Polyssifier runs a multitude of machine learning classifiers on your data. It reports scores, confusion matrices, predictions, and a plot of the scores ranked by classifier performance.
- Python 3.3 or higher.
- Keras
- Sklearn
- Numpy
- Pandas
- Nvidia GPU
- CUDA
- Cross validated results.
- Report with f1 score (scoring='f1') or ROC (scoring='auc')
- Parallel processing.
- Control the number of threads with 'concurrency'.
- We recommend setting concurrency to half the number of Cores in your system.
- Support for Nvidia GPUs (MLP only).
- Set theano flag "device=gpu".
- Saves trained models for future use in case of server malfunction.
- Set project_name for identifying a experiment.
- Activate feature selection step setting
- feature_selection=True
- Automatically scales your data with scale=True
Not compatible with Python 2 for the moment. We need a replacement for the "starmap" method in multiprocessing
Example: on sample/example.ipynb Example:
https://github.com/MRN-Code/polyssifier/blob/master/sample/example.ipynb
It includes the following classifiers:
- Multilayer Perceptron (see mlp.py )
- Nearest Neighbors
- Linear SVM
- RBF SVM
- Decision Tree
- Random Forest
- Logistic Regression
- Naive Bayes
- Voting Classifier
You can exclude some of this classifiers by providing a list of names as follows:
from polyssifier import poly, plot
scores, confusions, predictions = poly(data,label, n_folds=8, exclude=['Multilayer Perceptron'], verbose=1, concurrency=4)
plot(scores)