rhapsody.train package¶
This subpackage contains modules for training Rhapsody classifiers and assess their accuracy.
- rhapsody.train.calcScoreMetrics(y_test, y_pred, bootstrap=0, **resample_kwargs)[source]¶
Compute accuracy metrics of continuous values (optionally bootstrapped)
- rhapsody.train.calcClassMetrics(y_test, y_pred, bootstrap=0, **resample_kwargs)[source]¶
Compute accuracy metrics of binary labels (optionally bootstrapped)
- rhapsody.train.calcPathogenicityProbs(CV_info, num_bins=15, ppred_reliability_cutoff=200, pred_distrib_fig='predictions_distribution.png', path_prob_fig='pathogenicity_prob.png', **kwargs)[source]¶
Compute pathogenicity probabilities, from predictions on CV test sets
- rhapsody.train.trainRFclassifier(feat_matrix, n_estimators=1500, max_features=2, pickle_name='trained_classifier.pkl', feat_imp_fig='feat_importances.png', **kwargs)[source]¶
- rhapsody.train.extendDefaultTrainingDataset(names, arrays, base_default_featset='full')[source]¶
base : array Input array to extend.
names : string, sequence String or sequence of strings corresponding to the names of the new fields.
data : array or sequence of arrays Array or sequence of arrays storing the fields to add to the base.
- rhapsody.train.print_path_prob_figure(filename, bins, histo, dx, path_prob, smooth_plot=None, cutoff=200)[source]¶
Submodules¶
rhapsody.train.RFtraining module¶
This module defines functions for training Random Forest classifiers implementing Rhapsody’s classification schemes.
- rhapsody.train.RFtraining.calcScoreMetrics(y_test, y_pred, bootstrap=0, **resample_kwargs)[source]¶
Compute accuracy metrics of continuous values (optionally bootstrapped)
- rhapsody.train.RFtraining.calcClassMetrics(y_test, y_pred, bootstrap=0, **resample_kwargs)[source]¶
Compute accuracy metrics of binary labels (optionally bootstrapped)
- rhapsody.train.RFtraining.calcPathogenicityProbs(CV_info, num_bins=15, ppred_reliability_cutoff=200, pred_distrib_fig='predictions_distribution.png', path_prob_fig='pathogenicity_prob.png', **kwargs)[source]¶
Compute pathogenicity probabilities, from predictions on CV test sets
- rhapsody.train.RFtraining.RandomForestCV(feat_matrix, n_estimators=1500, max_features=2, **kwargs)[source]¶
- rhapsody.train.RFtraining.trainRFclassifier(feat_matrix, n_estimators=1500, max_features=2, pickle_name='trained_classifier.pkl', feat_imp_fig='feat_importances.png', **kwargs)[source]¶
- rhapsody.train.RFtraining.extendDefaultTrainingDataset(names, arrays, base_default_featset='full')[source]¶
base : array Input array to extend.
names : string, sequence String or sequence of strings corresponding to the names of the new fields.
data : array or sequence of arrays Array or sequence of arrays storing the fields to add to the base.