def _replicates(self,
records: List[TrainingRecord],
cv: int = 5,
comple_steps: int = 20,
conta_steps: int = 20,
n_replicates: int = 10):
"""
Generator function to yield test/training sets which will be fed into subprocesses for
_completeness_cv
:param records: the complete set of TrainingRecords
:param cv: number of folds in the crossvalidation to be performed
:param comple_steps: number of steps between 0 and 1 (relative completeness) to be simulated
:param conta_steps: number of steps between 0 and 1 (relative contamination level)
to be simulated
:param n_replicates: number of replicates for the entire crossvalidation
:return: parameter list to submit to worker process
"""
for r in range(n_replicates):
X, y, tn, ft = get_x_y_tn_ft(records)
skf = StratifiedKFold(n_splits=cv, random_state=self.random_state)
fold = 0
for train_index, test_index in skf.split(X, y):
fold += 1
# separate in training set lists:
training_records = [records[i] for i in train_index]
test_records = [records[i] for i in test_index]
starting_message = f"Starting comple/conta replicate {r + 1}/{n_replicates}: fold {fold}"
yield [
test_records, training_records, comple_steps, conta_steps,
self.logger.level, starting_message
]
def _validate_subset(self, records: List[TrainingRecord],
estimator: Pipeline):
"""
Use a fitted Pipeline to predict scores on resampled test data.
part of the compleconta crossvalidation where only validation is performed.
:param records: test records as a List of TrainingRecord objects
:param estimator: classifier previously trained as a sklearn.Pipeline object
:return: score
"""
X, y, tn, ft = get_x_y_tn_ft(records)
preds = estimator.predict(X)
score = self.scoring_method(y, preds)
return score
def _completeness_cv(self, param,
**kwargs) -> Dict[float, Dict[float, float]]:
"""
Perform completeness/contamination simulation and testing for one fold.
This is a separate function only called by run_cccv which spawns
subprocesses using a ProcessPoolExecutor from concurrent.futures
:param param: List [test_records, X_train, y_train, comple_steps, conta_steps, starting_message]
workaround to get multiple parameters into this function. (using processor.map)
"""
# unpack parameters
test_records, training_records, comple_steps, conta_steps, verb, starting_message = param
# needed to create a new logger, self.logger not accessible from a different process
logger = get_logger(__name__, verb=verb)
logger.info(starting_message)
classifier = copy.deepcopy(self.pipeline)
if self.reduce_features:
recursive_feature_elimination(training_records,
classifier,
n_features=self.n_features,
random_state=self.random_state)
X_train, y_train, tn, ft = get_x_y_tn_ft(training_records)
classifier.fit(X=X_train, y=y_train, **kwargs)
# initialize the resampler with the test_records only,
# so the samples are unknown to the classifier
resampler = TrainingRecordResampler(random_state=self.random_state,
verb=False)
resampler.fit(records=test_records)
cv_scores = {}
comple_increment = 1 / comple_steps
conta_increment = 1 / conta_steps
for comple in np.arange(0, 1.05, comple_increment):
comple = np.round(comple, 2)
cv_scores[comple] = {}
for conta in np.arange(0, 1.05, conta_increment):
conta = np.round(conta, 2)
resampled_set = [
resampler.get_resampled(x, comple, conta)
for x in test_records
]
cv_scores[comple][conta] = self._validate_subset(
resampled_set, classifier)
return cv_scores
def train(self,
records: List[TrainingRecord],
reduce_features: bool = False,
n_features: int = 10000,
**kwargs):
"""
Fit CountVectorizer and train LinearSVC on a list of TrainingRecord.
:param records: a List[TrainingRecord] for fitting of CountVectorizer and training of LinearSVC.
:param reduce_features: toggles feature reduction using recursive feature elimination
:param n_features: minimum number of features to retain when reducing features
:param kwargs: additional named arguments are passed to the fit() method of Pipeline.
:returns: Whether the Pipeline has been fitted on the records.
"""
self.logger.info("Begin training classifier.")
X, y, tn, ft = get_x_y_tn_ft(records)
if self.trait_name is not None or self.feature_type is not None:
self.logger.warning(
"Pipeline is already fitted. Refusing to fit again.")
return False
if reduce_features:
self.logger.info(
"using recursive feature elimination as feature selection strategy"
)
# use non-calibrated classifier
recursive_feature_elimination(records,
self.cv_pipeline,
n_features=n_features)
self.trait_name = tn
self.feature_type = ft
extra_explainer_arg = kwargs.pop('train_explainer', None)
if extra_explainer_arg is not None:
self.logger.warning(
f'{self.__class__.__name__} provides SHAP explanations without '
f'training an Explainer. Argument '
f'"train_explainer"={extra_explainer_arg} ignored.')
self.pipeline.fit(X=X, y=y, **kwargs)
self.logger.info("Classifier training completed.")
return self
def test_recursive_feature_elimination(self, trait_name, n_features):
"""
Perform feature compression tests only for SVM; counterindicated for XGB.
:param trait_name:
:return:
"""
training_records, genotype, phenotype, group = self.test_load_data(
trait_name, False)
svm = TrexSVM(verb=True, random_state=RANDOM_STATE)
recursive_feature_elimination(
records=training_records,
pipeline=svm.cv_pipeline,
step=0.01,
n_features=n_features,
)
vec = svm.cv_pipeline.named_steps["vec"]
vec._validate_vocabulary()
# check if vocabulary is set properly
assert vec.fixed_vocabulary_
# check if length of vocabulary is matching
assert len(vec.vocabulary_) >= n_features
X, y, tn, ft = get_x_y_tn_ft(training_records)
X_trans = vec.transform(X)
# check if number of unique features is matching
assert X_trans.shape[1] >= n_features
# check if all samples still have at least one feature present
one_is_zero = False
non_zero = X_trans.nonzero()
for x in non_zero:
if len(x) == 0:
one_is_zero = True
assert not one_is_zero
def crossvalidate(self,
records: List[TrainingRecord],
cv: int = 5,
scoring: Union[str, Callable] = DEFAULT_SCORING_FUNCTION,
n_jobs=-1,
n_replicates: int = 10,
groups: bool = False,
reduce_features: bool = False,
n_features: int = 10000,
demote=False,
**kwargs) -> Tuple[float, float, np.ndarray]:
"""
Perform cv-fold crossvalidation or leave-one(-group)-out validation if groups == True
:param records: training records to perform crossvalidation on.
:param scoring: String identifying scoring function of crossvalidation, or Callable.
If a callable is passed, it must take two parameters `y_true` and `y_pred`
(iterables of true and predicted class labels, respectively) and return a
(numeric) score.
:param cv: Number of folds in crossvalidation. Default: 5
:param n_jobs: Number of parallel jobs. Default: -1 (All processors used)
:param n_replicates: Number of replicates of the crossvalidation
:param groups: If True, use group information stored in records for splitting. Otherwise,
stratify split according to labels in records. This also resets n_replicates to 1.
:param reduce_features: toggles feature reduction using recursive feature elimination
:param n_features: minimum number of features to retain when reducing features
:param demote: toggles logger that is used. if true, msg is written to debug else info
:param kwargs: Unused
:return: A list of mean score, score SD, and the percentage of misclassifications per sample
"""
if n_jobs != 1 and self.n_jobs > 1:
self.logger.info(
f'Will use selected classifier parallelism instead of multithreading.'
)
n_jobs = self.n_jobs
if hasattr(scoring, '__call__'):
scoring_func = scoring
else:
scoring_func = self.scoring_function_mapping.get(scoring)
assert scoring_func is not None, f'invalid or missing scoring function: {scoring}.'
log_function = self.logger.debug if demote else self.logger.info
t1 = time()
X, y, tn, ft = get_x_y_tn_ft(records)
# unfortunately RFECV does not work with pipelines (need to use the vectorizer separately)
self.cv_pipeline.fit(X, y)
vec = self.cv_pipeline.named_steps["vec"]
clf = self.cv_pipeline.named_steps["clf"]
if not vec.vocabulary:
vec.fit(X)
X_trans = vec.transform(X)
misclassifications = np.zeros(len(y))
scores = []
if groups:
log_function(
"Begin Leave-One-Group-Out validation on training data.")
splitting_strategy = LeaveOneGroupOut()
group_ids = get_groups(records)
n_replicates = 1
else:
log_function("Begin cross-validation on training data.")
splitting_strategy = StratifiedKFold(
n_splits=cv, shuffle=True, random_state=self.random_state)
group_ids = None
for i in range(n_replicates):
inner_cv = StratifiedKFold(n_splits=cv,
shuffle=True,
random_state=self.random_state)
outer_cv = splitting_strategy
for tr, ts in outer_cv.split(X_trans, y, groups=group_ids):
if reduce_features:
est = RFECV(estimator=clf,
cv=inner_cv,
n_jobs=n_jobs,
step=DEFAULT_STEP_SIZE,
min_features_to_select=n_features,
scoring=DEFAULT_SCORING_FUNCTION)
else:
est = clf
est.fit(X_trans[tr], y[tr])
y_pred = est.predict(X_trans[ts])
mismatch = np.logical_xor(y[ts], y_pred)
mismatch_indices = ts[np.where(mismatch)]
misclassifications[mismatch_indices] += 1
score = scoring_func(y[ts], y_pred)
scores.append(score)
log_function(f"Finished replicate {i + 1} of {n_replicates}")
misclassifications /= n_replicates
score_mean, score_sd = float(np.mean(scores)), float(np.std(scores))
t2 = time()
log_function(f"Cross-validation completed.")
log_function(
f"Total duration of cross-validation: {np.round(t2 - t1, 2)} seconds."
)
return score_mean, score_sd, misclassifications
def parameter_search(self,
records: List[TrainingRecord],
search_params: Dict[str, List] = None,
cv: int = 5,
scoring: str = DEFAULT_SCORING_FUNCTION,
n_jobs: int = -1,
n_iter: int = 10,
return_optimized: bool = False):
"""
Perform stratified, randomized parameter search. If desired, return a new class instance
with optimized training parameters.
:param records: training records to perform crossvalidation on.
:param search_params: A dictionary of iterables of possible model training parameters.
If None, use default search parameters for the given classifier.
:param scoring: Scoring function of crossvalidation. Default: Balanced Accuracy.
:param cv: Number of folds in crossvalidation. Default: 5
:param n_jobs: Number of parallel jobs. Default: -1 (All processors used)
:param n_iter: Number of grid points to evaluate. Default: 10
:param return_optimized: Whether to return a ready-made classifier
with the optimized params instead of a dictionary of params.
:return: A dictionary containing best found parameters or an optimized class instance.
"""
if n_jobs != 1 and self.n_jobs > 1:
self.logger.info(
f'Will use selected classifier parallelism instead of multithreading.'
)
n_jobs = self.n_jobs
t1 = time()
self.logger.info(f'Performing randomized parameter search.')
X, y, tn, ft = get_x_y_tn_ft(records)
if search_params is None:
search_params = self.default_search_params
vec = clone(self.cv_pipeline.named_steps['vec'])
clf = clone(self.cv_pipeline.named_steps['clf'])
X_trans = vec.fit_transform(X)
cv = StratifiedKFold(n_splits=cv,
shuffle=True,
random_state=self.random_state)
rcv = RandomizedSearchCV(estimator=clf,
scoring=scoring,
param_distributions=search_params,
n_jobs=n_jobs,
n_iter=n_iter,
cv=cv,
iid=False,
verbose=1 if self.verb else 0)
rcv.fit(X_trans, y=y)
best_params = rcv.best_params_
t2 = time()
gc.collect(
) # essential due to imperfect memory management of XGBoost sklearn interface
self.logger.info(f'Optimized params:\n{pformat(best_params)}')
self.logger.info(
f'{np.round(t2 - t1)} sec elapsed during parameter search.')
if return_optimized:
self.logger.info(
f'Returning optimized instance of {self.__class__.__name__}.')
return self.get_instance(**best_params,
random_state=self.random_state_init,
verb=self.verb)
return best_params
def recursive_feature_elimination(records: List[TrainingRecord],
pipeline: Pipeline,
step: float = DEFAULT_STEP_SIZE,
n_features: int = None,
random_state: np.random.RandomState = None):
"""
Function to apply RFE to limit the vocabulary used by the CustomVectorizer, optional step.
:param records: list of TrainingRecords, entire training set.
:param pipeline: the pipeline which vocabulary should be modified
:param step: rate of features to eliminate at each step. the lower the number, the more steps
:param n_features: number of features to select (if None: half of the provided features)
:param random_state: random state for deterministic results
:return: number of features used
"""
t1 = time()
X, y, tn, ft = get_x_y_tn_ft(records)
vec = pipeline.named_steps["vec"]
estimator = pipeline.named_steps["clf"]
if not vec.vocabulary:
vec.fit(X)
previous_vocabulary = vec.vocabulary_
if not n_features:
n_features = len(previous_vocabulary) // 2
X_trans = vec.transform(X)
logger = get_logger(__name__, verb=True)
split = StratifiedKFold(shuffle=True,
n_splits=5,
random_state=random_state)
selector = RFECV(estimator,
step=step,
min_features_to_select=n_features,
cv=split,
n_jobs=5,
scoring=DEFAULT_SCORING_FUNCTION)
selector = selector.fit(X=X_trans, y=y)
original_size = len(previous_vocabulary)
support = selector.get_support()
support = support.nonzero()[0]
new_id = {support[x]: x for x in range(len(support))}
vocabulary = {
feature: new_id[i]
for feature, i in previous_vocabulary.items()
if new_id.get(i) is not None
}
size_after = selector.n_features_
t2 = time()
logger.info(
f"{size_after}/{original_size} features selected using Recursive Feature Eliminiation."
f" in {np.round(t2 - t1, 2)} seconds.")
# set vocabulary to vectorizer
pipeline.named_steps["vec"].vocabulary = vocabulary
pipeline.named_steps["vec"].vocabulary_ = vocabulary
pipeline.named_steps["vec"].fixed_vocabulary_ = True
return size_after
