Polyssifier runs a multitude of machine learning models on data. It reports scores, confusion matrices, predictions, and plots the scores ranked by classifier performance.
pip install polyssifierfrom polyssifier import poly
# Load data
data = np.load("/path/to/data.npy")
label = np.load("/path/to/labels.npy")
# Run analysis
report = poly(data,label, n_folds=8)
# Plot results
report.plot_scores()
report.plot_features(ntop=10)from polyssifier import polyr
# Load data
data = np.load("/path/to/data.npy")
target = np.load("/path/to/target.npy")
# Run analysis
report = polyr(data, target, n_folds=8)
# Plot results
report.plot_scores()
report.plot_features(ntop=10)poly data.npy label.npy --concurrency 10- Sklearn
- Numpy
- Pandas
- Cross validated scores.
- Report f1 score (scoring='f1') or ROC (scoring='auc') for classification
- Report MSE or R^2 for regression
- Feature ranking for compatible models (Logistic Regression, Linear SVM, Random Forest)
- Parallel processing.
- Control the number of threads with 'concurrency'.
- We recommend setting concurrency to half the number of Cores in your system.
- 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
Example: on sample/example.ipynb
It includes the following classifiers:
- Multilayer Perceptron
- Nearest Neighbors
- Linear SVM
- RBF SVM
- Decision Tree
- Random Forest
- Logistic Regression
- Naive Bayes
- Voting Classifier
and the following regressors:
- Linear Regression
- Bayesian Ridge
- PassiveAggressiveRegressor
- GaussianProcessRegressor
- Ridge
- Lasso
- Lars
- LassoLars
- OrthogonalMatchingPursuit
- ElasticNet
You can exclude some of this models by providing a list of names as follows:
from polyssifier import poly
report = poly(data,label, n_folds=8,
exclude=['Multilayer Perceptron'])