{"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_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"
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Multimodal Single-Cell Integration

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CITEseq - Ensembling

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\n\n## Acknowledgments:","metadata":{}},{"cell_type":"markdown","source":"#### Thanks to: @**pourchot**\n\n##### https://www.kaggle.com/code/pourchot/all-in-one-citeseq-multiome-with-keras/notebook","metadata":{}},{"cell_type":"markdown","source":"
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","metadata":{}},{"cell_type":"code","source":"import warnings # suppress warnings\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2022-10-14T14:16:52.282183Z","iopub.execute_input":"2022-10-14T14:16:52.28304Z","iopub.status.idle":"2022-10-14T14:16:52.309285Z","shell.execute_reply.started":"2022-10-14T14:16:52.282912Z","shell.execute_reply":"2022-10-14T14:16:52.308531Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os, gc\nimport numpy as np \nimport pandas as pd\nfrom tqdm import tqdm\n\n!ls ../input/*","metadata":{"execution":{"iopub.status.busy":"2022-10-14T14:16:58.784738Z","iopub.execute_input":"2022-10-14T14:16:58.785084Z","iopub.status.idle":"2022-10-14T14:16:59.833069Z","shell.execute_reply.started":"2022-10-14T14:16:58.785057Z","shell.execute_reply":"2022-10-14T14:16:59.830949Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
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CITEseq Technology

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(Given gene expression, predict protein levels)

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","metadata":{}},{"cell_type":"markdown","source":"#### \"cell_type\" is sorted according to the following order:\n\n1 - **MasP** = Mast Cell Progenitor (Number of **18090**)\n\n2 - **MkP** = Megakaryocyte Progenitor (Number of **10800**)\n\n3 - **NeuP** = Neutrophil Progenitor (Number of **21418**)\n\n4 - **MoP** = Monocyte Progenitor (Number of **1822**)\n\n5 - **EryP** = Erythrocyte Progenitor (Number of **24344**)\n\n6 - **HSC** = Hematoploetic Stem Cell (Number of **42874**)\n\n7 - **BP** = B-Cell Progenitor (Number of **303**)","metadata":{}},{"cell_type":"markdown","source":"
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\n\n## Ensembling & Submission","metadata":{}},{"cell_type":"code","source":"sub1 = pd.read_csv('../input/3-5-msci22-baseline-separate-models-citeseq/submission.csv')\n\nsub2 = pd.read_csv('../input/4-5-msci22-baseline-knn-model-citeseq/submission.csv')\n\nsub3 = pd.read_csv('../input/all-in-one-citeseq-multiome-with-keras/submission_lolo_total_ensembling.csv')","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub = sub1.copy()\nsub['target'] = (0.05 * sub1['target']) + (0.20 * sub2['target']) + (0.75 * sub3['target'])\n\ndisplay(sub)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub.to_csv('submission.csv', index=False)\n!ls","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
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Good Luck

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