tutorials/villin_headpiece/tICA_Cross_Validation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "010a2f99",
   "metadata": {},
   "outputs": [],
   "source": [
    "MDtrajs_dir=\"./MDtrajs/\"\n",
    "Featurization_dir=\"./Featurization/\"\n",
    "FSelection_dir = \"./FSel/\"\n",
    "GMRQ_dir=\"./GMRQ/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8843abd5",
   "metadata": {},
   "source": [
    "### GMRQ Cross Validation of tICA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "015317c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from msmbuilder.decomposition import tICA\n",
    "from msmbuilder.cluster import KMeans, KCenters, MiniBatchKMeans\n",
    "from msmbuilder.msm import MarkovStateModel\n",
    "import multiprocessing\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a18c728",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pickles(file_dir):\n",
    "    pickle_files = glob.glob(file_dir)\n",
    "    data_list = []\n",
    "    for file in pickle_files:\n",
    "        with open(file, \"rb\") as f:\n",
    "            data = pickle.load(f)\n",
    "            data_list.append(data)\n",
    "    return data_list\n",
    "\n",
    "ticaCV_dir = \"./GMRQ/tica/\"\n",
    "if not os.path.exists(ticaCV_dir):\n",
    "        os.mkdir(ticaCV_dir)\n",
    "\n",
    "for para in ['lagtime', 'n_tics', 'n_clusters']:\n",
    "    para_dir = ticaCV_dir + para + '/'\n",
    "    if not os.path.exists(para_dir):\n",
    "        os.mkdir(para_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8da282a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GMRQ\n",
    "oasis_trajs = np.load(FSelection_dir + 'oasis_trajs.npy', allow_pickle=True)\n",
    "\n",
    "def TICA_CV(lt=200, n_tics=4, n_clusters=200, trajs=oasis_trajs, runs=10, n_fold=5, para='lagtime'):\n",
    "    \n",
    "    output_files_dir = ticaCV_dir + para + '/'\n",
    "    \n",
    "    results = []\n",
    "    for run in range(runs):\n",
    "        cv = KFold(n_splits=n_fold, shuffle=True)\n",
    "        for train_index, test_index in cv.split(trajs):\n",
    "            \n",
    "            train_data = [trajs[i] for i in train_index]\n",
    "            test_data = [trajs[i] for i in test_index]\n",
    "            \n",
    "            tica = tICA(n_components=n_tics, lag_time=lt, kinetic_mapping=True)\n",
    "            tica.fit(train_data)\n",
    "            ttraj_train = tica.transform(train_data)\n",
    "            ttraj_test = tica.transform(test_data)\n",
    "            \n",
    "            cluster = KCenters(n_clusters=n_clusters)\n",
    "            cluster.fit(ttraj_train)\n",
    "            ctraj_train = cluster.predict(ttraj_train)\n",
    "            ctraj_test = cluster.predict(ttraj_test)\n",
    "            \n",
    "            msm = MarkovStateModel(n_timescales=3, lag_time=500, reversible_type='transpose', \n",
    "                                   verbose=False, ergodic_cutoff='off')\n",
    "            msm.fit(ctraj_train)\n",
    "            train_score = msm.score(ctraj_train)\n",
    "            test_score = msm.score(ctraj_test)\n",
    "            \n",
    "            results.append({\n",
    "                'type': 'train', 'score': train_score, \n",
    "                'lagtime': lt, 'n_tics': n_tics, 'n_clusters': n_clusters})\n",
    "            results.append({\n",
    "                'type': 'test', 'score': test_score, \n",
    "                'lagtime': lt, 'n_tics': n_tics, 'n_clusters': n_clusters})\n",
    "            \n",
    "        print('Run {} for {}lt, {}tics, {}clusters is complete'.format(run,lt,n_tics,n_clusters))\n",
    "    frame = pd.DataFrame(results)\n",
    "    frame.to_pickle(output_files_dir + \"{}lt_{}tics_{}clusters_gmrq.pickl\".format(lt,n_tics,n_clusters)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4f6308c",
   "metadata": {},
   "outputs": [],
   "source": [
    "para_list = [25, 50, 100, 200, 400]\n",
    "Parallel(n_jobs=5)(delayed(TICA_CV)\\\n",
    "                   (lt=i, n_tics=4, n_clusters=200, para='lagtime') for i in para_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "526126e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "para_list = [2, 3, 4, 5, 6]\n",
    "Parallel(n_jobs=5)(delayed(TICA_CV)\\\n",
    "                   (lt=200, n_tics=i, n_clusters=200, para='n_tics') for i in para_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d0d5244",
   "metadata": {},
   "outputs": [],
   "source": [
    "para_list = [50, 100, 200, 400, 800]\n",
    "Parallel(n_jobs=5)(delayed(TICA_CV)\\\n",
    "                   (lt=200, n_tics=4, n_clusters=i, para='n_clusters') for i in para_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "acc7f424",
   "metadata": {},
   "outputs": [
    {
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Tp04QosEZs2fPtpwe5fbbbw9CNMVXIK+DQKibO3euZXl+fYmsrCxt3rxZy5Yt06ZNm3T06NF8f3WP4o3+pH1C7VwuX77c1lhKlSqlpk2behWLr4rTGEMot0tJHmMIdrswxmAt2O3iDr55wCN9+/bVsmXL1LVrV4+2S0hI0P/+9z/9/PPPSkxM9E9wIe7kyZPatWuXVqxYoXXr1mn//v2WH3QIDVbTpUZFRalu3bpe11mvXj3LX1+sWrXK6zqLIl8Gg2kX9/z999/63//+51L25JNPqnHjxn7ZH+3iKjY21nLdgQkTJng1cDVu3DhTWZMmTQo9v7QLrGYaiIyMzHddDPjfzp07dd9995nKO3Xq5NMvRuEq0NdBeIbvRYFnNThVu3ZtVaxYMe/vY8eO6c0331Tbtm0VExOjCy+8UC1bttRFF12k8847T1FRUbriiiv08ssvW/7qHYEXiPcS/Un7hNK53Llzp06cOGEqv+SSS7yOJb/tA9GuxWWMIZTbpSSPMYRCuzDGYBYK7eIOkm822rFjhwzD8Otjx44dbsdz5g147qNWrVo+HedFF12k2bNn688//9SDDz6oOnXqyOFwmF4XExOjrl276v3339fu3bv12GOPlcg1Rh566CE1btxYsbGxqlmzppo3b66LL75YVatWVZkyZVSrVi3ddttt+uijj3TkyJFgh4v/z+oL5QUXXGD5b91dDodDF1xwgal869atXtdZFPkyGEy7FC47O1sDBgxwWRC2Tp06Gjp0qN/2SbuYPf3006ayFStW6N133/WonoULF+rjjz82lT/33HOFbku7lGxr1qzR1KlTTeVdunRRQkJCECIq2ZxOpyZPnqx27dpp586dLs9VrlxZEyZMCE5gxVAwroNwD9+LgmfdunWmskaNGkk6/fk0atQo1apVS0899ZSWLFminJwc0+tPnTqluXPnasiQIWrQoIHuuece7dq1y++xwyyQ7yX6k/YJpXOZXwLdlwH0/LbftWuX5WeKnYrLGEOotktJH2MIlXZhjKHwWKTgv1/ORfINXmvTpo0++OADbdmyRSkpKdqwYYP++usvrVy5Urt27VJqaqpmz56thx56SHFxccEON2g+/PBDrVmzxvJXONLpTP0XX3yhBx98UDVq1ND999/PLwlDwLkDY5JUrVo1n+u1qsOTpHpR5+tgMO1SuFdffdX0a6SPPvrIct5qu9AuZp06ddIDDzxgKn/iiSf0+uuv53tNONsPP/yga6+9VtnZ2S7lN954o26++eZCt6ddSi6n06kHHnjA5QvyGU8++WQQIire1q9frz///NPl8ccff+jnn3/WJ598ooceekg1atTQLbfcov3797tsW7duXc2bN081a9YMUvTFTzCug3AP34uCxypJVrVqVaWnp6tHjx568sknlZqa6nZ9OTk5Gj9+vJo2barffvvNzlDhhkC+l+hP2ieUzqVVLHbEY7V9bm6udu/e7VO9BSlOYwyh2i4lfYwhVNqFMYbCY7EjHrs/x0i+wRaxsbG66KKL1LJlSzVp0kTnn3++T1nvkurkyZNKSkpSkyZNlJSUFOxwSrRDhw6ZygpbeNQdVnVYzU1eHNkxGEy7FGzDhg0aOXKkS9kdd9zh8VTBnqJdrL333nvq06ePS5nT6dR//vMfNWrUSG+99Zb+/vtvHTt2TDk5OUpJSdH69es1btw4XX755bruuutM0yhcddVV+uyzz9zaP+1Scr388statGiRqfzaa69lakM/eOihh9SuXTuXx2WXXaZrrrlGAwcO1Icffqi9e/e6bBMbG6unn35aK1euVP369YMUefETrOsg7Mf3Inudm/iXpAoVKqhnz56aOXOm1/UeP35c3bt315dffulLePAjX99L9CftE0rn0ioWh8PhMhWtXbG4E4+3itsYQyi2C2MModUujDEUHEuw3y9WInwJBoB/ZGRk6P7779cff/yhTz/9lERmEBw7dsxUZsc0XfHx8aayo0eP+lxvUWDHYDDtkj+n06kBAwa4rPNQvnx5vfXWW37fN+1iLSIiQlOmTNHrr7+u4cOHKzMzM++5jRs3avDgwW7XFRUVpWeffVbPPfec21M40y4l0+zZszVs2DBTeUJCgt5///0gRISzxcbG6vnnn9f9999fYtdB9pdgXgfhP3wv8p3T6VRaWpqpfNy4caakXKVKlfTAAw/o6quv1gUXXKCEhAQdPnxY69at03fffafx48eb1hTLycnRgAED1LBhQzVp0sSvxwLvefteoj9pn1A6l1axxMTE+LxUjFUs7sTjreI2xhBq7cIYQ/6xBKtdGGMoOJZQ/Bwj+Qb4QalSpdS+fXt16dJFF198sRo0aKDzzjtP8fHxOnXqlI4fP66tW7dq4cKF+vbbb7VixQrLej777DOVK1dOb7/9dmAPAJZfUGNjY32u16qO9PR0n+sNdXYNBtMu+Xvvvfe0ePFil7JRo0bpvPPO8/u+aZf8hYWF6ZlnnlH//v313nvvacqUKfrnn3/c3r5Ro0a6+eab9cADD3j8Cy7apeTZsGGDbr75ZsspRz7++GPVqFEjCFHhbGlpaXr22Wf1ww8/6PHHH9cNN9ygsDAmI7FDMK+DyB/fi4Lv7IG5s52beLvzzjv13nvvmQadqlWrpmrVqumqq67SU089pb59++rvv/92ec3Jkyd14403av369YqMjLT3ACApeO8l+pP2CaVzGchY3InHG8VxjCHU2oUxhsDH4k48jDEEPhZ34skPyTfARu3atdOAAQPUt2/ffN+skZGRio2N1fnnn69OnTppyJAhmjdvnh588EFt3LjR9Pp33nlHzZs315133unv8HGWc+c/lk7/wsRXVl9Gs7KyfK43lNk5GEy7WNuxY4eGDBniUtalSxfdddddAdk/7VI4h8Oh6Ohoj38Vtm/fPv3zzz/asGGDxx1j2qVk2bdvn66++mrTNCKS9Nhjj6lv376BDwqWDMPQ4sWLtXjxYrVv314TJ05UnTp1gh1WkRbs6yDM+F4UOnJycgp9zX333aePP/640LuhLrjgAs2ZM0ddunTRX3/95fLcli1bNHHiRA0YMMCneOEq2O8l+pP2CaVzGchY3InHU8V1jCGU2iXYfSvapXAlfYwhVNvlXPzMErDRokWLNGDAAI8z7Z06ddLy5ct14403Wj7/3HPPKSMjw44Q4SarOcN9vXU5vzrc+UJcVNk9GEy7WLv//vtdfoVTpkwZffTRRwHbP+2Sv8zMTD311FOqVauWnn/+edNAVWGOHz+uzz//XJ06ddLll1+uLVu2uL0t7VJyHDlyRFdeeaXlotPXX3+93nzzzSBEVXLMmzdPhmG4PDIyMrRv3z4tWrRI77zzjjp27Gi57aJFi9SuXTutXbs2wFEXL8G+DsKM70Who7A70Ro0aKB3333X7WkI4+Li9Pnnnys6Otr03GuvvSbDMLyKE9aC/V6iP2mfUDqXgYzFnXg8UZzHGEKpXYLdt6Jd8scYQ+BjcSee/JB8A0JEVFSUPv/8c3Xu3Nn03N69e1mnJcCsfi1hR4fRqo7iOjWLPwaDaRez8ePHa9asWS5lQ4cOVd26dQMWA+1ibe/evWrVqpXefPNN05RP0dHRuvnmmzV69GhNmzZNCxYs0IwZMzR+/Hjdd999qlKliqm+BQsWqGnTpvr555/d2j/tUjIkJyerW7duWr9+vem57t2768svv7TlSwg8ExUVpSpVqqhdu3Z69NFHNX/+fK1cuVKtWrUyvfbw4cPq1q2bjh8/HoRIi75QuA7CXnwvslepUqUKfH7YsGEqU6aMR3XWq1dP/fv3N5Vv3rxZ69at86gu+I8d7yX6k/YJpXMZyFjcicddxX2MIVTaJRT6VrSLNcYYghOLO/Hkh+QbEEJKlSqlpKQkyzf0t99+G4SISq7SpUubyqxuafaU1W3KVvsq6vw1GEy7uDp48KBpQd2LL75YTz75ZEDjoF3Mjhw5os6dO1vezfLII49o586dmjx5sgYNGqSePXvqsssu09VXX63+/fsrKSlJO3bs0Pvvv2/6VXl6err69Omj+fPnFxoD7VL8paamqlu3blq+fLnpuc6dO2vq1KmFDroicJo0aaJFixZZ3oWwb98+PfHEE0GIqmgLlesg7Mf3IvuEh4fne9dUuXLldP3113tV78CBAy3L582b51V98A9f30v0J+0TSucykLG4E487SsIYQyi0S6j0rWgXM8YYgheLO/Hkh+QbEGLq1q2rW2+91VT+119/6ciRI0GIqGSKi4szlaWkpPhcb2pqqqns3EXNizp/DgbTLq4GDRrkcqdEWFiYPvnkk4D/apR2MXvwwQe1efNmlzKHw6FPP/1U7777bqGLVJcqVUoPPfSQFi9erMTERJfnTp06pTvuuEPJyckF1kG7FG/p6em65pprtGTJEtNzHTp00PTp0xUVFRWEyFCQiIgIffHFF7rkkktMz02aNEl79uwJQlRFV6hcB+EffC+yT379jnbt2nn9fmncuLGpjyJJf/75p1f1wX98eS/Rn7RPKJ3LQMbiTjzu1FsSxhhCoV1CpW9Fu5gxxhC8WNyJJz8k34AQ1LNnT1OZ0+nUsmXLghBNyVS+fHlTmdWc4p6yupBZ7auo8vdgMO3yr++++870a9UHH3xQbdu2DXgstIurhQsXasqUKaby//znP24tbH+2xo0b68svvzSV7969W2+//XaB29IuxVdGRoZ69OihP/74w/Rc27ZtNWPGDMXExAQhMrgjMjJSr7/+uqk8JyfH8rMD1kLpOgj/4XuRPapVq2ZZ3rRpU6/rdDgclj8kOHjwoNd1wn+8fS/Rn7RPKJ1Lq+czMzN16tQp22NxJ56ClKQxhmC3Syj1rWgXV4wxuBdLKH6OkXwDQlCLFi0sy/kiEziVK1c2ldlx/vfv3+/WvoqiQAwG0y6nnThxQoMGDXIpq1atml5++eWgxEO7uBo9erSprFy5cnruuee8qq979+7q2rWrqfzDDz+U0+nMdzvapXg6efKkevbsaTktSMuWLTVz5kzLXwEitFx55ZWWX+CsrqEwC7XrIPyH70X2qF27tmV5uXLlfKrX6nPs6NGjPtUJ//D2vUR/0j6hdC7ze97XeKxicSee/JS0MYZgtkuo9a1oF1eMMbgXix3x2P05Zl6ZDkDQVaxY0bL80KFDAY6k5LL6grpr1y6f6929e7db+ypqAjUYTLucNn/+fFOH4K677rKc/94be/bssZwyqGHDhpa32tMu/zIMQ7NnzzaV9+rVy6f3wG233aZff/3VpezgwYNas2aNmjRpYrkN7VL8ZGZmqnfv3pozZ47puWbNmmnWrFlKSEgIQmTwVHh4uJo2barffvvNpdyO92hJEGrXQfgP34vs0aBBA8vyc9d98ZTVoHdaWppPdcI/vH0v0Z+0Tyidy/ye37Vrl2rUqGFrLGXKlPFq0LokjjEEs11CrW9Fu/yLMQZrwW4Xd5F8A4oQOxaOhHvq1atnKtuxY4eysrK8nks8KytLO3fudGtfRUkgB4Npl9MMwzCVvfzyy7b9Km3s2LEaO3asqXzu3Lnq1KmTqZx2+deuXbssf/HdoUMHn+q99NJLLctXrFiRb8eYdileTp06pT59+lh+8WrcuLFmz56tsmXLBiEyeKtChQqmsrPX2ED+Qu06iMDje5Fn8rvryde1UaymZuJaVLQU9l6iP2mfUDqX+T3/zz//+PS95Z9//jGV1a1bVw6Hw6N6SuoYQzDbJdT6VrTLvxhjsBbsdnEX004CISi/X58xjVTgNGvWzFSWm5urtWvXel3nmjVrlJubayr3Za2FYAv0YDDtEppol38dPnzYsrxSpUo+1Zvfr6yOHDmS7za0S/GRlZWl66+/XjNnzjQ916hRI/3666/Ffp2U4ig9Pd1U5u2XVqC44nuRPdq2bavw8HBTua93EFptf9555/lUJ/zD2/cS/Un7hNK5rFChguVakCtXrvQ6lvy297RdS/IYQyi3S6DRLv9ijMFasNvFXSTfgBCU36LHvtw2C8+0bNlSYWHmj8jFixd7XafVtuHh4fn+EjXUBWMwmHYJTbTLv3JycizLIyMjfao3v+0Lmo+ddikesrOzddNNN2nGjBmm5xo0aKA5c+ZY3kGF0Ldnzx5Tma9fooHihu9F9khISFDbtm1N5X/99ZfXdWZlZWn16tWm8po1a3pdJ/zH2/cS/Un7hNq5bNOmja2xJCcna8OGDW7tJz+MMYRmuwQD7fIvxhjyVxTeLyTfgBA0ffp0y/JQ/2VKcZKYmKiWLVuayn/55Rev67TatmXLlkpMTPS6zmAJ1mAw7RKaaJd/5fdr74J+PeaO/H7tVtD7jHYp+nJyctS3b19NmzbN9Fz9+vU1Z86cfNdwQWg7ePCgVq1aZSpv2LBhEKIBQhffi+xzww03mMqWLl2qEydOeFXf77//rszMTFN5586dvaoP/uXte4n+pH1C7Vx27drVVLZ8+XKvv7f8+uuvlnevWO3HCmMMp4VauwQL7fIvxhjyVxTeLyTfgBCzdetWffXVV6byevXq8QvPAOvZs6epbNasWV59QT1+/LhmzZplKu/Vq5c3oQVVsAeDaRfpuuuuk2EYtjysDBs2zPK1Ba1zQ7uclt+//b///tunevP7ZXphX0Bpl6IrNzdX/fr103fffWd6rl69epozZ47Xiz4j+D7++GPLX5UyaO2eULwOwn58L7LXbbfdZvqVe2ZmpiZMmOBVfe+//76pzOFw6IorrvCqPviPr+8l+pP2CaVzee2115rWMHI6nfrmm288jkWSJk+ebCqrV6+eLrrookK3ZYzhX8Fql1DsW9EupzHGkL9Q+hzLD8k3IIRkZWXpvvvuU1ZWlum522+/PQgRlWy33Xab6UP81KlTSkpK8riuTz75xNSuDodD/fr18ynGQAuFwWDaJTTRLqfFx8erbt26pvJp06ZZ/oLKXVbvOUmWvzo7G+1SNDmdTt15552WXxrq1KmjOXPmqGrVqkGIDHZYt26dXn31VVN5QkKCrrnmmiBEBIQevhfZr2LFirrjjjtM5SNGjNCBAwc8quu3337T999/byq//vrrWYM0xNjxXqI/aZ9QOpfnn3++OnbsaCp///33802g5GfPnj2Wnwnu/BtjjMFVqLRLKKBdTmOMIX9F4v1iAPDZjBkzjCNHjvhUR0ZGhnHjjTcakkyPuLg4n+svjrZv3255vubOnWvbPnr06GGqv3z58sbRo0fdruPIkSNGuXLlTPX07NnTtjgDITc31+jXr5/lOa9Tp46xe/fugMVCu9jHqj2HDRvmVV20y2kPP/yw5XkdN26cV/WtXbvWCA8PN9XXpEkTt7anXYoWp9Np9O/f3/LfUO3atY1du3YFO8QS6f/+7/+M7777znA6nT7V8/fffxsVK1a09bMXvqEt7MX3otC2fft2IyoqynReL7vsMiM1NdWtOjZt2mRUrVrVVIfD4TBWrlzp5yMoOULtvVRS+pMlbYzhm2++sTzeCRMmeFTPXXfdZaqjdOnSxr59+wrcjjEGa8FuF18V1zGGYLYLYwz5C/X3C8k3wAa9e/c2oqOjjUGDBhmLFy/2eGBm3rx5RoMGDSw/LCQZ7733np8iL9oC0TFevHix5T569epl5ObmFrp9bm6u0bNnT8svp3/++adtcfpbqA0G0y72sbNjTLuctnDhQsvzEBsba6xYscKjuo4ePWpceOGFlvW9+uqrbtVBuxQtgwYNsmyv6Oho46uvvjIWL17s82P58uXBPswi5/LLLzckGRdffLHxyiuvGJs3b/Zo+61btxqPPPKI5ZdcScaFF15opKen+yl6FMTO6yD4XlQUvPLKK5bntnnz5oX2U6ZOnZrvDwgGDhwYmAMoIULtvVRS+pMlbYwhNzfXaNSokamuxMREt/s6X3/9teXxPPTQQwVuxxhDwXUFq13sUFzHGILZLowxFFxXKL9fSL4BNujdu7fLm7Nq1arG3XffbbzzzjvG3LlzjW3bthlHjhwxsrKyjPT0dGPPnj3GvHnzjJEjRxrNmjXLt0Msyejbt2+wDy9kBaJjbBiGceedd1ru55ZbbilwoCw9Pd3o27ev5bZ33XWXrTH6WygOBtMu9rCzY2wYtMsZ1157reWxxMfHG1999ZVbdSxdutSoU6eOZT1Vq1b1aKCedik6atasWWC/wI5HzZo1g32YRc6Z5NvZj7p16xo33XSTMXLkSGPSpEnG9OnTjQULFhhz5841fvjhB+OTTz4xHnnkEaN169YFtke5cuWM9evXB/sQSyy7r4MlHd+LQl9ubq5x9dVXW57jsLAwo1u3bsa7775r/Pjjj8aCBQuMqVOnGi+++GKB7dOmTRsjMzMz2IdWrITie6kk9CdL4hjDb7/9ZllfjRo1Ch3U/+KLL4xSpUpZ9m0Ku7OSMYaCBatd7GB334p2OY0xhvyF8vuF5Btgg3M7xnY9br75ZiMrKyvYhxcU+XV67Xp40nk+fvy4Ubt27Xw/yP/3v/8ZGzZsMDIzM43MzExj/fr1xltvvWWcf/75ltvUrl3bOH78uN/OnT+E4mAw7WIPq3PhS8eYdjlt06ZNRtmyZfP99960aVPj7bffNpYtW2YcOXLEyM7ONk6cOGFs3LjRGDduXL6DYtLpX4N98803HsVDuxQdofh5C+vkmx2P2rVrG2vXrg324ZVodl8HSzq+FxUNqampRocOHWxpm+bNm/t9CrOSKBTfS0W9P8kYQ/4ee+wxy3ojIyON+++/35g/f75x4sQJIycnx9i/f78xdepUo3v37vmei++//77QfYZin5d2sYfVvovTGEOw2oUxhoKF6vuF5BtgA7s7xvHx8caYMWOCfVhBFUodY8MwjDVr1hjly5f3eb/ly5c31qxZ45+T5keh2DE2DNrFDlbnw9dBR9rltN9//91yXRVfH2+99ZZX8dAuRUOoft6WdHYn3yIiIozBgwcbaWlpwT60Es8f18GSjO9FRUdGRobl+iaePG655RYjIyMj2IdSLIXqe6ko9ycZY8hfVlaW5RpO3jxeeeUVt/YZqn3ekt4udrDaf3EaYwhmuzDGkL9Qfb+QfANs8OKLLxrnnXeez2/ucuXKGY8//njA57YORaHWMTYMw1i9enW+v+pw51G7dm1j9erV9p+sAAjVjrFhlOx2sYPVObFj0JF2OW3JkiX5zqfu6SMhIcHjRYPPRbuEvlD+vC3Jli1bZgwZMsRo3ry54XA4vD73lStXNgYPHmxs2LAh2IeE/89f18GSiu9FRc/MmTON5s2be9Q+HTp0MH777bdgh16shfJ7qaj2JxljKFhmZqZx2223eR1LRESE8fbbb7u9v1Du85bkdrGDVRzFbYwhmO3CGEP+QvH9QvINsInT6TRWr15tfPzxx8YDDzxgtGvXzqhYsWKBAzSlS5c2WrdubTz88MPGV199ZZw8eTLYhxEyQrFjbBiGcezYMeOBBx4wwsPD3d5XeHi48eCDDxbpKdpCuWNsGCW3XexgdW7sGnSkXU5LT083hgwZYlSoUMGr90aZMmWMO++807ZBE9oF8M2JEyeMWbNmGSNHjjRuv/12o0OHDkbNmjWNxMREIzIy0ihVqpRRvnx5o3bt2kaLFi2MO+64w3jrrbeMBQsWGDk5OcEOH+fw53WwpOJ7UdG0ZMkS4/nnnze6dOliVK9e3YiKijIiIiKMChUqGA0bNjR69OhhjBo1irvfAyiU30tFsT/JGIN7PvvsM6NKlSoeHXuLFi2MP//806P9MMbgmUC1ix382beiXU5jjKFgofR+cRiGYQiA32RlZenAgQNKS0vTyZMnFR4ersTERCUkJCghIUFhYWHBDjEknTp1SitWrPBb/Q0bNlR8fLzX2+/atUtjx47VTz/9pFWrViknJ8fl+YiICDVp0kQ9evTQgAEDVKNGDV9DhhtoF8/997//NZV16tRJnTp1sm0ftMtpWVlZmjp1qn777TctWbJE69evV25uruVra9WqpdatW6tDhw7q16+fypcvb3s8tAs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\n",
      "text/plain": [
       "<Figure size 1800x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.size'] = 40\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 8), sharey=True,\n",
    "                         gridspec_kw={'width_ratios': [1, 1, 1]})\n",
    "\n",
    "pal = {'train': '#67a9cf', 'test': '#ed5736'}\n",
    "para = ['lagtime', 'n_tics', 'n_clusters']\n",
    "for i in range(len(para)):\n",
    "    file_dir = ticaCV_dir + para[i] + '/'\n",
    "    data = load_pickles(file_dir + '*.pickl')\n",
    "    df = pd.concat(data, axis=0)\n",
    "    df['lagtime'] = np.int16(df['lagtime'] * 0.2)\n",
    "    sns.boxplot(data=df, x=para[i], y='score', hue='type', \n",
    "                orient=\"v\", notch=False, \n",
    "                palette=pal, linewidth=3, whis=[2.5, 97.5], width=0.7, \n",
    "                sym=\".\", showfliers=False, ax=axes[i])\n",
    "    axes[i].set_ylabel(\"\")   \n",
    "    axes[i].set_ylim(2, 4)\n",
    "    axes[i].legend([], frameon=False)\n",
    "    axes[i].tick_params(axis='both', direction='in', width=2, length=8)\n",
    "    for spine in axes[i].spines.values():\n",
    "        spine.set_linewidth(4)\n",
    "        \n",
    "axes[0].set_xlabel(\"lagtime (ns)\")\n",
    "axes[1].set_xlabel(\"Number of tICs\")\n",
    "axes[2].set_xlabel(\"Number of clusters\")\n",
    "fig.text(0.01, 0.55, \"GMRQ Score\", ha='center', va='center', rotation=90)\n",
    "fig.text(0.5, 0.95, \"tICA-MSM Cross-Validation\", ha='center', va='center')\n",
    "# axes[2].legend(loc='upper center', bbox_to_anchor=(-0.6, 1.36), ncol=2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=0.05)\n",
    "plt.savefig('./png/gmrq_tica.png', dpi=1000, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "870dd6fb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}