All in one cite-double supervised.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2a785569",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, gc, pickle, datetime, scipy.sparse,random\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from colorama import Fore, Back, Style\n",
    "\n",
    "import sklearn \n",
    "from sklearn.model_selection import GroupKFold,train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import TruncatedSVD,PCA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import seaborn as sns\n",
    "from cycler import cycler\n",
    "from IPython.display import display\n",
    "\n",
    "import scipy.sparse\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "DATA_DIR = \"./\"\n",
    "FP_CELL_METADATA = os.path.join(DATA_DIR,\"metadata.csv\")\n",
    "\n",
    "FP_CITE_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_cite_inputs.h5\")\n",
    "FP_CITE_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_cite_targets.h5\")\n",
    "FP_CITE_TEST_INPUTS = os.path.join(DATA_DIR,\"test_cite_inputs.h5\")\n",
    "\n",
    "FP_SUBMISSION = os.path.join(DATA_DIR,\"sample_submission.csv\")\n",
    "FP_EVALUATION_IDS = os.path.join(DATA_DIR,\"evaluation_ids.csv\")\n",
    "\n",
    "VERBOSE = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fc81b6d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  1\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "import tensorflow_addons as tfa\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau, LearningRateScheduler, EarlyStopping\n",
    "from tensorflow.keras.layers import Dense, Input, Concatenate, Dropout, BatchNormalization, AlphaDropout\n",
    "import keras_tuner\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5ae4cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize\n",
    "def std(x):\n",
    "    empty_list = []\n",
    "    for item in x:\n",
    "        empty_list.append((item - np.mean(item)) / np.std(item))\n",
    "    return np.array(empty_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "83ebdf2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import anndata as ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37334300",
   "metadata": {},
   "outputs": [],
   "source": [
    "def seed_tensorflow(seed=1):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    tf.random.set_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "61e1f2a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constant cols: 1194\n",
      "important columns  84\n"
     ]
    }
   ],
   "source": [
    "# constant_cols = list(X.columns[(X == 0).all(axis=0).values]) + list(X_test.columns[(X_test == 0).all(axis=0).values])\n",
    "constant_cols = ['ENSG00000003137_CYP26B1', 'ENSG00000004848_ARX', 'ENSG00000006606_CCL26', 'ENSG00000010379_SLC6A13', 'ENSG00000010932_FMO1', 'ENSG00000017427_IGF1', 'ENSG00000022355_GABRA1', 'ENSG00000041982_TNC', 'ENSG00000060709_RIMBP2', 'ENSG00000064886_CHI3L2', 'ENSG00000065717_TLE2', 'ENSG00000067798_NAV3', 'ENSG00000069535_MAOB', 'ENSG00000073598_FNDC8', 'ENSG00000074219_TEAD2', 'ENSG00000074964_ARHGEF10L', 'ENSG00000077264_PAK3', 'ENSG00000078053_AMPH', 'ENSG00000082684_SEMA5B', 'ENSG00000083857_FAT1', 'ENSG00000084628_NKAIN1', 'ENSG00000084734_GCKR', 'ENSG00000086967_MYBPC2', 'ENSG00000087258_GNAO1', 'ENSG00000089505_CMTM1', 'ENSG00000091129_NRCAM', 'ENSG00000091986_CCDC80', 'ENSG00000092377_TBL1Y', 'ENSG00000092969_TGFB2', 'ENSG00000095397_WHRN', 'ENSG00000095970_TREM2', 'ENSG00000099715_PCDH11Y', 'ENSG00000100197_CYP2D6', 'ENSG00000100218_RSPH14', 'ENSG00000100311_PDGFB', 'ENSG00000100362_PVALB', 'ENSG00000100373_UPK3A', 'ENSG00000100625_SIX4', 'ENSG00000100867_DHRS2', 'ENSG00000100985_MMP9', 'ENSG00000101197_BIRC7', 'ENSG00000101298_SNPH', 'ENSG00000102387_TAF7L', 'ENSG00000103034_NDRG4', 'ENSG00000104059_FAM189A1', 'ENSG00000104112_SCG3', 'ENSG00000104313_EYA1', 'ENSG00000104892_KLC3', 'ENSG00000105088_OLFM2', 'ENSG00000105261_OVOL3', 'ENSG00000105290_APLP1', 'ENSG00000105507_CABP5', 'ENSG00000105642_KCNN1', 'ENSG00000105694_ELOCP28', 'ENSG00000105707_HPN', 'ENSG00000105894_PTN', 'ENSG00000106018_VIPR2', 'ENSG00000106541_AGR2', 'ENSG00000107317_PTGDS', 'ENSG00000108688_CCL7', 'ENSG00000108702_CCL1', 'ENSG00000108947_EFNB3', 'ENSG00000109193_SULT1E1', 'ENSG00000109794_FAM149A', 'ENSG00000109832_DDX25', 'ENSG00000110195_FOLR1', 'ENSG00000110375_UPK2', 'ENSG00000110436_SLC1A2', 'ENSG00000111339_ART4', 'ENSG00000111863_ADTRP', 'ENSG00000112761_WISP3', 'ENSG00000112852_PCDHB2', 'ENSG00000114251_WNT5A', 'ENSG00000114279_FGF12', 'ENSG00000114455_HHLA2', 'ENSG00000114757_PEX5L', 'ENSG00000115155_OTOF', 'ENSG00000115266_APC2', 'ENSG00000115297_TLX2', 'ENSG00000115590_IL1R2', 'ENSG00000115844_DLX2', 'ENSG00000116194_ANGPTL1', 'ENSG00000116661_FBXO2', 'ENSG00000116774_OLFML3', 'ENSG00000117322_CR2', 'ENSG00000117971_CHRNB4', 'ENSG00000118322_ATP10B', 'ENSG00000118402_ELOVL4', 'ENSG00000118520_ARG1', 'ENSG00000118946_PCDH17', 'ENSG00000118972_FGF23', 'ENSG00000119771_KLHL29', 'ENSG00000120549_KIAA1217', 'ENSG00000121316_PLBD1', 'ENSG00000121905_HPCA', 'ENSG00000122224_LY9', 'ENSG00000124194_GDAP1L1', 'ENSG00000124440_HIF3A', 'ENSG00000124657_OR2B6', 'ENSG00000125462_C1orf61', 'ENSG00000125895_TMEM74B', 'ENSG00000126838_PZP', 'ENSG00000128422_KRT17', 'ENSG00000128918_ALDH1A2', 'ENSG00000129170_CSRP3', 'ENSG00000129214_SHBG', 'ENSG00000129673_AANAT', 'ENSG00000129910_CDH15', 'ENSG00000130294_KIF1A', 'ENSG00000130307_USHBP1', 'ENSG00000130545_CRB3', 'ENSG00000131019_ULBP3', 'ENSG00000131044_TTLL9', 'ENSG00000131183_SLC34A1', 'ENSG00000131386_GALNT15', 'ENSG00000131400_NAPSA', 'ENSG00000131914_LIN28A', 'ENSG00000131941_RHPN2', 'ENSG00000131951_LRRC9', 'ENSG00000132170_PPARG', 'ENSG00000132681_ATP1A4', 'ENSG00000132958_TPTE2', 'ENSG00000133454_MYO18B', 'ENSG00000134545_KLRC1', 'ENSG00000134853_PDGFRA', 'ENSG00000135083_CCNJL', 'ENSG00000135100_HNF1A', 'ENSG00000135116_HRK', 'ENSG00000135312_HTR1B', 'ENSG00000135324_MRAP2', 'ENSG00000135436_FAM186B', 'ENSG00000135472_FAIM2', 'ENSG00000135898_GPR55', 'ENSG00000135929_CYP27A1', 'ENSG00000136002_ARHGEF4', 'ENSG00000136099_PCDH8', 'ENSG00000136274_NACAD', 'ENSG00000137078_SIT1', 'ENSG00000137142_IGFBPL1', 'ENSG00000137473_TTC29', 'ENSG00000137474_MYO7A', 'ENSG00000137491_SLCO2B1', 'ENSG00000137691_CFAP300', 'ENSG00000137731_FXYD2', 'ENSG00000137747_TMPRSS13', 'ENSG00000137878_GCOM1', 'ENSG00000138411_HECW2', 'ENSG00000138741_TRPC3', 'ENSG00000138769_CDKL2', 'ENSG00000138823_MTTP', 'ENSG00000139908_TSSK4', 'ENSG00000140832_MARVELD3', 'ENSG00000142178_SIK1', 'ENSG00000142538_PTH2', 'ENSG00000142910_TINAGL1', 'ENSG00000143217_NECTIN4', 'ENSG00000143858_SYT2', 'ENSG00000144130_NT5DC4', 'ENSG00000144214_LYG1', 'ENSG00000144290_SLC4A10', 'ENSG00000144366_GULP1', 'ENSG00000144583_MARCH4', 'ENSG00000144771_LRTM1', 'ENSG00000144891_AGTR1', 'ENSG00000145087_STXBP5L', 'ENSG00000145107_TM4SF19', 'ENSG00000146197_SCUBE3', 'ENSG00000146966_DENND2A', 'ENSG00000147082_CCNB3', 'ENSG00000147614_ATP6V0D2', 'ENSG00000147642_SYBU', 'ENSG00000147869_CER1', 'ENSG00000149403_GRIK4', 'ENSG00000149596_JPH2', 'ENSG00000150630_VEGFC', 'ENSG00000150722_PPP1R1C', 'ENSG00000151631_AKR1C6P', 'ENSG00000151704_KCNJ1', 'ENSG00000152154_TMEM178A', 'ENSG00000152292_SH2D6', 'ENSG00000152315_KCNK13', 'ENSG00000152503_TRIM36', 'ENSG00000153253_SCN3A', 'ENSG00000153902_LGI4', 'ENSG00000153930_ANKFN1', 'ENSG00000154040_CABYR', 'ENSG00000154118_JPH3', 'ENSG00000154175_ABI3BP', 'ENSG00000154645_CHODL', 'ENSG00000157060_SHCBP1L', 'ENSG00000157087_ATP2B2', 'ENSG00000157152_SYN2', 'ENSG00000157168_NRG1', 'ENSG00000157680_DGKI', 'ENSG00000158246_TENT5B', 'ENSG00000158477_CD1A', 'ENSG00000158481_CD1C', 'ENSG00000158488_CD1E', 'ENSG00000159189_C1QC', 'ENSG00000159217_IGF2BP1', 'ENSG00000160683_CXCR5', 'ENSG00000160801_PTH1R', 'ENSG00000160973_FOXH1', 'ENSG00000161594_KLHL10', 'ENSG00000162409_PRKAA2', 'ENSG00000162840_MT2P1', 'ENSG00000162873_KLHDC8A', 'ENSG00000162944_RFTN2', 'ENSG00000162949_CAPN13', 'ENSG00000163116_STPG2', 'ENSG00000163288_GABRB1', 'ENSG00000163531_NFASC', 'ENSG00000163618_CADPS', 'ENSG00000163637_PRICKLE2', 'ENSG00000163735_CXCL5', 'ENSG00000163873_GRIK3', 'ENSG00000163898_LIPH', 'ENSG00000164061_BSN', 'ENSG00000164078_MST1R', 'ENSG00000164123_C4orf45', 'ENSG00000164690_SHH', 'ENSG00000164761_TNFRSF11B', 'ENSG00000164821_DEFA4', 'ENSG00000164845_FAM86FP', 'ENSG00000164867_NOS3', 'ENSG00000166073_GPR176', 'ENSG00000166148_AVPR1A', 'ENSG00000166250_CLMP', 'ENSG00000166257_SCN3B', 'ENSG00000166268_MYRFL', 'ENSG00000166523_CLEC4E', 'ENSG00000166535_A2ML1', 'ENSG00000166819_PLIN1', 'ENSG00000166928_MS4A14', 'ENSG00000167210_LOXHD1', 'ENSG00000167306_MYO5B', 'ENSG00000167634_NLRP7', 'ENSG00000167748_KLK1', 'ENSG00000167889_MGAT5B', 'ENSG00000168140_VASN', 'ENSG00000168546_GFRA2', 'ENSG00000168646_AXIN2', 'ENSG00000168955_TM4SF20', 'ENSG00000168993_CPLX1', 'ENSG00000169075_Z99496.1', 'ENSG00000169194_IL13', 'ENSG00000169246_NPIPB3', 'ENSG00000169884_WNT10B', 'ENSG00000169900_PYDC1', 'ENSG00000170074_FAM153A', 'ENSG00000170075_GPR37L1', 'ENSG00000170289_CNGB3', 'ENSG00000170356_OR2A20P', 'ENSG00000170537_TMC7', 'ENSG00000170689_HOXB9', 'ENSG00000170827_CELP', 'ENSG00000171346_KRT15', 'ENSG00000171368_TPPP', 'ENSG00000171501_OR1N2', 'ENSG00000171532_NEUROD2', 'ENSG00000171611_PTCRA', 'ENSG00000171873_ADRA1D', 'ENSG00000171916_LGALS9C', 'ENSG00000172005_MAL', 'ENSG00000172987_HPSE2', 'ENSG00000173068_BNC2', 'ENSG00000173077_DEC1', 'ENSG00000173210_ABLIM3', 'ENSG00000173267_SNCG', 'ENSG00000173369_C1QB', 'ENSG00000173372_C1QA', 'ENSG00000173391_OLR1', 'ENSG00000173626_TRAPPC3L', 'ENSG00000173698_ADGRG2', 'ENSG00000173868_PHOSPHO1', 'ENSG00000174407_MIR1-1HG', 'ENSG00000174807_CD248', 'ENSG00000175206_NPPA', 'ENSG00000175746_C15orf54', 'ENSG00000175985_PLEKHD1', 'ENSG00000176043_AC007160.1', 'ENSG00000176399_DMRTA1', 'ENSG00000176510_OR10AC1', 'ENSG00000176697_BDNF', 'ENSG00000176826_FKBP9P1', 'ENSG00000176988_FMR1NB', 'ENSG00000177324_BEND2', 'ENSG00000177335_C8orf31', 'ENSG00000177535_OR2B11', 'ENSG00000177614_PGBD5', 'ENSG00000177707_NECTIN3', 'ENSG00000178033_CALHM5', 'ENSG00000178175_ZNF366', 'ENSG00000178462_TUBAL3', 'ENSG00000178732_GP5', 'ENSG00000178750_STX19', 'ENSG00000179058_C9orf50', 'ENSG00000179101_AL590139.1', 'ENSG00000179388_EGR3', 'ENSG00000179611_DGKZP1', 'ENSG00000179899_PHC1P1', 'ENSG00000179934_CCR8', 'ENSG00000180537_RNF182', 'ENSG00000180712_LINC02363', 'ENSG00000180988_OR52N2', 'ENSG00000181001_OR52N1', 'ENSG00000181616_OR52H1', 'ENSG00000181634_TNFSF15', 'ENSG00000182021_AL591379.1', 'ENSG00000182230_FAM153B', 'ENSG00000182853_VMO1', 'ENSG00000183090_FREM3', 'ENSG00000183562_AC131971.1', 'ENSG00000183615_FAM167B', 'ENSG00000183625_CCR3', 'ENSG00000183770_FOXL2', 'ENSG00000183779_ZNF703', 'ENSG00000183831_ANKRD45', 'ENSG00000183844_FAM3B', 'ENSG00000183960_KCNH8', 'ENSG00000184106_TREML3P', 'ENSG00000184227_ACOT1', 'ENSG00000184363_PKP3', 'ENSG00000184434_LRRC19', 'ENSG00000184454_NCMAP', 'ENSG00000184571_PIWIL3', 'ENSG00000184702_SEPT5', 'ENSG00000184908_CLCNKB', 'ENSG00000184923_NUTM2A', 'ENSG00000185070_FLRT2', 'ENSG00000185156_MFSD6L', 'ENSG00000185567_AHNAK2', 'ENSG00000185686_PRAME', 'ENSG00000186190_BPIFB3', 'ENSG00000186191_BPIFB4', 'ENSG00000186231_KLHL32', 'ENSG00000186431_FCAR', 'ENSG00000186715_MST1L', 'ENSG00000187116_LILRA5', 'ENSG00000187185_AC092118.1', 'ENSG00000187268_FAM9C', 'ENSG00000187554_TLR5', 'ENSG00000187867_PALM3', 'ENSG00000188153_COL4A5', 'ENSG00000188158_NHS', 'ENSG00000188163_FAM166A', 'ENSG00000188316_ENO4', 'ENSG00000188959_C9orf152', 'ENSG00000189013_KIR2DL4', 'ENSG00000189409_MMP23B', 'ENSG00000196092_PAX5', 'ENSG00000196260_SFTA2', 'ENSG00000197358_BNIP3P1', 'ENSG00000197446_CYP2F1', 'ENSG00000197540_GZMM', 'ENSG00000198049_AVPR1B', 'ENSG00000198134_AC007537.1', 'ENSG00000198156_NPIPB6', 'ENSG00000198221_AFDN-DT', 'ENSG00000198626_RYR2', 'ENSG00000198759_EGFL6', 'ENSG00000198822_GRM3', 'ENSG00000198963_RORB', 'ENSG00000199090_MIR326', 'ENSG00000199753_SNORD104', 'ENSG00000199787_RF00406', 'ENSG00000199872_RNU6-942P', 'ENSG00000200075_RF00402', 'ENSG00000200296_RNU1-83P', 'ENSG00000200683_RNU6-379P', 'ENSG00000201044_RNU6-268P', 'ENSG00000201343_RF00019', 'ENSG00000201564_RN7SKP50', 'ENSG00000201616_RNU1-91P', 'ENSG00000201737_RNU1-133P', 'ENSG00000202048_SNORD114-20', 'ENSG00000202415_RN7SKP269', 'ENSG00000203395_AC015969.1', 'ENSG00000203721_LINC00862', 'ENSG00000203727_SAMD5', 'ENSG00000203737_GPR52', 'ENSG00000203783_PRR9', 'ENSG00000203867_RBM20', 'ENSG00000203907_OOEP', 'ENSG00000203999_LINC01270', 'ENSG00000204010_IFIT1B', 'ENSG00000204044_SLC12A5-AS1', 'ENSG00000204091_TDRG1', 'ENSG00000204121_ECEL1P1', 'ENSG00000204165_CXorf65', 'ENSG00000204173_LRRC37A5P', 'ENSG00000204248_COL11A2', 'ENSG00000204424_LY6G6F', 'ENSG00000204539_CDSN', 'ENSG00000204583_LRCOL1', 'ENSG00000204677_FAM153C', 'ENSG00000204709_LINC01556', 'ENSG00000204711_C9orf135', 'ENSG00000204792_LINC01291', 'ENSG00000204850_AC011484.1', 'ENSG00000204851_PNMA8B', 'ENSG00000204909_SPINK9', 'ENSG00000205037_AC134312.1', 'ENSG00000205038_PKHD1L1', 'ENSG00000205089_CCNI2', 'ENSG00000205106_DKFZp779M0652', 'ENSG00000205364_MT1M', 'ENSG00000205502_C2CD4B', 'ENSG00000205746_AC126755.1', 'ENSG00000205856_C22orf42', 'ENSG00000206052_DOK6', 'ENSG00000206579_XKR4', 'ENSG00000206645_RF00019', 'ENSG00000206786_RNU6-701P', 'ENSG00000206846_RF00019', 'ENSG00000206848_RNU6-890P', 'ENSG00000207088_SNORA7B', 'ENSG00000207181_SNORA14B', 'ENSG00000207234_RNU6-125P', 'ENSG00000207326_RF00019', 'ENSG00000207359_RNU6-925P', 'ENSG00000211677_IGLC2', 'ENSG00000211699_TRGV3', 'ENSG00000211895_IGHA1', 'ENSG00000212385_RNU6-817P', 'ENSG00000212391_RF00554', 'ENSG00000212607_SNORA3B', 'ENSG00000212829_RPS26P3', 'ENSG00000213083_AC010731.1', 'ENSG00000213216_AC007066.1', 'ENSG00000213222_AC093724.1', 'ENSG00000213228_RPL12P38', 'ENSG00000213250_RBMS2P1', 'ENSG00000213272_RPL7AP9', 'ENSG00000213303_AC008481.1', 'ENSG00000213402_PTPRCAP', 'ENSG00000213471_TTLL13P', 'ENSG00000213588_ZBTB9', 'ENSG00000213609_RPL7AP50', 'ENSG00000213757_AC020898.1', 'ENSG00000213931_HBE1', 'ENSG00000213950_RPS10P2', 'ENSG00000213994_AL157395.1', 'ENSG00000214787_MS4A4E', 'ENSG00000214866_DCDC2C', 'ENSG00000214908_AL353678.1', 'ENSG00000214975_PPIAP29', 'ENSG00000215198_AL353795.1', 'ENSG00000215208_KRT18P60', 'ENSG00000215218_UBE2QL1', 'ENSG00000215297_AL354941.1', 'ENSG00000215464_AP000354.1', 'ENSG00000215483_LINC00598', 'ENSG00000215817_ZC3H11B', 'ENSG00000215861_AC245297.1', 'ENSG00000215910_C1orf167', 'ENSG00000216475_AL024474.1', 'ENSG00000217195_AL513475.1', 'ENSG00000217414_DDX18P3', 'ENSG00000217512_AL356776.1', 'ENSG00000218351_RPS3AP23', 'ENSG00000218418_AL591135.1', 'ENSG00000218749_AL033519.1', 'ENSG00000218766_AL450338.1', 'ENSG00000218792_HSPD1P16', 'ENSG00000219249_AMZ2P2', 'ENSG00000219395_HSPA8P15', 'ENSG00000219410_AC125494.1', 'ENSG00000219932_RPL12P8', 'ENSG00000220091_LAP3P1', 'ENSG00000220237_RPS24P12', 'ENSG00000220494_YAP1P1', 'ENSG00000221102_SNORA11B', 'ENSG00000221887_HMSD', 'ENSG00000222276_RNU2-33P', 'ENSG00000222370_SNORA36B', 'ENSG00000222421_RF00019', 'ENSG00000222431_RNU6-141P', 'ENSG00000223342_AL158817.1', 'ENSG00000223379_AL391987.3', 'ENSG00000223403_MEG9', 'ENSG00000223519_KIF28P', 'ENSG00000223576_AL355001.1', 'ENSG00000223668_EEF1A1P24', 'ENSG00000223741_PSMD4P1', 'ENSG00000223779_AC239800.1', 'ENSG00000223783_LINC01983', 'ENSG00000223784_LINP1', 'ENSG00000223855_HRAT92', 'ENSG00000223884_AC068481.1', 'ENSG00000223899_SEC13P1', 'ENSG00000224067_AL354877.1', 'ENSG00000224072_AL139811.1', 'ENSG00000224081_SLC44A3-AS1', 'ENSG00000224099_AC104823.1', 'ENSG00000224116_INHBA-AS1', 'ENSG00000224137_LINC01857', 'ENSG00000224155_AC073136.2', 'ENSG00000224321_RPL12P14', 'ENSG00000224402_OR6D1P', 'ENSG00000224479_AC104162.1', 'ENSG00000224599_BMS1P12', 'ENSG00000224689_ZNF812P', 'ENSG00000224848_AL589843.1', 'ENSG00000224908_TIMM8BP2', 'ENSG00000224957_LINC01266', 'ENSG00000224959_AC017002.1', 'ENSG00000224988_AL158207.1', 'ENSG00000224993_RPL29P12', 'ENSG00000225096_AL445250.1', 'ENSG00000225101_OR52K3P', 'ENSG00000225107_AC092484.1', 'ENSG00000225187_AC073283.1', 'ENSG00000225313_AL513327.1', 'ENSG00000225345_SNX18P3', 'ENSG00000225393_BX571846.1', 'ENSG00000225422_RBMS1P1', 'ENSG00000225423_TNPO1P1', 'ENSG00000225531_AL807761.2', 'ENSG00000225554_AL359764.1', 'ENSG00000225650_EIF2S2P5', 'ENSG00000225674_IPO7P2', 'ENSG00000225807_AC069281.1', 'ENSG00000226010_AL355852.1', 'ENSG00000226084_AC113935.1', 'ENSG00000226251_AL451060.1', 'ENSG00000226383_LINC01876', 'ENSG00000226491_FTOP1', 'ENSG00000226501_USF1P1', 'ENSG00000226545_AL357552.1', 'ENSG00000226564_FTH1P20', 'ENSG00000226617_RPL21P110', 'ENSG00000226647_AL365356.1', 'ENSG00000226800_CACTIN-AS1', 'ENSG00000226913_BSN-DT', 'ENSG00000226948_RPS4XP2', 'ENSG00000226970_AL450063.1', 'ENSG00000227006_AL136988.2', 'ENSG00000227051_C14orf132', 'ENSG00000227072_AL353706.1', 'ENSG00000227110_LMCD1-AS1', 'ENSG00000227192_AL023581.2', 'ENSG00000227198_C6orf47-AS1', 'ENSG00000227207_RPL31P12', 'ENSG00000227477_STK4-AS1', 'ENSG00000227541_SFR1P1', 'ENSG00000227590_ATP5MC1P5', 'ENSG00000227649_MTND6P32', 'ENSG00000227682_ATP5F1AP2', 'ENSG00000227740_AL513329.1', 'ENSG00000227742_CALR4P', 'ENSG00000228097_MTATP6P11', 'ENSG00000228140_AL031283.1', 'ENSG00000228175_GEMIN8P4', 'ENSG00000228212_OFD1P17', 'ENSG00000228232_GAPDHP1', 'ENSG00000228317_AL158070.1', 'ENSG00000228413_AC024937.1', 'ENSG00000228430_AL162726.3', 'ENSG00000228501_RPL15P18', 'ENSG00000228550_AC073583.1', 'ENSG00000228655_AC096558.1', 'ENSG00000228727_SAPCD1', 'ENSG00000228826_AL592494.1', 'ENSG00000228839_PIK3IP1-AS1', 'ENSG00000228863_AL121985.1', 'ENSG00000229066_AC093459.1', 'ENSG00000229150_CRYGEP', 'ENSG00000229154_KCNQ5-AS1', 'ENSG00000229163_NAP1L1P2', 'ENSG00000229236_TTTY10', 'ENSG00000229274_AL662860.1', 'ENSG00000229308_AC010737.1', 'ENSG00000229326_AC069154.1', 'ENSG00000229372_SZT2-AS1', 'ENSG00000229444_AL451062.1', 'ENSG00000229567_AL139421.1', 'ENSG00000229703_CR589904.1', 'ENSG00000229742_AC092809.1', 'ENSG00000229758_DYNLT3P2', 'ENSG00000229839_AC018462.1', 'ENSG00000229847_EMX2OS', 'ENSG00000229853_AL034418.1', 'ENSG00000229918_DOCK9-AS1', 'ENSG00000229953_AL590666.2', 'ENSG00000229992_HMGB3P9', 'ENSG00000230063_AL360091.2', 'ENSG00000230064_AL772161.1', 'ENSG00000230138_AC119428.2', 'ENSG00000230149_AL021707.3', 'ENSG00000230289_AL358781.2', 'ENSG00000230295_GTF2IP23', 'ENSG00000230479_AP000695.1', 'ENSG00000230508_RPL19P21', 'ENSG00000230519_HMGB1P49', 'ENSG00000230534_AL392046.1', 'ENSG00000230563_AL121757.1', 'ENSG00000230721_AL049597.1', 'ENSG00000230772_VN1R108P', 'ENSG00000230777_RPS29P5', 'ENSG00000230799_AC007279.1', 'ENSG00000230813_AL356583.3', 'ENSG00000230815_AL807757.1', 'ENSG00000230872_MFSD13B', 'ENSG00000230910_AL391807.1', 'ENSG00000230912_AL021707.4', 'ENSG00000230968_AC084149.2', 'ENSG00000230993_RPL12P15', 'ENSG00000231265_TRERNA1', 'ENSG00000231307_RPS3P2', 'ENSG00000231407_AL354732.1', 'ENSG00000231449_AC097359.1', 'ENSG00000231507_LINC01353', 'ENSG00000231531_HINT1P1', 'ENSG00000231548_OR55B1P', 'ENSG00000231731_AC010976.1', 'ENSG00000231742_LINC01273', 'ENSG00000231788_RPL31P50', 'ENSG00000231830_AC245140.1', 'ENSG00000231927_AC093734.1', 'ENSG00000231993_EP300-AS1', 'ENSG00000232027_AL671986.1', 'ENSG00000232028_AC007391.1', 'ENSG00000232065_LINC01063', 'ENSG00000232133_IMPDH1P10', 'ENSG00000232139_LINC00867', 'ENSG00000232273_FTH1P1', 'ENSG00000232333_RPS27AP2', 'ENSG00000232466_AL356133.1', 'ENSG00000232500_AP005273.1', 'ENSG00000232530_LIF-AS1', 'ENSG00000232568_RPL23AP35', 'ENSG00000232578_AC093311.1', 'ENSG00000232606_LINC01412', 'ENSG00000232654_FAM136BP', 'ENSG00000232656_IDI2-AS1', 'ENSG00000232719_AC007272.1', 'ENSG00000232803_SLCO4A1-AS1', 'ENSG00000232987_LINC01219', 'ENSG00000233025_CRYZP1', 'ENSG00000233093_LINC00892', 'ENSG00000233099_AC095030.1', 'ENSG00000233401_PRKAR1AP1', 'ENSG00000233427_AL009181.1', 'ENSG00000233540_DNM3-IT1', 'ENSG00000233674_AL451062.2', 'ENSG00000233825_AL391839.2', 'ENSG00000233862_AC016907.2', 'ENSG00000233994_GDI2P2', 'ENSG00000234026_AL157834.2', 'ENSG00000234106_SRP14P2', 'ENSG00000234145_NAP1L4P3', 'ENSG00000234174_AC016683.1', 'ENSG00000234271_Z98752.2', 'ENSG00000234425_AL138930.1', 'ENSG00000234488_AC096664.2', 'ENSG00000234630_AC245060.2', 'ENSG00000234645_YWHAEP5', 'ENSG00000234718_AC007161.1', 'ENSG00000234810_AL603840.1', 'ENSG00000235045_RPL7P8', 'ENSG00000235072_AC012074.1', 'ENSG00000235214_FAM83C-AS1', 'ENSG00000235288_AC099329.1', 'ENSG00000235376_RPEL1', 'ENSG00000235429_AC083875.1', 'ENSG00000235472_EIF4A1P7', 'ENSG00000235478_LINC01664', 'ENSG00000235531_MSC-AS1', 'ENSG00000235640_AC092646.2', 'ENSG00000235677_NPM1P26', 'ENSG00000235683_AC018442.1', 'ENSG00000235701_PCBP2P1', 'ENSG00000235740_PHACTR2-AS1', 'ENSG00000235774_AC023347.1', 'ENSG00000235802_HCFC1-AS1', 'ENSG00000235917_MTCO2P11', 'ENSG00000235958_UBOX5-AS1', 'ENSG00000236032_OR5H14', 'ENSG00000236180_AL445669.2', 'ENSG00000236254_MTND4P14', 'ENSG00000236283_AC019197.1', 'ENSG00000236290_EEF1GP7', 'ENSG00000236317_AC104333.2', 'ENSG00000236364_AL358115.1', 'ENSG00000236457_AC090617.1', 'ENSG00000236564_YWHAQP5', 'ENSG00000236671_PRKG1-AS1', 'ENSG00000236680_AL356000.1', 'ENSG00000236682_AC068282.1', 'ENSG00000236711_SMAD9-IT1', 'ENSG00000236806_RPL7AP15', 'ENSG00000236869_ZKSCAN7-AS1', 'ENSG00000236886_AC007563.2', 'ENSG00000236915_AL356270.1', 'ENSG00000236936_AL031005.1', 'ENSG00000237057_LINC02087', 'ENSG00000237101_AC092809.4', 'ENSG00000237276_ANO7L1', 'ENSG00000237317_AL022400.1', 'ENSG00000237387_AL022329.2', 'ENSG00000237618_BTBD7P2', 'ENSG00000237685_AL139039.3', 'ENSG00000237757_EEF1A1P30', 'ENSG00000237766_GGTA2P', 'ENSG00000237798_AC010894.4', 'ENSG00000238015_AC104837.2', 'ENSG00000238133_MAP3K20-AS1', 'ENSG00000238259_AC067940.1', 'ENSG00000238324_RN7SKP198', 'ENSG00000238358_AC004969.1', 'ENSG00000239219_AC008040.1', 'ENSG00000239316_RN7SL11P', 'ENSG00000239474_KLHL41', 'ENSG00000239527_RPS23P7', 'ENSG00000239642_MEIKIN', 'ENSG00000239650_GUSBP4', 'ENSG00000239686_AL158801.1', 'ENSG00000239701_AC006512.1', 'ENSG00000239705_AL354710.2', 'ENSG00000239797_RPL21P39', 'ENSG00000239830_RPS4XP22', 'ENSG00000239930_AP001625.3', 'ENSG00000240086_AC092969.1', 'ENSG00000240087_RPSAP12', 'ENSG00000240183_RN7SL297P', 'ENSG00000240219_AL512306.2', 'ENSG00000240498_CDKN2B-AS1', 'ENSG00000240809_AC026877.1', 'ENSG00000240993_RN7SL459P', 'ENSG00000241111_PRICKLE2-AS1', 'ENSG00000241135_LINC00881', 'ENSG00000241319_SETP6', 'ENSG00000241570_PAQR9-AS1', 'ENSG00000241631_RN7SL316P', 'ENSG00000241932_AC092324.1', 'ENSG00000241933_DENND6A-DT', 'ENSG00000242060_RPS3AP49', 'ENSG00000242107_LINC01100', 'ENSG00000242175_RN7SL127P', 'ENSG00000242431_AC107398.1', 'ENSG00000242551_POU5F1P6', 'ENSG00000242571_RPL21P11', 'ENSG00000242641_LINC00971', 'ENSG00000242747_AC090515.1', 'ENSG00000242992_FTH1P4', 'ENSG00000243055_GK-AS1', 'ENSG00000243498_UBA52P5', 'ENSG00000243592_RPL17P22', 'ENSG00000243709_LEFTY1', 'ENSG00000243830_AC092865.1', 'ENSG00000243836_WDR86-AS1', 'ENSG00000243961_PARAL1', 'ENSG00000244021_AC093591.1', 'ENSG00000244097_RPS4XP17', 'ENSG00000244151_AC010973.2', 'ENSG00000244183_PPIAP71', 'ENSG00000244242_IFITM10', 'ENSG00000244245_AC133134.1', 'ENSG00000244251_AC013356.1', 'ENSG00000244355_LY6G6D', 'ENSG00000244357_RN7SL145P', 'ENSG00000244476_ERVFRD-1', 'ENSG00000244482_LILRA6', 'ENSG00000244585_RPL12P33', 'ENSG00000244618_RN7SL334P', 'ENSG00000244703_CD46P1', 'ENSG00000245261_AL133375.1', 'ENSG00000245482_AC046130.1', 'ENSG00000246363_LINC02458', 'ENSG00000246863_AC012377.1', 'ENSG00000247199_AC091948.1', 'ENSG00000248121_SMURF2P1', 'ENSG00000248155_CR545473.1', 'ENSG00000248223_AC026785.2', 'ENSG00000248485_PCP4L1', 'ENSG00000248690_HAS2-AS1', 'ENSG00000248884_AC010280.2', 'ENSG00000248936_AC027607.1', 'ENSG00000249140_PRDX2P3', 'ENSG00000249363_AC011411.1', 'ENSG00000249381_LINC00500', 'ENSG00000249456_AL731577.2', 'ENSG00000249492_AC114956.3', 'ENSG00000249574_AC226118.1', 'ENSG00000249614_LINC02503', 'ENSG00000249691_AC026117.1', 'ENSG00000249695_AC026369.1', 'ENSG00000249803_AC112178.1', 'ENSG00000249825_CTD-2201I18.1', 'ENSG00000249848_AC112673.1', 'ENSG00000249850_KRT18P31', 'ENSG00000249884_RNF103-CHMP3', 'ENSG00000249978_TRGV7', 'ENSG00000250130_AC090519.1', 'ENSG00000250148_KRT8P31', 'ENSG00000250332_AC010460.3', 'ENSG00000250334_LINC00989', 'ENSG00000250539_KRT8P33', 'ENSG00000250548_LINC01303', 'ENSG00000250608_AC010210.1', 'ENSG00000250635_CXXC5-AS1', 'ENSG00000250645_AC010442.2', 'ENSG00000250733_C8orf17', 'ENSG00000250853_RNF138P1', 'ENSG00000250902_SMAD1-AS1', 'ENSG00000250950_AC093752.2', 'ENSG00000250982_GAPDHP35', 'ENSG00000251129_LINC02506', 'ENSG00000251152_AC025539.1', 'ENSG00000251250_AC091951.3', 'ENSG00000251288_AC018797.3', 'ENSG00000251468_AC135352.1', 'ENSG00000251537_AC005324.3', 'ENSG00000251538_LINC02201', 'ENSG00000251584_AC096751.2', 'ENSG00000251676_SNHG27', 'ENSG00000251916_RNU1-61P', 'ENSG00000252759_RF00019', 'ENSG00000253256_AC134043.1', 'ENSG00000253305_PCDHGB6', 'ENSG00000253394_LINC00534', 'ENSG00000253490_LINC02099', 'ENSG00000253537_PCDHGA7', 'ENSG00000253629_AP000426.1', 'ENSG00000253651_SOD1P3', 'ENSG00000253730_AC015909.2', 'ENSG00000253734_LINC01289', 'ENSG00000253767_PCDHGA8', 'ENSG00000253853_AC246817.1', 'ENSG00000253873_PCDHGA11', 'ENSG00000254028_AC083843.1', 'ENSG00000254048_AC105150.1', 'ENSG00000254054_AC087273.2', 'ENSG00000254122_PCDHGB7', 'ENSG00000254248_AC068189.1', 'ENSG00000254680_AC079329.1', 'ENSG00000254708_AL139174.1', 'ENSG00000254780_AC023232.1', 'ENSG00000254810_AP001189.3', 'ENSG00000254812_AC067930.3', 'ENSG00000254842_LINC02551', 'ENSG00000254846_AL355075.1', 'ENSG00000254862_AC100771.2', 'ENSG00000254897_AP003035.1', 'ENSG00000255002_LINC02324', 'ENSG00000255074_AC018523.1', 'ENSG00000255102_AP005436.1', 'ENSG00000255156_RNY1P9', 'ENSG00000255158_AC131934.1', 'ENSG00000255222_SETP17', 'ENSG00000255256_AL136146.2', 'ENSG00000255367_AC127526.2', 'ENSG00000255418_AC090092.1', 'ENSG00000255443_CD44-AS1', 'ENSG00000255446_AP003064.2', 'ENSG00000255479_AP001189.6', 'ENSG00000255487_AC087362.2', 'ENSG00000255867_DENND5B-AS1', 'ENSG00000255871_AC007529.1', 'ENSG00000256029_SNHG28', 'ENSG00000256571_AC079866.2', 'ENSG00000256588_AC027544.2', 'ENSG00000256712_AC134349.1', 'ENSG00000256746_AC018410.1', 'ENSG00000256813_AP000777.3', 'ENSG00000256967_AC018653.3', 'ENSG00000256968_SNRPEP2', 'ENSG00000257074_RPL29P33', 'ENSG00000257120_AL356756.1', 'ENSG00000257146_AC079905.2', 'ENSG00000257195_HNRNPA1P50', 'ENSG00000257327_AC012555.1', 'ENSG00000257345_LINC02413', 'ENSG00000257379_AC023509.1', 'ENSG00000257386_AC025257.1', 'ENSG00000257431_AC089998.1', 'ENSG00000257715_AC007298.1', 'ENSG00000257838_OTOAP1', 'ENSG00000257987_TEX49', 'ENSG00000258084_AC128707.1', 'ENSG00000258090_AC093014.1', 'ENSG00000258177_AC008149.1', 'ENSG00000258357_AC023161.2', 'ENSG00000258410_AC087386.1', 'ENSG00000258498_DIO3OS', 'ENSG00000258504_AL157871.1', 'ENSG00000258512_LINC00239', 'ENSG00000258867_LINC01146', 'ENSG00000258886_HIGD1AP17', 'ENSG00000259032_ENSAP2', 'ENSG00000259100_AL157791.1', 'ENSG00000259294_AC005096.1', 'ENSG00000259327_AC023906.3', 'ENSG00000259345_AC013652.1', 'ENSG00000259377_AC026770.1', 'ENSG00000259380_AC087473.1', 'ENSG00000259442_AC105339.3', 'ENSG00000259461_ANP32BP3', 'ENSG00000259556_AC090971.3', 'ENSG00000259569_AC013489.2', 'ENSG00000259617_AC020661.3', 'ENSG00000259684_AC084756.1', 'ENSG00000259719_LINC02284', 'ENSG00000259954_IL21R-AS1', 'ENSG00000259986_AC103876.1', 'ENSG00000260135_MMP2-AS1', 'ENSG00000260206_AC105020.2', 'ENSG00000260235_AC105020.3', 'ENSG00000260269_AC105036.3', 'ENSG00000260394_Z92544.1', 'ENSG00000260425_AL031709.1', 'ENSG00000260447_AC009065.3', 'ENSG00000260615_RPL23AP97', 'ENSG00000260871_AC093510.2', 'ENSG00000260877_AP005233.2', 'ENSG00000260979_AC022167.3', 'ENSG00000261051_AC107021.2', 'ENSG00000261113_AC009034.1', 'ENSG00000261168_AL592424.1', 'ENSG00000261253_AC137932.2', 'ENSG00000261269_AC093278.2', 'ENSG00000261552_AC109460.4', 'ENSG00000261572_AC097639.1', 'ENSG00000261602_AC092115.2', 'ENSG00000261630_AC007496.2', 'ENSG00000261644_AC007728.2', 'ENSG00000261734_AC116096.1', 'ENSG00000261773_AC244090.2', 'ENSG00000261837_AC046158.2', 'ENSG00000261838_AC092718.6', 'ENSG00000261888_AC144831.1', 'ENSG00000262061_AC129507.1', 'ENSG00000262097_LINC02185', 'ENSG00000262372_CR936218.1', 'ENSG00000262406_MMP12', 'ENSG00000262580_AC087741.1', 'ENSG00000262772_LINC01977', 'ENSG00000262833_AC016245.1', 'ENSG00000263006_ROCK1P1', 'ENSG00000263011_AC108134.4', 'ENSG00000263155_MYZAP', 'ENSG00000263393_AC011825.2', 'ENSG00000263426_RN7SL471P', 'ENSG00000263503_MAPK8IP1P2', 'ENSG00000263595_RN7SL823P', 'ENSG00000263878_DLGAP1-AS4', 'ENSG00000263940_RN7SL275P', 'ENSG00000264019_AC018521.2', 'ENSG00000264031_ABHD15-AS1', 'ENSG00000264044_AC005726.2', 'ENSG00000264070_DND1P1', 'ENSG00000264188_AC106037.1', 'ENSG00000264269_AC016866.1', 'ENSG00000264339_AP001020.1', 'ENSG00000264434_AC110603.1', 'ENSG00000264714_KIAA0895LP1', 'ENSG00000265010_AC087301.1', 'ENSG00000265073_AC010761.2', 'ENSG00000265107_GJA5', 'ENSG00000265179_AP000894.2', 'ENSG00000265218_AC103810.2', 'ENSG00000265334_AC130324.2', 'ENSG00000265439_RN7SL811P', 'ENSG00000265531_FCGR1CP', 'ENSG00000265845_AC024267.4', 'ENSG00000265907_AP000919.2', 'ENSG00000265942_RN7SL577P', 'ENSG00000266256_LINC00683', 'ENSG00000266456_AP001178.3', 'ENSG00000266733_TBC1D29', 'ENSG00000266835_GAPLINC', 'ENSG00000266844_AC093330.1', 'ENSG00000266903_AC243964.2', 'ENSG00000266944_AC005262.1', 'ENSG00000266946_MRPL37P1', 'ENSG00000266947_AC022916.1', 'ENSG00000267034_AC010980.2', 'ENSG00000267044_AC005757.1', 'ENSG00000267147_LINC01842', 'ENSG00000267175_AC105094.2', 'ENSG00000267191_AC006213.3', 'ENSG00000267275_AC020911.2', 'ENSG00000267288_AC138150.2', 'ENSG00000267313_KC6', 'ENSG00000267316_AC090409.2', 'ENSG00000267323_SLC25A1P5', 'ENSG00000267345_AC010632.1', 'ENSG00000267387_AC020931.1', 'ENSG00000267395_DM1-AS', 'ENSG00000267429_AC006116.6', 'ENSG00000267452_LINC02073', 'ENSG00000267491_AC100788.1', 'ENSG00000267529_AP005131.4', 'ENSG00000267554_AC015911.8', 'ENSG00000267601_AC022966.1', 'ENSG00000267638_AC023855.1', 'ENSG00000267665_AC021683.3', 'ENSG00000267681_AC135721.1', 'ENSG00000267703_AC020917.2', 'ENSG00000267731_AC005332.2', 'ENSG00000267733_AP005264.5', 'ENSG00000267750_RUNDC3A-AS1', 'ENSG00000267890_AC010624.2', 'ENSG00000267898_AC026803.2', 'ENSG00000267927_AC010320.1', 'ENSG00000268070_AC006539.2', 'ENSG00000268355_AC243960.3', 'ENSG00000268416_AC010329.1', 'ENSG00000268520_AC008750.5', 'ENSG00000268636_AC011495.2', 'ENSG00000268696_ZNF723', 'ENSG00000268777_AC020914.1', 'ENSG00000268849_SIGLEC22P', 'ENSG00000268903_AL627309.6', 'ENSG00000268983_AC005253.2', 'ENSG00000269019_HOMER3-AS1', 'ENSG00000269067_ZNF728', 'ENSG00000269103_RF00017', 'ENSG00000269274_AC078899.4', 'ENSG00000269288_AC092070.3', 'ENSG00000269352_PTOV1-AS2', 'ENSG00000269400_AC008734.2', 'ENSG00000269506_AC110792.2', 'ENSG00000269653_AC011479.3', 'ENSG00000269881_AC004754.1', 'ENSG00000269926_DDIT4-AS1', 'ENSG00000270048_AC068790.4', 'ENSG00000270050_AL035427.1', 'ENSG00000270503_YTHDF2P1', 'ENSG00000270706_PRMT1P1', 'ENSG00000270765_GAS2L2', 'ENSG00000270882_HIST2H4A', 'ENSG00000270906_MTND4P35', 'ENSG00000271013_LRRC37A9P', 'ENSG00000271129_AC009027.1', 'ENSG00000271259_AC010201.1', 'ENSG00000271524_BNIP3P17', 'ENSG00000271543_AC021443.1', 'ENSG00000271743_AF287957.1', 'ENSG00000271792_AC008667.4', 'ENSG00000271868_AC114810.1', 'ENSG00000271973_AC141002.1', 'ENSG00000271984_AL008726.1', 'ENSG00000271996_AC019080.4', 'ENSG00000272070_AC005618.1', 'ENSG00000272138_LINC01607', 'ENSG00000272150_NBPF25P', 'ENSG00000272265_AC034236.3', 'ENSG00000272279_AL512329.2', 'ENSG00000272473_AC006273.1', 'ENSG00000272510_AL121992.3', 'ENSG00000272582_AL031587.3', 'ENSG00000272695_GAS6-DT', 'ENSG00000272732_AC004982.1', 'ENSG00000272770_AC005696.2', 'ENSG00000272788_AP000864.1', 'ENSG00000272824_AC245100.7', 'ENSG00000272825_AL844908.1', 'ENSG00000272848_AL135910.1', 'ENSG00000272916_AC022400.6', 'ENSG00000273133_AC116651.1', 'ENSG00000273177_AC092954.2', 'ENSG00000273212_AC000068.2', 'ENSG00000273218_AC005776.2', 'ENSG00000273245_AC092653.1', 'ENSG00000273274_ZBTB8B', 'ENSG00000273312_AL121749.1', 'ENSG00000273325_AL008723.3', 'ENSG00000273369_AC096586.2', 'ENSG00000273474_AL157392.4', 'ENSG00000273599_AL731571.1', 'ENSG00000273724_AC106782.5', 'ENSG00000273870_AL138721.1', 'ENSG00000273920_AC103858.2', 'ENSG00000274023_AL360169.2', 'ENSG00000274029_AC069209.1', 'ENSG00000274114_ALOX15P1', 'ENSG00000274124_AC074029.3', 'ENSG00000274139_AC090164.2', 'ENSG00000274281_AC022929.2', 'ENSG00000274308_AC244093.1', 'ENSG00000274373_AC148476.1', 'ENSG00000274386_TMEM269', 'ENSG00000274403_AC090510.2', 'ENSG00000274570_SPDYE10P', 'ENSG00000274670_AC137590.2', 'ENSG00000274723_AC079906.1', 'ENSG00000274742_RF00017', 'ENSG00000274798_AC025166.1', 'ENSG00000274911_AL627230.2', 'ENSG00000275106_AC025594.2', 'ENSG00000275197_AC092794.2', 'ENSG00000275302_CCL4', 'ENSG00000275348_AC096861.1', 'ENSG00000275367_AC092111.1', 'ENSG00000275489_C17orf98', 'ENSG00000275527_AC100835.2', 'ENSG00000275995_AC109809.1', 'ENSG00000276070_CCL4L2', 'ENSG00000276255_AL136379.1', 'ENSG00000276282_AC022960.2', 'ENSG00000276547_PCDHGB5', 'ENSG00000276704_AL442067.2', 'ENSG00000276952_AL121772.3', 'ENSG00000276984_AL023881.1', 'ENSG00000276997_AL513314.2', 'ENSG00000277117_FP565260.3', 'ENSG00000277152_AC110048.2', 'ENSG00000277186_AC131212.1', 'ENSG00000277229_AC084781.1', 'ENSG00000277496_AL357033.4', 'ENSG00000277504_AC010536.3', 'ENSG00000277531_PNMA8C', 'ENSG00000278041_AL133325.3', 'ENSG00000278344_AC063943.1', 'ENSG00000278467_AC138393.3', 'ENSG00000278513_AC091046.2', 'ENSG00000278621_AC037198.2', 'ENSG00000278713_AC120114.2', 'ENSG00000278716_AC133540.1', 'ENSG00000278746_RN7SL660P', 'ENSG00000278774_RF00004', 'ENSG00000279091_AC026523.2', 'ENSG00000279130_AC091925.1', 'ENSG00000279141_LINC01451', 'ENSG00000279161_AC093503.3', 'ENSG00000279187_AC027601.5', 'ENSG00000279263_OR2L8', 'ENSG00000279315_AL158212.4', 'ENSG00000279319_AC105074.1', 'ENSG00000279332_AC090772.4', 'ENSG00000279339_AC100788.2', 'ENSG00000279365_AP000695.3', 'ENSG00000279378_AC009159.4', 'ENSG00000279384_AC080188.2', 'ENSG00000279404_AC008739.5', 'ENSG00000279417_AC019322.4', 'ENSG00000279444_AC135584.1', 'ENSG00000279486_OR2AG1', 'ENSG00000279530_AC092881.1', 'ENSG00000279590_AC005786.4', 'ENSG00000279619_AC020907.5', 'ENSG00000279633_AL137918.1', 'ENSG00000279636_LINC00216', 'ENSG00000279672_AP006621.5', 'ENSG00000279690_AP000280.1', 'ENSG00000279727_LINC02033', 'ENSG00000279861_AC073548.1', 'ENSG00000279913_AP001962.1', 'ENSG00000279970_AC023024.2', 'ENSG00000280055_TMEM75', 'ENSG00000280057_AL022069.2', 'ENSG00000280135_AL096816.1', 'ENSG00000280310_AC092437.1', 'ENSG00000280422_AC115284.2', 'ENSG00000280432_AP000962.2', 'ENSG00000280693_SH3PXD2A-AS1', 'ENSG00000281490_CICP14', 'ENSG00000281530_AC004461.2', 'ENSG00000281571_AC241585.2', 'ENSG00000282772_AL358790.1', 'ENSG00000282989_AP001206.1', 'ENSG00000282996_AC022021.1', 'ENSG00000283023_FRG1GP', 'ENSG00000283031_AC009242.1', 'ENSG00000283097_AL159152.1', 'ENSG00000283141_AL157832.3', 'ENSG00000283209_AC106858.1', 'ENSG00000283538_AC005972.3', 'ENSG00000284240_AC099062.1', 'ENSG00000284512_AC092718.8', 'ENSG00000284657_AL031432.5', 'ENSG00000284664_AL161756.3', 'ENSG00000284931_AC104389.5', 'ENSG00000285016_AC017002.6', 'ENSG00000285117_AC068724.4', 'ENSG00000285162_AC004593.3', 'ENSG00000285210_AL136382.1', 'ENSG00000285215_AC241377.4', 'ENSG00000285292_AC021097.2', 'ENSG00000285498_AC104389.6', 'ENSG00000285534_AL163541.1', 'ENSG00000285577_AC019127.1', 'ENSG00000285611_AC007132.1', 'ENSG00000285629_AL031847.2', 'ENSG00000285641_AL358472.6', 'ENSG00000285649_AL357079.2', 'ENSG00000285650_AL157827.2', 'ENSG00000285662_AL731733.1', 'ENSG00000285672_AL160396.2', 'ENSG00000285763_AL358777.1', 'ENSG00000285865_AC010285.3', 'ENSG00000285879_AC018628.2']\n",
    "print('Constant cols:', len(constant_cols))\n",
    "\n",
    "important_cols = ['ENSG00000135218_CD36',\n",
    " 'ENSG00000010278_CD9',\n",
    " 'ENSG00000204287_HLA-DRA',\n",
    " 'ENSG00000117091_CD48',\n",
    " 'ENSG00000004468_CD38',\n",
    " 'ENSG00000173762_CD7',\n",
    " 'ENSG00000137101_CD72',\n",
    " 'ENSG00000019582_CD74',\n",
    " 'ENSG00000169442_CD52',\n",
    " 'ENSG00000170458_CD14',\n",
    " 'ENSG00000272398_CD24',\n",
    " 'ENSG00000026508_CD44',\n",
    " 'ENSG00000114013_CD86',\n",
    " 'ENSG00000174059_CD34',\n",
    " 'ENSG00000139193_CD27',\n",
    " 'ENSG00000105383_CD33',\n",
    " 'ENSG00000085117_CD82',\n",
    " 'ENSG00000177455_CD19',\n",
    " 'ENSG00000002586_CD99',\n",
    " 'ENSG00000196126_HLA-DRB1',\n",
    " 'ENSG00000135404_CD63',\n",
    " 'ENSG00000012124_CD22',\n",
    " 'ENSG00000134061_CD180',\n",
    " 'ENSG00000105369_CD79A',\n",
    " 'ENSG00000116824_CD2',\n",
    " 'ENSG00000010610_CD4',\n",
    " 'ENSG00000139187_KLRG1',\n",
    " 'ENSG00000204592_HLA-E',\n",
    " 'ENSG00000090470_PDCD7',\n",
    " 'ENSG00000206531_CD200R1L',\n",
    "'ENSG00000166710_B2M',\n",
    " 'ENSG00000198034_RPS4X',\n",
    " 'ENSG00000188404_SELL',\n",
    " 'ENSG00000130303_BST2',\n",
    " 'ENSG00000128040_SPINK2',\n",
    " 'ENSG00000206503_HLA-A',\n",
    " 'ENSG00000108107_RPL28',\n",
    " 'ENSG00000143226_FCGR2A',\n",
    " 'ENSG00000133112_TPT1',\n",
    " 'ENSG00000166091_CMTM5',\n",
    " 'ENSG00000026025_VIM',\n",
    " 'ENSG00000205542_TMSB4X',\n",
    " 'ENSG00000109099_PMP22',\n",
    " 'ENSG00000145425_RPS3A',\n",
    " 'ENSG00000172247_C1QTNF4',\n",
    " 'ENSG00000072274_TFRC',\n",
    " 'ENSG00000234745_HLA-B',\n",
    " 'ENSG00000075340_ADD2',\n",
    " 'ENSG00000119865_CNRIP1',\n",
    " 'ENSG00000198938_MT-CO3',\n",
    " 'ENSG00000135046_ANXA1',\n",
    " 'ENSG00000235169_SMIM1',\n",
    " 'ENSG00000101200_AVP',\n",
    " 'ENSG00000167996_FTH1',\n",
    " 'ENSG00000163565_IFI16',\n",
    " 'ENSG00000117450_PRDX1',\n",
    " 'ENSG00000124570_SERPINB6',\n",
    " 'ENSG00000112077_RHAG',\n",
    " 'ENSG00000051523_CYBA',\n",
    " 'ENSG00000107130_NCS1',\n",
    " 'ENSG00000055118_KCNH2',\n",
    " 'ENSG00000029534_ANK1',\n",
    " 'ENSG00000169567_HINT1',\n",
    " 'ENSG00000142089_IFITM3',\n",
    " 'ENSG00000139278_GLIPR1',\n",
    " 'ENSG00000142227_EMP3',\n",
    " 'ENSG00000076662_ICAM3',\n",
    " 'ENSG00000143627_PKLR',\n",
    " 'ENSG00000130755_GMFG',\n",
    " 'ENSG00000160593_JAML',\n",
    " 'ENSG00000095932_SMIM24',\n",
    " 'ENSG00000197956_S100A6',\n",
    " 'ENSG00000171476_HOPX',\n",
    " 'ENSG00000116675_DNAJC6',\n",
    " 'ENSG00000100448_CTSG',\n",
    " 'ENSG00000100368_CSF2RB',\n",
    " 'ENSG00000047648_ARHGAP6',\n",
    " 'ENSG00000198918_RPL39',\n",
    " 'ENSG00000196154_S100A4',\n",
    " 'ENSG00000233968_AL157895.1',\n",
    " 'ENSG00000137642_SORL1',\n",
    " 'ENSG00000133816_MICAL2',\n",
    " 'ENSG00000130208_APOC1',\n",
    " 'ENSG00000105610_KLF1']\n",
    "print('important columns ',len(important_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01edd537",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd806ef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X0 shape (70988, 84) X0t shape (48663, 84)\n"
     ]
    }
   ],
   "source": [
    "metadata_df = pd.read_csv(FP_CELL_METADATA, index_col = 'cell_id')\n",
    "metadata_df = metadata_df[metadata_df.technology == \"citeseq\"]\n",
    "\n",
    "# Read train and convert to sparse matrix\n",
    "# X = pd.read_hdf(FP_CITE_TRAIN_INPUTS)\n",
    "X = pd.read_hdf(FP_CITE_TRAIN_INPUTS).drop(columns = constant_cols)\n",
    "# X = pd.read_csv(\"/gpfs/ysm/home/tl688/scrnahpc/tl688/openproblems/scGNN/outputdir/train_protein_recon.csv\",index_col=0).T.drop(columns = constant_cols)\n",
    "cell_index = X.index\n",
    "meta = metadata_df.reindex(cell_index)\n",
    "X0 = X[important_cols].values\n",
    "\n",
    "del X\n",
    "gc.collect()\n",
    "\n",
    "\n",
    "# Read test and convert to sparse matrix\n",
    "# Xt = pd.read_hdf(FP_CITE_TEST_INPUTS)\n",
    "Xt = pd.read_hdf(FP_CITE_TEST_INPUTS).drop(columns = constant_cols)\n",
    "cell_index_test = Xt.index\n",
    "meta_test = metadata_df.reindex(cell_index_test)\n",
    "X0t = Xt[important_cols].values\n",
    "\n",
    "del Xt\n",
    "gc.collect()\n",
    "\n",
    "st = StandardScaler()\n",
    "X0 = st.fit_transform(X0)\n",
    "X0t = st.transform(X0t)\n",
    "\n",
    "print(f'X0 shape {X0.shape} X0t shape {X0t.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a800ca92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70988, 512), (48663, 512))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('./input/targets-multiome-sparse-scaled/train_cite_512.pkl','rb') as f: X = pickle.load(f)\n",
    "with open('./input/targets-multiome-sparse-scaled/test_cite_512.pkl','rb') as f: Xt = pickle.load(f)\n",
    "\n",
    "X.shape, Xt.shape\n",
    "\n",
    "\n",
    "# with open('./input/targets-multiome-sparse-scaled/train_Citeseq_truncated_512.pkl','rb') as f: X = pickle.load(f)\n",
    "# with open('./input/targets-multiome-sparse-scaled/test_Citeseq_truncated_512.pkl','rb') as f: Xt = pickle.load(f)\n",
    "\n",
    "# X.shape, Xt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "659bfb18",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "62e102e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.3822762e+02,  3.1855104e+01,  2.3049294e+01, ...,\n",
       "        -1.3715878e+00, -2.8723223e+00, -1.4102858e+00],\n",
       "       [-2.3365659e+02,  4.0229019e+01,  1.9437603e+01, ...,\n",
       "         3.7163601e+00,  4.1906461e-01, -1.3358747e+00],\n",
       "       [-2.5014233e+02, -4.6703153e+00,  9.4395075e+00, ...,\n",
       "        -3.6248217e+00,  2.4677904e+00,  3.7641099e-01],\n",
       "       ...,\n",
       "       [-2.6739429e+02, -1.4522221e+01,  8.5594721e+00, ...,\n",
       "         2.5515324e-01,  2.7128370e+00, -1.5232078e+00],\n",
       "       [-2.7380966e+02, -2.3930012e+01, -3.1388956e+01, ...,\n",
       "        -2.1867669e+00, -3.8604670e+00,  5.6192124e-01],\n",
       "       [-2.3833578e+02,  1.1786343e+01,  1.1732360e+01, ...,\n",
       "        -3.7178817e+00,  8.2002485e-01, -1.9139581e+00]], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f48df2e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "57a99501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70988, 140)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = pd.read_hdf(FP_CITE_TRAIN_TARGETS)\n",
    "Y = Y.values\n",
    "Y -= Y.mean(axis=1).reshape(-1, 1)\n",
    "Y /= Y.std(axis=1).reshape(-1, 1)\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb044d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_number_pca = 66"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "492e7ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70988, 150)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.hstack((X[:,:feature_number_pca],X0))\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7922fab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-238.22762   ,   31.855104  ,   23.049294  , ...,   -0.364857  ,\n",
       "          -0.61251414,   -0.68285155],\n",
       "       [-233.65659   ,   40.22902   ,   19.437603  , ...,   -0.364857  ,\n",
       "          -0.61251414,   -0.68285155],\n",
       "       [-250.14233   ,   -4.6703153 ,    9.4395075 , ...,   -0.364857  ,\n",
       "           2.0863745 ,    1.9733372 ],\n",
       "       ...,\n",
       "       [-267.3943    ,  -14.522221  ,    8.559472  , ...,    2.6505954 ,\n",
       "           1.9836951 ,    1.6599795 ],\n",
       "       [-273.80966   ,  -23.930012  ,  -31.388956  , ...,    3.2577517 ,\n",
       "          -0.61251414,   -0.68285155],\n",
       "       [-238.33578   ,   11.786343  ,   11.73236   , ...,    2.94607   ,\n",
       "          -0.61251414,    1.6039475 ]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3bb5d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>donor</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>technology</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c2150f55becb</th>\n",
       "      <td>2</td>\n",
       "      <td>27678</td>\n",
       "      <td>HSC</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65b7edf8a4da</th>\n",
       "      <td>2</td>\n",
       "      <td>27678</td>\n",
       "      <td>HSC</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1b26cb1057b</th>\n",
       "      <td>2</td>\n",
       "      <td>27678</td>\n",
       "      <td>EryP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917168fa6f83</th>\n",
       "      <td>2</td>\n",
       "      <td>27678</td>\n",
       "      <td>NeuP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2b29feeca86d</th>\n",
       "      <td>2</td>\n",
       "      <td>27678</td>\n",
       "      <td>EryP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a9b4d99f1f50</th>\n",
       "      <td>7</td>\n",
       "      <td>31800</td>\n",
       "      <td>HSC</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0e2c1d0782af</th>\n",
       "      <td>7</td>\n",
       "      <td>31800</td>\n",
       "      <td>HSC</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a3cbc5aa0ec3</th>\n",
       "      <td>7</td>\n",
       "      <td>31800</td>\n",
       "      <td>MkP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75b350243add</th>\n",
       "      <td>7</td>\n",
       "      <td>31800</td>\n",
       "      <td>EryP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ad5a949989b2</th>\n",
       "      <td>7</td>\n",
       "      <td>31800</td>\n",
       "      <td>EryP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119651 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              day  donor cell_type technology\n",
       "cell_id                                      \n",
       "c2150f55becb    2  27678       HSC    citeseq\n",
       "65b7edf8a4da    2  27678       HSC    citeseq\n",
       "c1b26cb1057b    2  27678      EryP    citeseq\n",
       "917168fa6f83    2  27678      NeuP    citeseq\n",
       "2b29feeca86d    2  27678      EryP    citeseq\n",
       "...           ...    ...       ...        ...\n",
       "a9b4d99f1f50    7  31800       HSC    citeseq\n",
       "0e2c1d0782af    7  31800       HSC    citeseq\n",
       "a3cbc5aa0ec3    7  31800       MkP    citeseq\n",
       "75b350243add    7  31800      EryP    citeseq\n",
       "ad5a949989b2    7  31800      EryP    citeseq\n",
       "\n",
       "[119651 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6f307e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "ct_list = metadata_df.cell_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0208f463",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_celltype = {}\n",
    "count = 0\n",
    "for i in sorted(list(set(ct_list.values))):\n",
    "    dict_celltype[i] = count \n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c9dabde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BP': 0, 'EryP': 1, 'HSC': 2, 'MasP': 3, 'MkP': 4, 'MoP': 5, 'NeuP': 6}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_celltype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d83d45fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "celltype_new = [dict_celltype[i] for i in meta.cell_type.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8f815ab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "meta[\"cell_type_bin\"] = celltype_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a4528428",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_type_class = meta[\"cell_type_bin\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bd4eb93",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d62d1f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation_score(y_true, y_pred):\n",
    "    if type(y_true) == pd.DataFrame: y_true = y_true.values\n",
    "    if type(y_pred) == pd.DataFrame: y_pred = y_pred.values\n",
    "    corrsum = 0\n",
    "    for i in range(len(y_true)):\n",
    "        corrsum += np.corrcoef(y_true[i], y_pred[i])[1, 0]\n",
    "    return corrsum / len(y_true)\n",
    "\n",
    "def negative_correlation_loss(y_true, y_pred):\n",
    "    \n",
    "    y_true_label = y_true[:,140:]\n",
    "    y_pred_label =  y_pred[:,140:]\n",
    "    \n",
    "    y_true = y_true[:,:140]\n",
    "    y_pred = y_pred[:,:140]\n",
    "    \n",
    "    my = K.mean(tf.convert_to_tensor(y_pred), axis=1)\n",
    "    my = tf.tile(tf.expand_dims(my, axis=1), (1, y_true.shape[1]))\n",
    "    ym = y_pred - my\n",
    "    r_num = K.sum(tf.multiply(y_true, ym), axis=1)\n",
    "    r_den = tf.sqrt(K.sum(K.square(ym), axis=1) * float(y_true.shape[-1]))\n",
    "    r = tf.reduce_mean(r_num / r_den)\n",
    "    return - r + tf.keras.losses.SparseCategoricalCrossentropy()(y_true_label,  y_pred_label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a73b4960",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR_START = 5e-3#5e-3\n",
    "BATCH_SIZE = 512\n",
    "\n",
    "def create_model():\n",
    "    \n",
    "    reg1 = 9.613e-06\n",
    "    reg2 = 1e-07\n",
    "    REG1 = tf.keras.regularizers.l2(reg1)\n",
    "    REG2 = tf.keras.regularizers.l2(reg2)\n",
    "    DROP = 0.2\n",
    "\n",
    "    activation = 'selu'\n",
    "    inputs = Input(shape =(X.shape[1],))\n",
    "\n",
    "    x0 = Dense(512, \n",
    "              kernel_regularizer = REG1,\n",
    "              activation = activation,\n",
    "             )(inputs)\n",
    "    \n",
    "\n",
    "    x0 = Dropout(DROP)(x0)\n",
    "    \n",
    "    \n",
    "    \n",
    "    x1 = Dense(512, \n",
    "               kernel_regularizer = REG1,\n",
    "               activation = activation,\n",
    "             )(x0)\n",
    "\n",
    "    x1 = Dropout(DROP)(x1)\n",
    "    \n",
    "    \n",
    "    \n",
    "    x2 = Dense(512, \n",
    "               kernel_regularizer = REG1,\n",
    "               activation = activation,\n",
    "             )(x1+x0) \n",
    "    \n",
    "\n",
    "    x2 = Dropout(DROP)(x2)\n",
    "\n",
    "    \n",
    "    x3 = Dense(Y.shape[1],\n",
    "               kernel_regularizer = REG1,\n",
    "               activation = activation,\n",
    "             )(x2+x1)\n",
    "    \n",
    "\n",
    "    x3 = Dropout(DROP)(x3)\n",
    "\n",
    "    x = Concatenate()([\n",
    "                x0, \n",
    "                x1, \n",
    "                x2, \n",
    "                x3\n",
    "                ])\n",
    "    \n",
    "    x = Dense(Y.shape[1], \n",
    "                kernel_regularizer = REG2,\n",
    "                activation=\"linear\",\n",
    "                )(x)\n",
    "    \n",
    "    x_out = Dense(Y.shape[1], \n",
    "                kernel_regularizer = REG2,\n",
    "                activation=activation,\n",
    "                )(inputs)\n",
    "\n",
    "    x = x + x_out\n",
    "    \n",
    "    \n",
    "    x00 = Dense(64, \n",
    "              activation = activation,\n",
    "             )(x)\n",
    "    \n",
    "    \n",
    "    x10 = Dense(32, \n",
    "               activation = activation,\n",
    "             )(x00)\n",
    "\n",
    "\n",
    "    \n",
    "    x20 = Dense(7, \n",
    "               activation = \"softmax\",\n",
    "             )(x10)\n",
    "        \n",
    "    \n",
    "        \n",
    "    model = Model(inputs, Concatenate()([x,x20]))\n",
    "    \n",
    "\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4993e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cell_id\n",
       "45006fe3e4c8     HSC\n",
       "d02759a80ba2     HSC\n",
       "c016c6b0efa5    EryP\n",
       "ba7f733a4f75    NeuP\n",
       "fbcf2443ffb2    EryP\n",
       "                ... \n",
       "650ee456f0f3     HSC\n",
       "cc506e7707f5    EryP\n",
       "a91f1b55a520    EryP\n",
       "3a9882c98205    MasP\n",
       "c91b6b2ccd3d    EryP\n",
       "Name: cell_type, Length: 70988, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta.cell_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "48744648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.09460453, -0.16236241, -0.40533188, ...,  0.38325769,\n",
       "        -0.19745257,  2.        ],\n",
       "       [-0.25125083, -0.35797054, -0.16268902, ...,  0.40262994,\n",
       "         0.21424626,  2.        ],\n",
       "       [-0.50453472, -0.52717918, -0.66611427, ...,  0.98586214,\n",
       "         0.60952032,  1.        ],\n",
       "       ...,\n",
       "       [-0.21439953, -0.42171365, -0.15420543, ...,  1.36944079,\n",
       "         0.39634082,  1.        ],\n",
       "       [-0.59204561, -0.85295653, -0.36310589, ...,  0.1609816 ,\n",
       "         0.75275707,  3.        ],\n",
       "       [-0.64900035, -0.61859208, -0.27466986, ...,  1.65784419,\n",
       "         0.4573473 ,  1.        ]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack([Y[idx_tr], cell_type_class[idx_tr].reshape(-1,1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5133d8b3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a006dae5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6757f0da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./submissions/model_0/assets\n",
      "model saved\n",
      "776/776 [==============================] - 1s 1ms/step\n",
      "yes\n",
      "Fold 0, correlation =  0.89181\n",
      "INFO:tensorflow:Assets written to: ./submissions/model_1/assets\n",
      "model saved\n",
      "750/750 [==============================] - 1s 2ms/step\n",
      "yes\n",
      "Fold 1, correlation =  0.80785\n",
      "INFO:tensorflow:Assets written to: ./submissions/model_2/assets\n",
      "model saved\n",
      "694/694 [==============================] - 1s 2ms/step\n",
      "yes\n",
      "Fold 2, correlation =  0.85621\n",
      "\u001b[32m\u001b[1mMean corr = 0.85196\u001b[0m\n",
      "\u001b[34m\u001b[1mOof corr   = 0.85231\u001b[0m\n",
      "CPU times: user 5min 32s, sys: 7.13 s, total: 5min 39s\n",
      "Wall time: 9min 47s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "EPOCHS = 1100\n",
    "N_SPLITS = 3\n",
    "\n",
    "pred_train = np.zeros((Y.shape[0],Y.shape[1]))\n",
    "\n",
    "seed_tensorflow(1)\n",
    "score_list = []\n",
    "kf = GroupKFold(n_splits=N_SPLITS)\n",
    "# kf = sklearn.model_selection.KFold(5)\n",
    "score_list = []\n",
    "\n",
    "for fold, (idx_tr, idx_va) in enumerate(kf.split(X, groups=meta.donor)):\n",
    "    start_time = datetime.datetime.now()\n",
    "    model = None\n",
    "    gc.collect()\n",
    "    \n",
    "    X_tr = X[idx_tr]\n",
    "    y_tr = Y[idx_tr]\n",
    "    X_va = X[idx_va]\n",
    "    y_va = Y[idx_va]\n",
    "    \n",
    "    y_tr = np.hstack([Y[idx_tr], cell_type_class[idx_tr].reshape(-1,1)])\n",
    "    y_va = np.hstack([Y[idx_va], cell_type_class[idx_va].reshape(-1,1)])\n",
    "\n",
    "    lr = ReduceLROnPlateau(\n",
    "                    monitor = \"val_loss\",\n",
    "                    factor = 0.9, \n",
    "                    patience = 4, \n",
    "                    verbose = VERBOSE)\n",
    "    \n",
    "    \n",
    "    es = EarlyStopping(\n",
    "                    monitor = \"val_loss\",\n",
    "                    patience = 100, \n",
    "                    verbose = VERBOSE,\n",
    "                    mode = \"min\", \n",
    "                    restore_best_weights = True)\n",
    "\n",
    "    model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "                    filepath = './citeseq',\n",
    "                    save_weights_only = True,\n",
    "                    monitor = 'val_loss',\n",
    "                    mode = 'min',\n",
    "                    save_best_only = True)\n",
    "\n",
    "    callbacks = [   lr, \n",
    "                    es, \n",
    "                    model_checkpoint_callback\n",
    "                    ]\n",
    "    \n",
    "    model = create_model()\n",
    "    \n",
    "    \n",
    "    model.compile(\n",
    "                optimizer = tfa.optimizers.AdaBelief(learning_rate = LR_START),\n",
    "                metrics = [negative_correlation_loss],\n",
    "                loss = negative_correlation_loss\n",
    "                 )\n",
    "    # Training\n",
    "    model.fit(\n",
    "                X_tr,\n",
    "                y_tr, \n",
    "                validation_data=(\n",
    "                                X_va,\n",
    "                                y_va), \n",
    "                epochs = EPOCHS,\n",
    "                verbose = VERBOSE,\n",
    "                batch_size = BATCH_SIZE,\n",
    "                shuffle = True,\n",
    "                callbacks = callbacks)\n",
    "\n",
    "    del X_tr, y_tr \n",
    "    gc.collect()\n",
    "    \n",
    "    model.load_weights('./citeseq')\n",
    "    model.save(f\"./submissions/model_{fold}\")\n",
    "    print('model saved')\n",
    "    \n",
    "    #  Model validation\n",
    "    y_va_pred = model.predict(X_va)\n",
    "    print(\"yes\")\n",
    "    corrscore = correlation_score(y_va[:,0:140], y_va_pred[:,0:140])\n",
    "    pred_train[idx_va] = y_va_pred[:,0:140]\n",
    "    \n",
    "    print(f\"Fold {fold}, correlation =  {corrscore:.5f}\")\n",
    "    del X_va, y_va, y_va_pred\n",
    "    gc.collect()\n",
    "    score_list.append(corrscore)\n",
    "\n",
    "# Show overall score\n",
    "\n",
    "print(f\"{Fore.GREEN}{Style.BRIGHT}Mean corr = {np.array(score_list).mean():.5f}{Style.RESET_ALL}\")\n",
    "score_total = correlation_score(Y, pred_train[:,:140])\n",
    "print(f\"{Fore.BLUE}{Style.BRIGHT}Oof corr   = {score_total:.5f}{Style.RESET_ALL}\")\n",
    "\n",
    "\n",
    "\n",
    "# # multi layer opt\n",
    "# import warnings\n",
    "# warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# EPOCHS = 1100\n",
    "# N_SPLITS = 3\n",
    "\n",
    "# pred_train = np.zeros((Y.shape[0],Y.shape[1]))\n",
    "\n",
    "# # np.random.seed(1)\n",
    "# # tf.random.set_seed(1)\n",
    "# seed_tensorflow()\n",
    "# score_list = []\n",
    "# kf = GroupKFold(n_splits=N_SPLITS)\n",
    "# score_list = []\n",
    "\n",
    "# for fold, (idx_tr, idx_va) in enumerate(kf.split(X, groups=meta.donor)):\n",
    "#     start_time = datetime.datetime.now()\n",
    "#     model = None\n",
    "#     gc.collect()\n",
    "    \n",
    "#     X_tr = X[idx_tr]\n",
    "#     y_tr = Y[idx_tr]\n",
    "#     X_va = X[idx_va]\n",
    "#     y_va = Y[idx_va]\n",
    "\n",
    "#     lr = ReduceLROnPlateau(\n",
    "#                     monitor = \"val_loss\",\n",
    "#                     factor = 0.9, \n",
    "#                     patience = 4, \n",
    "#                     verbose = VERBOSE)\n",
    "\n",
    "#     es = EarlyStopping(\n",
    "#                     monitor = \"val_loss\",\n",
    "#                     patience = 100, \n",
    "#                     verbose = VERBOSE,\n",
    "#                     mode = \"min\", \n",
    "#                     restore_best_weights = True)\n",
    "\n",
    "#     model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "#                     filepath = './citeseq',\n",
    "#                     save_weights_only = True,\n",
    "#                     monitor = 'val_loss',\n",
    "#                     mode = 'min',\n",
    "#                     save_best_only = True)\n",
    "\n",
    "#     callbacks = [   #lr, \n",
    "#                     es, \n",
    "#                     model_checkpoint_callback\n",
    "#                     ]\n",
    "    \n",
    "#     model = create_model()\n",
    "    \n",
    "    \n",
    "#     optimizers = [\n",
    "#     tfa.optimizers.AdaBelief(learning_rate=5e-3),\n",
    "#     tfa.optimizers.AdaBelief(learning_rate=5e-2)\n",
    "#     ]\n",
    "    \n",
    "#     optimizers_and_layers = [(optimizers[0], model.layers[0:10]), (optimizers[1], model.layers[10:])]\n",
    "#     optimizer_multi = tfa.optimizers.MultiOptimizer(optimizers_and_layers)\n",
    "    \n",
    "#     model.compile(\n",
    "#                 optimizer = optimizer_multi,\n",
    "#                 metrics = [negative_correlation_loss],\n",
    "#                 loss = negative_correlation_loss\n",
    "#                  )\n",
    "#     # Training\n",
    "#     model.fit(\n",
    "#                 X_tr,\n",
    "#                 y_tr, \n",
    "#                 validation_data=(\n",
    "#                                 X_va,\n",
    "#                                 y_va), \n",
    "#                 epochs = EPOCHS,\n",
    "#                 verbose = VERBOSE,\n",
    "#                 batch_size = BATCH_SIZE,\n",
    "#                 shuffle = True,\n",
    "#                 callbacks = callbacks)\n",
    "\n",
    "#     del X_tr, y_tr \n",
    "#     gc.collect()\n",
    "    \n",
    "#     model.load_weights('./citeseq')\n",
    "#     model.save(f\"./submissions/model_{fold}\")\n",
    "#     print('model saved')\n",
    "    \n",
    "#     #  Model validation\n",
    "#     y_va_pred = model.predict(X_va)\n",
    "#     corrscore = correlation_score(y_va, y_va_pred)\n",
    "#     pred_train[idx_va] = y_va_pred\n",
    "    \n",
    "#     print(f\"Fold {fold}, correlation =  {corrscore:.5f}\")\n",
    "#     del X_va, y_va, y_va_pred\n",
    "#     gc.collect()\n",
    "#     score_list.append(corrscore)\n",
    "\n",
    "# # Show overall score\n",
    "\n",
    "# print(f\"{Fore.GREEN}{Style.BRIGHT}Mean corr = {np.array(score_list).mean():.5f}{Style.RESET_ALL}\")\n",
    "# score_total = correlation_score(Y, pred_train)\n",
    "# print(f\"{Fore.BLUE}{Style.BRIGHT}Oof corr   = {score_total:.5f}{Style.RESET_ALL}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93ef5aa2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dfaf4b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d7333841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48663, 168)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Xt = np.hstack((Xt[:,:feature_number_pca],X0t))\n",
    "# Xt.shape\n",
    "Xt = np.hstack((Xt[:,:feature_number_pca],X0t, X0t_celltype))\n",
    "Xt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6373862",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0c9d3166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.22474487,  0.        ,  1.22474487],\n",
       "       [ 1.22474487,  0.        , -1.22474487],\n",
       "       [ 0.70710678, -1.41421356,  0.70710678]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_list = np.array([[1,2,3],[3,2,1],[1,0,1]])\n",
    "std(test_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f591126c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [3, 2, 1],\n",
       "       [1, 0, 1]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9db7afe3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting with fold 0\n",
      "1521/1521 [==============================] - 2s 1ms/step\n",
      "Predicting with fold 1\n",
      "1521/1521 [==============================] - 2s 1ms/step\n",
      "Predicting with fold 2\n",
      "1521/1521 [==============================] - 2s 1ms/step\n",
      "48663\n",
      "[[ 9.4604529e-02 -1.6236241e-01 -4.0533188e-01 ... -2.6049733e-01\n",
      "   3.8325769e-01 -1.9745257e-01]\n",
      " [-2.5125083e-01 -3.5797054e-01 -1.6268902e-01 ... -1.9555242e-01\n",
      "   4.0262994e-01  2.1424626e-01]\n",
      " [-5.0453472e-01 -5.2717918e-01 -6.6611427e-01 ... -7.8993803e-01\n",
      "   9.8586214e-01  6.0952032e-01]\n",
      " ...\n",
      " [-8.1554993e+01 -3.1155190e+01 -5.0684601e+01 ... -7.5564728e+01\n",
      "  -5.1838104e+01  5.2822746e+01]\n",
      " [-8.0224876e+01 -6.4099884e+01 -4.0413280e+01 ... -7.7787079e+01\n",
      "   1.9851367e+02  8.0069778e+01]\n",
      " [-7.2409058e+01 -5.8891174e+01 -3.9310787e+01 ... -7.4846786e+01\n",
      "   2.4202838e+02  9.9897881e+01]]\n",
      "[[ 0.09460448 -0.16236246 -0.40533194 ... -0.2604974   0.38325763\n",
      "  -0.19745262]\n",
      " [-0.2512508  -0.3579705  -0.16268899 ... -0.1955524   0.40262997\n",
      "   0.21424629]\n",
      " [-0.50453466 -0.5271791  -0.6661142  ... -0.789938    0.9858622\n",
      "   0.6095204 ]\n",
      " ...\n",
      " [-0.39562437 -0.07664843 -0.20024836 ... -0.3577125  -0.2075488\n",
      "   0.45484054]\n",
      " [-0.6060044  -0.4580055  -0.24060436 ... -0.5836297   1.9523225\n",
      "   0.8652171 ]\n",
      " [-0.5492751  -0.42562565 -0.2465219  ... -0.57157326  2.3269155\n",
      "   1.0268337 ]]\n"
     ]
    }
   ],
   "source": [
    "test_pred = np.zeros((len(Xt), 140), dtype=np.float32)\n",
    "for fold in range(N_SPLITS):\n",
    "    print(f\"Predicting with fold {fold}\")\n",
    "    model = load_model(f\"./submissions/model_{fold}\",\n",
    "                       custom_objects={'negative_correlation_loss': negative_correlation_loss})\n",
    "    test_pred += model.predict(Xt)\n",
    "\n",
    "print(len(test_pred))\n",
    "# Copy the targets for the data leak but useless since the change in the public LB...\n",
    "test_pred[:7476] = Y[:7476]\n",
    "\n",
    "\n",
    "# from Juan Smith Perera to complete with the Multiome part :\n",
    "# submission = pd.read_csv('submission_lolo_1.csv',index_col='row_id', squeeze=True)\n",
    "\n",
    "print(test_pred)\n",
    "\n",
    "test_pred = std(test_pred)\n",
    "\n",
    "print(test_pred)\n",
    "# submission = pd.read_csv(\"/ysm-gpfs/pi/zhao/tl688/openproblems/submission_10_combine.csv\",index_col='row_id', squeeze=True)\n",
    "# submission.iloc[:len(test_pred.ravel())] = test_pred.ravel()\n",
    "# assert not submission.isna().any()\n",
    "\n",
    "# submission.to_csv('submission_onebestcite.csv')\n",
    "# display(submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c8d3b25c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_id\n",
       "0           0.094604\n",
       "1          -0.162362\n",
       "2          -0.405332\n",
       "3          -0.302582\n",
       "4           1.114355\n",
       "              ...   \n",
       "65744175    2.777528\n",
       "65744176   -0.369145\n",
       "65744177   -0.367802\n",
       "65744178    0.135783\n",
       "65744179    2.558738\n",
       "Name: target, Length: 65744180, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "submission = pd.read_csv(\"/ysm-gpfs/pi/zhao/tl688/openproblems/submission_testourcite.csv\",index_col='row_id', squeeze=True)\n",
    "submission.iloc[:len(test_pred.ravel())] = test_pred.ravel()\n",
    "assert not submission.isna().any()\n",
    "\n",
    "submission.to_csv('submission_onebestcite_updatekfold.csv')\n",
    "display(submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8ab4339",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d0718afe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.4604529e-02, -1.6236241e-01, -4.0533188e-01, ...,\n",
       "        -2.6049733e-01,  3.8325769e-01, -1.9745257e-01],\n",
       "       [-2.5125083e-01, -3.5797054e-01, -1.6268902e-01, ...,\n",
       "        -1.9555242e-01,  4.0262994e-01,  2.1424626e-01],\n",
       "       [-5.0453472e-01, -5.2717918e-01, -6.6611427e-01, ...,\n",
       "        -7.8993803e-01,  9.8586214e-01,  6.0952032e-01],\n",
       "       ...,\n",
       "       [-1.0985884e+02, -3.9327122e+01, -6.5025253e+01, ...,\n",
       "        -9.2144600e+01, -7.7501633e+01,  4.6865902e+01],\n",
       "       [-9.2803215e+01, -7.8483650e+01, -5.0464352e+01, ...,\n",
       "        -8.5875710e+01,  1.9630319e+02,  9.0480896e+01],\n",
       "       [-8.9690689e+01, -7.3419548e+01, -4.7673729e+01, ...,\n",
       "        -7.9953278e+01,  2.3358974e+02,  1.0799841e+02]], dtype=float32)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f77a907",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8643aae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_cite = pd.read_csv('submission_813.csv',index_col='row_id', squeeze=True)\n",
    "submission_multiome = pd.read_csv(\"standard_ensemble_812.csv\", index_col='row_id', squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1ba0983",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_cite.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dea33b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# submission_multiome.iloc[0:6812820] = submission_cite.iloc[0:6812820]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa9eb227",
   "metadata": {},
   "outputs": [],
   "source": [
    "# submission_multiome.to_csv(\"submission_ensemble_cite_standardmulti.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6e124722",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR_START = 5e-3#5e-3\n",
    "BATCH_SIZE = 512\n",
    "\n",
    "def create_model():\n",
    "    \n",
    "    reg1 = 9.613e-06\n",
    "    reg2 = 1e-05\n",
    "    REG1 = tf.keras.regularizers.l2(reg1)\n",
    "    REG2 = tf.keras.regularizers.l2(reg2)\n",
    "    DROP = 0.2\n",
    "\n",
    "    activation = 'selu'\n",
    "    inputs = Input(shape =(X.shape[1],))\n",
    "\n",
    "    x0 = Dense(1024, \n",
    "              kernel_regularizer = REG1,\n",
    "              activation = activation,\n",
    "             )(inputs)\n",
    "    \n",
    "\n",
    "    x0 = Dropout(DROP)(x0)\n",
    "    \n",
    "    \n",
    "    \n",
    "    x1 = Dense(1024, \n",
    "               kernel_regularizer = REG1,\n",
    "               activation = activation,\n",
    "             )(x0)\n",
    "\n",
    "    x1 = Dropout(DROP)(x1)\n",
    "    \n",
    "    \n",
    "    \n",
    "    x2 = Dense(1024, \n",
    "               kernel_regularizer = REG1,\n",
    "               activation = activation,\n",
    "             )(x1+x0) \n",
    "    \n",
    "\n",
    "    x2 = Dropout(DROP)(x2)\n",
    "\n",
    "    \n",
    "    x3 = Dense(1024,\n",
    "               kernel_regularizer = REG1,\n",
    "               activation = activation,\n",
    "             )(x2+x1)\n",
    "    \n",
    "\n",
    "    x3 = Dropout(DROP)(x3)\n",
    "\n",
    "    x = Concatenate()([\n",
    "                x0, \n",
    "                x1, \n",
    "                x2, \n",
    "                x3\n",
    "                ])\n",
    "    \n",
    "    x = Dense(Y.shape[1], \n",
    "                kernel_regularizer = REG2,\n",
    "                activation='linear',\n",
    "                )(x)\n",
    "    \n",
    "    x_out = Dense(Y.shape[1], \n",
    "                kernel_regularizer = REG2,\n",
    "                activation=activation,\n",
    "                )(inputs)\n",
    "    \n",
    "    \n",
    "    x = x + x_out\n",
    "    \n",
    "#     mean = Dense(1, \n",
    "#                 kernel_regularizer = REG2,\n",
    "#                 activation='linear',\n",
    "#                 )(inputs)\n",
    "        \n",
    "#     var = Dense(1, \n",
    "#                 kernel_regularizer = REG2,\n",
    "#                 activation='relu',\n",
    "#                 )(inputs)\n",
    "    \n",
    "    \n",
    "#     x = (x + x_out-mean)/(var+1e-5)\n",
    "\n",
    "        \n",
    "    model = Model(inputs, x)\n",
    "    \n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8a1eaebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We start working on seed, 1\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "File \u001b[0;32m<timed exec>:61\u001b[0m\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/keras/engine/training.py:1564\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1556\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1557\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1558\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1561\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1562\u001b[0m ):\n\u001b[1;32m   1563\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1564\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1565\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1566\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 915\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    944\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    945\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    946\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 947\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateless_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m    948\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    949\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    950\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m    951\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/tensorflow/python/eager/function.py:2496\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2493\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m   2494\u001b[0m   (graph_function,\n\u001b[1;32m   2495\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2496\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2497\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/tensorflow/python/eager/function.py:1862\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1858\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1859\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1860\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1861\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1862\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1863\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1864\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1865\u001b[0m     args,\n\u001b[1;32m   1866\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1867\u001b[0m     executing_eagerly)\n\u001b[1;32m   1868\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/tensorflow/python/eager/function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    497\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    498\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 499\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    505\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    506\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m    507\u001b[0m         \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m    508\u001b[0m         num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    511\u001b[0m         ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[1;32m    512\u001b[0m         cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 54\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     57\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "EPOCHS = 1100\n",
    "N_SPLITS = 3\n",
    "\n",
    "for seed_set in range(1,11):\n",
    "    print('We start working on seed,', seed_set)\n",
    "    pred_train = np.zeros((Y.shape[0],Y.shape[1]))\n",
    "\n",
    "    # np.random.seed(1)\n",
    "    # tf.random.set_seed(1)\n",
    "    seed_tensorflow(seed_set)\n",
    "    score_list = []\n",
    "    kf = GroupKFold(n_splits=N_SPLITS)\n",
    "    score_list = []\n",
    "\n",
    "    for fold, (idx_tr, idx_va) in enumerate(kf.split(X, groups=meta.donor)):\n",
    "        start_time = datetime.datetime.now()\n",
    "        model = None\n",
    "        gc.collect()\n",
    "\n",
    "        X_tr = X[idx_tr]\n",
    "        y_tr = Y[idx_tr]\n",
    "        X_va = X[idx_va]\n",
    "        y_va = Y[idx_va]\n",
    "\n",
    "        lr = ReduceLROnPlateau(\n",
    "                        monitor = \"val_loss\",\n",
    "                        factor = 0.9, \n",
    "                        patience = 4, \n",
    "                        verbose = VERBOSE)\n",
    "\n",
    "        es = EarlyStopping(\n",
    "                        monitor = \"val_loss\",\n",
    "                        patience = 100, \n",
    "                        verbose = VERBOSE,\n",
    "                        mode = \"min\", \n",
    "                        restore_best_weights = True)\n",
    "\n",
    "        model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "                        filepath = './citeseq',\n",
    "                        save_weights_only = True,\n",
    "                        monitor = 'val_loss',\n",
    "                        mode = 'min',\n",
    "                        save_best_only = True)\n",
    "\n",
    "        callbacks = [   lr, \n",
    "                        es, \n",
    "                        model_checkpoint_callback\n",
    "                        ]\n",
    "\n",
    "        model = create_model()\n",
    "\n",
    "        model.compile(\n",
    "                    optimizer = tfa.optimizers.AdaBelief(LR_START),\n",
    "                    metrics = [negative_correlation_loss],\n",
    "                    loss = negative_correlation_loss\n",
    "                     )\n",
    "        # Training\n",
    "        model.fit(\n",
    "                    X_tr,\n",
    "                    y_tr, \n",
    "                    validation_data=(\n",
    "                                    X_va,\n",
    "                                    y_va), \n",
    "                    epochs = EPOCHS,\n",
    "                    verbose = VERBOSE,\n",
    "                    batch_size = BATCH_SIZE,\n",
    "                    shuffle = True,\n",
    "                    callbacks = callbacks)\n",
    "\n",
    "        del X_tr, y_tr \n",
    "        gc.collect()\n",
    "\n",
    "        model.load_weights('./citeseq')\n",
    "        model.save(f\"./submissions/model_{fold}\")\n",
    "        print('model saved')\n",
    "\n",
    "        #  Model validation\n",
    "        y_va_pred = model.predict(X_va)\n",
    "        corrscore = correlation_score(y_va, y_va_pred)\n",
    "        pred_train[idx_va] = y_va_pred\n",
    "\n",
    "        print(f\"Fold {fold}, correlation =  {corrscore:.5f}\")\n",
    "        del X_va, y_va, y_va_pred\n",
    "        gc.collect()\n",
    "        score_list.append(corrscore)\n",
    "\n",
    "    # Show overall score\n",
    "\n",
    "    print(f\"{Fore.GREEN}{Style.BRIGHT}Mean corr = {np.array(score_list).mean():.5f}{Style.RESET_ALL}\")\n",
    "    score_total = correlation_score(Y, pred_train)\n",
    "    print(f\"{Fore.BLUE}{Style.BRIGHT}Oof corr   = {score_total:.5f}{Style.RESET_ALL}\")\n",
    "    \n",
    "    Xt = np.hstack((Xt[:,:feature_number_pca],X0t))\n",
    "    Xt.shape\n",
    "    \n",
    "    test_pred = np.zeros((len(Xt), 140), dtype=np.float32)\n",
    "    for fold in range(N_SPLITS):\n",
    "        print(f\"Predicting with fold {fold}\")\n",
    "        model = load_model(f\"./submissions/model_{fold}\",\n",
    "                           custom_objects={'negative_correlation_loss': negative_correlation_loss})\n",
    "        test_pred += model.predict(Xt)\n",
    "\n",
    "    print(len(test_pred))\n",
    "    # Copy the targets for the data leak but useless since the change in the public LB...\n",
    "    test_pred[:7476] = Y[:7476]\n",
    "    \n",
    "#     test_pred = std(test_pred)\n",
    "    \n",
    "    submission = pd.read_csv(\"./submission_ensemble_standard.csv\",index_col='row_id', squeeze=True)\n",
    "    submission.iloc[:len(test_pred.ravel())] = test_pred.ravel()\n",
    "    assert not submission.isna().any()\n",
    "    \n",
    "    save_path = \"./ensemblingwork/\" + \"submission_seed\" + str(seed_set) + \".csv\"\n",
    "    submission.to_csv(save_path)\n",
    "    display(submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14865f20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c051d5d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0d50fcda",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_list = [0.89437, 0.89435, 0.89413, 0.89442, 0.89420, 0.89457, 0.89439, 0.89469, 0.89429, 0.89418]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "135e3445",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "21030ecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv(\"./ensemblingwork/\" + \"submission_seed\" + str(1) + \".csv\", index_col=0)\n",
    "for seed_set in range(2,21):\n",
    "    save_path = \"./ensemblingwork/\" + \"submission_seed\" + str(seed_set) + \".csv\"\n",
    "    submission_save = pd.read_csv(save_path, index_col=0)\n",
    "    submission += submission_save\n",
    "submission = submission/20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54d6dbda",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b0207b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "submssion_sample = pd.read_csv(\"evaluation_ids.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3bce61d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>gene_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>c2150f55becb</td>\n",
       "      <td>CD86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>c2150f55becb</td>\n",
       "      <td>CD274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>c2150f55becb</td>\n",
       "      <td>CD270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>c2150f55becb</td>\n",
       "      <td>CD155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>c2150f55becb</td>\n",
       "      <td>CD112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>65744175</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "      <td>ENSG00000134419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>65744176</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "      <td>ENSG00000186862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>65744177</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "      <td>ENSG00000170959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>65744178</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "      <td>ENSG00000107874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>65744179</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "      <td>ENSG00000166012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            row_id       cell_id          gene_id\n",
       "0                0  c2150f55becb             CD86\n",
       "1                1  c2150f55becb            CD274\n",
       "2                2  c2150f55becb            CD270\n",
       "3                3  c2150f55becb            CD155\n",
       "4                4  c2150f55becb            CD112\n",
       "...            ...           ...              ...\n",
       "65744175  65744175  2c53aa67933d  ENSG00000134419\n",
       "65744176  65744176  2c53aa67933d  ENSG00000186862\n",
       "65744177  65744177  2c53aa67933d  ENSG00000170959\n",
       "65744178  65744178  2c53aa67933d  ENSG00000107874\n",
       "65744179  65744179  2c53aa67933d  ENSG00000166012\n",
       "\n",
       "[65744180 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submssion_sample "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "abe50aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# submission = pd.read_csv(\"./ensemblingwork/\" + \"submission_seed\" + str(8) + \".csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c075ecf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.094605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.162362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.405332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.302582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.114355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>4.425791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>-0.604866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>-0.597459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>0.237816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>3.961616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target\n",
       "row_id            \n",
       "0         0.094605\n",
       "1        -0.162362\n",
       "2        -0.405332\n",
       "3        -0.302582\n",
       "4         1.114355\n",
       "...            ...\n",
       "65744175  4.425791\n",
       "65744176 -0.604866\n",
       "65744177 -0.597459\n",
       "65744178  0.237816\n",
       "65744179  3.961616\n",
       "\n",
       "[65744180 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "764488d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission[\"cell_id\"] = submssion_sample[\"cell_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "459bafd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>cell_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.094605</td>\n",
       "      <td>c2150f55becb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.162362</td>\n",
       "      <td>c2150f55becb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.405332</td>\n",
       "      <td>c2150f55becb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.302582</td>\n",
       "      <td>c2150f55becb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.114355</td>\n",
       "      <td>c2150f55becb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>4.425791</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>-0.604866</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>-0.597459</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>0.237816</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>3.961616</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target       cell_id\n",
       "row_id                          \n",
       "0         0.094605  c2150f55becb\n",
       "1        -0.162362  c2150f55becb\n",
       "2        -0.405332  c2150f55becb\n",
       "3        -0.302582  c2150f55becb\n",
       "4         1.114355  c2150f55becb\n",
       "...            ...           ...\n",
       "65744175  4.425791  2c53aa67933d\n",
       "65744176 -0.604866  2c53aa67933d\n",
       "65744177 -0.597459  2c53aa67933d\n",
       "65744178  0.237816  2c53aa67933d\n",
       "65744179  3.961616  2c53aa67933d\n",
       "\n",
       "[65744180 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d984cf99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b71f00f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82c950c2ca104e70bdcd9ae0fadd812e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Standardizing:   0%|          | 0/65443 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Standardize submission per cell_id\n",
    "loop_list          = submission.groupby('cell_id', sort=False)\n",
    "vals               = []\n",
    "\n",
    "for idx, g in tqdm(loop_list, desc=f'Standardizing', miniters=1000):\n",
    "    vals.append(std(g.target).values)\n",
    "\n",
    "to_save_norm = np.concatenate(vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c4c357a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09460448, -0.16236246, -0.40533193, ..., -0.59745899,\n",
       "        0.23781598,  3.9616161 ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_save_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3004caa6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2ce5070",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ffae1261",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['target'] = to_save_norm.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bf2f76e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "del submission['cell_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a71fc7e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.094604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.162362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.405332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.302582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.114355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>4.425791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>-0.604866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>-0.597459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>0.237816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>3.961616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target\n",
       "row_id            \n",
       "0         0.094604\n",
       "1        -0.162362\n",
       "2        -0.405332\n",
       "3        -0.302582\n",
       "4         1.114355\n",
       "...            ...\n",
       "65744175  4.425791\n",
       "65744176 -0.604866\n",
       "65744177 -0.597459\n",
       "65744178  0.237816\n",
       "65744179  3.961616\n",
       "\n",
       "[65744180 rows x 1 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d877dcec",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_bestmulti = pd.read_csv(\"/ysm-gpfs/pi/zhao/tl688/openproblems/submission_onebestcite.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "932b69a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.iloc[6812820:] = submission_bestmulti[6812820:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ce87c96c",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"submission_modify_combine.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f6de4733",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.094604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.162362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.405332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.302582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.114355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>6.156170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>-0.852548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>-0.844325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>0.349796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>5.507162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target\n",
       "row_id            \n",
       "0         0.094604\n",
       "1        -0.162362\n",
       "2        -0.405332\n",
       "3        -0.302582\n",
       "4         1.114355\n",
       "...            ...\n",
       "65744175  6.156170\n",
       "65744176 -0.852548\n",
       "65744177 -0.844325\n",
       "65744178  0.349796\n",
       "65744179  5.507162\n",
       "\n",
       "[65744180 rows x 1 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e694d3d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4baae090",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "86b44301",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_comb = pd.read_csv(\"./ensemblingwork/\" + \"submission_seed\" + str(8) + \".csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "018bf091",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv(\"/ysm-gpfs/pi/zhao/tl688/openproblems/submission_ensemble_standard.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "766e5541",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_comb[\"cell_id\"] = submssion_sample[\"cell_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6af29703",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f773e68f38d4a738490f0678c2cce23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Standardizing:   0%|          | 0/65443 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Standardize submission per cell_id\n",
    "loop_list          = submission_comb.groupby('cell_id', sort=False)\n",
    "vals               = []\n",
    "\n",
    "for idx, g in tqdm(loop_list, desc=f'Standardizing', miniters=1000):\n",
    "    vals.append(std(g.target).values)\n",
    "\n",
    "to_save_norm = np.concatenate(vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "10f59a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_comb['target'] = to_save_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9ec512b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.iloc[0:6812820] = submission_comb[0:6812820]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f387d1c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b2f5716c",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"/ysm-gpfs/pi/zhao/tl688/openproblems/submission_modify_8.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2969577b",
   "metadata": {},
   "source": [
    "# Change input size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d5495d36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constant cols: 1194\n",
      "important columns  84\n"
     ]
    }
   ],
   "source": [
    "# constant_cols = list(X.columns[(X == 0).all(axis=0).values]) + list(X_test.columns[(X_test == 0).all(axis=0).values])\n",
    "constant_cols = ['ENSG00000003137_CYP26B1', 'ENSG00000004848_ARX', 'ENSG00000006606_CCL26', 'ENSG00000010379_SLC6A13', 'ENSG00000010932_FMO1', 'ENSG00000017427_IGF1', 'ENSG00000022355_GABRA1', 'ENSG00000041982_TNC', 'ENSG00000060709_RIMBP2', 'ENSG00000064886_CHI3L2', 'ENSG00000065717_TLE2', 'ENSG00000067798_NAV3', 'ENSG00000069535_MAOB', 'ENSG00000073598_FNDC8', 'ENSG00000074219_TEAD2', 'ENSG00000074964_ARHGEF10L', 'ENSG00000077264_PAK3', 'ENSG00000078053_AMPH', 'ENSG00000082684_SEMA5B', 'ENSG00000083857_FAT1', 'ENSG00000084628_NKAIN1', 'ENSG00000084734_GCKR', 'ENSG00000086967_MYBPC2', 'ENSG00000087258_GNAO1', 'ENSG00000089505_CMTM1', 'ENSG00000091129_NRCAM', 'ENSG00000091986_CCDC80', 'ENSG00000092377_TBL1Y', 'ENSG00000092969_TGFB2', 'ENSG00000095397_WHRN', 'ENSG00000095970_TREM2', 'ENSG00000099715_PCDH11Y', 'ENSG00000100197_CYP2D6', 'ENSG00000100218_RSPH14', 'ENSG00000100311_PDGFB', 'ENSG00000100362_PVALB', 'ENSG00000100373_UPK3A', 'ENSG00000100625_SIX4', 'ENSG00000100867_DHRS2', 'ENSG00000100985_MMP9', 'ENSG00000101197_BIRC7', 'ENSG00000101298_SNPH', 'ENSG00000102387_TAF7L', 'ENSG00000103034_NDRG4', 'ENSG00000104059_FAM189A1', 'ENSG00000104112_SCG3', 'ENSG00000104313_EYA1', 'ENSG00000104892_KLC3', 'ENSG00000105088_OLFM2', 'ENSG00000105261_OVOL3', 'ENSG00000105290_APLP1', 'ENSG00000105507_CABP5', 'ENSG00000105642_KCNN1', 'ENSG00000105694_ELOCP28', 'ENSG00000105707_HPN', 'ENSG00000105894_PTN', 'ENSG00000106018_VIPR2', 'ENSG00000106541_AGR2', 'ENSG00000107317_PTGDS', 'ENSG00000108688_CCL7', 'ENSG00000108702_CCL1', 'ENSG00000108947_EFNB3', 'ENSG00000109193_SULT1E1', 'ENSG00000109794_FAM149A', 'ENSG00000109832_DDX25', 'ENSG00000110195_FOLR1', 'ENSG00000110375_UPK2', 'ENSG00000110436_SLC1A2', 'ENSG00000111339_ART4', 'ENSG00000111863_ADTRP', 'ENSG00000112761_WISP3', 'ENSG00000112852_PCDHB2', 'ENSG00000114251_WNT5A', 'ENSG00000114279_FGF12', 'ENSG00000114455_HHLA2', 'ENSG00000114757_PEX5L', 'ENSG00000115155_OTOF', 'ENSG00000115266_APC2', 'ENSG00000115297_TLX2', 'ENSG00000115590_IL1R2', 'ENSG00000115844_DLX2', 'ENSG00000116194_ANGPTL1', 'ENSG00000116661_FBXO2', 'ENSG00000116774_OLFML3', 'ENSG00000117322_CR2', 'ENSG00000117971_CHRNB4', 'ENSG00000118322_ATP10B', 'ENSG00000118402_ELOVL4', 'ENSG00000118520_ARG1', 'ENSG00000118946_PCDH17', 'ENSG00000118972_FGF23', 'ENSG00000119771_KLHL29', 'ENSG00000120549_KIAA1217', 'ENSG00000121316_PLBD1', 'ENSG00000121905_HPCA', 'ENSG00000122224_LY9', 'ENSG00000124194_GDAP1L1', 'ENSG00000124440_HIF3A', 'ENSG00000124657_OR2B6', 'ENSG00000125462_C1orf61', 'ENSG00000125895_TMEM74B', 'ENSG00000126838_PZP', 'ENSG00000128422_KRT17', 'ENSG00000128918_ALDH1A2', 'ENSG00000129170_CSRP3', 'ENSG00000129214_SHBG', 'ENSG00000129673_AANAT', 'ENSG00000129910_CDH15', 'ENSG00000130294_KIF1A', 'ENSG00000130307_USHBP1', 'ENSG00000130545_CRB3', 'ENSG00000131019_ULBP3', 'ENSG00000131044_TTLL9', 'ENSG00000131183_SLC34A1', 'ENSG00000131386_GALNT15', 'ENSG00000131400_NAPSA', 'ENSG00000131914_LIN28A', 'ENSG00000131941_RHPN2', 'ENSG00000131951_LRRC9', 'ENSG00000132170_PPARG', 'ENSG00000132681_ATP1A4', 'ENSG00000132958_TPTE2', 'ENSG00000133454_MYO18B', 'ENSG00000134545_KLRC1', 'ENSG00000134853_PDGFRA', 'ENSG00000135083_CCNJL', 'ENSG00000135100_HNF1A', 'ENSG00000135116_HRK', 'ENSG00000135312_HTR1B', 'ENSG00000135324_MRAP2', 'ENSG00000135436_FAM186B', 'ENSG00000135472_FAIM2', 'ENSG00000135898_GPR55', 'ENSG00000135929_CYP27A1', 'ENSG00000136002_ARHGEF4', 'ENSG00000136099_PCDH8', 'ENSG00000136274_NACAD', 'ENSG00000137078_SIT1', 'ENSG00000137142_IGFBPL1', 'ENSG00000137473_TTC29', 'ENSG00000137474_MYO7A', 'ENSG00000137491_SLCO2B1', 'ENSG00000137691_CFAP300', 'ENSG00000137731_FXYD2', 'ENSG00000137747_TMPRSS13', 'ENSG00000137878_GCOM1', 'ENSG00000138411_HECW2', 'ENSG00000138741_TRPC3', 'ENSG00000138769_CDKL2', 'ENSG00000138823_MTTP', 'ENSG00000139908_TSSK4', 'ENSG00000140832_MARVELD3', 'ENSG00000142178_SIK1', 'ENSG00000142538_PTH2', 'ENSG00000142910_TINAGL1', 'ENSG00000143217_NECTIN4', 'ENSG00000143858_SYT2', 'ENSG00000144130_NT5DC4', 'ENSG00000144214_LYG1', 'ENSG00000144290_SLC4A10', 'ENSG00000144366_GULP1', 'ENSG00000144583_MARCH4', 'ENSG00000144771_LRTM1', 'ENSG00000144891_AGTR1', 'ENSG00000145087_STXBP5L', 'ENSG00000145107_TM4SF19', 'ENSG00000146197_SCUBE3', 'ENSG00000146966_DENND2A', 'ENSG00000147082_CCNB3', 'ENSG00000147614_ATP6V0D2', 'ENSG00000147642_SYBU', 'ENSG00000147869_CER1', 'ENSG00000149403_GRIK4', 'ENSG00000149596_JPH2', 'ENSG00000150630_VEGFC', 'ENSG00000150722_PPP1R1C', 'ENSG00000151631_AKR1C6P', 'ENSG00000151704_KCNJ1', 'ENSG00000152154_TMEM178A', 'ENSG00000152292_SH2D6', 'ENSG00000152315_KCNK13', 'ENSG00000152503_TRIM36', 'ENSG00000153253_SCN3A', 'ENSG00000153902_LGI4', 'ENSG00000153930_ANKFN1', 'ENSG00000154040_CABYR', 'ENSG00000154118_JPH3', 'ENSG00000154175_ABI3BP', 'ENSG00000154645_CHODL', 'ENSG00000157060_SHCBP1L', 'ENSG00000157087_ATP2B2', 'ENSG00000157152_SYN2', 'ENSG00000157168_NRG1', 'ENSG00000157680_DGKI', 'ENSG00000158246_TENT5B', 'ENSG00000158477_CD1A', 'ENSG00000158481_CD1C', 'ENSG00000158488_CD1E', 'ENSG00000159189_C1QC', 'ENSG00000159217_IGF2BP1', 'ENSG00000160683_CXCR5', 'ENSG00000160801_PTH1R', 'ENSG00000160973_FOXH1', 'ENSG00000161594_KLHL10', 'ENSG00000162409_PRKAA2', 'ENSG00000162840_MT2P1', 'ENSG00000162873_KLHDC8A', 'ENSG00000162944_RFTN2', 'ENSG00000162949_CAPN13', 'ENSG00000163116_STPG2', 'ENSG00000163288_GABRB1', 'ENSG00000163531_NFASC', 'ENSG00000163618_CADPS', 'ENSG00000163637_PRICKLE2', 'ENSG00000163735_CXCL5', 'ENSG00000163873_GRIK3', 'ENSG00000163898_LIPH', 'ENSG00000164061_BSN', 'ENSG00000164078_MST1R', 'ENSG00000164123_C4orf45', 'ENSG00000164690_SHH', 'ENSG00000164761_TNFRSF11B', 'ENSG00000164821_DEFA4', 'ENSG00000164845_FAM86FP', 'ENSG00000164867_NOS3', 'ENSG00000166073_GPR176', 'ENSG00000166148_AVPR1A', 'ENSG00000166250_CLMP', 'ENSG00000166257_SCN3B', 'ENSG00000166268_MYRFL', 'ENSG00000166523_CLEC4E', 'ENSG00000166535_A2ML1', 'ENSG00000166819_PLIN1', 'ENSG00000166928_MS4A14', 'ENSG00000167210_LOXHD1', 'ENSG00000167306_MYO5B', 'ENSG00000167634_NLRP7', 'ENSG00000167748_KLK1', 'ENSG00000167889_MGAT5B', 'ENSG00000168140_VASN', 'ENSG00000168546_GFRA2', 'ENSG00000168646_AXIN2', 'ENSG00000168955_TM4SF20', 'ENSG00000168993_CPLX1', 'ENSG00000169075_Z99496.1', 'ENSG00000169194_IL13', 'ENSG00000169246_NPIPB3', 'ENSG00000169884_WNT10B', 'ENSG00000169900_PYDC1', 'ENSG00000170074_FAM153A', 'ENSG00000170075_GPR37L1', 'ENSG00000170289_CNGB3', 'ENSG00000170356_OR2A20P', 'ENSG00000170537_TMC7', 'ENSG00000170689_HOXB9', 'ENSG00000170827_CELP', 'ENSG00000171346_KRT15', 'ENSG00000171368_TPPP', 'ENSG00000171501_OR1N2', 'ENSG00000171532_NEUROD2', 'ENSG00000171611_PTCRA', 'ENSG00000171873_ADRA1D', 'ENSG00000171916_LGALS9C', 'ENSG00000172005_MAL', 'ENSG00000172987_HPSE2', 'ENSG00000173068_BNC2', 'ENSG00000173077_DEC1', 'ENSG00000173210_ABLIM3', 'ENSG00000173267_SNCG', 'ENSG00000173369_C1QB', 'ENSG00000173372_C1QA', 'ENSG00000173391_OLR1', 'ENSG00000173626_TRAPPC3L', 'ENSG00000173698_ADGRG2', 'ENSG00000173868_PHOSPHO1', 'ENSG00000174407_MIR1-1HG', 'ENSG00000174807_CD248', 'ENSG00000175206_NPPA', 'ENSG00000175746_C15orf54', 'ENSG00000175985_PLEKHD1', 'ENSG00000176043_AC007160.1', 'ENSG00000176399_DMRTA1', 'ENSG00000176510_OR10AC1', 'ENSG00000176697_BDNF', 'ENSG00000176826_FKBP9P1', 'ENSG00000176988_FMR1NB', 'ENSG00000177324_BEND2', 'ENSG00000177335_C8orf31', 'ENSG00000177535_OR2B11', 'ENSG00000177614_PGBD5', 'ENSG00000177707_NECTIN3', 'ENSG00000178033_CALHM5', 'ENSG00000178175_ZNF366', 'ENSG00000178462_TUBAL3', 'ENSG00000178732_GP5', 'ENSG00000178750_STX19', 'ENSG00000179058_C9orf50', 'ENSG00000179101_AL590139.1', 'ENSG00000179388_EGR3', 'ENSG00000179611_DGKZP1', 'ENSG00000179899_PHC1P1', 'ENSG00000179934_CCR8', 'ENSG00000180537_RNF182', 'ENSG00000180712_LINC02363', 'ENSG00000180988_OR52N2', 'ENSG00000181001_OR52N1', 'ENSG00000181616_OR52H1', 'ENSG00000181634_TNFSF15', 'ENSG00000182021_AL591379.1', 'ENSG00000182230_FAM153B', 'ENSG00000182853_VMO1', 'ENSG00000183090_FREM3', 'ENSG00000183562_AC131971.1', 'ENSG00000183615_FAM167B', 'ENSG00000183625_CCR3', 'ENSG00000183770_FOXL2', 'ENSG00000183779_ZNF703', 'ENSG00000183831_ANKRD45', 'ENSG00000183844_FAM3B', 'ENSG00000183960_KCNH8', 'ENSG00000184106_TREML3P', 'ENSG00000184227_ACOT1', 'ENSG00000184363_PKP3', 'ENSG00000184434_LRRC19', 'ENSG00000184454_NCMAP', 'ENSG00000184571_PIWIL3', 'ENSG00000184702_SEPT5', 'ENSG00000184908_CLCNKB', 'ENSG00000184923_NUTM2A', 'ENSG00000185070_FLRT2', 'ENSG00000185156_MFSD6L', 'ENSG00000185567_AHNAK2', 'ENSG00000185686_PRAME', 'ENSG00000186190_BPIFB3', 'ENSG00000186191_BPIFB4', 'ENSG00000186231_KLHL32', 'ENSG00000186431_FCAR', 'ENSG00000186715_MST1L', 'ENSG00000187116_LILRA5', 'ENSG00000187185_AC092118.1', 'ENSG00000187268_FAM9C', 'ENSG00000187554_TLR5', 'ENSG00000187867_PALM3', 'ENSG00000188153_COL4A5', 'ENSG00000188158_NHS', 'ENSG00000188163_FAM166A', 'ENSG00000188316_ENO4', 'ENSG00000188959_C9orf152', 'ENSG00000189013_KIR2DL4', 'ENSG00000189409_MMP23B', 'ENSG00000196092_PAX5', 'ENSG00000196260_SFTA2', 'ENSG00000197358_BNIP3P1', 'ENSG00000197446_CYP2F1', 'ENSG00000197540_GZMM', 'ENSG00000198049_AVPR1B', 'ENSG00000198134_AC007537.1', 'ENSG00000198156_NPIPB6', 'ENSG00000198221_AFDN-DT', 'ENSG00000198626_RYR2', 'ENSG00000198759_EGFL6', 'ENSG00000198822_GRM3', 'ENSG00000198963_RORB', 'ENSG00000199090_MIR326', 'ENSG00000199753_SNORD104', 'ENSG00000199787_RF00406', 'ENSG00000199872_RNU6-942P', 'ENSG00000200075_RF00402', 'ENSG00000200296_RNU1-83P', 'ENSG00000200683_RNU6-379P', 'ENSG00000201044_RNU6-268P', 'ENSG00000201343_RF00019', 'ENSG00000201564_RN7SKP50', 'ENSG00000201616_RNU1-91P', 'ENSG00000201737_RNU1-133P', 'ENSG00000202048_SNORD114-20', 'ENSG00000202415_RN7SKP269', 'ENSG00000203395_AC015969.1', 'ENSG00000203721_LINC00862', 'ENSG00000203727_SAMD5', 'ENSG00000203737_GPR52', 'ENSG00000203783_PRR9', 'ENSG00000203867_RBM20', 'ENSG00000203907_OOEP', 'ENSG00000203999_LINC01270', 'ENSG00000204010_IFIT1B', 'ENSG00000204044_SLC12A5-AS1', 'ENSG00000204091_TDRG1', 'ENSG00000204121_ECEL1P1', 'ENSG00000204165_CXorf65', 'ENSG00000204173_LRRC37A5P', 'ENSG00000204248_COL11A2', 'ENSG00000204424_LY6G6F', 'ENSG00000204539_CDSN', 'ENSG00000204583_LRCOL1', 'ENSG00000204677_FAM153C', 'ENSG00000204709_LINC01556', 'ENSG00000204711_C9orf135', 'ENSG00000204792_LINC01291', 'ENSG00000204850_AC011484.1', 'ENSG00000204851_PNMA8B', 'ENSG00000204909_SPINK9', 'ENSG00000205037_AC134312.1', 'ENSG00000205038_PKHD1L1', 'ENSG00000205089_CCNI2', 'ENSG00000205106_DKFZp779M0652', 'ENSG00000205364_MT1M', 'ENSG00000205502_C2CD4B', 'ENSG00000205746_AC126755.1', 'ENSG00000205856_C22orf42', 'ENSG00000206052_DOK6', 'ENSG00000206579_XKR4', 'ENSG00000206645_RF00019', 'ENSG00000206786_RNU6-701P', 'ENSG00000206846_RF00019', 'ENSG00000206848_RNU6-890P', 'ENSG00000207088_SNORA7B', 'ENSG00000207181_SNORA14B', 'ENSG00000207234_RNU6-125P', 'ENSG00000207326_RF00019', 'ENSG00000207359_RNU6-925P', 'ENSG00000211677_IGLC2', 'ENSG00000211699_TRGV3', 'ENSG00000211895_IGHA1', 'ENSG00000212385_RNU6-817P', 'ENSG00000212391_RF00554', 'ENSG00000212607_SNORA3B', 'ENSG00000212829_RPS26P3', 'ENSG00000213083_AC010731.1', 'ENSG00000213216_AC007066.1', 'ENSG00000213222_AC093724.1', 'ENSG00000213228_RPL12P38', 'ENSG00000213250_RBMS2P1', 'ENSG00000213272_RPL7AP9', 'ENSG00000213303_AC008481.1', 'ENSG00000213402_PTPRCAP', 'ENSG00000213471_TTLL13P', 'ENSG00000213588_ZBTB9', 'ENSG00000213609_RPL7AP50', 'ENSG00000213757_AC020898.1', 'ENSG00000213931_HBE1', 'ENSG00000213950_RPS10P2', 'ENSG00000213994_AL157395.1', 'ENSG00000214787_MS4A4E', 'ENSG00000214866_DCDC2C', 'ENSG00000214908_AL353678.1', 'ENSG00000214975_PPIAP29', 'ENSG00000215198_AL353795.1', 'ENSG00000215208_KRT18P60', 'ENSG00000215218_UBE2QL1', 'ENSG00000215297_AL354941.1', 'ENSG00000215464_AP000354.1', 'ENSG00000215483_LINC00598', 'ENSG00000215817_ZC3H11B', 'ENSG00000215861_AC245297.1', 'ENSG00000215910_C1orf167', 'ENSG00000216475_AL024474.1', 'ENSG00000217195_AL513475.1', 'ENSG00000217414_DDX18P3', 'ENSG00000217512_AL356776.1', 'ENSG00000218351_RPS3AP23', 'ENSG00000218418_AL591135.1', 'ENSG00000218749_AL033519.1', 'ENSG00000218766_AL450338.1', 'ENSG00000218792_HSPD1P16', 'ENSG00000219249_AMZ2P2', 'ENSG00000219395_HSPA8P15', 'ENSG00000219410_AC125494.1', 'ENSG00000219932_RPL12P8', 'ENSG00000220091_LAP3P1', 'ENSG00000220237_RPS24P12', 'ENSG00000220494_YAP1P1', 'ENSG00000221102_SNORA11B', 'ENSG00000221887_HMSD', 'ENSG00000222276_RNU2-33P', 'ENSG00000222370_SNORA36B', 'ENSG00000222421_RF00019', 'ENSG00000222431_RNU6-141P', 'ENSG00000223342_AL158817.1', 'ENSG00000223379_AL391987.3', 'ENSG00000223403_MEG9', 'ENSG00000223519_KIF28P', 'ENSG00000223576_AL355001.1', 'ENSG00000223668_EEF1A1P24', 'ENSG00000223741_PSMD4P1', 'ENSG00000223779_AC239800.1', 'ENSG00000223783_LINC01983', 'ENSG00000223784_LINP1', 'ENSG00000223855_HRAT92', 'ENSG00000223884_AC068481.1', 'ENSG00000223899_SEC13P1', 'ENSG00000224067_AL354877.1', 'ENSG00000224072_AL139811.1', 'ENSG00000224081_SLC44A3-AS1', 'ENSG00000224099_AC104823.1', 'ENSG00000224116_INHBA-AS1', 'ENSG00000224137_LINC01857', 'ENSG00000224155_AC073136.2', 'ENSG00000224321_RPL12P14', 'ENSG00000224402_OR6D1P', 'ENSG00000224479_AC104162.1', 'ENSG00000224599_BMS1P12', 'ENSG00000224689_ZNF812P', 'ENSG00000224848_AL589843.1', 'ENSG00000224908_TIMM8BP2', 'ENSG00000224957_LINC01266', 'ENSG00000224959_AC017002.1', 'ENSG00000224988_AL158207.1', 'ENSG00000224993_RPL29P12', 'ENSG00000225096_AL445250.1', 'ENSG00000225101_OR52K3P', 'ENSG00000225107_AC092484.1', 'ENSG00000225187_AC073283.1', 'ENSG00000225313_AL513327.1', 'ENSG00000225345_SNX18P3', 'ENSG00000225393_BX571846.1', 'ENSG00000225422_RBMS1P1', 'ENSG00000225423_TNPO1P1', 'ENSG00000225531_AL807761.2', 'ENSG00000225554_AL359764.1', 'ENSG00000225650_EIF2S2P5', 'ENSG00000225674_IPO7P2', 'ENSG00000225807_AC069281.1', 'ENSG00000226010_AL355852.1', 'ENSG00000226084_AC113935.1', 'ENSG00000226251_AL451060.1', 'ENSG00000226383_LINC01876', 'ENSG00000226491_FTOP1', 'ENSG00000226501_USF1P1', 'ENSG00000226545_AL357552.1', 'ENSG00000226564_FTH1P20', 'ENSG00000226617_RPL21P110', 'ENSG00000226647_AL365356.1', 'ENSG00000226800_CACTIN-AS1', 'ENSG00000226913_BSN-DT', 'ENSG00000226948_RPS4XP2', 'ENSG00000226970_AL450063.1', 'ENSG00000227006_AL136988.2', 'ENSG00000227051_C14orf132', 'ENSG00000227072_AL353706.1', 'ENSG00000227110_LMCD1-AS1', 'ENSG00000227192_AL023581.2', 'ENSG00000227198_C6orf47-AS1', 'ENSG00000227207_RPL31P12', 'ENSG00000227477_STK4-AS1', 'ENSG00000227541_SFR1P1', 'ENSG00000227590_ATP5MC1P5', 'ENSG00000227649_MTND6P32', 'ENSG00000227682_ATP5F1AP2', 'ENSG00000227740_AL513329.1', 'ENSG00000227742_CALR4P', 'ENSG00000228097_MTATP6P11', 'ENSG00000228140_AL031283.1', 'ENSG00000228175_GEMIN8P4', 'ENSG00000228212_OFD1P17', 'ENSG00000228232_GAPDHP1', 'ENSG00000228317_AL158070.1', 'ENSG00000228413_AC024937.1', 'ENSG00000228430_AL162726.3', 'ENSG00000228501_RPL15P18', 'ENSG00000228550_AC073583.1', 'ENSG00000228655_AC096558.1', 'ENSG00000228727_SAPCD1', 'ENSG00000228826_AL592494.1', 'ENSG00000228839_PIK3IP1-AS1', 'ENSG00000228863_AL121985.1', 'ENSG00000229066_AC093459.1', 'ENSG00000229150_CRYGEP', 'ENSG00000229154_KCNQ5-AS1', 'ENSG00000229163_NAP1L1P2', 'ENSG00000229236_TTTY10', 'ENSG00000229274_AL662860.1', 'ENSG00000229308_AC010737.1', 'ENSG00000229326_AC069154.1', 'ENSG00000229372_SZT2-AS1', 'ENSG00000229444_AL451062.1', 'ENSG00000229567_AL139421.1', 'ENSG00000229703_CR589904.1', 'ENSG00000229742_AC092809.1', 'ENSG00000229758_DYNLT3P2', 'ENSG00000229839_AC018462.1', 'ENSG00000229847_EMX2OS', 'ENSG00000229853_AL034418.1', 'ENSG00000229918_DOCK9-AS1', 'ENSG00000229953_AL590666.2', 'ENSG00000229992_HMGB3P9', 'ENSG00000230063_AL360091.2', 'ENSG00000230064_AL772161.1', 'ENSG00000230138_AC119428.2', 'ENSG00000230149_AL021707.3', 'ENSG00000230289_AL358781.2', 'ENSG00000230295_GTF2IP23', 'ENSG00000230479_AP000695.1', 'ENSG00000230508_RPL19P21', 'ENSG00000230519_HMGB1P49', 'ENSG00000230534_AL392046.1', 'ENSG00000230563_AL121757.1', 'ENSG00000230721_AL049597.1', 'ENSG00000230772_VN1R108P', 'ENSG00000230777_RPS29P5', 'ENSG00000230799_AC007279.1', 'ENSG00000230813_AL356583.3', 'ENSG00000230815_AL807757.1', 'ENSG00000230872_MFSD13B', 'ENSG00000230910_AL391807.1', 'ENSG00000230912_AL021707.4', 'ENSG00000230968_AC084149.2', 'ENSG00000230993_RPL12P15', 'ENSG00000231265_TRERNA1', 'ENSG00000231307_RPS3P2', 'ENSG00000231407_AL354732.1', 'ENSG00000231449_AC097359.1', 'ENSG00000231507_LINC01353', 'ENSG00000231531_HINT1P1', 'ENSG00000231548_OR55B1P', 'ENSG00000231731_AC010976.1', 'ENSG00000231742_LINC01273', 'ENSG00000231788_RPL31P50', 'ENSG00000231830_AC245140.1', 'ENSG00000231927_AC093734.1', 'ENSG00000231993_EP300-AS1', 'ENSG00000232027_AL671986.1', 'ENSG00000232028_AC007391.1', 'ENSG00000232065_LINC01063', 'ENSG00000232133_IMPDH1P10', 'ENSG00000232139_LINC00867', 'ENSG00000232273_FTH1P1', 'ENSG00000232333_RPS27AP2', 'ENSG00000232466_AL356133.1', 'ENSG00000232500_AP005273.1', 'ENSG00000232530_LIF-AS1', 'ENSG00000232568_RPL23AP35', 'ENSG00000232578_AC093311.1', 'ENSG00000232606_LINC01412', 'ENSG00000232654_FAM136BP', 'ENSG00000232656_IDI2-AS1', 'ENSG00000232719_AC007272.1', 'ENSG00000232803_SLCO4A1-AS1', 'ENSG00000232987_LINC01219', 'ENSG00000233025_CRYZP1', 'ENSG00000233093_LINC00892', 'ENSG00000233099_AC095030.1', 'ENSG00000233401_PRKAR1AP1', 'ENSG00000233427_AL009181.1', 'ENSG00000233540_DNM3-IT1', 'ENSG00000233674_AL451062.2', 'ENSG00000233825_AL391839.2', 'ENSG00000233862_AC016907.2', 'ENSG00000233994_GDI2P2', 'ENSG00000234026_AL157834.2', 'ENSG00000234106_SRP14P2', 'ENSG00000234145_NAP1L4P3', 'ENSG00000234174_AC016683.1', 'ENSG00000234271_Z98752.2', 'ENSG00000234425_AL138930.1', 'ENSG00000234488_AC096664.2', 'ENSG00000234630_AC245060.2', 'ENSG00000234645_YWHAEP5', 'ENSG00000234718_AC007161.1', 'ENSG00000234810_AL603840.1', 'ENSG00000235045_RPL7P8', 'ENSG00000235072_AC012074.1', 'ENSG00000235214_FAM83C-AS1', 'ENSG00000235288_AC099329.1', 'ENSG00000235376_RPEL1', 'ENSG00000235429_AC083875.1', 'ENSG00000235472_EIF4A1P7', 'ENSG00000235478_LINC01664', 'ENSG00000235531_MSC-AS1', 'ENSG00000235640_AC092646.2', 'ENSG00000235677_NPM1P26', 'ENSG00000235683_AC018442.1', 'ENSG00000235701_PCBP2P1', 'ENSG00000235740_PHACTR2-AS1', 'ENSG00000235774_AC023347.1', 'ENSG00000235802_HCFC1-AS1', 'ENSG00000235917_MTCO2P11', 'ENSG00000235958_UBOX5-AS1', 'ENSG00000236032_OR5H14', 'ENSG00000236180_AL445669.2', 'ENSG00000236254_MTND4P14', 'ENSG00000236283_AC019197.1', 'ENSG00000236290_EEF1GP7', 'ENSG00000236317_AC104333.2', 'ENSG00000236364_AL358115.1', 'ENSG00000236457_AC090617.1', 'ENSG00000236564_YWHAQP5', 'ENSG00000236671_PRKG1-AS1', 'ENSG00000236680_AL356000.1', 'ENSG00000236682_AC068282.1', 'ENSG00000236711_SMAD9-IT1', 'ENSG00000236806_RPL7AP15', 'ENSG00000236869_ZKSCAN7-AS1', 'ENSG00000236886_AC007563.2', 'ENSG00000236915_AL356270.1', 'ENSG00000236936_AL031005.1', 'ENSG00000237057_LINC02087', 'ENSG00000237101_AC092809.4', 'ENSG00000237276_ANO7L1', 'ENSG00000237317_AL022400.1', 'ENSG00000237387_AL022329.2', 'ENSG00000237618_BTBD7P2', 'ENSG00000237685_AL139039.3', 'ENSG00000237757_EEF1A1P30', 'ENSG00000237766_GGTA2P', 'ENSG00000237798_AC010894.4', 'ENSG00000238015_AC104837.2', 'ENSG00000238133_MAP3K20-AS1', 'ENSG00000238259_AC067940.1', 'ENSG00000238324_RN7SKP198', 'ENSG00000238358_AC004969.1', 'ENSG00000239219_AC008040.1', 'ENSG00000239316_RN7SL11P', 'ENSG00000239474_KLHL41', 'ENSG00000239527_RPS23P7', 'ENSG00000239642_MEIKIN', 'ENSG00000239650_GUSBP4', 'ENSG00000239686_AL158801.1', 'ENSG00000239701_AC006512.1', 'ENSG00000239705_AL354710.2', 'ENSG00000239797_RPL21P39', 'ENSG00000239830_RPS4XP22', 'ENSG00000239930_AP001625.3', 'ENSG00000240086_AC092969.1', 'ENSG00000240087_RPSAP12', 'ENSG00000240183_RN7SL297P', 'ENSG00000240219_AL512306.2', 'ENSG00000240498_CDKN2B-AS1', 'ENSG00000240809_AC026877.1', 'ENSG00000240993_RN7SL459P', 'ENSG00000241111_PRICKLE2-AS1', 'ENSG00000241135_LINC00881', 'ENSG00000241319_SETP6', 'ENSG00000241570_PAQR9-AS1', 'ENSG00000241631_RN7SL316P', 'ENSG00000241932_AC092324.1', 'ENSG00000241933_DENND6A-DT', 'ENSG00000242060_RPS3AP49', 'ENSG00000242107_LINC01100', 'ENSG00000242175_RN7SL127P', 'ENSG00000242431_AC107398.1', 'ENSG00000242551_POU5F1P6', 'ENSG00000242571_RPL21P11', 'ENSG00000242641_LINC00971', 'ENSG00000242747_AC090515.1', 'ENSG00000242992_FTH1P4', 'ENSG00000243055_GK-AS1', 'ENSG00000243498_UBA52P5', 'ENSG00000243592_RPL17P22', 'ENSG00000243709_LEFTY1', 'ENSG00000243830_AC092865.1', 'ENSG00000243836_WDR86-AS1', 'ENSG00000243961_PARAL1', 'ENSG00000244021_AC093591.1', 'ENSG00000244097_RPS4XP17', 'ENSG00000244151_AC010973.2', 'ENSG00000244183_PPIAP71', 'ENSG00000244242_IFITM10', 'ENSG00000244245_AC133134.1', 'ENSG00000244251_AC013356.1', 'ENSG00000244355_LY6G6D', 'ENSG00000244357_RN7SL145P', 'ENSG00000244476_ERVFRD-1', 'ENSG00000244482_LILRA6', 'ENSG00000244585_RPL12P33', 'ENSG00000244618_RN7SL334P', 'ENSG00000244703_CD46P1', 'ENSG00000245261_AL133375.1', 'ENSG00000245482_AC046130.1', 'ENSG00000246363_LINC02458', 'ENSG00000246863_AC012377.1', 'ENSG00000247199_AC091948.1', 'ENSG00000248121_SMURF2P1', 'ENSG00000248155_CR545473.1', 'ENSG00000248223_AC026785.2', 'ENSG00000248485_PCP4L1', 'ENSG00000248690_HAS2-AS1', 'ENSG00000248884_AC010280.2', 'ENSG00000248936_AC027607.1', 'ENSG00000249140_PRDX2P3', 'ENSG00000249363_AC011411.1', 'ENSG00000249381_LINC00500', 'ENSG00000249456_AL731577.2', 'ENSG00000249492_AC114956.3', 'ENSG00000249574_AC226118.1', 'ENSG00000249614_LINC02503', 'ENSG00000249691_AC026117.1', 'ENSG00000249695_AC026369.1', 'ENSG00000249803_AC112178.1', 'ENSG00000249825_CTD-2201I18.1', 'ENSG00000249848_AC112673.1', 'ENSG00000249850_KRT18P31', 'ENSG00000249884_RNF103-CHMP3', 'ENSG00000249978_TRGV7', 'ENSG00000250130_AC090519.1', 'ENSG00000250148_KRT8P31', 'ENSG00000250332_AC010460.3', 'ENSG00000250334_LINC00989', 'ENSG00000250539_KRT8P33', 'ENSG00000250548_LINC01303', 'ENSG00000250608_AC010210.1', 'ENSG00000250635_CXXC5-AS1', 'ENSG00000250645_AC010442.2', 'ENSG00000250733_C8orf17', 'ENSG00000250853_RNF138P1', 'ENSG00000250902_SMAD1-AS1', 'ENSG00000250950_AC093752.2', 'ENSG00000250982_GAPDHP35', 'ENSG00000251129_LINC02506', 'ENSG00000251152_AC025539.1', 'ENSG00000251250_AC091951.3', 'ENSG00000251288_AC018797.3', 'ENSG00000251468_AC135352.1', 'ENSG00000251537_AC005324.3', 'ENSG00000251538_LINC02201', 'ENSG00000251584_AC096751.2', 'ENSG00000251676_SNHG27', 'ENSG00000251916_RNU1-61P', 'ENSG00000252759_RF00019', 'ENSG00000253256_AC134043.1', 'ENSG00000253305_PCDHGB6', 'ENSG00000253394_LINC00534', 'ENSG00000253490_LINC02099', 'ENSG00000253537_PCDHGA7', 'ENSG00000253629_AP000426.1', 'ENSG00000253651_SOD1P3', 'ENSG00000253730_AC015909.2', 'ENSG00000253734_LINC01289', 'ENSG00000253767_PCDHGA8', 'ENSG00000253853_AC246817.1', 'ENSG00000253873_PCDHGA11', 'ENSG00000254028_AC083843.1', 'ENSG00000254048_AC105150.1', 'ENSG00000254054_AC087273.2', 'ENSG00000254122_PCDHGB7', 'ENSG00000254248_AC068189.1', 'ENSG00000254680_AC079329.1', 'ENSG00000254708_AL139174.1', 'ENSG00000254780_AC023232.1', 'ENSG00000254810_AP001189.3', 'ENSG00000254812_AC067930.3', 'ENSG00000254842_LINC02551', 'ENSG00000254846_AL355075.1', 'ENSG00000254862_AC100771.2', 'ENSG00000254897_AP003035.1', 'ENSG00000255002_LINC02324', 'ENSG00000255074_AC018523.1', 'ENSG00000255102_AP005436.1', 'ENSG00000255156_RNY1P9', 'ENSG00000255158_AC131934.1', 'ENSG00000255222_SETP17', 'ENSG00000255256_AL136146.2', 'ENSG00000255367_AC127526.2', 'ENSG00000255418_AC090092.1', 'ENSG00000255443_CD44-AS1', 'ENSG00000255446_AP003064.2', 'ENSG00000255479_AP001189.6', 'ENSG00000255487_AC087362.2', 'ENSG00000255867_DENND5B-AS1', 'ENSG00000255871_AC007529.1', 'ENSG00000256029_SNHG28', 'ENSG00000256571_AC079866.2', 'ENSG00000256588_AC027544.2', 'ENSG00000256712_AC134349.1', 'ENSG00000256746_AC018410.1', 'ENSG00000256813_AP000777.3', 'ENSG00000256967_AC018653.3', 'ENSG00000256968_SNRPEP2', 'ENSG00000257074_RPL29P33', 'ENSG00000257120_AL356756.1', 'ENSG00000257146_AC079905.2', 'ENSG00000257195_HNRNPA1P50', 'ENSG00000257327_AC012555.1', 'ENSG00000257345_LINC02413', 'ENSG00000257379_AC023509.1', 'ENSG00000257386_AC025257.1', 'ENSG00000257431_AC089998.1', 'ENSG00000257715_AC007298.1', 'ENSG00000257838_OTOAP1', 'ENSG00000257987_TEX49', 'ENSG00000258084_AC128707.1', 'ENSG00000258090_AC093014.1', 'ENSG00000258177_AC008149.1', 'ENSG00000258357_AC023161.2', 'ENSG00000258410_AC087386.1', 'ENSG00000258498_DIO3OS', 'ENSG00000258504_AL157871.1', 'ENSG00000258512_LINC00239', 'ENSG00000258867_LINC01146', 'ENSG00000258886_HIGD1AP17', 'ENSG00000259032_ENSAP2', 'ENSG00000259100_AL157791.1', 'ENSG00000259294_AC005096.1', 'ENSG00000259327_AC023906.3', 'ENSG00000259345_AC013652.1', 'ENSG00000259377_AC026770.1', 'ENSG00000259380_AC087473.1', 'ENSG00000259442_AC105339.3', 'ENSG00000259461_ANP32BP3', 'ENSG00000259556_AC090971.3', 'ENSG00000259569_AC013489.2', 'ENSG00000259617_AC020661.3', 'ENSG00000259684_AC084756.1', 'ENSG00000259719_LINC02284', 'ENSG00000259954_IL21R-AS1', 'ENSG00000259986_AC103876.1', 'ENSG00000260135_MMP2-AS1', 'ENSG00000260206_AC105020.2', 'ENSG00000260235_AC105020.3', 'ENSG00000260269_AC105036.3', 'ENSG00000260394_Z92544.1', 'ENSG00000260425_AL031709.1', 'ENSG00000260447_AC009065.3', 'ENSG00000260615_RPL23AP97', 'ENSG00000260871_AC093510.2', 'ENSG00000260877_AP005233.2', 'ENSG00000260979_AC022167.3', 'ENSG00000261051_AC107021.2', 'ENSG00000261113_AC009034.1', 'ENSG00000261168_AL592424.1', 'ENSG00000261253_AC137932.2', 'ENSG00000261269_AC093278.2', 'ENSG00000261552_AC109460.4', 'ENSG00000261572_AC097639.1', 'ENSG00000261602_AC092115.2', 'ENSG00000261630_AC007496.2', 'ENSG00000261644_AC007728.2', 'ENSG00000261734_AC116096.1', 'ENSG00000261773_AC244090.2', 'ENSG00000261837_AC046158.2', 'ENSG00000261838_AC092718.6', 'ENSG00000261888_AC144831.1', 'ENSG00000262061_AC129507.1', 'ENSG00000262097_LINC02185', 'ENSG00000262372_CR936218.1', 'ENSG00000262406_MMP12', 'ENSG00000262580_AC087741.1', 'ENSG00000262772_LINC01977', 'ENSG00000262833_AC016245.1', 'ENSG00000263006_ROCK1P1', 'ENSG00000263011_AC108134.4', 'ENSG00000263155_MYZAP', 'ENSG00000263393_AC011825.2', 'ENSG00000263426_RN7SL471P', 'ENSG00000263503_MAPK8IP1P2', 'ENSG00000263595_RN7SL823P', 'ENSG00000263878_DLGAP1-AS4', 'ENSG00000263940_RN7SL275P', 'ENSG00000264019_AC018521.2', 'ENSG00000264031_ABHD15-AS1', 'ENSG00000264044_AC005726.2', 'ENSG00000264070_DND1P1', 'ENSG00000264188_AC106037.1', 'ENSG00000264269_AC016866.1', 'ENSG00000264339_AP001020.1', 'ENSG00000264434_AC110603.1', 'ENSG00000264714_KIAA0895LP1', 'ENSG00000265010_AC087301.1', 'ENSG00000265073_AC010761.2', 'ENSG00000265107_GJA5', 'ENSG00000265179_AP000894.2', 'ENSG00000265218_AC103810.2', 'ENSG00000265334_AC130324.2', 'ENSG00000265439_RN7SL811P', 'ENSG00000265531_FCGR1CP', 'ENSG00000265845_AC024267.4', 'ENSG00000265907_AP000919.2', 'ENSG00000265942_RN7SL577P', 'ENSG00000266256_LINC00683', 'ENSG00000266456_AP001178.3', 'ENSG00000266733_TBC1D29', 'ENSG00000266835_GAPLINC', 'ENSG00000266844_AC093330.1', 'ENSG00000266903_AC243964.2', 'ENSG00000266944_AC005262.1', 'ENSG00000266946_MRPL37P1', 'ENSG00000266947_AC022916.1', 'ENSG00000267034_AC010980.2', 'ENSG00000267044_AC005757.1', 'ENSG00000267147_LINC01842', 'ENSG00000267175_AC105094.2', 'ENSG00000267191_AC006213.3', 'ENSG00000267275_AC020911.2', 'ENSG00000267288_AC138150.2', 'ENSG00000267313_KC6', 'ENSG00000267316_AC090409.2', 'ENSG00000267323_SLC25A1P5', 'ENSG00000267345_AC010632.1', 'ENSG00000267387_AC020931.1', 'ENSG00000267395_DM1-AS', 'ENSG00000267429_AC006116.6', 'ENSG00000267452_LINC02073', 'ENSG00000267491_AC100788.1', 'ENSG00000267529_AP005131.4', 'ENSG00000267554_AC015911.8', 'ENSG00000267601_AC022966.1', 'ENSG00000267638_AC023855.1', 'ENSG00000267665_AC021683.3', 'ENSG00000267681_AC135721.1', 'ENSG00000267703_AC020917.2', 'ENSG00000267731_AC005332.2', 'ENSG00000267733_AP005264.5', 'ENSG00000267750_RUNDC3A-AS1', 'ENSG00000267890_AC010624.2', 'ENSG00000267898_AC026803.2', 'ENSG00000267927_AC010320.1', 'ENSG00000268070_AC006539.2', 'ENSG00000268355_AC243960.3', 'ENSG00000268416_AC010329.1', 'ENSG00000268520_AC008750.5', 'ENSG00000268636_AC011495.2', 'ENSG00000268696_ZNF723', 'ENSG00000268777_AC020914.1', 'ENSG00000268849_SIGLEC22P', 'ENSG00000268903_AL627309.6', 'ENSG00000268983_AC005253.2', 'ENSG00000269019_HOMER3-AS1', 'ENSG00000269067_ZNF728', 'ENSG00000269103_RF00017', 'ENSG00000269274_AC078899.4', 'ENSG00000269288_AC092070.3', 'ENSG00000269352_PTOV1-AS2', 'ENSG00000269400_AC008734.2', 'ENSG00000269506_AC110792.2', 'ENSG00000269653_AC011479.3', 'ENSG00000269881_AC004754.1', 'ENSG00000269926_DDIT4-AS1', 'ENSG00000270048_AC068790.4', 'ENSG00000270050_AL035427.1', 'ENSG00000270503_YTHDF2P1', 'ENSG00000270706_PRMT1P1', 'ENSG00000270765_GAS2L2', 'ENSG00000270882_HIST2H4A', 'ENSG00000270906_MTND4P35', 'ENSG00000271013_LRRC37A9P', 'ENSG00000271129_AC009027.1', 'ENSG00000271259_AC010201.1', 'ENSG00000271524_BNIP3P17', 'ENSG00000271543_AC021443.1', 'ENSG00000271743_AF287957.1', 'ENSG00000271792_AC008667.4', 'ENSG00000271868_AC114810.1', 'ENSG00000271973_AC141002.1', 'ENSG00000271984_AL008726.1', 'ENSG00000271996_AC019080.4', 'ENSG00000272070_AC005618.1', 'ENSG00000272138_LINC01607', 'ENSG00000272150_NBPF25P', 'ENSG00000272265_AC034236.3', 'ENSG00000272279_AL512329.2', 'ENSG00000272473_AC006273.1', 'ENSG00000272510_AL121992.3', 'ENSG00000272582_AL031587.3', 'ENSG00000272695_GAS6-DT', 'ENSG00000272732_AC004982.1', 'ENSG00000272770_AC005696.2', 'ENSG00000272788_AP000864.1', 'ENSG00000272824_AC245100.7', 'ENSG00000272825_AL844908.1', 'ENSG00000272848_AL135910.1', 'ENSG00000272916_AC022400.6', 'ENSG00000273133_AC116651.1', 'ENSG00000273177_AC092954.2', 'ENSG00000273212_AC000068.2', 'ENSG00000273218_AC005776.2', 'ENSG00000273245_AC092653.1', 'ENSG00000273274_ZBTB8B', 'ENSG00000273312_AL121749.1', 'ENSG00000273325_AL008723.3', 'ENSG00000273369_AC096586.2', 'ENSG00000273474_AL157392.4', 'ENSG00000273599_AL731571.1', 'ENSG00000273724_AC106782.5', 'ENSG00000273870_AL138721.1', 'ENSG00000273920_AC103858.2', 'ENSG00000274023_AL360169.2', 'ENSG00000274029_AC069209.1', 'ENSG00000274114_ALOX15P1', 'ENSG00000274124_AC074029.3', 'ENSG00000274139_AC090164.2', 'ENSG00000274281_AC022929.2', 'ENSG00000274308_AC244093.1', 'ENSG00000274373_AC148476.1', 'ENSG00000274386_TMEM269', 'ENSG00000274403_AC090510.2', 'ENSG00000274570_SPDYE10P', 'ENSG00000274670_AC137590.2', 'ENSG00000274723_AC079906.1', 'ENSG00000274742_RF00017', 'ENSG00000274798_AC025166.1', 'ENSG00000274911_AL627230.2', 'ENSG00000275106_AC025594.2', 'ENSG00000275197_AC092794.2', 'ENSG00000275302_CCL4', 'ENSG00000275348_AC096861.1', 'ENSG00000275367_AC092111.1', 'ENSG00000275489_C17orf98', 'ENSG00000275527_AC100835.2', 'ENSG00000275995_AC109809.1', 'ENSG00000276070_CCL4L2', 'ENSG00000276255_AL136379.1', 'ENSG00000276282_AC022960.2', 'ENSG00000276547_PCDHGB5', 'ENSG00000276704_AL442067.2', 'ENSG00000276952_AL121772.3', 'ENSG00000276984_AL023881.1', 'ENSG00000276997_AL513314.2', 'ENSG00000277117_FP565260.3', 'ENSG00000277152_AC110048.2', 'ENSG00000277186_AC131212.1', 'ENSG00000277229_AC084781.1', 'ENSG00000277496_AL357033.4', 'ENSG00000277504_AC010536.3', 'ENSG00000277531_PNMA8C', 'ENSG00000278041_AL133325.3', 'ENSG00000278344_AC063943.1', 'ENSG00000278467_AC138393.3', 'ENSG00000278513_AC091046.2', 'ENSG00000278621_AC037198.2', 'ENSG00000278713_AC120114.2', 'ENSG00000278716_AC133540.1', 'ENSG00000278746_RN7SL660P', 'ENSG00000278774_RF00004', 'ENSG00000279091_AC026523.2', 'ENSG00000279130_AC091925.1', 'ENSG00000279141_LINC01451', 'ENSG00000279161_AC093503.3', 'ENSG00000279187_AC027601.5', 'ENSG00000279263_OR2L8', 'ENSG00000279315_AL158212.4', 'ENSG00000279319_AC105074.1', 'ENSG00000279332_AC090772.4', 'ENSG00000279339_AC100788.2', 'ENSG00000279365_AP000695.3', 'ENSG00000279378_AC009159.4', 'ENSG00000279384_AC080188.2', 'ENSG00000279404_AC008739.5', 'ENSG00000279417_AC019322.4', 'ENSG00000279444_AC135584.1', 'ENSG00000279486_OR2AG1', 'ENSG00000279530_AC092881.1', 'ENSG00000279590_AC005786.4', 'ENSG00000279619_AC020907.5', 'ENSG00000279633_AL137918.1', 'ENSG00000279636_LINC00216', 'ENSG00000279672_AP006621.5', 'ENSG00000279690_AP000280.1', 'ENSG00000279727_LINC02033', 'ENSG00000279861_AC073548.1', 'ENSG00000279913_AP001962.1', 'ENSG00000279970_AC023024.2', 'ENSG00000280055_TMEM75', 'ENSG00000280057_AL022069.2', 'ENSG00000280135_AL096816.1', 'ENSG00000280310_AC092437.1', 'ENSG00000280422_AC115284.2', 'ENSG00000280432_AP000962.2', 'ENSG00000280693_SH3PXD2A-AS1', 'ENSG00000281490_CICP14', 'ENSG00000281530_AC004461.2', 'ENSG00000281571_AC241585.2', 'ENSG00000282772_AL358790.1', 'ENSG00000282989_AP001206.1', 'ENSG00000282996_AC022021.1', 'ENSG00000283023_FRG1GP', 'ENSG00000283031_AC009242.1', 'ENSG00000283097_AL159152.1', 'ENSG00000283141_AL157832.3', 'ENSG00000283209_AC106858.1', 'ENSG00000283538_AC005972.3', 'ENSG00000284240_AC099062.1', 'ENSG00000284512_AC092718.8', 'ENSG00000284657_AL031432.5', 'ENSG00000284664_AL161756.3', 'ENSG00000284931_AC104389.5', 'ENSG00000285016_AC017002.6', 'ENSG00000285117_AC068724.4', 'ENSG00000285162_AC004593.3', 'ENSG00000285210_AL136382.1', 'ENSG00000285215_AC241377.4', 'ENSG00000285292_AC021097.2', 'ENSG00000285498_AC104389.6', 'ENSG00000285534_AL163541.1', 'ENSG00000285577_AC019127.1', 'ENSG00000285611_AC007132.1', 'ENSG00000285629_AL031847.2', 'ENSG00000285641_AL358472.6', 'ENSG00000285649_AL357079.2', 'ENSG00000285650_AL157827.2', 'ENSG00000285662_AL731733.1', 'ENSG00000285672_AL160396.2', 'ENSG00000285763_AL358777.1', 'ENSG00000285865_AC010285.3', 'ENSG00000285879_AC018628.2']\n",
    "print('Constant cols:', len(constant_cols))\n",
    "\n",
    "important_cols = ['ENSG00000135218_CD36',\n",
    " 'ENSG00000010278_CD9',\n",
    " 'ENSG00000204287_HLA-DRA',\n",
    " 'ENSG00000117091_CD48',\n",
    " 'ENSG00000004468_CD38',\n",
    " 'ENSG00000173762_CD7',\n",
    " 'ENSG00000137101_CD72',\n",
    " 'ENSG00000019582_CD74',\n",
    " 'ENSG00000169442_CD52',\n",
    " 'ENSG00000170458_CD14',\n",
    " 'ENSG00000272398_CD24',\n",
    " 'ENSG00000026508_CD44',\n",
    " 'ENSG00000114013_CD86',\n",
    " 'ENSG00000174059_CD34',\n",
    " 'ENSG00000139193_CD27',\n",
    " 'ENSG00000105383_CD33',\n",
    " 'ENSG00000085117_CD82',\n",
    " 'ENSG00000177455_CD19',\n",
    " 'ENSG00000002586_CD99',\n",
    " 'ENSG00000196126_HLA-DRB1',\n",
    " 'ENSG00000135404_CD63',\n",
    " 'ENSG00000012124_CD22',\n",
    " 'ENSG00000134061_CD180',\n",
    " 'ENSG00000105369_CD79A',\n",
    " 'ENSG00000116824_CD2',\n",
    " 'ENSG00000010610_CD4',\n",
    " 'ENSG00000139187_KLRG1',\n",
    " 'ENSG00000204592_HLA-E',\n",
    " 'ENSG00000090470_PDCD7',\n",
    " 'ENSG00000206531_CD200R1L',\n",
    "'ENSG00000166710_B2M',\n",
    " 'ENSG00000198034_RPS4X',\n",
    " 'ENSG00000188404_SELL',\n",
    " 'ENSG00000130303_BST2',\n",
    " 'ENSG00000128040_SPINK2',\n",
    " 'ENSG00000206503_HLA-A',\n",
    " 'ENSG00000108107_RPL28',\n",
    " 'ENSG00000143226_FCGR2A',\n",
    " 'ENSG00000133112_TPT1',\n",
    " 'ENSG00000166091_CMTM5',\n",
    " 'ENSG00000026025_VIM',\n",
    " 'ENSG00000205542_TMSB4X',\n",
    " 'ENSG00000109099_PMP22',\n",
    " 'ENSG00000145425_RPS3A',\n",
    " 'ENSG00000172247_C1QTNF4',\n",
    " 'ENSG00000072274_TFRC',\n",
    " 'ENSG00000234745_HLA-B',\n",
    " 'ENSG00000075340_ADD2',\n",
    " 'ENSG00000119865_CNRIP1',\n",
    " 'ENSG00000198938_MT-CO3',\n",
    " 'ENSG00000135046_ANXA1',\n",
    " 'ENSG00000235169_SMIM1',\n",
    " 'ENSG00000101200_AVP',\n",
    " 'ENSG00000167996_FTH1',\n",
    " 'ENSG00000163565_IFI16',\n",
    " 'ENSG00000117450_PRDX1',\n",
    " 'ENSG00000124570_SERPINB6',\n",
    " 'ENSG00000112077_RHAG',\n",
    " 'ENSG00000051523_CYBA',\n",
    " 'ENSG00000107130_NCS1',\n",
    " 'ENSG00000055118_KCNH2',\n",
    " 'ENSG00000029534_ANK1',\n",
    " 'ENSG00000169567_HINT1',\n",
    " 'ENSG00000142089_IFITM3',\n",
    " 'ENSG00000139278_GLIPR1',\n",
    " 'ENSG00000142227_EMP3',\n",
    " 'ENSG00000076662_ICAM3',\n",
    " 'ENSG00000143627_PKLR',\n",
    " 'ENSG00000130755_GMFG',\n",
    " 'ENSG00000160593_JAML',\n",
    " 'ENSG00000095932_SMIM24',\n",
    " 'ENSG00000197956_S100A6',\n",
    " 'ENSG00000171476_HOPX',\n",
    " 'ENSG00000116675_DNAJC6',\n",
    " 'ENSG00000100448_CTSG',\n",
    " 'ENSG00000100368_CSF2RB',\n",
    " 'ENSG00000047648_ARHGAP6',\n",
    " 'ENSG00000198918_RPL39',\n",
    " 'ENSG00000196154_S100A4',\n",
    " 'ENSG00000233968_AL157895.1',\n",
    " 'ENSG00000137642_SORL1',\n",
    " 'ENSG00000133816_MICAL2',\n",
    " 'ENSG00000130208_APOC1',\n",
    " 'ENSG00000105610_KLF1']\n",
    "print('important columns ',len(important_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "071e3c78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fece1b27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X0 shape (70988, 84) X0t shape (48663, 84)\n"
     ]
    }
   ],
   "source": [
    "metadata_df = pd.read_csv(FP_CELL_METADATA, index_col = 'cell_id')\n",
    "metadata_df = metadata_df[metadata_df.technology == \"citeseq\"]\n",
    "\n",
    "# Read train and convert to sparse matrix\n",
    "# X = pd.read_hdf(FP_CITE_TRAIN_INPUTS)\n",
    "X = pd.read_hdf(FP_CITE_TRAIN_INPUTS).drop(columns = constant_cols)\n",
    "# X = pd.read_csv(\"/gpfs/ysm/home/tl688/scrnahpc/tl688/openproblems/scGNN/outputdir/train_protein_recon.csv\",index_col=0).T.drop(columns = constant_cols)\n",
    "cell_index = X.index\n",
    "meta = metadata_df.reindex(cell_index)\n",
    "X0 = X[important_cols].values\n",
    "\n",
    "del X\n",
    "gc.collect()\n",
    "\n",
    "\n",
    "# Read test and convert to sparse matrix\n",
    "# Xt = pd.read_hdf(FP_CITE_TEST_INPUTS)\n",
    "Xt = pd.read_hdf(FP_CITE_TEST_INPUTS).drop(columns = constant_cols)\n",
    "cell_index_test = Xt.index\n",
    "meta_test = metadata_df.reindex(cell_index_test)\n",
    "X0t = Xt[important_cols].values\n",
    "\n",
    "del Xt\n",
    "gc.collect()\n",
    "\n",
    "st = StandardScaler()\n",
    "X0 = st.fit_transform(X0)\n",
    "X0t = st.transform(X0t)\n",
    "\n",
    "print(f'X0 shape {X0.shape} X0t shape {X0t.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4d27fd0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70988, 512), (48663, 512))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('./input/targets-multiome-sparse-scaled/train_Citeseq_truncated_512.pkl','rb') as f: X = pickle.load(f)\n",
    "with open('./input/targets-multiome-sparse-scaled/test_Citeseq_truncated_512.pkl','rb') as f: Xt = pickle.load(f)\n",
    "\n",
    "X.shape, Xt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f99ce09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70988, 140)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = pd.read_hdf(FP_CITE_TRAIN_TARGETS)\n",
    "Y = Y.values\n",
    "Y -= Y.mean(axis=1).reshape(-1, 1)\n",
    "Y /= Y.std(axis=1).reshape(-1, 1)\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04c81132",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_number_pca = 75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "674fd3b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70988, 159)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.hstack((X[:,:feature_number_pca],X0))\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bccb4380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>donor</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>technology</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45006fe3e4c8</th>\n",
       "      <td>2</td>\n",
       "      <td>32606</td>\n",
       "      <td>HSC</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d02759a80ba2</th>\n",
       "      <td>2</td>\n",
       "      <td>32606</td>\n",
       "      <td>HSC</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c016c6b0efa5</th>\n",
       "      <td>2</td>\n",
       "      <td>32606</td>\n",
       "      <td>EryP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ba7f733a4f75</th>\n",
       "      <td>2</td>\n",
       "      <td>32606</td>\n",
       "      <td>NeuP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbcf2443ffb2</th>\n",
       "      <td>2</td>\n",
       "      <td>32606</td>\n",
       "      <td>EryP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650ee456f0f3</th>\n",
       "      <td>4</td>\n",
       "      <td>31800</td>\n",
       "      <td>HSC</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cc506e7707f5</th>\n",
       "      <td>4</td>\n",
       "      <td>31800</td>\n",
       "      <td>EryP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a91f1b55a520</th>\n",
       "      <td>4</td>\n",
       "      <td>31800</td>\n",
       "      <td>EryP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3a9882c98205</th>\n",
       "      <td>4</td>\n",
       "      <td>31800</td>\n",
       "      <td>MasP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c91b6b2ccd3d</th>\n",
       "      <td>4</td>\n",
       "      <td>31800</td>\n",
       "      <td>EryP</td>\n",
       "      <td>citeseq</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70988 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              day  donor cell_type technology\n",
       "cell_id                                      \n",
       "45006fe3e4c8    2  32606       HSC    citeseq\n",
       "d02759a80ba2    2  32606       HSC    citeseq\n",
       "c016c6b0efa5    2  32606      EryP    citeseq\n",
       "ba7f733a4f75    2  32606      NeuP    citeseq\n",
       "fbcf2443ffb2    2  32606      EryP    citeseq\n",
       "...           ...    ...       ...        ...\n",
       "650ee456f0f3    4  31800       HSC    citeseq\n",
       "cc506e7707f5    4  31800      EryP    citeseq\n",
       "a91f1b55a520    4  31800      EryP    citeseq\n",
       "3a9882c98205    4  31800      MasP    citeseq\n",
       "c91b6b2ccd3d    4  31800      EryP    citeseq\n",
       "\n",
       "[70988 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7a768922",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR_START = 5e-3#5e-3\n",
    "BATCH_SIZE = 512\n",
    "\n",
    "def create_model():\n",
    "    \n",
    "    reg1 = 9.613e-06\n",
    "    reg2 = 1e-05\n",
    "    REG1 = tf.keras.regularizers.l2(reg1)\n",
    "    REG2 = tf.keras.regularizers.l2(reg2)\n",
    "    DROP = 0.2\n",
    "\n",
    "    activation = 'selu'\n",
    "    inputs = Input(shape =(X.shape[1],))\n",
    "\n",
    "    x0 = Dense(1024, \n",
    "              kernel_regularizer = REG1,\n",
    "              activation = activation,\n",
    "             )(inputs)\n",
    "    \n",
    "\n",
    "    x0 = Dropout(DROP)(x0)\n",
    "    \n",
    "    \n",
    "    \n",
    "    x1 = Dense(1024, \n",
    "               kernel_regularizer = REG1,\n",
    "               activation = activation,\n",
    "             )(x0)\n",
    "\n",
    "    x1 = Dropout(DROP)(x1)\n",
    "    \n",
    "    \n",
    "    \n",
    "    x2 = Dense(1024, \n",
    "               kernel_regularizer = REG1,\n",
    "               activation = activation,\n",
    "             )(x1+x0) \n",
    "    \n",
    "\n",
    "    x2 = Dropout(DROP)(x2)\n",
    "\n",
    "    \n",
    "    x3 = Dense(1024,\n",
    "               kernel_regularizer = REG1,\n",
    "               activation = activation,\n",
    "             )(x2+x1)\n",
    "    \n",
    "\n",
    "    x3 = Dropout(DROP)(x3)\n",
    "\n",
    "    x = Concatenate()([\n",
    "                x0, \n",
    "                x1, \n",
    "                x2, \n",
    "                x3\n",
    "                ])\n",
    "    \n",
    "    x = Dense(Y.shape[1], \n",
    "                kernel_regularizer = REG2,\n",
    "                activation='linear',\n",
    "                )(x)\n",
    "    \n",
    "    x_out = Dense(Y.shape[1], \n",
    "                kernel_regularizer = REG2,\n",
    "                activation=activation,\n",
    "                )(inputs)\n",
    "    \n",
    "    \n",
    "    x = x + x_out\n",
    "    \n",
    "#     mean = Dense(1, \n",
    "#                 kernel_regularizer = REG2,\n",
    "#                 activation='linear',\n",
    "#                 )(inputs)\n",
    "        \n",
    "#     var = Dense(1, \n",
    "#                 kernel_regularizer = REG2,\n",
    "#                 activation='relu',\n",
    "#                 )(inputs)\n",
    "    \n",
    "    \n",
    "#     x = (x + x_out-mean)/(var+1e-5)\n",
    "\n",
    "        \n",
    "    model = Model(inputs, x)\n",
    "    \n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "520fe364",
   "metadata": {},
   "outputs": [],
   "source": [
    "def frobenius_norm(m):\n",
    "    \"\"\"This function is to calculate the frobenius norm of the matrix of all\n",
    "    layer's weight.\n",
    "\n",
    "    Args:\n",
    "        m: is a list of weights param for each layers.\n",
    "    \"\"\"\n",
    "    total_reduce_sum = 0\n",
    "    for i in range(len(m)):\n",
    "        total_reduce_sum = total_reduce_sum + tf.math.reduce_sum(m[i]**2)\n",
    "    norm = total_reduce_sum**0.5\n",
    "    return norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6c6b6338",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We start working on feature, 80\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-06 17:47:10.230984: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-11-06 17:47:15.459497: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22291 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:1d:00.0, compute capability: 8.6\n",
      "2022-11-06 17:47:29.694298: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./submissions/model_0/assets\n",
      "model saved\n",
      "776/776 [==============================] - 1s 1ms/step\n",
      "Fold 0, correlation =  0.89265\n",
      "INFO:tensorflow:Assets written to: ./submissions/model_1/assets\n",
      "model saved\n",
      "750/750 [==============================] - 1s 1ms/step\n",
      "Fold 1, correlation =  0.89716\n",
      "INFO:tensorflow:Assets written to: ./submissions/model_2/assets\n",
      "model saved\n",
      "694/694 [==============================] - 1s 1ms/step\n",
      "Fold 2, correlation =  0.89413\n",
      "\u001b[32m\u001b[1mMean corr = 0.89465\u001b[0m\n",
      "\u001b[34m\u001b[1mOof corr   = 0.89464\u001b[0m\n",
      "Predicting with fold 0\n",
      "1521/1521 [==============================] - 2s 1ms/step\n",
      "Predicting with fold 1\n",
      "1521/1521 [==============================] - 2s 1ms/step\n",
      "Predicting with fold 2\n",
      "1521/1521 [==============================] - 2s 1ms/step\n",
      "48663\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "row_id\n",
       "0           0.094605\n",
       "1          -0.162362\n",
       "2          -0.405332\n",
       "3          -0.302582\n",
       "4           1.114355\n",
       "              ...   \n",
       "65744175    4.406590\n",
       "65744176   -0.603898\n",
       "65744177   -0.595424\n",
       "65744178    0.224828\n",
       "65744179    3.913553\n",
       "Name: target, Length: 65744180, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11min 57s, sys: 30.3 s, total: 12min 28s\n",
      "Wall time: 15min 50s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "EPOCHS = 1100\n",
    "N_SPLITS = 3\n",
    "\n",
    "feature_number_pca_list = [80]\n",
    "for feature_number_pca in feature_number_pca_list:\n",
    "    \n",
    "    with open('./input/targets-multiome-sparse-scaled/train_Citeseq_truncated_512.pkl','rb') as f: X = pickle.load(f)\n",
    "    with open('./input/targets-multiome-sparse-scaled/test_Citeseq_truncated_512.pkl','rb') as f: Xt = pickle.load(f)\n",
    "\n",
    "    X = np.hstack((X[:,:feature_number_pca],X0))\n",
    "    \n",
    "\n",
    "    print('We start working on feature,', feature_number_pca)\n",
    "    pred_train = np.zeros((Y.shape[0],Y.shape[1]))\n",
    "\n",
    "    # np.random.seed(1)\n",
    "    # tf.random.set_seed(1)\n",
    "    seed_tensorflow(1)\n",
    "    score_list = []\n",
    "    kf = GroupKFold(n_splits=N_SPLITS)\n",
    "    score_list = []\n",
    "\n",
    "    for fold, (idx_tr, idx_va) in enumerate(kf.split(X, groups=meta.donor)):\n",
    "        start_time = datetime.datetime.now()\n",
    "        model = None\n",
    "        gc.collect()\n",
    "\n",
    "        X_tr = X[idx_tr]\n",
    "        y_tr = Y[idx_tr]\n",
    "        X_va = X[idx_va]\n",
    "        y_va = Y[idx_va]\n",
    "\n",
    "        lr = ReduceLROnPlateau(\n",
    "                        monitor = \"val_loss\",\n",
    "                        factor = 0.9, \n",
    "                        patience = 4, \n",
    "                        verbose = VERBOSE)\n",
    "\n",
    "        es = EarlyStopping(\n",
    "                        monitor = \"val_loss\",\n",
    "                        patience = 200, \n",
    "                        verbose = VERBOSE,\n",
    "                        mode = \"min\", \n",
    "                        restore_best_weights = True)\n",
    "\n",
    "        model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "                        filepath = './citeseq',\n",
    "                        save_weights_only = True,\n",
    "                        monitor = 'val_loss',\n",
    "                        mode = 'min',\n",
    "                        save_best_only = True)\n",
    "\n",
    "        callbacks = [   lr, \n",
    "                        es, \n",
    "                        model_checkpoint_callback\n",
    "                        ]\n",
    "\n",
    "        model = create_model()\n",
    "\n",
    "        model.compile(\n",
    "                    optimizer = tfa.optimizers.AdaBelief(LR_START),\n",
    "                    metrics = [negative_correlation_loss],\n",
    "                    loss = negative_correlation_loss\n",
    "                     )\n",
    "        # Training\n",
    "        model.fit(\n",
    "                    X_tr,\n",
    "                    y_tr, \n",
    "                    validation_data=(\n",
    "                                    X_va,\n",
    "                                    y_va), \n",
    "                    epochs = EPOCHS,\n",
    "                    verbose = VERBOSE,\n",
    "                    batch_size = BATCH_SIZE,\n",
    "                    shuffle = True,\n",
    "                    callbacks = callbacks)\n",
    "\n",
    "        del X_tr, y_tr \n",
    "        gc.collect()\n",
    "\n",
    "        model.load_weights('./citeseq')\n",
    "        model.save(f\"./submissions/model_{fold}\")\n",
    "        print('model saved')\n",
    "\n",
    "        #  Model validation\n",
    "        y_va_pred = model.predict(X_va)\n",
    "        corrscore = correlation_score(y_va, y_va_pred)\n",
    "        pred_train[idx_va] = y_va_pred\n",
    "\n",
    "        print(f\"Fold {fold}, correlation =  {corrscore:.5f}\")\n",
    "        del X_va, y_va, y_va_pred\n",
    "        gc.collect()\n",
    "        score_list.append(corrscore)\n",
    "\n",
    "    # Show overall score\n",
    "\n",
    "    print(f\"{Fore.GREEN}{Style.BRIGHT}Mean corr = {np.array(score_list).mean():.5f}{Style.RESET_ALL}\")\n",
    "    score_total = correlation_score(Y, pred_train)\n",
    "    print(f\"{Fore.BLUE}{Style.BRIGHT}Oof corr   = {score_total:.5f}{Style.RESET_ALL}\")\n",
    "    \n",
    "    Xt = np.hstack((Xt[:,:feature_number_pca],X0t))\n",
    "    Xt.shape\n",
    "    \n",
    "    test_pred = np.zeros((len(Xt), 140), dtype=np.float32)\n",
    "    for fold in range(N_SPLITS):\n",
    "        print(f\"Predicting with fold {fold}\")\n",
    "        model = load_model(f\"./submissions/model_{fold}\",\n",
    "                           custom_objects={'negative_correlation_loss': negative_correlation_loss})\n",
    "        test_pred += model.predict(Xt)\n",
    "\n",
    "    print(len(test_pred))\n",
    "    # Copy the targets for the data leak but useless since the change in the public LB...\n",
    "    test_pred[:7476] = Y[:7476]\n",
    "    \n",
    "#     test_pred = std(test_pred)\n",
    "    \n",
    "    submission = pd.read_csv(\"./submission_ensemble_cite_standardmulti.csv\",index_col='row_id', squeeze=True)\n",
    "    submission.iloc[:len(test_pred.ravel())] = test_pred.ravel()\n",
    "    assert not submission.isna().any()\n",
    "    \n",
    "    save_path = \"./ensemblingwork_pca/\" + \"submission_pca\" + str(feature_number_pca) + \".csv\"\n",
    "    submission.to_csv(save_path)\n",
    "    display(submission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3194b88a",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv(\"./ensemblingwork_pca/\" + \"submission_pca\" + str(70) + \".csv\", index_col=0)\n",
    "for seed_set in [71,72,73,74,75,76,77,78,79,80]:\n",
    "    save_path = \"./ensemblingwork_pca/\" + \"submission_pca\" + str(seed_set) + \".csv\"\n",
    "    submission_save = pd.read_csv(save_path, index_col=0)\n",
    "    submission += submission_save\n",
    "submission = submission/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e204f817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.104065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.178599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.445865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.332840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.225791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>4.847249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>-0.664288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>-0.654967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>0.247310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>4.304908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target\n",
       "row_id            \n",
       "0         0.104065\n",
       "1        -0.178599\n",
       "2        -0.445865\n",
       "3        -0.332840\n",
       "4         1.225791\n",
       "...            ...\n",
       "65744175  4.847249\n",
       "65744176 -0.664288\n",
       "65744177 -0.654967\n",
       "65744178  0.247310\n",
       "65744179  4.304908\n",
       "\n",
       "[65744180 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e1a02ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b7ba19e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8507454b",
   "metadata": {},
   "outputs": [],
   "source": [
    "submssion_sample = pd.read_csv(\"evaluation_ids.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "84f2b977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>gene_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>c2150f55becb</td>\n",
       "      <td>CD86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>c2150f55becb</td>\n",
       "      <td>CD274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>c2150f55becb</td>\n",
       "      <td>CD270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>c2150f55becb</td>\n",
       "      <td>CD155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>c2150f55becb</td>\n",
       "      <td>CD112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>65744175</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "      <td>ENSG00000134419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>65744176</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "      <td>ENSG00000186862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>65744177</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "      <td>ENSG00000170959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>65744178</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "      <td>ENSG00000107874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>65744179</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "      <td>ENSG00000166012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            row_id       cell_id          gene_id\n",
       "0                0  c2150f55becb             CD86\n",
       "1                1  c2150f55becb            CD274\n",
       "2                2  c2150f55becb            CD270\n",
       "3                3  c2150f55becb            CD155\n",
       "4                4  c2150f55becb            CD112\n",
       "...            ...           ...              ...\n",
       "65744175  65744175  2c53aa67933d  ENSG00000134419\n",
       "65744176  65744176  2c53aa67933d  ENSG00000186862\n",
       "65744177  65744177  2c53aa67933d  ENSG00000170959\n",
       "65744178  65744178  2c53aa67933d  ENSG00000107874\n",
       "65744179  65744179  2c53aa67933d  ENSG00000166012\n",
       "\n",
       "[65744180 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submssion_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b2d093e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission[\"cell_id\"] = submssion_sample [\"cell_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "997cb272",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>cell_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.104065</td>\n",
       "      <td>c2150f55becb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.178599</td>\n",
       "      <td>c2150f55becb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.445865</td>\n",
       "      <td>c2150f55becb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.332840</td>\n",
       "      <td>c2150f55becb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.225791</td>\n",
       "      <td>c2150f55becb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>4.847249</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>-0.664288</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>-0.654967</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>0.247310</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>4.304908</td>\n",
       "      <td>2c53aa67933d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target       cell_id\n",
       "row_id                          \n",
       "0         0.104065  c2150f55becb\n",
       "1        -0.178599  c2150f55becb\n",
       "2        -0.445865  c2150f55becb\n",
       "3        -0.332840  c2150f55becb\n",
       "4         1.225791  c2150f55becb\n",
       "...            ...           ...\n",
       "65744175  4.847249  2c53aa67933d\n",
       "65744176 -0.664288  2c53aa67933d\n",
       "65744177 -0.654967  2c53aa67933d\n",
       "65744178  0.247310  2c53aa67933d\n",
       "65744179  4.304908  2c53aa67933d\n",
       "\n",
       "[65744180 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfc90498",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4a5a4a02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1050ce1339e9485e89f76b869846335d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Standardizing:   0%|          | 0/65443 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Standardize submission per cell_id\n",
    "loop_list          = submission.groupby('cell_id', sort=False)\n",
    "vals               = []\n",
    "\n",
    "for idx, g in tqdm(loop_list, desc=f'Standardizing', miniters=1000):\n",
    "    vals.append(std(g.target).values)\n",
    "\n",
    "to_save_norm = np.concatenate(vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1169f0b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09460448, -0.16236246, -0.40533193, ..., -0.59542426,\n",
       "        0.2248275 ,  3.91355303])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_save_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25a6e34c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ce6b675",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8496e77b",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['target'] = to_save_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "12060a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "del submission['cell_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "75c5ee41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.094604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.162362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.405332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.302582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.114355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>4.406590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>-0.603898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>-0.595424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>0.224828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>3.913553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target\n",
       "row_id            \n",
       "0         0.094604\n",
       "1        -0.162362\n",
       "2        -0.405332\n",
       "3        -0.302582\n",
       "4         1.114355\n",
       "...            ...\n",
       "65744175  4.406590\n",
       "65744176 -0.603898\n",
       "65744177 -0.595424\n",
       "65744178  0.224828\n",
       "65744179  3.913553\n",
       "\n",
       "[65744180 rows x 1 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4f04c790",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"submission_10_pca_combine.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "00966131",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.094604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.162362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.405332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.302582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.114355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>4.406590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>-0.603898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>-0.595424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>0.224828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>3.913553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target\n",
       "row_id            \n",
       "0         0.094604\n",
       "1        -0.162362\n",
       "2        -0.405332\n",
       "3        -0.302582\n",
       "4         1.114355\n",
       "...            ...\n",
       "65744175  4.406590\n",
       "65744176 -0.603898\n",
       "65744177 -0.595424\n",
       "65744178  0.224828\n",
       "65744179  3.913553\n",
       "\n",
       "[65744180 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b93da0d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "aac66156",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_correct = pd.read_csv(\"/ysm-gpfs/pi/zhao/tl688/openproblems/submission_10_combine.csv\", index_col=0)\n",
    "submission_wrong = pd.read_csv(\"/ysm-gpfs/pi/zhao/tl688/openproblems/submission_wrongsubmission.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "52280bc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.094604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.162362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.405332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.302582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.114355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>6.156170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>-0.852548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>-0.844325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>0.349796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>5.507162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target\n",
       "row_id            \n",
       "0         0.094604\n",
       "1        -0.162362\n",
       "2        -0.405332\n",
       "3        -0.302582\n",
       "4         1.114355\n",
       "...            ...\n",
       "65744175  6.156170\n",
       "65744176 -0.852548\n",
       "65744177 -0.844325\n",
       "65744178  0.349796\n",
       "65744179  5.507162\n",
       "\n",
       "[65744180 rows x 1 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecee999b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6edbc83a",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_ty = pd.read_csv(\"tianyu_standard_cite.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f93d00a",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_sma = pd.read_csv(\"sma_standard_cite.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fa484e05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.094604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.162362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.405332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.302582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.114355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744175</th>\n",
       "      <td>6.156170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744176</th>\n",
       "      <td>-0.852548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744177</th>\n",
       "      <td>-0.844325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744178</th>\n",
       "      <td>0.349796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65744179</th>\n",
       "      <td>5.507162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65744180 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target\n",
       "row_id            \n",
       "0         0.094604\n",
       "1        -0.162362\n",
       "2        -0.405332\n",
       "3        -0.302582\n",
       "4         1.114355\n",
       "...            ...\n",
       "65744175  6.156170\n",
       "65744176 -0.852548\n",
       "65744177 -0.844325\n",
       "65744178  0.349796\n",
       "65744179  5.507162\n",
       "\n",
       "[65744180 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_ty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f33091f",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_sma.iloc[0:6812820] = submission_ty.iloc[0:6812820]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "98475961",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_sma.to_csv(\"submission_testourcite.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf21546c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "51c9f113",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"/ysm-gpfs/pi/zhao/tl688/openproblems/input/multimodal-single-cell-as-sparse-matrix/test_cite_inputs_values.sparse.npz\"\n",
    "train = scipy.sparse.load_npz(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa99c654",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f7c9bee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sklearn.decomposition.PCA(n_components=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b37a05a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/sklearn/utils/validation.py:727: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "train_out = model.fit_transform(train.todense())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5005b0a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48663, 512)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33a9bea5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86e13cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# u, s, vT = scipy.sparse.linalg.svds(train, k=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "45f568f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_out = u.dot(np.diag(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3c96844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70988, 128)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7f814156",
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl_filename = \"test_cite_512.pkl\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ee89fa2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(pkl_filename, 'wb') as file:\n",
    "    pickle.dump(train_out, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7424001",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a44ea6f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"/ysm-gpfs/pi/zhao/tl688/openproblems/input/multimodal-single-cell-as-sparse-matrix/train_cite_inputs_values.sparse.npz\"\n",
    "train = scipy.sparse.load_npz(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "07bd0cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "u, s, vT = scipy.sparse.linalg.svds(train, k=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b6a89c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_out = u.dot(np.diag(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "22e4f7df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.3822762e+02,  3.1855104e+01,  2.3049294e+01, ...,\n",
       "        -1.3715878e+00, -2.8723223e+00, -1.4102858e+00],\n",
       "       [-2.3365659e+02,  4.0229019e+01,  1.9437603e+01, ...,\n",
       "         3.7163601e+00,  4.1906461e-01, -1.3358747e+00],\n",
       "       [-2.5014233e+02, -4.6703153e+00,  9.4395075e+00, ...,\n",
       "        -3.6248217e+00,  2.4677904e+00,  3.7641099e-01],\n",
       "       ...,\n",
       "       [-2.6739429e+02, -1.4522221e+01,  8.5594721e+00, ...,\n",
       "         2.5515324e-01,  2.7128370e+00, -1.5232078e+00],\n",
       "       [-2.7380966e+02, -2.3930012e+01, -3.1388956e+01, ...,\n",
       "        -2.1867669e+00, -3.8604670e+00,  5.6192124e-01],\n",
       "       [-2.3833578e+02,  1.1786343e+01,  1.1732360e+01, ...,\n",
       "        -3.7178817e+00,  8.2002485e-01, -1.9139581e+00]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_out[:,::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bf99fb2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30.375257 , 26.91837  , 21.573349 , 18.739716 , 16.615198 ,\n",
       "       13.227979 , 11.472698 , 10.428644 ,  9.999443 ,  8.605583 ,\n",
       "        8.355967 ,  7.7108827,  7.2348576,  6.828668 ,  6.5244465,\n",
       "        6.126846 ,  5.8108683,  5.406348 ,  4.9885864,  4.825242 ,\n",
       "        4.7263103,  4.401399 ,  4.3118143,  4.1695824,  4.051741 ,\n",
       "        4.035068 ,  3.9138656,  3.8137624,  3.676365 ,  3.5272353,\n",
       "        3.5001812,  3.381876 ,  3.3740146,  3.2949069,  3.2515957,\n",
       "        3.1779337,  3.161723 ,  3.113488 ,  3.0597708,  3.027244 ,\n",
       "        2.9707816,  2.9378138,  2.9076996,  2.8964288,  2.884235 ,\n",
       "        2.8376637,  2.8027925,  2.7877994,  2.7554429,  2.7377884,\n",
       "        2.7335215,  2.716231 ,  2.694369 ,  2.6778047,  2.6538417,\n",
       "        2.6433966,  2.6243203,  2.6124542,  2.6013606,  2.5963352,\n",
       "        2.5789633,  2.5716212,  2.556418 ,  2.5486555,  2.5419617,\n",
       "        2.5316672,  2.5233636,  2.5103376,  2.5029907,  2.4968817,\n",
       "        2.495011 ,  2.486638 ,  2.4826837,  2.47716  ,  2.4732075,\n",
       "        2.4715102,  2.467796 ,  2.4634757,  2.4575574,  2.4520028,\n",
       "        2.450574 ,  2.4491582,  2.4477737,  2.4435177,  2.4406738,\n",
       "        2.4393868,  2.438908 ,  2.4378564,  2.4366877,  2.4349668,\n",
       "        2.434713 ,  2.4331183,  2.4313433,  2.4303913,  2.429515 ,\n",
       "        2.428823 ,  2.4278343,  2.4270399,  2.4268003,  2.4248066,\n",
       "        2.423944 ,  2.423137 ,  2.422512 ,  2.421782 ,  2.4214725,\n",
       "        2.419826 ,  2.4197168,  2.419273 ,  2.4183147,  2.4176948,\n",
       "        2.4172115,  2.4169266,  2.4164162,  2.4158163,  2.4154234,\n",
       "        2.4143946,  2.413669 ,  2.4125524,  2.412205 ,  2.4117517,\n",
       "        2.411379 ,  2.4103744,  2.4097683,  2.4092968,  2.4089885,\n",
       "        2.408428 ,  2.4079132,  2.4072945,  2.4068089,  2.4054167,\n",
       "        2.405225 ,  2.4043756,  2.4039738,  2.4035604,  2.4032168,\n",
       "        2.4025173,  2.4020784,  2.4017751,  2.401377 ,  2.4005709,\n",
       "        2.4002817,  2.39962  ,  2.3992543,  2.3988137,  2.398218 ,\n",
       "        2.3981566,  2.3969228,  2.3967903,  2.396304 ,  2.3954668,\n",
       "        2.395275 ,  2.3944745,  2.3942497,  2.3934486,  2.392741 ,\n",
       "        2.392645 ,  2.392178 ,  2.39167  ,  2.390984 ,  2.3906326,\n",
       "        2.389865 ,  2.3893907,  2.3890285,  2.3888736,  2.3884585,\n",
       "        2.387971 ,  2.3874798,  2.3870583,  2.3867211,  2.386524 ,\n",
       "        2.386127 ,  2.3854852,  2.3846874,  2.3840303,  2.3838162,\n",
       "        2.3835344,  2.3832414,  2.3830228,  2.3825674,  2.3823545,\n",
       "        2.3818839,  2.3813183,  2.381089 ,  2.3806367,  2.3799248,\n",
       "        2.3795507,  2.3794205,  2.3790963,  2.3783586,  2.377962 ,\n",
       "        2.377669 ,  2.3774905,  2.3771615,  2.3766518,  2.3763409,\n",
       "        2.3758535,  2.3756554,  2.3751092,  2.3748612,  2.3746457,\n",
       "        2.3743343,  2.3739758,  2.37346  ,  2.3731632,  2.372895 ,\n",
       "        2.3723009,  2.3720043,  2.3715394,  2.370839 ,  2.3706386,\n",
       "        2.3700137,  2.3697453,  2.3693924,  2.3690212,  2.3684366,\n",
       "        2.368185 ,  2.367893 ,  2.367649 ,  2.3674674,  2.3673835,\n",
       "        2.366713 ,  2.3666463,  2.3661494,  2.3659317,  2.3651714,\n",
       "        2.3649454,  2.3649366,  2.3643694,  2.363809 ,  2.3635163,\n",
       "        2.3634326,  2.3629396,  2.362445 ,  2.3621192,  2.361713 ,\n",
       "        2.3613842,  2.3613188,  2.3609848,  2.3606286,  2.3601913,\n",
       "        2.3598876,  2.3595648,  2.359322 ,  2.3588421,  2.3582516,\n",
       "        2.3578894,  2.357658 ,  2.357385 ,  2.3570666,  2.3568318,\n",
       "        2.3564994,  2.3562543,  2.3558898,  2.3555655,  2.3555455,\n",
       "        2.3548255,  2.3544383,  2.35418  ,  2.353839 ,  2.353318 ,\n",
       "        2.3531675,  2.35269  ,  2.3520544,  2.3518045,  2.3513467,\n",
       "        2.3510475,  2.3509743,  2.3506293,  2.350423 ,  2.3499842,\n",
       "        2.3495152,  2.349391 ,  2.3491702,  2.348747 ,  2.3483694,\n",
       "        2.3480453,  2.3477187,  2.3475478,  2.3471859,  2.3465352,\n",
       "        2.3463163,  2.3457763,  2.3454633,  2.3450599,  2.3448036,\n",
       "        2.3445373,  2.3444123,  2.3440952,  2.343576 ,  2.3434308,\n",
       "        2.343204 ,  2.3428483,  2.342619 ,  2.3423755,  2.3420165,\n",
       "        2.3418448,  2.341532 ,  2.341304 ,  2.3410418,  2.3403444,\n",
       "        2.3402164,  2.339941 ,  2.3397639,  2.3395867,  2.3393383,\n",
       "        2.3386912,  2.338547 ,  2.3385046,  2.337593 ,  2.33738  ,\n",
       "        2.336959 ,  2.336636 ,  2.336384 ,  2.33616  ,  2.3358595,\n",
       "        2.3357418,  2.335323 ,  2.3350546,  2.3347073,  2.3344474,\n",
       "        2.3342788,  2.334028 ,  2.33355  ,  2.333296 ,  2.3328054,\n",
       "        2.3326597,  2.3325572,  2.332356 ,  2.3321493,  2.3317425,\n",
       "        2.331467 ,  2.3311102,  2.330841 ,  2.3303444,  2.3299856,\n",
       "        2.3296988,  2.3294823,  2.3290498,  2.328796 ,  2.3283687,\n",
       "        2.3281438,  2.3278203,  2.327645 ,  2.3273365,  2.3270016,\n",
       "        2.3266082,  2.3264189,  2.3258252,  2.3256943,  2.3254929,\n",
       "        2.3253353,  2.3252048,  2.3250654,  2.324824 ,  2.3242915,\n",
       "        2.3239255,  2.3235135,  2.323422 ,  2.3225472,  2.3222716,\n",
       "        2.3221095,  2.3218615,  2.3215241,  2.3212965,  2.3212552,\n",
       "        2.3209488,  2.3208623,  2.320336 ,  2.320113 ,  2.3198535,\n",
       "        2.3195345,  2.3193572,  2.318814 ,  2.318602 ,  2.3183653,\n",
       "        2.3179412,  2.3178353,  2.3173566,  2.3171382,  2.3171089,\n",
       "        2.3168175,  2.3163517,  2.31608  ,  2.3157177,  2.3153827,\n",
       "        2.3152654,  2.3150594,  2.3147404,  2.3145986,  2.3142915,\n",
       "        2.3142016,  2.3138185,  2.3134465,  2.313239 ,  2.312975 ,\n",
       "        2.312634 ,  2.312163 ,  2.312013 ,  2.3115554,  2.3115375,\n",
       "        2.3112237,  2.311009 ,  2.310567 ,  2.310357 ,  2.3099914,\n",
       "        2.3098934,  2.3095362,  2.3092866,  2.3090894,  2.3086987,\n",
       "        2.308392 ,  2.3082366,  2.307913 ,  2.3075776,  2.3070533,\n",
       "        2.3068948,  2.3068216,  2.3065228,  2.3058906,  2.3056972,\n",
       "        2.305235 ,  2.3051374,  2.3048804,  2.3046734,  2.304429 ,\n",
       "        2.304277 ,  2.304075 ,  2.3037207,  2.303416 ,  2.303367 ,\n",
       "        2.3030112,  2.3028553,  2.3026953,  2.3023314,  2.302226 ,\n",
       "        2.301817 ,  2.3017392,  2.3010812,  2.3009892,  2.3007777,\n",
       "        2.3005013,  2.300212 ,  2.3001156,  2.3000216,  2.2994683,\n",
       "        2.2993557,  2.2991786,  2.2988381,  2.2986786,  2.2983747,\n",
       "        2.2979348,  2.2977788,  2.2974465,  2.297309 ,  2.2972214,\n",
       "        2.296949 ,  2.2966223,  2.2962353,  2.2960136,  2.2959425,\n",
       "        2.2955835,  2.2951024,  2.2949898,  2.2946599,  2.2944722,\n",
       "        2.2943382,  2.2941093,  2.2936497,  2.2935824,  2.293355 ,\n",
       "        2.2929993,  2.2926955,  2.2925227,  2.2922683,  2.292066 ,\n",
       "        2.2918358,  2.291657 ,  2.2915967,  2.2912755,  2.290965 ,\n",
       "        2.2906399,  2.2903786,  2.2903454,  2.289807 ,  2.2895253,\n",
       "        2.289465 ,  2.2891166,  2.2886167,  2.2884948,  2.2882104,\n",
       "        2.28802  ,  2.2878323,  2.2876062,  2.2872596,  2.287197 ,\n",
       "        2.2867894,  2.2864187,  2.2860606,  2.2857347,  2.2853994,\n",
       "        2.2850995,  2.2850049,  2.2849517,  2.2846265,  2.284377 ,\n",
       "        2.2843418,  2.2838101,  2.2837765,  2.2836564,  2.2832634,\n",
       "        2.2831786,  2.2827172], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(train_out[:,::-1], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bb54ab4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cccbd955",
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl_filename = \"train_cite_512.pkl\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bd67c745",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(pkl_filename, 'wb') as file:\n",
    "    pickle.dump(train_out[:,::-1], file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "056cee64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48663, 128)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "71b3ff45",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"/ysm-gpfs/pi/zhao/tl688/openproblems/input/multimodal-single-cell-as-sparse-matrix/test_cite_inputs_values.sparse.npz\"\n",
    "test = scipy.sparse.load_npz(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f4b85038",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_result = test @ np.transpose(vT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "658b3019",
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl_filename = \"test_cite_512.pkl\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "309ddfe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(pkl_filename, 'wb') as file:\n",
    "    pickle.dump(test_result[:,::-1], file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "155ad4e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([32.68644  , 25.180162 , 23.764141 , 21.73299  , 17.029184 ,\n",
       "       19.265198 , 14.848934 , 11.346035 ,  9.450535 , 12.354806 ,\n",
       "        9.460149 ,  8.3627205, 10.149748 ,  6.7108583,  5.003267 ,\n",
       "        6.4000144,  6.4030294,  5.3740826,  9.131994 ,  5.498298 ,\n",
       "        5.8245015,  4.7920375,  4.709583 ,  6.8064227,  5.04601  ,\n",
       "        4.0840592,  4.5082254,  3.9793437,  3.934653 ,  4.6338983,\n",
       "        4.4533544,  3.276511 ,  3.447385 ,  3.8420873,  3.9942055,\n",
       "        3.5782282,  4.075223 ,  3.5485573,  3.435077 ,  2.883861 ,\n",
       "        3.4538996,  2.8564909,  2.9337335,  3.0643988,  3.0189426,\n",
       "        3.3068073,  2.8263333,  2.835357 ,  2.8102505,  2.92223  ,\n",
       "        2.721527 ,  2.681071 ,  2.64365  ,  2.4874492,  2.5603101,\n",
       "        2.5583553,  2.7103102,  2.5181084,  2.416626 ,  2.3963344,\n",
       "        2.3147671,  2.4001465,  2.4201431,  2.3519557,  2.3321655,\n",
       "        2.3296359,  2.3533387,  2.285748 ,  2.2059114,  2.3186588,\n",
       "        2.238971 ,  2.1972866,  2.224758 ,  2.2879932,  2.1973608,\n",
       "        2.2174745,  2.1916008,  2.1425452,  2.1289024,  2.104885 ,\n",
       "        2.0527818,  2.170075 ,  2.1399972,  2.1103175,  2.060116 ,\n",
       "        2.143337 ,  2.0755332,  2.0279086,  2.0175142,  2.0841582,\n",
       "        2.0154216,  2.0342193,  1.9836828,  1.9981949,  2.0041559,\n",
       "        2.006249 ,  2.0074043,  2.0120347,  2.004029 ,  2.0321956,\n",
       "        1.9906472,  2.0059636,  1.98405  ,  1.9645349,  1.9800998,\n",
       "        1.9685122,  1.977833 ,  2.0123842,  1.9976474,  1.9778919,\n",
       "        1.9597725,  1.9761127,  1.9668143,  1.9828569,  1.9753643,\n",
       "        1.9548181,  2.00567  ,  1.9831817,  1.978444 ,  1.9701458,\n",
       "        1.9509451,  1.9807161,  1.9742856,  1.9721252,  1.9767767,\n",
       "        1.9818715,  2.046791 ,  1.9788344,  1.9669577,  1.959864 ,\n",
       "        1.9487511,  1.9690965,  1.9487928,  1.9460113,  1.9584321,\n",
       "        1.9582279,  1.9424658,  1.9650165,  1.9798666,  1.963574 ,\n",
       "        1.9936924,  1.9544888,  1.9389105,  1.9489894,  1.9549819,\n",
       "        1.9598652,  1.952506 ,  1.9590863,  1.9463828,  1.9566073,\n",
       "        1.9654517,  1.951099 ,  1.9473041,  1.9657346,  1.9605879,\n",
       "        1.9660648,  1.950519 ,  1.9615159,  1.9611951,  1.9518175,\n",
       "        1.9679979,  1.9528178,  1.9592704,  1.950964 ,  1.9287195,\n",
       "        1.9450325,  1.9658868,  1.9411154,  1.938111 ,  1.9380624,\n",
       "        1.9340546,  1.9380058,  1.9556946,  1.952395 ,  1.9513159,\n",
       "        1.942686 ,  1.9732612,  1.9455004,  1.9378678,  1.940876 ,\n",
       "        1.9397099,  1.9383622,  1.9304342,  1.9461972,  1.948106 ,\n",
       "        1.9452417,  1.9520673,  1.9374632,  1.9471895,  1.957811 ,\n",
       "        1.9616971,  1.9523721,  1.9343003,  1.9449114,  1.9669753,\n",
       "        1.9355116,  1.9561069,  1.9566214,  1.9472272,  1.9443799,\n",
       "        1.941839 ,  1.9467323,  1.9305248,  1.9461029,  1.9446517,\n",
       "        1.9492767,  1.9447532,  1.9619665,  1.9324212,  1.9388864,\n",
       "        1.9335656,  1.9478611,  1.9389297,  1.961744 ,  1.9684706,\n",
       "        1.938637 ,  1.9355799,  1.9398551,  1.9472632,  1.9423772,\n",
       "        1.9393821,  1.9490191,  1.9447001,  1.9559753,  1.9412496,\n",
       "        1.9547328,  1.9356761,  1.9373041,  1.9387728,  1.9323446,\n",
       "        1.9324473,  1.9550346,  1.9512675,  1.9517123,  1.9622239,\n",
       "        1.9473475,  1.9303787,  1.9450378,  1.9344363,  1.9370939,\n",
       "        1.950121 ,  1.9405705,  1.9318959,  1.9276305,  1.9422817,\n",
       "        1.9283938,  1.9385538,  1.941127 ,  1.9365662,  1.9392521,\n",
       "        1.9359024,  1.9316757,  1.9349418,  1.9444447,  1.9260589,\n",
       "        1.9325418,  1.939303 ,  1.9497266,  1.922501 ,  1.9261022,\n",
       "        1.9329042,  1.9520911,  1.9405761,  1.9416496,  1.9361292,\n",
       "        1.9258602,  1.9376653,  1.9347581,  1.9407322,  1.9337935,\n",
       "        1.933571 ,  1.933586 ,  1.940055 ,  1.9497532,  1.937218 ,\n",
       "        1.9330604,  1.9406664,  1.945356 ,  1.9385672,  1.9322044,\n",
       "        1.9315114,  1.9166931,  1.9255698,  1.9143693,  1.9354961,\n",
       "        1.9455996,  1.9239478,  1.9525262,  1.9212261,  1.9253491,\n",
       "        1.9353552,  1.9303282,  1.9465756,  1.9601703,  1.9258331,\n",
       "        1.9233861,  1.9324138,  1.9337152,  1.9233586,  1.9234226,\n",
       "        1.941998 ,  1.9208055,  1.9436985,  1.9291071,  1.918371 ,\n",
       "        1.9353881,  1.9295509,  1.9272722,  1.9401282,  1.9270505,\n",
       "        1.9274719,  1.9210758,  1.9153079,  1.9248197,  1.9355444,\n",
       "        1.9243248,  1.9244848,  1.9331987,  1.938639 ,  1.9328145,\n",
       "        1.9185008,  1.918046 ,  1.9167753,  1.9297383,  1.9293373,\n",
       "        1.9321951,  1.947792 ,  1.9350058,  1.9275169,  1.9365953,\n",
       "        1.9102901,  1.9194942,  1.9165138,  1.9261805,  1.9216117,\n",
       "        1.9356233,  1.9358937,  1.9266284,  1.9140581,  1.921874 ,\n",
       "        1.9216317,  1.9281734,  1.9242735,  1.9270228,  1.9252504,\n",
       "        1.9363676,  1.9308748,  1.9219193,  1.9240332,  1.9218014,\n",
       "        1.9377922,  1.9246007,  1.9225621,  1.9261473,  1.9247446,\n",
       "        1.9399893,  1.9152308,  1.9214375,  1.9197156,  1.9196947,\n",
       "        1.9214871,  1.9327532,  1.9257779,  1.916853 ,  1.9285854,\n",
       "        1.920805 ,  1.9194821,  1.9290426,  1.9208546,  1.9285048,\n",
       "        1.9330361,  1.9160279,  1.9215826,  1.9291664,  1.8971388,\n",
       "        1.9068558,  1.9281434,  1.9291652,  1.9063635,  1.9124936,\n",
       "        1.9168974,  1.9215983,  1.9264065,  1.9245908,  1.9276104,\n",
       "        1.9175017,  1.9492444,  1.9234184,  1.919204 ,  1.9233031,\n",
       "        1.9197172,  1.9161849,  1.9092324,  1.9182314,  1.9242824,\n",
       "        1.9224993,  1.9200243,  1.9331665,  1.9069374,  1.9106467,\n",
       "        1.9214932,  1.916918 ,  1.920233 ,  1.9144279,  1.9085658,\n",
       "        1.91517  ,  1.9160864,  1.9105046,  1.9114516,  1.9406353,\n",
       "        1.9118538,  1.9287246,  1.904719 ,  1.9197251,  1.9109873,\n",
       "        1.9238626,  1.9219205,  1.9206293,  1.9093311,  1.917933 ,\n",
       "        1.9151943,  1.9069812,  1.920931 ,  1.9098333,  1.9214963,\n",
       "        1.9148442,  1.9173393,  1.9288876,  1.9143311,  1.9127915,\n",
       "        1.9105376,  1.9043684,  1.9222382,  1.9107196,  1.9220362,\n",
       "        1.9128422,  1.9147214,  1.9237232,  1.9292247,  1.9131104,\n",
       "        1.9087955,  1.91658  ,  1.9088018,  1.9282905,  1.9211833,\n",
       "        1.901859 ,  1.9104484,  1.9136268,  1.924468 ,  1.9211687,\n",
       "        1.9098194,  1.9074017,  1.9059802,  1.912616 ,  1.9220152,\n",
       "        1.9094404,  1.9065435,  1.9122922,  1.9161415,  1.9287028,\n",
       "        1.9099585,  1.9165201,  1.9157567,  1.9051197,  1.9149715,\n",
       "        1.9237877,  1.8992928,  1.9171983,  1.9245101,  1.9121454,\n",
       "        1.9093157,  1.9122287,  1.9112773,  1.9056395,  1.9183621,\n",
       "        1.9066125,  1.9244292,  1.9041488,  1.9167244,  1.9274323,\n",
       "        1.9136541,  1.910645 ,  1.9039996,  1.9157882,  1.9120178,\n",
       "        1.9090828,  1.9111016,  1.91364  ,  1.914062 ,  1.9107215,\n",
       "        1.9168805,  1.9055905,  1.9132301,  1.903594 ,  1.9116294,\n",
       "        1.9236834,  1.9179463,  1.9054116,  1.9006292,  1.9064336,\n",
       "        1.9187837,  1.9300734,  1.9051156,  1.9165236,  1.9195591,\n",
       "        1.9348328,  1.9139359,  1.9070514,  1.8937443,  1.9094383,\n",
       "        1.9262148,  1.9010675], dtype=float32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(test_result[:,::-1], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c4c1f0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "59ecd3de",
   "metadata": {},
   "source": [
    "# Single prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb104054",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, gc, pickle, datetime, scipy.sparse,random\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from colorama import Fore, Back, Style\n",
    "\n",
    "import sklearn \n",
    "from sklearn.model_selection import GroupKFold,train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import TruncatedSVD,PCA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import seaborn as sns\n",
    "from cycler import cycler\n",
    "from IPython.display import display\n",
    "\n",
    "import scipy.sparse\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "DATA_DIR = \"./\"\n",
    "FP_CELL_METADATA = os.path.join(DATA_DIR,\"metadata.csv\")\n",
    "\n",
    "FP_CITE_TRAIN_INPUTS = os.path.join(DATA_DIR,\"train_cite_inputs.h5\")\n",
    "FP_CITE_TRAIN_TARGETS = os.path.join(DATA_DIR,\"train_cite_targets.h5\")\n",
    "FP_CITE_TEST_INPUTS = os.path.join(DATA_DIR,\"test_cite_inputs.h5\")\n",
    "\n",
    "FP_SUBMISSION = os.path.join(DATA_DIR,\"sample_submission.csv\")\n",
    "FP_EVALUATION_IDS = os.path.join(DATA_DIR,\"evaluation_ids.csv\")\n",
    "\n",
    "VERBOSE = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c02c5dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-11 00:14:21.236452: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-11 00:14:25.863028: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-11-11 00:14:25.863085: W tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-11-11 00:14:25.863113: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (c14n12.farnam.hpc.yale.internal): /proc/driver/nvidia/version does not exist\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "import tensorflow_addons as tfa\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau, LearningRateScheduler, EarlyStopping\n",
    "from tensorflow.keras.layers import Dense, Input, Concatenate, Dropout, BatchNormalization, AlphaDropout\n",
    "import keras_tuner\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "003433d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize\n",
    "def std(x):\n",
    "    empty_list = []\n",
    "    for item in x:\n",
    "        empty_list.append((item - np.mean(item)) / np.std(item))\n",
    "    return np.array(empty_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "677b203b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import anndata as ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "397fa220",
   "metadata": {},
   "outputs": [],
   "source": [
    "def seed_tensorflow(seed=1):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    tf.random.set_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c89b75e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constant cols: 1194\n",
      "important columns  84\n"
     ]
    }
   ],
   "source": [
    "# constant_cols = list(X.columns[(X == 0).all(axis=0).values]) + list(X_test.columns[(X_test == 0).all(axis=0).values])\n",
    "constant_cols = ['ENSG00000003137_CYP26B1', 'ENSG00000004848_ARX', 'ENSG00000006606_CCL26', 'ENSG00000010379_SLC6A13', 'ENSG00000010932_FMO1', 'ENSG00000017427_IGF1', 'ENSG00000022355_GABRA1', 'ENSG00000041982_TNC', 'ENSG00000060709_RIMBP2', 'ENSG00000064886_CHI3L2', 'ENSG00000065717_TLE2', 'ENSG00000067798_NAV3', 'ENSG00000069535_MAOB', 'ENSG00000073598_FNDC8', 'ENSG00000074219_TEAD2', 'ENSG00000074964_ARHGEF10L', 'ENSG00000077264_PAK3', 'ENSG00000078053_AMPH', 'ENSG00000082684_SEMA5B', 'ENSG00000083857_FAT1', 'ENSG00000084628_NKAIN1', 'ENSG00000084734_GCKR', 'ENSG00000086967_MYBPC2', 'ENSG00000087258_GNAO1', 'ENSG00000089505_CMTM1', 'ENSG00000091129_NRCAM', 'ENSG00000091986_CCDC80', 'ENSG00000092377_TBL1Y', 'ENSG00000092969_TGFB2', 'ENSG00000095397_WHRN', 'ENSG00000095970_TREM2', 'ENSG00000099715_PCDH11Y', 'ENSG00000100197_CYP2D6', 'ENSG00000100218_RSPH14', 'ENSG00000100311_PDGFB', 'ENSG00000100362_PVALB', 'ENSG00000100373_UPK3A', 'ENSG00000100625_SIX4', 'ENSG00000100867_DHRS2', 'ENSG00000100985_MMP9', 'ENSG00000101197_BIRC7', 'ENSG00000101298_SNPH', 'ENSG00000102387_TAF7L', 'ENSG00000103034_NDRG4', 'ENSG00000104059_FAM189A1', 'ENSG00000104112_SCG3', 'ENSG00000104313_EYA1', 'ENSG00000104892_KLC3', 'ENSG00000105088_OLFM2', 'ENSG00000105261_OVOL3', 'ENSG00000105290_APLP1', 'ENSG00000105507_CABP5', 'ENSG00000105642_KCNN1', 'ENSG00000105694_ELOCP28', 'ENSG00000105707_HPN', 'ENSG00000105894_PTN', 'ENSG00000106018_VIPR2', 'ENSG00000106541_AGR2', 'ENSG00000107317_PTGDS', 'ENSG00000108688_CCL7', 'ENSG00000108702_CCL1', 'ENSG00000108947_EFNB3', 'ENSG00000109193_SULT1E1', 'ENSG00000109794_FAM149A', 'ENSG00000109832_DDX25', 'ENSG00000110195_FOLR1', 'ENSG00000110375_UPK2', 'ENSG00000110436_SLC1A2', 'ENSG00000111339_ART4', 'ENSG00000111863_ADTRP', 'ENSG00000112761_WISP3', 'ENSG00000112852_PCDHB2', 'ENSG00000114251_WNT5A', 'ENSG00000114279_FGF12', 'ENSG00000114455_HHLA2', 'ENSG00000114757_PEX5L', 'ENSG00000115155_OTOF', 'ENSG00000115266_APC2', 'ENSG00000115297_TLX2', 'ENSG00000115590_IL1R2', 'ENSG00000115844_DLX2', 'ENSG00000116194_ANGPTL1', 'ENSG00000116661_FBXO2', 'ENSG00000116774_OLFML3', 'ENSG00000117322_CR2', 'ENSG00000117971_CHRNB4', 'ENSG00000118322_ATP10B', 'ENSG00000118402_ELOVL4', 'ENSG00000118520_ARG1', 'ENSG00000118946_PCDH17', 'ENSG00000118972_FGF23', 'ENSG00000119771_KLHL29', 'ENSG00000120549_KIAA1217', 'ENSG00000121316_PLBD1', 'ENSG00000121905_HPCA', 'ENSG00000122224_LY9', 'ENSG00000124194_GDAP1L1', 'ENSG00000124440_HIF3A', 'ENSG00000124657_OR2B6', 'ENSG00000125462_C1orf61', 'ENSG00000125895_TMEM74B', 'ENSG00000126838_PZP', 'ENSG00000128422_KRT17', 'ENSG00000128918_ALDH1A2', 'ENSG00000129170_CSRP3', 'ENSG00000129214_SHBG', 'ENSG00000129673_AANAT', 'ENSG00000129910_CDH15', 'ENSG00000130294_KIF1A', 'ENSG00000130307_USHBP1', 'ENSG00000130545_CRB3', 'ENSG00000131019_ULBP3', 'ENSG00000131044_TTLL9', 'ENSG00000131183_SLC34A1', 'ENSG00000131386_GALNT15', 'ENSG00000131400_NAPSA', 'ENSG00000131914_LIN28A', 'ENSG00000131941_RHPN2', 'ENSG00000131951_LRRC9', 'ENSG00000132170_PPARG', 'ENSG00000132681_ATP1A4', 'ENSG00000132958_TPTE2', 'ENSG00000133454_MYO18B', 'ENSG00000134545_KLRC1', 'ENSG00000134853_PDGFRA', 'ENSG00000135083_CCNJL', 'ENSG00000135100_HNF1A', 'ENSG00000135116_HRK', 'ENSG00000135312_HTR1B', 'ENSG00000135324_MRAP2', 'ENSG00000135436_FAM186B', 'ENSG00000135472_FAIM2', 'ENSG00000135898_GPR55', 'ENSG00000135929_CYP27A1', 'ENSG00000136002_ARHGEF4', 'ENSG00000136099_PCDH8', 'ENSG00000136274_NACAD', 'ENSG00000137078_SIT1', 'ENSG00000137142_IGFBPL1', 'ENSG00000137473_TTC29', 'ENSG00000137474_MYO7A', 'ENSG00000137491_SLCO2B1', 'ENSG00000137691_CFAP300', 'ENSG00000137731_FXYD2', 'ENSG00000137747_TMPRSS13', 'ENSG00000137878_GCOM1', 'ENSG00000138411_HECW2', 'ENSG00000138741_TRPC3', 'ENSG00000138769_CDKL2', 'ENSG00000138823_MTTP', 'ENSG00000139908_TSSK4', 'ENSG00000140832_MARVELD3', 'ENSG00000142178_SIK1', 'ENSG00000142538_PTH2', 'ENSG00000142910_TINAGL1', 'ENSG00000143217_NECTIN4', 'ENSG00000143858_SYT2', 'ENSG00000144130_NT5DC4', 'ENSG00000144214_LYG1', 'ENSG00000144290_SLC4A10', 'ENSG00000144366_GULP1', 'ENSG00000144583_MARCH4', 'ENSG00000144771_LRTM1', 'ENSG00000144891_AGTR1', 'ENSG00000145087_STXBP5L', 'ENSG00000145107_TM4SF19', 'ENSG00000146197_SCUBE3', 'ENSG00000146966_DENND2A', 'ENSG00000147082_CCNB3', 'ENSG00000147614_ATP6V0D2', 'ENSG00000147642_SYBU', 'ENSG00000147869_CER1', 'ENSG00000149403_GRIK4', 'ENSG00000149596_JPH2', 'ENSG00000150630_VEGFC', 'ENSG00000150722_PPP1R1C', 'ENSG00000151631_AKR1C6P', 'ENSG00000151704_KCNJ1', 'ENSG00000152154_TMEM178A', 'ENSG00000152292_SH2D6', 'ENSG00000152315_KCNK13', 'ENSG00000152503_TRIM36', 'ENSG00000153253_SCN3A', 'ENSG00000153902_LGI4', 'ENSG00000153930_ANKFN1', 'ENSG00000154040_CABYR', 'ENSG00000154118_JPH3', 'ENSG00000154175_ABI3BP', 'ENSG00000154645_CHODL', 'ENSG00000157060_SHCBP1L', 'ENSG00000157087_ATP2B2', 'ENSG00000157152_SYN2', 'ENSG00000157168_NRG1', 'ENSG00000157680_DGKI', 'ENSG00000158246_TENT5B', 'ENSG00000158477_CD1A', 'ENSG00000158481_CD1C', 'ENSG00000158488_CD1E', 'ENSG00000159189_C1QC', 'ENSG00000159217_IGF2BP1', 'ENSG00000160683_CXCR5', 'ENSG00000160801_PTH1R', 'ENSG00000160973_FOXH1', 'ENSG00000161594_KLHL10', 'ENSG00000162409_PRKAA2', 'ENSG00000162840_MT2P1', 'ENSG00000162873_KLHDC8A', 'ENSG00000162944_RFTN2', 'ENSG00000162949_CAPN13', 'ENSG00000163116_STPG2', 'ENSG00000163288_GABRB1', 'ENSG00000163531_NFASC', 'ENSG00000163618_CADPS', 'ENSG00000163637_PRICKLE2', 'ENSG00000163735_CXCL5', 'ENSG00000163873_GRIK3', 'ENSG00000163898_LIPH', 'ENSG00000164061_BSN', 'ENSG00000164078_MST1R', 'ENSG00000164123_C4orf45', 'ENSG00000164690_SHH', 'ENSG00000164761_TNFRSF11B', 'ENSG00000164821_DEFA4', 'ENSG00000164845_FAM86FP', 'ENSG00000164867_NOS3', 'ENSG00000166073_GPR176', 'ENSG00000166148_AVPR1A', 'ENSG00000166250_CLMP', 'ENSG00000166257_SCN3B', 'ENSG00000166268_MYRFL', 'ENSG00000166523_CLEC4E', 'ENSG00000166535_A2ML1', 'ENSG00000166819_PLIN1', 'ENSG00000166928_MS4A14', 'ENSG00000167210_LOXHD1', 'ENSG00000167306_MYO5B', 'ENSG00000167634_NLRP7', 'ENSG00000167748_KLK1', 'ENSG00000167889_MGAT5B', 'ENSG00000168140_VASN', 'ENSG00000168546_GFRA2', 'ENSG00000168646_AXIN2', 'ENSG00000168955_TM4SF20', 'ENSG00000168993_CPLX1', 'ENSG00000169075_Z99496.1', 'ENSG00000169194_IL13', 'ENSG00000169246_NPIPB3', 'ENSG00000169884_WNT10B', 'ENSG00000169900_PYDC1', 'ENSG00000170074_FAM153A', 'ENSG00000170075_GPR37L1', 'ENSG00000170289_CNGB3', 'ENSG00000170356_OR2A20P', 'ENSG00000170537_TMC7', 'ENSG00000170689_HOXB9', 'ENSG00000170827_CELP', 'ENSG00000171346_KRT15', 'ENSG00000171368_TPPP', 'ENSG00000171501_OR1N2', 'ENSG00000171532_NEUROD2', 'ENSG00000171611_PTCRA', 'ENSG00000171873_ADRA1D', 'ENSG00000171916_LGALS9C', 'ENSG00000172005_MAL', 'ENSG00000172987_HPSE2', 'ENSG00000173068_BNC2', 'ENSG00000173077_DEC1', 'ENSG00000173210_ABLIM3', 'ENSG00000173267_SNCG', 'ENSG00000173369_C1QB', 'ENSG00000173372_C1QA', 'ENSG00000173391_OLR1', 'ENSG00000173626_TRAPPC3L', 'ENSG00000173698_ADGRG2', 'ENSG00000173868_PHOSPHO1', 'ENSG00000174407_MIR1-1HG', 'ENSG00000174807_CD248', 'ENSG00000175206_NPPA', 'ENSG00000175746_C15orf54', 'ENSG00000175985_PLEKHD1', 'ENSG00000176043_AC007160.1', 'ENSG00000176399_DMRTA1', 'ENSG00000176510_OR10AC1', 'ENSG00000176697_BDNF', 'ENSG00000176826_FKBP9P1', 'ENSG00000176988_FMR1NB', 'ENSG00000177324_BEND2', 'ENSG00000177335_C8orf31', 'ENSG00000177535_OR2B11', 'ENSG00000177614_PGBD5', 'ENSG00000177707_NECTIN3', 'ENSG00000178033_CALHM5', 'ENSG00000178175_ZNF366', 'ENSG00000178462_TUBAL3', 'ENSG00000178732_GP5', 'ENSG00000178750_STX19', 'ENSG00000179058_C9orf50', 'ENSG00000179101_AL590139.1', 'ENSG00000179388_EGR3', 'ENSG00000179611_DGKZP1', 'ENSG00000179899_PHC1P1', 'ENSG00000179934_CCR8', 'ENSG00000180537_RNF182', 'ENSG00000180712_LINC02363', 'ENSG00000180988_OR52N2', 'ENSG00000181001_OR52N1', 'ENSG00000181616_OR52H1', 'ENSG00000181634_TNFSF15', 'ENSG00000182021_AL591379.1', 'ENSG00000182230_FAM153B', 'ENSG00000182853_VMO1', 'ENSG00000183090_FREM3', 'ENSG00000183562_AC131971.1', 'ENSG00000183615_FAM167B', 'ENSG00000183625_CCR3', 'ENSG00000183770_FOXL2', 'ENSG00000183779_ZNF703', 'ENSG00000183831_ANKRD45', 'ENSG00000183844_FAM3B', 'ENSG00000183960_KCNH8', 'ENSG00000184106_TREML3P', 'ENSG00000184227_ACOT1', 'ENSG00000184363_PKP3', 'ENSG00000184434_LRRC19', 'ENSG00000184454_NCMAP', 'ENSG00000184571_PIWIL3', 'ENSG00000184702_SEPT5', 'ENSG00000184908_CLCNKB', 'ENSG00000184923_NUTM2A', 'ENSG00000185070_FLRT2', 'ENSG00000185156_MFSD6L', 'ENSG00000185567_AHNAK2', 'ENSG00000185686_PRAME', 'ENSG00000186190_BPIFB3', 'ENSG00000186191_BPIFB4', 'ENSG00000186231_KLHL32', 'ENSG00000186431_FCAR', 'ENSG00000186715_MST1L', 'ENSG00000187116_LILRA5', 'ENSG00000187185_AC092118.1', 'ENSG00000187268_FAM9C', 'ENSG00000187554_TLR5', 'ENSG00000187867_PALM3', 'ENSG00000188153_COL4A5', 'ENSG00000188158_NHS', 'ENSG00000188163_FAM166A', 'ENSG00000188316_ENO4', 'ENSG00000188959_C9orf152', 'ENSG00000189013_KIR2DL4', 'ENSG00000189409_MMP23B', 'ENSG00000196092_PAX5', 'ENSG00000196260_SFTA2', 'ENSG00000197358_BNIP3P1', 'ENSG00000197446_CYP2F1', 'ENSG00000197540_GZMM', 'ENSG00000198049_AVPR1B', 'ENSG00000198134_AC007537.1', 'ENSG00000198156_NPIPB6', 'ENSG00000198221_AFDN-DT', 'ENSG00000198626_RYR2', 'ENSG00000198759_EGFL6', 'ENSG00000198822_GRM3', 'ENSG00000198963_RORB', 'ENSG00000199090_MIR326', 'ENSG00000199753_SNORD104', 'ENSG00000199787_RF00406', 'ENSG00000199872_RNU6-942P', 'ENSG00000200075_RF00402', 'ENSG00000200296_RNU1-83P', 'ENSG00000200683_RNU6-379P', 'ENSG00000201044_RNU6-268P', 'ENSG00000201343_RF00019', 'ENSG00000201564_RN7SKP50', 'ENSG00000201616_RNU1-91P', 'ENSG00000201737_RNU1-133P', 'ENSG00000202048_SNORD114-20', 'ENSG00000202415_RN7SKP269', 'ENSG00000203395_AC015969.1', 'ENSG00000203721_LINC00862', 'ENSG00000203727_SAMD5', 'ENSG00000203737_GPR52', 'ENSG00000203783_PRR9', 'ENSG00000203867_RBM20', 'ENSG00000203907_OOEP', 'ENSG00000203999_LINC01270', 'ENSG00000204010_IFIT1B', 'ENSG00000204044_SLC12A5-AS1', 'ENSG00000204091_TDRG1', 'ENSG00000204121_ECEL1P1', 'ENSG00000204165_CXorf65', 'ENSG00000204173_LRRC37A5P', 'ENSG00000204248_COL11A2', 'ENSG00000204424_LY6G6F', 'ENSG00000204539_CDSN', 'ENSG00000204583_LRCOL1', 'ENSG00000204677_FAM153C', 'ENSG00000204709_LINC01556', 'ENSG00000204711_C9orf135', 'ENSG00000204792_LINC01291', 'ENSG00000204850_AC011484.1', 'ENSG00000204851_PNMA8B', 'ENSG00000204909_SPINK9', 'ENSG00000205037_AC134312.1', 'ENSG00000205038_PKHD1L1', 'ENSG00000205089_CCNI2', 'ENSG00000205106_DKFZp779M0652', 'ENSG00000205364_MT1M', 'ENSG00000205502_C2CD4B', 'ENSG00000205746_AC126755.1', 'ENSG00000205856_C22orf42', 'ENSG00000206052_DOK6', 'ENSG00000206579_XKR4', 'ENSG00000206645_RF00019', 'ENSG00000206786_RNU6-701P', 'ENSG00000206846_RF00019', 'ENSG00000206848_RNU6-890P', 'ENSG00000207088_SNORA7B', 'ENSG00000207181_SNORA14B', 'ENSG00000207234_RNU6-125P', 'ENSG00000207326_RF00019', 'ENSG00000207359_RNU6-925P', 'ENSG00000211677_IGLC2', 'ENSG00000211699_TRGV3', 'ENSG00000211895_IGHA1', 'ENSG00000212385_RNU6-817P', 'ENSG00000212391_RF00554', 'ENSG00000212607_SNORA3B', 'ENSG00000212829_RPS26P3', 'ENSG00000213083_AC010731.1', 'ENSG00000213216_AC007066.1', 'ENSG00000213222_AC093724.1', 'ENSG00000213228_RPL12P38', 'ENSG00000213250_RBMS2P1', 'ENSG00000213272_RPL7AP9', 'ENSG00000213303_AC008481.1', 'ENSG00000213402_PTPRCAP', 'ENSG00000213471_TTLL13P', 'ENSG00000213588_ZBTB9', 'ENSG00000213609_RPL7AP50', 'ENSG00000213757_AC020898.1', 'ENSG00000213931_HBE1', 'ENSG00000213950_RPS10P2', 'ENSG00000213994_AL157395.1', 'ENSG00000214787_MS4A4E', 'ENSG00000214866_DCDC2C', 'ENSG00000214908_AL353678.1', 'ENSG00000214975_PPIAP29', 'ENSG00000215198_AL353795.1', 'ENSG00000215208_KRT18P60', 'ENSG00000215218_UBE2QL1', 'ENSG00000215297_AL354941.1', 'ENSG00000215464_AP000354.1', 'ENSG00000215483_LINC00598', 'ENSG00000215817_ZC3H11B', 'ENSG00000215861_AC245297.1', 'ENSG00000215910_C1orf167', 'ENSG00000216475_AL024474.1', 'ENSG00000217195_AL513475.1', 'ENSG00000217414_DDX18P3', 'ENSG00000217512_AL356776.1', 'ENSG00000218351_RPS3AP23', 'ENSG00000218418_AL591135.1', 'ENSG00000218749_AL033519.1', 'ENSG00000218766_AL450338.1', 'ENSG00000218792_HSPD1P16', 'ENSG00000219249_AMZ2P2', 'ENSG00000219395_HSPA8P15', 'ENSG00000219410_AC125494.1', 'ENSG00000219932_RPL12P8', 'ENSG00000220091_LAP3P1', 'ENSG00000220237_RPS24P12', 'ENSG00000220494_YAP1P1', 'ENSG00000221102_SNORA11B', 'ENSG00000221887_HMSD', 'ENSG00000222276_RNU2-33P', 'ENSG00000222370_SNORA36B', 'ENSG00000222421_RF00019', 'ENSG00000222431_RNU6-141P', 'ENSG00000223342_AL158817.1', 'ENSG00000223379_AL391987.3', 'ENSG00000223403_MEG9', 'ENSG00000223519_KIF28P', 'ENSG00000223576_AL355001.1', 'ENSG00000223668_EEF1A1P24', 'ENSG00000223741_PSMD4P1', 'ENSG00000223779_AC239800.1', 'ENSG00000223783_LINC01983', 'ENSG00000223784_LINP1', 'ENSG00000223855_HRAT92', 'ENSG00000223884_AC068481.1', 'ENSG00000223899_SEC13P1', 'ENSG00000224067_AL354877.1', 'ENSG00000224072_AL139811.1', 'ENSG00000224081_SLC44A3-AS1', 'ENSG00000224099_AC104823.1', 'ENSG00000224116_INHBA-AS1', 'ENSG00000224137_LINC01857', 'ENSG00000224155_AC073136.2', 'ENSG00000224321_RPL12P14', 'ENSG00000224402_OR6D1P', 'ENSG00000224479_AC104162.1', 'ENSG00000224599_BMS1P12', 'ENSG00000224689_ZNF812P', 'ENSG00000224848_AL589843.1', 'ENSG00000224908_TIMM8BP2', 'ENSG00000224957_LINC01266', 'ENSG00000224959_AC017002.1', 'ENSG00000224988_AL158207.1', 'ENSG00000224993_RPL29P12', 'ENSG00000225096_AL445250.1', 'ENSG00000225101_OR52K3P', 'ENSG00000225107_AC092484.1', 'ENSG00000225187_AC073283.1', 'ENSG00000225313_AL513327.1', 'ENSG00000225345_SNX18P3', 'ENSG00000225393_BX571846.1', 'ENSG00000225422_RBMS1P1', 'ENSG00000225423_TNPO1P1', 'ENSG00000225531_AL807761.2', 'ENSG00000225554_AL359764.1', 'ENSG00000225650_EIF2S2P5', 'ENSG00000225674_IPO7P2', 'ENSG00000225807_AC069281.1', 'ENSG00000226010_AL355852.1', 'ENSG00000226084_AC113935.1', 'ENSG00000226251_AL451060.1', 'ENSG00000226383_LINC01876', 'ENSG00000226491_FTOP1', 'ENSG00000226501_USF1P1', 'ENSG00000226545_AL357552.1', 'ENSG00000226564_FTH1P20', 'ENSG00000226617_RPL21P110', 'ENSG00000226647_AL365356.1', 'ENSG00000226800_CACTIN-AS1', 'ENSG00000226913_BSN-DT', 'ENSG00000226948_RPS4XP2', 'ENSG00000226970_AL450063.1', 'ENSG00000227006_AL136988.2', 'ENSG00000227051_C14orf132', 'ENSG00000227072_AL353706.1', 'ENSG00000227110_LMCD1-AS1', 'ENSG00000227192_AL023581.2', 'ENSG00000227198_C6orf47-AS1', 'ENSG00000227207_RPL31P12', 'ENSG00000227477_STK4-AS1', 'ENSG00000227541_SFR1P1', 'ENSG00000227590_ATP5MC1P5', 'ENSG00000227649_MTND6P32', 'ENSG00000227682_ATP5F1AP2', 'ENSG00000227740_AL513329.1', 'ENSG00000227742_CALR4P', 'ENSG00000228097_MTATP6P11', 'ENSG00000228140_AL031283.1', 'ENSG00000228175_GEMIN8P4', 'ENSG00000228212_OFD1P17', 'ENSG00000228232_GAPDHP1', 'ENSG00000228317_AL158070.1', 'ENSG00000228413_AC024937.1', 'ENSG00000228430_AL162726.3', 'ENSG00000228501_RPL15P18', 'ENSG00000228550_AC073583.1', 'ENSG00000228655_AC096558.1', 'ENSG00000228727_SAPCD1', 'ENSG00000228826_AL592494.1', 'ENSG00000228839_PIK3IP1-AS1', 'ENSG00000228863_AL121985.1', 'ENSG00000229066_AC093459.1', 'ENSG00000229150_CRYGEP', 'ENSG00000229154_KCNQ5-AS1', 'ENSG00000229163_NAP1L1P2', 'ENSG00000229236_TTTY10', 'ENSG00000229274_AL662860.1', 'ENSG00000229308_AC010737.1', 'ENSG00000229326_AC069154.1', 'ENSG00000229372_SZT2-AS1', 'ENSG00000229444_AL451062.1', 'ENSG00000229567_AL139421.1', 'ENSG00000229703_CR589904.1', 'ENSG00000229742_AC092809.1', 'ENSG00000229758_DYNLT3P2', 'ENSG00000229839_AC018462.1', 'ENSG00000229847_EMX2OS', 'ENSG00000229853_AL034418.1', 'ENSG00000229918_DOCK9-AS1', 'ENSG00000229953_AL590666.2', 'ENSG00000229992_HMGB3P9', 'ENSG00000230063_AL360091.2', 'ENSG00000230064_AL772161.1', 'ENSG00000230138_AC119428.2', 'ENSG00000230149_AL021707.3', 'ENSG00000230289_AL358781.2', 'ENSG00000230295_GTF2IP23', 'ENSG00000230479_AP000695.1', 'ENSG00000230508_RPL19P21', 'ENSG00000230519_HMGB1P49', 'ENSG00000230534_AL392046.1', 'ENSG00000230563_AL121757.1', 'ENSG00000230721_AL049597.1', 'ENSG00000230772_VN1R108P', 'ENSG00000230777_RPS29P5', 'ENSG00000230799_AC007279.1', 'ENSG00000230813_AL356583.3', 'ENSG00000230815_AL807757.1', 'ENSG00000230872_MFSD13B', 'ENSG00000230910_AL391807.1', 'ENSG00000230912_AL021707.4', 'ENSG00000230968_AC084149.2', 'ENSG00000230993_RPL12P15', 'ENSG00000231265_TRERNA1', 'ENSG00000231307_RPS3P2', 'ENSG00000231407_AL354732.1', 'ENSG00000231449_AC097359.1', 'ENSG00000231507_LINC01353', 'ENSG00000231531_HINT1P1', 'ENSG00000231548_OR55B1P', 'ENSG00000231731_AC010976.1', 'ENSG00000231742_LINC01273', 'ENSG00000231788_RPL31P50', 'ENSG00000231830_AC245140.1', 'ENSG00000231927_AC093734.1', 'ENSG00000231993_EP300-AS1', 'ENSG00000232027_AL671986.1', 'ENSG00000232028_AC007391.1', 'ENSG00000232065_LINC01063', 'ENSG00000232133_IMPDH1P10', 'ENSG00000232139_LINC00867', 'ENSG00000232273_FTH1P1', 'ENSG00000232333_RPS27AP2', 'ENSG00000232466_AL356133.1', 'ENSG00000232500_AP005273.1', 'ENSG00000232530_LIF-AS1', 'ENSG00000232568_RPL23AP35', 'ENSG00000232578_AC093311.1', 'ENSG00000232606_LINC01412', 'ENSG00000232654_FAM136BP', 'ENSG00000232656_IDI2-AS1', 'ENSG00000232719_AC007272.1', 'ENSG00000232803_SLCO4A1-AS1', 'ENSG00000232987_LINC01219', 'ENSG00000233025_CRYZP1', 'ENSG00000233093_LINC00892', 'ENSG00000233099_AC095030.1', 'ENSG00000233401_PRKAR1AP1', 'ENSG00000233427_AL009181.1', 'ENSG00000233540_DNM3-IT1', 'ENSG00000233674_AL451062.2', 'ENSG00000233825_AL391839.2', 'ENSG00000233862_AC016907.2', 'ENSG00000233994_GDI2P2', 'ENSG00000234026_AL157834.2', 'ENSG00000234106_SRP14P2', 'ENSG00000234145_NAP1L4P3', 'ENSG00000234174_AC016683.1', 'ENSG00000234271_Z98752.2', 'ENSG00000234425_AL138930.1', 'ENSG00000234488_AC096664.2', 'ENSG00000234630_AC245060.2', 'ENSG00000234645_YWHAEP5', 'ENSG00000234718_AC007161.1', 'ENSG00000234810_AL603840.1', 'ENSG00000235045_RPL7P8', 'ENSG00000235072_AC012074.1', 'ENSG00000235214_FAM83C-AS1', 'ENSG00000235288_AC099329.1', 'ENSG00000235376_RPEL1', 'ENSG00000235429_AC083875.1', 'ENSG00000235472_EIF4A1P7', 'ENSG00000235478_LINC01664', 'ENSG00000235531_MSC-AS1', 'ENSG00000235640_AC092646.2', 'ENSG00000235677_NPM1P26', 'ENSG00000235683_AC018442.1', 'ENSG00000235701_PCBP2P1', 'ENSG00000235740_PHACTR2-AS1', 'ENSG00000235774_AC023347.1', 'ENSG00000235802_HCFC1-AS1', 'ENSG00000235917_MTCO2P11', 'ENSG00000235958_UBOX5-AS1', 'ENSG00000236032_OR5H14', 'ENSG00000236180_AL445669.2', 'ENSG00000236254_MTND4P14', 'ENSG00000236283_AC019197.1', 'ENSG00000236290_EEF1GP7', 'ENSG00000236317_AC104333.2', 'ENSG00000236364_AL358115.1', 'ENSG00000236457_AC090617.1', 'ENSG00000236564_YWHAQP5', 'ENSG00000236671_PRKG1-AS1', 'ENSG00000236680_AL356000.1', 'ENSG00000236682_AC068282.1', 'ENSG00000236711_SMAD9-IT1', 'ENSG00000236806_RPL7AP15', 'ENSG00000236869_ZKSCAN7-AS1', 'ENSG00000236886_AC007563.2', 'ENSG00000236915_AL356270.1', 'ENSG00000236936_AL031005.1', 'ENSG00000237057_LINC02087', 'ENSG00000237101_AC092809.4', 'ENSG00000237276_ANO7L1', 'ENSG00000237317_AL022400.1', 'ENSG00000237387_AL022329.2', 'ENSG00000237618_BTBD7P2', 'ENSG00000237685_AL139039.3', 'ENSG00000237757_EEF1A1P30', 'ENSG00000237766_GGTA2P', 'ENSG00000237798_AC010894.4', 'ENSG00000238015_AC104837.2', 'ENSG00000238133_MAP3K20-AS1', 'ENSG00000238259_AC067940.1', 'ENSG00000238324_RN7SKP198', 'ENSG00000238358_AC004969.1', 'ENSG00000239219_AC008040.1', 'ENSG00000239316_RN7SL11P', 'ENSG00000239474_KLHL41', 'ENSG00000239527_RPS23P7', 'ENSG00000239642_MEIKIN', 'ENSG00000239650_GUSBP4', 'ENSG00000239686_AL158801.1', 'ENSG00000239701_AC006512.1', 'ENSG00000239705_AL354710.2', 'ENSG00000239797_RPL21P39', 'ENSG00000239830_RPS4XP22', 'ENSG00000239930_AP001625.3', 'ENSG00000240086_AC092969.1', 'ENSG00000240087_RPSAP12', 'ENSG00000240183_RN7SL297P', 'ENSG00000240219_AL512306.2', 'ENSG00000240498_CDKN2B-AS1', 'ENSG00000240809_AC026877.1', 'ENSG00000240993_RN7SL459P', 'ENSG00000241111_PRICKLE2-AS1', 'ENSG00000241135_LINC00881', 'ENSG00000241319_SETP6', 'ENSG00000241570_PAQR9-AS1', 'ENSG00000241631_RN7SL316P', 'ENSG00000241932_AC092324.1', 'ENSG00000241933_DENND6A-DT', 'ENSG00000242060_RPS3AP49', 'ENSG00000242107_LINC01100', 'ENSG00000242175_RN7SL127P', 'ENSG00000242431_AC107398.1', 'ENSG00000242551_POU5F1P6', 'ENSG00000242571_RPL21P11', 'ENSG00000242641_LINC00971', 'ENSG00000242747_AC090515.1', 'ENSG00000242992_FTH1P4', 'ENSG00000243055_GK-AS1', 'ENSG00000243498_UBA52P5', 'ENSG00000243592_RPL17P22', 'ENSG00000243709_LEFTY1', 'ENSG00000243830_AC092865.1', 'ENSG00000243836_WDR86-AS1', 'ENSG00000243961_PARAL1', 'ENSG00000244021_AC093591.1', 'ENSG00000244097_RPS4XP17', 'ENSG00000244151_AC010973.2', 'ENSG00000244183_PPIAP71', 'ENSG00000244242_IFITM10', 'ENSG00000244245_AC133134.1', 'ENSG00000244251_AC013356.1', 'ENSG00000244355_LY6G6D', 'ENSG00000244357_RN7SL145P', 'ENSG00000244476_ERVFRD-1', 'ENSG00000244482_LILRA6', 'ENSG00000244585_RPL12P33', 'ENSG00000244618_RN7SL334P', 'ENSG00000244703_CD46P1', 'ENSG00000245261_AL133375.1', 'ENSG00000245482_AC046130.1', 'ENSG00000246363_LINC02458', 'ENSG00000246863_AC012377.1', 'ENSG00000247199_AC091948.1', 'ENSG00000248121_SMURF2P1', 'ENSG00000248155_CR545473.1', 'ENSG00000248223_AC026785.2', 'ENSG00000248485_PCP4L1', 'ENSG00000248690_HAS2-AS1', 'ENSG00000248884_AC010280.2', 'ENSG00000248936_AC027607.1', 'ENSG00000249140_PRDX2P3', 'ENSG00000249363_AC011411.1', 'ENSG00000249381_LINC00500', 'ENSG00000249456_AL731577.2', 'ENSG00000249492_AC114956.3', 'ENSG00000249574_AC226118.1', 'ENSG00000249614_LINC02503', 'ENSG00000249691_AC026117.1', 'ENSG00000249695_AC026369.1', 'ENSG00000249803_AC112178.1', 'ENSG00000249825_CTD-2201I18.1', 'ENSG00000249848_AC112673.1', 'ENSG00000249850_KRT18P31', 'ENSG00000249884_RNF103-CHMP3', 'ENSG00000249978_TRGV7', 'ENSG00000250130_AC090519.1', 'ENSG00000250148_KRT8P31', 'ENSG00000250332_AC010460.3', 'ENSG00000250334_LINC00989', 'ENSG00000250539_KRT8P33', 'ENSG00000250548_LINC01303', 'ENSG00000250608_AC010210.1', 'ENSG00000250635_CXXC5-AS1', 'ENSG00000250645_AC010442.2', 'ENSG00000250733_C8orf17', 'ENSG00000250853_RNF138P1', 'ENSG00000250902_SMAD1-AS1', 'ENSG00000250950_AC093752.2', 'ENSG00000250982_GAPDHP35', 'ENSG00000251129_LINC02506', 'ENSG00000251152_AC025539.1', 'ENSG00000251250_AC091951.3', 'ENSG00000251288_AC018797.3', 'ENSG00000251468_AC135352.1', 'ENSG00000251537_AC005324.3', 'ENSG00000251538_LINC02201', 'ENSG00000251584_AC096751.2', 'ENSG00000251676_SNHG27', 'ENSG00000251916_RNU1-61P', 'ENSG00000252759_RF00019', 'ENSG00000253256_AC134043.1', 'ENSG00000253305_PCDHGB6', 'ENSG00000253394_LINC00534', 'ENSG00000253490_LINC02099', 'ENSG00000253537_PCDHGA7', 'ENSG00000253629_AP000426.1', 'ENSG00000253651_SOD1P3', 'ENSG00000253730_AC015909.2', 'ENSG00000253734_LINC01289', 'ENSG00000253767_PCDHGA8', 'ENSG00000253853_AC246817.1', 'ENSG00000253873_PCDHGA11', 'ENSG00000254028_AC083843.1', 'ENSG00000254048_AC105150.1', 'ENSG00000254054_AC087273.2', 'ENSG00000254122_PCDHGB7', 'ENSG00000254248_AC068189.1', 'ENSG00000254680_AC079329.1', 'ENSG00000254708_AL139174.1', 'ENSG00000254780_AC023232.1', 'ENSG00000254810_AP001189.3', 'ENSG00000254812_AC067930.3', 'ENSG00000254842_LINC02551', 'ENSG00000254846_AL355075.1', 'ENSG00000254862_AC100771.2', 'ENSG00000254897_AP003035.1', 'ENSG00000255002_LINC02324', 'ENSG00000255074_AC018523.1', 'ENSG00000255102_AP005436.1', 'ENSG00000255156_RNY1P9', 'ENSG00000255158_AC131934.1', 'ENSG00000255222_SETP17', 'ENSG00000255256_AL136146.2', 'ENSG00000255367_AC127526.2', 'ENSG00000255418_AC090092.1', 'ENSG00000255443_CD44-AS1', 'ENSG00000255446_AP003064.2', 'ENSG00000255479_AP001189.6', 'ENSG00000255487_AC087362.2', 'ENSG00000255867_DENND5B-AS1', 'ENSG00000255871_AC007529.1', 'ENSG00000256029_SNHG28', 'ENSG00000256571_AC079866.2', 'ENSG00000256588_AC027544.2', 'ENSG00000256712_AC134349.1', 'ENSG00000256746_AC018410.1', 'ENSG00000256813_AP000777.3', 'ENSG00000256967_AC018653.3', 'ENSG00000256968_SNRPEP2', 'ENSG00000257074_RPL29P33', 'ENSG00000257120_AL356756.1', 'ENSG00000257146_AC079905.2', 'ENSG00000257195_HNRNPA1P50', 'ENSG00000257327_AC012555.1', 'ENSG00000257345_LINC02413', 'ENSG00000257379_AC023509.1', 'ENSG00000257386_AC025257.1', 'ENSG00000257431_AC089998.1', 'ENSG00000257715_AC007298.1', 'ENSG00000257838_OTOAP1', 'ENSG00000257987_TEX49', 'ENSG00000258084_AC128707.1', 'ENSG00000258090_AC093014.1', 'ENSG00000258177_AC008149.1', 'ENSG00000258357_AC023161.2', 'ENSG00000258410_AC087386.1', 'ENSG00000258498_DIO3OS', 'ENSG00000258504_AL157871.1', 'ENSG00000258512_LINC00239', 'ENSG00000258867_LINC01146', 'ENSG00000258886_HIGD1AP17', 'ENSG00000259032_ENSAP2', 'ENSG00000259100_AL157791.1', 'ENSG00000259294_AC005096.1', 'ENSG00000259327_AC023906.3', 'ENSG00000259345_AC013652.1', 'ENSG00000259377_AC026770.1', 'ENSG00000259380_AC087473.1', 'ENSG00000259442_AC105339.3', 'ENSG00000259461_ANP32BP3', 'ENSG00000259556_AC090971.3', 'ENSG00000259569_AC013489.2', 'ENSG00000259617_AC020661.3', 'ENSG00000259684_AC084756.1', 'ENSG00000259719_LINC02284', 'ENSG00000259954_IL21R-AS1', 'ENSG00000259986_AC103876.1', 'ENSG00000260135_MMP2-AS1', 'ENSG00000260206_AC105020.2', 'ENSG00000260235_AC105020.3', 'ENSG00000260269_AC105036.3', 'ENSG00000260394_Z92544.1', 'ENSG00000260425_AL031709.1', 'ENSG00000260447_AC009065.3', 'ENSG00000260615_RPL23AP97', 'ENSG00000260871_AC093510.2', 'ENSG00000260877_AP005233.2', 'ENSG00000260979_AC022167.3', 'ENSG00000261051_AC107021.2', 'ENSG00000261113_AC009034.1', 'ENSG00000261168_AL592424.1', 'ENSG00000261253_AC137932.2', 'ENSG00000261269_AC093278.2', 'ENSG00000261552_AC109460.4', 'ENSG00000261572_AC097639.1', 'ENSG00000261602_AC092115.2', 'ENSG00000261630_AC007496.2', 'ENSG00000261644_AC007728.2', 'ENSG00000261734_AC116096.1', 'ENSG00000261773_AC244090.2', 'ENSG00000261837_AC046158.2', 'ENSG00000261838_AC092718.6', 'ENSG00000261888_AC144831.1', 'ENSG00000262061_AC129507.1', 'ENSG00000262097_LINC02185', 'ENSG00000262372_CR936218.1', 'ENSG00000262406_MMP12', 'ENSG00000262580_AC087741.1', 'ENSG00000262772_LINC01977', 'ENSG00000262833_AC016245.1', 'ENSG00000263006_ROCK1P1', 'ENSG00000263011_AC108134.4', 'ENSG00000263155_MYZAP', 'ENSG00000263393_AC011825.2', 'ENSG00000263426_RN7SL471P', 'ENSG00000263503_MAPK8IP1P2', 'ENSG00000263595_RN7SL823P', 'ENSG00000263878_DLGAP1-AS4', 'ENSG00000263940_RN7SL275P', 'ENSG00000264019_AC018521.2', 'ENSG00000264031_ABHD15-AS1', 'ENSG00000264044_AC005726.2', 'ENSG00000264070_DND1P1', 'ENSG00000264188_AC106037.1', 'ENSG00000264269_AC016866.1', 'ENSG00000264339_AP001020.1', 'ENSG00000264434_AC110603.1', 'ENSG00000264714_KIAA0895LP1', 'ENSG00000265010_AC087301.1', 'ENSG00000265073_AC010761.2', 'ENSG00000265107_GJA5', 'ENSG00000265179_AP000894.2', 'ENSG00000265218_AC103810.2', 'ENSG00000265334_AC130324.2', 'ENSG00000265439_RN7SL811P', 'ENSG00000265531_FCGR1CP', 'ENSG00000265845_AC024267.4', 'ENSG00000265907_AP000919.2', 'ENSG00000265942_RN7SL577P', 'ENSG00000266256_LINC00683', 'ENSG00000266456_AP001178.3', 'ENSG00000266733_TBC1D29', 'ENSG00000266835_GAPLINC', 'ENSG00000266844_AC093330.1', 'ENSG00000266903_AC243964.2', 'ENSG00000266944_AC005262.1', 'ENSG00000266946_MRPL37P1', 'ENSG00000266947_AC022916.1', 'ENSG00000267034_AC010980.2', 'ENSG00000267044_AC005757.1', 'ENSG00000267147_LINC01842', 'ENSG00000267175_AC105094.2', 'ENSG00000267191_AC006213.3', 'ENSG00000267275_AC020911.2', 'ENSG00000267288_AC138150.2', 'ENSG00000267313_KC6', 'ENSG00000267316_AC090409.2', 'ENSG00000267323_SLC25A1P5', 'ENSG00000267345_AC010632.1', 'ENSG00000267387_AC020931.1', 'ENSG00000267395_DM1-AS', 'ENSG00000267429_AC006116.6', 'ENSG00000267452_LINC02073', 'ENSG00000267491_AC100788.1', 'ENSG00000267529_AP005131.4', 'ENSG00000267554_AC015911.8', 'ENSG00000267601_AC022966.1', 'ENSG00000267638_AC023855.1', 'ENSG00000267665_AC021683.3', 'ENSG00000267681_AC135721.1', 'ENSG00000267703_AC020917.2', 'ENSG00000267731_AC005332.2', 'ENSG00000267733_AP005264.5', 'ENSG00000267750_RUNDC3A-AS1', 'ENSG00000267890_AC010624.2', 'ENSG00000267898_AC026803.2', 'ENSG00000267927_AC010320.1', 'ENSG00000268070_AC006539.2', 'ENSG00000268355_AC243960.3', 'ENSG00000268416_AC010329.1', 'ENSG00000268520_AC008750.5', 'ENSG00000268636_AC011495.2', 'ENSG00000268696_ZNF723', 'ENSG00000268777_AC020914.1', 'ENSG00000268849_SIGLEC22P', 'ENSG00000268903_AL627309.6', 'ENSG00000268983_AC005253.2', 'ENSG00000269019_HOMER3-AS1', 'ENSG00000269067_ZNF728', 'ENSG00000269103_RF00017', 'ENSG00000269274_AC078899.4', 'ENSG00000269288_AC092070.3', 'ENSG00000269352_PTOV1-AS2', 'ENSG00000269400_AC008734.2', 'ENSG00000269506_AC110792.2', 'ENSG00000269653_AC011479.3', 'ENSG00000269881_AC004754.1', 'ENSG00000269926_DDIT4-AS1', 'ENSG00000270048_AC068790.4', 'ENSG00000270050_AL035427.1', 'ENSG00000270503_YTHDF2P1', 'ENSG00000270706_PRMT1P1', 'ENSG00000270765_GAS2L2', 'ENSG00000270882_HIST2H4A', 'ENSG00000270906_MTND4P35', 'ENSG00000271013_LRRC37A9P', 'ENSG00000271129_AC009027.1', 'ENSG00000271259_AC010201.1', 'ENSG00000271524_BNIP3P17', 'ENSG00000271543_AC021443.1', 'ENSG00000271743_AF287957.1', 'ENSG00000271792_AC008667.4', 'ENSG00000271868_AC114810.1', 'ENSG00000271973_AC141002.1', 'ENSG00000271984_AL008726.1', 'ENSG00000271996_AC019080.4', 'ENSG00000272070_AC005618.1', 'ENSG00000272138_LINC01607', 'ENSG00000272150_NBPF25P', 'ENSG00000272265_AC034236.3', 'ENSG00000272279_AL512329.2', 'ENSG00000272473_AC006273.1', 'ENSG00000272510_AL121992.3', 'ENSG00000272582_AL031587.3', 'ENSG00000272695_GAS6-DT', 'ENSG00000272732_AC004982.1', 'ENSG00000272770_AC005696.2', 'ENSG00000272788_AP000864.1', 'ENSG00000272824_AC245100.7', 'ENSG00000272825_AL844908.1', 'ENSG00000272848_AL135910.1', 'ENSG00000272916_AC022400.6', 'ENSG00000273133_AC116651.1', 'ENSG00000273177_AC092954.2', 'ENSG00000273212_AC000068.2', 'ENSG00000273218_AC005776.2', 'ENSG00000273245_AC092653.1', 'ENSG00000273274_ZBTB8B', 'ENSG00000273312_AL121749.1', 'ENSG00000273325_AL008723.3', 'ENSG00000273369_AC096586.2', 'ENSG00000273474_AL157392.4', 'ENSG00000273599_AL731571.1', 'ENSG00000273724_AC106782.5', 'ENSG00000273870_AL138721.1', 'ENSG00000273920_AC103858.2', 'ENSG00000274023_AL360169.2', 'ENSG00000274029_AC069209.1', 'ENSG00000274114_ALOX15P1', 'ENSG00000274124_AC074029.3', 'ENSG00000274139_AC090164.2', 'ENSG00000274281_AC022929.2', 'ENSG00000274308_AC244093.1', 'ENSG00000274373_AC148476.1', 'ENSG00000274386_TMEM269', 'ENSG00000274403_AC090510.2', 'ENSG00000274570_SPDYE10P', 'ENSG00000274670_AC137590.2', 'ENSG00000274723_AC079906.1', 'ENSG00000274742_RF00017', 'ENSG00000274798_AC025166.1', 'ENSG00000274911_AL627230.2', 'ENSG00000275106_AC025594.2', 'ENSG00000275197_AC092794.2', 'ENSG00000275302_CCL4', 'ENSG00000275348_AC096861.1', 'ENSG00000275367_AC092111.1', 'ENSG00000275489_C17orf98', 'ENSG00000275527_AC100835.2', 'ENSG00000275995_AC109809.1', 'ENSG00000276070_CCL4L2', 'ENSG00000276255_AL136379.1', 'ENSG00000276282_AC022960.2', 'ENSG00000276547_PCDHGB5', 'ENSG00000276704_AL442067.2', 'ENSG00000276952_AL121772.3', 'ENSG00000276984_AL023881.1', 'ENSG00000276997_AL513314.2', 'ENSG00000277117_FP565260.3', 'ENSG00000277152_AC110048.2', 'ENSG00000277186_AC131212.1', 'ENSG00000277229_AC084781.1', 'ENSG00000277496_AL357033.4', 'ENSG00000277504_AC010536.3', 'ENSG00000277531_PNMA8C', 'ENSG00000278041_AL133325.3', 'ENSG00000278344_AC063943.1', 'ENSG00000278467_AC138393.3', 'ENSG00000278513_AC091046.2', 'ENSG00000278621_AC037198.2', 'ENSG00000278713_AC120114.2', 'ENSG00000278716_AC133540.1', 'ENSG00000278746_RN7SL660P', 'ENSG00000278774_RF00004', 'ENSG00000279091_AC026523.2', 'ENSG00000279130_AC091925.1', 'ENSG00000279141_LINC01451', 'ENSG00000279161_AC093503.3', 'ENSG00000279187_AC027601.5', 'ENSG00000279263_OR2L8', 'ENSG00000279315_AL158212.4', 'ENSG00000279319_AC105074.1', 'ENSG00000279332_AC090772.4', 'ENSG00000279339_AC100788.2', 'ENSG00000279365_AP000695.3', 'ENSG00000279378_AC009159.4', 'ENSG00000279384_AC080188.2', 'ENSG00000279404_AC008739.5', 'ENSG00000279417_AC019322.4', 'ENSG00000279444_AC135584.1', 'ENSG00000279486_OR2AG1', 'ENSG00000279530_AC092881.1', 'ENSG00000279590_AC005786.4', 'ENSG00000279619_AC020907.5', 'ENSG00000279633_AL137918.1', 'ENSG00000279636_LINC00216', 'ENSG00000279672_AP006621.5', 'ENSG00000279690_AP000280.1', 'ENSG00000279727_LINC02033', 'ENSG00000279861_AC073548.1', 'ENSG00000279913_AP001962.1', 'ENSG00000279970_AC023024.2', 'ENSG00000280055_TMEM75', 'ENSG00000280057_AL022069.2', 'ENSG00000280135_AL096816.1', 'ENSG00000280310_AC092437.1', 'ENSG00000280422_AC115284.2', 'ENSG00000280432_AP000962.2', 'ENSG00000280693_SH3PXD2A-AS1', 'ENSG00000281490_CICP14', 'ENSG00000281530_AC004461.2', 'ENSG00000281571_AC241585.2', 'ENSG00000282772_AL358790.1', 'ENSG00000282989_AP001206.1', 'ENSG00000282996_AC022021.1', 'ENSG00000283023_FRG1GP', 'ENSG00000283031_AC009242.1', 'ENSG00000283097_AL159152.1', 'ENSG00000283141_AL157832.3', 'ENSG00000283209_AC106858.1', 'ENSG00000283538_AC005972.3', 'ENSG00000284240_AC099062.1', 'ENSG00000284512_AC092718.8', 'ENSG00000284657_AL031432.5', 'ENSG00000284664_AL161756.3', 'ENSG00000284931_AC104389.5', 'ENSG00000285016_AC017002.6', 'ENSG00000285117_AC068724.4', 'ENSG00000285162_AC004593.3', 'ENSG00000285210_AL136382.1', 'ENSG00000285215_AC241377.4', 'ENSG00000285292_AC021097.2', 'ENSG00000285498_AC104389.6', 'ENSG00000285534_AL163541.1', 'ENSG00000285577_AC019127.1', 'ENSG00000285611_AC007132.1', 'ENSG00000285629_AL031847.2', 'ENSG00000285641_AL358472.6', 'ENSG00000285649_AL357079.2', 'ENSG00000285650_AL157827.2', 'ENSG00000285662_AL731733.1', 'ENSG00000285672_AL160396.2', 'ENSG00000285763_AL358777.1', 'ENSG00000285865_AC010285.3', 'ENSG00000285879_AC018628.2']\n",
    "print('Constant cols:', len(constant_cols))\n",
    "\n",
    "important_cols = ['ENSG00000135218_CD36',\n",
    " 'ENSG00000010278_CD9',\n",
    " 'ENSG00000204287_HLA-DRA',\n",
    " 'ENSG00000117091_CD48',\n",
    " 'ENSG00000004468_CD38',\n",
    " 'ENSG00000173762_CD7',\n",
    " 'ENSG00000137101_CD72',\n",
    " 'ENSG00000019582_CD74',\n",
    " 'ENSG00000169442_CD52',\n",
    " 'ENSG00000170458_CD14',\n",
    " 'ENSG00000272398_CD24',\n",
    " 'ENSG00000026508_CD44',\n",
    " 'ENSG00000114013_CD86',\n",
    " 'ENSG00000174059_CD34',\n",
    " 'ENSG00000139193_CD27',\n",
    " 'ENSG00000105383_CD33',\n",
    " 'ENSG00000085117_CD82',\n",
    " 'ENSG00000177455_CD19',\n",
    " 'ENSG00000002586_CD99',\n",
    " 'ENSG00000196126_HLA-DRB1',\n",
    " 'ENSG00000135404_CD63',\n",
    " 'ENSG00000012124_CD22',\n",
    " 'ENSG00000134061_CD180',\n",
    " 'ENSG00000105369_CD79A',\n",
    " 'ENSG00000116824_CD2',\n",
    " 'ENSG00000010610_CD4',\n",
    " 'ENSG00000139187_KLRG1',\n",
    " 'ENSG00000204592_HLA-E',\n",
    " 'ENSG00000090470_PDCD7',\n",
    " 'ENSG00000206531_CD200R1L',\n",
    "'ENSG00000166710_B2M',\n",
    " 'ENSG00000198034_RPS4X',\n",
    " 'ENSG00000188404_SELL',\n",
    " 'ENSG00000130303_BST2',\n",
    " 'ENSG00000128040_SPINK2',\n",
    " 'ENSG00000206503_HLA-A',\n",
    " 'ENSG00000108107_RPL28',\n",
    " 'ENSG00000143226_FCGR2A',\n",
    " 'ENSG00000133112_TPT1',\n",
    " 'ENSG00000166091_CMTM5',\n",
    " 'ENSG00000026025_VIM',\n",
    " 'ENSG00000205542_TMSB4X',\n",
    " 'ENSG00000109099_PMP22',\n",
    " 'ENSG00000145425_RPS3A',\n",
    " 'ENSG00000172247_C1QTNF4',\n",
    " 'ENSG00000072274_TFRC',\n",
    " 'ENSG00000234745_HLA-B',\n",
    " 'ENSG00000075340_ADD2',\n",
    " 'ENSG00000119865_CNRIP1',\n",
    " 'ENSG00000198938_MT-CO3',\n",
    " 'ENSG00000135046_ANXA1',\n",
    " 'ENSG00000235169_SMIM1',\n",
    " 'ENSG00000101200_AVP',\n",
    " 'ENSG00000167996_FTH1',\n",
    " 'ENSG00000163565_IFI16',\n",
    " 'ENSG00000117450_PRDX1',\n",
    " 'ENSG00000124570_SERPINB6',\n",
    " 'ENSG00000112077_RHAG',\n",
    " 'ENSG00000051523_CYBA',\n",
    " 'ENSG00000107130_NCS1',\n",
    " 'ENSG00000055118_KCNH2',\n",
    " 'ENSG00000029534_ANK1',\n",
    " 'ENSG00000169567_HINT1',\n",
    " 'ENSG00000142089_IFITM3',\n",
    " 'ENSG00000139278_GLIPR1',\n",
    " 'ENSG00000142227_EMP3',\n",
    " 'ENSG00000076662_ICAM3',\n",
    " 'ENSG00000143627_PKLR',\n",
    " 'ENSG00000130755_GMFG',\n",
    " 'ENSG00000160593_JAML',\n",
    " 'ENSG00000095932_SMIM24',\n",
    " 'ENSG00000197956_S100A6',\n",
    " 'ENSG00000171476_HOPX',\n",
    " 'ENSG00000116675_DNAJC6',\n",
    " 'ENSG00000100448_CTSG',\n",
    " 'ENSG00000100368_CSF2RB',\n",
    " 'ENSG00000047648_ARHGAP6',\n",
    " 'ENSG00000198918_RPL39',\n",
    " 'ENSG00000196154_S100A4',\n",
    " 'ENSG00000233968_AL157895.1',\n",
    " 'ENSG00000137642_SORL1',\n",
    " 'ENSG00000133816_MICAL2',\n",
    " 'ENSG00000130208_APOC1',\n",
    " 'ENSG00000105610_KLF1']\n",
    "print('important columns ',len(important_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3bea8657",
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation_score(y_true, y_pred):\n",
    "    if type(y_true) == pd.DataFrame: y_true = y_true.values\n",
    "    if type(y_pred) == pd.DataFrame: y_pred = y_pred.values\n",
    "    corrsum = 0\n",
    "    for i in range(len(y_true)):\n",
    "        corrsum += np.corrcoef(y_true[i], y_pred[i])[1, 0]\n",
    "    return corrsum / len(y_true)\n",
    "\n",
    "def negative_correlation_loss(y_true, y_pred):\n",
    "    my = K.mean(tf.convert_to_tensor(y_pred), axis=1)\n",
    "    my = tf.tile(tf.expand_dims(my, axis=1), (1, y_true.shape[1]))\n",
    "    ym = y_pred - my\n",
    "    r_num = K.sum(tf.multiply(y_true, ym), axis=1)\n",
    "    r_den = tf.sqrt(K.sum(K.square(ym), axis=1) * float(y_true.shape[-1]))\n",
    "    r = tf.reduce_mean(r_num / r_den)\n",
    "    return - r\n",
    "\n",
    "def correlation_score_new(y_true, y_pred):\n",
    "    if type(y_true) == pd.DataFrame: y_true = y_true.values\n",
    "    if type(y_pred) == pd.DataFrame: y_pred = y_pred.values\n",
    "    return np.corrcoef(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d034a909",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba39e1ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b068807",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "472fc09f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "622daa4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_score_new(np.array([1,0,1]), np.array([2,1,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7bd3d8c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,1,1]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecc1abce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b1263abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_df = pd.read_csv(FP_CELL_METADATA, index_col = 'cell_id')\n",
    "metadata_df = metadata_df[metadata_df.technology == \"citeseq\"]\n",
    "\n",
    "# Read train and convert to sparse matrix\n",
    "# X = pd.read_hdf(FP_CITE_TRAIN_INPUTS)\n",
    "X = pd.read_hdf(FP_CITE_TRAIN_INPUTS).drop(columns = constant_cols)\n",
    "# X = pd.read_csv(\"/gpfs/ysm/home/tl688/scrnahpc/tl688/openproblems/scGNN/outputdir/train_protein_recon.csv\",index_col=0).T.drop(columns = constant_cols)\n",
    "cell_index = X.index\n",
    "meta = metadata_df.reindex(cell_index)\n",
    "\n",
    "\n",
    "# Read test and convert to sparse matrix\n",
    "# Xt = pd.read_hdf(FP_CITE_TEST_INPUTS)\n",
    "Xt = pd.read_hdf(FP_CITE_TEST_INPUTS).drop(columns = constant_cols)\n",
    "cell_index_test = Xt.index\n",
    "meta_test = metadata_df.reindex(cell_index_test)\n",
    "\n",
    "\n",
    "\n",
    "# st = StandardScaler()\n",
    "# X = st.fit_transform(X)\n",
    "# Xt = st.transform(Xt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7a6b63c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "st = StandardScaler()\n",
    "X_s = st.fit_transform(X.values)\n",
    "Xt_s = st.transform(Xt.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a553ea64",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.iloc[:,:] = X_s\n",
    "Xt.iloc[:,:] = Xt_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4cecaf11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gene_id</th>\n",
       "      <th>ENSG00000121410_A1BG</th>\n",
       "      <th>ENSG00000268895_A1BG-AS1</th>\n",
       "      <th>ENSG00000175899_A2M</th>\n",
       "      <th>ENSG00000245105_A2M-AS1</th>\n",
       "      <th>ENSG00000128274_A4GALT</th>\n",
       "      <th>ENSG00000094914_AAAS</th>\n",
       "      <th>ENSG00000081760_AACS</th>\n",
       "      <th>ENSG00000109576_AADAT</th>\n",
       "      <th>ENSG00000103591_AAGAB</th>\n",
       "      <th>ENSG00000115977_AAK1</th>\n",
       "      <th>...</th>\n",
       "      <th>ENSG00000153975_ZUP1</th>\n",
       "      <th>ENSG00000086827_ZW10</th>\n",
       "      <th>ENSG00000174442_ZWILCH</th>\n",
       "      <th>ENSG00000122952_ZWINT</th>\n",
       "      <th>ENSG00000198205_ZXDA</th>\n",
       "      <th>ENSG00000198455_ZXDB</th>\n",
       "      <th>ENSG00000070476_ZXDC</th>\n",
       "      <th>ENSG00000162378_ZYG11B</th>\n",
       "      <th>ENSG00000159840_ZYX</th>\n",
       "      <th>ENSG00000074755_ZZEF1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45006fe3e4c8</th>\n",
       "      <td>-0.15239</td>\n",
       "      <td>-0.236089</td>\n",
       "      <td>-0.066677</td>\n",
       "      <td>-0.213677</td>\n",
       "      <td>-0.162106</td>\n",
       "      <td>-0.876378</td>\n",
       "      <td>-0.827405</td>\n",
       "      <td>-0.285128</td>\n",
       "      <td>-0.697792</td>\n",
       "      <td>-1.093317</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.589172</td>\n",
       "      <td>-0.767604</td>\n",
       "      <td>-0.865788</td>\n",
       "      <td>-1.641808</td>\n",
       "      <td>-0.12657</td>\n",
       "      <td>-0.237671</td>\n",
       "      <td>-0.551475</td>\n",
       "      <td>-0.664261</td>\n",
       "      <td>0.617446</td>\n",
       "      <td>-0.551096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d02759a80ba2</th>\n",
       "      <td>-0.15239</td>\n",
       "      <td>-0.236089</td>\n",
       "      <td>-0.066677</td>\n",
       "      <td>-0.213677</td>\n",
       "      <td>-0.162106</td>\n",
       "      <td>-0.876378</td>\n",
       "      <td>-0.827405</td>\n",
       "      <td>-0.285128</td>\n",
       "      <td>-0.697792</td>\n",
       "      <td>0.822107</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.589172</td>\n",
       "      <td>-0.767604</td>\n",
       "      <td>-0.865788</td>\n",
       "      <td>0.305982</td>\n",
       "      <td>-0.12657</td>\n",
       "      <td>-0.237671</td>\n",
       "      <td>-0.551475</td>\n",
       "      <td>-0.664261</td>\n",
       "      <td>-1.336204</td>\n",
       "      <td>-0.551096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c016c6b0efa5</th>\n",
       "      <td>-0.15239</td>\n",
       "      <td>-0.236089</td>\n",
       "      <td>-0.066677</td>\n",
       "      <td>-0.213677</td>\n",
       "      <td>6.292244</td>\n",
       "      <td>-0.876378</td>\n",
       "      <td>1.113428</td>\n",
       "      <td>3.547482</td>\n",
       "      <td>-0.697792</td>\n",
       "      <td>-1.093317</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.589172</td>\n",
       "      <td>-0.767604</td>\n",
       "      <td>1.078776</td>\n",
       "      <td>0.542346</td>\n",
       "      <td>-0.12657</td>\n",
       "      <td>-0.237671</td>\n",
       "      <td>-0.551475</td>\n",
       "      <td>1.459987</td>\n",
       "      <td>0.501443</td>\n",
       "      <td>-0.551096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ba7f733a4f75</th>\n",
       "      <td>-0.15239</td>\n",
       "      <td>-0.236089</td>\n",
       "      <td>-0.066677</td>\n",
       "      <td>-0.213677</td>\n",
       "      <td>-0.162106</td>\n",
       "      <td>0.861680</td>\n",
       "      <td>0.906359</td>\n",
       "      <td>-0.285128</td>\n",
       "      <td>-0.697792</td>\n",
       "      <td>1.046975</td>\n",
       "      <td>...</td>\n",
       "      <td>1.449651</td>\n",
       "      <td>-0.767604</td>\n",
       "      <td>1.213454</td>\n",
       "      <td>0.778843</td>\n",
       "      <td>-0.12657</td>\n",
       "      <td>-0.237671</td>\n",
       "      <td>-0.551475</td>\n",
       "      <td>1.233349</td>\n",
       "      <td>0.628716</td>\n",
       "      <td>-0.551096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbcf2443ffb2</th>\n",
       "      <td>-0.15239</td>\n",
       "      <td>-0.236089</td>\n",
       "      <td>-0.066677</td>\n",
       "      <td>-0.213677</td>\n",
       "      <td>-0.162106</td>\n",
       "      <td>-0.876378</td>\n",
       "      <td>1.289741</td>\n",
       "      <td>-0.285128</td>\n",
       "      <td>-0.697792</td>\n",
       "      <td>-1.093317</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.589172</td>\n",
       "      <td>1.440852</td>\n",
       "      <td>1.255428</td>\n",
       "      <td>0.381821</td>\n",
       "      <td>-0.12657</td>\n",
       "      <td>-0.237671</td>\n",
       "      <td>1.616453</td>\n",
       "      <td>1.652962</td>\n",
       "      <td>0.344437</td>\n",
       "      <td>-0.551096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650ee456f0f3</th>\n",
       "      <td>-0.15239</td>\n",
       "      <td>-0.236089</td>\n",
       "      <td>-0.066677</td>\n",
       "      <td>-0.213677</td>\n",
       "      <td>-0.162106</td>\n",
       "      <td>-0.876378</td>\n",
       "      <td>-0.827405</td>\n",
       "      <td>-0.285128</td>\n",
       "      <td>-0.697792</td>\n",
       "      <td>-1.093317</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.589172</td>\n",
       "      <td>1.546469</td>\n",
       "      <td>1.356873</td>\n",
       "      <td>0.809844</td>\n",
       "      <td>-0.12657</td>\n",
       "      <td>-0.237671</td>\n",
       "      <td>-0.551475</td>\n",
       "      <td>-0.664261</td>\n",
       "      <td>0.764249</td>\n",
       "      <td>2.158987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cc506e7707f5</th>\n",
       "      <td>-0.15239</td>\n",
       "      <td>-0.236089</td>\n",
       "      <td>-0.066677</td>\n",
       "      <td>-0.213677</td>\n",
       "      <td>-0.162106</td>\n",
       "      <td>1.137101</td>\n",
       "      <td>1.526039</td>\n",
       "      <td>-0.285128</td>\n",
       "      <td>-0.697792</td>\n",
       "      <td>-1.093317</td>\n",
       "      <td>...</td>\n",
       "      <td>1.772733</td>\n",
       "      <td>-0.767604</td>\n",
       "      <td>1.492180</td>\n",
       "      <td>0.277978</td>\n",
       "      <td>-0.12657</td>\n",
       "      <td>-0.237671</td>\n",
       "      <td>-0.551475</td>\n",
       "      <td>-0.664261</td>\n",
       "      <td>0.565517</td>\n",
       "      <td>-0.551096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a91f1b55a520</th>\n",
       "      <td>-0.15239</td>\n",
       "      <td>-0.236089</td>\n",
       "      <td>-0.066677</td>\n",
       "      <td>-0.213677</td>\n",
       "      <td>-0.162106</td>\n",
       "      <td>-0.876378</td>\n",
       "      <td>1.441519</td>\n",
       "      <td>-0.285128</td>\n",
       "      <td>1.760215</td>\n",
       "      <td>0.715930</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.589172</td>\n",
       "      <td>-0.767604</td>\n",
       "      <td>-0.865788</td>\n",
       "      <td>0.526894</td>\n",
       "      <td>-0.12657</td>\n",
       "      <td>-0.237671</td>\n",
       "      <td>1.799451</td>\n",
       "      <td>1.819083</td>\n",
       "      <td>-1.336204</td>\n",
       "      <td>-0.551096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3a9882c98205</th>\n",
       "      <td>-0.15239</td>\n",
       "      <td>-0.236089</td>\n",
       "      <td>-0.066677</td>\n",
       "      <td>-0.213677</td>\n",
       "      <td>-0.162106</td>\n",
       "      <td>1.096361</td>\n",
       "      <td>-0.827405</td>\n",
       "      <td>-0.285128</td>\n",
       "      <td>1.807321</td>\n",
       "      <td>1.670767</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.589172</td>\n",
       "      <td>-0.767604</td>\n",
       "      <td>1.451063</td>\n",
       "      <td>0.762318</td>\n",
       "      <td>-0.12657</td>\n",
       "      <td>-0.237671</td>\n",
       "      <td>-0.551475</td>\n",
       "      <td>-0.664261</td>\n",
       "      <td>-1.336204</td>\n",
       "      <td>1.852928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c91b6b2ccd3d</th>\n",
       "      <td>-0.15239</td>\n",
       "      <td>-0.236089</td>\n",
       "      <td>-0.066677</td>\n",
       "      <td>-0.213677</td>\n",
       "      <td>-0.162106</td>\n",
       "      <td>-0.876378</td>\n",
       "      <td>-0.827405</td>\n",
       "      <td>-0.285128</td>\n",
       "      <td>-0.697792</td>\n",
       "      <td>-1.093317</td>\n",
       "      <td>...</td>\n",
       "      <td>1.896145</td>\n",
       "      <td>-0.767604</td>\n",
       "      <td>-0.865788</td>\n",
       "      <td>0.378289</td>\n",
       "      <td>-0.12657</td>\n",
       "      <td>-0.237671</td>\n",
       "      <td>-0.551475</td>\n",
       "      <td>-0.664261</td>\n",
       "      <td>0.664884</td>\n",
       "      <td>2.030782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70988 rows × 20856 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gene_id       ENSG00000121410_A1BG  ENSG00000268895_A1BG-AS1  \\\n",
       "cell_id                                                        \n",
       "45006fe3e4c8              -0.15239                 -0.236089   \n",
       "d02759a80ba2              -0.15239                 -0.236089   \n",
       "c016c6b0efa5              -0.15239                 -0.236089   \n",
       "ba7f733a4f75              -0.15239                 -0.236089   \n",
       "fbcf2443ffb2              -0.15239                 -0.236089   \n",
       "...                            ...                       ...   \n",
       "650ee456f0f3              -0.15239                 -0.236089   \n",
       "cc506e7707f5              -0.15239                 -0.236089   \n",
       "a91f1b55a520              -0.15239                 -0.236089   \n",
       "3a9882c98205              -0.15239                 -0.236089   \n",
       "c91b6b2ccd3d              -0.15239                 -0.236089   \n",
       "\n",
       "gene_id       ENSG00000175899_A2M  ENSG00000245105_A2M-AS1  \\\n",
       "cell_id                                                      \n",
       "45006fe3e4c8            -0.066677                -0.213677   \n",
       "d02759a80ba2            -0.066677                -0.213677   \n",
       "c016c6b0efa5            -0.066677                -0.213677   \n",
       "ba7f733a4f75            -0.066677                -0.213677   \n",
       "fbcf2443ffb2            -0.066677                -0.213677   \n",
       "...                           ...                      ...   \n",
       "650ee456f0f3            -0.066677                -0.213677   \n",
       "cc506e7707f5            -0.066677                -0.213677   \n",
       "a91f1b55a520            -0.066677                -0.213677   \n",
       "3a9882c98205            -0.066677                -0.213677   \n",
       "c91b6b2ccd3d            -0.066677                -0.213677   \n",
       "\n",
       "gene_id       ENSG00000128274_A4GALT  ENSG00000094914_AAAS  \\\n",
       "cell_id                                                      \n",
       "45006fe3e4c8               -0.162106             -0.876378   \n",
       "d02759a80ba2               -0.162106             -0.876378   \n",
       "c016c6b0efa5                6.292244             -0.876378   \n",
       "ba7f733a4f75               -0.162106              0.861680   \n",
       "fbcf2443ffb2               -0.162106             -0.876378   \n",
       "...                              ...                   ...   \n",
       "650ee456f0f3               -0.162106             -0.876378   \n",
       "cc506e7707f5               -0.162106              1.137101   \n",
       "a91f1b55a520               -0.162106             -0.876378   \n",
       "3a9882c98205               -0.162106              1.096361   \n",
       "c91b6b2ccd3d               -0.162106             -0.876378   \n",
       "\n",
       "gene_id       ENSG00000081760_AACS  ENSG00000109576_AADAT  \\\n",
       "cell_id                                                     \n",
       "45006fe3e4c8             -0.827405              -0.285128   \n",
       "d02759a80ba2             -0.827405              -0.285128   \n",
       "c016c6b0efa5              1.113428               3.547482   \n",
       "ba7f733a4f75              0.906359              -0.285128   \n",
       "fbcf2443ffb2              1.289741              -0.285128   \n",
       "...                            ...                    ...   \n",
       "650ee456f0f3             -0.827405              -0.285128   \n",
       "cc506e7707f5              1.526039              -0.285128   \n",
       "a91f1b55a520              1.441519              -0.285128   \n",
       "3a9882c98205             -0.827405              -0.285128   \n",
       "c91b6b2ccd3d             -0.827405              -0.285128   \n",
       "\n",
       "gene_id       ENSG00000103591_AAGAB  ENSG00000115977_AAK1  ...  \\\n",
       "cell_id                                                    ...   \n",
       "45006fe3e4c8              -0.697792             -1.093317  ...   \n",
       "d02759a80ba2              -0.697792              0.822107  ...   \n",
       "c016c6b0efa5              -0.697792             -1.093317  ...   \n",
       "ba7f733a4f75              -0.697792              1.046975  ...   \n",
       "fbcf2443ffb2              -0.697792             -1.093317  ...   \n",
       "...                             ...                   ...  ...   \n",
       "650ee456f0f3              -0.697792             -1.093317  ...   \n",
       "cc506e7707f5              -0.697792             -1.093317  ...   \n",
       "a91f1b55a520               1.760215              0.715930  ...   \n",
       "3a9882c98205               1.807321              1.670767  ...   \n",
       "c91b6b2ccd3d              -0.697792             -1.093317  ...   \n",
       "\n",
       "gene_id       ENSG00000153975_ZUP1  ENSG00000086827_ZW10  \\\n",
       "cell_id                                                    \n",
       "45006fe3e4c8             -0.589172             -0.767604   \n",
       "d02759a80ba2             -0.589172             -0.767604   \n",
       "c016c6b0efa5             -0.589172             -0.767604   \n",
       "ba7f733a4f75              1.449651             -0.767604   \n",
       "fbcf2443ffb2             -0.589172              1.440852   \n",
       "...                            ...                   ...   \n",
       "650ee456f0f3             -0.589172              1.546469   \n",
       "cc506e7707f5              1.772733             -0.767604   \n",
       "a91f1b55a520             -0.589172             -0.767604   \n",
       "3a9882c98205             -0.589172             -0.767604   \n",
       "c91b6b2ccd3d              1.896145             -0.767604   \n",
       "\n",
       "gene_id       ENSG00000174442_ZWILCH  ENSG00000122952_ZWINT  \\\n",
       "cell_id                                                       \n",
       "45006fe3e4c8               -0.865788              -1.641808   \n",
       "d02759a80ba2               -0.865788               0.305982   \n",
       "c016c6b0efa5                1.078776               0.542346   \n",
       "ba7f733a4f75                1.213454               0.778843   \n",
       "fbcf2443ffb2                1.255428               0.381821   \n",
       "...                              ...                    ...   \n",
       "650ee456f0f3                1.356873               0.809844   \n",
       "cc506e7707f5                1.492180               0.277978   \n",
       "a91f1b55a520               -0.865788               0.526894   \n",
       "3a9882c98205                1.451063               0.762318   \n",
       "c91b6b2ccd3d               -0.865788               0.378289   \n",
       "\n",
       "gene_id       ENSG00000198205_ZXDA  ENSG00000198455_ZXDB  \\\n",
       "cell_id                                                    \n",
       "45006fe3e4c8              -0.12657             -0.237671   \n",
       "d02759a80ba2              -0.12657             -0.237671   \n",
       "c016c6b0efa5              -0.12657             -0.237671   \n",
       "ba7f733a4f75              -0.12657             -0.237671   \n",
       "fbcf2443ffb2              -0.12657             -0.237671   \n",
       "...                            ...                   ...   \n",
       "650ee456f0f3              -0.12657             -0.237671   \n",
       "cc506e7707f5              -0.12657             -0.237671   \n",
       "a91f1b55a520              -0.12657             -0.237671   \n",
       "3a9882c98205              -0.12657             -0.237671   \n",
       "c91b6b2ccd3d              -0.12657             -0.237671   \n",
       "\n",
       "gene_id       ENSG00000070476_ZXDC  ENSG00000162378_ZYG11B  \\\n",
       "cell_id                                                      \n",
       "45006fe3e4c8             -0.551475               -0.664261   \n",
       "d02759a80ba2             -0.551475               -0.664261   \n",
       "c016c6b0efa5             -0.551475                1.459987   \n",
       "ba7f733a4f75             -0.551475                1.233349   \n",
       "fbcf2443ffb2              1.616453                1.652962   \n",
       "...                            ...                     ...   \n",
       "650ee456f0f3             -0.551475               -0.664261   \n",
       "cc506e7707f5             -0.551475               -0.664261   \n",
       "a91f1b55a520              1.799451                1.819083   \n",
       "3a9882c98205             -0.551475               -0.664261   \n",
       "c91b6b2ccd3d             -0.551475               -0.664261   \n",
       "\n",
       "gene_id       ENSG00000159840_ZYX  ENSG00000074755_ZZEF1  \n",
       "cell_id                                                   \n",
       "45006fe3e4c8             0.617446              -0.551096  \n",
       "d02759a80ba2            -1.336204              -0.551096  \n",
       "c016c6b0efa5             0.501443              -0.551096  \n",
       "ba7f733a4f75             0.628716              -0.551096  \n",
       "fbcf2443ffb2             0.344437              -0.551096  \n",
       "...                           ...                    ...  \n",
       "650ee456f0f3             0.764249               2.158987  \n",
       "cc506e7707f5             0.565517              -0.551096  \n",
       "a91f1b55a520            -1.336204              -0.551096  \n",
       "3a9882c98205            -1.336204               1.852928  \n",
       "c91b6b2ccd3d             0.664884               2.030782  \n",
       "\n",
       "[70988 rows x 20856 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6909b52a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "85ae8520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gene_id</th>\n",
       "      <th>ENSG00000121410_A1BG</th>\n",
       "      <th>ENSG00000268895_A1BG-AS1</th>\n",
       "      <th>ENSG00000175899_A2M</th>\n",
       "      <th>ENSG00000245105_A2M-AS1</th>\n",
       "      <th>ENSG00000128274_A4GALT</th>\n",
       "      <th>ENSG00000094914_AAAS</th>\n",
       "      <th>ENSG00000081760_AACS</th>\n",
       "      <th>ENSG00000109576_AADAT</th>\n",
       "      <th>ENSG00000103591_AAGAB</th>\n",
       "      <th>ENSG00000115977_AAK1</th>\n",
       "      <th>...</th>\n",
       "      <th>ENSG00000153975_ZUP1</th>\n",
       "      <th>ENSG00000086827_ZW10</th>\n",
       "      <th>ENSG00000174442_ZWILCH</th>\n",
       "      <th>ENSG00000122952_ZWINT</th>\n",
       "      <th>ENSG00000198205_ZXDA</th>\n",
       "      <th>ENSG00000198455_ZXDB</th>\n",
       "      <th>ENSG00000070476_ZXDC</th>\n",
       "      <th>ENSG00000162378_ZYG11B</th>\n",
       "      <th>ENSG00000159840_ZYX</th>\n",
       "      <th>ENSG00000074755_ZZEF1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c2150f55becb</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.090185</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65b7edf8a4da</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.039545</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.039545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1b26cb1057b</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.847321</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.847321</td>\n",
       "      <td>3.847321</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.847321</td>\n",
       "      <td>4.529743</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.847321</td>\n",
       "      <td>3.847321</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917168fa6f83</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.436846</td>\n",
       "      <td>3.436846</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.513782</td>\n",
       "      <td>...</td>\n",
       "      <td>3.436846</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.113780</td>\n",
       "      <td>5.020215</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.436846</td>\n",
       "      <td>4.113780</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2b29feeca86d</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.196826</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.196826</td>\n",
       "      <td>4.196826</td>\n",
       "      <td>4.196826</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.51861</td>\n",
       "      <td>4.196826</td>\n",
       "      <td>3.518610</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a9b4d99f1f50</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.719836</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.719836</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.719836</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0e2c1d0782af</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.026206</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.026206</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.026206</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.710393</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a3cbc5aa0ec3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.306634</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.306634</td>\n",
       "      <td>6.933096</td>\n",
       "      <td>4.993019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75b350243add</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.624848</td>\n",
       "      <td>3.624848</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.624848</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.624848</td>\n",
       "      <td>3.624848</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.624848</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ad5a949989b2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.452700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.377115</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.377115</td>\n",
       "      <td>3.377115</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.737466</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48663 rows × 20856 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gene_id       ENSG00000121410_A1BG  ENSG00000268895_A1BG-AS1  \\\n",
       "cell_id                                                        \n",
       "c2150f55becb                   0.0                       0.0   \n",
       "65b7edf8a4da                   0.0                       0.0   \n",
       "c1b26cb1057b                   0.0                       0.0   \n",
       "917168fa6f83                   0.0                       0.0   \n",
       "2b29feeca86d                   0.0                       0.0   \n",
       "...                            ...                       ...   \n",
       "a9b4d99f1f50                   0.0                       0.0   \n",
       "0e2c1d0782af                   0.0                       0.0   \n",
       "a3cbc5aa0ec3                   0.0                       0.0   \n",
       "75b350243add                   0.0                       0.0   \n",
       "ad5a949989b2                   0.0                       0.0   \n",
       "\n",
       "gene_id       ENSG00000175899_A2M  ENSG00000245105_A2M-AS1  \\\n",
       "cell_id                                                      \n",
       "c2150f55becb                  0.0                      0.0   \n",
       "65b7edf8a4da                  0.0                      0.0   \n",
       "c1b26cb1057b                  0.0                      0.0   \n",
       "917168fa6f83                  0.0                      0.0   \n",
       "2b29feeca86d                  0.0                      0.0   \n",
       "...                           ...                      ...   \n",
       "a9b4d99f1f50                  0.0                      0.0   \n",
       "0e2c1d0782af                  0.0                      0.0   \n",
       "a3cbc5aa0ec3                  0.0                      0.0   \n",
       "75b350243add                  0.0                      0.0   \n",
       "ad5a949989b2                  0.0                      0.0   \n",
       "\n",
       "gene_id       ENSG00000128274_A4GALT  ENSG00000094914_AAAS  \\\n",
       "cell_id                                                      \n",
       "c2150f55becb                0.000000              0.000000   \n",
       "65b7edf8a4da                0.000000              0.000000   \n",
       "c1b26cb1057b                3.847321              0.000000   \n",
       "917168fa6f83                0.000000              3.436846   \n",
       "2b29feeca86d                0.000000              0.000000   \n",
       "...                              ...                   ...   \n",
       "a9b4d99f1f50                0.000000              0.000000   \n",
       "0e2c1d0782af                0.000000              0.000000   \n",
       "a3cbc5aa0ec3                0.000000              0.000000   \n",
       "75b350243add                0.000000              3.624848   \n",
       "ad5a949989b2                0.000000              0.000000   \n",
       "\n",
       "gene_id       ENSG00000081760_AACS  ENSG00000109576_AADAT  \\\n",
       "cell_id                                                     \n",
       "c2150f55becb              0.000000               0.000000   \n",
       "65b7edf8a4da              0.000000               0.000000   \n",
       "c1b26cb1057b              3.847321               3.847321   \n",
       "917168fa6f83              3.436846               0.000000   \n",
       "2b29feeca86d              4.196826               0.000000   \n",
       "...                            ...                    ...   \n",
       "a9b4d99f1f50              0.000000               0.000000   \n",
       "0e2c1d0782af              0.000000               4.026206   \n",
       "a3cbc5aa0ec3              0.000000               0.000000   \n",
       "75b350243add              3.624848               0.000000   \n",
       "ad5a949989b2              4.452700               0.000000   \n",
       "\n",
       "gene_id       ENSG00000103591_AAGAB  ENSG00000115977_AAK1  ...  \\\n",
       "cell_id                                                    ...   \n",
       "c2150f55becb               0.000000              0.000000  ...   \n",
       "65b7edf8a4da               0.000000              4.039545  ...   \n",
       "c1b26cb1057b               0.000000              0.000000  ...   \n",
       "917168fa6f83               0.000000              4.513782  ...   \n",
       "2b29feeca86d               0.000000              0.000000  ...   \n",
       "...                             ...                   ...  ...   \n",
       "a9b4d99f1f50               3.719836              0.000000  ...   \n",
       "0e2c1d0782af               0.000000              0.000000  ...   \n",
       "a3cbc5aa0ec3               0.000000              0.000000  ...   \n",
       "75b350243add               0.000000              3.624848  ...   \n",
       "ad5a949989b2               3.377115              0.000000  ...   \n",
       "\n",
       "gene_id       ENSG00000153975_ZUP1  ENSG00000086827_ZW10  \\\n",
       "cell_id                                                    \n",
       "c2150f55becb              0.000000              0.000000   \n",
       "65b7edf8a4da              0.000000              0.000000   \n",
       "c1b26cb1057b              0.000000              0.000000   \n",
       "917168fa6f83              3.436846              0.000000   \n",
       "2b29feeca86d              0.000000              4.196826   \n",
       "...                            ...                   ...   \n",
       "a9b4d99f1f50              0.000000              0.000000   \n",
       "0e2c1d0782af              4.026206              0.000000   \n",
       "a3cbc5aa0ec3              0.000000              0.000000   \n",
       "75b350243add              0.000000              0.000000   \n",
       "ad5a949989b2              3.377115              3.377115   \n",
       "\n",
       "gene_id       ENSG00000174442_ZWILCH  ENSG00000122952_ZWINT  \\\n",
       "cell_id                                                       \n",
       "c2150f55becb                0.000000               0.000000   \n",
       "65b7edf8a4da                0.000000               4.039545   \n",
       "c1b26cb1057b                3.847321               4.529743   \n",
       "917168fa6f83                4.113780               5.020215   \n",
       "2b29feeca86d                4.196826               4.196826   \n",
       "...                              ...                    ...   \n",
       "a9b4d99f1f50                0.000000               3.719836   \n",
       "0e2c1d0782af                0.000000               4.026206   \n",
       "a3cbc5aa0ec3                4.306634               0.000000   \n",
       "75b350243add                0.000000               3.624848   \n",
       "ad5a949989b2                0.000000               4.737466   \n",
       "\n",
       "gene_id       ENSG00000198205_ZXDA  ENSG00000198455_ZXDB  \\\n",
       "cell_id                                                    \n",
       "c2150f55becb              0.000000                   0.0   \n",
       "65b7edf8a4da              0.000000                   0.0   \n",
       "c1b26cb1057b              0.000000                   0.0   \n",
       "917168fa6f83              0.000000                   0.0   \n",
       "2b29feeca86d              0.000000                   0.0   \n",
       "...                            ...                   ...   \n",
       "a9b4d99f1f50              0.000000                   0.0   \n",
       "0e2c1d0782af              0.000000                   0.0   \n",
       "a3cbc5aa0ec3              0.000000                   0.0   \n",
       "75b350243add              3.624848                   0.0   \n",
       "ad5a949989b2              0.000000                   0.0   \n",
       "\n",
       "gene_id       ENSG00000070476_ZXDC  ENSG00000162378_ZYG11B  \\\n",
       "cell_id                                                      \n",
       "c2150f55becb               0.00000                0.000000   \n",
       "65b7edf8a4da               0.00000                0.000000   \n",
       "c1b26cb1057b               0.00000                3.847321   \n",
       "917168fa6f83               0.00000                3.436846   \n",
       "2b29feeca86d               3.51861                4.196826   \n",
       "...                            ...                     ...   \n",
       "a9b4d99f1f50               0.00000                0.000000   \n",
       "0e2c1d0782af               0.00000                0.000000   \n",
       "a3cbc5aa0ec3               0.00000                4.306634   \n",
       "75b350243add               0.00000                3.624848   \n",
       "ad5a949989b2               0.00000                0.000000   \n",
       "\n",
       "gene_id       ENSG00000159840_ZYX  ENSG00000074755_ZZEF1  \n",
       "cell_id                                                   \n",
       "c2150f55becb             4.090185               0.000000  \n",
       "65b7edf8a4da             0.000000               0.000000  \n",
       "c1b26cb1057b             3.847321               0.000000  \n",
       "917168fa6f83             4.113780               0.000000  \n",
       "2b29feeca86d             3.518610               0.000000  \n",
       "...                           ...                    ...  \n",
       "a9b4d99f1f50             3.719836               0.000000  \n",
       "0e2c1d0782af             4.710393               0.000000  \n",
       "a3cbc5aa0ec3             6.933096               4.993019  \n",
       "75b350243add             0.000000               0.000000  \n",
       "ad5a949989b2             0.000000               0.000000  \n",
       "\n",
       "[48663 rows x 20856 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a9333f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['CD86', 'CD274', 'CD270', 'CD155', 'CD112', 'CD47', 'CD48', 'CD40',\n",
      "       'CD154', 'CD52',\n",
      "       ...\n",
      "       'CD94', 'CD162', 'CD85j', 'CD23', 'CD328', 'HLA-E', 'CD82', 'CD101',\n",
      "       'CD88', 'CD224'],\n",
      "      dtype='object', name='gene_id', length=140)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(70988, 140)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = pd.read_hdf(FP_CITE_TRAIN_TARGETS)\n",
    "print(Y.columns)\n",
    "Y = Y.values\n",
    "Y -= Y.mean(axis=1).reshape(-1, 1)\n",
    "Y /= Y.std(axis=1).reshape(-1, 1)\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50b07d9d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f26dcd28",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "aa81d6fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR_START = 5e-4#5e-3\n",
    "BATCH_SIZE = 512\n",
    "\n",
    "def create_model():\n",
    "    \n",
    "    reg1 = 9.613e-06\n",
    "    reg2 = 1e-07\n",
    "    REG1 = tf.keras.regularizers.l2(reg1)\n",
    "    REG2 = tf.keras.regularizers.l2(reg2)\n",
    "    DROP = 0.1\n",
    "\n",
    "    activation = 'selu'\n",
    "    inputs = Input(shape =(100,))\n",
    "\n",
    "    x0 = Dense(64, \n",
    "              kernel_regularizer = REG1,\n",
    "              activation = activation,\n",
    "             )(inputs)\n",
    "    \n",
    "\n",
    "    x0 = Dropout(DROP)(x0)\n",
    "    \n",
    "    \n",
    "    \n",
    "    x2 = Dense(32, \n",
    "               kernel_regularizer = REG1,\n",
    "               activation = activation,\n",
    "             )(x0) \n",
    "    \n",
    "    x2 = Dropout(DROP)(x2)\n",
    "    \n",
    "    x = Dense(1, \n",
    "                kernel_regularizer = REG2,\n",
    "                activation=\"linear\",\n",
    "                )(x2)\n",
    "    \n",
    "    \n",
    "    model = Model(inputs, x)\n",
    "    \n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "51b4b310",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_table = pd.read_csv(\"/ysm-gpfs/pi/zhao/tl688/openproblems/top100features_cite.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2b0d26ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "protein_names = correlation_table.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "72ce7216",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CD86', 'CD274', 'CD270', 'CD155', 'CD112', 'CD47', 'CD48', 'CD40',\n",
       "       'CD154', 'CD52',\n",
       "       ...\n",
       "       'CD94', 'CD162', 'CD85j', 'CD23', 'CD328', 'HLA-E', 'CD82', 'CD101',\n",
       "       'CD88', 'CD224'],\n",
       "      dtype='object', length=140)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1512de29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.       , 0.       , 4.090185 , ..., 0.       , 6.4727488,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 6.967164 ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 6.312475 ,\n",
       "        0.       ],\n",
       "       ...,\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 5.9930754,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 3.9009068, ..., 0.       , 7.0593896,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 5.968553 ,\n",
       "        0.       ]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[correlation_table.loc[:,protein_names[0]]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "597e0170",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_list = []\n",
    "# for i in range(len(protein_names)):\n",
    "#     model_list.append(create_model()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c984ddb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_4 (InputLayer)        [(None, 100)]             0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 64)                6464      \n",
      "                                                                 \n",
      " dropout_6 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 16)                1040      \n",
      "                                                                 \n",
      " dropout_7 (Dropout)         (None, 16)                0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 7,521\n",
      "Trainable params: 7,521\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "290a08a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.       , 0.       , 4.090185 , ..., 0.       , 6.4727488,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 6.967164 ,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 6.312475 ,\n",
       "        0.       ],\n",
       "       ...,\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 5.9930754,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 3.9009068, ..., 0.       , 7.0593896,\n",
       "        0.       ],\n",
       "       [0.       , 0.       , 0.       , ..., 0.       , 5.968553 ,\n",
       "        0.       ]], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[correlation_table.loc[:,protein_names[0]]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d9b0975e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start training\n",
      "\n",
      "Epoch 1: val_loss improved from inf to 0.73517, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 2: val_loss improved from 0.73517 to 0.42353, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 3: val_loss improved from 0.42353 to 0.33713, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 4: val_loss improved from 0.33713 to 0.29627, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 5: val_loss improved from 0.29627 to 0.27399, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "end training\n",
      "INFO:tensorflow:Assets written to: ./submissions/model_0_0/assets\n",
      "model saved\n",
      "880/880 [==============================] - 1s 843us/step\n",
      "Fold 0, correlation =  nan\n",
      "start training\n",
      "\n",
      "Epoch 1: val_loss improved from inf to 1.09867, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 2: val_loss improved from 1.09867 to 0.40677, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 3: val_loss improved from 0.40677 to 0.25313, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 4: val_loss improved from 0.25313 to 0.19586, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 5: val_loss improved from 0.19586 to 0.15969, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "end training\n",
      "INFO:tensorflow:Assets written to: ./submissions/model_0_1/assets\n",
      "model saved\n",
      "880/880 [==============================] - 1s 842us/step\n",
      "Fold 0, correlation =  nan\n",
      "start training\n",
      "\n",
      "Epoch 1: val_loss improved from inf to 1.03030, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 2: val_loss improved from 1.03030 to 0.44385, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 3: val_loss improved from 0.44385 to 0.24590, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 4: val_loss improved from 0.24590 to 0.17632, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 5: val_loss improved from 0.17632 to 0.13811, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "end training\n",
      "INFO:tensorflow:Assets written to: ./submissions/model_0_2/assets\n",
      "model saved\n",
      "880/880 [==============================] - 1s 842us/step\n",
      "Fold 0, correlation =  nan\n",
      "start training\n",
      "\n",
      "Epoch 1: val_loss improved from inf to 0.92954, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 2: val_loss improved from 0.92954 to 0.62834, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 3: val_loss improved from 0.62834 to 0.48399, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 4: val_loss improved from 0.48399 to 0.42971, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 5: val_loss improved from 0.42971 to 0.40366, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "end training\n",
      "INFO:tensorflow:Assets written to: ./submissions/model_0_3/assets\n",
      "model saved\n",
      "880/880 [==============================] - 1s 844us/step\n",
      "Fold 0, correlation =  nan\n",
      "start training\n",
      "\n",
      "Epoch 1: val_loss improved from inf to 0.76538, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 2: val_loss improved from 0.76538 to 0.44739, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 3: val_loss improved from 0.44739 to 0.36901, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 4: val_loss improved from 0.36901 to 0.31108, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "\n",
      "Epoch 5: val_loss improved from 0.31108 to 0.28104, saving model to ./citeseq_new/checkpointfile.hdf5\n",
      "end training\n",
      "INFO:tensorflow:Assets written to: ./submissions/model_0_4/assets\n",
      "model saved\n",
      "880/880 [==============================] - 1s 846us/step\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "File \u001b[0;32m<timed exec>:91\u001b[0m\n",
      "Cell \u001b[0;32mIn [40], line 6\u001b[0m, in \u001b[0;36mcorrelation_score\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m      4\u001b[0m corrsum \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(y_true)):\n\u001b[0;32m----> 6\u001b[0m     corrsum \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcorrcoef\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m corrsum \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mlen\u001b[39m(y_true)\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mcorrcoef\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/numpy/lib/function_base.py:2859\u001b[0m, in \u001b[0;36mcorrcoef\u001b[0;34m(x, y, rowvar, bias, ddof, dtype)\u001b[0m\n\u001b[1;32m   2854\u001b[0m c \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m=\u001b[39m stddev[\u001b[38;5;28;01mNone\u001b[39;00m, :]\n\u001b[1;32m   2856\u001b[0m \u001b[38;5;66;03m# Clip real and imaginary parts to [-1, 1].  This does not guarantee\u001b[39;00m\n\u001b[1;32m   2857\u001b[0m \u001b[38;5;66;03m# abs(a[i,j]) <= 1 for complex arrays, but is the best we can do without\u001b[39;00m\n\u001b[1;32m   2858\u001b[0m \u001b[38;5;66;03m# excessive work.\u001b[39;00m\n\u001b[0;32m-> 2859\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclip\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreal\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreal\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2860\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39miscomplexobj(c):\n\u001b[1;32m   2861\u001b[0m     np\u001b[38;5;241m.\u001b[39mclip(c\u001b[38;5;241m.\u001b[39mimag, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, out\u001b[38;5;241m=\u001b[39mc\u001b[38;5;241m.\u001b[39mimag)\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mclip\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/numpy/core/fromnumeric.py:2154\u001b[0m, in \u001b[0;36mclip\u001b[0;34m(a, a_min, a_max, out, **kwargs)\u001b[0m\n\u001b[1;32m   2085\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_clip_dispatcher)\n\u001b[1;32m   2086\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mclip\u001b[39m(a, a_min, a_max, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   2087\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   2088\u001b[0m \u001b[38;5;124;03m    Clip (limit) the values in an array.\u001b[39;00m\n\u001b[1;32m   2089\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2152\u001b[0m \n\u001b[1;32m   2153\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mclip\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ma_min\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ma_max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/numpy/core/fromnumeric.py:57\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _wrapit(obj, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 57\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbound\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m     59\u001b[0m     \u001b[38;5;66;03m# A TypeError occurs if the object does have such a method in its\u001b[39;00m\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;66;03m# class, but its signature is not identical to that of NumPy's. This\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[38;5;66;03m# Call _wrapit from within the except clause to ensure a potential\u001b[39;00m\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;66;03m# exception has a traceback chain.\u001b[39;00m\n\u001b[1;32m     66\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _wrapit(obj, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/numpy/core/_methods.py:135\u001b[0m, in \u001b[0;36m_clip\u001b[0;34m(a, min, max, out, casting, **kwargs)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _clip_dep_is_byte_swapped(a) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _clip_dep_is_byte_swapped(out):\n\u001b[1;32m    134\u001b[0m     using_deprecated_nan \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 135\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43m_clip_dep_is_scalar_nan\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mmin\u001b[39;49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    136\u001b[0m         \u001b[38;5;28mmin\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minf\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    137\u001b[0m         using_deprecated_nan \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m/gpfs/ysm/project/zhao/tl688/conda_envs/tf117/lib/python3.9/site-packages/numpy/core/_methods.py:94\u001b[0m, in \u001b[0;36m_clip_dep_is_scalar_nan\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_clip_dep_is_scalar_nan\u001b[39m(a):\n\u001b[1;32m     93\u001b[0m     \u001b[38;5;66;03m# guarded to protect circular imports\u001b[39;00m\n\u001b[0;32m---> 94\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfromnumeric\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ndim\n\u001b[1;32m     95\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ndim(a) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     96\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "EPOCHS = 5\n",
    "N_SPLITS = 3\n",
    "\n",
    "pred_train = np.zeros((Y.shape[0],1))\n",
    "\n",
    "seed_tensorflow(1)\n",
    "score_list = []\n",
    "kf = GroupKFold(n_splits=N_SPLITS)\n",
    "# kf = sklearn.model_selection.KFold(5)\n",
    "score_list = []\n",
    "\n",
    "for fold, (idx_tr, idx_va) in enumerate(kf.split(X, groups=meta.day)):\n",
    "    start_time = datetime.datetime.now()\n",
    "    model = None\n",
    "    gc.collect()\n",
    "\n",
    "    for protein_index in range(140):\n",
    "        X_tr = X[correlation_table.loc[:,protein_names[protein_index]]].values[idx_tr]\n",
    "        y_tr = Y[idx_tr,protein_index].reshape(-1,1)\n",
    "        \n",
    "        X_va = X[correlation_table.loc[:,protein_names[protein_index]]].values[idx_va]\n",
    "        y_va = Y[idx_va,protein_index].reshape(-1,1)\n",
    "\n",
    "        lr = ReduceLROnPlateau(\n",
    "                        monitor = \"val_loss\",\n",
    "                        factor = 0.9, \n",
    "                        patience = 4, \n",
    "                        verbose = VERBOSE)\n",
    "\n",
    "\n",
    "        es = EarlyStopping(\n",
    "                        monitor = \"val_loss\",\n",
    "                        patience = 100, \n",
    "                        verbose = VERBOSE,\n",
    "                        mode = \"min\", \n",
    "                        restore_best_weights = True)\n",
    "        \n",
    "        \n",
    "        checkpoint_filepath = \"./citeseq_new/checkpointfile.hdf5\"\n",
    "\n",
    "        model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "                        filepath = checkpoint_filepath,\n",
    "                        save_weights_only = True,\n",
    "                        monitor = 'val_loss',\n",
    "                        mode = 'min',\n",
    "                        verbose=1,\n",
    "                        save_best_only = True)\n",
    "\n",
    "        callbacks = [   lr, \n",
    "                        es, \n",
    "                        model_checkpoint_callback\n",
    "                        ]\n",
    "\n",
    "        model = create_model()\n",
    "\n",
    "\n",
    "        model.compile(\n",
    "                    optimizer = tfa.optimizers.AdaBelief(learning_rate = LR_START),\n",
    "                    metrics = [\"mse\"],\n",
    "                    loss = \"mse\"\n",
    "                     )\n",
    "        # Training\n",
    "        \n",
    "        print(\"start training\")\n",
    "        \n",
    "        model.fit( X_tr,\n",
    "                    y_tr, \n",
    "                    validation_data=(\n",
    "                                    X_va,\n",
    "                                    y_va), \n",
    "                    epochs = EPOCHS,\n",
    "                    verbose = VERBOSE,\n",
    "                    batch_size = BATCH_SIZE,\n",
    "                    shuffle = True,\n",
    "                    callbacks = callbacks)\n",
    "\n",
    "        del X_tr, y_tr \n",
    "        gc.collect()\n",
    "\n",
    "        print(\"end training\")\n",
    "        \n",
    "        model.load_weights(checkpoint_filepath)\n",
    "        model.save(f\"./submissions/model_{fold}_{protein_index}\")\n",
    "        print('model saved')\n",
    "\n",
    "        #  Model validation\n",
    "        y_va_pred = model.predict(X_va)\n",
    "        corrscore = correlation_score(y_va, y_va_pred)\n",
    "        pred_train[idx_va] = y_va_pred\n",
    "\n",
    "        print(f\"Fold {fold}, correlation =  {corrscore:.5f}\")\n",
    "        del X_va, y_va, y_va_pred\n",
    "        gc.collect()\n",
    "        score_list.append(corrscore)\n",
    "\n",
    "# Show overall score\n",
    "\n",
    "print(f\"{Fore.GREEN}{Style.BRIGHT}Mean corr = {np.array(score_list).mean():.5f}{Style.RESET_ALL}\")\n",
    "score_total = correlation_score(Y, pred_train)\n",
    "print(f\"{Fore.BLUE}{Style.BRIGHT}Oof corr   = {score_total:.5f}{Style.RESET_ALL}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dde70c99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "80fa31c7",
   "metadata": {},
   "source": [
    "# Encode meta information into our input"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d6e42f7",
   "metadata": {},
   "source": [
    "We have some meta information in this competition, including three explicit meta information (days, donors, and cell tyoe information), and we also have one implicit meta information (gender). We can encode them into our input based on one hot encoder or embedding. The similar approach was used by JAE in the 1st open problems in single cell competition, and they won the track: joint embedding analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "956fd581",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}