DAGMA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from dagma.linear import DagmaLinear\n",
    "from dagma.nonlinear import DagmaMLP, DagmaNonlinear\n",
    "\n",
    "from castle.common import GraphDAG\n",
    "from castle.metrics import MetricsDAG\n",
    "from castle.datasets import IIDSimulation, DAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-07-23 13:41:47,132 - /opt/homebrew/Caskroom/miniconda/base/envs/causal/lib/python3.12/site-packages/castle/datasets/simulator.py[line:270] - INFO: Finished synthetic dataset\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fa457cfe2a74d2b891f92704a84afde",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1120000.0 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'fdr': 0.0, 'tpr': 1.0, 'fpr': 0.0, 'shd': 0, 'nnz': 2, 'precision': np.float64(1.0), 'recall': np.float64(1.0), 'F1': np.float64(1.0), 'gscore': np.float64(1.0)}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniconda/base/envs/causal/lib/python3.12/site-packages/castle/metrics/evaluation.py:193: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  num_tp =  (W_p + W_true).applymap(lambda elem:1 if elem==2 else 0).sum(axis=1).sum()\n",
      "/opt/homebrew/Caskroom/miniconda/base/envs/causal/lib/python3.12/site-packages/castle/metrics/evaluation.py:195: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  num_fn_r = (W_p - W_true).applymap(lambda elem:1 if elem==1 else 0).sum(axis=1).sum()\n",
      "/opt/homebrew/Caskroom/miniconda/base/envs/causal/lib/python3.12/site-packages/castle/metrics/evaluation.py:221: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  TP = (W_p + W_true).applymap(lambda elem:1 if elem==2 else 0).sum(axis=1).sum()\n"
     ]
    }
   ],
   "source": [
    "dag = DAG.scale_free(n_nodes=3, n_edges=4)\n",
    "data = IIDSimulation(W=dag, n=100, method='linear', sem_type='logistic')\n",
    "\n",
    "model = DagmaLinear(loss_type='logistic')\n",
    "W_est = (model.fit(data.X, max_iter=1e6) > 0.5).astype(\"int32\")\n",
    "GraphDAG(W_est, data.B)\n",
    "\n",
    "metrics = MetricsDAG(W_est, data.B)\n",
    "print(metrics.metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}