predict.py

# Copyright (c) 2015. Mount Sinai School of Medicine
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from collections import OrderedDict

import pandas as pd

from .class1_allele_specific import load
from .common import normalize_allele_name, UnsupportedAllele


def predict(alleles, peptides, loaders=None):
    """
    Make predictions across all combinations of the specified alleles and
    peptides.

    Parameters
    ----------
    alleles : list of str
        Names of alleles to make predictions for.

    peptides : list of str
        Peptide amino acid sequences.

    loaders : list of Class1AlleleSpecificPredictorLoader, optional
        Loaders to try. Will be tried in the order given.

    Returns DataFrame with columns "Allele", "Peptide", and "Prediction"
    """
    if loaders is None:
        loaders = [
            load.get_loader_for_downloaded_models(),
        ]
    result_dict = OrderedDict([
        ("Allele", []),
        ("Peptide", []),
        ("Prediction", []),
    ])
    for allele in alleles:
        allele = normalize_allele_name(allele)
        exceptions = {}  # loader -> UnsupportedAllele exception
        model = None
        for loader in loaders:
            try:
                model = loader.from_allele_name(allele)
                break
            except UnsupportedAllele as e:
                exceptions[loader] = e
        if model is None:
            raise UnsupportedAllele(
                "No loaders support allele '%s'. Errors were:\n%s" % (
                    allele,
                    "\n".join(
                        ("\t%-20s : %s" % (k, v))
                        for (k, v) in exceptions.items())))

        for i, ic50 in enumerate(model.predict(peptides)):
            result_dict["Allele"].append(allele)
            result_dict["Peptide"].append(peptides[i])
            result_dict["Prediction"].append(ic50)
    return pd.DataFrame(result_dict)