Gossipcat

Latest version: v0.3.36

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0.1.40

- Add `bayesOot` to the `core` as a function to tune hyper parameters.

Dec 15, 2017
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0.1.32

- Split `Feature` from `core` as a class. Automatical feature engineering on technical level.
- Split `Glimpse` from `core`.
- Polished `SimAnneal`, `core`.

Dec 14, 2017
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0.1.24

- Split `Report` from `core` as a class.
- Polished `SimAnneal`.

Dec 13, 2017
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0.1.10

Tested and modified functions. Add `param_1` as default params for `simAnneal` function.

Dec 12, 2017
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**gossipcat.py**

Add several new functions to do feature engineering:
- `features_dup`: This function checks first n_head rows and obtains duplicated features.
- `features_clf`: This function divides features into sublists according to their data type.
- `corr_pairs`: This function computes correlated feature pairs with correlated coefficient larger than gamma.
- `features_new`: This function builds new features based on correlated feature pairs if the new feature has an AUC greater than auc_score with logistic regression.

Dec 11, 2017
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**gossipcat.py**

- `glimpse`: This function prints a general infomation of the dataset and plot the distribution of target value.
- `simAnneal`: This function uses the simulated annealing to find the optimal hyper parameters and return an optimized classifier.
- `report`: This function prints model report with a classifier on test dataset.
- `report_CM`: This function prints the recall rate of the classifier on test data and plots out confusion matrix.
- `report_PR`: This function plots precision-recall curve and gives average precision.

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