evaluate_encoder_multiout.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Needed to reload modified modules\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-06 15:21:34.050703: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-02-06 15:21:43.772515: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-02-06 15:21:46.289634: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/kiliakis/install/anaconda3/lib:/home/kiliakis/install/lib\n",
      "2024-02-06 15:21:46.289654: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2024-02-06 15:22:19.780162: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/kiliakis/install/anaconda3/lib:/home/kiliakis/install/lib\n",
      "2024-02-06 15:22:19.780430: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/kiliakis/install/anaconda3/lib:/home/kiliakis/install/lib\n",
      "2024-02-06 15:22:19.780440: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the ML model\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from utils import unnormalize_params\n",
    "from utils import fast_tensor_load\n",
    "from models import EncoderMulti\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "# data_dir = './tomo_data/datasets_encoder_TF_03-03-23'\n",
    "# data_dir = './tomo_data/datasets_encoder_TF_24-03-23'\n",
    "data_dir = './tomo_data/datasets_encoder_TF_08-11-23'\n",
    "\n",
    "\n",
    "dataset_percent = 0.5\n",
    "normalization = 'minmax'\n",
    "img_normalize = 'off'\n",
    "num_Turns_Case = 1\n",
    "\n",
    "var_names = ['phEr', 'enEr', 'bl',\n",
    "             'inten', 'Vrf', 'mu', 'VrfSPS']\n",
    "# timestamp = get_best_model_timestamp('./trials', model='enc')\n",
    "# timestamp = 'best_encoder_TF'\n",
    "timestamp = 'best_encoder_TF_no_intensity'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading ./tomo_data/datasets_encoder_TF_08-11-23/ML_data/testing-00.npz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-06 15:23:14.423662: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/kiliakis/install/anaconda3/lib:/home/kiliakis/install/lib\n",
      "2024-02-06 15:23:14.437426: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2024-02-06 15:23:14.437472: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (sy153): /proc/driver/nvidia/version does not exist\n",
      "2024-02-06 15:23:14.636054: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Testing files:  697\n",
      "Elapsed time: 3.165, Per file: 0.005\n"
     ]
    }
   ],
   "source": [
    "# Initialize parameters\n",
    "import time\n",
    "\n",
    "# Initialize train/ test / validation paths\n",
    "ML_dir = os.path.join(data_dir, 'ML_data')\n",
    "assert os.path.exists(ML_dir)\n",
    "\n",
    "assert normalization == 'minmax'\n",
    "assert img_normalize == 'off'\n",
    "TESTING_PATH = os.path.join(ML_dir, 'testing-??.npz')\n",
    "\n",
    "# First the training data\n",
    "start_t = time.time()\n",
    "x_test, y_test = fast_tensor_load(TESTING_PATH, dataset_percent)\n",
    "print('Number of Testing files: ', len(y_test))\n",
    "total_time = time.time() - start_t\n",
    "print(f'Elapsed time: {total_time:.3f}, Per file: {total_time/len(y_test):.3f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_encoder_TF_no_intensity\n"
     ]
    }
   ],
   "source": [
    "# Model to load\n",
    "\n",
    "print(timestamp)\n",
    "\n",
    "# Initialize directories\n",
    "trial_dir = os.path.join('./trials/', timestamp)\n",
    "weights_dir = os.path.join(trial_dir, 'weights')\n",
    "plots_dir = os.path.join(trial_dir, 'plots')\n",
    "assert os.path.exists(weights_dir)\n",
    "os.makedirs(plots_dir, exist_ok=True)\n",
    "\n",
    "encoder = EncoderMulti()\n",
    "encoder.load(weights_dir)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Input (InputLayer)          [(None, 128, 128, 1)]     0         \n",
      "                                                                 \n",
      " Crop (Cropping2D)           (None, 128, 128, 1)       0         \n",
      "                                                                 \n",
      " phEr_CNN_1 (Conv2D)         (None, 64, 64, 8)         392       \n",
      "                                                                 \n",
      " phEr_CNN_2 (Conv2D)         (None, 32, 32, 16)        6272      \n",
      "                                                                 \n",
      " phEr_CNN_3 (Conv2D)         (None, 16, 16, 32)        25088     \n",
      "                                                                 \n",
      " phEr_Flatten (Flatten)      (None, 8192)              0         \n",
      "                                                                 \n",
      " phEr_Dense_1 (Dense)        (None, 1024)              8389632   \n",
      "                                                                 \n",
      " phEr_Dense_2 (Dense)        (None, 512)               524800    \n",
      "                                                                 \n",
      " phEr_Dense_3 (Dense)        (None, 32)                16416     \n",
      "                                                                 \n",
      " phEr (Dense)                (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 8,962,633\n",
      "Trainable params: 8,962,633\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Input (InputLayer)          [(None, 128, 128, 1)]     0         \n",
      "                                                                 \n",
      " Crop (Cropping2D)           (None, 128, 128, 1)       0         \n",
      "                                                                 \n",
      " enEr_CNN_1 (Conv2D)         (None, 64, 64, 8)         648       \n",
      "                                                                 \n",
      " enEr_CNN_2 (Conv2D)         (None, 32, 32, 16)        10368     \n",
      "                                                                 \n",
      " enEr_CNN_3 (Conv2D)         (None, 16, 16, 32)        41472     \n",
      "                                                                 \n",
      " enEr_Flatten (Flatten)      (None, 8192)              0         \n",
      "                                                                 \n",
      " enEr_Dense_1 (Dense)        (None, 1024)              8389632   \n",
      "                                                                 \n",
      " enEr_Dense_2 (Dense)        (None, 256)               262400    \n",
      "                                                                 \n",
      " enEr_Dense_3 (Dense)        (None, 64)                16448     \n",
      "                                                                 \n",
      " enEr (Dense)                (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 8,721,033\n",
      "Trainable params: 8,721,033\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Input (InputLayer)          [(None, 128, 128, 1)]     0         \n",
      "                                                                 \n",
      " Crop (Cropping2D)           (None, 128, 128, 1)       0         \n",
      "                                                                 \n",
      " bl_CNN_1 (Conv2D)           (None, 64, 64, 16)        400       \n",
      "                                                                 \n",
      " bl_CNN_2 (Conv2D)           (None, 32, 32, 32)        12800     \n",
      "                                                                 \n",
      " bl_CNN_3 (Conv2D)           (None, 16, 16, 64)        51200     \n",
      "                                                                 \n",
      " bl_Flatten (Flatten)        (None, 16384)             0         \n",
      "                                                                 \n",
      " bl_Dense_1 (Dense)          (None, 1024)              16778240  \n",
      "                                                                 \n",
      " bl_Dense_2 (Dense)          (None, 512)               524800    \n",
      "                                                                 \n",
      " bl_Dense_3 (Dense)          (None, 64)                32832     \n",
      "                                                                 \n",
      " bl (Dense)                  (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 17,400,337\n",
      "Trainable params: 17,400,337\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Input (InputLayer)          [(None, 128, 128, 1)]     0         \n",
      "                                                                 \n",
      " Crop (Cropping2D)           (None, 128, 128, 1)       0         \n",
      "                                                                 \n",
      " inten_CNN_1 (Conv2D)        (None, 64, 64, 8)         80        \n",
      "                                                                 \n",
      " inten_CNN_2 (Conv2D)        (None, 32, 32, 16)        1168      \n",
      "                                                                 \n",
      " inten_CNN_3 (Conv2D)        (None, 16, 16, 32)        4640      \n",
      "                                                                 \n",
      " inten_Flatten (Flatten)     (None, 8192)              0         \n",
      "                                                                 \n",
      " inten_Dense_1 (Dense)       (None, 1024)              8389632   \n",
      "                                                                 \n",
      " inten_Dense_2 (Dense)       (None, 256)               262400    \n",
      "                                                                 \n",
      " inten_Dense_3 (Dense)       (None, 64)                16448     \n",
      "                                                                 \n",
      " inten (Dense)               (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 8,674,433\n",
      "Trainable params: 8,674,433\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Input (InputLayer)          [(None, 128, 128, 1)]     0         \n",
      "                                                                 \n",
      " Crop (Cropping2D)           (None, 104, 104, 1)       0         \n",
      "                                                                 \n",
      " Vrf_CNN_1 (Conv2D)          (None, 52, 52, 8)         72        \n",
      "                                                                 \n",
      " Vrf_CNN_2 (Conv2D)          (None, 26, 26, 16)        1152      \n",
      "                                                                 \n",
      " Vrf_CNN_3 (Conv2D)          (None, 13, 13, 32)        4608      \n",
      "                                                                 \n",
      " Vrf_Flatten (Flatten)       (None, 5408)              0         \n",
      "                                                                 \n",
      " Vrf_Dense_1 (Dense)         (None, 1024)              5538816   \n",
      "                                                                 \n",
      " Vrf_Dense_2 (Dense)         (None, 256)               262400    \n",
      "                                                                 \n",
      " Vrf_Dense_3 (Dense)         (None, 64)                16448     \n",
      "                                                                 \n",
      " Vrf (Dense)                 (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5,823,561\n",
      "Trainable params: 5,823,561\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "Model: \"model_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Input (InputLayer)          [(None, 128, 128, 1)]     0         \n",
      "                                                                 \n",
      " Crop (Cropping2D)           (None, 128, 128, 1)       0         \n",
      "                                                                 \n",
      " mu_CNN_1 (Conv2D)           (None, 64, 64, 8)         208       \n",
      "                                                                 \n",
      " mu_CNN_2 (Conv2D)           (None, 32, 32, 16)        3216      \n",
      "                                                                 \n",
      " mu_CNN_3 (Conv2D)           (None, 16, 16, 32)        12832     \n",
      "                                                                 \n",
      " mu_Flatten (Flatten)        (None, 8192)              0         \n",
      "                                                                 \n",
      " mu_Dense_1 (Dense)          (None, 1024)              8389632   \n",
      "                                                                 \n",
      " mu_Dense_2 (Dense)          (None, 256)               262400    \n",
      "                                                                 \n",
      " mu_Dense_3 (Dense)          (None, 64)                16448     \n",
      "                                                                 \n",
      " mu (Dense)                  (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 8,684,801\n",
      "Trainable params: 8,684,801\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "Model: \"model_6\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Input (InputLayer)          [(None, 128, 128, 1)]     0         \n",
      "                                                                 \n",
      " Crop (Cropping2D)           (None, 116, 116, 1)       0         \n",
      "                                                                 \n",
      " VrfSPS_CNN_1 (Conv2D)       (None, 58, 58, 8)         208       \n",
      "                                                                 \n",
      " VrfSPS_CNN_2 (Conv2D)       (None, 29, 29, 16)        3216      \n",
      "                                                                 \n",
      " VrfSPS_CNN_3 (Conv2D)       (None, 15, 15, 32)        12832     \n",
      "                                                                 \n",
      " VrfSPS_Flatten (Flatten)    (None, 7200)              0         \n",
      "                                                                 \n",
      " VrfSPS_Dense_1 (Dense)      (None, 1024)              7373824   \n",
      "                                                                 \n",
      " VrfSPS_Dense_2 (Dense)      (None, 256)               262400    \n",
      "                                                                 \n",
      " VrfSPS_Dense_3 (Dense)      (None, 64)                16448     \n",
      "                                                                 \n",
      " VrfSPS (Dense)              (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 7,668,993\n",
      "Trainable params: 7,668,993\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n"
     ]
    }
   ],
   "source": [
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'visualkeras'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_3960337/1798128716.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mvisualkeras\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     visualkeras.layered_view(model.model, to_file=os.path.join(plots_dir, f'encoder-{model.output_name}-visual.png'), \n\u001b[1;32m      5\u001b[0m                          legend=True, index_ignore=[1], max_z=100, max_xy=400, draw_volume=True).show()\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'visualkeras'"
     ]
    }
   ],
   "source": [
    "import visualkeras\n",
    "\n",
    "for model in encoder.model:\n",
    "    visualkeras.layered_view(model.model, to_file=os.path.join(plots_dir, f'encoder-{model.output_name}-visual.png'), \n",
    "                         legend=True, index_ignore=[1], max_z=100, max_xy=400, draw_volume=True).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(697, 7)\n"
     ]
    }
   ],
   "source": [
    "test_pred = encoder.predict(x_test)\n",
    "print(test_pred.shape)\n",
    "test_mse = mean_squared_error(y_test, test_pred, multioutput='raw_values')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_losses = {}\n",
    "# for var_name in var_names:\n",
    "#     model = encoder.model[var_names.index(var_name)]\n",
    "#     # if var_name == 'inten':\n",
    "#     #     continue\n",
    "#     idx = var_names.index(var_name)\n",
    "#     # print(f'------ Evaluating {var_name} ------')\n",
    "#     test_loss = model.evaluate(x_test, tf.gather(y_test, idx, axis=1))\n",
    "#     # print(test_loss)\n",
    "#     if isinstance(test_loss, float):\n",
    "#         test_losses[var_name] = test_loss\n",
    "#     else:\n",
    "#         test_losses[var_name] = test_loss[0]\n",
    "\n",
    "# for name, mse in test_losses.items():\n",
    "#     print(f'{name}:\\t{mse:.3e}')\n",
    "\n",
    "# print(f'\\nMean loss: {np.mean([v for v in test_losses.values()]):.3e}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 180x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate table and save\n",
    "plt.figure(figsize=(1.8, 3))\n",
    "plt.axis('off')\n",
    "rowLabels = ['PhaseEr', 'EnergyEr', 'B.Length', 'B.Inten', 'LHC_VRF', 'mu', 'SPS_VRF']\n",
    "rowColours = ['xkcd:light grey'] * len(rowLabels)\n",
    "colColours = ['xkcd:light grey']\n",
    "cellColours = [['white'] for i in range(len(rowLabels))]\n",
    "cellText = [[f'{l:.1e}'] for l in test_mse]\n",
    "\n",
    "plt.table(cellText=cellText, rowLabels=rowLabels, colLabels=['MSE'],\n",
    "          loc='center', rowColours=rowColours, colColours=colColours,\n",
    "          cellColours=cellColours)\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{plots_dir}/mse-table.png',\n",
    "            dpi=400, bbox_inches='tight', transparent=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(697, 7)\n",
      "phEr:\t7.444e-02\t1.832e-01\n",
      "enEr:\t4.576e-01\t1.193e+00\n",
      "bl:\t5.089e-12\t1.186e-11\n",
      "inten:\t1.428e+11\t2.748e+11\n",
      "Vrf:\t1.782e-02\t4.548e-02\n",
      "mu:\t1.914e+00\t3.605e+00\n",
      "VrfSPS:\t3.410e+00\t6.712e+00\n",
      "(7,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "y_test_unnorm = unnormalize_params(\n",
    "    y_test[:, 0], y_test[:, 1], y_test[:, 2],\n",
    "    y_test[:, 3], y_test[:, 4], y_test[:, 5],\n",
    "    y_test[:, 6], normalization=normalization)\n",
    "\n",
    "y_pred_unnorm = unnormalize_params(\n",
    "    test_pred[:, 0], test_pred[:, 1], test_pred[:, 2],\n",
    "    test_pred[:, 3], test_pred[:, 4], test_pred[:, 5],\n",
    "    test_pred[:, 6], normalization=normalization)\n",
    "\n",
    "y_test_unnorm = np.array(y_test_unnorm).T\n",
    "y_pred_unnorm = np.array(y_pred_unnorm).T\n",
    "\n",
    "# I want to calcualte the mean absolute error, and the 95th percentile of the absolute error, for each parameter\n",
    "diffs = np.abs(np.array(y_pred_unnorm) - np.array(y_test_unnorm))\n",
    "print(diffs.shape)\n",
    "\n",
    "for name, mae, perc95 in zip(var_names, np.mean(diffs, axis=0), np.percentile(diffs, 95, axis=0)):\n",
    "    print(f'{name}:\\t{mae:.3e}\\t{perc95:.3e}')\n",
    "\n",
    "test_mse_unnorm = mean_absolute_error(y_test_unnorm, y_pred_unnorm, multioutput='raw_values')\n",
    "test_mse_unnorm = np.array([mse * mult for mse, mult in zip(test_mse_unnorm, [1, 1, 1e12, 1e-9, 1, 1, 1])])\n",
    "print(test_mse_unnorm.shape)\n",
    "\n",
    "\n",
    "# plt.figure(figsize=(1.8, 3))\n",
    "plt.axis('off')\n",
    "rowLabels = ['PhaseEr', 'EnergyEr', 'B.Length', 'B.Inten', 'LHC_VRF', 'mu', 'SPS_VRF']\n",
    "rowColours = ['xkcd:light grey'] * len(rowLabels)\n",
    "colColours = ['xkcd:light grey', 'xkcd:light grey']\n",
    "cellColours = [['white', 'white'] for i in range(len(rowLabels))]\n",
    "cellText = [[f'{l:.3f}', unit] for l, unit in zip(test_mse_unnorm, ['deg', 'MeV', 'ps', 'p', 'MV', 'a.u.', 'MV'])]\n",
    "\n",
    "plt.table(cellText=cellText, rowLabels=rowLabels, colLabels=['MAE', 'Units'],\n",
    "          loc='center', rowColours=rowColours, colColours=colColours,\n",
    "          cellColours=cellColours)\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{plots_dir}/mse-table-unnorm.png',\n",
    "            dpi=400, bbox_inches='tight', transparent=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 51 is out of bounds for axis 0 with size 51",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_335618/1480802804.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0mcumsum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdiffs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbisect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbisect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.95\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m     plt.axvline(x=bins[b+1], color='tab:orange',\n\u001b[0m\u001b[1;32m     57\u001b[0m                 label=f'95% < {bins[b+1]:.2f}')\n\u001b[1;32m     58\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'xticks'\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;31mIndexError\u001b[0m: index 51 is out of bounds for axis 0 with size 51"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import bisect\n",
    "\n",
    "# Encoder, graphical evaluation\n",
    "\n",
    "evaluation_config = {\n",
    "    0: {'xlabel': 'Phase Error [deg]',\n",
    "        'range': (0, 2.5),\n",
    "        'xticks': np.arange(0, 2.6, 0.5),\n",
    "        'desired': 2,\n",
    "        'multiplier': 1},\n",
    "    1: {'xlabel': 'Energy Error [MeV]',\n",
    "        'range': (0, 3.),\n",
    "        'xticks': np.arange(0, 3.1, 0.5),\n",
    "        'desired': 2,\n",
    "        'multiplier': 1},\n",
    "    2: {'xlabel': 'Bunch Length [ps]',\n",
    "        'range': (0, 30),\n",
    "        'xticks': np.arange(0, 30.5, 5),\n",
    "        'desired': 25,\n",
    "        'multiplier': 1e12},\n",
    "    3: {'xlabel': 'Bunch Intensity [1e9]',\n",
    "        'range': (0, 2),\n",
    "        'xticks': np.arange(0, 2.1, 0.5),\n",
    "        'desired': 1.5,\n",
    "        'multiplier': 1e-9},\n",
    "    4: {'xlabel': 'V_rf [MV]',\n",
    "        'range': (0, 0.15),\n",
    "        'xticks': np.arange(0, 0.16, 0.05),\n",
    "        'desired': 0.1,\n",
    "        'multiplier': 1},\n",
    "    5: {'xlabel': 'mu [a.u.]',\n",
    "        'range': (0, 0.4),\n",
    "        'xticks': np.arange(0, 0.41, 0.1),\n",
    "        'desired': 0.2,\n",
    "        'multiplier': 1},\n",
    "    6: {'xlabel': 'V_rf SPS [MV]',\n",
    "        'range': (0, 0.25),\n",
    "        'xticks': np.arange(0, 0.26, 0.05),\n",
    "        'desired': 0.1,\n",
    "        'multiplier': 1},\n",
    "}\n",
    "\n",
    "fig, axes = plt.subplots(ncols=3, nrows=3, sharex=False,\n",
    "                         sharey=False, figsize=(7, 6))\n",
    "axes = np.ravel(axes, order='F')\n",
    "\n",
    "for idx, ax in enumerate(axes):\n",
    "    if idx == 7:\n",
    "        break\n",
    "    plt.sca(ax)\n",
    "    config = evaluation_config[idx]\n",
    "    hist, bins, _ = plt.hist(\n",
    "        diffs[:, idx]*config['multiplier'], bins=50, range=config['range'])\n",
    "    cumsum = np.cumsum(hist) / diffs.shape[0]\n",
    "    b = bisect.bisect(cumsum, 0.95)\n",
    "    plt.axvline(x=bins[b+1], color='tab:orange',\n",
    "                label=f'95% < {bins[b+1]:.2f}')\n",
    "    plt.xticks(config['xticks'])\n",
    "    plt.yticks([], [])\n",
    "    plt.xlabel(config['xlabel'], fontweight='heavy')\n",
    "    if idx < 3:\n",
    "        plt.ylabel('No. Samples')\n",
    "    plt.axvline(x=config['desired'], color='black',\n",
    "                label=f'Desired < {config[\"desired\"]:.1f}')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.gca().set_facecolor('0.85')\n",
    "\n",
    "# delete last\n",
    "fig.delaxes(axes[-1])\n",
    "fig.delaxes(axes[-2])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{plots_dir}/enc-abs-error.png', transparent=True,\n",
    "            dpi=400, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import bisect\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, nrows=4, sharex=False,\n",
    "                         sharey=True, figsize=(8, 8))\n",
    "fig.suptitle(f'Model id: {timestamp}')\n",
    "\n",
    "plt.sca(axes[0][0])\n",
    "n, bins, patches = plt.hist(diffs[:, 0], bins=50, range=(0, 10),\n",
    "                            cumulative=True, density=True, label='CumHist')\n",
    "b = bisect.bisect(n, 0.95)\n",
    "plt.axvline(x=bins[b+1], color='tab:orange', label='95% max error')\n",
    "plt.xticks(np.arange(0, 10.5))\n",
    "plt.xlabel('Phase Error [deg]')\n",
    "plt.ylabel('No. Samples')\n",
    "plt.axvline(x=2, color='black', label='Desired')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "plt.sca(axes[1][0])\n",
    "n, bins, patches = plt.hist(diffs[:, 1], bins=50, range=(0, 10),\n",
    "                            cumulative=True, density=True, label='CumHist')\n",
    "b = bisect.bisect(n, 0.95)\n",
    "plt.axvline(x=bins[b+1], color='tab:orange', label='95% max error')\n",
    "plt.xticks(np.arange(0, 10.5))\n",
    "plt.xlabel('Energy Error [MeV]')\n",
    "plt.ylabel('No. Samples')\n",
    "plt.axvline(x=2, color='black', label='Desired')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "plt.sca(axes[2][0])\n",
    "n, bins, patches = plt.hist(\n",
    "    diffs[:, 2]*1e12, bins=50, range=(0, 50),\n",
    "    cumulative=True, density=True, label='CumHist')\n",
    "b = bisect.bisect(n, 0.95)\n",
    "plt.axvline(x=bins[b+1], color='tab:orange', label='95% max error')\n",
    "plt.xticks(np.arange(0, 50.5, 5))\n",
    "plt.xlabel('Bunch Length [ps]')\n",
    "plt.ylabel('No. Samples')\n",
    "plt.axvline(x=25, color='black', label='Desired')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "\n",
    "plt.sca(axes[3][0])\n",
    "n, bins, patches = plt.hist(\n",
    "    diffs[:, 3]*1e-9, bins=50, range=(0, 10),\n",
    "    cumulative=True, density=True, label='CumHist')\n",
    "b = bisect.bisect(n, 0.95)\n",
    "plt.axvline(x=bins[b+1], color='tab:orange', label='95% max error')\n",
    "plt.xticks(np.arange(0, 10.5, 1))\n",
    "plt.xlabel('Bunch Intensity [1e9]')\n",
    "plt.ylabel('No. Samples')\n",
    "plt.axvline(x=1, color='black', label='Desired')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "plt.sca(axes[0][1])\n",
    "n, bins, patches = plt.hist(diffs[:, 4], bins=50, range=(0, 0.3),\n",
    "                            cumulative=True, density=True, label='CumHist')\n",
    "b = bisect.bisect(n, 0.95)\n",
    "plt.axvline(x=bins[b+1], color='tab:orange', label='95% max error')\n",
    "plt.xlabel('V_rf [MV]')\n",
    "plt.axvline(x=0.1, color='black', label='Desired')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "plt.sca(axes[1][1])\n",
    "n, bins, patches = plt.hist(diffs[:, 5], bins=50, range=(0, 0.7),\n",
    "                            cumulative=True, density=True, label='CumHist')\n",
    "b = bisect.bisect(n, 0.95)\n",
    "plt.axvline(x=bins[b+1], color='tab:orange', label='95% max error')\n",
    "plt.xticks(np.arange(0, 0.71, 0.1))\n",
    "plt.xlabel('mu [a.u.]')\n",
    "plt.axvline(x=0.2, color='black', label='Desired')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "plt.sca(axes[2][1])\n",
    "n, bins, patches = plt.hist(diffs[:, 6], bins=50, range=(0, 1),\n",
    "                            cumulative=True, density=True, label='CumHist')\n",
    "b = bisect.bisect(n, 0.95)\n",
    "plt.axvline(x=bins[b+1], color='tab:orange', label='95% max error')\n",
    "plt.xticks(np.arange(0, 1.1, 0.25))\n",
    "plt.xlabel('V_rf SPS [MV]')\n",
    "plt.axvline(x=0.1, color='black', label='Desired')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "fig.delaxes(axes[3][1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'plots/enc-cumulative-hist-{timestamp}.jpg', dpi=400)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Also generate MSE histogram\n",
    "# print(valid_pred.shape)\n",
    "# square_errors = ((y_test - test_pred) * (y_test - test_pred))\n",
    "\n",
    "# fig, axes = plt.subplots(ncols=1, nrows=len(var_names), sharex=False,\n",
    "# sharey=True, figsize=(8, 16))\n",
    "# for i, name in enumerate(var_names):\n",
    "#     hist,edges = np.histogram(square_errors[:, i], bins=10, density=False)\n",
    "#     hist = hist / len(square_errors[:, i])\n",
    "#     print(name, hist)\n",
    "#     ax = axes[i]\n",
    "#     plt.sca(ax)\n",
    "#     plt.bar(np.arange(len(hist)), hist, width=0.8)\n",
    "#     plt.title(name)\n",
    "#     edges = [f'{e:.4f}' for e in edges]\n",
    "#     plt.xticks(np.arange(len(hist)), edges[1:])\n",
    "#     plt.xlabel('Error')\n",
    "#     plt.ylabel('Density')\n",
    "#     plt.tight_layout()\n"
   ]
  }
 ],
 "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": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "79ab8fd01a8cec42884b8b2a5d7fb4751c5402d97e9e61d151ed5c6a6352873c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}