Image Classification Program.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b9332f2",
   "metadata": {},
   "source": [
    "### Prepare the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96185f66",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install lightgbm --no-warn-script-location\n",
    "!pip install lazypredict --no-warn-script-location\n",
    "\n",
    "# referenced code from [1] - [8] in https://kapernikov.com/tutorial-image-classification-with-scikit-learn/\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "24583e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "from skimage import io, color\n",
    "from skimage.transform import resize\n",
    "\n",
    "def resize_all(src, pklname, width=150, height=None):\n",
    "    \"\"\"\n",
    "    load images from path, resize them and write them as arrays to a dictionary, \n",
    "    together with labels and metadata. The dictionary is written to a pickle file \n",
    "    named '{pklname}_{width}x{height}px.pkl'.\n",
    "     \n",
    "    Parameter\n",
    "    ---------\n",
    "    src: str\n",
    "        path to data\n",
    "    pklname: str\n",
    "        path to output file\n",
    "    width: int\n",
    "        target width of the image in pixels\n",
    "    include: set[str]\n",
    "        set containing str\n",
    "    \"\"\"\n",
    "     \n",
    "    height = height if height is not None else width\n",
    "     \n",
    "    data = dict()\n",
    "    data['description'] = 'resized ({0}x{1})western blot images in rgb'.format(int(width), int(height))\n",
    "    data['label'] = []\n",
    "    data['filename'] = []\n",
    "    data['data'] = []   \n",
    "     \n",
    "    pklname = f\"{pklname}_{width}x{height}px.pkl\"\n",
    "    \n",
    "    # read all images in PATH, resize and write to DESTINATION_PATH\n",
    "    for file in os.listdir(src):\n",
    "        img_path = os.path.join(src, file)\n",
    "        im = io.imread(img_path)\n",
    "        im = resize(im, (width, height))\n",
    "        data['label'].append(img_path[-8:-4])\n",
    "        data['filename'].append(img_path)\n",
    "        data['data'].append(im)\n",
    " \n",
    "        joblib.dump(data, pklname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4917df3d",
   "metadata": {},
   "source": [
    "### List of images used (48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a408fdd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aTub_Lerit_Lab_2022-07-12_13h11m29s(Chemiluminescence)_mani.png',\n",
       " 'aTub_Lerit_Lab_2022-07-12_13h11m29s(Chemiluminescence)_orig.png',\n",
       " 'ECA_mani.png',\n",
       " 'ECA_orig.png',\n",
       " 'Figure2C--actin_mani.png',\n",
       " 'Figure2C--actin_orig.png',\n",
       " 'Figure2F_CD9+-actin_mani.png',\n",
       " 'Figure2F_CD9+-actin_orig.png',\n",
       " 'pone.0237759.s005_1_mani.png',\n",
       " 'pone.0237759.s005_1_orig.png',\n",
       " 'pone.0257259.s004_1_mani.png',\n",
       " 'pone.0257259.s004_1_orig.png',\n",
       " 'pone.0257259.s004_2_mani.png',\n",
       " 'pone.0257259.s004_2_orig.png',\n",
       " 'Raw_images_wetsern_blot_1_mani.png',\n",
       " 'Raw_images_wetsern_blot_1_orig.png',\n",
       " 'Raw_images_wetsern_blot_2_mani.png',\n",
       " 'Raw_images_wetsern_blot_2_orig.png',\n",
       " 'Raw_images_wetsern_blot_3_mani.png',\n",
       " 'Raw_images_wetsern_blot_3_orig.png',\n",
       " 'Raw_images_wetsern_blot_4_mani.png',\n",
       " 'Raw_images_wetsern_blot_4_orig.png',\n",
       " 'Raw_images_wetsern_blot_5_mani.png',\n",
       " 'Raw_images_wetsern_blot_5_orig.png',\n",
       " 'Raw_images_wetsern_blot_6_mani.png',\n",
       " 'Raw_images_wetsern_blot_6_orig.png',\n",
       " 'Raw_images_wetsern_blot_7_mani.png',\n",
       " 'Raw_images_wetsern_blot_7_orig.png',\n",
       " 'Raw_images_wetsern_blot_8_mani.png',\n",
       " 'Raw_images_wetsern_blot_8_orig.png',\n",
       " 'SCC25DDP_BMI1_mani.jpg',\n",
       " 'SCC25DDP_BMI1_orig.jpg',\n",
       " 'SCC25DDP_GAPDH_mani.jpg',\n",
       " 'SCC25DDP_GAPDH_orig.jpg',\n",
       " 'TSCCADDP_BMI1_mani.jpg',\n",
       " 'TSCCADDP_BMI1_orig.jpg',\n",
       " 'TSCCADDP_GAPDH_mani.jpg',\n",
       " 'TSCCADDP_GAPDH_orig.jpg',\n",
       " 'Western_blot_images_revised_COX-2_mani.png',\n",
       " 'Western_blot_images_revised_COX-2_orig.png',\n",
       " 'Western_blot_images_revised_GAPDH_mani.png',\n",
       " 'Western_blot_images_revised_GAPDH_orig.png',\n",
       " 'Western_blot_images_revised_pNF-kB_mani.png',\n",
       " 'Western_blot_images_revised_pNF-kB_orig.png',\n",
       " 'Western_blot_images_revised_TLR4_mani.png',\n",
       " 'Western_blot_images_revised_TLR4_orig.png',\n",
       " 'Western_blot_images_revised_tNF-kB_mani.png',\n",
       " 'Western_blot_images_revised_tNF-kB_orig.png']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = r'C:\\Users\\athenaz2\\Documents\\Western-Blot-Data\\Images'\n",
    "os.listdir(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fee569e",
   "metadata": {},
   "source": [
    "### Resize all images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "853aba26",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_name = 'wb_img'\n",
    "width = 312 \n",
    "\n",
    "resize_all(src = data_path, pklname = base_name, width = width)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d97d5066",
   "metadata": {},
   "source": [
    "### Summary of image data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70153524",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of samples:  48\n",
      "keys:  ['description', 'label', 'filename', 'data']\n",
      "description:  resized (312x312)western blot images in rgb\n",
      "image shape:  (312, 312)\n",
      "labels: ['mani' 'orig']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Counter({'mani': 24, 'orig': 24})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    " \n",
    "data = joblib.load(f'{base_name}_{width}x{width}px.pkl')\n",
    "    \n",
    "print('number of samples: ', len(data['data']))\n",
    "print('keys: ', list(data.keys()))\n",
    "print('description: ', data['description'])\n",
    "print('image shape: ', data['data'][0].shape)\n",
    "print('labels:', np.unique(data['label']))\n",
    " \n",
    "Counter(data['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "96d56458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use np.unique to get all unique values in the list of labels\n",
    "labels = np.unique(data['label'])\n",
    " \n",
    "# set up the matplotlib figure and axes, based on the number of labels\n",
    "fig, axes = plt.subplots(1, len(labels))\n",
    "fig.set_size_inches(15,4)\n",
    "fig.tight_layout()\n",
    " \n",
    "# make a plot for every label (equipment) type. The index method returns the \n",
    "# index of the first item corresponding to its search string, label in this case\n",
    "for ax, label in zip(axes, labels):\n",
    "    idx = data['label'].index(label)\n",
    "     \n",
    "    ax.imshow(data['data'][idx])\n",
    "    ax.axis('off')\n",
    "    ax.set_title(label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5810121b",
   "metadata": {},
   "source": [
    "### Split data into test set and training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "768f25d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import Lazy Predict classification library\n",
    "from lazypredict.Supervised import LazyClassifier, LazyRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# input data X\n",
    "X = np.array(data['data']).reshape(48, -1)\n",
    "# results (labels)\n",
    "y = np.array(data['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe79a4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    " \n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, \n",
    "    y, \n",
    "    test_size=0.5, \n",
    "#    shuffle=True,\n",
    "    random_state=42,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c69d76",
   "metadata": {},
   "source": [
    "### Classify dataset with LazyPredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc65d25f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29/29 [01:05<00:00,  2.25s/it]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <th>ROC AUC</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Time Taken</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CalibratedClassifierCV</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.67</td>\n",
       "      <td>None</td>\n",
       "      <td>0.67</td>\n",
       "      <td>17.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBMClassifier</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.50</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DummyClassifier</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.50</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.45</td>\n",
       "      <td>None</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsClassifier</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.43</td>\n",
       "      <td>None</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BernoulliNB</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.43</td>\n",
       "      <td>None</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RidgeClassifier</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.43</td>\n",
       "      <td>None</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RidgeClassifierCV</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.43</td>\n",
       "      <td>None</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QuadraticDiscriminantAnalysis</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.43</td>\n",
       "      <td>None</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.41</td>\n",
       "      <td>None</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NearestCentroid</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.41</td>\n",
       "      <td>None</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassiveAggressiveClassifier</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.39</td>\n",
       "      <td>None</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGDClassifier</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.39</td>\n",
       "      <td>None</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NuSVC</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.39</td>\n",
       "      <td>None</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptron</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.39</td>\n",
       "      <td>None</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreeClassifier</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.36</td>\n",
       "      <td>None</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearDiscriminantAnalysis</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.34</td>\n",
       "      <td>None</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>None</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>None</td>\n",
       "      <td>0.34</td>\n",
       "      <td>5.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BaggingClassifier</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.32</td>\n",
       "      <td>None</td>\n",
       "      <td>0.27</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.29</td>\n",
       "      <td>None</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTreesClassifier</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.29</td>\n",
       "      <td>None</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTreeClassifier</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.29</td>\n",
       "      <td>None</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoostClassifier</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.29</td>\n",
       "      <td>None</td>\n",
       "      <td>0.21</td>\n",
       "      <td>6.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Accuracy  Balanced Accuracy ROC AUC  F1 Score  \\\n",
       "Model                                                                          \n",
       "CalibratedClassifierCV             0.67               0.67    None      0.67   \n",
       "LGBMClassifier                     0.42               0.50    None      0.25   \n",
       "DummyClassifier                    0.42               0.50    None      0.25   \n",
       "SVC                                0.38               0.45    None      0.23   \n",
       "KNeighborsClassifier               0.42               0.43    None      0.42   \n",
       "BernoulliNB                        0.42               0.43    None      0.42   \n",
       "RidgeClassifier                    0.42               0.43    None      0.42   \n",
       "RidgeClassifierCV                  0.42               0.43    None      0.42   \n",
       "QuadraticDiscriminantAnalysis      0.42               0.43    None      0.42   \n",
       "GaussianNB                         0.42               0.41    None      0.42   \n",
       "NearestCentroid                    0.38               0.41    None      0.35   \n",
       "PassiveAggressiveClassifier        0.38               0.39    None      0.37   \n",
       "SGDClassifier                      0.42               0.39    None      0.40   \n",
       "NuSVC                              0.33               0.39    None      0.26   \n",
       "Perceptron                         0.42               0.39    None      0.40   \n",
       "ExtraTreeClassifier                0.33               0.36    None      0.32   \n",
       "LinearDiscriminantAnalysis         0.33               0.34    None      0.33   \n",
       "LogisticRegression                 0.33               0.33    None      0.34   \n",
       "LinearSVC                          0.33               0.33    None      0.34   \n",
       "BaggingClassifier                  0.29               0.32    None      0.27   \n",
       "RandomForestClassifier             0.25               0.29    None      0.21   \n",
       "ExtraTreesClassifier               0.25               0.29    None      0.21   \n",
       "DecisionTreeClassifier             0.25               0.29    None      0.21   \n",
       "AdaBoostClassifier                 0.25               0.29    None      0.21   \n",
       "\n",
       "                               Time Taken  \n",
       "Model                                      \n",
       "CalibratedClassifierCV              17.97  \n",
       "LGBMClassifier                       1.73  \n",
       "DummyClassifier                      1.29  \n",
       "SVC                                  1.40  \n",
       "KNeighborsClassifier                 1.43  \n",
       "BernoulliNB                          1.33  \n",
       "RidgeClassifier                      1.33  \n",
       "RidgeClassifierCV                    1.32  \n",
       "QuadraticDiscriminantAnalysis        1.43  \n",
       "GaussianNB                           1.36  \n",
       "NearestCentroid                      1.31  \n",
       "PassiveAggressiveClassifier          1.38  \n",
       "SGDClassifier                        1.35  \n",
       "NuSVC                                1.39  \n",
       "Perceptron                           1.35  \n",
       "ExtraTreeClassifier                  1.29  \n",
       "LinearDiscriminantAnalysis           1.70  \n",
       "LogisticRegression                   2.46  \n",
       "LinearSVC                            5.36  \n",
       "BaggingClassifier                    2.70  \n",
       "RandomForestClassifier               1.46  \n",
       "ExtraTreesClassifier                 1.44  \n",
       "DecisionTreeClassifier               1.45  \n",
       "AdaBoostClassifier                   6.47  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)\n",
    "models,predictions = clf.fit(X_train, X_test, y_train, y_test)\n",
    "models"
   ]
  }
 ],
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