insight-nb7.ipynb

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   "cell_type": "code",
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     "text": [
      "Rate: 1e-05\n",
      "New optimal hyperparameters\n",
      "learning_rate                                               1e-05\n",
      "model            <__main__.ExplicitMF instance at 0x7fa542e4b5f0>\n",
      "n_iter                                                        200\n",
      "test_mse                                                 1.006278\n",
      "train_mse                                                1.071964\n",
      "dtype: object\n",
      "Rate: 0.0001\n",
      "New optimal hyperparameters\n",
      "learning_rate                                              0.0001\n",
      "model            <__main__.ExplicitMF instance at 0x7fa542e67950>\n",
      "n_iter                                                        200\n",
      "test_mse                                                0.8663474\n",
      "train_mse                                               0.8787174\n",
      "dtype: object\n",
      "Rate: 0.001\n",
      "New optimal hyperparameters\n",
      "learning_rate                                               0.001\n",
      "model            <__main__.ExplicitMF instance at 0x7fa542e67908>\n",
      "n_iter                                                        100\n",
      "test_mse                                                0.8061162\n",
      "train_mse                                               0.7461311\n",
      "dtype: object\n",
      "Rate: 0.01\n"
     ]
    }
   ],
   "source": [
    "iter_array = [1, 2, 5, 10, 25, 50, 100, 200]\n",
    "learning_rates = [1e-5, 1e-4, 1e-3, 1e-2]\n",
    "\n",
    "best_params = {}\n",
    "best_params['learning_rate'] = None\n",
    "best_params['n_iter'] = 0\n",
    "best_params['train_mse'] = np.inf\n",
    "best_params['test_mse'] = np.inf\n",
    "best_params['model'] = None\n",
    "\n",
    "\n",
    "for rate in learning_rates:\n",
    "    print 'Rate: {}'.format(rate)\n",
    "    MF_SGD = ExplicitMF(train, n_factors=40, learning='sgd')\n",
    "    MF_SGD.calculate_learning_curve(iter_array, test, learning_rate=rate)\n",
    "    min_idx = np.argmin(MF_SGD.test_mse)\n",
    "    if MF_SGD.test_mse[min_idx] < best_params['test_mse']:\n",
    "        best_params['n_iter'] = iter_array[min_idx]\n",
    "        best_params['learning_rate'] = rate\n",
    "        best_params['train_mse'] = MF_SGD.train_mse[min_idx]\n",
    "        best_params['test_mse'] = MF_SGD.test_mse[min_idx]\n",
    "        best_params['model'] = MF_SGD\n",
    "        print 'New optimal hyperparameters'\n",
    "        print pd.Series(best_params)"
   ]
  }
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