobj.Scatter(
x=point_cloud[:, 0],
y=point_cloud[:, 1],
mode="markers",
marker={
"size": 4,
"color": list(range(point_cloud.shape[0])),
"colorscale": "Viridis",
"opacity": 0.8
}
))

elif dimension == 3:
scene = {
"xaxis": {
"title": "0th",
"type": "linear",
"showexponent": "all",
"exponentformat": "e"
},
},
"yaxis": {
"title": "1st",
"type": "linear",
"showexponent": "all",
"exponentformat": "e"
},
},
"zaxis": {
"title": "2nd",
"type": "linear",
"showexponent": "all",
"exponentformat": "e"
}
}
}

fig = gobj.Figure()
fig.update_layout(scene=scene)

fig.add_trace(gobj.Scatter3d(x=point_cloud[:, 0],
y=point_cloud[:, 1],
z=point_cloud[:, 2],
mode='markers',
marker=dict(size=4,
color=list(range(
point_cloud.shape[0])),
colorscale='Viridis',
opacity=0.8)))
fig.add_trace(gobj.Scatter3d(
x=point_cloud[:, 0],
y=point_cloud[:, 1],
z=point_cloud[:, 2],
mode="markers",
marker={
"size": 4,
"color": list(range(point_cloud.shape[0])),
"colorscale": "Viridis",
"opacity": 0.8
}
))

# Update trace and layout according to user input
if plotly_params:
Expand Down