four_analyte_not_prob_LB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython import get_ipython\n",
    "ipython = get_ipython()\n",
    "if '__IPYTHON__' in globals():\n",
    "    ipython.magic('load_ext autoreload')\n",
    "    ipython.magic('autoreload 2')\n",
    "\n",
    "import argparse\n",
    "import os\n",
    "from glob import glob\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "# import tensorflow_probability as tfp\n",
    "import tensorflow_datasets as tfds\n",
    "\n",
    "from mymodels.four_analyte_model_bounded import (conv, ds_to_array,get_multiple_data,\n",
    "                                                get_multiple_data_v2, make_bijector,\n",
    "                                                predict, print_metric, rmse,\n",
    "                                                predict_non_prob, merge_datasets, make_preprocess, \n",
    "                                                shifted_zscore, conv_drop_last, tf_pmse, natural_keys)\n",
    "\n",
    "def plot_concentrations(y):\n",
    "    fig, axs = plt.subplots(2, 2, sharey=False, tight_layout=True)\n",
    "    for ip in range(0,4):\n",
    "        axs[np.unravel_index(ip, axs.shape)].hist(y[:,ip])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CF017', 'CF025', 'CF027', 'CF045', 'CF056', 'CF057', 'CF058', 'CF059', 'CF060', 'CF062', 'CF064', 'CF065', 'CF066', 'CF067', 'CF068', 'CF069', 'CF070', 'CF071', 'CF072', 'CF073', 'CF074', 'CF078', 'CF081', 'CF082']\n",
      "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1')\n",
      "hold probe : CF078\n",
      "validation probe : CF025\n",
      "Training model on probes ['CF027', 'CF057', 'CF064', 'CF066', 'CF081', 'CF082'].\n",
      "Loading slow data\n"
     ]
    }
   ],
   "source": [
    "speed = 'slow'\n",
    "\n",
    "bi_type = 5\n",
    "\n",
    "# drop_prob = .8\n",
    "drop_prob = .5\n",
    "# drop_prob = .1 # default\n",
    "# drop_prob = 0. # disable\n",
    "\n",
    "optimizer = 'Adadelta'\n",
    "learning_rate = 0.85\n",
    "\n",
    "# optimizer = 'Adam'\n",
    "# # learning_rate = 0.000001\n",
    "# # learning_rate = 0.00001\n",
    "# # learning_rate = 0.00005\n",
    "# # learning_rate = 0.0001\n",
    "# learning_rate = 0.0005\n",
    "# # learning_rate = 0.001\n",
    "# # beta_1 = 0.9\n",
    "# beta_1 = 0.1\n",
    "# # beta_2 = 0.999\n",
    "# beta_2 = 0.95\n",
    "\n",
    "tfk = tf.keras\n",
    "tfkl = tf.keras.layers\n",
    "tfkr = tf.keras.regularizers\n",
    "# tfpl = tfp.layers\n",
    "# tfb = tfp.bijectors\n",
    "dtype = tf.float32\n",
    "tf.keras.backend.set_floatx('float32')\n",
    "npdt = np.float32\n",
    "names = ['DA', '5HT', 'pH', 'NE']\n",
    "prefix = os.path.join('/mnt/nfs/proj/in-vitro/Mark/four_analyte/', speed,\n",
    "                      'allin')\n",
    "probes = [x.split('/')[-1] for x in glob(prefix + '/CF*')]\n",
    "probes.sort()\n",
    "print(probes)\n",
    "\n",
    "mirrored_strategy = tf.distribute.MirroredStrategy()\n",
    "\n",
    "good_probes = [\n",
    "    'CF025', 'CF027', 'CF057', 'CF064', 'CF066', 'CF078', 'CF081', 'CF082'\n",
    "]\n",
    "# Only use good data\n",
    "probes = good_probes  # probes.pop(probes.index(good_probes[args.probe]))\n",
    "hold_probe = probes.pop(5)\n",
    "print('hold probe :', hold_probe)\n",
    "\n",
    "# val_probe=None\n",
    "val_probe=probes.pop(0)\n",
    "print('validation probe :', val_probe)\n",
    "\n",
    "print(f'Training model on probes {probes}.')\n",
    "print(f'Loading {speed} data')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# batch_size = 16384\n",
    "batch_size = 18495 # about 20 mini-batches\n",
    "base_depth = 20\n",
    "# base_depth = 15\n",
    "kernel_size = 5\n",
    "force_reload = True\n",
    "epochs = 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using CF025 probe for validation and merging TFRecords original validation data into training\n",
      "Data loaded\n"
     ]
    }
   ],
   "source": [
    "# train_data, val_data, hold_data = get_multiple_data(prefix,\n",
    "#                                                     probes,\n",
    "#                                                     hold_probe,\n",
    "#                                                     batch_size=batch_size,\n",
    "#                                                     weight=True,\n",
    "#                                                     allin=True,\n",
    "#                                                     bi_type=bi_type)\n",
    "train_data, val_data, hold_data = get_multiple_data_v2(prefix,\n",
    "                                                    probes,\n",
    "                                                    hold_probe,\n",
    "                                                    val_probe,\n",
    "                                                    batch_size=batch_size,\n",
    "                                                    weight=True,\n",
    "                                                    allin=True,\n",
    "                                                    bi_type=bi_type)\n",
    "print('Data loaded')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.0'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds = tf.data.TFRecordDataset(train_data[0])\n",
    "# ds = tf.map()\n",
    "# mr = 5\n",
    "# for (i,r) in enumerate(ds):\n",
    "#     x = r.numpy()\n",
    "# #     print(x.__class__)\n",
    "#     v, l = preprocess(r)\n",
    "#     if i > mr:\n",
    "#         break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# def shifted_zscore(x, inverse=False):\n",
    "#     # without zeros shifted by 10 std to avoid negative values\n",
    "#     mean = [-10410.677490234375, -10414.565185546875, 6.00097793340683, -10413.194091796875]\n",
    "#     std = [1266.3062, 1266.7249, 0.13912384, 1266.6195]\n",
    "\n",
    "#     if inverse:\n",
    "#         x = (x*std)+mean\n",
    "#     else:\n",
    "#         x = (x-mean)/std\n",
    "    \n",
    "#     return x\n",
    "\n",
    "# def preprocess(serialized_example):\n",
    "#     features = tf.io.parse_example(\n",
    "#         serialized_example,\n",
    "#         features={\n",
    "#             'gram': tf.io.FixedLenFeature([], tf.string),\n",
    "#             'label': tf.io.FixedLenFeature([], tf.string)\n",
    "#         })\n",
    "#     data = tf.io.decode_raw(features['gram'], tf.float32)\n",
    "#     label = tf.io.decode_raw(features['label'], tf.float32)\n",
    "#     data.set_shape((None, 999))\n",
    "#     label.set_shape((None, 4))\n",
    "#     return data, label\n",
    "\n",
    "# def merge_datasets(vfiles, batch_size, prep, asnumpy=False):\n",
    "#     yv = []\n",
    "#     yl = []\n",
    "#     for filename in vfiles:\n",
    "#         ds = tf.data.TFRecordDataset(filename)\n",
    "#         ds = ds.batch(batch_size=batch_size)\n",
    "#         ds = ds.map(map_func=preprocess)\n",
    "#         for v,l in ds:\n",
    "#             v = np.array(v).astype(npdt)\n",
    "#             l = np.array(l).astype(npdt)\n",
    "#             yv.append(v)\n",
    "#             l = np.apply_along_axis(shifted_zscore, axis=1, arr=l) \n",
    "#             yl.append(l)\n",
    "        \n",
    "#     x = np.vstack(yv)\n",
    "#     y = np.vstack(yl)\n",
    "\n",
    "#     if asnumpy:\n",
    "#         return x,y\n",
    "#     else:\n",
    "#         d = tf.data.Dataset.from_tensor_slices((x, y))\n",
    "#         return d\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # train_data_b = merge_datasets(train_data, batch_size, prep)\n",
    "# # train_data_b = merge_datasets(train_data, batch_size, shifted_zscore)\n",
    "# train_data = merge_datasets(train_data, batch_size, shifted_zscore)\n",
    "# train_data = train_data.shuffle(buffer_size=100_000)\n",
    "# train_data = train_data.batch(batch_size)\n",
    "# train_data = train_data.prefetch(tf.data.experimental.AUTOTUNE)\n",
    "# #     val_data = merge_datasets(val_list, batch_size, prep)\n",
    "# #     if not allin:\n",
    "# #         test_data = test_list(train_data, batch_size, prep)\n",
    "# #     hold_data = merge_datasets(hold_list, batch_size, prep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, axs = plt.subplots(2, 2, sharey=False, sharex=False, tight_layout=True)\n",
    "# fig.set_size_inches(10,10)\n",
    "\n",
    "# # dist = norm\n",
    "# zeros = shifted_zscore(np.zeros(4))\n",
    "# for ic in range(0,4):\n",
    "#     idx = np.unravel_index(ic, axs.shape)\n",
    "#     x = train_y[:,ic]\n",
    "#     x = np.array([y for y in x if y > zeros[ic]])\n",
    "#     print(np.mean(x), np.std(x), np.min(x), np.max(x))\n",
    "#     axs[idx].hist(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dense depth: 128\n",
      "Dropout disabled for layer 0\n",
      "Dropout disabled for layer 1\n",
      "Dropout disabled for layer 2\n",
      "Dropout disabled for layer 3\n",
      "Dropout disabled for layer 4\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "Dense layers with drop_prob 0.50 and 0.25\n",
      "optimizer Adadelta\n",
      "learning rate 0.85\n"
     ]
    }
   ],
   "source": [
    "input_shape = 999\n",
    "output_shape = 4\n",
    "with mirrored_strategy.scope():\n",
    "\n",
    "    dense_depth=int(64/(1-drop_prob))\n",
    "    print(f'Dense depth: {dense_depth}', flush=True)\n",
    "#     dense_depth=int(64/.2)\n",
    "#     print(f'Fixed Dense depth: {dense_depth}', flush=True)\n",
    "    \n",
    "#     vae = conv(input_dim=input_shape,# 999\n",
    "    vae = conv_drop_last(input_dim=input_shape,# 999\n",
    "                    encoded_size=output_shape,# 4\n",
    "                    base_depth=base_depth, # 15\n",
    "                    kernel_size=kernel_size, # 5\n",
    "                    norm=True, # true\n",
    "                    interpool=False, # False\n",
    "                    dense_depth=dense_depth,\n",
    "                    drop_prob=drop_prob)\n",
    "\n",
    "    checkpoint = (\n",
    "        f'four_analyte/checkpoints/{speed}'\n",
    "        f'/conv_holdout_allin'\n",
    "        f'_manual_{hold_probe}_{len(probes)}_{base_depth}_{kernel_size}_{bi_type}_{optimizer}.h5')\n",
    "\n",
    "    if not force_reload:\n",
    "        try:\n",
    "            vae.load_weights(checkpoint)\n",
    "            print('Found previous model weights.')\n",
    "        except (OSError, ValueError):\n",
    "            print('Did not find usable previous model weights.')\n",
    "\n",
    "    print(f'optimizer {optimizer}', flush=True)\n",
    "    print(f'learning rate {learning_rate}', flush=True)\n",
    "    if optimizer == 'Adam':\n",
    "        print(f'beta_1 {beta_1}, beta_2 {beta_2}', flush=True)\n",
    "        opt = tfk.optimizers.Adam(learning_rate=learning_rate, beta_2=beta_2, beta_1=beta_1)\n",
    "#         lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "#             learning_rate,\n",
    "#             decay_steps=100000,\n",
    "#             decay_rate=0.96,\n",
    "#             staircase=True)\n",
    "#         opt = tfk.optimizers.Adam(learning_rate=lr_schedule, beta_2=0.99)\n",
    "    elif optimizer == 'RMSprop':\n",
    "        opt = tfk.optimizers.RMSprop(learning_rate=learning_rate)\n",
    "    elif optimizer == 'Adadelta':\n",
    "        opt = tfk.optimizers.Adadelta(learning_rate=learning_rate,\n",
    "                                  epsilon=tfk.backend.epsilon())\n",
    "    else:\n",
    "        raise ValueError(\"Unknown optimizer %s\"%optimizer)\n",
    "\n",
    "    vae.compile(optimizer=opt,\n",
    "                loss='mse',\n",
    "#                 loss=tf_pmse,\n",
    "                metrics=[tf_pmse, tfk.metrics.RootMeanSquaredError(),], # ,'mse'\n",
    "                experimental_run_tf_function=False)\n",
    "    model_file = (\n",
    "        f'four_analyte/models/{speed}'\n",
    "        f'/conv_holdout_allin'\n",
    "        f'_manual_{hold_probe}_{len(probes)}_{base_depth}_{kernel_size}_{bi_type}_{optimizer}.h5')\n",
    "\n",
    "    tfk.models.save_model(vae, model_file)\n",
    "\n",
    "cb = [\n",
    "    tfk.callbacks.ModelCheckpoint(\n",
    "        checkpoint,\n",
    "        monitor='val_loss',\n",
    "        verbose=1,\n",
    "        save_best_only=True,\n",
    "        save_weights_only=True,\n",
    "        mode='auto',\n",
    "        save_freq='epoch',\n",
    "    ),\n",
    "    tfk.callbacks.TerminateOnNaN(),\n",
    "    tf.keras.callbacks.TensorBoard(\n",
    "        log_dir=os.path.join('four_analyte', 'checkpoints', speed, 'logs',\n",
    "                             'holdout', 'allin', hold_probe),\n",
    "        histogram_freq=1,\n",
    "        write_graph=False,\n",
    "        write_images=True,\n",
    "        update_freq='epoch',\n",
    "        # profile_batch=\"20, 25\",\n",
    "        embeddings_freq=0,\n",
    "        embeddings_metadata=None),\n",
    "    # tfk.callbacks.ReduceLROnPlateau(monitor='val_loss',\n",
    "    #                                 factor=0.8,\n",
    "    #                                 patience=10,\n",
    "    #                                 verbose=1,\n",
    "    #                                 mode='min',\n",
    "    #                                 min_delta=0.0001,\n",
    "    #                                 cooldown=0,\n",
    "    #                                 min_lr=0.05)\n",
    "]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"conv\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 999)]             0         \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 999, 1)            0         \n",
      "_________________________________________________________________\n",
      "conv_0 (Conv1D)              (None, 995, 20)           120       \n",
      "_________________________________________________________________\n",
      "conv_norm_0 (SyncBatchNormal (None, 995, 20)           80        \n",
      "_________________________________________________________________\n",
      "conv_1 (Conv1D)              (None, 986, 20)           4020      \n",
      "_________________________________________________________________\n",
      "conv_pool_1 (MaxPooling1D)   (None, 493, 20)           0         \n",
      "_________________________________________________________________\n",
      "conv_norm_1 (SyncBatchNormal (None, 493, 20)           80        \n",
      "_________________________________________________________________\n",
      "conv_2 (Conv1D)              (None, 479, 40)           12040     \n",
      "_________________________________________________________________\n",
      "conv_norm_2 (SyncBatchNormal (None, 479, 40)           160       \n",
      "_________________________________________________________________\n",
      "conv_3 (Conv1D)              (None, 460, 40)           32040     \n",
      "_________________________________________________________________\n",
      "conv_pool_3 (MaxPooling1D)   (None, 230, 40)           0         \n",
      "_________________________________________________________________\n",
      "conv_norm_3 (SyncBatchNormal (None, 230, 40)           160       \n",
      "_________________________________________________________________\n",
      "conv_4 (Conv1D)              (None, 206, 80)           80080     \n",
      "_________________________________________________________________\n",
      "conv_norm_4 (SyncBatchNormal (None, 206, 80)           320       \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 16480)             0         \n",
      "_________________________________________________________________\n",
      "dense_0 (Dense)              (None, 128)               2109568   \n",
      "_________________________________________________________________\n",
      "dense_drop_0 (Dropout)       (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_drop_1 (Dropout)       (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "loc (Dense)                  (None, 4)                 260       \n",
      "=================================================================\n",
      "Total params: 2,247,184\n",
      "Trainable params: 2,246,784\n",
      "Non-trainable params: 400\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vae.summary()\n",
    "# tfk.utils.plot_model(vae, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "INFO:tensorflow:batch_all_reduce: 3 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:batch_all_reduce: 3 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:batch_all_reduce: 3 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:batch_all_reduce: 3 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:batch_all_reduce: 3 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:batch_all_reduce: 3 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:batch_all_reduce: 3 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:batch_all_reduce: 3 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:batch_all_reduce: 3 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:batch_all_reduce: 3 all-reduces with algorithm = nccl, num_packs = 1\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 66.06651, saving model to four_analyte/checkpoints/slow/conv_holdout_allin_manual_CF078_6_20_5_5_Adadelta.h5\n",
      "20/20 - 15s - loss: 46.6589 - tf_pmse: 6428.1182 - root_mean_squared_error: 6.8307 - val_loss: 66.0665 - val_tf_pmse: 8466.7559 - val_root_mean_squared_error: 8.1281\n",
      "Epoch 2/500\n",
      "\n",
      "Epoch 00002: val_loss improved from 66.06651 to 61.40208, saving model to four_analyte/checkpoints/slow/conv_holdout_allin_manual_CF078_6_20_5_5_Adadelta.h5\n",
      "20/20 - 11s - loss: 15.4894 - tf_pmse: 4249.7163 - root_mean_squared_error: 3.9357 - val_loss: 61.4021 - val_tf_pmse: 8137.7065 - val_root_mean_squared_error: 7.8359\n",
      "Epoch 3/500\n",
      "\n",
      "Epoch 00003: val_loss improved from 61.40208 to 57.95105, saving model to four_analyte/checkpoints/slow/conv_holdout_allin_manual_CF078_6_20_5_5_Adadelta.h5\n",
      "20/20 - 11s - loss: 14.3935 - tf_pmse: 4075.0288 - root_mean_squared_error: 3.7939 - val_loss: 57.9510 - val_tf_pmse: 7892.4102 - val_root_mean_squared_error: 7.6126\n",
      "Epoch 4/500\n",
      "\n",
      "Epoch 00004: val_loss improved from 57.95105 to 49.31543, saving model to four_analyte/checkpoints/slow/conv_holdout_allin_manual_CF078_6_20_5_5_Adadelta.h5\n",
      "20/20 - 11s - loss: 11.2846 - tf_pmse: 3634.2109 - root_mean_squared_error: 3.3593 - val_loss: 49.3154 - val_tf_pmse: 7250.1475 - val_root_mean_squared_error: 7.0225\n",
      "Epoch 5/500\n",
      "\n",
      "Epoch 00005: val_loss improved from 49.31543 to 38.12007, saving model to four_analyte/checkpoints/slow/conv_holdout_allin_manual_CF078_6_20_5_5_Adadelta.h5\n",
      "20/20 - 11s - loss: 15.6318 - tf_pmse: 4160.4248 - root_mean_squared_error: 3.9537 - val_loss: 38.1201 - val_tf_pmse: 6321.0430 - val_root_mean_squared_error: 6.1741\n",
      "Epoch 6/500\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-7dd3274615cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m                       \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mremainder\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m                       \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m                       callbacks=cb)\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0mall_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mstats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvae\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhold_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/apps/software/TensorFlow/2.2.0-fosscuda-2019b-Python-3.7.4/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/apps/software/TensorFlow/2.2.0-fosscuda-2019b-Python-3.7.4/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m    846\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    847\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    849\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/apps/software/TensorFlow/2.2.0-fosscuda-2019b-Python-3.7.4/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/apps/software/TensorFlow/2.2.0-fosscuda-2019b-Python-3.7.4/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/apps/software/TensorFlow/2.2.0-fosscuda-2019b-Python-3.7.4/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2419\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/apps/software/TensorFlow/2.2.0-fosscuda-2019b-Python-3.7.4/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1664\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/apps/software/TensorFlow/2.2.0-fosscuda-2019b-Python-3.7.4/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/apps/software/TensorFlow/2.2.0-fosscuda-2019b-Python-3.7.4/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m/opt/apps/software/TensorFlow/2.2.0-fosscuda-2019b-Python-3.7.4/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "verb = 2\n",
    "remainder = epochs\n",
    "all_history = []\n",
    "# all_stats = []\n",
    "while remainder > 10:\n",
    "    history = vae.fit(train_data,\n",
    "                      validation_data=val_data,\n",
    "                      epochs=remainder,\n",
    "                      verbose=verb,\n",
    "                      callbacks=cb)\n",
    "    all_history.append(history.history)\n",
    "    stats = vae.evaluate(hold_data)\n",
    "#     all_stats.append(stats)\n",
    "    print(stats)\n",
    "    vae.load_weights(checkpoint)\n",
    "    remainder -= len(history.history['loss'])\n",
    "\n",
    "print(checkpoint)\n",
    "# vae.load_weights(checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = vae.evaluate(hold_data)\n",
    "print(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Epoch 00010: val_loss improved from 3.24315 to 2.77304, saving model to four_analyte/checkpoints/slow/conv_holdout_allin_manual_CF078_6_20_5_5_Adam.h5\n",
    "# 20/20 - 12s - root_mean_squared_error: 2.1603 - tf_pmse: 2291.8496 - loss: 4.6668 - val_root_mean_squared_error: 1.6652 - val_tf_pmse: 1783.9392 - val_loss: 2.7730\n",
    "# Epoch 00403: val_loss improved from 0.21302 to 0.20346, saving model to four_analyte/checkpoints/slow/conv_holdout_allin_manual_CF078_6_20_5_5_Adam.h5\n",
    "# 20/20 - 12s - root_mean_squared_error: 0.7143 - tf_pmse: 760.9315 - loss: 0.5103 - val_root_mean_squared_error: 0.4511 - val_tf_pmse: 387.6716 - val_loss: 0.2035\n",
    "\n",
    "# Epoch 00010: val_loss did not improve from 1.67947\n",
    "# 20/20 - 8s - loss: 4.6731 - root_mean_squared_error: 2.1617 - tf_pmse: 2294.1250 - val_loss: 3.8590 - val_root_mean_squared_error: 1.9644 - val_tf_pmse: 2122.7517\n",
    "# Epoch 00158: val_loss improved from 0.10149 to 0.09772, saving model to four_analyte/checkpoints/slow/conv_holdout_allin_manual_CF025_6_15_5_5_Adam.h5\n",
    "# 20/20 - 9s - loss: 0.5840 - root_mean_squared_error: 0.7642 - tf_pmse: 814.4987 - val_loss: 0.0977 - val_root_mean_squared_error: 0.3126 - val_tf_pmse: 265.2802\n",
    "# Epoch 00300: val_loss did not improve from 0.09537\n",
    "# 20/20 - 9s - loss: 0.5066 - root_mean_squared_error: 0.7117 - tf_pmse: 762.6978 - val_loss: 0.1298 - val_root_mean_squared_error: 0.3603 - val_tf_pmse: 276.1533\n",
    "\n",
    "# Epoch 00010: val_loss improved from 659.09766 to 570.89612, saving model to four_analyte/checkpoints/slow/conv_holdout_allin_manual_CF025_7_15_5_5_Adadelta.h5\n",
    "# 42/42 - 9s - loss: 411.0308 - mse: 1.1620 - mean_absolute_percentage_error: 8.5353 - val_loss: 570.8961 - val_mse: 1.5896 - val_mean_absolute_percentage_error: 10.6714\n",
    "# Epoch 11/500\n",
    "# optimal\n",
    "# Epoch 00481: val_loss improved from 39.06295 to 31.78666, saving model to four_analyte/checkpoints/slow/conv_holdout_allin_manual_CF025_7_15_5_5_Adadelta.h5\n",
    "# 42/42 - 9s - loss: 10.2691 - mse: 0.0291 - mean_absolute_percentage_error: 1.3824 - val_loss: 31.7867 - val_mse: 0.0877 - val_mean_absolute_percentage_error: 1.8936\n",
    "# Epoch 482/500\n",
    "\n",
    "# out_prefix = os.path.join('/mnt/nfs/proj/in-vitro/',\n",
    "#                           'Mark/four_analyte/good_probe_holdout/', 'pred_csvs',\n",
    "#                           'allin')\n",
    "out_prefix = os.path.join('/home/leonardo.barbosa/projects/voltametry/results/',\n",
    "                          'four_analyte/good_probe_holdout/', 'pred_csvs',\n",
    "                          'allin/non_prob')\n",
    "\n",
    "print('Saving history...')\n",
    "\n",
    "hist_file = os.path.join(out_prefix, f'manual_{speed}_{hold_probe}_history.pickle')\n",
    "print(hist_file)\n",
    "with open(hist_file, 'wb') as handle:\n",
    "    pickle.dump(all_history, handle)\n",
    "\n",
    "print('History Saved.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "z values shifted by 10 std\n"
     ]
    }
   ],
   "source": [
    "lab_bijector = make_bijector(4, bi_type=bi_type)\n",
    "# train_data, val_data, hold_data = get_multiple_data(prefix, \n",
    "#                                                     probes, \n",
    "#                                                     hold_probe, \n",
    "#                                                     batch_size=batch_size, \n",
    "#                                                     weight=True,\n",
    "#                                                     bi_type=2,\n",
    "#                                                     allin=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    x_arr, y_arr = list(zip(*hold_data))\n",
    "except ValueError:\n",
    "    x_arr, y_arr, _ = list(zip(*hold_data))\n",
    "y_arr = np.concatenate(y_arr, axis=0)\n",
    "x_arr = np.concatenate(x_arr, axis=0)\n",
    "\n",
    "# y_arr[-10:,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one prediction\n",
    "\n",
    "# for x, *_ in hold_data:\n",
    "#     ys2 = [vae(x) for _ in range(10)]\n",
    "#     print(len(ys2))\n",
    "#     break\n",
    "\n",
    "# z = np.median([y for y in ys2], axis=0)\n",
    "\n",
    "# print(z.shape)\n",
    "# print(z[-10:,])\n",
    "\n",
    "# z_pred = lab_bijector.inverse(z).numpy()\n",
    "\n",
    "# z_pred[-10:,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_x_arr, hold_y_arr = ds_to_array(lab_bijector, hold_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-20, 20)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ul, ulidx = np.unique(hold_y_arr, return_index=True, axis=0)\n",
    "\n",
    "fig, axs = plt.subplots()\n",
    "fig.set_size_inches(20,10)\n",
    "\n",
    "for idx in ulidx:\n",
    "    axs.plot(hold_x_arr[idx,:])\n",
    "axs.set_ylim(-20,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_concentrations(hold_y_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "10\n",
      "10\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "hold_conc_pred = predict_non_prob(lab_bijector, vae, hold_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_concentrations(hold_conc_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------\n",
      "\n",
      "\n",
      "Holdout Results: \n",
      "RMSE:\n",
      "DA: 353.5242614746094\n",
      "5HT: 236.11410522460938\n",
      "pH: 0.08497358113527298\n",
      "NE: 455.54150390625\n",
      "R2 Score:\n",
      "DA: 0.9321831072225272\n",
      "5HT: 0.9697532673082169\n",
      "pH: 0.6272074806440443\n",
      "NE: 0.8874826787258907\n",
      "----------------------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print('----------------------------------------------------\\n\\n')\n",
    "# print('Test Results: ')\n",
    "# print('RMSE:')\n",
    "# print_metric(rmse, names, test_y_arr, conc_pred)\n",
    "# print('R2 Score:')\n",
    "# print_metric(r2_score, names, test_y_arr, conc_pred)\n",
    "print('----------------------------------------------------\\n\\n')\n",
    "print('Holdout Results: ')\n",
    "print('RMSE:')\n",
    "print_metric(rmse, names, hold_y_arr, hold_conc_pred)\n",
    "print('R2 Score:')\n",
    "print_metric(r2_score, names, hold_y_arr, hold_conc_pred)\n",
    "print('----------------------------------------------------\\n\\n')\n",
    "\n",
    "# ----------------------------------------------------\n",
    "# Holdout Results: \n",
    "# RMSE:\n",
    "# DA: 353.5242614746094\n",
    "# 5HT: 236.11410522460938\n",
    "# pH: 0.08497358113527298\n",
    "# NE: 455.54150390625\n",
    "# R2 Score:\n",
    "# DA: 0.9321831072225272\n",
    "# 5HT: 0.9697532673082169\n",
    "# pH: 0.6272074806440443\n",
    "# NE: 0.8874826787258907\n",
    "# ----------------------------------------------------\n",
    "\n",
    "\n",
    "# Validation Results: \n",
    "# RMSE:\n",
    "# DA: 597.5407104492188\n",
    "# 5HT: 264.5990295410156\n",
    "# pH: 0.08884887397289276\n",
    "# NE: 473.3499450683594\n",
    "# R2 Score:\n",
    "# DA: 0.8064105187021449\n",
    "# 5HT: 0.9620229399535662\n",
    "# pH: 0.5924154300191126\n",
    "# NE: 0.8785643254997075\n",
    "# ----------------------------------------------------\n",
    "\n",
    "# dropout .8, 1000 epochs\n",
    "# Holdout Results: \n",
    "# RMSE:\n",
    "# DA: 381.420166015625\n",
    "# 5HT: 527.9290771484375\n",
    "# pH: 0.06234191730618477\n",
    "# NE: 437.3533630371094\n",
    "# R2 Score:\n",
    "# DA: 0.9211229248684787\n",
    "# 5HT: 0.8488202086130727\n",
    "# pH: 0.7993339927940516\n",
    "# NE: 0.8963309835252766\n",
    "# ----------------------------------------------------\n",
    "\n",
    "# Holdout Results: \n",
    "# RMSE:\n",
    "# DA: 328.60009765625\n",
    "# 5HT: 222.7253875732422\n",
    "# pH: 0.07421305775642395\n",
    "# NE: 451.2471008300781\n",
    "# R2 Score:\n",
    "# DA: 0.9414564676317353\n",
    "# 5HT: 0.9730915512198245\n",
    "# pH: 0.7156313500325053\n",
    "# NE: 0.8896402558562272\n",
    "# ----------------------------------------------------\n",
    "\n",
    "# Holdout Results: \n",
    "# RMSE:\n",
    "# DA: 228.89488006843868\n",
    "# 5HT: 159.28784949678047\n",
    "# pH: 0.06615879457003582\n",
    "# NE: 608.8871904838503\n",
    "# R2 Score:\n",
    "# DA: 0.9715707969368755\n",
    "# 5HT: 0.9862343635691533\n",
    "# pH: 0.773862756812846\n",
    "# NE: 0.79898298650915\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Saving predictions for hold out...')\n",
    "\n",
    "# if not (os.path.exists(out_prefix)):\n",
    "#     os.makedirs(out_prefix, exist_ok=True)\n",
    "# np.savetxt(\n",
    "#     os.path.join(out_prefix, f'{speed}_{hold_probe}_outprobe_ytest.csv'),\n",
    "#     hold_y_arr, delimiter=',')\n",
    "# np.savetxt(\n",
    "#     os.path.join(out_prefix, f'{speed}_{hold_probe}_outprobe_ypred.csv'),\n",
    "#     hold_conc_pred, delimiter=',')\n",
    "# # np.savetxt(\n",
    "# #     os.path.join(out_prefix, f'{speed}_{hold_probe}_outprobe_ypred_05.csv'),\n",
    "# #     hold_conc_05, delimiter=',')\n",
    "# # np.savetxt(\n",
    "# #     os.path.join(out_prefix, f'{speed}_{hold_probe}_outprobe_ypred_95.csv'),\n",
    "# #     hold_conc_95, delimiter=',')\n",
    "\n",
    "# print('done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "10\n",
      "10\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "val_x_arr, val_y_arr = ds_to_array(lab_bijector, val_data)\n",
    "\n",
    "val_conc_pred = predict_non_prob(lab_bijector, vae, val_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_concentrations(val_y_arr)\n",
    "plot_concentrations(val_conc_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------\n",
      "\n",
      "\n",
      "Validation Results: \n",
      "RMSE:\n",
      "DA: 367.68609619140625\n",
      "5HT: 300.8308410644531\n",
      "pH: 0.08805300295352936\n",
      "NE: 489.2878723144531\n",
      "R2 Score:\n",
      "DA: 0.9267021994991452\n",
      "5HT: 0.9509105504055534\n",
      "pH: 0.5996846245496753\n",
      "NE: 0.870247002963068\n",
      "----------------------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print('----------------------------------------------------\\n\\n')\n",
    "print('Validation Results: ')\n",
    "print('RMSE:')\n",
    "print_metric(rmse, names, val_y_arr, val_conc_pred)\n",
    "print('R2 Score:')\n",
    "print_metric(r2_score, names, val_y_arr, val_conc_pred)\n",
    "print('----------------------------------------------------\\n\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Validation Results: \n",
    "# RMSE:\n",
    "# DA: 276.2057189941406\n",
    "# 5HT: 235.17147827148438\n",
    "# pH: 0.060689620673656464\n",
    "# NE: 329.8268737792969\n",
    "# R2 Score:\n",
    "# DA: 0.9586037289435875\n",
    "# 5HT: 0.9699943467196164\n",
    "# pH: 0.8098400536200738\n",
    "# NE: 0.9410155599970595\n",
    "# ----------------------------------------------------\n",
    "\n",
    "# print('Saving predictions for validation...')\n",
    "\n",
    "# # np.savetxt(os.path.join(out_prefix,\n",
    "# #                         f'{speed}_{hold_probe}_inprobe_ytest.csv'),\n",
    "# #            test_y_arr)\n",
    "# # np.savetxt(os.path.join(out_prefix,\n",
    "# #                         f'{speed}_{hold_probe}_inprobe_ypred.csv'),\n",
    "# #            conc_pred)\n",
    "# # np.savetxt(\n",
    "# #     os.path.join(out_prefix, f'{speed}_{hold_probe}_inprobe_ypred_05.csv'),\n",
    "# #     conc_05)\n",
    "# # np.savetxt(\n",
    "# #     os.path.join(out_prefix, f'{speed}_{hold_probe}_inprobe_ypred_95.csv'),\n",
    "# #     conc_95)\n",
    "# np.savetxt(os.path.join(out_prefix, f'{speed}_{hold_probe}_yval.csv'),\n",
    "#            val_y_arr, delimiter=',')\n",
    "# np.savetxt(os.path.join(out_prefix, f'{speed}_{hold_probe}_yval_pred.csv'),\n",
    "#            val_conc_pred, delimiter=',')\n",
    "# np.savetxt(os.path.join(out_prefix, f'{speed}_{hold_probe}_yval_pred_05.csv'),\n",
    "#            val_conc_05, delimiter=',')\n",
    "# np.savetxt(os.path.join(out_prefix, f'{speed}_{hold_probe}_yval_pred_95.csv'),\n",
    "#            val_conc_95, delimiter=',')\n",
    "# # if not (os.path.exists('./pred_csvs/four_analyte/')):\n",
    "# #     os.makedirs('./pred_csvs/four_analyte/', exist_ok=True)\n",
    "# # np.savetxt(f'pred_csvs/four_analyte/holdout/ytest_{speed}_{hold_probe}.csv',\n",
    "# #            test_y_arr)\n",
    "# # np.savetxt(f'pred_csvs/four_analyte/holdout/ypred_{speed}_{hold_probe}.csv',\n",
    "# #            conc_pred)\n",
    "# # np.savetxt(f'pred_csvs/four_analyte/holdout/yval_{speed}_{hold_probe}.csv',\n",
    "# #            val_y_arr)\n",
    "# # np.savetxt(\n",
    "# #     f'pred_csvs/four_analyte/holdout/yval_pred_{speed}_{hold_probe}.csv',\n",
    "# #     val_conc_pred)\n",
    "\n",
    "# print('Predictions Saved.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_x_arr, train_y_arr = ds_to_array(lab_bijector, train_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_x_arr.shape[0]/20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "compare to probabilitic predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # hold_y_arr = np.loadtxt(f'/home/leonardo.barbosa/projects/voltametry/results/four_analyte/good_probe_holdout/pred_csvs/allin/{speed}_{hold_probe}_outprobe_ytest.csv',\n",
    "# #     delimiter=',')\n",
    "# # hold_conc_pred = np.loadtxt(f'/home/leonardo.barbosa/projects/voltametry/results/four_analyte/good_probe_holdout/pred_csvs/allin/{speed}_{hold_probe}_outprobe_ypred.csv',\n",
    "# #     delimiter=',')\n",
    "\n",
    "# hold_y_arr = np.loadtxt(f'/home/leonardo.barbosa/projects/voltametry/results/four_analyte/good_probe_holdout/pred_csvs/allin/non_prob/slow_CF025_1_outprobe_ytest.csv',\n",
    "#     delimiter=',')\n",
    "# hold_conc_pred = np.loadtxt(f'/home/leonardo.barbosa/projects/voltametry/results/four_analyte/good_probe_holdout/pred_csvs/allin/non_prob/slow_CF025_1_outprobe_ypred.csv',\n",
    "#     delimiter=',')\n",
    "\n",
    "\n",
    "# print('-------------------- Identity ----------------------\\n\\n')\n",
    "# print('Holdout Results: ')\n",
    "# print('RMSE:')\n",
    "# print_metric(rmse, names, hold_y_arr, hold_conc_pred)\n",
    "# print('R2 Score:')\n",
    "# print_metric(r2_score, names, hold_y_arr, hold_conc_pred)\n",
    "# print('----------------------------------------------------\\n\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# hold_y_arr = np.loadtxt(f'/home/leonardo.barbosa/projects/voltametry/results/four_analyte/good_probe_holdout/pred_csvs/allin/non_prob/slow_CF025_outprobe_ytest.csv',\n",
    "#     delimiter=',')\n",
    "# hold_conc_pred = np.loadtxt(f'/home/leonardo.barbosa/projects/voltametry/results/four_analyte/good_probe_holdout/pred_csvs/allin/non_prob/slow_CF025_outprobe_ypred.csv',\n",
    "#     delimiter=',')\n",
    "\n",
    "# print('-------------------- Original ----------------------\\n\\n')\n",
    "# print('Holdout Results: ')\n",
    "# print('RMSE:')\n",
    "# print_metric(rmse, names, hold_y_arr, hold_conc_pred)\n",
    "# print('R2 Score:')\n",
    "# print_metric(r2_score, names, hold_y_arr, hold_conc_pred)\n",
    "# print('----------------------------------------------------\\n\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# hold_y_arr = np.loadtxt(f'/home/leonardo.barbosa/projects/voltametry/results/four_analyte/good_probe_holdout/pred_csvs/allin/non_prob/slow_CF025_5_Adadelta_outprobe_ytest.csv',\n",
    "#     delimiter=',')\n",
    "# hold_conc_pred = np.loadtxt(f'/home/leonardo.barbosa/projects/voltametry/results/four_analyte/good_probe_holdout/pred_csvs/allin/non_prob/slow_CF025_5_Adadelta_outprobe_ypred.csv',\n",
    "#     delimiter=',')\n",
    "\n",
    "# print('------------------ shifted z-values -----------------\\n\\n')\n",
    "# print('Holdout Results: ')\n",
    "# print('RMSE:')\n",
    "# print_metric(rmse, names, hold_y_arr, hold_conc_pred)\n",
    "# print('R2 Score:')\n",
    "# print_metric(r2_score, names, hold_y_arr, hold_conc_pred)\n",
    "# print('----------------------------------------------------\\n\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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