M1_cv_64d_bottleneck.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross-validation of models for Scala et al."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import sparseRRR\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import importlib\n",
    "\n",
    "import bottleneck_networks_tf2\n",
    "\n",
    "import time\n",
    "import pickle\n",
    "import warnings\n",
    "\n",
    "dtype = tf.float32\n",
    "\n",
    "def sns_styleset():\n",
    "    sns.set_style('ticks', {'axes.linewidth': .75, \"xtick.major.size\":4, \"ytick.major.size\":4})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "scala = pickle.load(open('data/scala_2020/scala2020.pickle', 'rb'))\n",
    "genes=scala['X']\n",
    "ephys=scala['Y']\n",
    "gene_names=scala['gene_names']\n",
    "cell_types=scala['celltypes']\n",
    "cell_id_to_cell_type=scala['map']\n",
    "ephys_names=scala['ephys_features']\n",
    "colors=scala['colors']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us find K-mean clusters to perform pre-training. One could also pre-train on celltype classification. This has actually improved performance even more but could be argued to be biasing the results too much. K-means allows for an unsupervised more general setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can cluster according to amounts of classes or amounts of celltypes. Change the argument of n_clusters accordingly.\n",
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters=20, random_state=17, n_init=1000).fit(ephys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "amount_of_cells_in_clusters = {cluster_id: sum(clusters==cluster_id) for cluster_id in np.unique(clusters)}\n",
    "for_every_cell_number_of_cells_of_their_cluster = np.array([amount_of_cells_in_clusters[cell] for cell in clusters])\n",
    "ind_pre_training_clusters = for_every_cell_number_of_cells_of_their_cluster >= 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster  0 represented by 62  cells.\n",
      "cluster  1 represented by 87  cells.\n",
      "cluster  2 represented by 52  cells.\n",
      "cluster  3 represented by 76  cells.\n",
      "cluster  4 represented by 74  cells.\n",
      "cluster  5 represented by 37  cells.\n",
      "cluster  6 represented by 54  cells.\n",
      "cluster  7 represented by 61  cells.\n",
      "cluster  8 represented by 30  cells.\n",
      "cluster  9 represented by 74  cells.\n",
      "cluster  10 represented by 40  cells.\n",
      "cluster  11 represented by 85  cells.\n",
      "cluster  12 represented by 116  cells.\n",
      "cluster  13 represented by 45  cells.\n",
      "cluster  14 represented by 76  cells.\n",
      "cluster  15 represented by 74  cells.\n",
      "cluster  16 represented by 42  cells.\n",
      "cluster  17 represented by 50  cells.\n",
      "cluster  18 represented by 58  cells.\n",
      "cluster  19 represented by 20  cells.\n",
      "average # of cells per cluster:  60.65\n",
      "std of # of cells per cluster:  22.100395924055295\n"
     ]
    }
   ],
   "source": [
    "c_n=[]\n",
    "for i in np.unique(clusters):\n",
    "    print('cluster ', i, 'represented by', sum(clusters==i), ' cells.')\n",
    "    c_n.append(sum(clusters==i))\n",
    "print('average # of cells per cluster: ', np.mean(np.array(c_n)))\n",
    "print('std of # of cells per cluster: ', np.std(np.array(c_n)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras initialisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras import metrics\n",
    "from tensorflow.keras.losses import CategoricalCrossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.models import load_model\n",
    "import tensorflow.keras.backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.compat.v1.disable_eager_execution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You should assume you get two TensorFlow variables as input\n",
    "y_actual = tf.compat.v1.placeholder(dtype = tf.float32)\n",
    "y_predicted = tf.compat.v1.placeholder(dtype = tf.float32)\n",
    "def r2_score(y_truth, y_pred):\n",
    "    residual = tf.add(y_truth, -y_pred, name='residual')\n",
    "    return 1-tf.reduce_sum(input_tensor=tf.square(residual)) / tf.reduce_sum(input_tensor=tf.square(y_truth))\n",
    "r2_score_result = r2_score(y_actual, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ElasticNet(tf.keras.regularizers.Regularizer): \n",
    "    def __init__(self, l1=1e-8, l2=1.0): \n",
    "        self.l1 = l1\n",
    "        self.l2 = l2\n",
    "\n",
    "    def __call__(self, x): \n",
    "        return self.l2*tf.math.reduce_sum(input_tensor=tf.math.square(x)) + \\\n",
    "                             self.l1*tf.reduce_sum(input_tensor=tf.norm(tensor=x, ord = 2, axis = 1))\n",
    "\n",
    "    def get_config(self): \n",
    "        return {'l1': float(self.l1), 'l2': float(self.l2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross validation to various training strategies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group lasso + ridge penalty straight regression (no pre-training) model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bottleneck_networks_tf2 import StraightRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CVFOLD:  0\n",
      "WARNING:tensorflow:AutoGraph could not transform <function r2_score at 0x7ff44a3d5950> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function r2_score at 0x7ff44a3d5950> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "[INFO] training network...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/keras/engine/training_v1.py:2057: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates = self.state_updates\n",
      "/usr/local/lib/python3.7/dist-packages/keras/engine/training_v1.py:2079: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 25 genes that make it:  ['Col24a1' 'Kcnip2' 'Pde1a' '6720468P15Rik' 'Enpp2' 'Gm49948' 'Ndnf'\n",
      " 'Elmo1' 'Mybpc1' 'Atp1a3' 'Reln' 'Tafa1' 'Ndn' 'Htr3a' 'Fxyd6' 'Thsd7a'\n",
      " 'Galnt14' 'Cplx1' 'Erbb4' 'Tac2' 'Lamp5' 'Sst' 'Gad1' 'Vip' 'Pvalb']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.4926719665527344\n",
      "Test R^2 after pruning:  0.46250760555267334\n",
      "Train R^2 after pruning:  0.4627925753593445\n",
      "Test R^2 after pruning:  0.45104753971099854\n",
      "\n",
      "CVFOLD:  1\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Reln' 'Kcnab3' 'Cdc42se2' 'Slc6a1' 'Gm43670' 'Elmo1' 'Galnt14' 'Plch2'\n",
      " 'Cck' 'Htr3a' 'Ndn' 'Gm49948' 'Enpp2' 'Tafa1' 'Kcnc2' 'Erbb4' 'Cplx1'\n",
      " 'Fxyd6' 'Tac2' 'Atp1a3' 'Sst' 'Lamp5' 'Gad1' 'Pvalb' 'Vip']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.4821015000343323\n",
      "Test R^2 after pruning:  0.44340699911117554\n",
      "Train R^2 after pruning:  0.46663379669189453\n",
      "Test R^2 after pruning:  0.4234775900840759\n",
      "\n",
      "CVFOLD:  2\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Unc13c' 'Sparcl1' 'Gabra1' 'Cxcl14' 'Col24a1' 'Gm11549' 'Enpp2' 'Npy'\n",
      " 'Atp1a3' 'Mas1' 'Kcnc2' 'Chat' 'Htr3a' 'Galnt14' 'Tafa1' 'Fxyd6' 'Ndn'\n",
      " 'Cplx1' 'Erbb4' 'Tac2' 'Gad1' 'Sst' 'Lamp5' 'Vip' 'Pvalb']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.5065426826477051\n",
      "Test R^2 after pruning:  0.404279887676239\n",
      "Train R^2 after pruning:  0.460915744304657\n",
      "Test R^2 after pruning:  0.39655351638793945\n",
      "\n",
      "CVFOLD:  3\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Vwa5b1' 'Npy' 'Satb1' 'Elmo1' 'Zfp11' 'Tafa1' 'Mybpc1' 'Kcnc2' 'Mas1'\n",
      " 'Zfp873' 'Ndn' 'Ndst3' 'Thsd7a' 'Galnt14' 'Tac2' 'Erbb4' 'Mgst1' 'Cplx1'\n",
      " 'Fxyd6' 'Htr3a' 'Lamp5' 'Vip' 'Pvalb' 'Gad1' 'Sst']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.4932137131690979\n",
      "Test R^2 after pruning:  0.43491965532302856\n",
      "Train R^2 after pruning:  0.4478744864463806\n",
      "Test R^2 after pruning:  0.41316425800323486\n",
      "\n",
      "CVFOLD:  4\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Ifit1' 'Phactr1' 'Elmo1' 'Mas1' 'Plch2' 'Ndst3' 'Mybpc1' 'Gas7' 'Cck'\n",
      " 'Gm49948' 'Thsd7a' 'Fxyd6' 'Cplx1' 'Galnt14' 'Ndn' 'Npy' 'Erbb4' 'Tac2'\n",
      " 'Tafa1' 'Gad1' 'Lamp5' 'Htr3a' 'Sst' 'Pvalb' 'Vip']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.5948697030544281\n",
      "Test R^2 after pruning:  0.4253491759300232\n",
      "Train R^2 after pruning:  0.4682654142379761\n",
      "Test R^2 after pruning:  0.4199920892715454\n",
      "\n",
      "CVFOLD:  5\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Nell1' 'Enpp2' 'Cck' 'Galnt14' 'Slc6a1' 'Plch2' 'Slc5a7' 'Mybpc1'\n",
      " 'Tafa1' 'Reln' 'Cxcl14' 'Npy' 'Chrnb3' 'Ndn' 'Chat' 'Erbb4' 'Fxyd6'\n",
      " 'Htr3a' 'Cplx1' 'Tac2' 'Gad1' 'Lamp5' 'Sst' 'Pvalb' 'Vip']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.5018016993999481\n",
      "Test R^2 after pruning:  0.4126942753791809\n",
      "Train R^2 after pruning:  0.463506281375885\n",
      "Test R^2 after pruning:  0.380032479763031\n",
      "\n",
      "CVFOLD:  6\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Cck' 'Tiam2' 'Calb1' 'Enpp2' 'Cdh13' 'Lingo2' 'Chrnb3' 'Elmo1' 'Ndn'\n",
      " 'Gas7' 'Ndst3' 'Galnt14' 'Gm49948' 'Atp1a3' 'Erbb4' 'Fxyd6' 'Tafa1'\n",
      " 'Cplx1' 'Gad1' 'Tac2' 'Htr3a' 'Pvalb' 'Vip' 'Lamp5' 'Sst']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.55128613114357\n",
      "Test R^2 after pruning:  0.4341445565223694\n",
      "Train R^2 after pruning:  0.46989405155181885\n",
      "Test R^2 after pruning:  0.417818546295166\n",
      "\n",
      "CVFOLD:  7\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Enpp2' 'Slc6a1' 'Lingo2' 'Cck' 'Gabrq' 'Npy1r' 'Reln' 'Pdyn' 'Elmo1'\n",
      " 'Gm49948' 'Tafa1' 'Atp1a3' 'Cplx1' 'Ndst3' 'Ndn' 'Fxyd6' 'Htr3a'\n",
      " 'Galnt14' 'Erbb4' 'Tac2' 'Sst' 'Gad1' 'Lamp5' 'Vip' 'Pvalb']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.4957360029220581\n",
      "Test R^2 after pruning:  0.4572346806526184\n",
      "Train R^2 after pruning:  0.4634057879447937\n",
      "Test R^2 after pruning:  0.4546589255332947\n",
      "\n",
      "CVFOLD:  8\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Npy' 'Kcnab3' 'Plch2' 'Cbln2' 'Tafa1' 'Atp1b2' 'Calb1' 'Cxcl14' 'Cck'\n",
      " 'Reln' 'Erbb4' 'Gabra1' 'Thsd7a' 'Cplx1' 'Galnt14' 'Tac2' 'Atp1a3'\n",
      " 'Gm49948' 'Htr3a' 'Ndn' 'Sst' 'Lamp5' 'Pvalb' 'Gad1' 'Vip']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.48613232374191284\n",
      "Test R^2 after pruning:  0.41080594062805176\n",
      "Train R^2 after pruning:  0.47787177562713623\n",
      "Test R^2 after pruning:  0.4035717844963074\n",
      "\n",
      "CVFOLD:  9\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Plch2' 'Cck' 'Gas7' 'Lingo2' 'Kcnc2' 'Scg2' 'Chat' 'Gabra1' 'Npy'\n",
      " 'Galnt14' 'Tafa1' 'Atp1a3' 'Fxyd6' 'Thsd7a' 'Ndn' 'Tac2' 'Cplx1' 'Htr3a'\n",
      " 'Pdyn' 'Erbb4' 'Lamp5' 'Gad1' 'Sst' 'Pvalb' 'Vip']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.5068955421447754\n",
      "Test R^2 after pruning:  0.4503887891769409\n",
      "Train R^2 after pruning:  0.4728933572769165\n",
      "Test R^2 after pruning:  0.41833752393722534\n",
      "Time: 0.0h  9m 36s\n"
     ]
    }
   ],
   "source": [
    "# Settings\n",
    "l2_params = np.logspace(-10, -10, num=1)\n",
    "l1_params = np.array([1e-1])\n",
    "\n",
    "act = 'elu'\n",
    "folds = 10\n",
    "seed = 42\n",
    "lr=0.0001\n",
    "epochs=50\n",
    "bs=32\n",
    "prune=True\n",
    "patience=epochs\n",
    "\n",
    "r2_straight_regr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_straight_regr_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_straight_regr_ap = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_straight_regr_ap_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_straight_regr_hist = np.zeros((folds, l1_params.shape[0], l2_params.shape[0], 4*epochs))\n",
    "r2_straight_regr_hist_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0], 4*epochs))\n",
    "\n",
    "t = time.time()\n",
    "np.random.seed(seed)\n",
    "n = genes.shape[0]\n",
    "ind = np.random.permutation(n)\n",
    "X = genes[ind,:]\n",
    "Y = ephys[ind,:]\n",
    "\n",
    "nodes_list=[512,128,64,128,512]\n",
    "\n",
    "# CV folds\n",
    "for cvfold in range(folds):\n",
    "    print('\\nCVFOLD: ', cvfold)\n",
    "    indtest  = np.arange(cvfold*int(n/folds), (cvfold+1)*int(n/folds))\n",
    "    indtrain = np.setdiff1d(np.arange(n), indtest)\n",
    "    x_train = np.copy(X[indtrain,:])\n",
    "    y_train = np.copy(Y[indtrain,:])\n",
    "    x_test = np.copy(X[indtest,:])\n",
    "    y_test = np.copy(Y[indtest,:])\n",
    "\n",
    "    # Mean centering (like sRRR)\n",
    "    X_mean = np.mean(x_train, axis=0)\n",
    "    x_train -= X_mean\n",
    "    x_test -= X_mean\n",
    "    Y_mean = np.mean(y_train, axis=0)\n",
    "    y_train -= Y_mean\n",
    "    y_test -= Y_mean\n",
    "    \n",
    "    input_dim = x_train.shape[1]\n",
    "    output_dim = y_train.shape[1]\n",
    "    \n",
    "    for i, l2 in enumerate(l2_params):\n",
    "        #print('Ridge penalty: ', l2)\n",
    "        for j, l1 in enumerate(l1_params):\n",
    "            #print('Lasso penalty: ', l1)\n",
    "            BN_StraightRegression = StraightRegression(l1=l1, l2=l2, lr=lr, act=act, \\\n",
    "                                                       input_dim=input_dim, output_dim=output_dim, nodes_list=nodes_list)\n",
    "            r2_train, r2_test, \\\n",
    "                r2_ap_train, r2_ap_test, \\\n",
    "                train_hist, test_hist = BN_StraightRegression.train(x_train=x_train, y_train=y_train, \\\n",
    "                                                                                   x_test=x_test, y_test=y_test, \\\n",
    "                                                                                   epochs=epochs, bs=bs, patience=patience, \\\n",
    "                                                                                   cvfold_id=cvfold, l1_id=j, l2_id=i, \\\n",
    "                                                                                   prune=prune, geneNames=gene_names, \\\n",
    "                                                                                   verbose=0)\n",
    "            r2_straight_regr[cvfold, j, i] = r2_test\n",
    "            r2_straight_regr_tr[cvfold, j, i] = r2_train\n",
    "            r2_straight_regr_ap[cvfold, j, i] = r2_ap_test\n",
    "            r2_straight_regr_ap_tr[cvfold, j, i] = r2_ap_train\n",
    "            r2_straight_regr_hist[cvfold, j, i] = test_hist\n",
    "            r2_straight_regr_hist_tr[cvfold, j, i] = train_hist\n",
    "            #print('Test R^2 score (straight regression, no pre-tr): ', r2_straight_regr[cvfold, j, i])\n",
    "            #print('Test R^2 score after pruning (straight regression, no pre-tr): ', r2_straight_regr_ap[cvfold, j, i])\n",
    "\n",
    "t = time.time() - t\n",
    "m,s = divmod(t, 60)\n",
    "h,m = divmod(m, 60)\n",
    "print('Time: {}h {:2.0f}m {:2.0f}s'.format(h,m,s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_r2_straight_regr_lasso_no_bottleneck.pickle', 'wb') as f:\n",
    "    pickle.dump([r2_straight_regr, r2_straight_regr_tr, \\\n",
    "                 r2_straight_regr_ap, r2_straight_regr_ap_tr, \\\n",
    "                 r2_straight_regr_hist, r2_straight_regr_hist_tr], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group lasso + ridge penalty classification pre-training model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bottleneck_networks_tf2 import ClassificationPreTrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CVFOLD:  0\n",
      "[INFO] training network...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/keras/engine/training_v1.py:2057: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates = self.state_updates\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "\n",
      "CVFOLD:  1\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "\n",
      "CVFOLD:  2\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "Time: 0.0h  1m 32s\n"
     ]
    }
   ],
   "source": [
    "# Immediately for different penalties, but we'll be only interested in the 1e-1 penalty (for now), i.e. see extensions later\n",
    "l2_params = np.logspace(-10, -10, num=1)\n",
    "l1_params = np.array([1e-4, 1e-2, 1e-1, 1])\n",
    "\n",
    "act = 'elu'\n",
    "folds = 3\n",
    "seed = 42\n",
    "lr=0.0001\n",
    "epochs=50\n",
    "bs=32\n",
    "patience=epochs\n",
    "\n",
    "\n",
    "t = time.time()\n",
    "np.random.seed(seed)\n",
    "n = genes.shape[0]\n",
    "ind = np.random.permutation(n)\n",
    "X = genes[ind,:]\n",
    "Y = ephys[ind,:]\n",
    "clusters_ = clusters[ind]\n",
    "ind_pre_train = ind_pre_training_clusters[ind]\n",
    "\n",
    "CE_loss = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "CE_loss_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "pre_tr_hist = np.zeros((folds, l1_params.shape[0], l2_params.shape[0], epochs))\n",
    "pre_tr_hist_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0], epochs))\n",
    "\n",
    "nodes_list=[512,128,64,128,512]\n",
    "\n",
    "# CV folds\n",
    "for cvfold in range(folds):\n",
    "    print('\\nCVFOLD: ', cvfold)\n",
    "    indtest  = np.arange(cvfold*int(n/folds), (cvfold+1)*int(n/folds))\n",
    "    indtrain = np.setdiff1d(np.arange(n), indtest)\n",
    "    x_train = np.copy(X[indtrain,:])\n",
    "    clusters_train = np.copy(clusters_[indtrain])\n",
    "    y_train = np.copy(Y[indtrain,:])\n",
    "    x_test = np.copy(X[indtest,:])\n",
    "    clusters_test = np.copy(clusters_[indtest])\n",
    "    y_test = np.copy(Y[indtest,:])\n",
    "\n",
    "    # Mean centering (like sRRR)\n",
    "    X_mean = np.mean(x_train, axis=0)\n",
    "    x_train -= X_mean\n",
    "    x_test -= X_mean\n",
    "    Y_mean = np.mean(y_train, axis=0)\n",
    "    y_train -= Y_mean\n",
    "    y_test -= Y_mean\n",
    "\n",
    "    # In our training set we now look for a 'pretraining' training and test data set that we can use\n",
    "    # for pretraing a classification network, i.e. picking the best model will be dones with a seperate val set in our\n",
    "    # training set for regression.\n",
    "\n",
    "    x_train_pre_tr = x_train[ind_pre_train[indtrain], :]\n",
    "    clusters_train_pre_tr = clusters_train[ind_pre_train[indtrain]]\n",
    "    \n",
    "    pre_train_size = 0.6\n",
    "    train_cnt = round(x_train_pre_tr.shape[0]*pre_train_size)\n",
    "    x_train_pre_tr_ = x_train_pre_tr[:train_cnt, :]\n",
    "    x_test_pre_tr_ = x_train_pre_tr[train_cnt:, :]\n",
    "    clusters_train_pre_tr_ = clusters_train_pre_tr[:train_cnt]\n",
    "    clusters_test_pre_tr_ = clusters_train_pre_tr[train_cnt:]\n",
    "    \n",
    "    for i, l2 in enumerate(l2_params):\n",
    "        #print('Ridge penalty: ', l2)\n",
    "        for j, l1 in enumerate(l1_params):\n",
    "            #print('Lasso penalty: ', l1)\n",
    "\n",
    "\n",
    "            input_dim = x_train.shape[1]\n",
    "            output_dim = to_categorical(clusters_train_pre_tr).shape[1]\n",
    "            BN_Classification = ClassificationPreTrain(l1=l1, l2=l2, lr=lr, act=act, input_dim=input_dim, \\\n",
    "                                                       output_dim=output_dim, nodes_list=nodes_list)\n",
    "            CE_train, CE_test, train_hist, test_hist = BN_Classification.train(x_train=x_train_pre_tr_, \\\n",
    "                                                                               cluster_train=clusters_train_pre_tr_, \\\n",
    "                                                                               x_test=x_test_pre_tr_, \\\n",
    "                                                                               cluster_test=clusters_test_pre_tr_, \\\n",
    "                                                                               epochs=epochs, bs=bs, patience=patience, \\\n",
    "                                                                               cvfold_id=cvfold, l1_id=j, l2_id=i, \\\n",
    "                                                                               verbose=0)\n",
    "            CE_loss[cvfold, j, i] = CE_test\n",
    "            CE_loss_tr[cvfold, j, i] = CE_train\n",
    "            pre_tr_hist[cvfold, j, i] = test_hist\n",
    "            pre_tr_hist_tr[cvfold, j, i] = train_hist\n",
    "            #print('Test CE loss (pre-trainin): ', CE_loss[cvfold, j, i])\n",
    "            \n",
    "t = time.time() - t\n",
    "m,s = divmod(t, 60)\n",
    "h,m = divmod(m, 60)\n",
    "print('Time: {}h {:2.0f}m {:2.0f}s'.format(h,m,s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_pre_tr_lasso_no_bottleneck.pickle', 'wb') as f:\n",
    "    pickle.dump([CE_loss, CE_loss_tr, pre_tr_hist, pre_tr_hist_tr], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group lasso + ridge penalty pre-train and regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CVFOLD:  0\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Unc13c' 'Arhgap32' 'Tafa1' 'Grm7' 'Th' 'Idh3a' 'Gm49948' 'Spon1' 'Elfn1'\n",
      " 'Enpp2' 'Gabra1' 'Ndn' 'Sema3c' 'Mas1' 'Fxyd6' 'Cplx1' 'Atp1a3' 'Kcnc2'\n",
      " 'Thsd7a' 'Erbb4' 'Lamp5' 'Gad1' 'Tac2' 'Vip' 'Pvalb']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.521702915430069\n",
      "Test R^2 after pruning:  0.42676854133605957\n",
      "Train R^2 after pruning:  0.47844940423965454\n",
      "Test R^2 after pruning:  0.4096892476081848\n",
      "\n",
      "CVFOLD:  1\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Ncald' 'Nos1' 'Bdnf' 'Cck' 'Vsir' 'Kcnc2' 'Ndst3' 'Cplx1' 'Rtn3' 'Tafa1'\n",
      " 'Gas7' 'Fxyd6' 'Cdh13' 'Erbb4' 'Mybpc1' 'Galnt14' 'Tac2' 'Thsd7a' 'Pamr1'\n",
      " 'Lamp5' 'Htr3a' 'Gad1' 'Vip' 'Sst' 'Pvalb']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.5548890233039856\n",
      "Test R^2 after pruning:  0.42968833446502686\n",
      "Train R^2 after pruning:  0.48152124881744385\n",
      "Test R^2 after pruning:  0.4006425738334656\n",
      "\n",
      "CVFOLD:  2\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Npy1r' 'Cck' 'Fxyd6' 'Sst' 'Hpse' 'Atp1b2' 'Kcnc2' 'Gabrd' 'Atp1a3'\n",
      " 'Pamr1' 'Afap1' 'Gabra1' 'Scg2' 'Npy' 'Cplx1' 'Erbb4' 'Pvalb' 'Elmo1'\n",
      " 'Lamp5' 'Ndn' 'Htr3a' 'Galnt14' 'Gad1' 'Vip' 'Pdyn']\n",
      "[INFO] training network...\n",
      "Train R^2 before pruning:  0.5235995948314667\n",
      "Test R^2 after pruning:  0.43582189083099365\n",
      "Train R^2 after pruning:  0.48452329635620117\n",
      "Test R^2 after pruning:  0.4047980308532715\n",
      "Time: 0.0h  2m 28s\n"
     ]
    }
   ],
   "source": [
    "# Settings\n",
    "l2_params = np.logspace(-10, -10, num=1)\n",
    "l1_params = np.array([1e-1])\n",
    "\n",
    "act = 'elu'\n",
    "folds = 3\n",
    "seed = 42\n",
    "lr=0.0001\n",
    "epochs=50\n",
    "bs=32\n",
    "patience=epochs\n",
    "pre_trained_weights=True\n",
    "prune=True\n",
    "\n",
    "r2_pretr_straight_regr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_pretr_straight_regr_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_pretr_straight_regr_ap = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_pretr_straight_regr_ap_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_pretr_straight_regr_hist = np.zeros((folds, l1_params.shape[0], l2_params.shape[0], 4*epochs))\n",
    "r2_pretr_straight_regr_hist_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0], 4*epochs))\n",
    "\n",
    "t = time.time()\n",
    "np.random.seed(seed)\n",
    "n = genes.shape[0]\n",
    "ind = np.random.permutation(n)\n",
    "X = genes[ind,:]\n",
    "Y = ephys[ind,:]\n",
    "\n",
    "nodes_list=[512,128,64,128,512]\n",
    "\n",
    "# CV folds\n",
    "for cvfold in range(folds):\n",
    "    print('\\nCVFOLD: ', cvfold)\n",
    "    indtest  = np.arange(cvfold*int(n/folds), (cvfold+1)*int(n/folds))\n",
    "    indtrain = np.setdiff1d(np.arange(n), indtest)\n",
    "    x_train = np.copy(X[indtrain,:])\n",
    "    y_train = np.copy(Y[indtrain,:])\n",
    "    x_test = np.copy(X[indtest,:])\n",
    "    y_test = np.copy(Y[indtest,:])\n",
    "\n",
    "    # Mean centering (like sRRR)\n",
    "    X_mean = np.mean(x_train, axis=0)\n",
    "    x_train -= X_mean\n",
    "    x_test -= X_mean\n",
    "    Y_mean = np.mean(y_train, axis=0)\n",
    "    y_train -= Y_mean\n",
    "    y_test -= Y_mean\n",
    "    \n",
    "    input_dim = x_train.shape[1]\n",
    "    output_dim = y_train.shape[1]\n",
    "    \n",
    "    for i, l2 in enumerate(l2_params):\n",
    "        #print('Ridge penalty: ', l2)\n",
    "        for j, l1 in enumerate(l1_params):\n",
    "            #print('Lasso penalty: ', l1)\n",
    "            \n",
    "            pre_trained_weights_h5 = 'KerasSavedModels/Classification_last_weights_{}_{}_{}.h5'.format(cvfold, 2, i)\n",
    "            \n",
    "            BN_PreTrStraightRegression = StraightRegression(l1=l1, l2=l2, lr=lr, act=act, \\\n",
    "                                            input_dim=input_dim, output_dim=output_dim, \\\n",
    "                                            pre_trained_weights=pre_trained_weights, \\\n",
    "                                            pre_trained_weights_h5=pre_trained_weights_h5, nodes_list=nodes_list)\n",
    "            r2_train, r2_test, \\\n",
    "                r2_train_ap, r2_test_ap, \\\n",
    "                train_hist, test_hist = BN_PreTrStraightRegression.train(x_train=x_train, y_train=y_train, \\\n",
    "                                                                                   x_test=x_test, y_test=y_test, \\\n",
    "                                                                                   epochs=epochs, bs=bs, patience=patience, \\\n",
    "                                                                                   cvfold_id=cvfold, l1_id=j, l2_id=i, \\\n",
    "                                                                                   prune=prune, geneNames=gene_names, \\\n",
    "                                                                                   verbose=0)\n",
    "            r2_pretr_straight_regr[cvfold, j, i] = r2_test\n",
    "            r2_pretr_straight_regr_tr[cvfold, j, i] = r2_train\n",
    "            r2_pretr_straight_regr_ap[cvfold, j, i] = r2_test_ap\n",
    "            r2_pretr_straight_regr_ap_tr[cvfold, j, i] = r2_train_ap            \n",
    "            r2_pretr_straight_regr_hist[cvfold, j, i] = test_hist\n",
    "            r2_pretr_straight_regr_hist_tr[cvfold, j, i] = train_hist\n",
    "            #print('Test R^2 score (regression, after pre-tr): ', r2_pretr_straight_regr[cvfold, j, i])\n",
    "            #print('Test R^2 score (regression, after pre-tr), after pruning: ', r2_pretr_straight_regr_ap[cvfold, j, i])\n",
    "\n",
    "t = time.time() - t\n",
    "m,s = divmod(t, 60)\n",
    "h,m = divmod(m, 60)\n",
    "print('Time: {}h {:2.0f}m {:2.0f}s'.format(h,m,s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_r2_pre_tr_and_straight_regr_lasso_no_bottleneck.pickle', 'wb') as f:\n",
    "    pickle.dump([r2_pretr_straight_regr, r2_pretr_straight_regr_tr, \\\n",
    "                 r2_pretr_straight_regr_ap, r2_pretr_straight_regr_ap_tr, \\\n",
    "                 r2_pretr_straight_regr_hist, r2_pretr_straight_regr_hist_tr], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group lasso + ridge penalty pre-train + freeze the first 2 layers + unfreeze model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'bottleneck_networks_tf2' from '/gpfs01/berens/user/ybernaerts/EphysAnalysis/data/ephys/sBNN/bottleneck_networks_tf2.py'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(bottleneck_networks_tf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bottleneck_networks_tf2 import FreezeUnfreeze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CVFOLD:  0\n",
      "WARNING:tensorflow:AutoGraph could not transform <function r2_score at 0x7fda021e1d40> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function r2_score at 0x7fda021e1d40> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: 'arguments' object has no attribute 'posonlyargs'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/keras/engine/training_v1.py:2079: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 25 genes that make it:  ['Kcnc2' 'Cadps2' 'Lancl3' 'Ap1s2' 'Cmtr2' 'Cacna1e' 'Fstl5' 'Pvalb'\n",
      " 'Ccr5' 'Baiap2' 'Slc2a3' 'Lingo2' 'Rora' 'Tmem44' 'Trpm2' 'Kctd21' 'Egr2'\n",
      " 'Opcml' 'Ndst3' 'Rab11a' 'Cep83os' 'Dmrt2' 'Pamr1' 'Nbr1' 'Col24a1']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.6303770244121552\n",
      "Test R^2 before unfreezing:  0.3773595094680786\n",
      "Train R^2 after unfreezing:  0.7510461658239365\n",
      "Test R^2 after unfreezing:  0.3784949779510498\n",
      "Train R^2 after unfreezing and pruning:  0.4021405577659607\n",
      "Test R^2 after unfreezing and pruning:  0.32771188020706177\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Coa5' 'Epha4' 'Sstr2' 'Neat1' 'Col6a1' 'Col6a2' 'Gad2' 'Scube1'\n",
      " 'Gm11549' 'Cd34' 'Ak5' 'Enpp2' 'Ccn1' 'Pamr1' 'Nrp1' 'Crym' 'Blnk'\n",
      " 'Fezf2' 'Gm48745' 'Neurod6' 'Scara3' 'Mas1' 'Tmem63a' 'Tafa1' 'Adcyap1']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.6387826502323151\n",
      "Test R^2 before unfreezing:  0.40611493587493896\n",
      "Train R^2 after unfreezing:  0.7589810490608215\n",
      "Test R^2 after unfreezing:  0.41571760177612305\n",
      "Train R^2 after unfreezing and pruning:  0.27925097942352295\n",
      "Test R^2 after unfreezing and pruning:  0.1849672794342041\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Sparcl1' 'Tafa1' 'Idh3a' 'Atp1a3' 'Aldoc' 'Reln' 'Calb1' 'Ndn' 'Sst'\n",
      " 'Kcnab3' 'Fxyd6' 'Enpp2' 'Unc13c' 'Cbln2' 'Cplx1' 'Elfn1' 'Thsd7a'\n",
      " 'Spon1' 'Kcnc2' 'Erbb4' 'Gad1' 'Tac2' 'Vip' 'Lamp5' 'Pvalb']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.5169857144355774\n",
      "Test R^2 before unfreezing:  0.4092499613761902\n",
      "Train R^2 after unfreezing:  0.5552446842193604\n",
      "Test R^2 after unfreezing:  0.43781256675720215\n",
      "Train R^2 after unfreezing and pruning:  0.49206143617630005\n",
      "Test R^2 after unfreezing and pruning:  0.4256552457809448\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Phactr1' 'Gm49948' 'Kcnc2' 'Cdh13' 'Ptk2b' 'Chl1' 'Galnt14' 'Crhbp'\n",
      " 'Ndn' 'Npas1' 'Nell2' 'Lamp5' 'Kcnip2' 'Sparcl1' 'Fxyd6' 'Elfn1' 'Gad1'\n",
      " 'Unc13c' 'Atp1a3' 'Vip' 'Sst' 'Cacna2d1' 'Tac2' 'Erbb4' 'Pvalb']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.3985506296157837\n",
      "Test R^2 before unfreezing:  0.3952842354774475\n",
      "Train R^2 after unfreezing:  0.3807337284088135\n",
      "Test R^2 after unfreezing:  0.369093656539917\n",
      "Train R^2 after unfreezing and pruning:  0.453518271446228\n",
      "Test R^2 after unfreezing and pruning:  0.41152364015579224\n",
      "\n",
      "CVFOLD:  1\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Uqcrfs1' 'Calb1' 'Gpcpd1' 'Prkcg' 'Lyn' 'Ccl4' 'Zfp536' 'Gm49948'\n",
      " 'Thsd7a' 'Sucla2' 'Stx12' 'Atp6v1g2' '9530068E07Rik' 'Dusp14' 'Sema3e'\n",
      " 'Tmem167b' 'Aldh1a1' 'Penk' 'Zmat4' 'Ano3' 'Stxbp2' 'Mbp' 'Reln' 'Rgs2'\n",
      " 'Aars']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.6231914460659027\n",
      "Test R^2 before unfreezing:  0.3552224636077881\n",
      "Train R^2 after unfreezing:  0.6997499465942383\n",
      "Test R^2 after unfreezing:  0.361428439617157\n",
      "Train R^2 after unfreezing and pruning:  0.39680415391921997\n",
      "Test R^2 after unfreezing and pruning:  0.27554333209991455\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Sema3e' 'Gad1' 'Kctd21' 'Akr1c18' 'Prox1' 'Neat1' 'A430033K04Rik'\n",
      " 'Gm24105' 'Ndst3' 'Neurod6' 'Pvalb' 'Tmem200a' 'AI597479' 'Calb1'\n",
      " 'Baiap3' 'Pdgfra' 'Kcnv1' 'Tekt5' 'Klhl23' 'Gm11549' 'Zfp11' 'Vwa5b1'\n",
      " 'Arhgap45' 'Mas1' 'Tafa1']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.6291668117046356\n",
      "Test R^2 before unfreezing:  0.3732430338859558\n",
      "Train R^2 after unfreezing:  0.7457538545131683\n",
      "Test R^2 after unfreezing:  0.3881445527076721\n",
      "Train R^2 after unfreezing and pruning:  0.4295840263366699\n",
      "Test R^2 after unfreezing and pruning:  0.3479735851287842\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Ndn' 'Ndst3' 'Pde1a' 'Gas7' 'Cplx3' 'Chat' 'Bdnf' 'Pdyn' 'Galnt14'\n",
      " 'Cplx1' 'Cck' 'Tac2' 'Cdh13' 'Fxyd6' 'Erbb4' 'Tafa1' 'Thsd7a' 'Mybpc1'\n",
      " 'Lamp5' 'Pamr1' 'Gad1' 'Htr3a' 'Vip' 'Pvalb' 'Sst']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.5276319980621338\n",
      "Test R^2 before unfreezing:  0.41985470056533813\n",
      "Train R^2 after unfreezing:  0.5631581842899323\n",
      "Test R^2 after unfreezing:  0.43176549673080444\n",
      "Train R^2 after unfreezing and pruning:  0.49414485692977905\n",
      "Test R^2 after unfreezing and pruning:  0.4155595898628235\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Adamts1' 'Bmp3' 'Nell2' 'Kcnip2' 'Gm49948' 'Ndst3' 'Cacna2d3' 'Chl1'\n",
      " 'Dach2' 'Ndn' 'Tac1' 'Ptk2b' 'Unc13c' 'Plch2' 'Vip' 'Cacna1e' 'Bdnf'\n",
      " 'Tac2' 'Gad1' 'Erbb4' 'Elfn1' 'Lamp5' 'Htr3a' 'Sst' 'Pvalb']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.40408724546432495\n",
      "Test R^2 before unfreezing:  0.37556636333465576\n",
      "Train R^2 after unfreezing:  0.40863925218582153\n",
      "Test R^2 after unfreezing:  0.3698195815086365\n",
      "Train R^2 after unfreezing and pruning:  0.44595998525619507\n",
      "Test R^2 after unfreezing and pruning:  0.39670735597610474\n",
      "\n",
      "CVFOLD:  2\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Homer1' 'Tcf4' 'Shroom2' 'Sst' '9930012K11Rik' 'Lancl3' 'Fut9' 'Oxtr'\n",
      " 'Cstf2' 'Kcnip2' 'Irf5' 'Fos' 'Nr2e1' 'Luzp2' 'Rab11a' 'Aars' 'Cplx3'\n",
      " 'Tomm70a' 'Trf' 'Gad1' 'Poglut1' 'Pamr1' 'Gpcpd1' 'Tnfrsf1b' 'Anxa3']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.6237137615680695\n",
      "Test R^2 before unfreezing:  0.36059069633483887\n",
      "Train R^2 after unfreezing:  0.7316611111164093\n",
      "Test R^2 after unfreezing:  0.36053186655044556\n",
      "Train R^2 after unfreezing and pruning:  0.3854888677597046\n",
      "Test R^2 after unfreezing and pruning:  0.3128971457481384\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Rtl6' 'Sv2b' 'Npas4' 'Tiam2' 'Kcnv1' 'Gad1' 'Elmo1' 'Cplx3' 'Cav1'\n",
      " 'Serpina3n' 'Sdr42e1' 'Cbln4' 'Tekt5' 'Mas1' 'Lmo7' 'Ptgs2' 'Gm11549'\n",
      " 'Sstr2' 'Neurod6' 'Poglut3' 'Scube1' 'Ptk2b' 'Adcyap1' 'Klhl23' 'Tafa1']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.6399842798709869\n",
      "Test R^2 before unfreezing:  0.3903394937515259\n",
      "Train R^2 after unfreezing:  0.7159861028194427\n",
      "Test R^2 after unfreezing:  0.4032290577888489\n",
      "Train R^2 after unfreezing and pruning:  0.31016725301742554\n",
      "Test R^2 after unfreezing and pruning:  0.22873824834823608\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Afap1' 'Enpp2' 'Npas1' 'Hpse' 'Atp1a3' 'Gm49948' 'Gabrd' 'Col6a3'\n",
      " 'Nell1' 'Cck' 'Erbb4' 'Elmo1' 'Pamr1' 'Gabra1' 'Npy' 'Sst' 'Kcnc2' 'Ndn'\n",
      " 'Htr3a' 'Pvalb' 'Lamp5' 'Galnt14' 'Gad1' 'Vip' 'Pdyn']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.5253017246723175\n",
      "Test R^2 before unfreezing:  0.40164148807525635\n",
      "Train R^2 after unfreezing:  0.5363775789737701\n",
      "Test R^2 after unfreezing:  0.43356573581695557\n",
      "Train R^2 after unfreezing and pruning:  0.48074227571487427\n",
      "Test R^2 after unfreezing and pruning:  0.4011848568916321\n",
      "[INFO] training network...\n",
      "[INFO] training network...\n",
      "The 25 genes that make it:  ['Flt3' 'Phactr1' 'Tex15' 'Satb1' 'Reln' 'Cdh13' 'Npy1r' 'Elfn1' 'Vip'\n",
      " 'Pald1' 'Atp1a3' 'Gad1' 'Gpm6a' 'Ndn' 'Npas1' 'Tac2' 'Gabrd' 'Gabra1'\n",
      " 'Cacna2d1' 'Lamp5' 'Galnt14' 'Erbb4' 'Htr3a' 'Sst' 'Pvalb']\n",
      "[INFO] training network...\n",
      "Train R^2 before unfreezing:  0.3935062289237976\n",
      "Test R^2 before unfreezing:  0.3948572278022766\n",
      "Train R^2 after unfreezing:  0.3974141478538513\n",
      "Test R^2 after unfreezing:  0.39172548055648804\n",
      "Train R^2 after unfreezing and pruning:  0.4498305320739746\n",
      "Test R^2 after unfreezing and pruning:  0.40939587354660034\n",
      "Time: 0.0h  7m 23s\n"
     ]
    }
   ],
   "source": [
    "# Settings\n",
    "l2_params = np.logspace(-10, -10, num=1)\n",
    "l1_params = np.array([1e-4, 1e-2, 1e-1, 1])\n",
    "\n",
    "act = 'elu'\n",
    "folds = 3\n",
    "seed = 42\n",
    "lr=0.0001\n",
    "epochs=50\n",
    "bs=32\n",
    "patience=epochs\n",
    "pre_trained_weights=True\n",
    "unfreeze = [False, False, True, True, True, True] # last 4 layers can be trained only first\n",
    "prune=True\n",
    "\n",
    "r2_before_unfreezing = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_before_unfreezing_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_after_unfreezing = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_after_unfreezing_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_after_unfreezing_ap = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_after_unfreezing_ap_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0]))\n",
    "r2_freeze_unfreeze_hist = np.zeros((folds, l1_params.shape[0], l2_params.shape[0], 4*epochs))\n",
    "r2_freeze_unfreeze_hist_tr = np.zeros((folds, l1_params.shape[0], l2_params.shape[0], 4*epochs))\n",
    "\n",
    "t = time.time()\n",
    "np.random.seed(seed)\n",
    "n = genes.shape[0]\n",
    "ind = np.random.permutation(n)\n",
    "X = genes[ind,:]\n",
    "Y = ephys[ind,:]\n",
    "\n",
    "nodes_list=[512,128,64,128,512]\n",
    "\n",
    "# CV folds\n",
    "for cvfold in range(folds):\n",
    "    print('\\nCVFOLD: ', cvfold)\n",
    "    indtest  = np.arange(cvfold*int(n/folds), (cvfold+1)*int(n/folds))\n",
    "    indtrain = np.setdiff1d(np.arange(n), indtest)\n",
    "    x_train = np.copy(X[indtrain,:])\n",
    "    y_train = np.copy(Y[indtrain,:])\n",
    "    x_test = np.copy(X[indtest,:])\n",
    "    y_test = np.copy(Y[indtest,:])\n",
    "\n",
    "    # Mean centering (like sRRR)\n",
    "    X_mean = np.mean(x_train, axis=0)\n",
    "    x_train -= X_mean\n",
    "    x_test -= X_mean\n",
    "    Y_mean = np.mean(y_train, axis=0)\n",
    "    y_train -= Y_mean\n",
    "    y_test -= Y_mean\n",
    "    \n",
    "    input_dim = x_train.shape[1]\n",
    "    output_dim = y_train.shape[1]\n",
    "    \n",
    "    for i, l2 in enumerate(l2_params):\n",
    "        #print('Ridge penalty: ', l2)\n",
    "        for j, l1 in enumerate(l1_params):\n",
    "            #print('Lasso penalty: ', l1)\n",
    "            \n",
    "            pre_trained_weights_h5 = 'KerasSavedModels/Classification_last_weights_{}_{}_{}.h5'.format(cvfold, j, i)\n",
    "            input_dim = x_train.shape[1]\n",
    "            output_dim = y_train.shape[1]\n",
    "            BN_PreTrFreezeUnfreeze = FreezeUnfreeze(l1=l1, l2=l2, lr=lr, act=act, input_dim=input_dim, output_dim=output_dim, \\\n",
    "                                                       unfreeze = unfreeze, \\\n",
    "                                                       pre_trained_weights=pre_trained_weights, \\\n",
    "                                                       pre_trained_weights_h5=pre_trained_weights_h5, nodes_list=nodes_list)\n",
    "            r2_before_unfreezing_train, r2_before_unfreezing_test, \\\n",
    "                r2_after_unfreezing_train, r2_after_unfreezing_test, \\\n",
    "                r2_after_unfreezing_train_ap, r2_after_unfreezing_test_ap, \\\n",
    "                train_hist, test_hist = BN_PreTrFreezeUnfreeze.train(x_train=x_train, y_train=y_train, \\\n",
    "                                                                    x_test=x_test, y_test=y_test, \\\n",
    "                                                                    epochs=epochs, bs=bs, patience=patience, \\\n",
    "                                                                    cvfold_id=cvfold, l1_id=j, l2_id=i, \\\n",
    "                                                                    prune=prune, geneNames=gene_names, \\\n",
    "                                                                    verbose=0,\n",
    "                                                                    report_individual_ephys_feature_test_R2=True)\n",
    "            r2_before_unfreezing[cvfold, j, i] = r2_before_unfreezing_test\n",
    "            r2_before_unfreezing_tr[cvfold, j, i] = r2_before_unfreezing_train\n",
    "            r2_after_unfreezing[cvfold, j, i] = r2_after_unfreezing_test\n",
    "            r2_after_unfreezing_tr[cvfold, j, i] = r2_after_unfreezing_train\n",
    "            r2_after_unfreezing_ap[cvfold, j, i] = r2_after_unfreezing_test_ap\n",
    "            r2_after_unfreezing_ap_tr[cvfold, j, i] = r2_after_unfreezing_train_ap\n",
    "            r2_freeze_unfreeze_hist[cvfold, j, i] = test_hist\n",
    "            r2_freeze_unfreeze_hist_tr[cvfold, j, i] = train_hist\n",
    "            #print('Test R^2 score (before unfreezing): ', r2_before_unfreezing[cvfold, j, i])\n",
    "            #print('Test R^2 score (after unfreezing): ', r2_after_unfreezing[cvfold, j, i])\n",
    "            #print('Test R^2 score (after unfreezing and after pruning): ', r2_after_unfreezing_ap[cvfold, j, i])\n",
    "            \n",
    "t = time.time() - t\n",
    "m,s = divmod(t, 60)\n",
    "h,m = divmod(m, 60)\n",
    "print('Time: {}h {:2.0f}m {:2.0f}s'.format(h,m,s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_r2_pre_tr_and_freeze_unfreeze_lasso_no_bottleneck.pickle', 'wb') as f:\n",
    "    pickle.dump([r2_before_unfreezing, r2_before_unfreezing_tr, \\\n",
    "                 r2_after_unfreezing, r2_after_unfreezing_tr, \\\n",
    "                 r2_after_unfreezing_ap, r2_after_unfreezing_ap_tr, \\\n",
    "                 r2_freeze_unfreeze_hist, r2_freeze_unfreeze_hist_tr], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score on all:  0.39728141725063326\n",
      "\n",
      "individual contributions: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'AP amp': 0.5233319103717804,\n",
       " 'AP amp adapt': 0.3731975754102071,\n",
       " 'AP amp CV': -0.009308358033498129,\n",
       " 'AP thresh': 0.08034402132034302,\n",
       " 'AP width': 0.731165329615275,\n",
       " 'AHP': 0.6177818973859152,\n",
       " 'AI': 0.326288362344106,\n",
       " 'ISI CV': 0.43613457679748535,\n",
       " 'R_input': 0.5323546628157297,\n",
       " 'Max AP num': 0.7207182745138804,\n",
       " 'Tau': 0.3486028512318929,\n",
       " 'Rebound': 0.36925144990285236,\n",
       " 'V_rest': 0.1597218910853068,\n",
       " 'Rheobase': 0.36861759424209595,\n",
       " 'Sag': 0.26075098911921185,\n",
       " 'UDR': 0.7739744285742441}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "individual_ephys_test_R2_scores=np.zeros((folds,Y.shape[1]))\n",
    "for i in range(folds):\n",
    "    individual_ephys_test_R2_scores[i,:]=np.load('KerasSavedModels/individual_ephys_feature_test_R2_{}_2_0_nb.npz'\\\n",
    "                                                             .format(i))['R2']\n",
    "individual_R2_scores={}\n",
    "individual_R2_scores_std={}\n",
    "for i in range(Y.shape[1]):\n",
    "    individual_R2_scores.update(\n",
    "        {ephys_names[i]:np.mean(individual_ephys_test_R2_scores, axis=0)[i]}\n",
    "                               )\n",
    "    individual_R2_scores_std.update(\n",
    "        {ephys_names[i]:np.std(individual_ephys_test_R2_scores, axis=0)[i]}\n",
    "                               )\n",
    "R2_full=np.mean(r2_freeze_unfreeze_hist[:,2,0,-1],axis=0)\n",
    "print('R^2 score on all: ', R2_full)\n",
    "print('\\nindividual contributions: ')\n",
    "individual_R2_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "individual contributions \\w their std's\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'AP amp': 0.010493994707849516,\n",
       " 'AP amp adapt': 0.01755518679301579,\n",
       " 'AP amp CV': 0.018152087572414276,\n",
       " 'AP thresh': 0.025826706277906897,\n",
       " 'AP width': 0.015773169923619206,\n",
       " 'AHP': 0.02460160237648746,\n",
       " 'AI': 0.02995057295004485,\n",
       " 'ISI CV': 0.01557241312315267,\n",
       " 'R_input': 0.04981063064791512,\n",
       " 'Max AP num': 0.012143026042797633,\n",
       " 'Tau': 0.007508997361585351,\n",
       " 'Rebound': 0.06851535327601407,\n",
       " 'V_rest': 0.02833875593662878,\n",
       " 'Rheobase': 0.05678037203519015,\n",
       " 'Sag': 0.036822647212034876,\n",
       " 'UDR': 0.011077186217225142}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"individual contributions \\w their std's\")\n",
    "individual_R2_scores_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/ind_feature_scores_nb.pickle', 'wb') as f:\n",
    "    pickle.dump([individual_R2_scores, individual_R2_scores_std], f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "order=np.argsort(np.array([individual_R2_scores[key] for key in individual_R2_scores]))[::-1]\n",
    "fig, ax = plt.subplots(1,1,figsize=(8,5))\n",
    "ax.errorbar(np.array([key for key in individual_R2_scores])[order],\n",
    "            np.array([individual_R2_scores[key] for key in individual_R2_scores])[order],\n",
    "            np.array([individual_R2_scores_std[key] for key in individual_R2_scores_std])[order],\n",
    "            marker='.', color='k', markersize=10, capsize=5, linestyle='none')\n",
    "ax.hlines(R2_full,\n",
    "          xmin=ax.get_xlim()[0],\n",
    "          xmax=ax.get_xlim()[1],\n",
    "          linestyle='dashed')\n",
    "ax.set_ylim(ymin=0,ymax=1)\n",
    "ax.annotate(r'Multivariate $R^2$={0:.2f}'.format(R2_full),\n",
    "           (ax.get_xlim()[0]+1.3*(ax.get_xlim()[1]-ax.get_xlim()[0])/2, R2_full+0.02),\n",
    "            fontsize=13)\n",
    "plt.xticks(rotation='80', fontsize=13)\n",
    "ax.set_ylabel(r'Cross validated $R^2$', fontsize=13)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now analyse and visualise the differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambdas = np.arange(.1,2,.1)\n",
    "alphas = np.array([1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/sRRR_vs_Bottleneck_rank2_CV_lasso.pickle', 'rb') as f:\n",
    "    r2_sRRR_rank2, r2_sRRR_relaxed_rank2, r2_sRRR_rank2_tr, r2_sRRR_relaxed_rank2_tr, \\\n",
    "                nonzero_rank2, corrs_rank2, corrs_relaxed_rank2 = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/sRRR_vs_Bottleneck_rank16_CV_lasso.pickle', 'rb') as f:\n",
    "    r2_sRRR_rank16, r2_sRRR_relaxed_rank16, r2_sRRR_rank16_tr, r2_sRRR_relaxed_rank16_tr, \\\n",
    "                nonzero_rank16, corrs_rank16, corrs_relaxed_rank16 = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_pre_tr_lasso.pickle', 'rb') as f:\n",
    "    CE_loss, CE_loss_tr, pre_tr_hist, pre_tr_hist_tr = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_pre_tr_lasso_no_bottleneck.pickle', 'rb') as f:\n",
    "    CE_loss_nb, CE_loss_tr_nb, pre_tr_hist_nb, pre_tr_hist_tr_nb = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_r2_straight_regr_lasso.pickle', 'rb') as f:\n",
    "    r2_straight_regr, r2_straight_regr_tr, \\\n",
    "    r2_straight_regr_ap, r2_straight_regr_ap_tr, \\\n",
    "    r2_straight_regr_hist, r2_straight_regr_hist_tr = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_r2_straight_regr_lasso_no_bottleneck.pickle', 'rb') as f:\n",
    "    r2_straight_regr_nb, r2_straight_regr_tr_nb, \\\n",
    "    r2_straight_regr_ap_nb, r2_straight_regr_ap_tr_nb, \\\n",
    "    r2_straight_regr_hist_nb, r2_straight_regr_hist_tr_nb = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_r2_pre_tr_and_straight_regr_lasso.pickle', 'rb') as f:\n",
    "    r2_pretr_straight_regr, r2_pretr_straight_regr_tr, \\\n",
    "    r2_pretr_straight_regr_ap, r2_pretr_straight_regr_ap_tr, \\\n",
    "    r2_pretr_straight_regr_hist, r2_pretr_straight_regr_hist_tr = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_r2_pre_tr_and_straight_regr_lasso_no_bottleneck.pickle', 'rb') as f:\n",
    "    r2_pretr_straight_regr_nb, r2_pretr_straight_regr_tr_nb, \\\n",
    "    r2_pretr_straight_regr_ap_nb, r2_pretr_straight_regr_ap_tr_nb, \\\n",
    "    r2_pretr_straight_regr_hist_nb, r2_pretr_straight_regr_hist_tr_nb = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_r2_pre_tr_and_freeze_unfreeze_lasso.pickle', 'rb') as f:\n",
    "    r2_before_unfreezing, r2_before_unfreezing_tr, \\\n",
    "    r2_after_unfreezing, r2_after_unfreezing_tr, \\\n",
    "    r2_after_unfreezing_ap, r2_after_unfreezing_ap_tr, \\\n",
    "    r2_freeze_unfreeze_hist, r2_freeze_unfreeze_hist_tr = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('KerasSavedModels/scala_2020_tf2/Bottleneck_r2_pre_tr_and_freeze_unfreeze_lasso_no_bottleneck.pickle', 'rb') as f:\n",
    "    r2_before_unfreezing_nb, r2_before_unfreezing_tr_nb, \\\n",
    "    r2_after_unfreezing_nb, r2_after_unfreezing_tr_nb, \\\n",
    "    r2_after_unfreezing_ap_nb, r2_after_unfreezing_ap_tr_nb, \\\n",
    "    r2_freeze_unfreeze_hist_nb, r2_freeze_unfreeze_hist_tr_nb = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select closest to 25 genes value\n",
    "# suppressing \"mean of empty slice\" warnings\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n",
    "    n_rank2 = np.nanmean(nonzero_rank2, axis=0)[0, :]\n",
    "    r_rank2_r = np.nanmean(r2_sRRR_relaxed_rank2, axis=0)[0, :]\n",
    "    n_rank16 = np.nanmean(nonzero_rank16, axis=0)[0, :]\n",
    "    r_rank16_r = np.nanmean(r2_sRRR_relaxed_rank16, axis=0)[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.39811512463141036 +/- 0.016844028073453247\n"
     ]
    }
   ],
   "source": [
    "print(r_rank16_r[np.isclose(n_rank16, 25, atol=3)][0], '+/-',\n",
    "      np.nanstd(r2_sRRR_relaxed_rank16, axis=0)[0, :][np.isclose(n_rank16, 25, atol=3)][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35036089702192025 +/- 0.019618703438733235\n"
     ]
    }
   ],
   "source": [
    "print(r_rank2_r[np.isclose(n_rank2, 25, atol=3)][0], '+/-',\n",
    "      np.nanstd(r2_sRRR_relaxed_rank2, axis=0)[0, :][np.isclose(n_rank2, 25, atol=3)][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.39728141725063326 +/- 0.02221465147618321\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(r2_freeze_unfreeze_hist,axis=0)[2,0,-1], '+/-', np.std(r2_freeze_unfreeze_hist,axis=0)[2,0,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4085913896560669 +/- 0.011105088393733078\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(r2_freeze_unfreeze_hist_nb,axis=0)[2,0,-1], '+/-', np.std(r2_freeze_unfreeze_hist_nb,axis=0)[2,0,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3740154504776001 +/- 0.021394069010219213\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(r2_straight_regr_hist,axis=0)[0,0,-1], '+/-', np.std(r2_straight_regr_hist,axis=0)[0,0,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.412414887547493 +/- 0.02264313753349095\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(r2_straight_regr_hist_nb,axis=0)[0,0,-1], '+/-', np.std(r2_straight_regr_hist_nb,axis=0)[0,0,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 648x288 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7fd9fb0da850>)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All together in one plot\n",
    "linewidth = 2\n",
    "sns_styleset()\n",
    "\n",
    "epochs_used=200\n",
    "fig, ax3 = plt.subplots(1,1,figsize=(9,4))\n",
    "ax3.plot(range(epochs_used), np.mean(r2_freeze_unfreeze_hist, axis=(0))[2,0,:], \\\n",
    "        c='#1b9e77', linewidth=linewidth, label='Pre-training, sBNN-2')\n",
    "ax3.plot(range(epochs_used), np.mean(r2_freeze_unfreeze_hist_nb, axis=(0))[2,0,:], \\\n",
    "        c='#d95f02', linewidth=linewidth, label='Pre-training, sBNN-64')\n",
    "ax3.plot(range(epochs_used), np.mean(r2_straight_regr_hist, axis=(0))[0,0,:], \\\n",
    "        '--', c='#1b9e77', linewidth=linewidth, label='No pre-training, sBNN-2')\n",
    "ax3.plot(range(epochs_used), np.mean(r2_straight_regr_hist_nb, axis=(0))[0,0,:], \\\n",
    "        '--', c='#d95f02', linewidth=linewidth, label='No pre-training, sBNN-64')\n",
    "ax3.hlines(r_rank16_r[np.isclose(n_rank16, 25, atol=3)], xmin = 0, xmax = epochs_used, \\\n",
    "            colors = np.array([0, 0, 0])/256, linewidth=linewidth)\n",
    "ax3.hlines(r_rank2_r[np.isclose(n_rank2, 25, atol=3)], xmin = 0, xmax = epochs_used, \\\n",
    "            colors = np.array([0, 0, 0])/256, linewidth=linewidth)\n",
    "ax3.set_ylim([0.3,0.45])\n",
    "ax3.set_xlim([0,200])\n",
    "ax3.set_xticks(np.arange(0, 225, 25))\n",
    "ax3.annotate('sRRR, r=16', (178, r_rank16_r[np.isclose(n_rank16, 25, atol=3)]+0.003), fontsize=15)\n",
    "ax3.annotate('sRRR, r=2', (178, r_rank2_r[np.isclose(n_rank2, 25, atol=3)]+0.003), fontsize=15)\n",
    "ax3.set_title('sBNN', fontsize = 19, y=1.02)\n",
    "ax3.set_xlabel('Epoch', fontsize = 17)\n",
    "ax3.set_ylabel(r'Cross validated $R^2$', fontsize = 17)\n",
    "ax3.tick_params(axis='both', which='major', labelsize=15)\n",
    "ax3.tick_params(axis='both', which='minor', labelsize=15)\n",
    "ax3.legend(loc='lower right', fontsize=12)\n",
    "ax3.vlines(x=50, ymin=ax3.get_ylim()[0], ymax=ax3.get_ylim()[1], linestyles='dashed')\n",
    "ax3.vlines(x=100, ymin=ax3.get_ylim()[0], ymax=ax3.get_ylim()[1], linestyles='dashed')\n",
    "\n",
    "# plt.text(-0.25, 1.04, 'a', transform = ax1.transAxes, fontsize=25, fontweight='bold')\n",
    "# plt.text(-0.11, 1.04, 'b', transform = ax2.transAxes, fontsize=25, fontweight='bold')\n",
    "# plt.text(-0.13, 1.04, 'c', transform = ax3.transAxes, fontsize=25, fontweight='bold')\n",
    "sns.despine()\n",
    "\n",
    "plt.savefig('figures/Lasso_training_curves_w_no_bottleneck.pdf', bbox_inches='tight',\n",
    "    pad_inches=0.1)\n",
    "plt.savefig('figures/Lasso_training_curves_w_no_bottleneck.png', bbox_inches='tight',\n",
    "    pad_inches=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x324 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All together in one plot\n",
    "linewidth = 2\n",
    "sns_styleset()\n",
    "\n",
    "# suppressing \"mean of empty slice\" warnings\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n",
    "    n_rank2 = np.nanmean(nonzero_rank2, axis=0)[0, :]\n",
    "    r_rank2_r = np.nanmean(r2_sRRR_relaxed_rank2, axis=0)[0, :]\n",
    "    n_rank16 = np.nanmean(nonzero_rank16, axis=0)[0, :]\n",
    "    r_rank16_r = np.nanmean(r2_sRRR_relaxed_rank16, axis=0)[0, :]\n",
    "\n",
    "# All together in one plot for the paper\n",
    "fig = plt.figure(figsize=(19, 4.5))\n",
    "widths = [1, 1, 2]\n",
    "heights = [1]\n",
    "spec = fig.add_gridspec(ncols=3, nrows=1, width_ratios=widths,\n",
    "                          height_ratios=heights)\n",
    "\n",
    "ax1 = fig.add_subplot(spec[0, 0])\n",
    "ax1.plot(n_2, cr_2, '.-', c = np.array([0, 0, 0])/256, linewidth=2)\n",
    "ax1.plot(n_16[1:], cr_16[1:], '.-', c = np.array([0, 0, 0]) / 256, linewidth=2)\n",
    "ax1.set_ylim([0.3,0.45])\n",
    "ax1.set_xlabel('Number of selected genes', fontsize = 17)\n",
    "ax1.set_ylabel(r'Cross validated $R^2$', fontsize = 17)\n",
    "ax1.set_title('sRRR', fontsize = 20, y=1.02)\n",
    "ax1.tick_params(axis='both', which='major', labelsize=15)\n",
    "ax1.tick_params(axis='both', which='minor', labelsize=15)\n",
    "ax1.vlines(x=25, ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1], linestyles='dashed')\n",
    "ax1.annotate('sRRR, r=16', (70, np.max(cr_16)+0.0003), fontsize=15)\n",
    "ax1.annotate('sRRR, r=2', (70, np.max(cr_2)+0.0003), fontsize=15)\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(spec[0, 1])\n",
    "ax2.plot(range(50), np.mean(pre_tr_hist, axis=(0, 2))[2, :], \\\n",
    "         c = '#1b9e77', linewidth=linewidth, label='sBNN-2')\n",
    "ax2.plot(range(50), np.mean(pre_tr_hist_tr, axis=(0, 2))[2, :], \\\n",
    "         '--', c = '#1b9e77', linewidth=linewidth)\n",
    "ax2.plot(range(50), np.mean(pre_tr_hist_nb, axis=(0, 2))[2, :], \\\n",
    "         c = '#d95f02', linewidth=linewidth, label='sBNN-64')\n",
    "ax2.plot(range(50), np.mean(pre_tr_hist_tr_nb, axis=(0, 2))[2, :], \\\n",
    "         '--', c = '#d95f02', linewidth=linewidth)\n",
    "ax2.set_title('Pretraining', fontsize = 19, y=1.02)\n",
    "ax2.set_xlabel('Epoch', fontsize = 17)\n",
    "ax2.set_ylabel('Cross entropy loss', fontsize = 17)\n",
    "ax2.tick_params(axis='both', which='major', labelsize=15)\n",
    "ax2.tick_params(axis='both', which='minor', labelsize=15)\n",
    "ax2.set_ylim([0, ax2.get_ylim()[1]])\n",
    "ax2.annotate('Training', (35, 1), fontsize=15)\n",
    "ax2.annotate('Validation', (35, 2.42), fontsize=15)\n",
    "ax2.set_xticks(np.arange(0, 60, 10))\n",
    "ax2.legend(loc='lower left', fontsize=12)\n",
    "\n",
    "epochs_used=200\n",
    "ax3 = fig.add_subplot(spec[0, 2])\n",
    "ax3.plot(range(epochs_used), np.mean(r2_freeze_unfreeze_hist_tr, axis=(0))[2,0,:], \\\n",
    "        c='#1b9e77', linewidth=linewidth, label='Pre-training, sBNN-2')\n",
    "ax3.plot(range(epochs_used), np.mean(r2_freeze_unfreeze_hist_tr_nb, axis=(0))[2,0,:], \\\n",
    "        c='#d95f02', linewidth=linewidth, label='Pre-training, sBNN-64')\n",
    "ax3.plot(range(epochs_used), np.mean(r2_straight_regr_hist_tr, axis=(0))[0,0,:], \\\n",
    "        '--', c='#1b9e77', linewidth=linewidth, label='No pre-training, sBNN-2')\n",
    "ax3.plot(range(epochs_used), np.mean(r2_straight_regr_hist_tr_nb, axis=(0))[0,0,:], \\\n",
    "        '--', c='#d95f02', linewidth=linewidth, label='No pre-training, sBNN-64')\n",
    "ax3.hlines(r_rank16_r[np.isclose(n_rank16, 25, atol=3)], xmin = 0, xmax = epochs_used, \\\n",
    "            colors = np.array([0, 0, 0])/256, linewidth=linewidth)\n",
    "ax3.hlines(r_rank2_r[np.isclose(n_rank2, 25, atol=3)], xmin = 0, xmax = epochs_used, \\\n",
    "            colors = np.array([0, 0, 0])/256, linewidth=linewidth)\n",
    "ax3.set_ylim([0.2,0.6])\n",
    "ax3.set_xlim([0,200])\n",
    "ax3.set_xticks(np.arange(0, 225, 25))\n",
    "ax3.annotate('sRRR, r=16', (178, r_rank16_r[np.isclose(n_rank16, 25, atol=3)]+0.003), fontsize=15)\n",
    "ax3.annotate('sRRR, r=2', (178, r_rank2_r[np.isclose(n_rank2, 25, atol=3)]+0.003), fontsize=15)\n",
    "ax3.set_title('sBNN', fontsize = 19, y=1.02)\n",
    "ax3.set_xlabel('Epoch', fontsize = 17)\n",
    "ax3.set_ylabel(r'Cross validated $R^2$', fontsize = 17)\n",
    "ax3.tick_params(axis='both', which='major', labelsize=15)\n",
    "ax3.tick_params(axis='both', which='minor', labelsize=15)\n",
    "ax3.legend(loc='lower right', fontsize=12)\n",
    "ax3.vlines(x=50, ymin=ax3.get_ylim()[0], ymax=ax3.get_ylim()[1], linestyles='dashed')\n",
    "ax3.vlines(x=100, ymin=ax3.get_ylim()[0], ymax=ax3.get_ylim()[1], linestyles='dashed')\n",
    "\n",
    "plt.text(-0.25, 1.04, 'a', transform = ax2.transAxes, fontsize=25, fontweight='bold')\n",
    "plt.text(-0.11, 1.04, 'b', transform = ax3.transAxes, fontsize=25, fontweight='bold')\n",
    "sns.despine()\n",
    "\n",
    "plt.savefig('figures/Lasso_training_curves_w_no_bottleneck_tr.pdf', bbox_inches='tight',\n",
    "    pad_inches=0.1)\n",
    "plt.savefig('figures/Lasso_training_curves_w_no_bottleneck_tr.png', bbox_inches='tight',\n",
    "    pad_inches=0.1)"
   ]
  }
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