{"cells":[{"metadata":{},"cell_type":"markdown","source":"## Weighted Average [image + tabular]","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"import glob\nimport pandas as pd","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"FILES = glob.glob('../input/*/prediction_*.csv', recursive=True)\nFILES = [\n    #'../input/effcientnetb6-384x384-fold-1-3/prediction_fold_3.csv', ##LB 0.9387\n    '../input/mysample/tabular_6928.csv', ##LB 0.6928\n    #'../input/random-forest-melanoma/submission_random_forest.csv'\n    #'../input/catboost/cat_baseline_sub.csv', ## LB 0.81\n    '../input/sub-blend/submission_945_15_folds.csv',\n    '../input/sub-blend/submission_945_5_folds.csv',\n    '../input/melanoma-sub-single-9516/submission_comb(1).csv',\n    #'../input/sub-chris/submission.csv',\n    #'../input/sub-blend/submission_939.csv',\n    '../input/train-cv-melanoma/submission.csv',\n    '../input/enet-cv-9430/submission.csv',\n    '../input/mel-9488/submission.csv',\n    '../input/mel-b6-9453/submission.csv'\n]","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"sub = pd.read_csv(\"../input/siim-isic-melanoma-classification/sample_submission.csv\")\ndel sub['target']\n\nw = [0.05, 0.1, 0.1, 0.1, 0.15, 0.15, 0.15, 0.2]\n\n\n# w = [0.15, 0.25, 0.2, 0.2, 0.2] ## 9597\n\n\n##w = [0.85, 0.15]\n\n#w = [0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.1]\n\n#w = [0.0, 0.0, 0.0, 0.25, 0.25, 0.25, 0.25] ## 0.955\n\n#w = [0.0, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2]\n\n\n# w = [0.0, 0.15, 0.2, 0.25, 0.2, 0.2]\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for counter, f in enumerate(FILES):\n    print(counter)\n    print(f)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv(f)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.columns = ['image_name', str(counter)]\n\ndf[str(counter)] *= w[counter]\n\ndf.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for counter, f in enumerate(FILES):\n    df = pd.read_csv(f)\n    df.columns = ['image_name', str(counter)]\n    df[str(counter)] *= w[counter]\n    sub = sub.merge(df, on=\"image_name\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"image_name = sub.image_name\n\nsub = sub.drop(columns = [\"image_name\"])\ntarget = sub.sum(axis = 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# for i in range(len(target)):\n#     if target[i] < 0.02:\n#         target[i] = 0.0\n#     elif target[i] > 0.9:\n#         target[i] = 1.0","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"target","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.DataFrame({\n    'image_name' : image_name,\n    'target' : target\n}).to_csv('submission_b.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv(\"submission_b.csv\")\nimport matplotlib.pyplot as plt\nf, ax = plt.subplots(figsize=(18,6))\nax.hist(df['target'], bins=100)\nax.grid(axis='y', color='0.95')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df","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}