Develop

README.md

@@ -92,7 +92,9 @@ from tdamapper.plot import MapperPlot
 
 # Generate toy dataset
 X, labels = make_circles(n_samples=5000, noise=0.05, factor=0.3, random_state=42)
-plt.scatter(X[:,0], X[:,1], c=labels, cmap='jet', s=0.25)
+plt.figure(figsize=(5, 5))
+plt.scatter(X[:,0], X[:,1], c=labels, s=0.25, cmap="jet")
+plt.axis("off")
 plt.show()
 
 # Apply PCA as lens
@@ -105,7 +107,7 @@ graph = MapperAlgorithm(cover, clust).fit_transform(X, y)
 
 # Visualize the Mapper graph
 fig = MapperPlot(graph, dim=2, seed=42, iterations=60).plot_plotly(colors=labels)
-fig.show(config={'scrollZoom': True})
+fig.show(config={"scrollZoom": True})
 ```
 
 | Original Dataset | Mapper Graph |

docs/source/notebooks/circles_online.py

@@ -17,74 +17,63 @@
 
 # %%
 import numpy as np
-
 from matplotlib import pyplot as plt
-
+from sklearn.cluster import DBSCAN
 from sklearn.datasets import make_circles
 from sklearn.decomposition import PCA
-from sklearn.cluster import DBSCAN
 
-from tdamapper.learn import MapperAlgorithm
 from tdamapper.cover import CubicalCover
+from tdamapper.learn import MapperAlgorithm
 from tdamapper.plot import MapperPlot
 
-X, y = make_circles(                # load a labelled dataset
-    n_samples=5000,
-    noise=0.05,
-    factor=0.3,
-    random_state=42
-)
-lens = PCA(2, random_state=42).fit_transform(X)
+width, height, dpi = 500, 500, 100
+
+# Generate toy dataset
+X, labels = make_circles(n_samples=5000, noise=0.05, factor=0.3, random_state=42)
+
+fig = plt.figure(figsize=(width / dpi, height / dpi), dpi=dpi)
+plt.scatter(X[:, 0], X[:, 1], c=labels, s=0.25, cmap="jet")
+plt.axis("off")
+plt.show()
+# fig.savefig("circles_dataset.png", dpi=dpi)
+
+# Apply PCA as lens
+y = PCA(2, random_state=42).fit_transform(X)
 
-plt.scatter(lens[:, 0], lens[:, 1], c=y, cmap='jet')
 
 # %% [markdown]
 # ### Build Mapper graph
 
 # %%
-mapper_algo = MapperAlgorithm(
-    cover=CubicalCover(
-        n_intervals=10,
-        overlap_frac=0.3
-    ),
-    clustering=DBSCAN()
-)
-
-mapper_graph = mapper_algo.fit_transform(X, lens)
+cover = CubicalCover(n_intervals=10, overlap_frac=0.3)
+clust = DBSCAN()
+mapper = MapperAlgorithm(cover=cover, clustering=clust)
+graph = mapper.fit_transform(X, y)
 
 # %% [markdown]
 # ### Plot Mapper graph with mean
 
 # %%
-mapper_plot = MapperPlot(
-    mapper_graph,
-    dim=2,
-    iterations=60,
-    seed=42
-)
+plot = MapperPlot(graph, dim=2, iterations=60, seed=42)
 
-fig = mapper_plot.plot_plotly(
-    colors=y,                       # color according to categorical values
-    cmap='jet',                     # Jet colormap, for classes
-    agg=np.nanmean,                 # aggregate on nodes according to mean
+fig = plot.plot_plotly(
+    colors=labels,  # color according to categorical values
+    cmap="jet",  # Jet colormap, for classes
+    agg=np.nanmean,  # aggregate on nodes according to mean
     width=600,
-    height=600
+    height=600,
 )
 
-fig.show(
-    renderer='notebook_connected',
-    config={'scrollZoom': True}
-)
+fig.show(renderer="notebook_connected", config={"scrollZoom": True})
+# fig.write_image("circles_mean.png", width=width, height=height)
 
 # %%
-mapper_plot.plot_plotly_update(
-    fig,                            # update the old figure
-    colors=y,
-    cmap='viridis',                 # viridis colormap, for ranges
-    agg=np.nanstd                   # aggregate on nodes according to std
-)                  
-
-fig.show(
-    renderer='notebook_connected',
-    config={'scrollZoom': True}
+plot.plot_plotly_update(
+    fig,  # update the old figure
+    colors=labels,
+    cmap="viridis",  # viridis colormap, for ranges
+    agg=np.nanstd,  # aggregate on nodes according to std
 )
+
+fig.show(renderer="notebook_connected", config={"scrollZoom": True})
+# fig.write_image("circles_std.png", width=width, height=height)

docs/source/notebooks/digits_online.py

@@ -17,74 +17,78 @@
 
 # %%
 import numpy as np
-
-from sklearn.datasets import load_digits
 from sklearn.cluster import AgglomerativeClustering
+from sklearn.datasets import load_digits
 from sklearn.decomposition import PCA
 
-from tdamapper.learn import MapperAlgorithm
-from tdamapper.cover import CubicalCover
 from tdamapper.clustering import FailSafeClustering
+from tdamapper.cover import CubicalCover
+from tdamapper.learn import MapperAlgorithm
 from tdamapper.plot import MapperPlot
 
+# We load a labelled dataset
+X, labels = load_digits(return_X_y=True)
 
-X, y = load_digits(return_X_y=True)                 # We load a labelled dataset
-lens = PCA(2, random_state=42).fit_transform(X)     # We compute the lens values
+# Apply PCA as lens
+y = PCA(2, random_state=42).fit_transform(X)
 
 # %% [markdown]
 # ### Build Mapper graph
 
 # %%
-mapper_algo = MapperAlgorithm(
-    cover=CubicalCover(
-        n_intervals=10,
-        overlap_frac=0.65
-    ),
+algo = MapperAlgorithm(
+    cover=CubicalCover(n_intervals=10, overlap_frac=0.5),
     clustering=AgglomerativeClustering(10),
-    verbose=False
+    verbose=False,
 )
 
-mapper_graph = mapper_algo.fit_transform(X, lens)
+graph = algo.fit_transform(X, y)
 
 # %% [markdown]
 # ### Plot Mapper graph with mean
 
 # %%
-mapper_plot = MapperPlot(
-    mapper_graph,
-    dim=2,
-    iterations=400,
-    seed=42
-)
+plot = MapperPlot(graph, dim=3, iterations=400, seed=42)
 
-fig = mapper_plot.plot_plotly(
-    colors=y,                        # We color according to digit values
-    cmap='jet',                      # Jet colormap, used for classes
-    agg=np.nanmean,                  # We aggregate on graph nodes according to mean
-    title='digit (mean)',
+fig = plot.plot_plotly(
+    colors=labels,  # We color according to digit values
+    cmap="jet",  # Jet colormap, used for classes
+    agg=np.nanmean,  # We aggregate on graph nodes according to mean
+    title="digit (mean)",
     width=600,
-    height=600
+    height=600,
 )
 
-fig.show(
-    renderer='notebook_connected',
-    config={'scrollZoom': True}
-)
+fig.show(renderer="notebook_connected", config={"scrollZoom": True})
 
 # %% [markdown]
 # ### Plot Mapper graph with standard deviation
 
 # %%
-fig = mapper_plot.plot_plotly(
-    colors=y,                        
-    cmap='viridis',                  # Viridis colormap, used for ranges
-    agg=np.nanstd,                   # We aggregate on graph nodes according to std
-    title='digit (std)',
+fig = plot.plot_plotly(
+    colors=labels,
+    cmap="viridis",  # Viridis colormap, used for ranges
+    agg=np.nanstd,  # We aggregate on graph nodes according to std
+    title="digit (std)",
     width=600,
-    height=600
+    height=600,
 )
 
-fig.show(
-    renderer='notebook_connected',
-    config={'scrollZoom': True}
-)
+fig.show(renderer="notebook_connected", config={"scrollZoom": True})
+
+# %% [markdown]
+# ### Inspect interesting nodes
+
+# %%
+from matplotlib import pyplot as plt
+
+# By interacting with the plot we see that node 140 is joining the cluster of
+# digit 0 with the cluster of digit 4. Let's see how digits inside look like!
+
+node_140 = [X[i, :] for i in graph.nodes()[140]["ids"]]
+fig, axes = plt.subplots(1, len(node_140))
+for dgt, ax in zip(node_140, axes):
+    ax.imshow(dgt.reshape(8, 8), cmap="gray")
+    ax.axis("off")
+plt.tight_layout()
+plt.show()