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{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "eea9f1b8-4240-4a68-a412-2b4071b2c04a",
"metadata": {},
"outputs": [],
"source": [
"from drawdata import ScatterWidget"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "207dbcfd-e731-4758-8035-d1f429aa10d4",
"metadata": {},
"outputs": [],
"source": [
"widget = ScatterWidget()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b77030f4-c895-4a39-96ce-b79d6a8a6d69",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from IPython.core.display import HTML\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.inspection import DecisionBoundaryDisplay\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"import matplotlib.pylab as plt \n",
"import numpy as np\n",
"import ipywidgets"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b9d36c79-3d1d-4084-a1a6-43f3b95c06fe",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6b21ddf1e80a4a34a786547610d52aa0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(ScatterWidget(), Output()))"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"widget = ScatterWidget()\n",
"output = ipywidgets.Output()\n",
"\n",
"\n",
"@output.capture(clear_output=True)\n",
"def on_change(change):\n",
" df = widget.data_as_pandas\n",
" if len(df) and (df['color'].nunique() > 1):\n",
" X = df[['x', 'y']].values\n",
" y = df['color']\n",
" display(HTML(\"<br><br><br>\"))\n",
" fig = plt.figure(figsize=(12, 12));\n",
" classifier = DecisionTreeClassifier().fit(X, y)\n",
" disp = DecisionBoundaryDisplay.from_estimator(\n",
" classifier, X, \n",
" ax=fig.add_subplot(111),\n",
" response_method=\"predict_proba\" if len(np.unique(df['color'])) == 2 else \"predict\",\n",
" xlabel=\"x\", ylabel=\"y\",\n",
" alpha=0.5,\n",
" );\n",
" disp.ax_.scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\");\n",
" plt.title(f\"{classifier.__class__.__name__}\");\n",
" disp.ax_.set_title(f\"{classifier.__class__.__name__}\");\n",
" plt.show();\n",
"widget.observe(on_change, names=[\"data\"])\n",
"on_change(None)\n",
"page = ipywidgets.HBox([widget, output])\n",
"page"
]
},
{
"cell_type": "markdown",
"id": "5f9bb7ce-8a7f-4879-9ddf-5b256bb0ff64",
"metadata": {},
"source": [
"\n",
"p<br><br><br><br><br><br><br><br><br><br><br><br>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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