{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.9","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\n\n# df = pd.read_csv('../input/vin-big-data-predit/submission.csv')\n# df1 = pd.read_csv('../input/noisy-vin-big-data-predit/submission.csv')\n# df_densenet = pd.read_csv('../input/densenet201-vin-big-data-predit/submission.csv')\n\n# df.columns\n# df[['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10',\n#        '11', '12', '13', '14']] = df[['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10',\n#        '11', '12', '13', '14']] * 0.25 + df1[['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10',\n#        '11', '12', '13', '14']] * 0.5 + df_densenet[['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10',\n#        '11', '12', '13', '14']] * 0.25","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.231953Z","iopub.execute_input":"2022-10-09T04:33:22.232299Z","iopub.status.idle":"2022-10-09T04:33:22.236323Z","shell.execute_reply.started":"2022-10-09T04:33:22.232266Z","shell.execute_reply":"2022-10-09T04:33:22.235215Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"for i in range(10):\n    globals()[f'df{i}'] = pd.read_csv(f'../input/vin-15-cnn-predict/submission{i}.csv')\nfor i in range(10,15):\n\n    globals()[f'df{i}'] = pd.read_csv(f'../input/vin-15-cnn-predict-1/submission{i - 10}.csv')\n    \n    \nlist1 = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10','11', '12', '13', '14']\n\n\ndf0[list1] = df0[list1] * 0.05 + df1[list1] * 0.05 + df2[list1] * 0.05 + df3[list1] * 0.05 + df4[list1] * 0.05 + df5[list1] * 0.1 + df6[list1] * 0.1 + df7[list1] * 0.1 + df8[list1] * 0.1 + df9[list1] * 0.1 + df10[list1] * 0.05 + df11[list1] * 0.05 + df12[list1] * 0.05 + df13[list1] * 0.05 + df14[list1] * 0.05\ndf = df0.copy()","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.245675Z","iopub.execute_input":"2022-10-09T04:33:22.245976Z","iopub.status.idle":"2022-10-09T04:33:22.278719Z","shell.execute_reply.started":"2022-10-09T04:33:22.24595Z","shell.execute_reply":"2022-10-09T04:33:22.276394Z"},"trusted":true},"execution_count":5,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-5-e98215805483>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m 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5\u001b[0m     \u001b[0mglobals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf'df{i}'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'../input/vin-15-cnn-predict-1/submission{i - 10}.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    686\u001b[0m     )\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    946\u001b[0m             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\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../input/vin-15-cnn-predict/submission0.csv'"],"ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: '../input/vin-15-cnn-predict/submission0.csv'","output_type":"error"}]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.280811Z","iopub.status.idle":"2022-10-09T04:33:22.281242Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#df2 = pd.read_csv('../input/vinbigdata-sub-23-211/submission (18).csv')\ndf3 = pd.read_csv('../input/0284-norm/cascade_rcnn_x101_32x4d_fpn_20e_OHEM_fpn.4_with_raw.4_ensemble.5.bbox.json.filter.norm (1).csv')","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.282411Z","iopub.status.idle":"2022-10-09T04:33:22.283172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df4 = pd.merge(df, df3, on = 'image_id', how = 'left')","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.284353Z","iopub.status.idle":"2022-10-09T04:33:22.285059Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# for i in range(3000):\n#     list2 = df4.loc[i,'PredictionString'].split()\n\n#     h = ''\n#     for j in range(int(len(df4.loc[i,'PredictionString'].split())/6)):\n#         if list2[0 + 6 * j] == '0' or list2[0 + 6 * j] == '14' or list2[0 + 6 * j] == '7'or list2[0 + 6 * j] == '13':\n\n            \n#             continue\n            \n#         h = h + ' ' + list2[0 + 6 * j] + ' ' + list2[1 + 6 * j] + ' ' + list2[2 + 6 * j] + ' ' + list2[3 + 6 * j] + ' ' + list2[4 + 6 * j]+ ' ' + list2[5 + 6 * j]\n    \n#         df4.loc[i,'PredictionString'] = h\n# for i in range(3000):\n#     list2 = df4.loc[i,'PredictionString'].split()\n#     df4.loc[i,'PredictionString'] = ' '.join(list2)","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.286289Z","iopub.status.idle":"2022-10-09T04:33:22.287151Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df4.iloc[1,16]\n","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.288388Z","iopub.status.idle":"2022-10-09T04:33:22.289033Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# df5 = pd.merge(df, df2, on = 'image_id', how = 'left')","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.290341Z","iopub.status.idle":"2022-10-09T04:33:22.290972Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#df4[['PredictionString']] = df4[['PredictionString']] +' '+ df5[['PredictionString']]","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.292324Z","iopub.status.idle":"2022-10-09T04:33:22.292962Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"list1 = [0,1,2,3,4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n\n\nfor i in range(df4.shape[0]):\n    if df4.loc[i,'PredictionString'] == '14 1 0 0 1 1':\n        continue\n    a = df4.loc[i,'PredictionString']\n    b = a.split()\n    for j in range(int(len(a.split())/6)):\n        for k in list1:\n            if int(b[0 + 6 * j]) == k:\n                if df4.loc[i,f'{k}'] < 0.92:\n                    continue\n                c = b[0 + 6 * j + 1]               \n                b[0 + 6 * j + 1] = str(df4.loc[i,f'{k}']* 0.15 + float(c) * 0.85)# * 0.9 + float(c) * 0.1\n\n                \n                # #         if int(b[0 + 6 * j]) == 0:\n# #                 if df4.loc[i,f'{k}'] > 0.92:\n                    \n# #                     c = b[0 + 6 * j + 1]               \n# #                     b[0 + 6 * j + 1] = str(df4.loc[i,f'{k}']* 0.5 + float(c) * 0.5)# * 0.9 + float(c) * 0.1\n        \n# #         if int(b[0 + 6 * j]) == 3:\n# #                 if df4.loc[i,f'{k}'] > 0.92:\n                    \n#                     c = b[0 + 6 * j + 1]               \n#                     b[0 + 6 * j + 1] = str(df4.loc[i,f'{k}']* 0.5 + float(c) * 0.5)# * 0.9 + float(c) * 0.1\n\n        \n    df4.loc[i,'PredictionString'] = ' '.join(b)\n#     df4.loc[i,'PredictionString'] = df4.loc[i,'PredictionString'] + ' ' + str(df4.loc[i,'14'])","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.294245Z","iopub.status.idle":"2022-10-09T04:33:22.294959Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# for i in range(df5.shape[0]):\n#     a = df5.loc[i,'PredictionString']\n#     b = a.split()\n#     for j in range(int(len(a.split())/6)):\n#         for k in range(15):\n#             if int(b[0 + 6 * j]) == k:\n#                 c = b[0 + 6 * j + 1]\n#                 b[0 + 6 * j + 1] = str(df5.loc[i,f'{k}'] * 0.9 + float(c) * 0.1)\n#                 df4.loc[i,'PredictionString'] = ' '.join(b)+ ' ' + str(df4.loc[i,'14'])","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.483052Z","iopub.execute_input":"2022-10-09T04:33:22.483419Z","iopub.status.idle":"2022-10-09T04:33:22.487504Z","shell.execute_reply.started":"2022-10-09T04:33:22.483385Z","shell.execute_reply":"2022-10-09T04:33:22.486584Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"for i in range(df4.shape[0]):\n#     #df4.loc[i,'PredictionString'] = df4.loc[i,'PredictionString'] + ' ' + '14' + ' ' + str(df4.loc[i,'14']) + ' 0' +' 0' +' 1'+' 1'\n    if df4.loc[i,'14'] > 0.999:\n        df4.loc[i,'PredictionString'] = '14 1 0 0 1 1'\ndf_final = df4[['image_id', 'PredictionString']]\ndf_final.to_csv('submission.csv',index = False)\n","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.48945Z","iopub.execute_input":"2022-10-09T04:33:22.490172Z","iopub.status.idle":"2022-10-09T04:33:22.503953Z","shell.execute_reply.started":"2022-10-09T04:33:22.49011Z","shell.execute_reply":"2022-10-09T04:33:22.502361Z"},"trusted":true},"execution_count":7,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-7-809d3c2b1514>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#     #df4.loc[i,'PredictionString'] = df4.loc[i,'PredictionString'] + ' ' + '14' + ' ' + str(df4.loc[i,'14']) + ' 0' +' 0' +' 1'+' 1'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdf4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'14'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.999\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mdf4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'PredictionString'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'14 1 0 0 1 1'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf_final\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'image_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'PredictionString'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'df4' is not defined"],"ename":"NameError","evalue":"name 'df4' is not defined","output_type":"error"}]},{"cell_type":"code","source":"df_final\n","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.505155Z","iopub.status.idle":"2022-10-09T04:33:22.505733Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# for i in range(df4.shape[0]):\n#     if df5.loc[i,'14'] > 0.999:\n#         df5.loc[i,'PredictionString'] = '14 1 0 0 1 1\n# df_final1 = df5[['image_id', 'PredictionString']]\n# df_final1.to_csv('submission1.csv',index = False)","metadata":{"execution":{"iopub.status.busy":"2022-10-09T04:33:22.507516Z","iopub.status.idle":"2022-10-09T04:33:22.508631Z"},"trusted":true},"execution_count":null,"outputs":[]}]}