Merge pull request #219 from lucasimi/ui-to-plotly

UI to plotly

Author: lucasimi

README.md

@@ -39,7 +39,7 @@ and in the
 
 - **Flexible visualization**
 
-    Multiple visualization backends supported (e.g., Plotly, Matplotlib) for
+    Multiple visualization backends supported (Plotly, Matplotlib, PyVis) for
     generating high-quality Mapper graph representations with adjustable 
     layouts and styling.
 

app/requirements.txt

@@ -3,5 +3,5 @@ numpy>=1.25.2,<2.0.0
 scikit-learn>=1.5.0,<1.6.0
 umap-learn>=0.5.7,<0.6.0
 pandas>=2.1.0,<3.0.0
-tda-mapper>=0.10.0,<0.11.0
+tda-mapper>=0.11.0,<0.12.0
 plotly>=6.0.0,<7.0.0

app/streamlit_app.py

@@ -22,6 +22,8 @@
 from sklearn.decomposition import PCA
 from umap import UMAP
 
+from tdamapper._plot_plotly import _marker_size
+from tdamapper.core import aggregate_graph
 from tdamapper.cover import BallCover, CubicalCover
 from tdamapper.learn import MapperAlgorithm
 from tdamapper.plot import MapperPlot
@@ -100,6 +102,16 @@
 
 V_CMAP_TWILIGHT = "Twilight (Cyclic)"
 
+V_CMAPS = {
+    V_CMAP_JET: "Jet",
+    V_CMAP_VIRIDIS: "Viridis",
+    V_CMAP_CIVIDIS: "Cividis",
+    V_CMAP_SPECTRAL: "Spectral",
+    V_CMAP_PORTLAND: "Portland",
+    V_CMAP_HSV: "HSV",
+    V_CMAP_TWILIGHT: "Twilight",
+}
+
 GIT_REPO_URL = "https://github.com/lucasimi/tda-mapper-python"
 
 ICON_URL = f"{GIT_REPO_URL}/raw/main/docs/source/logos/tda-mapper-logo-icon.png"
@@ -165,14 +177,6 @@ def _fix_data(data):
     return df
 
 
-def _get_dim(fig):
-    dim = 2
-    for trace in fig.data:
-        if "3d" in trace.type:
-            dim = 3
-    return dim
-
-
 def _get_graph_no_attribs(graph):
     graph_no_attribs = nx.Graph()
     graph_no_attribs.add_nodes_from(graph.nodes())
@@ -561,50 +565,6 @@ def plot_agg_input_section():
     return agg, agg_name
 
 
-def plot_cmap_input_section():
-    cmap_type = st.selectbox(
-        "Colormap",
-        options=[
-            V_CMAP_JET,
-            V_CMAP_VIRIDIS,
-            V_CMAP_CIVIDIS,
-            V_CMAP_PORTLAND,
-            V_CMAP_SPECTRAL,
-            V_CMAP_HSV,
-            V_CMAP_TWILIGHT,
-        ],
-    )
-    cmap = None
-    if cmap_type == V_CMAP_JET:
-        cmap = "Jet"
-    elif cmap_type == V_CMAP_VIRIDIS:
-        cmap = "Viridis"
-    elif cmap_type == V_CMAP_CIVIDIS:
-        cmap = "Cividis"
-    elif cmap_type == V_CMAP_PORTLAND:
-        cmap = "Portland"
-    elif cmap_type == V_CMAP_SPECTRAL:
-        cmap = "Spectral"
-    elif cmap_type == V_CMAP_HSV:
-        cmap = "HSV"
-    elif cmap_type == V_CMAP_TWILIGHT:
-        cmap = "Twilight"
-    return cmap
-
-
-def plot_color_input_section(df_X, df_y):
-    X_cols = list(df_X.columns)
-    y_cols = list(df_y.columns)
-    col_feat = st.selectbox(
-        "Color",
-        options=y_cols + X_cols,
-    )
-    if col_feat in X_cols:
-        return df_X[col_feat].to_numpy(), col_feat
-    elif col_feat in y_cols:
-        return df_y[col_feat].to_numpy(), col_feat
-
-
 @st.cache_data(
     hash_funcs={
         "networkx.classes.graph.Graph": lambda g: _encode_graph(
@@ -654,15 +614,13 @@ def mapper_plot_section(mapper_graph):
     hash_funcs={"tdamapper.plot.MapperPlot": lambda mp: mp.positions},
     show_spinner="Rendering Mapper",
 )
-def compute_mapper_fig(
-    mapper_plot, colors, node_size, cmap, _agg, agg_name, colors_feat
-):
+def compute_mapper_fig(mapper_plot, colors, node_size, cmap, _agg, agg_name):
     logger.info("Generating Mapper figure")
     mapper_fig = mapper_plot.plot_plotly(
         colors,
         node_size=node_size,
         agg=_agg,
-        title=f"{agg_name} of {colors_feat}",
+        title=[f"{agg_name} of {c}" for c in colors.columns],
         cmap=cmap,
         width=600,
         height=600,
@@ -673,23 +631,17 @@ def compute_mapper_fig(
 def mapper_figure_section(df_X, df_y, mapper_plot):
     st.header("🎨 Plot")
     agg, agg_name = plot_agg_input_section()
-    cmap = plot_cmap_input_section()
-    colors, colors_feat = plot_color_input_section(df_X, df_y)
-    node_size = st.slider("Node size", min_value=0.1, max_value=10.0, value=1.0)
+    colors = pd.concat([df_y, df_X], axis=1)
     mapper_fig = compute_mapper_fig(
         mapper_plot,
         colors=colors,
-        node_size=node_size,
+        node_size=1.0,
         _agg=agg,
-        cmap=cmap,
+        cmap=["Jet", "Viridis", "Cividis"],
         agg_name=agg_name,
-        colors_feat=colors_feat,
     )
-    dim = _get_dim(mapper_fig)
     mapper_fig.update_layout(
-        dragmode="orbit" if dim == 3 else "pan",
-        uirevision="constant",
-        margin=dict(b=0, l=0, r=0, t=0),
+        margin=dict(b=5, l=5, r=5, t=5),
     )
     mapper_fig.update_xaxes(
         showline=False,
@@ -699,16 +651,41 @@ def mapper_figure_section(df_X, df_y, mapper_plot):
         scaleanchor="x",
         scaleratio=1,
     )
+
     return mapper_fig
 
 
-def mapper_rendering_section(mapper_graph, mapper_fig):
+def _compute_colors_agg(mapper_plot, df_X, df_y, col_feat, agg):
+    X_cols = list(df_X.columns)
+    y_cols = list(df_y.columns)
+    colors = np.array([])
+    if col_feat in X_cols:
+        colors = df_X[col_feat].to_numpy()
+    elif col_feat in y_cols:
+        colors = df_y[col_feat].to_numpy()
+    return aggregate_graph(colors, mapper_plot.graph, agg)
+
+
+def _edge_colors(mapper_plot, df_X, df_y, col_feat, agg):
+    colors_avg = []
+    colors_agg = _compute_colors_agg(mapper_plot, df_X, df_y, col_feat, agg)
+    for edge in mapper_plot.graph.edges():
+        c0, c1 = colors_agg[edge[0]], colors_agg[edge[1]]
+        colors_avg.append(c0)
+        colors_avg.append(c1)
+        colors_avg.append(c1)
+    return colors_avg
+
+
+def mapper_rendering_section(mapper_fig):
     config = {
         "scrollZoom": True,
         "displaylogo": False,
         "modeBarButtonsToRemove": ["zoom", "pan"],
     }
-    st.plotly_chart(mapper_fig, use_container_width=True, config=config)
+    st.plotly_chart(
+        mapper_fig, use_container_width=True, config=config, key="mapper_plot"
+    )
 
 
 def data_summary_section(df_X, df_y, mapper_graph):
@@ -826,7 +803,7 @@ def main():
     with col_0:
         data_summary_section(df_X, df_y, mapper_graph)
     with col_1:
-        mapper_rendering_section(mapper_graph, mapper_fig)
+        mapper_rendering_section(mapper_fig)
     col_2, col_3 = st.columns([1, 3])
     with col_2:
         data_download_button(df_X, df_y)

benchmarks/benchmark.py

@@ -9,7 +9,7 @@
 from sklearn.decomposition import PCA
 
 import tdamapper as tm
-from tdamapper.clustering import TrivialClustering
+from tdamapper.core import TrivialClustering
 
 
 def _segment(cardinality, dimension, noise=0.1, start=None, end=None):
@@ -81,7 +81,7 @@ def run_gm(X, n, p):
 
 def run_tm(X, n, p):
     t0 = time.time()
-    tm.core.MapperAlgorithm(
+    tm.learn.MapperAlgorithm(
         cover=tm.cover.CubicalCover(
             n_intervals=n,
             overlap_frac=p,

docs/source/index.rst

@@ -50,19 +50,27 @@ Core features
 
 - **Efficient construction**
 
-    Leverages optimized spatial search techniques and parallelization to accelerate the construction of Mapper graphs, supporting the analysis of high-dimensional datasets.
+   Leverages optimized spatial search techniques and parallelization to 
+   accelerate the construction of Mapper graphs, supporting the analysis of
+   high-dimensional datasets.
 
 - **Scikit-learn integration**
 
-    Provides custom estimators that are fully compatible with scikit-learn's API, enabling seamless integration into scikit-learn pipelines for tasks such as dimensionality reduction, clustering, and feature extraction.
+   Provides custom estimators that are fully compatible with scikit-learn's
+   API, enabling seamless integration into scikit-learn pipelines for tasks
+   such as dimensionality reduction, clustering, and feature extraction.
 
 - **Flexible visualization**
 
-    Multiple visualization backends supported (e.g., Plotly, Matplotlib) for generating high-quality Mapper graph representations with adjustable layouts and styling.
+   Multiple visualization backends supported (Plotly, Matplotlib, PyVis) for
+   generating high-quality Mapper graph representations with adjustable 
+   layouts and styling.
 
 - **Interactive app**
 
-    Provides an interactive web-based interface (via Streamlit) for dynamic exploration of Mapper graph structures, offering real-time adjustments to parameters and visualizations.
+   Provides an interactive web-based interface (via Streamlit) for dynamic
+   exploration of Mapper graph structures, offering real-time adjustments to
+   parameters and visualizations.
 
 
 Background

docs/source/notebooks/circles.py

@@ -77,6 +77,7 @@
     cover=CubicalCover(n_intervals=10, overlap_frac=0.3), clustering=DBSCAN()
 )
 graph = mapper.fit_transform(X, y)
+print(f"nodes: {len(graph.nodes())}, edges: {len(graph.edges())}")
 
 # %% [markdown]
 # ### Visualization
@@ -92,9 +93,15 @@
 # %%
 plot = MapperPlot(graph, dim=2, iterations=60, seed=42)
 
-fig = plot.plot_plotly(colors=labels, cmap="jet", agg=np.nanmean, width=600, height=600)
+fig = plot.plot_plotly(
+    colors=labels,
+    cmap=["jet", "viridis", "cividis"],
+    agg=np.nanmean,
+    width=600,
+    height=600,
+)
 
-fig.show(config={"scrollZoom": True})
+fig.show(config={"scrollZoom": True}, renderer="notebook_connected")
 # fig.write_image("circles_mean.png", width=500, height=500)
 
 # %% [markdown]
@@ -107,14 +114,16 @@
 # data where such ambiguity is common.
 
 # %%
-plot.plot_plotly_update(
-    fig,
+
+fig = plot.plot_plotly(
     colors=labels,
-    cmap="viridis",
+    cmap=["jet", "viridis", "cividis"],
     agg=np.nanstd,
+    width=600,
+    height=600,
 )
 
-fig.show(config={"scrollZoom": True})
+fig.show(config={"scrollZoom": True}, renderer="notebook_connected")
 # fig.write_image("circles_std.png", width=500, height=500)
 
 # %% [markdown]

docs/source/notebooks/digits.py

@@ -72,6 +72,7 @@
 )
 
 graph = mapper.fit_transform(X, y)
+print(f"nodes: {len(graph.nodes())}, edges: {len(graph.edges())}")
 
 # %% [markdown]
 # ### Visualization
@@ -100,15 +101,15 @@ def mode(arr):
 
 fig = plot.plot_plotly(
     colors=labels,
-    cmap="jet",
+    cmap=["jet", "viridis", "cividis"],
     agg=mode,
     title="mode of digits",
     width=600,
     height=600,
     node_size=0.5,
 )
 
-fig.show(config={"scrollZoom": True})
+fig.show(config={"scrollZoom": True}, renderer="notebook_connected")
 
 # %% [markdown]
 # We also color the nodes by the **entropy** of their digit labels, which
@@ -131,15 +132,15 @@ def entropy(arr):
 
 fig = plot.plot_plotly(
     colors=labels,
-    cmap="viridis",
+    cmap=["jet", "viridis", "cividis"],
     agg=entropy,
     title="entropy of digits",
     width=600,
     height=600,
     node_size=0.5,
 )
 
-fig.show(config={"scrollZoom": True})
+fig.show(config={"scrollZoom": True}, renderer="notebook_connected")
 
 # %% [markdown]
 # ### Identifying high-entropy

docs/source/quickstart.rst

@@ -27,11 +27,11 @@ Development
 
    pip install git+https://github.com/lucasimi/tda-mapper-python
 
-- To install from the latest commit of develop branch
+- To install from the latest commit of a branch
 
 .. code:: bash
 
-   pip install git+https://github.com/lucasimi/tda-mapper-python@develop
+   pip install git+https://github.com/lucasimi/tda-mapper-python@[name-of-the-branch]
 
 
 How To Use

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tda-mapper"
-version = "0.10.0"
+version = "0.11.0"
 description = "A simple and efficient Python implementation of Mapper algorithm for Topological Data Analysis"
 readme = "README.md"
 authors = [{ name = "Luca Simi", email = "[email protected]" }]
@@ -49,6 +49,7 @@ dev = [
     "black[jupyter]",
     "isort",
     "flake8",
+    "nbformat>=4.2.0",
 ]
 
 [project.urls]