{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import os\nimport pandas as pd\nimport numpy as np\n\nimport tensorflow as tf\nfrom keras import layers, models, optimizers\nfrom keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, smart_resize, array_to_img\nfrom keras.callbacks import EarlyStopping, LearningRateScheduler, ReduceLROnPlateau, ModelCheckpoint\n\nfrom sklearn.preprocessing import MultiLabelBinarizer\n\nimport matplotlib.pyplot as plt\nimport plotly.graph_objects as go\nimport plotly.express as px","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_csv = pd.read_csv(\"../input/hpa-single-cell-image-classification/train.csv\")\nprint(\"<<<<<<<<<<     Train_csv head data    >>>>>>>>>>\")\nprint(train_csv.head(),'\\n')\nprint(\"<<<<<<<<<<     Check Nan value     >>>>>>>>>>\")\nprint(train_csv.isna().sum(axis=0))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"origin_colname = train_csv.columns\nlabel_name = [\"Nucleoplasm\", \"Nuclear Membrane\", \"Nucleoli\", \"Nucleoli Fibrillar Center\", \"Nuclear Speckles\", \"Nuclear Bodies\",\n              \"Endoplasmic Reticulum\", \"Golgi Apparatus\", \"Intermediate Filaments\", \"Actin Filaments\", \"Microtubules\", \"Mitotic Spindle\",\n              \"Centrosome\", \"Plasma Membrane\", \"Mitochondria\", \"Aggresome\", \"Cytosol\", \"Vesicles\", \"Negative\"]\n\nmlb = MultiLabelBinarizer()\ntrain_csv['Label'] = train_csv['Label'].apply(lambda x: list(map(int,x.split(\"|\"))))\ntrain_csv[list(range(19))] = mlb.fit_transform(train_csv['Label'])\nprint('Classes list : ', mlb.classes_)\n\ntrain_csv.columns = [\"ID\", \"Label\", \"Nucleoplasm\", \"Nuclear Membrane\", \"Nucleoli\", \"Nucleoli Fibrillar Center\", \"Nuclear Speckles\", \"Nuclear Bodies\",\n                     \"Endoplasmic Reticulum\", \"Golgi Apparatus\", \"Intermediate Filaments\", \"Actin Filaments\", \"Microtubules\", \"Mitotic Spindle\",\n                     \"Centrosome\", \"Plasma Membrane\", \"Mitochondria\", \"Aggresome\", \"Cytosol\", \"Vesicles\", \"Negative\"]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"collapsed":true},"cell_type":"code","source":"train_csv.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"label_count = train_csv.iloc[:, 2:].sum(axis=0)\nprint(label_count)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"px.bar(label_count)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def GetImage(csv, index):\n    train_dir = '../input/hpa-single-cell-image-classification/train'\n    for i in range(index):\n        image_b = train_dir + '/' + csv.iloc[i,0] + '_blue.png'\n        image_g = train_dir + '/' + csv.iloc[i,0] + '_green.png'\n        image_r = train_dir + '/' + csv.iloc[i,0] + '_red.png'\n        image_y = train_dir + '/' + csv.iloc[i,0] + '_yellow.png'\n        \n        img_b = load_img(image_b)\n        img_g = load_img(image_g)\n        img_r = load_img(image_r)\n        img_y = load_img(image_y)\n        \n        print()\n        fig=plt.figure(figsize=(20, 20))\n        cols = 4\n        rows = 1\n        \n        img = [img_b, img_g, img_r, img_y]\n        \n        for i in range(0, cols):\n            fig.add_subplot(rows, cols, i+1)\n            plt.imshow(img[i])\n            \n        plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"GetImage(train_csv, 3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}