imprv_AUC_VAE_v1.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "44d70d2a-70de-476a-ac02-4e198f01d3c7",
   "metadata": {},
   "source": [
    "### Encode cell-line expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ae0eeb9-0fb4-4fec-9144-ed863c7efde0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 latent dims: [50, 100, 150]\n",
    "# to compare with corresponding gradients \n",
    "# from UMAP and differential gene selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "581a3a1f-f77a-4f04-9b74-9fdb72ad1d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2023-07-26 update\n",
    "# Observed 45, 41 minute training initialization on n = 100\n",
    "# so adding [10, 25] latent dims"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af06c86c-cab9-4021-8052-d62bd15173ea",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6dec8d8c-80ec-4135-bd04-5b677a1bba99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import BatchNormalization, Activation, Layer\n",
    "from tensorflow.keras import metrics, optimizers\n",
    "from tensorflow.keras.losses import binary_crossentropy\n",
    "from tensorflow.keras.callbacks import Callback\n",
    "import tensorflow.compat.v1.keras.backend as K\n",
    "tf.compat.v1.disable_eager_execution() # error with tf 3.11\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c6c3418-7268-4bcf-8094-72f32673122c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# VAE functions\n",
    "def compute_latent(x):\n",
    "    mu, sigma = x\n",
    "    batch = K.shape(mu)[0]\n",
    "    dim = K.shape(mu)[1]\n",
    "    eps = K.random_normal(shape=(batch,dim), mean=0., stddev=1.0 )\n",
    "    return mu + K.exp(sigma/2)*eps\n",
    "\n",
    "class CustomVariationalLayer(Layer):\n",
    "    \"\"\"\n",
    "    Define a custom layer\n",
    "    \"\"\"\n",
    "    def __init__(self, **kwargs):\n",
    "        self.is_placeholder = True\n",
    "        super(CustomVariationalLayer, self).__init__(**kwargs)\n",
    "\n",
    "    def vae_loss(self, x_input, x_decoded):\n",
    "        reconstruction_loss = original_dim * metrics.binary_crossentropy(\n",
    "            x_input, x_decoded)\n",
    "        kl_loss = - 0.5 * K.sum(1 + z_log_var_encoded - K.square(z_mean_encoded) - \n",
    "                                K.exp(z_log_var_encoded), axis=-1)\n",
    "        return K.mean(reconstruction_loss + (K.get_value(beta) * kl_loss))\n",
    "\n",
    "    def call(self, inputs):\n",
    "        x = inputs[0]\n",
    "        x_decoded = inputs[1]\n",
    "        loss = self.vae_loss(x, x_decoded)\n",
    "        self.add_loss(loss, inputs=inputs)\n",
    "        return x\n",
    "    \n",
    "class WarmUpCallback(Callback):\n",
    "    def __init__(self, beta, kappa):\n",
    "        self.beta = beta\n",
    "        self.kappa = kappa\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        if K.get_value(self.beta) <= 1:\n",
    "            K.set_value(self.beta, K.get_value(self.beta) + self.kappa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7e1266d-f222-4ea6-b1ac-0c67d10a5862",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss plot visualization to determine degree of model fitting\n",
    "def plot_loss(loss_dict, latent_dim, train_file, epochs):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(loss_dict['vs0'], marker='o', linestyle='-', color='b')\n",
    "    plt.grid(True, linestyle='--', alpha=0.6)\n",
    "    plt.title('CPTAC '+'GEXP'+' VAE Loss', fontsize=16, fontweight='bold')\n",
    "    plt.xlabel('Epoch', fontsize=14)\n",
    "    plt.ylabel('Loss', fontsize=14)\n",
    "    plt.annotate(\n",
    "        'Samples: {}'.format(\n",
    "            len(train_file))+'\\nLatent dim: '+str(latent_dim)+'\\nRaw features: {}'.format(len(train_file.columns)), \n",
    "                 xy=(0.7, 0.3), xycoords='axes fraction', \n",
    "                 bbox=dict(boxstyle='round, pad=0.5', fc='white', ec='black'),\n",
    "                 fontsize=12)\n",
    "    plt.savefig('AUC_v1/VAE_loss_plots/dim.'+str(latent_dim)+'_epochs.'+str(epochs)+'_vae_loss.png')\n",
    "    # plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d422e1e-1d36-4024-aa87-d2625a14d1fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note, no loss plots for gene diff and UMAP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860587d1-2b09-4728-bb2e-9456cede6379",
   "metadata": {},
   "source": [
    "### Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d39e502-cb0c-4f30-a5af-4404def59f3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "vae_inpt = pd.read_csv('AUC_v1/exprsn_dot_T.tsv',\n",
    "                      sep = '\\t',\n",
    "                      index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00c037b4-a720-466f-84a4-aaaa650792d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_file = vae_inpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad6136ca-1d91-427c-ba70-c8b66c4fb0d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "train_file = pd.DataFrame(\n",
    "    scaler.fit_transform(train_file),\n",
    "    columns=train_file.columns,\n",
    "    index=train_file.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f09d28-e984-465a-a651-371b8f637a08",
   "metadata": {},
   "source": [
    "#### Mid-experiment feature count decrease  \n",
    "* adding epochs to loss plot to track iterations  \n",
    "attempt intentional underfit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "64669bab-03d0-4d86-b6f9-9f580a897e7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 10\n",
    "epochs = 50\n",
    "latent_header_prefix = 'cl_ln_gxp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5f741b4f-75c2-4504-8373-d9370d5aef32",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 25\n",
    "epochs = 35\n",
    "latent_header_prefix = 'cl_ln_gxp'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f8fc738-8ed8-4ed2-9e95-9d4e08f1136b",
   "metadata": {},
   "source": [
    "### Initial schematic version  \n",
    "10 minute MLP, trnsfrmr training initialization  \n",
    "at 100k subsample rate for n=50  \n",
    "\n",
    "40+ minute @ n=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "66c8e840-4a57-46cf-937d-f1096d175d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 50\n",
    "epochs = 25\n",
    "latent_header_prefix = 'cl_ln_gxp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f5f7a387-5a49-4054-9899-1f191b76f309",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 100\n",
    "epochs = 15\n",
    "latent_header_prefix = 'cl_ln_gxp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "42977644-640b-4d23-a496-a4a7eec7d602",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 150\n",
    "epochs = 10\n",
    "latent_header_prefix = 'cl_ln_gxp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2eb13cd7-98e8-45fb-99a2-2e9c824d5cae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Output {0} missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to custom_variational_layer_3.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-26 14:00:48.725965: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-07-26 14:00:48.789895: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-07-26 14:00:48.827610: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1007 samples\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-26 14:00:49.018182: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-07-26 14:00:49.090855: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-07-26 14:00:49.125541: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1007/1007 [==============================] - 2s 2ms/sample - loss: 21210.2259\n",
      "Epoch 2/50\n",
      " 250/1007 [======>.......................] - ETA: 0s - loss: 20904.5137"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-26 14:00:50.735450: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1007/1007 [==============================] - 1s 554us/sample - loss: 20746.3371\n",
      "Epoch 3/50\n",
      "1007/1007 [==============================] - 1s 537us/sample - loss: 20269.8906\n",
      "Epoch 4/50\n",
      "1007/1007 [==============================] - 1s 521us/sample - loss: 19794.0725\n",
      "Epoch 5/50\n",
      "1007/1007 [==============================] - 1s 525us/sample - loss: 19334.0666\n",
      "Epoch 6/50\n",
      "1007/1007 [==============================] - 1s 526us/sample - loss: 18850.8273\n",
      "Epoch 7/50\n",
      "1007/1007 [==============================] - 1s 517us/sample - loss: 18406.4424\n",
      "Epoch 8/50\n",
      "1007/1007 [==============================] - 1s 524us/sample - loss: 17990.4994\n",
      "Epoch 9/50\n",
      "1007/1007 [==============================] - 1s 525us/sample - loss: 17552.3471\n",
      "Epoch 10/50\n",
      "1007/1007 [==============================] - 1s 519us/sample - loss: 17227.9443\n",
      "Epoch 11/50\n",
      "1007/1007 [==============================] - 1s 536us/sample - loss: 16853.0963\n",
      "Epoch 12/50\n",
      "1007/1007 [==============================] - 1s 520us/sample - loss: 16530.7857\n",
      "Epoch 13/50\n",
      "1007/1007 [==============================] - 1s 522us/sample - loss: 16172.6233\n",
      "Epoch 14/50\n",
      "1007/1007 [==============================] - 1s 537us/sample - loss: 15817.6471\n",
      "Epoch 15/50\n",
      "1007/1007 [==============================] - 1s 522us/sample - loss: 15567.8644\n",
      "Epoch 16/50\n",
      "1007/1007 [==============================] - 1s 518us/sample - loss: 15287.1998\n",
      "Epoch 17/50\n",
      "1007/1007 [==============================] - 1s 517us/sample - loss: 15009.3125\n",
      "Epoch 18/50\n",
      "1007/1007 [==============================] - 1s 519us/sample - loss: 14847.6002\n",
      "Epoch 19/50\n",
      "1007/1007 [==============================] - 1s 520us/sample - loss: 14565.9642\n",
      "Epoch 20/50\n",
      "1007/1007 [==============================] - 1s 516us/sample - loss: 14365.6075\n",
      "Epoch 21/50\n",
      "1007/1007 [==============================] - 1s 522us/sample - loss: 14167.0333\n",
      "Epoch 22/50\n",
      "1007/1007 [==============================] - 1s 520us/sample - loss: 14000.1254\n",
      "Epoch 23/50\n",
      "1007/1007 [==============================] - 1s 520us/sample - loss: 13879.4224\n",
      "Epoch 24/50\n",
      "1007/1007 [==============================] - 1s 522us/sample - loss: 13674.7759\n",
      "Epoch 25/50\n",
      "1007/1007 [==============================] - 1s 519us/sample - loss: 13541.2941\n",
      "Epoch 26/50\n",
      "1007/1007 [==============================] - 1s 520us/sample - loss: 13437.1703\n",
      "Epoch 27/50\n",
      "1007/1007 [==============================] - 1s 526us/sample - loss: 13254.0204\n",
      "Epoch 28/50\n",
      "1007/1007 [==============================] - 1s 522us/sample - loss: 13117.6209\n",
      "Epoch 29/50\n",
      "1007/1007 [==============================] - 1s 517us/sample - loss: 13075.5877\n",
      "Epoch 30/50\n",
      "1007/1007 [==============================] - 1s 519us/sample - loss: 12958.4512\n",
      "Epoch 31/50\n",
      "1007/1007 [==============================] - 1s 525us/sample - loss: 12875.2416\n",
      "Epoch 32/50\n",
      "1007/1007 [==============================] - 1s 521us/sample - loss: 12837.1757\n",
      "Epoch 33/50\n",
      "1007/1007 [==============================] - 1s 519us/sample - loss: 12710.5206\n",
      "Epoch 34/50\n",
      "1007/1007 [==============================] - 1s 522us/sample - loss: 12670.1979\n",
      "Epoch 35/50\n",
      "1007/1007 [==============================] - 1s 522us/sample - loss: 12560.1850\n",
      "Epoch 36/50\n",
      "1007/1007 [==============================] - 1s 517us/sample - loss: 12505.1152\n",
      "Epoch 37/50\n",
      "1007/1007 [==============================] - 1s 521us/sample - loss: 12414.3613\n",
      "Epoch 38/50\n",
      "1007/1007 [==============================] - 1s 520us/sample - loss: 12383.4289\n",
      "Epoch 39/50\n",
      "1007/1007 [==============================] - 1s 514us/sample - loss: 12375.4161\n",
      "Epoch 40/50\n",
      "1007/1007 [==============================] - 1s 516us/sample - loss: 12322.8604\n",
      "Epoch 41/50\n",
      "1007/1007 [==============================] - 1s 522us/sample - loss: 12248.0638\n",
      "Epoch 42/50\n",
      "1007/1007 [==============================] - 1s 522us/sample - loss: 12216.2903\n",
      "Epoch 43/50\n",
      "1007/1007 [==============================] - 1s 522us/sample - loss: 12173.3211\n",
      "Epoch 44/50\n",
      "1007/1007 [==============================] - 1s 521us/sample - loss: 12127.8069\n",
      "Epoch 45/50\n",
      "1007/1007 [==============================] - 1s 517us/sample - loss: 12088.7270\n",
      "Epoch 46/50\n",
      "1007/1007 [==============================] - 1s 516us/sample - loss: 12077.0027\n",
      "Epoch 47/50\n",
      "1007/1007 [==============================] - 1s 521us/sample - loss: 12071.0255\n",
      "Epoch 48/50\n",
      "1007/1007 [==============================] - 1s 519us/sample - loss: 12009.5403\n",
      "Epoch 49/50\n",
      "1007/1007 [==============================] - 1s 519us/sample - loss: 12002.1650\n",
      "Epoch 50/50\n",
      "1007/1007 [==============================] - 1s 520us/sample - loss: 11950.5851\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/tf_M1_py3dot9/lib/python3.9/site-packages/keras/engine/training_v1.py:2356: 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",
      "2023-07-26 14:01:16.596494: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VAE done, latent object written to disk\n"
     ]
    }
   ],
   "source": [
    "# Variational autoencoder for feature dimensionality reduction\n",
    "\n",
    "loss_dict = {}\n",
    "vs_list = ['vs0']\n",
    "validation_split = vs_list[0]\n",
    "\n",
    "features = train_file.columns\n",
    "\n",
    "original_dim = len(features)\n",
    "feature_dim = len(features)\n",
    "latent_dim = latent_dim\n",
    "batch_size = 50\n",
    "\n",
    "encoder_inputs = keras.Input(shape=(feature_dim,))\n",
    "z_mean_dense_linear = layers.Dense(\n",
    "    latent_dim, kernel_initializer='glorot_uniform', name=\"encoder_1\")(encoder_inputs)\n",
    "z_mean_dense_batchnorm = layers.BatchNormalization()(z_mean_dense_linear)\n",
    "z_mean_encoded = layers.Activation('relu')(z_mean_dense_batchnorm)\n",
    "\n",
    "z_log_var_dense_linear = layers.Dense(\n",
    "    latent_dim, kernel_initializer='glorot_uniform', name=\"encoder_2\")(encoder_inputs)\n",
    "z_log_var_dense_batchnorm = layers.BatchNormalization()(z_log_var_dense_linear)\n",
    "z_log_var_encoded = layers.Activation('relu')(z_log_var_dense_batchnorm)\n",
    "\n",
    "latent_space = layers.Lambda(\n",
    "    compute_latent, output_shape=(\n",
    "        latent_dim,), name=\"latent_space\")([z_mean_encoded, z_log_var_encoded])\n",
    "\n",
    "decoder_to_reconstruct = layers.Dense(\n",
    "    feature_dim, kernel_initializer='glorot_uniform', activation='sigmoid')\n",
    "decoder_outputs = decoder_to_reconstruct(latent_space)\n",
    "\n",
    "learning_rate = 0.0005\n",
    "\n",
    "kappa = 1\n",
    "beta = K.variable(0)\n",
    "\n",
    "adam = optimizers.Adam(learning_rate=learning_rate)\n",
    "vae_layer = CustomVariationalLayer()([encoder_inputs, decoder_outputs])\n",
    "vae = Model(encoder_inputs, vae_layer)\n",
    "vae.compile(optimizer=adam, loss=None, loss_weights=[beta])\n",
    "\n",
    "history = vae.fit(train_file,\n",
    "            epochs=epochs,\n",
    "                  batch_size=batch_size,\n",
    "                  shuffle=True,\n",
    "                  callbacks=[WarmUpCallback(beta, kappa)],\n",
    "                  verbose=1)\n",
    "loss_dict[validation_split] = history.history['loss']\n",
    "\n",
    "encoder = Model(encoder_inputs, z_mean_encoded)\n",
    "latent_object = pd.DataFrame(\n",
    "    encoder.predict(train_file),\n",
    "    index=train_file.index\n",
    ")\n",
    "latent_object.index.name = train_file.index.name\n",
    "\n",
    "# Convert latent object headers to dtype specific strings for input to transformer\n",
    "new_column_headers = []\n",
    "for column_header in latent_object.columns:\n",
    "    new_column_header = latent_header_prefix+'_' + str(column_header)\n",
    "    new_column_headers.append(new_column_header)\n",
    "latent_object.columns = new_column_headers\n",
    "\n",
    "latent_object.to_csv(\n",
    "    'AUC_v1/VAE_expr_'+str(latent_dim)+'.tsv',\n",
    "    sep = '\\t')\n",
    "print('VAE done, latent object written to disk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc871fa1-7a40-43a3-9b7f-ec13bbfe3755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(loss_dict, latent_dim, train_file, epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "71bf4b8b-0965-442d-b394-f348ba8b20d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(loss_dict, latent_dim, train_file, epochs) # appears slightly under fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbf2b088-35ec-445e-83e4-e96c2c44ad6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Observation: Epochs decrease with increasing latent dimension to maintain shape of loss curve"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}