Examples/Feyn.ipynb

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   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sys; sys.path.append('../')\n",
    "from misc import h5file\n",
    "import numpy as np\n",
    "\n",
    "import scipy.io as sio\n",
    "import sympy\n",
    "from sympy import Symbol, parse_expr, sympify, lambdify, Lambda\n",
    "\n",
    "import pandas as pd\n",
    "import subprocess\n",
    "\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "MAIN_SEED = 1234"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "822a3532",
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_lv = 30\n",
    "denoising_mode = 'ksvd'\n",
    "smoother_name = 'none'\n",
    "n_components = 32\n",
    "transform_n_nonzero = '_none'\n",
    "undenoised = False\n",
    "\n",
    "fp1 = f\"../PMS_data/{denoising_mode}/{smoother_name}/transform_n_nonzero_coefs{transform_n_nonzero}/burgers_pms_noise30_dictlearn{n_components}.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84aa7d2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['X_pre', 'avg_weak_coeff', 'best_subsets', 'un', 'y_pre']\n"
     ]
    }
   ],
   "source": [
    "_, _, _, un, _ = h5file(file_path=fp1, mode='r', return_dict=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "77a6c130",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sio.loadmat('../Datasets/burgers.mat')\n",
    "x = data['x'].real.reshape(-1, 1)\n",
    "del data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "517355b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = un[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b87827ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This version of feyn and the QLattice is available for academic, personal, and non-commercial use. By using the community version of this software you agree to the terms and conditions which can be found at `https://abzu.ai/eula`.\n"
     ]
    }
   ],
   "source": [
    "import feyn\n",
    "ql = feyn.QLattice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc1d5192",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "train = pd.DataFrame({'x':x.flatten(), 'y':y})\n",
    "# starting from max_complexity = 10\n",
    "models = ql.auto_run(train, output_name = 'y', max_complexity=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bccb4ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "best = models[0]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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     "data": {
      "text/latex": [
       "$\\displaystyle 0.00168798 + 1.02307 e^{- 4.23536 \\left(0.495412 x + 1\\right)^{2}}$"
      ],
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       "0.00168798 + 1.02307*exp(-4.23536*(0.495412*x + 1)**2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sympy_model = best.sympify()\n",
    "sympy_model.as_expr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "639a1f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Save equation to file\n",
    "save_to = f\"hof.pkl\"\n",
    "# save_to = f\"hof_{smoother_name}.pkl\"\n",
    "with open(save_to, 'wb') as f:\n",
    "    pickle.dump(sympy_model.as_expr(), f)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
   "id": "ab1458f0",
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    {
     "data": {
      "text/latex": [
       "$\\displaystyle 0.00168798 + 1.02307 e^{- 4.23536 \\left(0.495412 x + 1\\right)^{2}}$"
      ],
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       "0.00168798 + 1.02307*exp(-4.23536*(0.495412*x + 1)**2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Load equation from file\n",
    "with open(save_to, 'rb') as f:\n",
    "    eq = pickle.load(f)\n",
    "eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0b0310e4",
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   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 0.00168798 + 1.02307 e^{- 4.23536 \\left(0.495412 x + 1\\right)^{2}}$"
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      "text/plain": [
       "0.00168798 + 1.02307*exp(-4.23536*(0.495412*x + 1)**2)"
      ]
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eq"
   ]
  }
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