examples/iris-clustering.ipynb

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    "flex_title = \"Iris K-Means Clustering\"\n",
    "flex_source_code = \"https://github.com/danielfrg/jupyter-flex/blob/master/examples/iris-clustering.ipynb\""
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   "execution_count": null,
   "metadata": {
    "tags": [
     "meta"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import datasets\n",
    "np.random.seed(5)\n",
    "\n",
    "import ipywidgets as widgets\n",
    "import plotly.graph_objects as go\n",
    "import colorlover as cl"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": [
     "sidebar"
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   "source": [
    "## Sidebar"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = [(fname, i) for i, fname in enumerate(iris.feature_names)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
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   "source": [
    "x_label = widgets.Label(value=\"X Variable:\")\n",
    "x_var = widgets.Dropdown(options=options, value=0)\n",
    "y_label = widgets.Label(value=\"Y Variable:\")\n",
    "y_var = widgets.Dropdown(options=options, value=1)\n",
    "cluster_label = widgets.Label(value=\"Cluster count:\")\n",
    "cluster_count = widgets.BoundedIntText(value=3, min=1, max=9, disabled=False)\n",
    "\n",
    "widgets.VBox([x_label, x_var, y_label, y_var, cluster_label, cluster_count])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_model():\n",
    "    X = iris.data[:, [x_var.value, y_var.value]]\n",
    "    y = iris.target\n",
    "    estimator = KMeans(n_clusters=cluster_count.value)\n",
    "    estimator.fit(X)\n",
    "    return X, estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = cl.scales[\"3\"][\"qual\"][\"Set1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = go.FigureWidget()\n",
    "plot = go.Scatter(x=[], y=[], mode=\"markers\", marker=dict(color=[], colorscale=scale, size=10))\n",
    "fig.add_trace(plot)\n",
    "\n",
    "margin = go.layout.Margin(l=20, r=20, b=20, t=30)\n",
    "fig = fig.update_layout(margin=margin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_value_change(change):\n",
    "    X, estimator = fit_model()\n",
    "    fig.data[0]['x'] = X[:, 0]\n",
    "    fig.data[0]['y'] = X[:, 1]\n",
    "    fig.data[0]['marker']['color'] = estimator.labels_\n",
    "    scale = cl.scales[str(cluster_count.value)][\"qual\"][\"Set1\"] if cluster_count.value > 2 else None\n",
    "    fig.data[0]['marker']['colorscale'] = scale\n",
    "    fig.update_xaxes(title_text=x_var.label)\n",
    "    fig.update_yaxes(title_text=y_var.label)\n",
    "\n",
    "x_var.observe(on_value_change, names='value')\n",
    "y_var.observe(on_value_change, names='value')\n",
    "cluster_count.observe(on_value_change, names='value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "chart"
    ]
   },
   "outputs": [],
   "source": [
    "on_value_change(None)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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