notebooks/1.0-Dataset_exploration.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration phase (**optional**)\n",
    "\n",
    "In this exploration phase we will visualize some features of your dataset to help you make more informed decisitions in case you want to **adapt the default parameters** to your dataset.\n",
    "\n",
    "You can always skip this part and go with the default parameters but if you want to have a classifier as efficient as possible you should consider spending five minutes running this analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Distribution of classes\n",
    "**Relevant parameters:** `use_class_weights`\n",
    "\n",
    "We can visualize the distributions of the classes to check is the dataset is highly imbalanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing /srv/phyto-plankton-classification/data/demo-images: 0it [00:00, ?it/s]\n",
      "Processing /srv/phyto-plankton-classification/data/demo-images/Actinoptychus: 100%|██████████| 14/14 [00:00<00:00, 24324.88it/s]\n",
      "Processing /srv/phyto-plankton-classification/data/demo-images/Bacillariophyceae: 100%|██████████| 16/16 [00:00<00:00, 24519.13it/s]\n",
      "Processing /srv/phyto-plankton-classification/data/demo-images/Ciliophora: 100%|██████████| 19/19 [00:00<00:00, 8065.97it/s]\n",
      "Processing /srv/phyto-plankton-classification/data/demo-images/Dinoflagellata: 100%|██████████| 20/20 [00:00<00:00, 34044.68it/s]\n",
      "Processing /srv/phyto-plankton-classification/data/demo-images/Noctilucales: 100%|██████████| 23/23 [00:00<00:00, 17001.94it/s]\n",
      "Processing /srv/phyto-plankton-classification/data/demo-images/Tripos: 100%|██████████| 13/13 [00:00<00:00, 15605.60it/s]\n",
      "Searching files: 7it [00:00, 64.98it/s]\n",
      "Writing /srv/phyto-plankton-classification/data/demo-dataset_files/train.txt: 100%|██████████| 87/87 [00:00<00:00, 127100.12it/s]\n",
      "Writing /srv/phyto-plankton-classification/data/demo-dataset_files/test.txt: 100%|██████████| 13/13 [00:00<00:00, 50161.87it/s]\n",
      "Writing /srv/phyto-plankton-classification/data/demo-dataset_files/val.txt: 100%|██████████| 5/5 [00:00<00:00, 3612.66it/s]\n",
      "Writing classes file: 100%|██████████| 6/6 [00:00<00:00, 20476.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading train data...\n",
      "Loading val data...\n",
      "Loading test data...\n",
      "Loading class names...\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import os\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "from planktonclas import paths, config\n",
    "from planktonclas.data_utils import load_image, load_data_splits, augment, load_class_names ,create_data_splits\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "import yaml\n",
    "conf_path = \"/srv/phyto-plankton-classification/etc/demo-config.yaml\"\n",
    "with open(conf_path, 'r') as f:\n",
    "    CONF = yaml.safe_load(f)\n",
    "\n",
    "CONF = config.get_conf_dict(CONF)\n",
    "\n",
    "\n",
    "#if not using demo\n",
    "splits_dir = paths.get_splits_dir()\n",
    "\n",
    "#if using DEMO\n",
    "splits_dir=\"/srv/phyto-plankton-classification/data/demo-dataset_files\"\n",
    "\n",
    "\n",
    "\n",
    "if 'train.txt' not in os.listdir(splits_dir ):\n",
    "    if not (CONF['dataset']['split_ratios']):\n",
    "        if (CONF['training']['use_validation']) & (CONF['testing']['use_test']):\n",
    "            split_ratios=[0.7,0.15,0.15]\n",
    "        elif (CONF['training']['use_validation']) & (~CONF['testing']['use_test']):\n",
    "            split_ratios=[0.9,0.1,0]\n",
    "        else:\n",
    "            split_ratios=[1,0,0]\n",
    "    else:\n",
    "        split_ratios=(CONF['dataset']['split_ratios'])\n",
    "    create_data_splits(splits_dir=splits_dir,\n",
    "                                    im_dir=CONF['general']['images_directory'],\n",
    "                                    split_ratios=split_ratios)\n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "# Load the training data\n",
    "X_train, y_train = load_data_splits(splits_dir=splits_dir,\n",
    "                                    im_dir=CONF['general']['images_directory'],\n",
    "                                    split_name='train')\n",
    "\n",
    "# Load the validation data\n",
    "if (CONF['training']['use_validation']) and ('val.txt' in os.listdir(splits_dir)):\n",
    "    X_val, y_val = load_data_splits(splits_dir=splits_dir,\n",
    "                                    im_dir=CONF['general']['images_directory'],\n",
    "                                    split_name='val')\n",
    "else:\n",
    "    print('No validation data.')\n",
    "    X_val, y_val = None, None\n",
    "    CONF['training']['use_validation'] = False\n",
    "\n",
    "    \n",
    "if (CONF['testing']['use_test']) and ('test.txt' in os.listdir(splits_dir)):\n",
    "    X_test, y_test = load_data_splits(splits_dir=splits_dir,\n",
    "                                    im_dir=CONF['general']['images_directory'],\n",
    "                                    split_name='test')\n",
    "else:\n",
    "    print('No validation data.')\n",
    "    X_test, y_test = None, None\n",
    "    CONF['training']['use_test'] = False\n",
    "    \n",
    "# Load the class names\n",
    "class_names = load_class_names(splits_dir=splits_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "# Concatenate the datasets\n",
    "all_data = np.concatenate((X_train, X_test, X_val))\n",
    "\n",
    "# Extract class names from file paths for all datasets\n",
    "class_names_all = [path.split('/')[-2] for path in all_data]\n",
    "\n",
    "# Count occurrences of each class for all datasets\n",
    "class_counts_all = Counter(class_names_all)\n",
    "\n",
    "# Extract class names and counts for all datasets\n",
    "classes_all = list(class_counts_all.keys())\n",
    "counts_all = list(class_counts_all.values())\n",
    "\n",
    "# Plot histogram for all datasets combined\n",
    "plt.figure(figsize=(20, 8))\n",
    "plt.bar(classes_all, counts_all, color='skyblue')\n",
    "plt.xlabel('Class Names')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Total Class Distribution Histogram')\n",
    "plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Plot histogram for each dataset separately\n",
    "datasets = {'X_train': X_train, 'X_test': X_test, 'X_val': X_val}\n",
    "for dataset_name, dataset in datasets.items():\n",
    "    class_names = [path.split('/')[-2] for path in dataset]\n",
    "    class_counts = Counter(class_names)\n",
    "    classes = list(class_counts.keys())\n",
    "    counts = list(class_counts.values())\n",
    "\n",
    "    plt.figure(figsize=(20, 8))\n",
    "    plt.bar(classes, counts, color='skyblue')\n",
    "    plt.xlabel('Class Names')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.title(f'{dataset_name} Class Distribution Histogram')\n",
    "    plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Training: 19.0\n",
      "Min Training: 11.0\n",
      "Mean Training: 14.5\n",
      "Median Training: 14.5\n",
      "\n",
      "\n",
      "Max Validation: 2.0\n",
      "Min Validation: 0.0\n",
      "Mean Validation: 0.8333333333333334\n",
      "Median Validation: 1.0\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define some parameters\n",
    "log_scale = False\n",
    "show_names = False\n",
    "\n",
    "# Plot the histograms\n",
    "fig, axs = plt.subplots(1, 2, figsize=(20,5))\n",
    "\n",
    "def plot_hist(ax, y, set_name=''):\n",
    "    n, bins, patches = ax.hist(y, bins=len(class_names), log=log_scale)\n",
    "    mean, med = np.mean(n), np.median(n)\n",
    "    ax.axhline(mean, linestyle= '--', color='#ce9b3b', label='mean')\n",
    "    ax.axhline(med, linestyle= '--', color='#fc0c70', label='median')\n",
    "    ax.set_title('{} set'.format(set_name))\n",
    "    ax.legend()\n",
    "    if show_names:\n",
    "        ax.set_xticks(range(len(class_names)))\n",
    "        ax.set_xticklabels(class_names, rotation='vertical')\n",
    "\n",
    "    print('Max {}: {}'.format(set_name, np.amax(n)))\n",
    "    print('Min {}: {}'.format(set_name, np.amin(n)))\n",
    "    print('Mean {}: {}'.format(set_name, mean))\n",
    "    print('Median {}: {}'.format(set_name, med))\n",
    "    print('\\n')\n",
    "\n",
    "plot_hist(axs[0], y_train, set_name='Training')\n",
    "\n",
    "axs[1].set_title('Validation set')\n",
    "if y_val is not None:\n",
    "    plot_hist(axs[1], y_val, set_name='Validation')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So if your dataset is **very** imbalanced you can consider setting the `use_class_weights` to `True` to compensate for this imbalance during the training."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Distribution of image sizes\n",
    "**Relevant parameters:** `im_size`\n",
    "\n",
    "We can visualize how the sizes of your images are tipically distributed in case you want to use a bigger image size resolution than the default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 87/87 [00:00<00:00, 114.36it/s]\n"
     ]
    }
   ],
   "source": [
    "# Load all images and retrieve their shapes\n",
    "height, width = [], []\n",
    "for img_path in tqdm(X_train):\n",
    "    img = load_image(img_path)\n",
    "    w, h, channels = img.shape\n",
    "    width.append(w)\n",
    "    height.append(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Heights: 436\n",
      "Min Heights: 30\n",
      "Mean Heights: 87.6\n",
      "Median Heights: 68.0\n",
      "Heights 5th percentile: 39.3\n",
      "\n",
      "\n",
      "Max Widths: 555\n",
      "Min Widths: 37\n",
      "Mean Widths: 106.6\n",
      "Median Widths: 79.0\n",
      "Widths 5th percentile: 44.9\n",
      "\n",
      "\n",
      "Max Combined: 555\n",
      "Min Combined: 30\n",
      "Mean Combined: 97.1\n",
      "Median Combined: 74.0\n",
      "Combined 5th percentile: 41.65\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define some parameters\n",
    "bins = 100\n",
    "log_scale = True\n",
    "lower_percentile = 5\n",
    "\n",
    "def plot_hist(ax, values, set_name=''):\n",
    "    \n",
    "    # Print some stats\n",
    "    mean, med, per = np.mean(values), np.median(values), np.percentile(values, lower_percentile)\n",
    "\n",
    "    print('Max {}: {}'.format(set_name, np.amax(values)))\n",
    "    print('Min {}: {}'.format(set_name, np.amin(values)))\n",
    "    print('Mean {}: {:.1f}'.format(set_name, mean))\n",
    "    print('Median {}: {}'.format(set_name, med))\n",
    "    print('{} {}th percentile: {}'.format(set_name, lower_percentile, per))\n",
    "    print('\\n')\n",
    "      \n",
    "    # Plot the histogram\n",
    "    ax.hist(values, bins=bins, log=log_scale)\n",
    "    ax.set_title('{} histogram'.format(set_name))\n",
    "    ax.axvline(mean, linestyle= '--', color='#ce9b3b', label='mean')\n",
    "    ax.axvline(med, linestyle= '--', color='#fc0c70', label='median')\n",
    "    ax.axvline(per, linestyle= '--', color='#0ea541', label='{}th percentile'.format(lower_percentile))\n",
    "    ax.legend()\n",
    "\n",
    "    \n",
    "fig, axs = plt.subplots(1, 3, figsize=(30,5))\n",
    "axs = axs.flatten()\n",
    "\n",
    "plot_hist(axs[0], height, set_name='Heights')\n",
    "plot_hist(axs[1], width, set_name='Widths')\n",
    "plot_hist(axs[2], height + width, set_name='Combined')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So after examining these plots and stats you can consider adapting the parameter `im_size` to better suit your usecase (maybe to a value in between the `Min combined` and the `Combined 5th percentile`).\n",
    "\n",
    "When **increasing** this parameter from the default value you have to consider that:\n",
    "* you keep more details in the image so the accuracy of the model *might* (or might not) increase.\n",
    "* bigger images size means longer training and inference times\n",
    "* bigger images means more model parameters. So it is possible to that model becomes too big to be trained on a GPU. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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