Polyssifier

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.

Installation

pip install polyssifier

How to use

from polyssifier import poly, plot

data = np.load("/path/to/data.npy")
label = np.load("/path/to/labels.npy")
scores, confusions, predictions, probs = poly(data,label, n_folds=8, verbose=1, concurrency=4)
plot(scores)

Requirements

Python 3.3 or higher.
Keras
Sklearn
Numpy
Pandas
MLP

Optional

Nvidia GPU
CUDA

Features

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

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)