{"cells":[{"metadata":{},"cell_type":"markdown","source":"# THIS KERNAL IS BLEND OF So awesome kernels present Right now\n# Vote if you love blend. \n\n## Kernels used comming from these awesome people:\n### For the TF-IDF submissions they are comming from this kernel:\n[NB-SVM strong linear baseline](https://www.kaggle.com/hamditarek/nb-svm-strong-linear-baseline)\n\n[[TPU-Inference] Super Fast XLMRoberta](https://www.kaggle.com/shonenkov/tpu-inference-super-fast-xlmroberta)\n\n[Jigsaw TPU: BERT with Huggingface and Keras](https://www.kaggle.com/miklgr500/jigsaw-tpu-bert-with-huggingface-and-keras)\n\n[inference of bert tpu model ml w/ validation](https://www.kaggle.com/abhishek/inference-of-bert-tpu-model-ml-w-validation)\n[Train from MLM finetuned XLM-R large](https://www.kaggle.com/riblidezso/train-from-mlm-finetuned-xlm-roberta-large)","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"# phase 1 [Ensemble]","execution_count":null},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n# 0.9422 /kaggle/input/train-from-mlm-finetuned-xlmr-large/submission (71).csv","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"#submission1 = pd.read_csv('/kaggle/input/train-from-mlm-finetuned-xlmr-large/submission (71).csv')\nsubmission2 = pd.read_csv('/kaggle/input/009473-v/submission - 2020-06-23T075709.806.csv')\nsubmission1 = pd.read_csv('/kaggle/input/009488/submission - 2020-06-23T074910.830.csv')\nsubmission3 = pd.read_csv('../input/tpuinference-super-fast-xlmroberta/submission (47).csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Hist Graph of scores","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.set()\nplt.hist(submission1['toxic'],bins=100)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.set()\nplt.hist(submission2['toxic'],bins=100)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission1['toxic'] = submission1['toxic']*0.9 + submission2['toxic']*0.1","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission1.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":4}