2.train-VAE/2.beta_vae_training_extract_model_components.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import sys\n",
    "import pathlib \n",
    "import joblib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import optuna\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from pathlib import Path\n",
    "script_directory = pathlib.Path(\"../2.train-VAE/utils/\").resolve()\n",
    "sys.path.insert(0, str(script_directory))\n",
    "from betavae import BetaVAE, compile_vae, extract_latent_dimensions, weights\n",
    "from optimize_utils import get_optimizer\n",
    "\n",
    "script_directory = pathlib.Path(\"../utils/\").resolve()\n",
    "sys.path.insert(0, str(script_directory))\n",
    "from data_loader import load_train_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data_directory = pathlib.Path(\"../0.data-download/data\").resolve()\n",
    "train_data, test_data, val_data, load_gene_stats = load_train_test_data(\n",
    "    data_directory, train_or_test=\"all\", load_gene_stats=True, zero_one_normalize=True\n",
    ")\n",
    "\n",
    "train_tensor = torch.tensor(train_data, dtype=torch.float32)\n",
    "val_tensor = torch.tensor(val_data, dtype=torch.float32)\n",
    "test_tensor = torch.tensor(test_data, dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the best hyperparameters\n",
    "study_name = \"BetaVAE-Optimization\"\n",
    "storage_name = f\"sqlite:///./{study_name}.db\"\n",
    "study = optuna.load_study(study_name=study_name, storage=storage_name)\n",
    "best_trial = study.best_trial\n",
    "\n",
    "latent_dim = best_trial.params['latent_dim']\n",
    "beta = best_trial.params['beta']\n",
    "learning_rate = best_trial.params['learning_rate']\n",
    "batch_size = best_trial.params['batch_size']\n",
    "epochs = best_trial.params['epochs']\n",
    "optimizer = best_trial.params['optimizer_type']\n",
    "\n",
    "# Create DataLoader\n",
    "train_loader = DataLoader(TensorDataset(train_tensor), batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(TensorDataset(val_tensor), batch_size=batch_size, shuffle=False)\n",
    "test_loader = DataLoader(TensorDataset(test_tensor), batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Train Loss: 194.66850440181904, Val Loss: 257.75120120578345, Test Loss: 248.47984653049045\n",
      "Epoch 2, Train Loss: 162.64236969449627, Val Loss: 137.9633674621582, Test Loss: 137.05179426405164\n",
      "Epoch 3, Train Loss: 144.69420730818564, Val Loss: 160.32896677652994, Test Loss: 168.54310099283853\n",
      "Epoch 4, Train Loss: 132.4755713619403, Val Loss: 125.69814046223958, Test Loss: 130.5127156575521\n",
      "Epoch 5, Train Loss: 120.82267519822761, Val Loss: 108.132384830051, Test Loss: 111.62621434529622\n",
      "Epoch 6, Train Loss: 113.17906439482276, Val Loss: 94.01677322387695, Test Loss: 91.98748694525824\n",
      "Epoch 7, Train Loss: 105.44901542094216, Val Loss: 95.73658413357205, Test Loss: 96.95867072211371\n",
      "Epoch 8, Train Loss: 101.29374125466418, Val Loss: 91.76888953314887, Test Loss: 91.97513241238065\n",
      "Epoch 9, Train Loss: 96.64669134794777, Val Loss: 80.00533252292209, Test Loss: 81.35176383124457\n",
      "Epoch 10, Train Loss: 91.32313396396921, Val Loss: 76.14555994669597, Test Loss: 75.68353292677138\n",
      "Epoch 11, Train Loss: 87.82226198111007, Val Loss: 73.00046624077692, Test Loss: 72.96810531616211\n",
      "Epoch 12, Train Loss: 84.89504066581156, Val Loss: 74.54003397623698, Test Loss: 75.55868530273438\n",
      "Epoch 13, Train Loss: 80.09089155054804, Val Loss: 70.3190746307373, Test Loss: 69.44927893744574\n",
      "Epoch 14, Train Loss: 77.13921262826493, Val Loss: 64.45355033874512, Test Loss: 63.20593388875326\n",
      "Epoch 15, Train Loss: 73.99993604973182, Val Loss: 61.44918780856662, Test Loss: 61.507565604315865\n",
      "Epoch 16, Train Loss: 70.58625542939599, Val Loss: 62.8267936706543, Test Loss: 61.543820487128365\n",
      "Epoch 17, Train Loss: 67.71652176131063, Val Loss: 61.91774050394694, Test Loss: 61.88053978814019\n",
      "Epoch 18, Train Loss: 64.89106609287546, Val Loss: 59.64818127950033, Test Loss: 59.02599546644423\n",
      "Epoch 19, Train Loss: 62.44770143423507, Val Loss: 57.06490241156684, Test Loss: 56.39604165818956\n",
      "Epoch 20, Train Loss: 59.97674232596782, Val Loss: 55.94578742980957, Test Loss: 55.083468967013886\n",
      "Epoch 21, Train Loss: 58.01610781541511, Val Loss: 55.36812867058648, Test Loss: 55.23077731662326\n",
      "Epoch 22, Train Loss: 56.379667822994406, Val Loss: 54.4043820699056, Test Loss: 52.875129063924156\n",
      "Epoch 23, Train Loss: 54.757445560284516, Val Loss: 53.61885070800781, Test Loss: 52.83237308926053\n",
      "Epoch 24, Train Loss: 53.37748294659515, Val Loss: 52.88231510586209, Test Loss: 50.72059037950304\n",
      "Epoch 25, Train Loss: 52.07158020930504, Val Loss: 52.3829157087538, Test Loss: 50.71602461073134\n",
      "Epoch 26, Train Loss: 51.02915184818097, Val Loss: 51.638729095458984, Test Loss: 49.58654001024034\n",
      "Epoch 27, Train Loss: 50.548551189365675, Val Loss: 50.762881173027885, Test Loss: 49.014463636610245\n",
      "Epoch 28, Train Loss: 49.686011106576494, Val Loss: 50.389211654663086, Test Loss: 48.45280350579156\n",
      "Epoch 29, Train Loss: 49.195196077716886, Val Loss: 50.52221319410536, Test Loss: 48.0925661722819\n",
      "Epoch 30, Train Loss: 48.568881180037316, Val Loss: 49.532280604044594, Test Loss: 47.21479733784994\n",
      "Epoch 31, Train Loss: 48.54600738980877, Val Loss: 49.0779480404324, Test Loss: 47.48920652601454\n",
      "Epoch 32, Train Loss: 48.144086513234605, Val Loss: 49.43660439385308, Test Loss: 47.43550660875108\n",
      "Epoch 33, Train Loss: 47.68618892840485, Val Loss: 48.80977906121148, Test Loss: 46.40244992574056\n",
      "Epoch 34, Train Loss: 47.5838282343167, Val Loss: 48.861656188964844, Test Loss: 46.52022573682997\n",
      "Epoch 35, Train Loss: 47.57506048857276, Val Loss: 48.96756066216363, Test Loss: 46.93081601460775\n",
      "Epoch 36, Train Loss: 47.36634375728778, Val Loss: 48.6935666402181, Test Loss: 46.3835998111301\n",
      "Epoch 37, Train Loss: 47.34521666569496, Val Loss: 49.053244484795464, Test Loss: 46.42178153991699\n",
      "Epoch 38, Train Loss: 47.207676400711286, Val Loss: 48.460715187920464, Test Loss: 46.21986707051595\n",
      "Epoch 39, Train Loss: 47.13247161409748, Val Loss: 48.42003885904948, Test Loss: 46.14719729953342\n",
      "Epoch 40, Train Loss: 47.04980049702659, Val Loss: 48.10816362169054, Test Loss: 46.21478356255425\n",
      "Epoch 41, Train Loss: 47.15801601125233, Val Loss: 48.45532480875651, Test Loss: 46.160239961412216\n",
      "Epoch 42, Train Loss: 46.96987869490439, Val Loss: 48.27118343777127, Test Loss: 46.12104818556044\n",
      "Epoch 43, Train Loss: 47.119322090718285, Val Loss: 47.629943211873375, Test Loss: 46.088897705078125\n",
      "Epoch 44, Train Loss: 47.007721038362874, Val Loss: 47.93163850572374, Test Loss: 46.148179372151695\n",
      "Epoch 45, Train Loss: 46.92799035826726, Val Loss: 48.17212677001953, Test Loss: 46.00121815999349\n",
      "Epoch 46, Train Loss: 46.90489119344683, Val Loss: 48.35160700480143, Test Loss: 45.94131490919325\n",
      "Epoch 47, Train Loss: 46.877862822119866, Val Loss: 47.937873416476776, Test Loss: 45.68816884358724\n",
      "Epoch 48, Train Loss: 46.65896050752099, Val Loss: 48.16115697224935, Test Loss: 45.99300787183974\n",
      "Epoch 49, Train Loss: 46.78554395988806, Val Loss: 47.97483126322428, Test Loss: 45.95209948221842\n",
      "Epoch 50, Train Loss: 46.78959796962453, Val Loss: 47.77984513176812, Test Loss: 45.87497435675727\n",
      "Epoch 51, Train Loss: 46.786802559468285, Val Loss: 47.876556396484375, Test Loss: 45.97543970743815\n",
      "Epoch 52, Train Loss: 46.67694638380364, Val Loss: 47.93451711866591, Test Loss: 46.06672837999132\n",
      "Epoch 53, Train Loss: 46.713877572586284, Val Loss: 47.90624321831597, Test Loss: 45.85900772942437\n",
      "Epoch 54, Train Loss: 46.897772125699625, Val Loss: 47.74081484476725, Test Loss: 45.90469190809462\n",
      "Epoch 55, Train Loss: 46.8214160520639, Val Loss: 48.123113844129776, Test Loss: 45.737928814358185\n",
      "Epoch 56, Train Loss: 46.726751435692634, Val Loss: 47.432149463229706, Test Loss: 45.89039124382867\n",
      "Epoch 57, Train Loss: 46.70906126107742, Val Loss: 47.9592473771837, Test Loss: 46.00473743014865\n",
      "Epoch 58, Train Loss: 46.638833117129195, Val Loss: 48.103576024373375, Test Loss: 45.90448104010688\n",
      "Epoch 59, Train Loss: 46.812833415928175, Val Loss: 47.69326252407498, Test Loss: 45.881537755330406\n",
      "Epoch 60, Train Loss: 46.65212748513293, Val Loss: 47.76114421420627, Test Loss: 45.61609713236491\n",
      "Epoch 61, Train Loss: 46.844367457147854, Val Loss: 47.793548795912, Test Loss: 46.21317058139377\n",
      "Epoch 62, Train Loss: 46.74031016791045, Val Loss: 47.79875225490994, Test Loss: 45.82820574442545\n",
      "Epoch 63, Train Loss: 46.764210806319966, Val Loss: 48.012054655287, Test Loss: 45.541198094685875\n",
      "Epoch 64, Train Loss: 46.75731565560868, Val Loss: 47.759822845458984, Test Loss: 45.66124937269423\n",
      "Epoch 65, Train Loss: 46.59755895813899, Val Loss: 47.74345058865018, Test Loss: 45.14014116923014\n",
      "Epoch 66, Train Loss: 46.88129809934701, Val Loss: 47.77824804517958, Test Loss: 45.358239279852974\n",
      "Epoch 67, Train Loss: 46.57779486357276, Val Loss: 47.98768679300944, Test Loss: 45.66029527452257\n",
      "Epoch 68, Train Loss: 46.60065098093517, Val Loss: 47.65880097283257, Test Loss: 45.69080204433865\n",
      "Epoch 69, Train Loss: 46.78545632433536, Val Loss: 47.39796765645345, Test Loss: 45.91980001661513\n",
      "Epoch 70, Train Loss: 46.70014921729244, Val Loss: 47.71239471435547, Test Loss: 45.91599231296115\n",
      "Epoch 71, Train Loss: 46.70260027985075, Val Loss: 47.63447083367242, Test Loss: 45.72827445136176\n",
      "Epoch 72, Train Loss: 46.650313192339084, Val Loss: 47.77252197265625, Test Loss: 45.75883229573568\n",
      "Epoch 73, Train Loss: 46.57277212569963, Val Loss: 47.81435690985786, Test Loss: 45.63545248243544\n",
      "Epoch 74, Train Loss: 46.717996443563436, Val Loss: 47.79647911919488, Test Loss: 45.800402111477325\n",
      "Epoch 75, Train Loss: 46.59321234404151, Val Loss: 47.98550245496962, Test Loss: 45.75628556145562\n",
      "Epoch 76, Train Loss: 46.570092409048506, Val Loss: 47.83714781867133, Test Loss: 45.79479683770074\n",
      "Epoch 77, Train Loss: 46.755069561858676, Val Loss: 47.97862180074056, Test Loss: 45.75355254279243\n",
      "Epoch 78, Train Loss: 46.91678120627332, Val Loss: 47.678142335679794, Test Loss: 45.92289034525553\n",
      "Epoch 79, Train Loss: 46.71489403568097, Val Loss: 47.78413433498807, Test Loss: 45.92521709865994\n",
      "Epoch 80, Train Loss: 46.75390114855411, Val Loss: 47.61335712009006, Test Loss: 45.89155197143555\n",
      "Epoch 81, Train Loss: 46.68621844391325, Val Loss: 47.94412485758463, Test Loss: 45.52873611450195\n",
      "Epoch 82, Train Loss: 46.77995313957556, Val Loss: 47.83979585435655, Test Loss: 45.61969248453776\n",
      "Epoch 83, Train Loss: 46.64495831389925, Val Loss: 47.7603210873074, Test Loss: 45.58842510647244\n",
      "Epoch 84, Train Loss: 46.713140413654386, Val Loss: 47.362602445814346, Test Loss: 45.862611558702255\n",
      "Epoch 85, Train Loss: 46.722607604069495, Val Loss: 47.50602828131782, Test Loss: 45.82206281026205\n",
      "Epoch 86, Train Loss: 46.699024530666975, Val Loss: 48.135599560207794, Test Loss: 45.70594829983182\n",
      "Epoch 87, Train Loss: 46.78668231110075, Val Loss: 47.8754391140408, Test Loss: 45.56063334147135\n",
      "Epoch 88, Train Loss: 46.6370262943097, Val Loss: 47.99103461371528, Test Loss: 45.544123543633354\n",
      "Epoch 89, Train Loss: 46.87450825705457, Val Loss: 47.82603073120117, Test Loss: 45.55605909559462\n",
      "Epoch 90, Train Loss: 46.72885614651353, Val Loss: 47.969726138644745, Test Loss: 45.45306078592936\n",
      "Epoch 91, Train Loss: 46.73519961229011, Val Loss: 48.01618893941244, Test Loss: 45.851081212361656\n",
      "Epoch 92, Train Loss: 46.80788738193797, Val Loss: 47.94234148661295, Test Loss: 46.01603359646268\n",
      "Epoch 93, Train Loss: 46.64742121909981, Val Loss: 47.95391824510362, Test Loss: 45.64886834886339\n",
      "Epoch 94, Train Loss: 46.86004948402519, Val Loss: 47.56826803419325, Test Loss: 45.51266776190864\n",
      "Epoch 95, Train Loss: 46.667822630013994, Val Loss: 47.97035598754883, Test Loss: 45.54585181342231\n",
      "Epoch 96, Train Loss: 46.82984327629431, Val Loss: 47.67261780632867, Test Loss: 45.56430668301053\n",
      "Epoch 97, Train Loss: 46.53951270260028, Val Loss: 47.813568327162, Test Loss: 45.5395081837972\n",
      "Epoch 98, Train Loss: 46.61410440473414, Val Loss: 47.78787104288737, Test Loss: 45.72050476074219\n",
      "Epoch 99, Train Loss: 46.77188811800373, Val Loss: 48.16870477464464, Test Loss: 45.70842488606771\n",
      "Epoch 100, Train Loss: 46.59261128439832, Val Loss: 47.78073755900065, Test Loss: 45.41566806369357\n",
      "Epoch 101, Train Loss: 46.92207650711287, Val Loss: 47.95787027147081, Test Loss: 45.74182891845703\n",
      "Epoch 102, Train Loss: 46.56490405637827, Val Loss: 47.635790295071075, Test Loss: 45.81051254272461\n",
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      "Epoch 105, Train Loss: 46.74421031439482, Val Loss: 48.14439985487196, Test Loss: 45.70453855726454\n",
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      "Epoch 110, Train Loss: 46.61887316347948, Val Loss: 47.691692140367294, Test Loss: 45.37525537278917\n",
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      "Epoch 112, Train Loss: 46.717550067047576, Val Loss: 48.06606780158149, Test Loss: 45.68317222595215\n",
      "Epoch 113, Train Loss: 46.64547264896222, Val Loss: 47.70320955912272, Test Loss: 45.6343150668674\n",
      "Epoch 114, Train Loss: 46.54971887389226, Val Loss: 47.697312884860565, Test Loss: 45.33294910854764\n",
      "Epoch 115, Train Loss: 46.61528484287547, Val Loss: 47.98769399854872, Test Loss: 45.446764416164825\n",
      "Epoch 116, Train Loss: 46.6219482421875, Val Loss: 47.89507526821561, Test Loss: 45.62144321865506\n",
      "Epoch 117, Train Loss: 46.49348490700793, Val Loss: 48.026603274875214, Test Loss: 45.90478812323676\n",
      "Epoch 118, Train Loss: 46.70716898903918, Val Loss: 47.68962732950846, Test Loss: 45.422615475124786\n",
      "Epoch 119, Train Loss: 46.58807081535681, Val Loss: 47.69887034098307, Test Loss: 45.56263118320041\n",
      "Epoch 120, Train Loss: 46.70640887360074, Val Loss: 47.69588745964898, Test Loss: 45.78101348876953\n",
      "Epoch 121, Train Loss: 46.682989046466886, Val Loss: 47.65119849310981, Test Loss: 45.49588245815701\n",
      "Epoch 122, Train Loss: 46.68947116225513, Val Loss: 47.97758060031467, Test Loss: 45.53364457024468\n",
      "Epoch 123, Train Loss: 46.681406505072296, Val Loss: 47.775954988267685, Test Loss: 45.641830656263565\n",
      "Epoch 124, Train Loss: 46.695287357159515, Val Loss: 47.48281245761447, Test Loss: 45.676161236233185\n",
      "Epoch 125, Train Loss: 46.75185583313899, Val Loss: 47.89943525526259, Test Loss: 45.54969173007541\n",
      "Epoch 126, Train Loss: 46.65127627244636, Val Loss: 47.568892584906685, Test Loss: 45.76889567905002\n",
      "Epoch 127, Train Loss: 46.8197364010028, Val Loss: 47.609288957383896, Test Loss: 45.70613267686632\n",
      "Epoch 128, Train Loss: 46.67485060051306, Val Loss: 47.69905556572808, Test Loss: 45.39601813422309\n",
      "Epoch 129, Train Loss: 46.757496210354475, Val Loss: 47.37439939710829, Test Loss: 45.445528242323135\n",
      "Epoch 130, Train Loss: 46.61022712365905, Val Loss: 47.91905699835883, Test Loss: 45.4786811404758\n",
      "Epoch 131, Train Loss: 46.53175139925373, Val Loss: 47.51250987582736, Test Loss: 45.18792533874512\n",
      "Epoch 132, Train Loss: 46.611046087919775, Val Loss: 47.61685201856825, Test Loss: 45.46457438998752\n",
      "Epoch 133, Train Loss: 46.66409347306436, Val Loss: 47.626512951321075, Test Loss: 45.5700929429796\n",
      "Epoch 134, Train Loss: 46.70475199685168, Val Loss: 47.79643461439345, Test Loss: 45.5401725769043\n",
      "Epoch 135, Train Loss: 46.54645613485308, Val Loss: 47.47902297973633, Test Loss: 45.793945948282875\n",
      "Epoch 136, Train Loss: 46.58689566085588, Val Loss: 47.59051831563314, Test Loss: 45.7518196105957\n",
      "Epoch 137, Train Loss: 46.470974412604946, Val Loss: 47.89279789394803, Test Loss: 45.74469142489963\n",
      "Epoch 138, Train Loss: 46.68315047079058, Val Loss: 47.543713887532554, Test Loss: 45.63041623433431\n",
      "Epoch 139, Train Loss: 46.61964493936567, Val Loss: 47.97617191738553, Test Loss: 45.850595474243164\n",
      "Epoch 140, Train Loss: 46.59529045446595, Val Loss: 47.88434516059028, Test Loss: 45.541956159803604\n",
      "Epoch 141, Train Loss: 46.60564638963386, Val Loss: 47.942827224731445, Test Loss: 45.69730122884115\n",
      "Epoch 142, Train Loss: 46.57751974988339, Val Loss: 47.46153746710883, Test Loss: 45.84753735860189\n",
      "Epoch 143, Train Loss: 46.669518860774254, Val Loss: 47.64595815870497, Test Loss: 45.7827271355523\n",
      "Epoch 144, Train Loss: 46.722300424148784, Val Loss: 47.868831634521484, Test Loss: 45.93573358323839\n",
      "Epoch 145, Train Loss: 46.607899042385725, Val Loss: 48.15309969584147, Test Loss: 45.556034088134766\n",
      "Epoch 146, Train Loss: 46.807588947353075, Val Loss: 47.51482031080458, Test Loss: 45.52357588873969\n",
      "Epoch 147, Train Loss: 46.71604277197994, Val Loss: 47.705959955851235, Test Loss: 45.60748693678114\n",
      "Epoch 148, Train Loss: 46.71322877798507, Val Loss: 47.60478973388672, Test Loss: 45.28574180603027\n",
      "Epoch 149, Train Loss: 46.83592219566231, Val Loss: 47.65342818366157, Test Loss: 45.67314338684082\n",
      "Epoch 150, Train Loss: 46.76229102291278, Val Loss: 47.69579060872396, Test Loss: 45.720263163248696\n",
      "Epoch 151, Train Loss: 46.69701000612174, Val Loss: 47.428961012098526, Test Loss: 45.66625637478299\n",
      "Epoch 152, Train Loss: 46.4167334713153, Val Loss: 47.96374003092448, Test Loss: 45.630080964830185\n",
      "Epoch 153, Train Loss: 46.647547115496735, Val Loss: 47.68123584323459, Test Loss: 45.309218088785805\n",
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      "Epoch 327, Train Loss: 46.753560809235076, Val Loss: 47.59827698601617, Test Loss: 45.59180810716417\n",
      "Epoch 328, Train Loss: 46.539429439715484, Val Loss: 47.559072494506836, Test Loss: 45.5721321105957\n",
      "Epoch 329, Train Loss: 46.675303353836284, Val Loss: 47.50606897142198, Test Loss: 45.64765654669868\n",
      "Epoch 330, Train Loss: 46.52457330048974, Val Loss: 47.71203634474013, Test Loss: 45.57783783806695\n",
      "Epoch 331, Train Loss: 46.549926940007, Val Loss: 47.66115125020345, Test Loss: 45.15918646918403\n",
      "Epoch 332, Train Loss: 46.59239228661381, Val Loss: 47.61276647779677, Test Loss: 45.77518865797255\n",
      "Epoch 333, Train Loss: 46.535232771688435, Val Loss: 47.97521464029948, Test Loss: 45.66602325439453\n",
      "Epoch 334, Train Loss: 46.53402099609375, Val Loss: 47.640764236450195, Test Loss: 45.674295849270294\n",
      "Epoch 335, Train Loss: 46.74710784456623, Val Loss: 48.11658732096354, Test Loss: 45.189978493584526\n",
      "Epoch 336, Train Loss: 46.63669105643657, Val Loss: 47.595545026991104, Test Loss: 45.76955223083496\n",
      "Epoch 337, Train Loss: 46.56978213182136, Val Loss: 47.44266552395291, Test Loss: 45.114584392971466\n",
      "Epoch 338, Train Loss: 46.554787706972945, Val Loss: 47.63476859198676, Test Loss: 45.64474550882975\n",
      "Epoch 339, Train Loss: 46.60429523524953, Val Loss: 47.78347142537435, Test Loss: 45.46250364515517\n",
      "Epoch 340, Train Loss: 46.58295552267957, Val Loss: 47.59285079108344, Test Loss: 45.576294369167755\n",
      "Epoch 341, Train Loss: 46.359483770114274, Val Loss: 47.76376639472114, Test Loss: 45.34066857231988\n",
      "Epoch 342, Train Loss: 46.85511675023321, Val Loss: 47.55472289191352, Test Loss: 45.69083107842339\n",
      "Epoch 343, Train Loss: 46.52911049003032, Val Loss: 47.774082607693146, Test Loss: 45.867589738633896\n",
      "Epoch 344, Train Loss: 46.55294772475513, Val Loss: 47.67517704433865, Test Loss: 45.839426040649414\n",
      "Epoch 345, Train Loss: 46.627298201375936, Val Loss: 47.5924621158176, Test Loss: 45.82576158311632\n",
      "Epoch 346, Train Loss: 46.708083241021455, Val Loss: 47.788914362589516, Test Loss: 45.745473861694336\n",
      "Epoch 347, Train Loss: 46.51086116050606, Val Loss: 47.43847359551324, Test Loss: 45.89106814066569\n",
      "Epoch 348, Train Loss: 46.684004416394586, Val Loss: 47.65527004665799, Test Loss: 45.44542588127984\n",
      "Epoch 349, Train Loss: 46.7182005924965, Val Loss: 47.79856533474393, Test Loss: 45.27839554680718\n",
      "Epoch 350, Train Loss: 46.63771243878265, Val Loss: 47.500122706095375, Test Loss: 45.81210242377387\n",
      "Epoch 351, Train Loss: 46.61859750320662, Val Loss: 47.99353218078613, Test Loss: 45.73668649461534\n",
      "Epoch 352, Train Loss: 46.74470579232742, Val Loss: 47.6589552561442, Test Loss: 45.434601889716255\n",
      "Epoch 353, Train Loss: 46.4763866823111, Val Loss: 47.490670098198784, Test Loss: 45.539525773790146\n",
      "Epoch 354, Train Loss: 46.68114396280317, Val Loss: 47.466778649224175, Test Loss: 45.530877431233726\n",
      "Epoch 355, Train Loss: 46.40683247580457, Val Loss: 47.811449474758575, Test Loss: 45.68881585862901\n",
      "Epoch 356, Train Loss: 46.696497310809235, Val Loss: 47.655557208591034, Test Loss: 45.66933822631836\n",
      "Epoch 357, Train Loss: 46.47670497609608, Val Loss: 47.28860431247287, Test Loss: 45.49816343519423\n",
      "Epoch 358, Train Loss: 46.6173583984375, Val Loss: 47.49133427937826, Test Loss: 45.57519976298014\n",
      "Epoch 359, Train Loss: 46.57468862960588, Val Loss: 47.91675334506564, Test Loss: 45.4746159447564\n",
      "Epoch 360, Train Loss: 46.788855344857744, Val Loss: 47.702708350287544, Test Loss: 45.49746343824599\n",
      "Epoch 361, Train Loss: 46.69777285447761, Val Loss: 47.8056148952908, Test Loss: 45.637948989868164\n",
      "Epoch 362, Train Loss: 46.60212457002099, Val Loss: 47.832188288370766, Test Loss: 45.768411212497284\n",
      "Epoch 363, Train Loss: 46.69958241021455, Val Loss: 47.82225036621094, Test Loss: 45.5125355190701\n",
      "Epoch 364, Train Loss: 46.495510545417446, Val Loss: 47.98050626118978, Test Loss: 45.57134246826172\n",
      "Epoch 365, Train Loss: 46.48879959334189, Val Loss: 47.88965076870389, Test Loss: 45.66844516330295\n",
      "Epoch 366, Train Loss: 46.67159715339319, Val Loss: 47.56551424662272, Test Loss: 45.39691183302138\n",
      "Epoch 367, Train Loss: 46.703587045242536, Val Loss: 48.002239015367294, Test Loss: 45.48225996229384\n",
      "Epoch 368, Train Loss: 46.49901487435868, Val Loss: 47.97198359171549, Test Loss: 45.743496365017364\n",
      "Epoch 369, Train Loss: 46.580750058302236, Val Loss: 47.685055838690865, Test Loss: 45.3463380601671\n",
      "Epoch 370, Train Loss: 46.687634368440996, Val Loss: 47.53437741597494, Test Loss: 45.52942827012804\n",
      "Epoch 371, Train Loss: 46.727490234375, Val Loss: 47.46883010864258, Test Loss: 45.63233672247993\n",
      "Epoch 372, Train Loss: 46.67393817047574, Val Loss: 47.604285134209526, Test Loss: 45.61666297912598\n",
      "Epoch 373, Train Loss: 46.59054119053172, Val Loss: 47.32286707560221, Test Loss: 45.365405824449326\n",
      "Epoch 374, Train Loss: 46.683124234783115, Val Loss: 47.486626942952476, Test Loss: 46.10022587246365\n",
      "Epoch 375, Train Loss: 46.56372452920942, Val Loss: 47.63973956637912, Test Loss: 45.981160693698456\n",
      "Epoch 376, Train Loss: 46.611566617595614, Val Loss: 47.33979309929742, Test Loss: 45.51838684082031\n",
      "Epoch 377, Train Loss: 46.56920639721315, Val Loss: 47.83532185024686, Test Loss: 45.562573962741425\n",
      "Epoch 378, Train Loss: 46.70559136689599, Val Loss: 47.59818988376193, Test Loss: 45.42803128560384\n",
      "Epoch 379, Train Loss: 46.78548911934468, Val Loss: 47.285127851698135, Test Loss: 45.29438336690267\n",
      "Epoch 380, Train Loss: 46.57177388205457, Val Loss: 47.86471197340224, Test Loss: 45.68112246195475\n",
      "Epoch 381, Train Loss: 46.72968075880364, Val Loss: 48.283648596869575, Test Loss: 45.16725624932183\n",
      "Epoch 382, Train Loss: 46.65470025361474, Val Loss: 48.14944627549913, Test Loss: 45.73381106058756\n"
     ]
    }
   ],
   "source": [
    "#Initialize the model and optimizer\n",
    "model = BetaVAE(input_dim=train_data.shape[1], latent_dim=latent_dim, beta=beta)\n",
    "optimizer = get_optimizer(optimizer, model.parameters(), learning_rate)\n",
    "\n",
    "# Training model\n",
    "train_loss_history, val_loss_history, test_loss_history = compile_vae(model, train_loader, val_loader, test_loader, optimizer, epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model\n",
    "model_path = pathlib.Path(\"results/best_vae_model.pth\")\n",
    "torch.save(model.state_dict(), model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save training history\n",
    "history = {\n",
    "    'loss': train_loss_history,\n",
    "    'val_loss': val_loss_history,\n",
    "    'test_loss': test_loss_history\n",
    "}\n",
    "\n",
    "# Convert the history dictionary into a DataFrame\n",
    "history_df = pd.DataFrame(history)\n",
    "\n",
    "# Define the path for the parquet file\n",
    "history_path = pathlib.Path(\"results/training_history.parquet\")\n",
    "\n",
    "# Save the DataFrame to a parquet file\n",
    "history_df.to_parquet(history_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x2500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot and save the figure\n",
    "save_path = pathlib.Path(\"../1.data-exploration/figures/training_curve.png\")\n",
    "\n",
    "plt.figure(figsize=(6, 5), dpi=500)\n",
    "plt.plot(train_loss_history, label=\"Training data\")\n",
    "plt.plot(val_loss_history, label=\"Validation data\")\n",
    "plt.plot(test_loss_history, label=\"Testing data\")\n",
    "plt.yscale(\"log\")\n",
    "plt.ylabel(\"MSE + KL Divergence\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.legend()\n",
    "plt.savefig(save_path)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x2500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_path = pathlib.Path(\"../1.data-exploration/figures/training_curve_elbow.png\")\n",
    "\n",
    "plt.figure(figsize=(6, 5), dpi=500)\n",
    "plt.xlim(-1,100)\n",
    "plt.ylim(0,300)\n",
    "plt.plot(val_loss_history, label=\"Validation data\")\n",
    "plt.plot(test_loss_history, label=\"Testing data\")\n",
    "plt.plot(train_loss_history, label=\"Training data\")\n",
    "plt.ylabel(\"MSE + KL Divergence\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.legend()\n",
    "plt.savefig(save_path)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      ModelID         0    1         2    3         4    5    6    7    8  \\\n",
      "0  ACH-000499  0.116793  0.0  3.545689  0.0  5.529771  0.0  0.0  0.0  0.0   \n",
      "1  ACH-001668  0.000000  0.0  3.077717  0.0  4.795898  0.0  0.0  0.0  0.0   \n",
      "2  ACH-001211  0.153375  0.0  3.289188  0.0  4.810534  0.0  0.0  0.0  0.0   \n",
      "3  ACH-000997  0.981759  0.0  3.474591  0.0  5.523706  0.0  0.0  0.0  0.0   \n",
      "4  ACH-001745  0.718717  0.0  2.982288  0.0  4.480220  0.0  0.0  0.0  0.0   \n",
      "\n",
      "   ...        26        27   28        29   30        31        32        33  \\\n",
      "0  ...  0.000000  4.155077  0.0  1.326112  0.0  4.327004  2.289593  2.347978   \n",
      "1  ...  0.000000  3.540287  0.0  1.125518  0.0  3.794856  2.156937  1.999025   \n",
      "2  ...  0.000000  3.672348  0.0  1.189166  0.0  4.018786  2.493559  2.113322   \n",
      "3  ...  0.038345  3.702794  0.0  0.957900  0.0  4.090379  2.449086  2.523274   \n",
      "4  ...  0.000000  3.362561  0.0  1.117164  0.0  3.605763  2.393928  2.124230   \n",
      "\n",
      "    34        35  \n",
      "0  0.0  2.050914  \n",
      "1  0.0  1.775838  \n",
      "2  0.0  2.088114  \n",
      "3  0.0  2.623637  \n",
      "4  0.0  2.599002  \n",
      "\n",
      "[5 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "# Extract the latent space dimensions\n",
    "metadata_df_dir = pathlib.Path(\"../0.data-download/data/metadata_df.parquet\")\n",
    "metadata = pd.read_parquet(metadata_df_dir)\n",
    "\n",
    "train_and_test_subbed_dir = pathlib.Path(\"../0.data-download/data/train_and_test_subbed.parquet\")\n",
    "train_and_test_subbed = pd.read_parquet(train_and_test_subbed_dir)\n",
    "\n",
    "\n",
    "# Convert DataFrame to NumPy and then Tensor\n",
    "train_test_array = train_and_test_subbed.to_numpy()\n",
    "train_test_tensor = torch.tensor(train_test_array, dtype=torch.float32)\n",
    "\n",
    "#Create TensorDataset and DataLoader\n",
    "tensor_dataset = TensorDataset(train_test_tensor)\n",
    "train_and_test_subbed_loader = DataLoader(tensor_dataset, batch_size=32, shuffle=False)\n",
    "\n",
    "path = \"./results/latent_df.parquet\"\n",
    "\n",
    "latent_df = extract_latent_dimensions(model, train_and_test_subbed_loader, metadata, path)\n",
    "print(latent_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data_directory = pathlib.Path(\"../0.data-download/data\").resolve()\n",
    "train_df = load_train_test_data(\n",
    "    data_directory, train_or_test=\"train\"\n",
    ")\n",
    "\n",
    "# create dataframe containing the genes that passed an initial QC (see Pan et al. 2022) and their corresponding gene label and extract the gene labels\n",
    "gene_dict_df = pd.read_parquet(\n",
    "    \"../0.data-download/data/CRISPR_gene_dictionary.parquet\"\n",
    ")\n",
    "gene_list_passed_qc = gene_dict_df.loc[\n",
    "    gene_dict_df[\"qc_pass\"], \"dependency_column\"\n",
    "].tolist()\n",
    "\n",
    "#Extract and process weights \n",
    "path = \"./results/weight_matrix_gsea.parquet\"\n",
    "train_data = train_df.filter(gene_list_passed_qc, axis=1)\n",
    "final_gene_weights_df = weights(model, train_data, path)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gene_dependency_representations",
   "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": "3.12.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "dea262fe473c2a7265de16dc303f906b3f1b3217055f2dca7dbe6a97a0d596fd"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}