Model Selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Our program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from face_ult.model_updater import ModelUpdater"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = ModelUpdater()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the face data of the master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   ],
   "source": [
    "mu.load_master_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "524"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu.master_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data of our artist dataset"
   ]
  },
  {
   "cell_type": "code",
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    },
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   ],
   "source": [
    "mu.load_stranger_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "524"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu.stranger_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = mu.X\n",
    "y = mu.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.5, random_state=777)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_parameters = [\n",
    "    {\n",
    "        'kernel': ['rbf', 'sigmod'], \n",
    "        'gamma': [1e-3, 1e-4, 1e-5, 'scale', 'auto'],\n",
    "        'C': [1, 10, 100, 1000]\n",
    "    }\n",
    "]\n",
    "\n",
    "scores = ['precision', 'recall']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Tuning hyper-parameters for precision\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.256 (+/-0.004) for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1, 'gamma': 0.001, 'kernel': 'sigmod'}\n",
      "0.256 (+/-0.004) for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1, 'gamma': 0.0001, 'kernel': 'sigmod'}\n",
      "0.256 (+/-0.004) for {'C': 1, 'gamma': 1e-05, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1, 'gamma': 1e-05, 'kernel': 'sigmod'}\n",
      "0.899 (+/-0.059) for {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1, 'gamma': 'scale', 'kernel': 'sigmod'}\n",
      "0.775 (+/-0.048) for {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1, 'gamma': 'auto', 'kernel': 'sigmod'}\n",
      "0.796 (+/-0.110) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 10, 'gamma': 0.001, 'kernel': 'sigmod'}\n",
      "0.256 (+/-0.004) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 10, 'gamma': 0.0001, 'kernel': 'sigmod'}\n",
      "0.256 (+/-0.004) for {'C': 10, 'gamma': 1e-05, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 10, 'gamma': 1e-05, 'kernel': 'sigmod'}\n",
      "0.923 (+/-0.059) for {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 10, 'gamma': 'scale', 'kernel': 'sigmod'}\n",
      "0.856 (+/-0.058) for {'C': 10, 'gamma': 'auto', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 10, 'gamma': 'auto', 'kernel': 'sigmod'}\n",
      "0.864 (+/-0.057) for {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 100, 'gamma': 0.001, 'kernel': 'sigmod'}\n",
      "0.796 (+/-0.110) for {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 100, 'gamma': 0.0001, 'kernel': 'sigmod'}\n",
      "0.256 (+/-0.004) for {'C': 100, 'gamma': 1e-05, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 100, 'gamma': 1e-05, 'kernel': 'sigmod'}\n",
      "0.914 (+/-0.049) for {'C': 100, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 100, 'gamma': 'scale', 'kernel': 'sigmod'}\n",
      "0.882 (+/-0.067) for {'C': 100, 'gamma': 'auto', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 100, 'gamma': 'auto', 'kernel': 'sigmod'}\n",
      "0.884 (+/-0.071) for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1000, 'gamma': 0.001, 'kernel': 'sigmod'}\n",
      "0.864 (+/-0.057) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'sigmod'}\n",
      "0.796 (+/-0.110) for {'C': 1000, 'gamma': 1e-05, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1000, 'gamma': 1e-05, 'kernel': 'sigmod'}\n",
      "0.914 (+/-0.049) for {'C': 1000, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1000, 'gamma': 'scale', 'kernel': 'sigmod'}\n",
      "0.900 (+/-0.037) for {'C': 1000, 'gamma': 'auto', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1000, 'gamma': 'auto', 'kernel': 'sigmod'}\n",
      "\n",
      "Detailed classification report:\n",
      "\n",
      "The model is trained on the full development set.\n",
      "The scores are computed on the full evaluation set.\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.94      0.94       256\n",
      "           1       0.94      0.95      0.95       268\n",
      "\n",
      "    accuracy                           0.94       524\n",
      "   macro avg       0.94      0.94      0.94       524\n",
      "weighted avg       0.94      0.94      0.94       524\n",
      "\n",
      "# Tuning hyper-parameters for recall\n",
      "\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.500 (+/-0.000) for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1, 'gamma': 0.001, 'kernel': 'sigmod'}\n",
      "0.500 (+/-0.000) for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1, 'gamma': 0.0001, 'kernel': 'sigmod'}\n",
      "0.500 (+/-0.000) for {'C': 1, 'gamma': 1e-05, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1, 'gamma': 1e-05, 'kernel': 'sigmod'}\n",
      "0.894 (+/-0.060) for {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1, 'gamma': 'scale', 'kernel': 'sigmod'}\n",
      "0.768 (+/-0.035) for {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1, 'gamma': 'auto', 'kernel': 'sigmod'}\n",
      "0.777 (+/-0.100) for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 10, 'gamma': 0.001, 'kernel': 'sigmod'}\n",
      "0.500 (+/-0.000) for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 10, 'gamma': 0.0001, 'kernel': 'sigmod'}\n",
      "0.500 (+/-0.000) for {'C': 10, 'gamma': 1e-05, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 10, 'gamma': 1e-05, 'kernel': 'sigmod'}\n",
      "0.922 (+/-0.060) for {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 10, 'gamma': 'scale', 'kernel': 'sigmod'}\n",
      "0.847 (+/-0.062) for {'C': 10, 'gamma': 'auto', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 10, 'gamma': 'auto', 'kernel': 'sigmod'}\n",
      "0.855 (+/-0.063) for {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 100, 'gamma': 0.001, 'kernel': 'sigmod'}\n",
      "0.777 (+/-0.100) for {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 100, 'gamma': 0.0001, 'kernel': 'sigmod'}\n",
      "0.500 (+/-0.000) for {'C': 100, 'gamma': 1e-05, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 100, 'gamma': 1e-05, 'kernel': 'sigmod'}\n",
      "0.913 (+/-0.050) for {'C': 100, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 100, 'gamma': 'scale', 'kernel': 'sigmod'}\n",
      "0.877 (+/-0.066) for {'C': 100, 'gamma': 'auto', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 100, 'gamma': 'auto', 'kernel': 'sigmod'}\n",
      "0.879 (+/-0.071) for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1000, 'gamma': 0.001, 'kernel': 'sigmod'}\n",
      "0.855 (+/-0.063) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1000, 'gamma': 0.0001, 'kernel': 'sigmod'}\n",
      "0.777 (+/-0.100) for {'C': 1000, 'gamma': 1e-05, 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1000, 'gamma': 1e-05, 'kernel': 'sigmod'}\n",
      "0.913 (+/-0.050) for {'C': 1000, 'gamma': 'scale', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1000, 'gamma': 'scale', 'kernel': 'sigmod'}\n",
      "0.898 (+/-0.039) for {'C': 1000, 'gamma': 'auto', 'kernel': 'rbf'}\n",
      "nan (+/-nan) for {'C': 1000, 'gamma': 'auto', 'kernel': 'sigmod'}\n",
      "\n",
      "Detailed classification report:\n",
      "\n",
      "The model is trained on the full development set.\n",
      "The scores are computed on the full evaluation set.\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.94      0.94       256\n",
      "           1       0.94      0.95      0.95       268\n",
      "\n",
      "    accuracy                           0.94       524\n",
      "   macro avg       0.94      0.94      0.94       524\n",
      "weighted avg       0.94      0.94      0.94       524\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for score in scores:\n",
    "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
    "    print()\n",
    "\n",
    "    clf = GridSearchCV(\n",
    "        SVC(), tuned_parameters, scoring='%s_macro' % score\n",
    "    )\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    print(\"Best parameters set found on development set:\")\n",
    "    print()\n",
    "    print(clf.best_params_)\n",
    "    print()\n",
    "    print(\"Grid scores on development set:\")\n",
    "    print()\n",
    "    means = clf.cv_results_['mean_test_score']\n",
    "    stds = clf.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "        print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "              % (mean, std * 2, params))\n",
    "    print()\n",
    "\n",
    "    print(\"Detailed classification report:\")\n",
    "    print()\n",
    "    print(\"The model is trained on the full development set.\")\n",
    "    print(\"The scores are computed on the full evaluation set.\")\n",
    "    print()\n",
    "    y_true, y_pred = y_test, clf.predict(X_test)\n",
    "    print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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