import collections
import time
from copy import copy
import logging
import pandas
import numpy
from sklearn.base import clone
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from ..common import assert_no_null, drop_nulls_and_warn
def build_presentation_models(term_dict, formulas, **kwargs):
"""
Convenience function for creating multiple final models based on
shared terms.
Parameters
------------
term_dict : dict of string -> (
list of PresentationComponentModel,
list of string)
Terms are named with arbitrary strings (e.g. "A_ms") and are
associated with some presentation component models and some
expressions (e.g. ["log(affinity_percentile_rank + .001)"]).
formulas : list of string
A formula is a string containing terms separated by "+". For example:
"A_ms + A_cleavage + A_expression".
**kwargs : dict
Passed to PresentationModel constructor
Returns
------------
dict of string -> PresentationModel
The keys of the result dict are formulas, and the values are (untrained)
PresentationModel instances.
"""
result = collections.OrderedDict()
for formula in formulas:
term_names = [x.strip() for x in formula.split("+")]
inputs = []
expressions = []
for name in term_names:
(term_inputs, term_expressions) = term_dict[name]
inputs.extend(term_inputs)
expressions.extend(term_expressions)
assert len(set(expressions)) == len(expressions), expressions
presentation_model = PresentationModel(
inputs,
expressions,
**kwargs)
result[formula] = presentation_model
return result
class PresentationModel(object):
"""
A predictor for whether a peptide is detected via mass-spec. Uses
"final model inputs" (e.g. expression, cleavage, mhc affinity) which
themselves may need to be fit.
Parameters
------------
component_models : list of PresentationComponentModel
feature_expressions : list of string
Expressions to use to generate features for the final model based
on the columns generated by the final model inputs.
Example: ["log(expression + .01)"]
decoy_strategy : DecoyStrategy
Decoy strategy to use for training the final model. (The final
model inputs handle their own decoys.)
random-state : int
Random state to use for picking cross validation folds. We are
careful to be deterministic here (i.e. same folds used if the
random state is the same) because we want to have cache hits
for final model inputs that are being used more than once in
multiple final models fit to the same data.
ensemble_size : int
If specified, train an ensemble of each final model input, and use
the out-of-bag predictors to generate predictions to fit the final
model. If not specified (default), a two-fold fit is used.
"""
def __init__(
self,
component_models,
feature_expressions,
decoy_strategy,
predictor=LogisticRegression(),
random_state=0,
ensemble_size=None):
columns = set()
self.component_models_require_fitting = False
for component_model in component_models:
model_cols = component_model.column_names()
assert not columns.intersection(model_cols), model_cols
columns.update(model_cols)
if component_model.requires_fitting():
self.component_models_require_fitting = True
self.component_models = component_models
self.ensemble_size = ensemble_size
self.feature_expressions = feature_expressions
self.decoy_strategy = decoy_strategy
self.random_state = random_state
self.predictor = predictor
self.trained_component_models = None
self.presentation_models_predictors = None
self.fit_experiments = None
@property
def has_been_fit(self):
return self.fit_experiments is not None
def clone(self):
return copy(self)
def reset_cache(self):
for model in self.component_models:
model.reset_cache()
if self.trained_component_models is not None:
for models in self.trained_component_models:
for ensemble_group in models:
for model in ensemble_group:
model.reset_cache()
def fit(self, hits_df):
"""
Train the final model and its inputs (if necessary).
Parameters
-----------
hits_df : pandas.DataFrame
dataframe of hits with columns 'experiment_name' and 'peptide'
"""
start = time.time()
assert not self.has_been_fit
assert 'experiment_name' in hits_df.columns
assert 'peptide' in hits_df.columns
assert self.trained_component_models is None
assert self.presentation_models_predictors is None
hits_df = hits_df.reset_index(drop=True).copy()
self.fit_experiments = set(hits_df.experiment_name.unique())
if self.component_models_require_fitting and not self.ensemble_size:
# Use two fold CV to train model inputs then final models.
# In this strategy, we fit the component models on half the data,
# and train the final predictor (usually logistic regression) on
# the other half. We do this twice to end up with two final.
# At prediction time, the results of these predictors are averaged.
cv = StratifiedKFold(
n_splits=2, shuffle=True, random_state=self.random_state)
self.trained_component_models = []
self.presentation_models_predictors = []
fold_num = 1
for (fold1, fold2) in cv.split(hits_df, hits_df.experiment_name):
print("Two fold fit: fitting fold %d" % fold_num)
fold_num += 1
assert len(fold1) > 0
assert len(fold2) > 0
model_input_training_hits_df = hits_df.iloc[fold1]
hits_and_decoys_df = make_hits_and_decoys_df(
hits_df.iloc[fold2],
self.decoy_strategy)
self.trained_component_models.append([])
for sub_model in self.component_models:
sub_model = sub_model.clone_and_fit(
model_input_training_hits_df)
self.trained_component_models[-1].append((sub_model,))
predictions = sub_model.predict(hits_and_decoys_df)
for (col, values) in predictions.items():
hits_and_decoys_df[col] = values
final_predictor = self.fit_final_predictor(hits_and_decoys_df)
self.presentation_models_predictors.append(final_predictor)
else:
# Use an ensemble of component predictors. Each component model is
# trained on a random half of the data (self.ensemble_size folds
# in total). Predictions are generated using the out of bag
# predictors. A single final model predictor is trained.
if self.component_models_require_fitting:
print("Using ensemble fit, ensemble size: %d" % (
self.ensemble_size))
else:
print("Using single fold fit.")
component_model_index_to_stratification_groups = []
stratification_groups_to_ensemble_folds = {}
for (i, component_model) in enumerate(self.component_models):
if component_model.requires_fitting():
stratification_groups = tuple(
component_model.stratification_groups(hits_df))
component_model_index_to_stratification_groups.append(
stratification_groups)
stratification_groups_to_ensemble_folds[
stratification_groups
] = []
for (i, (stratification_groups, ensemble_folds)) in enumerate(
stratification_groups_to_ensemble_folds.items()):
print("Preparing folds for stratification group %d / %d" % (
i + 1, len(stratification_groups_to_ensemble_folds)))
while len(ensemble_folds) < self.ensemble_size:
cv = StratifiedKFold(
n_splits=2,
shuffle=True,
random_state=self.random_state + len(ensemble_folds))
for (indices, _) in cv.split(
hits_df, stratification_groups):
ensemble_folds.append(indices)
# We may have one extra fold.
if len(ensemble_folds) == self.ensemble_size + 1:
ensemble_folds.pop()
def fit_and_predict_component(model, fit_df, predict_df):
assert component_model.requires_fitting()
model = component_model.clone_and_fit(fit_df)
predictions = model.predict(predict_df)
return (model, predictions)
# Note: we depend on hits coming before decoys here, so that
# indices into hits_df are also indices into hits_and_decoys_df.
hits_and_decoys_df = make_hits_and_decoys_df(
hits_df, self.decoy_strategy)
self.trained_component_models = [[]]
for (i, component_model) in enumerate(self.component_models):
if component_model.requires_fitting():
print("Training component model %d / %d: %s" % (
i + 1, len(self.component_models), component_model))
stratification_groups = (
component_model_index_to_stratification_groups[i])
ensemble_folds = stratification_groups_to_ensemble_folds[
stratification_groups
]
(models, predictions) = train_and_predict_ensemble(
component_model,
hits_and_decoys_df,
ensemble_folds)
else:
models = (component_model,)
predictions = component_model.predict(hits_and_decoys_df)
self.trained_component_models[0].append(models)
for (col, values) in predictions.items():
hits_and_decoys_df[col] = values
final_predictor = self.fit_final_predictor(hits_and_decoys_df)
self.presentation_models_predictors = [final_predictor]
assert len(self.presentation_models_predictors) == \
len(self.trained_component_models)
for models_group in self.trained_component_models:
assert isinstance(models_group, list)
assert len(models_group) == len(self.component_models)
assert all(
isinstance(ensemble_group, tuple)
for ensemble_group in models_group)
print("Fit final model in %0.1f sec." % (time.time() - start))
# Decoy strategy is no longer required after fitting.
self.decoy_strategy = None
def fit_final_predictor(
self, hits_and_decoys_with_component_predictions_df):
"""
Private helper method.
"""
(x, y) = self.make_features_and_target(
hits_and_decoys_with_component_predictions_df)
print("Training final model predictor on data of shape %s" % (
str(x.shape)))
final_predictor = clone(self.predictor)
final_predictor.fit(x.values, y.values)
return final_predictor
def evaluate_expressions(self, input_df):
result = pandas.DataFrame()
for expression in self.feature_expressions:
# We use numpy module as globals here so math functions
# like log, log1p, exp, are in scope.
try:
values = eval(expression, numpy.__dict__, input_df)
except SyntaxError:
logging.error("Syntax error in expression: %s" % expression)
raise
assert len(values) == len(input_df), expression
if hasattr(values, 'values'):
values = values.values
series = pandas.Series(values)
assert_no_null(series, expression)
result[expression] = series
assert len(result) == len(input_df)
return result
def make_features_and_target(self, hits_and_decoys_df):
"""
Private helper method.
"""
assert 'peptide' in hits_and_decoys_df
assert 'hit' in hits_and_decoys_df
df = self.evaluate_expressions(hits_and_decoys_df)
df['hit'] = hits_and_decoys_df.hit.values
new_df = drop_nulls_and_warn(df, hits_and_decoys_df)
y = new_df["hit"]
del new_df["hit"]
return (new_df, y)
def predict_to_df(self, peptides_df):
"""
Predict for the given peptides_df, which should have columns
'experiment_name' and 'peptide'.
Returns a dataframe giving the predictions. If this final
model's inputs required fitting and therefore the final model
has two predictors trained each fold, the resulting dataframe
will have predictions for both final model predictors.
"""
assert self.has_been_fit
assert 'experiment_name' in peptides_df.columns
assert 'peptide' in peptides_df.columns
assert len(self.presentation_models_predictors) == \
len(self.trained_component_models)
prediction_cols = []
presentation_model_predictions = {}
zipped = enumerate(
zip(
self.trained_component_models,
self.presentation_models_predictors))
for (i, (component_models, presentation_model_predictor)) in zipped:
df = pandas.DataFrame()
for ensemble_models in component_models:
start_t = time.time()
predictions = ensemble_predictions(
ensemble_models, peptides_df)
print(
"Component '%s' (ensemble size=%d) generated %d "
"predictions in %0.2f sec." % (
ensemble_models[0],
len(ensemble_models),
len(peptides_df),
(time.time() - start_t)))
for (col, values) in predictions.items():
values = pandas.Series(values)
assert_no_null(values)
df[col] = values
x_df = self.evaluate_expressions(df)
assert_no_null(x_df)
prediction_col = "Prediction (Model %d)" % (i + 1)
assert prediction_col not in presentation_model_predictions
presentation_model_predictions[prediction_col] = (
presentation_model_predictor
.predict_proba(x_df.values)[:, 1])
prediction_cols.append(prediction_col)
if len(prediction_cols) == 1:
presentation_model_predictions["Prediction"] = (
presentation_model_predictions[prediction_cols[0]])
del presentation_model_predictions[prediction_cols[0]]
else:
presentation_model_predictions["Prediction"] = numpy.mean(
[
presentation_model_predictions[col]
for col in prediction_cols
],
axis=0)
return pandas.DataFrame(presentation_model_predictions)
def predict(self, peptides_df):
"""
Predict for the given peptides_df, which should have columns
'experiment_name' and 'peptide'.
Returns an array of floats giving the predictions for each
row in peptides_df. If the final model was trained in two
folds, the predictions from the two final model predictors
are averaged.
"""
assert self.has_been_fit
df = self.predict_to_df(peptides_df)
return df.Prediction.values
def score_from_peptides_df(
self, peptides_df, include_hit_indices=True):
"""
Given a DataFrame with columns 'peptide', 'experiment_name', and
'hit', calculate the PPV score. Return a dict of scoring info.
If include_hit_indices is True (default), then the indices the
hits occur in after sorting by prediction score, is also returned.
The top predicted peptide will have index 0.
"""
assert self.has_been_fit
assert 'peptide' in peptides_df.columns
assert 'experiment_name' in peptides_df.columns
assert 'hit' in peptides_df.columns
peptides_df = peptides_df.copy()
peptides_df["prediction"] = self.predict(peptides_df)
top_n = float(peptides_df.hit.sum())
if not include_hit_indices:
top = peptides_df.nlargest(top_n, "prediction")
result = {
'score': top.hit.mean()
}
else:
ranks = peptides_df.prediction.rank(ascending=False)
hit_indices = ranks[peptides_df.hit > 0].values
hit_lengths = peptides_df.peptide[
peptides_df.hit > 0
].str.len().values
result = {
'hit_indices': hit_indices,
'hit_lengths': hit_lengths,
'total_peptides': len(peptides_df),
}
result['score'] = (
numpy.sum(result['hit_indices'] <= top_n) / top_n)
return result
def score_from_hits_and_decoy_strategy(self, hits_df, decoy_strategy):
"""
Compute positive predictive value on the given hits_df.
Parameters
-----------
hits_df : pandas.DataFrame
dataframe of hits with columns 'experiment_name' and 'peptide'
decoy_strategy : DecoyStrategy
Strategy for selecting decoys
Returns
-----------
dict of scoring info, with keys 'score', 'hit_indices', and
'total_peptides'
"""
assert self.has_been_fit
peptides_df = make_hits_and_decoys_df(
hits_df,
decoy_strategy)
return self.score_from_peptides_df(peptides_df)
def get_fit(self):
"""
Return fit (i.e. trained) parameters.
"""
assert self.has_been_fit
result = {
'trained_component_model_fits': [],
'presentation_models_predictors': (
self.presentation_models_predictors),
'fit_experiments': self.fit_experiments,
'feature_expressions': self.feature_expressions,
}
for final_predictor_models_group in self.trained_component_models:
fits = []
for ensemble_group in final_predictor_models_group:
fits.append(tuple(model.get_fit() for model in ensemble_group))
result['trained_component_model_fits'].append(fits)
return result
def restore_fit(self, fit):
"""
Restore fit parameters.
Parameters
------------
fit : object
What was returned from a call to get_fit().
"""
assert not self.has_been_fit
fit = dict(fit)
self.presentation_models_predictors = (
fit.pop('presentation_models_predictors'))
self.fit_experiments = fit.pop('fit_experiments')
model_input_fits = fit.pop('trained_component_model_fits')
feature_expressions = fit.pop('feature_expressions', [])
if feature_expressions != self.feature_expressions:
logging.warn(
"Feature expressions restored from fit: '%s' do not match "
"those of this PresentationModel: '%s'" % (
feature_expressions, self.feature_expressions))
assert not fit, "Unhandled data in fit: %s" % fit
assert (
len(model_input_fits) == len(self.presentation_models_predictors))
self.trained_component_models = []
for model_input_fits_for_fold in model_input_fits:
self.trained_component_models.append([])
for (sub_model, sub_model_fits) in zip(
self.component_models,
model_input_fits_for_fold):
restored_models = tuple(
sub_model.clone_and_restore_fit(sub_model_fit)
for sub_model_fit in sub_model_fits)
self.trained_component_models[-1].append(restored_models)
def make_hits_and_decoys_df(hits_df, decoy_strategy):
"""
Given some hits (with columns 'experiment_name' and 'peptide'),
and a decoy strategy, return a "peptides_df", which has columns
'experiment_name', 'peptide', and 'hit.'
"""
hits_df = hits_df.copy()
hits_df["hit"] = 1
decoys_df = decoy_strategy.decoys(hits_df)
decoys_df["hit"] = 0
peptides_df = pandas.concat(
[hits_df, decoys_df],
ignore_index=True)
return peptides_df
# TODO: paralellize this.
def train_and_predict_ensemble(model, peptides_df, ensemble_folds):
assert model.requires_fitting()
fit_models = tuple(
model.clone_and_fit(peptides_df.iloc[indices])
for indices in ensemble_folds)
return (
fit_models,
ensemble_predictions(fit_models, peptides_df, ensemble_folds))
def ensemble_predictions(models, peptides_df, mask_indices_list=None):
typical_model = models[0]
panel = pandas.Panel(
items=numpy.arange(len(models)),
major_axis=peptides_df.index,
minor_axis=typical_model.column_names(),
dtype=numpy.float32)
for (i, model) in enumerate(models):
predictions = model.predict(peptides_df)
for (key, values) in predictions.items():
panel.loc[i, :, key] = values
if mask_indices_list is not None:
for (i, indices) in enumerate(mask_indices_list):
panel.iloc[i, indices] = numpy.nan
result = {}
for col in typical_model.column_names():
values = panel.ix[:, :, col]
assert values.shape == (len(peptides_df), len(models))
result[col] = model.combine_ensemble_predictions(col, values.values)
assert_no_null(result[col])
return result