12. Improve Performance with Algorithm Tuening.ipynb

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      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "#Grid Search\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] \n",
    "data = pd.read_csv('datasets/pima-indians-diabetes.csv', names=names)\n",
    "array = data.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "text": [
      "0.27961755931297233\n",
      "1.0\n"
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   "source": [
    "alphas = np.array([1, 0.1, 0.01, 0.001, 0.0001, 0])\n",
    "param_grid = dict(alpha = alphas)\n",
    "model = Ridge()\n",
    "grid = GridSearchCV(estimator=model, param_grid= param_grid)\n",
    "grid.fit(X, Y)\n",
    "print(grid.best_score_)\n",
    "print(grid.best_estimator_.alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Search Parameter Tuening \n",
    "from scipy.stats import uniform\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2796171270305109\n",
      "0.9779895119966027\n"
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   "source": [
    "param_grid = {'alpha': uniform()}\n",
    "model = Ridge()\n",
    "rsearch = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_iter = 100, random_state = 7)\n",
    "rsearch.fit(X, Y)\n",
    "print(rsearch.best_score_)\n",
    "print(rsearch.best_estimator_.alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save and Load Machine Learning Model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from pickle import dump\n",
    "from pickle import load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train, Y_train)\n",
    "filename = 'final_model.sav'\n",
    "dump(model, open(filename, 'wb+'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7559055118110236\n"
     ]
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   ],
   "source": [
    "#some time later load from disk\n",
    "loaded_model = load(open(filename, 'rb'))\n",
    "result = loaded_model.score(X_test, Y_test)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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