generate_trials_to_calculate.ipynb

generate_trials_to_calculate.ipynb

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     "text": [
      "{'par1': 5.0, 'par2': 50.0, 'par3': 3.0}           \n",
      "{'par1': 6.0, 'par2': 51.0, 'par3': 4.0}           \n",
      "{'par1': 6.0, 'par2': 29.0, 'par3': 8.0}           \n",
      "{'par1': 5.0, 'par2': 44.0, 'par3': 5.0}                     \n",
      "{'par1': 5.0, 'par2': 54.0, 'par3': 8.0}                     \n",
      "{'par1': 2.0, 'par2': 14.0, 'par3': 7.0}                     \n",
      "{'par1': 9.0, 'par2': 67.0, 'par3': 2.0}                     \n",
      "100%|██████████| 7/7 [00:00<00:00, 53.12it/s, best loss: 2.0]\n"
     ]
    }
   ],
   "source": [
    "from hyperopt import Trials, Domain, hp, tpe, fmin\n",
    "from hyperopt.fmin import generate_trials_to_calculate\n",
    "\n",
    "\n",
    "spaceVar = {'par1' : hp.quniform('par1', 1, 9, 1),\n",
    "            'par2' : hp.quniform('par2', 1, 100, 1),\n",
    "            'par3' : hp.quniform('par3', 2, 9, 1)}\n",
    "\n",
    "def objective(space):\n",
    "    print (space)\n",
    "    return int(space['par1'])\n",
    "\n",
    "trials = generate_trials_to_calculate([{'par1' : 5, 'par2' : 50, 'par3' : 3}, {'par1' : 6, 'par2' : 51, 'par3' : 4}])\n",
    "best = fmin(fn=objective, space=spaceVar, trials=trials, algo=tpe.suggest, max_evals=5)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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