from __future__ import print_function
from os.path import join, exists, abspath
from os import mkdir
from socket import gethostname
from getpass import getuser
import time
import json
import hashlib
import logging
import collections
from six import string_types
import numpy
import pandas
from .version import __version__
from .class1_neural_network import DEFAULT_PREDICT_BATCH_SIZE
from .encodable_sequences import EncodableSequences
from .downloads import get_default_class1_cleavage_models_dir
from .class1_cleavage_neural_network import Class1CleavageNeuralNetwork
from .common import save_weights, load_weights, NumpyJSONEncoder
class Class1CleavagePredictor(object):
def __init__(
self,
models,
manifest_df=None,
metadata_dataframes=None):
self.models = models
self._manifest_df = manifest_df
self.metadata_dataframes = (
dict(metadata_dataframes) if metadata_dataframes else {})
def add_models(self, models):
new_model_names = []
original_manifest = self.manifest_df
new_manifest_rows = []
for model in models:
model_name = self.model_name(len(self.models))
row = pandas.Series(collections.OrderedDict([
("model_name", model_name),
("config_json", json.dumps(
model.get_config(), cls=NumpyJSONEncoder)),
("model", model),
])).to_frame().T
new_manifest_rows.append(row)
self.models.append(model)
new_model_names.append(model_name)
self._manifest_df = pandas.concat(
[original_manifest] + new_manifest_rows,
ignore_index=True)
self.check_consistency()
return new_model_names
@property
def manifest_df(self):
"""
A pandas.DataFrame describing the models included in this predictor.
Returns
-------
pandas.DataFrame
"""
if self._manifest_df is None:
rows = []
for (i, model) in enumerate(self.models):
model_config = model.get_config()
rows.append((
self.model_name(i),
json.dumps(model_config, cls=NumpyJSONEncoder),
model
))
self._manifest_df = pandas.DataFrame(
rows,
columns=["model_name", "config_json", "model"])
return self._manifest_df
@staticmethod
def model_name(num):
"""
Generate a model name
Returns
-------
string
"""
random_string = hashlib.sha1(
str(time.time()).encode()).hexdigest()[:16]
return "CLEAVAGE-CLASSI-%d-%s" % (
num,
random_string)
@staticmethod
def weights_path(models_dir, model_name):
"""
Generate the path to the weights file for a model
Parameters
----------
models_dir : string
model_name : string
Returns
-------
string
"""
return join(models_dir, "weights_%s.npz" % model_name)
def predict(
self,
peptides,
n_flanks,
c_flanks,
batch_size=DEFAULT_PREDICT_BATCH_SIZE):
return self.predict_to_dataframe(
peptides=peptides,
n_flanks=n_flanks,
c_flanks=c_flanks,
batch_size=batch_size).score.values
def predict_to_dataframe(
self,
peptides,
n_flanks,
c_flanks,
batch_size=DEFAULT_PREDICT_BATCH_SIZE):
for (name, value) in [
("peptides", peptides),
("n_flanks", n_flanks),
("c_flanks", c_flanks)]:
if isinstance(value, string_types):
raise TypeError(
"%s must be a list or array, not a string" % name)
peptides = EncodableSequences.create(peptides)
n_flanks = EncodableSequences.create(n_flanks)
c_flanks = EncodableSequences.create(c_flanks)
score_array = []
for (i, network) in enumerate(self.models):
predictions = network.predict(
peptides=peptides,
n_flanks=n_flanks,
c_flanks=c_flanks,
batch_size=batch_size)
score_array.append(predictions)
score_array = numpy.array(score_array)
result_df = pandas.DataFrame({
"peptide": peptides.sequences,
"n_flank": n_flanks.sequences,
"c_flank": c_flanks.sequences,
"score": numpy.mean(score_array, axis=0),
})
return result_df
def check_consistency(self):
"""
Verify that self.manifest_df is consistent with instance variables.
Currently only checks for agreement on the total number of models.
Throws AssertionError if inconsistent.
"""
assert len(self.manifest_df) == len(self.models), (
"Manifest seems out of sync with models: %d vs %d entries: \n%s"% (
len(self.manifest_df),
len(self.models),
str(self.manifest_df)))
def save(self, models_dir, model_names_to_write=None, write_metadata=True):
"""
Serialize the predictor to a directory on disk. If the directory does
not exist it will be created.
The serialization format consists of a file called "manifest.csv" with
the configurations of each Class1CleavageNeuralNetwork, along with
per-network files giving the model weights.
Parameters
----------
models_dir : string
Path to directory. It will be created if it doesn't exist.
"""
self.check_consistency()
if model_names_to_write is None:
# Write all models
model_names_to_write = self.manifest_df.model_name.values
if not exists(models_dir):
mkdir(models_dir)
sub_manifest_df = self.manifest_df.loc[
self.manifest_df.model_name.isin(model_names_to_write)
].copy()
# Network JSON configs may have changed since the models were added,
# so we update the JSON configs here also.
updated_network_config_jsons = []
for (_, row) in sub_manifest_df.iterrows():
updated_network_config_jsons.append(
json.dumps(row.model.get_config(), cls=NumpyJSONEncoder))
weights_path = self.weights_path(models_dir, row.model_name)
save_weights(row.model.get_weights(), weights_path)
logging.info("Wrote: %s", weights_path)
sub_manifest_df["config_json"] = updated_network_config_jsons
self.manifest_df.loc[
sub_manifest_df.index,
"config_json"
] = updated_network_config_jsons
write_manifest_df = self.manifest_df[[
c for c in self.manifest_df.columns if c != "model"
]]
manifest_path = join(models_dir, "manifest.csv")
write_manifest_df.to_csv(manifest_path, index=False)
logging.info("Wrote: %s", manifest_path)
if write_metadata:
# Write "info.txt"
info_path = join(models_dir, "info.txt")
rows = [
("trained on", time.asctime()),
("package ", "mhcflurry %s" % __version__),
("hostname ", gethostname()),
("user ", getuser()),
]
pandas.DataFrame(rows).to_csv(
info_path, sep="\t", header=False, index=False)
if self.metadata_dataframes:
for (name, df) in self.metadata_dataframes.items():
metadata_df_path = join(models_dir, "%s.csv.bz2" % name)
df.to_csv(metadata_df_path, index=False, compression="bz2")
@classmethod
def load(cls, models_dir=None, max_models=None):
"""
Deserialize a predictor from a directory on disk.
Parameters
----------
models_dir : string
Path to directory. If unspecified the default downloaded models are
used.
max_models : int, optional
Maximum number of models to load
Returns
-------
`Class1CleavagePredictor` instance
"""
if models_dir is None:
models_dir = get_default_class1_cleavage_models_dir()
manifest_path = join(models_dir, "manifest.csv")
manifest_df = pandas.read_csv(manifest_path, nrows=max_models)
models = []
for (_, row) in manifest_df.iterrows():
weights_filename = cls.weights_path(models_dir, row.model_name)
config = json.loads(row.config_json)
model = Class1CleavageNeuralNetwork.from_config(
config,
weights=load_weights(abspath(weights_filename)))
models.append(model)
manifest_df["model"] = models
logging.info("Loaded %d class1 cleavage models", len(models))
result = cls(
models=models,
manifest_df=manifest_df)
return result