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   "source": [
    "# Different ways to load an input graph\n",
    "\n",
    "We recommend using the GML graph format to load a graph. You can also use the DOT format, which requires additional dependencies (either pydot or pygraphviz). \n",
    "\n",
    "DoWhy supports both loading a graph as a string, or as a file (with the extensions 'gml' or 'dot').\n",
    "\n",
    "Below is an example showing the different ways of loading the same graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import random\n",
    "sys.path.append(os.path.abspath(\"../../../\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import dowhy\n",
    "from dowhy import CausalModel\n",
    "from IPython.display import Image, display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Generating dummy data\n",
    "We generate some dummy data for three variables: X, Y and Z. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=[i for i in range(10)]\n",
    "random.shuffle(z)\n",
    "df = pd.DataFrame(data = {'Z': z, 'X': range(0,10), 'Y': range(0,100,10)})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Loading GML or DOT graphs\n",
    "### GML format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With GML string\n",
    "model=CausalModel(\n",
    "        data = df,\n",
    "        treatment='X',\n",
    "        outcome='Y',\n",
    "        graph=\"\"\"graph[directed 1 node[id \"Z\" label \"Z\"]  \n",
    "                    node[id \"X\" label \"X\"]\n",
    "                    node[id \"Y\" label \"Y\"]      \n",
    "                    edge[source \"Z\" target \"X\"]    \n",
    "                    edge[source \"Z\" target \"Y\"]     \n",
    "                    edge[source \"X\" target \"Y\"]]\"\"\"\n",
    "                    \n",
    "        )\n",
    "model.view_model()\n",
    "\n",
    "\n",
    "display(Image(filename=\"causal_model.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With GML file\n",
    "model=CausalModel(\n",
    "        data = df,\n",
    "        treatment='X',\n",
    "        outcome='Y',\n",
    "        graph=\"../example_graphs/simple_graph_example.gml\"\n",
    "        )\n",
    "model.view_model()\n",
    "\n",
    "\n",
    "display(Image(filename=\"causal_model.png\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DOT format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With DOT string\n",
    "model=CausalModel(\n",
    "        data = df,\n",
    "        treatment='X',\n",
    "        outcome='Y',\n",
    "        graph=\"digraph {Z -> X;Z -> Y;X -> Y;}\"\n",
    "        )\n",
    "model.view_model()\n",
    "\n",
    "from IPython.display import Image, display\n",
    "display(Image(filename=\"causal_model.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With DOT file\n",
    "model=CausalModel(\n",
    "        data = df,\n",
    "        treatment='X',\n",
    "        outcome='Y',\n",
    "        graph=\"../example_graphs/simple_graph_example.dot\"\n",
    "        )\n",
    "model.view_model()\n",
    "\n",
    "\n",
    "display(Image(filename=\"causal_model.png\"))"
   ]
  }
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