sharonwoo
/ hashing_perceptron.py
Created
January 29, 2019 06:36
— forked from kastnerkyle/hashing_perceptron.py
Hashing perceptron from https://www.kaggle.com/c/criteo-display-ad-challenge/forums/t/10322/beat-the-benchmark-with-less-then-200mb-of-memory/53674
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| # Original code from tinrtgu on Kaggle under WTFPL license | |
| # Relicensed to BSD 3-clause (it does say do what you want...) | |
| # Authors: Kyle Kastner | |
| # License: BSD 3-clause | |
| # Reference links: | |
| # Adaptive learning: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41159.pdf | |
| # Criteo scalable response prediction: http://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf | |
| # Vowpal Wabbit (hashing trick): https://github.com/JohnLangford/vowpal_wabbit/ | |
| # Hashing Trick: http://arxiv.org/pdf/0902.2206.pdf |
sharonwoo
/ outlier.py
Created
October 5, 2018 10:06
— forked from ivan-mitb/outlier.py
annamalai detection using GMM
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| # load READY.DAT (56 cols) | |
| from dataload import load_object | |
| x_train, x_test, y_train, y_test = load_object('ready.dat') | |
| x_train.max(axis=0) | |
| del x_test, y_test # we don't need the test set for now | |
| # under-sample the big classes to make the set manageable | |
| from imblearn.under_sampling import RandomUnderSampler | |
| rus = RandomUnderSampler(ratio={'normal':50000, 'dos':50000}, random_state=4129) | |
| x_train, y_train = rus.fit_sample(x_train, y_train.attack_type) |