{"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 in \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 \"../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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"datafolder = '/kaggle/input/bengaliai-cv19/'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# imports\nimport gc\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom keras.models import load_model","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"code","source":"datafolder = '/kaggle/input/bengaliai-cv19/'\n# initialize submission dataframe\nrow_ids = []\npred = []\n\n# initialize variables\nroots = ['vowel_diacritic','grapheme_root','consonant_diacritic']\n\n#model = load_model('/kaggle/input/bengaliai-trained-models-mjy-v3/mjy_vowel_diacritic_v4.h5')\n\n# start loop to score test data (we have 4 test datasets [0,3])\nstart = 0\nfor z in range(4):\n    # load data to dataframe\n    img_df = pd.read_parquet(datafolder + 'test_image_data_'+str(z)+'.parquet')\n    # get the number of training images from the target\\id dataset\n    N = img_df.shape[0]\n    # drop ids\n    #img_df = img_df.drop('image_id', axis = 1)\n    # convert to numpy, reshape and normalize\n    #x_test = img_df.values.reshape((N,137,236,1)) / 255\n\n    # loop through models, score test data, and update submission dataframe\n    for root in roots:\n        # get predictions\n        pred.extend([1 for k in range(N)])\n        # create row id\n        row_ids.extend(['Test_'+str(j)+'_'+root for j in range(start,start+N)])\n    start += N\n\n    # clean up\n    del img_df\n    #del x_test\n    gc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submit_df = pd.DataFrame({'row_id':row_ids,'target':pred},\n                         columns = ['row_id','target'])\nsubmit_df.head(100)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# clean up\ndel pred, row_ids\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submit_df.to_csv('submission.csv',index=False)","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":1}