fixes

downloads-generation/models_class1_pan_unselected/README.md

 # Class I pan-allele models (ensemble)
 
-This download contains trained MHC Class I MHCflurry models.
+This download contains trained MHC Class I MHCflurry models before model selection.
 
 To generate this download run:
 

downloads-generation/models_class1_pan_unselected/write_validation_data.py

-"""
-Write and summarize model validation data, which is obtained by taking a full
-dataset and removing the data used for training.
-
-"""
-import argparse
-import sys
-from os.path import abspath
-
-import pandas
-import numpy
-from sklearn.model_selection import StratifiedKFold
-
-parser = argparse.ArgumentParser(usage = __doc__)
-
-parser.add_argument(
-    "--include",
-    metavar="INPUT.csv",
-    nargs="+",
-    help="Input CSV to include")
-parser.add_argument(
-    "--exclude",
-    metavar="INPUT.csv",
-    nargs="+",
-    help="Input CSV to exclude")
-parser.add_argument(
-    "--out-data",
-    metavar="RESULT.csv",
-    help="Output dadta CSV")
-parser.add_argument(
-    "--out-summary",
-    metavar="RESULT.csv",
-    help="Output summary CSV")
-parser.add_argument(
-    "--mass-spec-regex",
-    metavar="REGEX",
-    default="mass[- ]spec",
-    help="Regular expression for mass-spec data. Runs on measurement_source col."
-    "Default: %(default)s.")
-parser.add_argument(
-    "--only-alleles-present-in-exclude",
-    action="store_true",
-    default=False,
-    help="Filter to only alleles that are present in files given by --exclude. "
-    "Useful for filtering to only alleles supported by a predictor, where the "
-    "training data for the predictor is given by --exclude.")
-
-
-def run(argv):
-    args = parser.parse_args(argv)
-
-    dfs = []
-    for input in args.include:
-        df = pandas.read_csv(input)
-        dfs.append(df)
-    df = pandas.concat(dfs, ignore_index=True)
-    print("Loaded data with shape: %s" % str(df.shape))
-    del dfs
-
-    df = df.ix[
-        (df.peptide.str.len() >= 8) & (df.peptide.str.len() <= 15)
-    ]
-    print("Subselected to 8-15mers: %s" % (str(df.shape)))
-
-    if args.exclude:
-        exclude_dfs = []
-        for exclude in args.exclude:
-            exclude_df = pandas.read_csv(exclude)
-            exclude_dfs.append(exclude_df)
-        exclude_df = pandas.concat(exclude_dfs, ignore_index=True)
-        del exclude_dfs
-
-        df["_key"] = df.allele + "__" + df.peptide
-        exclude_df["_key"] = exclude_df.allele + "__" + exclude_df.peptide
-        df["_excluded"] = df._key.isin(exclude_df._key.unique())
-        print("Excluding measurements per allele (counts): ")
-        print(df.groupby("allele")._excluded.sum())
-
-        print("Excluding measurements per allele (fractions): ")
-        print(df.groupby("allele")._excluded.mean())
-
-        df = df.loc[~df._excluded]
-        del df["_excluded"]
-        del df["_key"]
-
-        if args.only_alleles_present_in_exclude:
-            df = df.loc[df.allele.isin(exclude_df.allele.unique())]
-
-    df["mass_spec"] = df.measurement_source.str.contains(args.mass_spec_regex)
-    df.loc[df.mass_spec , "measurement_inequality"] = "mass_spec"
-
-    if args.out_summary:
-        summary_df = df.groupby(
-            ["allele", "measurement_inequality"]
-        )["measurement_value"].count().unstack().fillna(0).astype(int)
-        summary_df["total"] = summary_df.sum(1)
-        summary_df.to_csv(args.out_summary)
-        print("Wrote: %s" % args.out_summary)
-
-    if args.out_data:
-        df.to_csv(args.out_data, index=False)
-        print("Wrote: %s" % args.out_data)
-
-if __name__ == '__main__':
-    run(sys.argv[1:])