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Final.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "896cacc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import MultiTaskLasso, Lasso\n",
+    "import gradio as gr\n",
+    "\n",
+    "rng = np.random.RandomState(42)\n",
+    "\n",
+    "# Generate some 2D coefficients with sine waves with random frequency and phase\n",
+    "def make_plot(n_samples, n_features, n_tasks, n_relevant_features, alpha):\n",
+    "     \n",
+    "    coef = np.zeros((n_tasks, n_features))\n",
+    "    times = np.linspace(0, 2 * np.pi, n_tasks)\n",
+    "    for k in range(n_relevant_features):\n",
+    "        coef[:, k] = np.sin((1.0 + rng.randn(1)) * times + 3 * rng.randn(1))\n",
+    "    \n",
+    "    X = rng.randn(n_samples, n_features)\n",
+    "    Y = np.dot(X, coef.T) + rng.randn(n_samples, n_tasks)\n",
+    "    \n",
+    "    coef_lasso_ = np.array([Lasso(alpha=0.5).fit(X, y).coef_ for y in Y.T])\n",
+    "    coef_multi_task_lasso_ = MultiTaskLasso(alpha=alpha).fit(X, Y).coef_\n",
+    "    \n",
+    "    fig = plt.figure(figsize=(8, 5))\n",
+    "    \n",
+    "    feature_to_plot = 0\n",
+    "    fig = plt.figure()\n",
+    "    lw = 2\n",
+    "    plt.plot(coef[:, feature_to_plot], color=\"seagreen\", linewidth=lw, label=\"Ground truth\")\n",
+    "    plt.plot(\n",
+    "        coef_lasso_[:, feature_to_plot], color=\"cornflowerblue\", linewidth=lw, label=\"Lasso\"\n",
+    "    )\n",
+    "    plt.plot(\n",
+    "        coef_multi_task_lasso_[:, feature_to_plot],\n",
+    "        color=\"gold\",\n",
+    "        linewidth=lw,\n",
+    "        label=\"MultiTaskLasso\",\n",
+    "    )\n",
+    "    plt.legend(loc=\"upper center\")\n",
+    "    plt.axis(\"tight\")\n",
+    "    plt.ylim([-1.1, 1.1])\n",
+    "    fig.suptitle(\"Lasso, MultiTaskLasso and Ground truth time series\")\n",
+    "    return fig\n",
+    "   \n",
+    "    \n",
+    "model_card=f\"\"\"\n",
+    "## Description\n",
+    "The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected\n",
+    "features to be the same across tasks. This example simulates sequential measurements, each task \n",
+    "is a time instant, and the relevant features vary in amplitude over time while being the same. \n",
+    "The multi-task lasso imposes that features that are selected at one time point are select \n",
+    "for all time point. This makes feature selection by the Lasso more stable.\n",
+    "## Model\n",
+    "currentmodule: sklearn.linear_model\n",
+    "class:`Lasso` and class: `MultiTaskLasso` are used in this example.\n",
+    "Plots represent Lasso, MultiTaskLasso and Ground truth time series\n",
+    "\"\"\"\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    gr.Markdown('''\n",
+    "            <div>\n",
+    "            <h1 style='text-align: center'> Joint feature selection with multi-task Lasso </h1>\n",
+    "            </div>\n",
+    "        ''')\n",
+    "    gr.Markdown(model_card)\n",
+    "    gr.Markdown(\"Original example Author: Alexandre Gramfort <[email protected]>\")\n",
+    "    gr.Markdown(\n",
+    "        \"Iterative conversion by: <a href=\\\"https://github.com/DeaMariaLeon\\\">Dea María Léon</a>\"\n",
+    "    )\n",
+    "    n_samples = gr.Slider(50,500,value=100,step=50,label='Select number of samples')\n",
+    "    n_features = gr.Slider(5,50,value=30,step=5,label='Select number of features')\n",
+    "    n_tasks = gr.Slider(5,50,value=40,step=5,label='Select number of tasks')\n",
+    "    n_relevant_features = gr.Slider(1,10,value=5,step=1,label='Select number of relevant_features')\n",
+    "    with gr.Column():\n",
+    "        with gr.Tab('Select Alpha Range'):\n",
+    "            alpha = gr.Slider(0,10,value=1.0,step=0.5,label='alpha')\n",
+    "            \n",
+    "    btn = gr.Button(value = 'Submit')\n",
+    "\n",
+    "    btn.click(make_plot,inputs=[n_samples,n_features, n_tasks, n_relevant_features, alpha],outputs=[gr.Plot()])\n",
+    "\n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8043d31",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "scikit-ex",
+   "language": "python",
+   "name": "scikit-ex"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

requirements.txt

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+matplotlib==3.6.3
+scikit-learn==1.2.1