comparison model_prediction.py @ 11:7d7379dfef8b draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ba6a47bdf76bbf4cb276206ac1a8cbf61332fd16"

10:153f237ddb36

import argparse
import json
import numpy as np
import pandas as pd
import warnings

from scipy.io import mmread
from sklearn.pipeline import Pipeline

from galaxy_ml.utils import (load_model, read_columns,
                             get_module, try_get_attr)


N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))

        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)

11:7d7379dfef8b

import argparse
import json
import numpy as np
import pandas as pd
import tabix
import warnings

from scipy.io import mmread
from sklearn.pipeline import Pipeline

from galaxy_ml.externals.selene_sdk.sequences import Genome
from galaxy_ml.utils import (load_model, read_columns,
                             get_module, try_get_attr)


N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))

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

        pred_data_generator.fit()

        variants = pred_data_generator.variants
        # TODO : remove the following block after galaxy-ml v0.7.13
        blacklist_tabix = getattr(pred_data_generator.reference_genome_,
                                  '_blacklist_tabix', None)
        clean_variants = []
        if blacklist_tabix:
            start_radius = pred_data_generator.start_radius_
            end_radius = pred_data_generator.end_radius_

            for chrom, pos, name, ref, alt, strand in variants:
                center = pos + len(ref) // 2
                start = center - start_radius
                end = center + end_radius

                if isinstance(pred_data_generator.reference_genome_, Genome):
                    if "chr" not in chrom:
                        chrom = "chr" + chrom
                    if "MT" in chrom:
                        chrom = chrom[:-1]
                try:
                    rows = blacklist_tabix.query(chrom, start, end)
                    found = 0
                    for row in rows:
                        found = 1
                        break
                    if found:
                        continue
                except tabix.TabixError:
                    pass

                clean_variants.append((chrom, pos, name, ref, alt, strand))
        else:
            clean_variants = variants

        setattr(pred_data_generator, 'variants', clean_variants)

        variants = np.array(clean_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)