diff --git a/Christmas Mapper.ipynb b/Christmas Mapper.ipynb
index 7dece46..28b32d7 100644
--- a/Christmas Mapper.ipynb
+++ b/Christmas Mapper.ipynb
@@ -8,7 +8,9 @@
"\n",
"_Created by:_ Davide Burba, December 2019\n",
"\n",
- "In this notebook we apply the [Mapper algorithm](https://research.math.osu.edu/tgda/mapperPBG.pdf) to point cloud data by using [giotto-learn](https://giotto.ai), an open-source Python library for topological data analysis."
+ "In this notebook, we apply the [Mapper algorithm](https://research.math.osu.edu/tgda/mapperPBG.pdf)\n",
+ "to point cloud data by using [giotto-tda](https://giotto.ai),\n",
+ "an open-source Python library for topological data analysis."
]
},
{
@@ -20,7 +22,7 @@
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+ "execution_count": null,
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@@ -30,7 +32,7 @@
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@@ -39,20 +41,22 @@
"import numpy as np\n",
"\n",
"# tda magic\n",
- "from giotto.mapper.filter import Projection\n",
- "from giotto.mapper.cover import (\n",
+ "from gtda.mapper.filter import Projection\n",
+ "from gtda.mapper.cover import (\n",
" OneDimensionalCover, \n",
" CubicalCover\n",
")\n",
- "from giotto.mapper.pipeline import make_mapper_pipeline\n",
- "from giotto.mapper.visualization import (\n",
- " create_network_2d,\n",
- " create_network_3d,\n",
- " create_interactive_network,\n",
+ "\n",
+ "\n",
+ "from gtda.mapper.pipeline import make_mapper_pipeline\n",
+ "from gtda.mapper.visualization import (\n",
+ " plot_static_mapper_graph,\n",
+ " plot_interactive_mapper_graph\n",
")\n",
"\n",
"# data viz\n",
"import plotly.graph_objects as go\n",
+ "from matplotlib.colors import to_rgba_array, to_rgba\n",
"\n",
"# ml tools\n",
"from sklearn.cluster import DBSCAN\n",
@@ -76,90 +80,9 @@
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@@ -81119,7 +133,7 @@
"- **nodes**: clusters\n",
"- **edges**: non-empty intersections between clusters\n",
"\n",
- "Even if its formulation could seem a bit intimidating, the best way to intuitively understand how the Mapper algorithm works is to use it and \"play\" with its parameters. The API of [giotto-learn](https://giotto.ai) is scikit-learn compatible and provides a very convenient way to fit the Mapper through a pipeline object.\n",
+ "Even if its formulation could seem a bit intimidating, the best way to intuitively understand how the Mapper algorithm works is to use it and \"play\" with its parameters. The API of [giotto-tda](https://giotto.ai) is scikit-learn compatible and provides a very convenient way to fit the Mapper through a pipeline object.\n",
"\n",
"We will apply the Mapper algorithm to the Santa Claus point cloud (the so-called \"Santa Cloud\"). Our goal is to get a minimal graph which preserves the topological properties of the original figure. \n",
"\n",
@@ -81128,7 +142,7 @@
},
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+ "execution_count": null,
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@@ -81148,11685 +162,50 @@
},
{
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+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_mapper_graph_from_pipeline(pipe):\n",
+ " \"\"\" A helper function, that plots the graph\n",
+ " resulting from applying the pipeline `pipe`\n",
+ " on the data.\n",
+ " Parameters\n",
+ " -----------\n",
+ " pipe : \n",
+ " \"\"\"\n",
+ " # generate topological graph from point cloud\n",
+ " graph = pipeline.fit_transform(coords)\n",
+ "\n",
+ " # get cluster member indices\n",
+ " node_elements = graph[\"node_metadata\"][\"node_elements\"]\n",
+ "\n",
+ " # configure choice of layout (x,z values)\n",
+ " layout = np.array([np.mean(coords[el], axis=0)[[0,2]] for el in node_elements])\n",
+ "\n",
+ " # define node coloring\n",
+ " node_colors = np.array([data.loc[el, \"color\"].value_counts().index[0] for el in node_elements])\n",
+ "\n",
+ " plotly_kwargs = {\n",
+ " 'node_trace_marker_colorscale': None,\n",
+ " 'node_trace_marker_showscale': False\n",
+ " }\n",
+ "\n",
+ " # initialise and display figure\n",
+ " fig = plot_static_mapper_graph(pipeline, coords,\n",
+ " layout, plotly_kwargs=plotly_kwargs)\n",
+ " fig.update_traces(patch={'hoverlabel_bgcolor': node_colors,\n",
+ " 'marker_color': node_colors})\n",
+ " fig.show(config={'scrollZoom':True})\n",
+ " \n",
+ " return fig"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
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+ "outputs": [],
"source": [
"# configure Mapper pipeline\n",
"pipeline = make_mapper_pipeline(\n",
@@ -92835,19 +214,8 @@
" clusterer=DBSCAN(),\n",
")\n",
"\n",
- "# generate topological graph from point cloud\n",
- "graph = pipeline.fit_transform(coords)\n",
- "\n",
- "# get cluster member indices\n",
- "node_elements = graph[\"node_metadata\"][\"node_elements\"]\n",
- "# configure choice of layout (x,z values)\n",
- "layout = [np.mean(coords[el], axis=0)[[0,2]] for el in node_elements]\n",
- "# define node coloring\n",
- "node_colors = [data.loc[el, \"color\"].value_counts().index[0] for el in node_elements]\n",
- "\n",
- "# initialise and display figure\n",
- "fig = create_network_2d(graph, coords, layout, node_colors)\n",
- "fig.show(config={'scrollZoom':True})"
+ "# plot the graph arising from applying the pipeline\n",
+ "fig = plot_mapper_graph_from_pipeline(pipeline)"
]
},
{
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- " filter_func=Projection(column_indices=[0,2]),\n",
+ " filter_func=Projection(columns=[0,2]),\n",
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+ " filter_func=Projection(columns=2),\n",
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+ "# plot the graph arising from applying the pipeline\n",
+ "fig = plot_mapper_graph_from_pipeline(pipeline)"
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"source": [
"# configure Mapper pipeline\n",
"pipeline = make_mapper_pipeline(\n",
- " filter_func=Projection(column_indices=2),\n",
+ " filter_func=Projection(columns=2),\n",
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- "# get cluster member indices\n",
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- "# configure choice of layout (x,z values)\n",
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- "# define node coloring\n",
- "node_colors = [data.loc[el, \"color\"].value_counts().index[0] for el in node_elements]\n",
- "\n",
- "# initialise and display figure\n",
- "fig = create_network_2d(graph, coords, layout, node_colors)\n",
- "fig.show(config={'scrollZoom':True})"
+ "# plot the graph arising from applying the pipeline\n",
+ "fig = plot_mapper_graph_from_pipeline(pipeline)"
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+ " filter_func=Projection(columns=2),\n",
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+ "# plot the graph arising from applying the pipeline\n",
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- "linecolor": "rgb(36,36,36)",
- "showgrid": false,
- "showline": true,
- "ticks": "outside",
- "title": {
- "standoff": 15
- },
- "zeroline": false,
- "zerolinecolor": "rgb(36,36,36)"
- }
- }
- },
- "updatemenus": [
- {
- "buttons": [
- {
- "args": [
- {
- "marker.colorscale": [
- null,
- "Blackbody"
- ]
- }
- ],
- "label": "Blackbody",
- "method": "restyle"
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- {
- "args": [
- {
- "marker.colorscale": [
- null,
- "Bluered"
- ]
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- "label": "Bluered",
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- null,
- "Blues"
- ]
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- "method": "restyle"
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- {
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- null,
- "Earth"
- ]
- }
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- "label": "Earth",
- "method": "restyle"
- },
- {
- "args": [
- {
- "marker.colorscale": [
- null,
- "Electric"
- ]
- }
- ],
- "label": "Electric",
- "method": "restyle"
- },
- {
- "args": [
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- null,
- "Greens"
- ]
- }
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- "label": "Greens",
- "method": "restyle"
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- {
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- null,
- "Greys"
- ]
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- "label": "Greys",
- "method": "restyle"
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- null,
- "Hot"
- ]
- }
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- "label": "Hot",
- "method": "restyle"
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- null,
- "Jet"
- ]
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- "method": "restyle"
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- null,
- "Picnic"
- ]
- }
- ],
- "label": "Picnic",
- "method": "restyle"
- },
- {
- "args": [
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- "marker.colorscale": [
- null,
- "Portland"
- ]
- }
- ],
- "label": "Portland",
- "method": "restyle"
- },
- {
- "args": [
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- null,
- "Rainbow"
- ]
- }
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- "label": "Rainbow",
- "method": "restyle"
- },
- {
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- null,
- "RdBu"
- ]
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- "label": "RdBu",
- "method": "restyle"
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- "Reds"
- ]
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- "Viridis"
- ]
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- "label": "Viridis",
- "method": "restyle"
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- {
- "args": [
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- null,
- "YlGnBu"
- ]
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- "method": "restyle"
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- "direction": "down",
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- "x": 0.12,
- "xanchor": "left",
- "y": 1.1,
- "yanchor": "top"
- },
- {
- "direction": "down",
- "pad": {
- "r": 10,
- "t": 10
- },
- "showactive": true,
- "x": 0.42,
- "xanchor": "left",
- "y": 1.1,
- "yanchor": "top"
- }
- ],
- "xaxis": {
- "showgrid": false,
- "showline": false,
- "showticklabels": false,
- "ticks": "",
- "title": {
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- "yaxis": {
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- "ticks": "",
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- "zeroline": false
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- "text/html": [
- "\n",
- " \n",
- " \n",
- "
\n",
- " \n",
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# configure Mapper pipeline\n",
"pipeline = make_mapper_pipeline(\n",
- " filter_func=Projection(column_indices=2),\n",
+ " filter_func=Projection(columns=2),\n",
" cover=OneDimensionalCover(n_intervals=20, overlap_frac=0.35),\n",
" clusterer=DBSCAN(eps=0.06),\n",
")\n",
"\n",
- "# generate topological graph from point cloud \n",
- "graph = pipeline.fit_transform(coords)\n",
- "\n",
- "# get cluster member indices\n",
- "node_elements = graph[\"node_metadata\"][\"node_elements\"]\n",
- "# configure choice of layout (x,z values)\n",
- "layout = [np.mean(coords[el], axis=0)[[0, 2]] for el in node_elements]\n",
- "# define node coloring\n",
- "node_colors = [data.loc[el, \"color\"].value_counts().index[0] for el in node_elements]\n",
- "\n",
- "# initialise and display figure\n",
- "fig = create_network_2d(graph, coords, layout, node_colors)\n",
- "fig.show(config={'scrollZoom':True})"
+ "# plot the graph arising from applying the pipeline\n",
+ "fig = plot_mapper_graph_from_pipeline(pipeline)"
]
},
{
@@ -101786,53 +405,24 @@
"source": [
"# Interactive mode\n",
"\n",
- "As we saw above, it takes several steps of interation to obtain a topological Mapper graph that accurately captures the underlying structure of the point cloud. To help speedup the iterations, [giotto-learn](https://giotto.ai) provides the option to dynamically change the Mapper parameters and see how this affects the output!"
+ "As we saw above, it takes several steps of interation to obtain a topological Mapper graph that accurately captures the underlying structure of the point cloud. To help speedup the iterations, [giotto-tda](https://giotto.ai) provides the option to dynamically change the Mapper parameters and see how this affects the output!"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "27ea52d34dbb4440af973aa8d522dd3c",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "VBox(children=(HBox(children=(IntSlider(value=10, description='n_intervals', max=50, min=1), FloatSlider(value…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "54674591fdf14fb2abc88ba8bed30fb3",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Output()"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# configure Mapper pipeline\n",
"pipeline = make_mapper_pipeline(\n",
- " filter_func=Projection(column_indices=2),\n",
+ " filter_func=Projection(columns=2),\n",
" cover=OneDimensionalCover(),\n",
" clusterer=DBSCAN(),\n",
")\n",
"\n",
"# generate figure\n",
- "create_interactive_network(pipeline, coords)"
+ "plot_interactive_mapper_graph(pipeline, coords)"
]
},
{
@@ -101859,7 +449,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.4"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/README.md b/README.md
index 7214cfd..9125b66 100644
--- a/README.md
+++ b/README.md
@@ -66,9 +66,5 @@ Enjoy!
## Requirements
In order to run the notebook, the following python packages are required:
-- giotto-learn-nightly
+- giotto-tda>=0.1.4
- pandas>=0.25.1
-- numpy>=1.17.2
-- plotly>=4.4.1
-- scikit-learn>=0.22
-- python-igraph
diff --git a/requirements.txt b/requirements.txt
index 96ed463..6f83b74 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,2 @@
-giotto-learn-nightly
-python-igraph >= 0.7.1.post6
+giotto-tda>=0.1.4
pandas>=0.25.1
-numpy>=1.17.2
-plotly>=4.4.1
-ipywidgets>=7.5.1
-scikit-learn==0.22