Pysmo serialization code example

pysmo-ser.ipynb

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   "cell_type": "code",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pyomo.environ as pyo\n",
    "from idaes.surrogate.pysmo.polynomial_regression import PolynomialRegression\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc4bdc5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def serialize_terms(feature_vector, terms):\n",
    "    columns = list(feature_vector)\n",
    "    termlist = [serialize_term(t) for t in terms]\n",
    "    result = {\"data_columns\": columns, \"terms\": termlist}\n",
    "    return result\n",
    "\n",
    "def serialize_term(t):\n",
    "    args = [str(p.index()) for p in t.args]\n",
    "    return (t.name, args)\n",
    "\n",
    "def deserialize_terms(j):\n",
    "    result = []\n",
    "    columns = j[\"data_columns\"]\n",
    "    param = build_param(columns)\n",
    "    for term in j[\"terms\"]:\n",
    "        func_name, index = term\n",
    "        func = getattr(pyo, func_name)\n",
    "        # XXX: this can only handle one index\n",
    "        result.append(func(param[index[0]]))\n",
    "    return result\n",
    "\n",
    "def build_param(columns):\n",
    "    # XXX: we want IndexedParam, but this is returning instead\n",
    "    # XXX: the type 'Unattached _ParamData'\n",
    "    p = pyo.Param(columns, mutable=True, initialize=0)\n",
    "    p.index_set().construct()\n",
    "    p.construct()\n",
    "    return p\n",
    "        \n",
    "def print_terms(terms):\n",
    "    termlist = [t.to_string() for t in terms]\n",
    "    print(\",\".join(termlist))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d09089c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===========================Polynomial Regression===============================================\n",
      "\n",
      "The number of cross-validation cases (3) is used.\n",
      "The default training/cross-validation split of 0.75 is used.\n",
      "No iterations will be run.\n",
      "Default parameter estimation method is used.\n",
      "Parameter estimation method:  pyomo \n",
      "\n"
     ]
    }
   ],
   "source": [
    "xy_data = pd.DataFrame.from_dict({'A': [1,2,3], 'B': [4,5,6], 'C': [4,3,9], \n",
    "                                  'D':[1,12,24]}, orient='index', \n",
    "                                 columns=['X1', 'X2', 'Y'])\n",
    "A = PolynomialRegression(xy_data, xy_data,\n",
    "                         maximum_polynomial_order=2)\n",
    "p = A.get_feature_vector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f9add47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"data_columns\": [\"X1\", \"X2\"], \"terms\": [[\"sin\", [\"X1\"]], [\"cos\", [\"X1\"]]]}\n",
      "sin(IndexedParam[X1]),cos(IndexedParam[X1])\n",
      "sin([Unattached _ParamData]),cos([Unattached _ParamData])\n"
     ]
    }
   ],
   "source": [
    "add_terms = [ pyo.sin(p['X1']) , pyo.cos(p['X1']) ]\n",
    "j = serialize_terms(p, add_terms)\n",
    "print(json.dumps(j))\n",
    "rt_terms = deserialize_terms(j)\n",
    "print_terms(add_terms)\n",
    "print_terms(rt_terms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "396d5383",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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