{"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":"Published on June 29, 2023. By Marília Prata, mpwolke","metadata":{}},{"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\n\nimport 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\n\n#Ignore warnings\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# Input data files are available in the read-only \"../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# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-06-29T16:38:54.452883Z","iopub.execute_input":"2023-06-29T16:38:54.453330Z","iopub.status.idle":"2023-06-29T16:38:56.254089Z","shell.execute_reply.started":"2023-06-29T16:38:54.453293Z","shell.execute_reply":"2023-06-29T16:38:56.252927Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#\"Those who don't know history, are destined to repeat it\", Edmund Burke","metadata":{}},{"cell_type":"markdown","source":"#Copying shared High Score Notebooks near the end of the Competition is a kind of Cheating.\n\nThose who copy the high score code, many times got better results without any effort than the others.\n\nSo, Don't do that. It's a bad practice sharing the code and mostly forking it without edits.\n\nThat competition is so different that everybody, till now, have the same Score: 1\n\n\nIt's about delivering essays. At the end, all shall be Public : ) 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"}}},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/2023-kaggle-ai-report/kaggle_writeups_20230510.csv')\ndf.head(2)","metadata":{"execution":{"iopub.status.busy":"2023-06-29T16:44:53.887506Z","iopub.execute_input":"2023-06-29T16:44:53.888138Z","iopub.status.idle":"2023-06-29T16:44:54.326462Z","shell.execute_reply.started":"2023-06-29T16:44:53.888077Z","shell.execute_reply":"2023-06-29T16:44:54.325207Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#People love winners, but \"villains\"/cheaters attract a lot of attention too (319 votes/236 comments)\n\nThe winner, 1st place Solution Summary had only 78 votes.\n\n![image.png](attachment:4a2ffc4f-287a-4b87-8fa1-b94ca5ca2313.png)","metadata":{},"attachments":{"4a2ffc4f-287a-4b87-8fa1-b94ca5ca2313.png":{"image/png":"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"}}},{"cell_type":"code","source":"pet = df[(df['Title of Competition']=='PetFinder.my Adoption Prediction')].reset_index(drop=True)\npet.head(20)","metadata":{"execution":{"iopub.status.busy":"2023-06-29T16:47:23.419640Z","iopub.execute_input":"2023-06-29T16:47:23.420204Z","iopub.status.idle":"2023-06-29T16:47:23.445743Z","shell.execute_reply.started":"2023-06-29T16:47:23.420162Z","shell.execute_reply":"2023-06-29T16:47:23.444382Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#1st place Solution above. It was just someone (176th) that wrote: \"1st place solution\". Is that tricky or Cheating?\n\nThe real 1st place was: \"1st Place Solution Summary\" on the 7th row.","metadata":{}},{"cell_type":"markdown","source":"#Let's remind us what happens to cheaters and how kagglers think about that:\n\nWORDS by the COMPETITION HOST\n\n\"The 1st Place Team has been disqualified from the contest for cheating. The Kaggle Grandmaster cheater has also been permanently banned on this platform as the evidence points towards him being the key party behind this fraudulent activity.\"\n\n\"Here is what the disqualified team did in the contest:\"\n\n\"They fraudulently obtained adoption speed answers for the private test data (possibly by scraping our website)\"\n\n\"This fraudulent activity was discovered by BM — a diligent, hardworking and compassionate winner who has been working closely with our team to implement the winning solutions. The violation was reported to Kaggle, which proceeded to conduct an in-depth investigation and took corresponding action.\"\n\n\"Unfortunately, the prize money has already been disbursed and is irrecoverable. Kaggle has revised the leaderboard accordingly, with relevant scores / medals awarded to the participants.\"\n\n\"It is very sad indeed that such brilliant people, including a highly respected Kaggle Grandmaster, have gone to such lengths to defraud a welfare competition aimed at saving precious animal lives, solely for their own financial gain.\"\n\n\"The hosts sincerely apologized for discovering this issue too late, as this is the first time they are organizing such an AI competition and genuinely did not expect such unscrupulous and irresponsible behavior.\"\n\n\"However, let’s not allow such black sheep to tarnish the reputation of this amazing community. The hosts were immensely grateful to Kaggle and all of you for your generous support, and will continue their diligent effort in implementing the AI tools based on the winning solutions.\"\n\n\"The disqualified team has issued an apology and the hosts had received their refund of the prize money.\"\n\n\"Upon consultation with the Kaggle team, it was advised that since the competition has long concluded, the reimbursed sum will be donated to the host organization to fund further development of our AI animal welfare solutions. Winners and participants of this Kaggle competition will see an increase in their ranks and points accordingly.\"\n\nhttps://www.kaggle.com/competitions/petfinder-adoption-prediction/discussion/125436","metadata":{}},{"cell_type":"markdown","source":"#What about Kagglers opinion about cheating (236 comments!) Though I picked up just a few.\n\n\"Discovering that they cheated was obviously frustrating, but overall working together with PetFinder.my has been wonderful. I learnt a lot, including, but definitely not limited to this experience ;)\nI am working on a kernel showing some information on how they cheated.\"  By BM\n\n\"Every time I read about such things I become more confident that in the long run, serious work, continuous learning and perseverance will be rewarded, even if results are slow to appear now. So I continue to work hard.\"\n\n\"Why was the team leader the only one punished? His team mates must have known and approved the cheating. This is going to damage Kaggle and Kagglers reputation quite a bit, hence appropriate corrective action should be taken IMHO.\"\n\n\"100% agreed, from what I read; I think going \"easy\" with these kind of incidents caused worse incidents. Kaggle should be really serious about taking actions against cheating.\"\n\n\"As far as I can see, only one of their teammates is not blocked. Two were both blocked.\"\n\n\"This is an important development that sends a crucial signal to our growing community. I want for all the honest and hardworking Kagglers to be proud of their achievements, medals, and ranks. Being a Kaggle Master or Grandmaster increasingly means something in the \"real\" world, and this is the way it should be. But cheaters like the disqualified undermined this situation, and they tarnished the experience and the satisfaction of many members of our community. I'm very happy that steps are taken to punish and deter cheaters, and I hope that Kaggle continues to address these cases appropriately and timely.\"\n\n\"As always, Kaggle team take action where they have sufficient evidence. And you can be very sure there was a thorough investigation here.\"\n\nhttps://www.kaggle.com/competitions/petfinder-adoption-prediction/discussion/125436\n","metadata":{}},{"cell_type":"markdown","source":"#What about Plagiarism? In some Kagglers words.\n\n\"There is no original author who works with the plagiarist. This is the problem with plagiarism. You say you're motivated, but in fact you just used the code as it was, and others think it's a similar piece of work. I would like you to know how much stress I have suffered during not few periods of time due to your personal greed.\"\n\n\"Instead of plagiarism, I decided to take it positively. I hope it becomes a template for Kaggle and one day I can make a tutorial for the official Kaggle team.\"\n\n\"Hi SA, just saw your comment on my Netflix EDA. You're 100% correct - I had it done to me recently too so I know how you feel. Wasn't done with malice, just haste. I've added your name & a link to the notebook.\"\n\n\"Sure, I gain inspiration from all over Kaggle, etc. I'll have a look through my notebooks. I am new to Kaggle - only just starting using it properly a months or so ago - so am happy to address it. To me it's not about stealing credit, it's about telling the story for the particular project. So I am more than happy to credit where it is due. I'm here to learn, not to argue 😃\"\n\n\"You should at least reference the person who wrote this code .. :)\"\n\n\"Hey! I see that some comments and code were taken from my 🪐 G2Net Getting Started + EDA ☄️. I am super glad you found it useful but would be great if you could reference it :)\"\n\n\"Thanks for copying many markdown cells and even the snippets from GD. that I added to my Riroriro Notebook. When I read the Riroriro installation in your code I expected to read something different, innovative. It takes time to read papers, edit, choose what we can add to our work and mostly provide sources. Since Edward C. wrote that you copied some comments and code from him, seems it's a bad habit from you. At least, you included his name on your work on a new version. Reproduce others work without forgeting citations, references, sources.Copying from others without credit is fast. Loosing respect by ANY community is faster. Avoid that bad practice\"\n\n\"The paragraphs/comments that you copied exactly from my Notebook aren't even the way that they were written on the papers, and Not by Geir D. too, because I edited the papers with double ticks and provided their sources, just like when we make reviews. Even that order are exactly how I've choosen and transcribed to my Notebook. If you've read Geir's work, you or anyone else will notice that. Even the code cell you didn't copy from the original work. I usually wrote \" Hash code by author's name httml …\" And that line was there too. If that is your signature in each and every work go forward.\"\n\nOk, the last two paragraphs were mine since a Jackass plagiarist had copied Edward C. and even my Markdown cells. Who on earth copy even the Markdown cells? Markdown cells are our insights of the work. \nWhich means you have nothing on your mind to say. Just been a despicable Plagiarist. \n\nGreat way to lose respect  by any community as I wrote above. ","metadata":{}},{"cell_type":"markdown","source":"#Cheaters and Plagiarists. \"Plagiarism usually occurs out of laziness.\" Are you lazy?\n\nBy School of Social Work - University of Washington\n\nI loved that line: \"Plagiarism usually occurs out of laziness.\"\n\nLet's check some insights about Academic Honesty:\n\n\"You are guilty of cheating whenever you present as your own work something that you did not do. You are also guilty of cheating if you help someone else to cheat.\"\n\nPLAGIARISM ways\n\n\"One of the most common forms of cheating is plagiarism, using another's words or ideas without proper citation. When students plagiarize, they usually do so in one of the following ways:\"\n\n\"Using another writer's words without proper citation. If you use another writer's words, you must place quotation marks around the quoted material and include a footnote or other indication of the source of the quotation.\"\n\n\"Using another writer's ideas without proper citation. When you use another author's ideas, you must indicate with footnotes or other means where this information can be found. Your instructors want to know which ideas and judgments are yours and which you arrived at by consulting other sources. Even if you arrived at the same judgment on your own, you need to acknowledge that the writer you consulted also came up with the idea.\"\n\nDON'T FORGET QUOTATION MARKS\n\n\"Citing your source but reproducing the exact words of a printed source without quotation marks. This makes it appear that you have paraphrased rather than borrowed the author's exact words.\"\n\n\"Borrowing the structure of another author's phrases or sentences without crediting the author from whom it came. This kind of plagiarism usually occurs out of laziness: it is easier to replicate another writer's style than to think about what you have read and then put it in your own words.\"\n \n\"Borrowing all or part of another kaggler's paper or using someone else's outline to write your own paper.\"\n\n\"Using a paper writing \"service\" or having a friend write the paper for you. Regardless of whether you pay a stranger or have a friend do it, it is a breach of academic honesty to hand in work that is not your own or to use parts of another kaggler's paper.\"\n\n\"In computer programming classes, borrowing computer code from another kaggler and presenting it as your own.\"\n\nhttps://socialwork.uw.edu/students/academic-standards/academic-honesty-cheating-plagiarism","metadata":{}},{"cell_type":"markdown","source":"#Why they behave like that?\n\nI prefer to think that people with this behavior do it in search of results. Including achieving goal/ranking number 1. As well mentioned by the disqualified GM.\n\nSuch an attitude demonstrates immaturity, lack of ethics incompatible with the titles of Master or GM.\nIt may be that with repentance and hard work this fault can be corrected. Especially because we expect more from those who have already reached a certain professional level. In addition, we often idealize such individuals and want to be inspired by them.","metadata":{}},{"cell_type":"markdown","source":"#Keep in mind: Impermanence is the essence of life. Going Down in a Blaze of Glory.\n\nEven if you reach your goal to be number 1, once you stop to participate all your rankings will fall down. Faster than London Bridge. \n\nWhat will remain on your page: your actual position and the higher you once had reached.\n\nIn other words dear Kaggler: You're going Down in a blaze of Glory, lol. ","metadata":{}},{"cell_type":"markdown","source":"#\"The following image is fictional and does Not depict any actual person or event\"\n\nLaw and Order opening disclaimer.  Those who were here more than 4 years ago will get it.\n\nThat's a Bonus Notebook since I had already published a couple of work on this 2023 Kaggle AI Report\n\n![](https://66.media.tumblr.com/2b7d346d230955db1c2261e05f77707b/tumblr_no23d6Pq6g1r45uaso1_400.gifv)","metadata":{}}]}