ComputerVision/knn-image-classifier.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# general lib\nfrom tqdm import tqdm\nimport cv2\n\n# models\nfrom sklearn.neighbors import KNeighborsClassifier\n\n# score metrics\nfrom sklearn.metrics import f1_score","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"# Loading the dataset , testset\ntrain_df = pd.read_csv('/kaggle/input/hackerearth-deep-learning-identify-the-snake-breed/dataset/train.csv')\ntest_df  = pd.read_csv('/kaggle/input/hackerearth-deep-learning-identify-the-snake-breed/dataset/test.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Appending .jpg against file name\n\ntrain = train_df.copy()\ntest = test_df.copy()\n\ntrain['image_id'] = train['image_id']+str('.jpg')\ntest['image_id'] = test['image_id']+str('.jpg')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# converting and loading images in numpy format with predefined size\nIMG_SIZE = 224\n\ntrain_img=[]\ntrain_label=[]\nj=0\npath='../input/hackerearth-deep-learning-identify-the-snake-breed/dataset/train'\nfor i in tqdm(train['image_id']):\n    final_path=os.path.join(path,i)\n    img=cv2.imread(final_path)\n    img=cv2.resize(img,(IMG_SIZE,IMG_SIZE))\n    img=img.astype('float32')\n    train_img.append(img.flatten())\n    train_label.append(train['breed'][j])\n    j=j+1","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(train_img[0])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# impoting test image same way as train\ntest_img=[]\npath='../input/hackerearth-deep-learning-identify-the-snake-breed/dataset/test'\nfor i in tqdm(test['image_id']):\n    final_path=os.path.join(path,i)\n    img=cv2.imread(final_path)\n    img=cv2.resize(img,(IMG_SIZE,IMG_SIZE))\n    img=img.astype('float32')\n    test_img.append(img.flatten())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Splitting data for validation and accuracy testing\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(train_img, train_label, test_size=0.2, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# garbage collector to free up ram\nimport gc\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# KNN model\nnclass = 35 # number of classes\n\nneigh = KNeighborsClassifier(n_neighbors=nclass)\nneigh.fit(X_train, y_train)\n100*f1_score(y_test, pred, average='weighted')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import gc\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# for storing the model\n\nimport pickle \nknnPickle = open('knnpickle_file', 'wb') \n\n# store the model in bin format\npickle.dump(neigh, knnPickle)\n\n# Load The model\nloaded_model = pickle.load(open(filename, 'rb'))\nresult = loaded_model.score(X_test, y_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# predicting on test data\npred = neigh.predict(test)\npred","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# converting predictions to dataframe\noutput = pd.DataFrame({'image_id': test.image_id, 'breed': pred})\nfilename = 'solution.csv'\noutput.to_csv(filename, index=False) \noutput.head()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}