Day_060_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA 範例\n",
    "* 以 PCA + 邏輯斯迴歸判斷手寫辨識資料集, 觀察不同 component 下正確率的變化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 載入套件\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定義 PCA 與隨後的羅吉斯迴歸函數\n",
    "# penalty 改為 L1, max_iter 改為 100\n",
    "logistic = SGDClassifier(loss='log', penalty='l1', max_iter=100, tol=1e-5, random_state=0)\n",
    "pca = PCA()\n",
    "pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])\n",
    "\n",
    "# 載入手寫數字辨識集\n",
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data\n",
    "y_digits = digits.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業\n",
    "* 將參數 penalty 改為 L1, max_iter 改為 100, 觀察 PCA 與羅吉斯迴歸做手寫辨識的效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameter (CV score=0.911):\n",
      "{'logistic__alpha': 0.01, 'pca__n_components': 40}\n"
     ]
    }
   ],
   "source": [
    "# 執行 GridSearchCV 跑出最佳參數\n",
    "param_grid = {\n",
    "    'pca__n_components': [4, 10, 20, 30, 40, 50, 64],\n",
    "    'logistic__alpha': np.logspace(-4, 4, 5),\n",
    "}\n",
    "search = GridSearchCV(pipe, param_grid, iid=False, cv=5, return_train_score=False)\n",
    "search.fit(X_digits, y_digits)\n",
    "print(\"Best parameter (CV score=%0.3f):\" % search.best_score_)\n",
    "print(search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x230846a9a20>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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29zXu/kH4vhzYChRG8ZndIi01hbOOCMKZ/75qJb1dRyMfRRJZY2PsT3N0mEjcfau7z3D3Qe4+2N0vd/dofnsOB0ojtsvCfYcknI4lA4h8bPR7YZPXPWaWeajX7AznTggTiZq3erWsrCx27NjRY5NJ+e4qysN+EulZ3J3a2lo2bdpE376xrYTZYV9H+ADi9cCoyPLu/sWOTm1l3yH9bzOzocBjwNVh3wzAHcDHBMllDsEa7rNbOXcWYc0plkXt23L2EYMwg4XrdlJRU0/fzC5dtVgSVFFREWVlZWzbti3eoXSJ3ZV1AOzJTo9zJNIV0tLSyMvLo6AgtkFD0fz2ew74C/BHoOEQrl0GFEdsFwFRz3ZoZrnACwQPP77VtN/dN4dva8zsYeBfWzvf3ecQJBpKSko6/c/FAX0zmFSczzsbd/PG2u185ughHZ8kPU56ejqjR4+OdxgicRVNIsl299sO49qLgPFmNpqgf2UGcHk0J5pZBvA74Nctn6A3s6HuvtmCGdb+AXjvMGLrFOceOYh3Nu5m/vtblUhEpNeKprP9eTP77KFe2N3rgZuBl4FVwNPuvsLMZpvZNAAzO9nMyoBLgF+a2Yrw9EuBs4BrWhnm+z9mthxYDhQA/3aosXWWcyKGAffUNnLp3b717Ht869m4/a0mSSKaGsktwJ1mVgPUEfR9uLvndnSiu78IvNhi310R7xcRNHm1PO9x4PE2rnluFDF3i6OH5TI4N5Mte2tYUb6XY4bnxTskkU6Vla7VuKVj0cy1ldNRmd7KzDjnyEHMXVTKgtVblUikx/nG/2n56JfIwaL6c8PM+pvZ5PBJ9LPM7KyuDixZTDkyGAb81rqdcY5ERCQ+ohn+ex1B81YRsBQ4FXgTSJgmpng6amjQwvfhtq5bxlIkXu747TIAfvCF4+IciSSyaGoktwAnAxvc/RzgBKBnDpo/DEX9s8lIS2Hznmr219THOxyRTpWfnUF+dq9cEFUOQTSd7dXuXm1mmFmmu79vZkd2eWRJIjXFGFPQl/c/3se6bfs5rii/45NEksRtUyfEOwRJAtHUSMrCCROfBV41s+c4hAcLe4Oxhf0ANW+JSO8Uzaiti8K3d5vZfCAPeKlLo0oyYwuDeWrWblUikZ7lX3/zLgA/ueT4OEciiazNRGJmue6+18wGROxeHn7tB2iYUmjsoLBGslVTykvPMiwvK94hSBJor0byBPA5YAnBZIvW4uuYLo8uSTQ1ba1V05b0MF+9QN2h0rE2E4m7fy6cz+psd9/YjTElnTFh09aGHRXUNTSSnqqngUWk92j3N164xO7vuimWpJWdkcbw/D7UNTilOyvjHY5Ip7l17jvcOvedeIchCS6aP53fMrOTuzySJNfUT6IOd+lJxhT2Y0zYdCvSlmieIzkHuMHMNgAVfDJpox51jTC2sC9/XrOND7epw116jn/59Ph4hyBJIJpEcmGXR9EDjBukZ0lEpHeKZs32De6+AagiGK3V9OqQmU01s9VmttbMbm/l+Flm9raZ1ZvZxS2OXW1mH4SvqyP2n2Rmy8Nr/iIcEBB3zSO31LQlPcjNT7zNzU+8He8wJMF1mEjMbJqZfQB8BPwJWA/8IYrzUoF7CWo0E4GZZtZyTuqNwDUEQ40jzx0AfBs4BZgMfNvM+oeH7yNYi318+JraUSzdIbJGokWupKeYOCyXicM6XHpIerloOtu/SzDj7xp3Hw18GvhrFOdNBta6+zp3rwXmAtMjC7j7endfBjS2OPczwKvuvtPddwGvAlPNbCiQ6+5vhiPKfk2w3G7cDeybQV6fdPZV17NtX028wxHpFP93yjj+75Rx8Q5DElw0iaTO3XcAKWaW4u7zgUkdnQQMB0ojtsvCfdFo69zh4fvDuWaXMrNPpkpRP4mI9CLRJJLdZtYP+DPBeuk/B6KZL721voto23zaOjfqa5rZLDNbbGaLt23rnlnvP2ne0sgt6RlufGwJNz62JN5hSIKLJpFMByqBrxBM1vgh8PkozisDiiO2i4h+1uC2zi3jwDXe27ymu89x9xJ3LyksLIzyY2PTPAuwOtylhzhxZD4njtTSCNK+aIb/zgJ+4+5lwKOHcO1FwHgzGw1sAmYAl0d57svA9yM62C8A7nD3nWa2z8xOBRYC/wT85yHE1KU0nbz0NLPOGhvvECQJRFMjyQVeNrO/mNmXzGxwNBd293rgZoKksAp42t1XmNlsM5sGYGYnm1kZcAnwSzNbEZ67k6CTf1H4mh3uA7gJeBBYS1A76nAEWXdpbtpSjUREehGLdqiqmR0HXAb8I1Dm7ud1ZWCdqaSkxBcvXtzln1Pf0MjEu16mtqGRFd/5DH0zo6nwiSSu6x5dBMCDV2uWpN7IzJa4e0lH5Q5lmtqtwMfADmDQ4QbWk6WlpjC6IBi5tU4d7tIDnD62gNPHFsQ7DElw0TyQeJOZLQBeAwqA6zXPVtvGDmoaArwvzpGIxO6LZ47mi2eOjncYkuCiaXsZCdzq7ku7Opie4JORW6qRiEjvEM2a7QfNkSVt0+SN0pNc/au/A/DoFyfHORJJZOoN7mSavFF6kvOOUneodEyJpJM1Lbu7fkcF9Q2NpGnZXUliV502Kt4hSBLQb7lOdsCyu7uq4h2OiEiXazORhE+Q723r1Z1BJpumWomatyTZXfHgW1zx4FvxDkMSXJtNW+6eA2BmswmeH3mMYNLEK4CcbokuSY0t7MdfPtjO2q37OX9iVBMBiCSkzx03LN4hSBKIpo/kM+5+SsT2fWa2EPhxF8WU9I4cEuTZVZtVcZPkNnPyiHiHIEkgmj6SBjO7wsxSzSzFzK4AGro6sGR27PA8AJZv2hPnSEREul40ieRy4FJgS/i6hOhn8e2VjhicQ0ZaCh9tr2BPVV28wxE5bJf98k0u++Wb8Q5DElw0DySup8USudK+jLQUjhqay7ulu1mxaQ+nj9NcRZKcLj6pqONC0utFM9fWEWb2mpm9F24fZ2bfjObiZjbVzFab2VozO+gJeTPLNLOnwuMLzWxUuP8KM1sa8Wo0s0nhsQXhNZuOJeQTU8eFzVvL1LwlSeySkmIuKSnuuKD0atE0bT0A3AHUAbj7MoJFqtplZqnAvcCFwERgpplNbFHsWmCXu48D7gF+FH7G/7j7JHefBFwFrG8x19cVTcfdfWsU99Dtji0K+0nKlEgkedU1NFLX0BjvMCTBRZNIst397y32RbNm+2Rgrbuvc/daYC4HN5FN55NVF58BPm1mLddlnwk8GcXnJZTjippqJLvjHInI4bvywYVc+eDCeIchCS6a4b/bzWws4ABmdjGwOYrzhgOlEdtlwCltlXH3ejPbAwwEtkeUuYyDE9DDZtYA/C/wbx7t6lzdaFxhP7LSUyjdWcWuilr6982Id0gih2zGZDVrSceiqZF8CfglMMHMNgG3Eix325GWNQsIk1G0ZczsFKDS3d+LOH6Fux8LfCp8XdXqh5vNMrPFZrZ427ZtUYTbudJSUzh6mIYBS3K76IQiLjpBHe7Svg4TSdg0dR5QCExw9zPDkVwdKQMi/5wpAsrbKmNmaUAesDPi+AxaNGu5+6bw6z7gCYImtNbinuPuJe5eUlhYGEW4nU/Pk0iyq6ptoKpWj41J+zps2jKzTIJ12kcBaU1dGO4+u4NTFwHjzWw0sIkgKbR8/mQecDXwJnAx8HpTM5WZpRA8s3JWRCxpQL67bzezdOBzwB87uod4ae4nKVM/iSSnax4OukefuuG0OEciiSyaPpLngD3AEqAm2guHfR43Ay8DqcCv3H1FOHfXYnefBzwEPGZmawlqIpGjwc4Cytx9XcS+TODlMImkEiSRB6KNqbsdp5FbkuSuPHVkvEOQJBBNIily96mHc3F3fxF4scW+uyLeVxPUOlo7dwFwaot9FcBJhxNLPIwp6EffjFTK91SzbV8NhTmZ8Q5J5JB8/nhN2igdi6az/W9mdmyXR9IDpaQYx4T9JO+pn0SS0N7qOvZWa5ofaV80ieRMYEn4NPkyM1tuZsu6OrCe4pN+EiUSST7XP7qY6x9dHO8wJMFF07R1YZdH0YMdW5QPwHI9mChJ6J/PGBXvECQJtJlIzCzX3fcC+7oxnh6nec4t1UgkCU09Zmi8Q5Ak0F6N5AmC4bVLCB4SjHx40IExXRhXjzFyYDY5WWls3VfDlr3VDM7NindIIlHbWVELwADNzCDtaLOPxN0/F34d7e5jwq9NLyWRKJmZ+kkkad30+BJuenxJvMOQBBdNHwlm1h8YDzT/Oe3uf+6qoHqaY4fn89e1O1hetltruEtSuf5T+ptROhbNk+3XAbcQTHGylODZjjeBc7s2tJ7jk5mAVSOR5HKe/vCRKEQz/PcW4GRgg7ufA5wAdP8siEmsec6tsj0k4ETFIm3auq+arfuq4x2GJLhoEkl1+AQ6Zpbp7u8DR3ZtWD1LUf8+9M9OZ0dFLeV79J9SkseXn3iHLz/xTrzDkAQXTSIpM7N84FngVTN7joNn8ZV2mFnz8yRvfKDKnCSPm6aM5aYpY+MdhiS4DvtI3P2i8O3dZjafYKr3l7o0qh7oc8cN5c9rtvGTV9Yw9Zih5PVJj3dIIh2acuSgeIcgSaDNGomZDWj5ApYDbwD9ui3CHuLiE4s4aWR/tu2r4aevrI53OCJRKd9dRfnuqniHIQmuvaatJcDi8GvLlybfOUQpKcb3LjqG1BTjsbc28G6ppkyRxPeVp5bylaeWxjsMSXDtPZDY2oOIh/RAoplNDSd7XGtmt7dyPNPMngqPLzSzUeH+UWZWZWZLw9f9EeecFE4cudbMfmFNK20lgQlDcrnuzNG4wzeeXU5Do0ZwSWL78rnj+fK54+MdhiS4aDrbMbMvmNl/mNlPzewfojwnFbiXYNLHicBMM5vYoti1wC53HwfcA/wo4tiH7j4pfN0Ysf8+YBbBA5LjgcNaKyVebjlvPMPz+/Depr089ub6eIcj0q4zxxdw5viCeIchCa7DRGJm/w3cSNA/8h5wo5ndG8W1JwNrwzXfa4G5wPQWZaYDj4bvnwE+3V4Nw8yGArnu/ma4JO+vgagSW6LIzkjj7mlHA/CTV9awZa+GA0vi2rijko07KuMdhiS4aGokZwOfcfeH3f1h4LPAlCjOGw6URmyXhftaLePu9QRL+g4Mj402s3fM7E9m9qmI8mUdXDPhnT9xMOdPHMz+mnpmP78y3uGItOnrz7zL1595N95hSIKLZq6t1cAIYEO4XQxEs7BVazWLlp0CbZXZDIxw9x1mdhLwrJkdHeU1gwubzSJoAmPEiBFRhNu97p52NG98sJ0Xlm3m9LEbuOIUrY0tiecr5x8R7xAkCURTIxkIrDKzBWa2AFgJDDKzeWY2r53zygiSTpMiDn6QsbmMmaURPKOy091r3H0HgLsvAT4EjgjLF3VwTcLz5rh7ibuXFBYWRnGb3Wt4fh++Mz1o4vrWs+/x6sotcY5I5GCnjhnIqWMGdlxQerVoaiR3Hea1FwHjzWw0sAmYAVzeosw84GqCSSAvBl53dzezQoKE0mBmYwg61de5+04z22dmpwILgX8C/vMw44u7S0uK2bSrip+/9gFffvJtnrj+VE4c0T/eYYk0+3DbfgDGFurRMWlbNIlkm7sf0JBvZlPcfUF7J7l7vZndDLwMpAK/cvcVZjYbWOzu84CHgMfMbC2wkyDZAJwFzDazeqABuNHdd4bHbgIeAfoAfwhfSevW88bz8Z5qnlpcyrWPLOJ/bzqdMfpPKwnizt8uB+CpG06LcySSyKyj2WjN7D2C0VH/TrAeyY+BEndPmp+skpISX7w4cZ+hrG9o5PpfL2b+6m0UD+jDb286g8KczHiHJcKSDcHfbyeNHBDnSCQezGyJu5d0VC6aPpJTCDrb/0bQXFUOnBFbeBIpLTWFe684keOL8ijdWcUXH1lEVW1DvMMS4aSRA5REpEPRJJI6oIqgKSkL+MjdG7s0ql4oOyONh645mREDslm+aQ9ff+ZdrV0icbf6432s/nhfvMOQBBdNIllEkEhOBs4keEL9mS6Nqpcq6JfJg1eX0DcjleeXbea+P30Y75Ckl7vrufe467n34h2GJLhoEsm17n6Xu9e5+8fuPh14rqsD662OGJzDz2acAMC/v7ya11Y39mFUAAAca0lEQVRpWLDEz52fPYo7P3tUvMOQBNdhInH3xWZ2ppn9M4CZFRBMJS9d5PyJg/na+UfgDrfMXcrarWpakPg4vjif44vz4x2GJLho5tr6NnAbcEe4KwN4vCuDErj53HH8n2OHsr+mnut/vYQ9lXXxDkl6oRXle1hRvifeYUiCi6Zp6yJgGlAB4O7lQE5XBiXB8rz/fslxHDU0l4+2V3DVrxayrExrmEj3mv37lcz+veaDk/ZFk0hqw5l2HcDM+nZtSNIkOyONB/7pJIbkZrGsbA/T7/0rX//Nu2zdpxmDpXvc9fmJ3PX5lqs/iBwomkTytJn9Esg3s+uBPwIPdG1Y0qSofzavfvUsbjhrDGkpxm+WlHHOvy/gvgUfUlOvZ02kax09LI+jh+XFOwxJcB0+2Q5gZucDFxDMvvuyu7/a1YF1pkR/sj1aH22v4HsvrOSPq7YCMCwvi3/59Hj+8aQi0lOjWqNM5JA0LQmtDvfeKdon26NKJMmupySSJn9es43vvbCK1VuC0VwjBmRz63njmT5pOKkpSbPysCSBy375JqC5tnorJZIIPS2RADQ2Os8v38zPXl3Duu0VAIwt7Mt/zjyRicNy4xyd9BRNT7UfOUTja3ojJZIIPTGRNKlvaOTZpeX8/LU1lO6soqh/H1748qfIy06Pd2gikuQ6c9LGlhcuNrOvH15Y0tnSUlO4+KQiXv3K2RxXlEfZrir+VfN0SSdZsmFn8wzAIm2JKpGYWYGZ3WRmfwYWAIOjPG+qma02s7VmdnsrxzPN7Knw+EIzGxXuP9/MlpjZ8vDruRHnLAivuTR8DYomlp4uKz2Vey8/kdysNF5duYWH3vgo3iFJD/Djl1bz45dWxzsMSXBtLmxlZjkEDyNeTrDM7e+AMe5e1NY5Lc5PBe4FzidYIneRmc1rsUjWtcAudx9nZjOAHwGXAduBz7t7uZkdQ7A41vCI865w957ZVhWD4gHZ/OSS45n12BJ++If3OWFEf04aqRUX5fB9/wvHxjsESQLt1Ui2Evyi/x4w1t2/BtQewrUnA2vdfZ271wJzgektykwHHg3fPwN82szM3d8Jn6AHWAFkmZlWeorCBUcP4bozR1Pf6Nz8xNvsrDiUfzKRA40t7KdldqVD7SWSOwnWH7kPuMPMxh7itYcDpRHbZRxYqzigjLvXA3uAgS3K/CPwjrvXROx7OGzW+paZabxrC7ddOIETR+SzeU81X3lqKaU7K9VnIoflrXU7eGvdjniHIQmuzUTi7ve4+ykE82wZ8CwwzMxuM7Mjorh2a7/gW/42a7eMmR1N0Nx1Q8TxK9z9WOBT4euqVj/cbJaZLTazxdu2bYsi3J4jPTWF/7r8RPKz0/nTmm186sfzOf47r3DZL9/kO79fwUvvfazEIlG559U13PPqmniHIQnukIb/mtmxwEzgMndvt4ZiZqcBd7v7Z8LtOwDc/QcRZV4Oy7xpZmnAx0Chu7uZFQGvA//s7n9t4zOuIVg//ub2YunJw3/b8/bGXdzz6hpWlO89qInrkpOK+N5Fx5KRpifipW0bd1QCMGJgdpwjkXiIdvhve53t44DBkb/E3X25mfUHfhVFDIuA8WY2GtgEzCDouI80D7gaeBO4GHg9TCL5wAvAHZGfHyabfHffbmbpwOcI5v6SVpw4oj+PXXsK7s7WfTWs3LyXd0t3c/+fPuQ3S8rYuLOS+688if59M+IdqiQoJRCJRnt/jv4MaG1FpUrgno4uHPZ53Eww4moV8LS7rzCz2WY2LSz2EDDQzNYCXwWahgjfDIwDvtVimG8m8LKZLQOWEiQoTSDZATNjcG4W5xw5iFvPO4KnbziNQTmZLPxoJxf991/5cNv+eIcoCeqND7bzxgfb4x2GJLg2m7bM7D13P6aNY8vDfoqk0FubttqzeU8V1z6ymJWb95Kblcbd045mbGE/CnIyKeiXQWZaarxDlASgubZ6t5inSDGzte4+7lCPJSIlktZV1NRzy9yl/LGVdeFzstI4a3wht02doOaNXqx8dxUAw/L7xDkSiYfOSCRPEvRZPNBi/7XABe5+WadE2g2USNrW0Og89MY6/vbhDrbvr2Hbvhp27K+lvjH4uchITeGLZ47m5nPH0S+zzS41EemBOiORDCZ4mr0WWBLuLiFYs/0id/+4k2Ltckokh6ax0dm0u4p7/riG3769CYCCfpn8v88cyeeOH0p2hhJKb7FgdbD2zZQjNRNRb9Rps/+a2TlAU1/JCnd/vRPi61ZKJIdvaeluZv9+BW9v/GS9+NysNIbkZTEkrw9Dc7MYO6gvRwzO4cghOQzJzULPiPYc6iPp3TqjRpIF3Egwemo58FA4EivpKJHExt2Z9245/z3/Qz7aUUFtfWObZXMy0xg3uB+jBvaluH8figdkM2JANuMH5zBAw4yTztZ91QAMysmKcyQSD52RSJ4C6oC/ABcC69391k6NspsokXQed2dXZR0f76nm471VbNpVxQdb97P6432s2bKPXZV1rZ6XmmJMPWYIXzxjFCeO6K9ai0gS6IxE0jzEN3wQ8O/ufmLnhtk9lEi6h7uzfX8tH2zdR9nOKkp3VbJxZyUbdlSyfNMeGsIO/GOH53HN6aP43PFDNcw4wf1xZTCi77yJUa0cIT1MzE+2E9RGgODhQv0FKR0xMwpzMinMyYQWE+hs3lPF429t4ImFG1m+aQ9f+827fOPZ5RwzLI/jivI5vjiPScX5DM3rQ3qqqcaSIB74yzpAiUTa116NpAGoaNoE+hA81W6Au3vSLAyuGkniqK5rYN7Sch59cz0ryve2WS4zLYXMtBSy0lMpHpDNEYNzmDAkhyMG5zB+cD/6Z2eQmqJk09Wa5mhT/1bvpDXbIyiRJKZdFbUs27SHd0t3827pbpZt2sPuylrqGqL7mczOSKVfZho5WWnkZKUzsG8GA/pmMKBfBgV9M8nPTqdfZhp9w1e/zDSG5GZpPXuRKHVG05ZIl+rfN4Ozjyjk7CMKD9jf0OjU1jdSW99IRW09H22vYPXH+4LXln2s27affTX1VNY2UFnbwNZ9NW18QuuG5/fhqKG5TByWy8ShOQzOzaJPRip90lPJSk+lT0YqOZlpal4DXnpvMwBTjxka50gkkSmRSMJJTbHgF3tGKnnZ6QzL78MZ4woOKNPY6FTWNbC/up591XXsqapjZ0UtOypqg6/7a9ldVUtFTT0VNQ3sr6lnf009m3ZVsWl38GptapgmmWkpDM3LYmheH4bmZzE0L4ucrHT6ZqSSnZFG38xUcrPSGZyXxbC8PvTJ6JmDBh7+63pAiUTap0QiSSklxejX1FyVF/0zDg2NzvodFaws38vKzXtZtXkvuyrrqK5toKoueFXW1FNR28D6HZWsD9fj6Ej/7HSG5vVhWH4Wg3KzGJKbxeDcTAbnZtE/OyOo6aSnkpWeQlZGKllpqUkxqOCBqzts1RDp2j4SM5sK/BxIBR509x+2OJ4J/Bo4CdhBsGDW+vDYHQRrxjcA/+LuL0dzzdaoj0QO1f6aejbvrqJ8TzWbd1exZW8NFbX1VIRNavtr6tlTWcfmvVV8vKc66n6dSGZNgwpSyUxLIT01hdQUIy3Fgq+pKUHiSQtqZ03vM9JSgldqCulpKWSnp5KfnU5edgb5fdLJz04nNyudfllBos1MS0n4hCWJKe59JGaWCtwLnE+wXvsiM5vn7isjil0L7HL3cWY2g2BZ3cvMbCLBQlhHA8OAP0Ys79vRNUVi1i8zjfGDcxg/OKfDso2NzvaKGjbvrmbznmq27qvm4z3VbNlbw5a91eytrqMqrPFU1zVSVVtPbUMjdQ1OdV0j1XVtzxTQGdJSjH5ZaUGiMiPFghpdavjKSE0hLdVISwmSU1ZGKn3SU+iTnsrWfTVkpqVwXFE+OVlp5Galk5OVRnZmGqnWlPCsOQGmpaSQnhokwbQUIys9lb6ZQQ0sRaPseqyubNqaDKx193UAZjYXmA5E/tKfDtwdvn8G+C8L/nSaDsx19xrgo3Dhq8lhuY6uKdKtUlKMQTlZDMrJ4vji6M9rGlRQXddATX0j9Y2NNDQ6dQ0efm2kpr4hTDafJKJgIEIDtQ1NAxIa2FNVx57KOnZX1bK7so591fXN/UK19Y3sbmPGgWjNX70tpvMhGGWXnZHaXPOKrIGlh8ksPSWF9LRgOystrIWFgyCamgezM1LDZBccz0gNamiZYW0t8pzIMqkpid+UmKy6MpEMB0ojtsuAU9oqEz70uAcYGO5/q8W5w8P3HV1TJClEDiroSjX1DVTUNFDX0EijB0mqsREa3KkPa0b1jcHXuobGIGGFI+L2VNVRVVdPdV0j+6rrw1cdVXUNNDQ69Y1OY6NT1+g0NDZSH16jvtGpb3Cq6xqoqA3ObxplFy9mkJ4aNgmmGilmmAUP0lp4PHjX+rlBGftkOyzfdOzgcw7e2+rVuzi33XreEUw7fliXfkZXJpLWvj0tG5LbKtPW/taWBm61cdrMZgGzAEaMGNF2lCI9XNAHE99RZQ2N3jyQoa7RaQiTV31Y82qqgdU1eHMyqq5roLq+garayBpZA1W1DVSGX2vqG6itb6Qm8hXW8Kpqg/Or6xqaa3lNw8p7k71VsdVGo9GViaQMiKzoFwHlbZQpC+fzygN2dnBuR9cEwN3nAHMg6Gw/vFsQ6d1+904ZABedUBTTdVIjRtnFS1Oyqm1opK6+EQca3cGh0cFb/5uUsAjuTtPYJA/LNx07+JyD97Ze7nDvJnqF/TK7/DO68l91ETDezEYDmwg6zy9vUWYecDXwJnAxwYqMbmbzgCfM7D8IOtvHA38nqKl0dE0R6SRz/x60JMeaSBJBMLgg6DuRztVliSTs87gZeJlgqO6v3H2Fmc0GFrv7POAh4LGwM30nQWIgLPc0QSd6PfAld28AaO2aXXUPIr3d49epC1I6prm2RESkVdE+R9Ja57WICAC/WVzKbxaXdlxQejUlEhFp0zNLynhmSVm8w5AE1yuatsxsG7DhME8vALZ3YjjdLdnjh+S/B8Uff8l+D/GKf6S7F3ZUqFckkliY2eJo2ggTVbLHD8l/D4o//pL9HhI9fjVtiYhITJRIREQkJkokHZsT7wBilOzxQ/Lfg+KPv2S/h4SOX30kIiISE9VIREQkJkokIiISEyUSERGJiRKJiIjERIlERERiokQiIiIxUSIREZGYKJGIiEhMlEhERCQmSiQiIhITJRIREYmJEomIiMREiURERGKiRCIiIjFRIhERkZgokYiISEyUSEREJCZKJCIiEhMlEhERiYkSiYiIxCShEomZ/crMtprZe20cNzP7hZmtNbNlZnZid8coIiIHSqhEAjwCTG3n+IXA+PA1C7ivG2ISEZF2JFQicfc/AzvbKTId+LUH3gLyzWxo90QnIiKtSahEEoXhQGnEdlm4T0RE4iQt3gEcImtln7da0GwWQfMXffv2PWnChAldGZeISI+zZMmS7e5e2FG5ZEskZUBxxHYRUN5aQXefA8wBKCkp8cWLF3d9dCIiPYiZbYimXLI1bc0D/ikcvXUqsMfdN8c7KBGR3iyhaiRm9iQwBSgwszLg20A6gLvfD7wIfBZYC1QC/xyfSEVEpElCJRJ3n9nBcQe+1E3hiIhIFJKtaUtERBKMEomIiMREiURERGKiRCIiIjFRIhERkZgokYiISEyUSEREJCZKJCIiEhMlEhERiYkSiYiIxESJREREYqJEIiIiMVEiERGRmCiRiIhITJRIREQkJkokIiISEyUSERGJiRKJiIjERIlERERiokQiIiIxSbhEYmZTzWy1ma01s9tbOT7CzOab2TtmtszMPhuPOEVEJJBQicTMUoF7gQuBicBMM5vYotg3gafd/QRgBvDf3RuliIhESqhEAkwG1rr7OnevBeYC01uUcSA3fJ8HlHdjfCIi0kJavANoYThQGrFdBpzSoszdwCtm9mWgL3Be94QmIiKtSbQaibWyz1tszwQecfci4LPAY2Z20H2Y2SwzW2xmi7dt29YFoYqICCReIikDiiO2izi46epa4GkAd38TyAIKWl7I3ee4e4m7lxQWFnZRuCIikmiJZBEw3sxGm1kGQWf6vBZlNgKfBjCzowgSiaocIiJxklCJxN3rgZuBl4FVBKOzVpjZbDObFhb7GnC9mb0LPAlc4+4tm79ERKSbJFpnO+7+IvBii313RbxfCZzR3XGJiEjrEqpGIiIiyUeJREREYqJEIiIiMVEiERGRmCiRiIhITJRIREQkJkokIiISEyUSERGJiRKJiIjERIlERERiokQiIiIxUSIREZGYKJGIiEhMlEhERCQmSiQiIhITJRIREYmJEomIiMREiURERGKiRCIiIjFRIhERkZgokYiISEwSLpGY2VQzW21ma83s9jbKXGpmK81shZk90d0xiojIJ9LiHUAkM0sF7gXOB8qARWY2z91XRpQZD9wBnOHuu8xsUHyiFRERSLwayWRgrbuvc/daYC4wvUWZ64F73X0XgLtv7eYYRUQkQqIlkuFAacR2Wbgv0hHAEWb2VzN7y8ymdlt0IiJykIRq2gKslX3eYjsNGA9MAYqAv5jZMe6++4ALmc0CZgGMGDGi8yMVEREg8WokZUBxxHYRUN5Kmefcvc7dPwJWEySWA7j7HHcvcfeSwsLCLgtYRKS3S7REsggYb2ajzSwDmAHMa1HmWeAcADMrIGjqWtetUYqISLOESiTuXg/cDLwMrAKedvcVZjbbzKaFxV4GdpjZSmA+8HV33xGfiEVExNxbdkH0PCUlJb548eJ4hyEiklTMbIm7l3RULqFqJCIiknyUSEREJCZKJCIiEhMlEhERiYkSiYiIxESJREREYqJEIiIiMVEiERGRmCiRiIhITJRIREQkJkokIiISEyUSERGJiRKJiIjERIlERERiokQiIiIxUSIREZGYKJGIiEhMlEhERCQmSiQiIhITJRIREYmJEomIiMQk4RKJmU01s9VmttbMbm+n3MVm5mZW0p3xiYjIgRIqkZhZKnAvcCEwEZhpZhNbKZcD/AuwsHsjFBGRlhIqkQCTgbXuvs7da4G5wPRWyn0X+DFQ3Z3BiYjIwRItkQwHSiO2y8J9zczsBKDY3Z9v70JmNsvMFpvZ4m3btnV+pCIiAiReIrFW9nnzQbMU4B7gax1dyN3nuHuJu5cUFhZ2YogiIhIp0RJJGVAcsV0ElEds5wDHAAvMbD1wKjBPHe4iIvGTaIlkETDezEabWQYwA5jXdNDd97h7gbuPcvdRwFvANHdfHJ9wRUQkoRKJu9cDNwMvA6uAp919hZnNNrNp8Y1ORERakxbvAFpy9xeBF1vsu6uNslO6IyYREWlbQtVIREQk+SiRiIhITJRIREQkJkokIiISEyUSERGJiRKJiIjERIlERERiokQiIiIxUSIREZGYKJGIiEhMlEhERCQmSiQiIhITJRIREYmJEomIiMREiURERGKiRCIiIjFRIhERkZgokYiISEyUSEREJCZKJCIiEpOESyRmNtXMVpvZWjO7vZXjXzWzlWa2zMxeM7OR8YhTREQCCZVIzCwVuBe4EJgIzDSziS2KvQOUuPtxwDPAj7s3ShERiZRQiQSYDKx193XuXgvMBaZHFnD3+e5eGW6+BRR1c4wiIhIh0RLJcKA0Yrss3NeWa4E/dGlEIiLSrrR4B9CCtbLPWy1odiVQApzdxvFZwCyAESNGdFZ8IiLSQqLVSMqA4ojtIqC8ZSEzOw/4BjDN3Wtau5C7z3H3EncvKSws7JJgRUQk8RLJImC8mY02swxgBjAvsoCZnQD8kiCJbI1DjCIiEiGhEom71wM3Ay8Dq4Cn3X2Fmc02s2lhsX8H+gG/MbOlZjavjcuJiEg3SLQ+Etz9ReDFFvvuinh/XrcHJSIibUqoGomIiCQfJRIREYmJEomIiMREiURERGKiRCIiIjFRIhERkZgokYiISEyUSEREJCZKJCIiEhMlEhERiYkSiYiIxESJREREYqJEIiIiMVEiERGRmCiRiIhITJRIREQkJkokIiISEyUSERGJiRKJiIjERIlERERiokQiIiIxSbhEYmZTzWy1ma01s9tbOZ5pZk+Fxxea2ajuj1JERJokVCIxs1TgXuBCYCIw08wmtih2LbDL3ccB9wA/6t4oRUQkUkIlEmAysNbd17l7LTAXmN6izHTg0fD9M8Cnzcy6MUYREYmQaIlkOFAasV0W7mu1jLvXA3uAgd0SnYiIHCQt3gG00FrNwg+jDGY2C5gVbu43s9WHGVMBsP0wz00EyR4/JP89KP74S/Z7iFf8I6MplGiJpAwojtguAsrbKFNmZmlAHrCz5YXcfQ4wJ9aAzGyxu5fEep14Sfb4IfnvQfHHX7LfQ6LHn2hNW4uA8WY22swygBnAvBZl5gFXh+8vBl5394NqJCIi0j0Sqkbi7vVmdjPwMpAK/MrdV5jZbGCxu88DHgIeM7O1BDWRGfGLWEREEiqRALj7i8CLLfbdFfG+GrikG0OKuXkszpI9fkj+e1D88Zfs95DQ8ZtahUREJBaJ1kciIiJJRomkHR1N15JozOxXZrbVzN6L2DfAzF41sw/Cr/3jGWN7zKzYzOab2SozW2Fmt4T7k+IezCzLzP5uZu+G8X8n3D86nM7ng3B6n4x4x9oeM0s1s3fM7PlwO9niX29my81sqZktDvclxc8QgJnlm9kzZvZ++H/htESPX4mkDVFO15JoHgGmtth3O/Cau48HXgu3E1U98DV3Pwo4FfhS+D1PlnuoAc519+OBScBUMzuVYBqfe8L4dxFM85PIbgFWRWwnW/wA57j7pIghs8nyMwTwc+Ald58AHE/wb5HY8bu7Xq28gNOAlyO27wDuiHdcUcQ9CngvYns1MDR8PxRYHe8YD+FengPOT8Z7ALKBt4FTCB4kSwv3H/BzlWgvgme3XgPOBZ4neAA4aeIPY1wPFLTYlxQ/Q0Au8BFh/3WyxK8aSduima4lGQx2980A4ddBcY4nKuGszicAC0miewibhZYCW4FXgQ+B3R5M5wOJ/3P0M+D/AY3h9kCSK34IZrp4xcyWhDNcQPL8DI0BtgEPh82LD5pZXxI8fiWStkU1FYt0PjPrB/wvcKu77413PIfC3RvcfRLBX/aTgaNaK9a9UUXHzD4HbHX3JZG7WymakPFHOMPdTyRolv6SmZ0V74AOQRpwInCfu58AVJBozVitUCJpWzTTtSSDLWY2FCD8ujXO8bTLzNIJksj/uPtvw91JdQ8A7r4bWEDQ15MfTucDif1zdAYwzczWE8y8fS5BDSVZ4gfA3cvDr1uB3xEk9GT5GSoDytx9Ybj9DEFiSej4lUjaFs10LckgckqZqwn6HRJSuBzAQ8Aqd/+PiENJcQ9mVmhm+eH7PsB5BB2l8wmm84EEjt/d73D3IncfRfDz/rq7X0GSxA9gZn3NLKfpPXAB8B5J8jPk7h8DpWZ2ZLjr08BKEjx+PZDYDjP7LMFfZE3TtXwvziG1y8yeBKYQzBS6Bfg28CzwNDAC2Ahc4u4HTXKZCMzsTOAvwHI+aaO/k6CfJOHvwcyOI1grJ5Xgj7Sn3X22mY0h+At/APAOcKW718Qv0o6Z2RTgX939c8kUfxjr78LNNOAJd/+emQ0kCX6GAMxsEvAgkAGsA/6Z8OeJBI1fiURERGKipi0REYmJEomIiMREiURERGKiRCIiIjFRIhERkZgokYiISEyUSEREJCZKJCIiEpP/D6yHTlZYx9voAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 繪製不同 components 的 PCA explained variance ratio\n",
    "pca.fit(X_digits)\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(6, 6))\n",
    "ax0.plot(pca.explained_variance_ratio_, linewidth=2)\n",
    "ax0.set_ylabel('PCA explained variance')\n",
    "\n",
    "ax0.axvline(search.best_estimator_.named_steps['pca'].n_components, linestyle=':', label='n_components chosen')\n",
    "ax0.legend(prop=dict(size=12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 繪製 7 個不同採樣點的 accuracy\n",
    "results = pd.DataFrame(search.cv_results_)\n",
    "components_col = 'param_pca__n_components'\n",
    "best_clfs = results.groupby(components_col).apply(lambda g: g.nlargest(1, 'mean_test_score'))\n",
    "\n",
    "best_clfs.plot(x=components_col, y='mean_test_score', yerr='std_test_score', legend=False, ax=ax1)\n",
    "ax1.set_ylabel('Classification accuracy (val)')\n",
    "ax1.set_xlabel('n_components')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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