comparison model_prediction.py @ 2:38c4f8a98038 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"

1:c1b0c8232816

            options['blacklist_regions'] = None

        pred_data_generator = klass(
            ref_genome_path=ref_seq, vcf_path=vcf_path, **options)

        pred_data_generator.fit()

        preds = estimator.model_.predict_generator(
            pred_data_generator.flow(batch_size=32),
            workers=N_JOBS,
            use_multiprocessing=True)

        if preds.min() < 0. or preds.max() > 1.:
            warnings.warn('Network returning invalid probability values. '
                          'The last layer might not normalize predictions '
                          'into probabilities '
                          '(like softmax or sigmoid would).')

        if params['method'] == 'predict_proba' and preds.shape[1] == 1:
            # first column is probability of class 0 and second is of class 1
            preds = np.hstack([1 - preds, preds])

        elif params['method'] == 'predict':
            if preds.shape[-1] > 1:
                # if the last activation is `softmax`, the sum of all
                # probibilities will 1, the classification is considered as
                # multi-class problem, otherwise, we take it as multi-label.
                act = getattr(estimator.model_.layers[-1], 'activation', None)
                if act and act.__name__ == 'softmax':
                    classes = preds.argmax(axis=-1)
                else:
                    preds = (preds > 0.5).astype('int32')
            else:
                classes = (preds > 0.5).astype('int32')

            preds = estimator.classes_[classes]
    # end input

    # output
    if input_type == 'variant_effect':   # TODO: save in batchs
        rval = pd.DataFrame(preds)
        meta = pd.DataFrame(
            pred_data_generator.variants,
            columns=['chrom', 'pos', 'name', 'ref', 'alt', 'strand'])

        rval = pd.concat([meta, rval], axis=1)

    elif len(preds.shape) == 1:
        rval = pd.DataFrame(preds, columns=['Predicted'])
    else:
        rval = pd.DataFrame(preds)

    rval.to_csv(outfile_predict, sep='\t',
                header=True, index=False)


if __name__ == '__main__':
    aparser = argparse.ArgumentParser()
    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)

2:38c4f8a98038

            options['blacklist_regions'] = None

        pred_data_generator = klass(
            ref_genome_path=ref_seq, vcf_path=vcf_path, **options)

        pred_data_generator.set_processing_attrs()

        variants = pred_data_generator.variants

        # predict 1600 sample at once then write to file
        gen_flow = pred_data_generator.flow(batch_size=1600)

        file_writer = open(outfile_predict, 'w')
        header_row = '\t'.join(['chrom', 'pos', 'name', 'ref',
                                'alt', 'strand'])
        file_writer.write(header_row)
        header_done = False

        steps_done = 0

        # TODO: multiple threading
        try:
            while steps_done < len(gen_flow):
                index_array = next(gen_flow.index_generator)
                batch_X = gen_flow._get_batches_of_transformed_samples(
                    index_array)

                if params['method'] == 'predict':
                    batch_preds = estimator.predict(
                        batch_X,
                        # The presence of `pred_data_generator` below is to
                        # override model carrying data_generator if there
                        # is any.
                        data_generator=pred_data_generator)
                else:
                    batch_preds = estimator.predict_proba(
                        batch_X,
                        # The presence of `pred_data_generator` below is to
                        # override model carrying data_generator if there
                        # is any.
                        data_generator=pred_data_generator)

                if batch_preds.ndim == 1:
                    batch_preds = batch_preds[:, np.newaxis]

                batch_meta = variants[index_array]
                batch_out = np.column_stack([batch_meta, batch_preds])

                if not header_done:
                    heads = np.arange(batch_preds.shape[-1]).astype(str)
                    heads_str = '\t'.join(heads)
                    file_writer.write("\t%s\n" % heads_str)
                    header_done = True

                for row in batch_out:
                    row_str = '\t'.join(row)
                    file_writer.write("%s\n" % row_str)

                steps_done += 1

        finally:
            file_writer.close()
            # TODO: make api `pred_data_generator.close()`
            pred_data_generator.close()
        return 0
    # end input

    # output
    if len(preds.shape) == 1:
        rval = pd.DataFrame(preds, columns=['Predicted'])
    else:
        rval = pd.DataFrame(preds)

    rval.to_csv(outfile_predict, sep='\t', header=True, index=False)


if __name__ == '__main__':
    aparser = argparse.ArgumentParser()
    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)