def main(inputs, infile_estimator, outfile_eval, infile1=None, infile2=None):
"""
Parameter
---------
inputs : str
File path to galaxy tool parameter
infile_estimator : strgit
File path to trained estimator input
outfile_eval : str
File path to save the evalulation results, tabular
infile1 : str
File path to dataset containing features
infile2 : str
File path to dataset containing target values
"""
warnings.filterwarnings('ignore')
with open(inputs, 'r') as param_handler:
params = json.load(param_handler)
X_test, y_test = _get_X_y(params, infile1, infile2)
# load model
estimator = load_model_from_h5(infile_estimator)
estimator = clean_params(estimator)
# handle scorer, convert to scorer dict
scoring = params['scoring']
scorer = get_scoring(scoring)
if not isinstance(scorer, (dict, list)):
scorer = [scoring['primary_scoring']]
scorer = _check_multimetric_scoring(estimator, scoring=scorer)
if hasattr(estimator, 'evaluate'):
scores = estimator.evaluate(X_test, y_test=y_test, scorer=scorer)
else:
scores = _score(estimator, X_test, y_test, scorer)
# handle output
for name, score in scores.items():
scores[name] = [score]
df = pd.DataFrame(scores)
df = df[sorted(df.columns)]
df.to_csv(path_or_buf=outfile_eval, sep='\t', header=True, index=False)
def main(inputs,
infile_estimator,
infile1,
infile2,
outfile_result,
outfile_object=None,
outfile_weights=None,
outfile_y_true=None,
outfile_y_preds=None,
groups=None,
ref_seq=None,
intervals=None,
targets=None,
fasta_path=None):
"""
Parameter
---------
inputs : str
File path to galaxy tool parameter
infile_estimator : str
File path to estimator
infile1 : str
File path to dataset containing features
infile2 : str
File path to dataset containing target values
outfile_result : str
File path to save the results, either cv_results or test result
outfile_object : str, optional
File path to save searchCV object
outfile_weights : str, optional
File path to save deep learning model weights
outfile_y_true : str, optional
File path to target values for prediction
outfile_y_preds : str, optional
File path to save deep learning model weights
groups : str
File path to dataset containing groups labels
ref_seq : str
File path to dataset containing genome sequence file
intervals : str
File path to dataset containing interval file
targets : str
File path to dataset compressed target bed file
fasta_path : str
File path to dataset containing fasta file
"""
warnings.simplefilter('ignore')
with open(inputs, 'r') as param_handler:
params = json.load(param_handler)
# load estimator
with open(infile_estimator, 'rb') as estimator_handler:
estimator = load_model(estimator_handler)
estimator = clean_params(estimator)
# swap hyperparameter
swapping = params['experiment_schemes']['hyperparams_swapping']
swap_params = _eval_swap_params(swapping)
estimator.set_params(**swap_params)
estimator_params = estimator.get_params()
# store read dataframe object
loaded_df = {}
input_type = params['input_options']['selected_input']
# tabular input
if input_type == 'tabular':
header = 'infer' if params['input_options']['header1'] else None
column_option = (params['input_options']['column_selector_options_1']
['selected_column_selector_option'])
if column_option in [
'by_index_number', 'all_but_by_index_number', 'by_header_name',
'all_but_by_header_name'
]:
c = params['input_options']['column_selector_options_1']['col1']
else:
c = None
df_key = infile1 + repr(header)
df = pd.read_csv(infile1, sep='\t', header=header, parse_dates=True)
loaded_df[df_key] = df
X = read_columns(df, c=c, c_option=column_option).astype(float)
# sparse input
elif input_type == 'sparse':
X = mmread(open(infile1, 'r'))
# fasta_file input
elif input_type == 'seq_fasta':
pyfaidx = get_module('pyfaidx')
sequences = pyfaidx.Fasta(fasta_path)
n_seqs = len(sequences.keys())
X = np.arange(n_seqs)[:, np.newaxis]
for param in estimator_params.keys():
if param.endswith('fasta_path'):
estimator.set_params(**{param: fasta_path})
break
else:
raise ValueError(
"The selected estimator doesn't support "
"fasta file input! Please consider using "
"KerasGBatchClassifier with "
"FastaDNABatchGenerator/FastaProteinBatchGenerator "
"or having GenomeOneHotEncoder/ProteinOneHotEncoder "
"in pipeline!")
elif input_type == 'refseq_and_interval':
path_params = {
'data_batch_generator__ref_genome_path': ref_seq,
'data_batch_generator__intervals_path': intervals,
'data_batch_generator__target_path': targets
}
estimator.set_params(**path_params)
n_intervals = sum(1 for line in open(intervals))
X = np.arange(n_intervals)[:, np.newaxis]
# Get target y
header = 'infer' if params['input_options']['header2'] else None
column_option = (params['input_options']['column_selector_options_2']
['selected_column_selector_option2'])
if column_option in [
'by_index_number', 'all_but_by_index_number', 'by_header_name',
'all_but_by_header_name'
]:
c = params['input_options']['column_selector_options_2']['col2']
else:
c = None
df_key = infile2 + repr(header)
if df_key in loaded_df:
infile2 = loaded_df[df_key]
else:
infile2 = pd.read_csv(infile2,
sep='\t',
header=header,
parse_dates=True)
loaded_df[df_key] = infile2
y = read_columns(infile2,
c=c,
c_option=column_option,
sep='\t',
header=header,
parse_dates=True)
if len(y.shape) == 2 and y.shape[1] == 1:
y = y.ravel()
if input_type == 'refseq_and_interval':
estimator.set_params(data_batch_generator__features=y.ravel().tolist())
y = None
# end y
# load groups
if groups:
groups_selector = (params['experiment_schemes']['test_split']
['split_algos']).pop('groups_selector')
header = 'infer' if groups_selector['header_g'] else None
column_option = \
(groups_selector['column_selector_options_g']
['selected_column_selector_option_g'])
if column_option in [
'by_index_number', 'all_but_by_index_number', 'by_header_name',
'all_but_by_header_name'
]:
c = groups_selector['column_selector_options_g']['col_g']
else:
c = None
df_key = groups + repr(header)
if df_key in loaded_df:
groups = loaded_df[df_key]
groups = read_columns(groups,
c=c,
c_option=column_option,
sep='\t',
header=header,
parse_dates=True)
groups = groups.ravel()
# del loaded_df
del loaded_df
# cache iraps_core fits could increase search speed significantly
memory = joblib.Memory(location=CACHE_DIR, verbose=0)
main_est = get_main_estimator(estimator)
if main_est.__class__.__name__ == 'IRAPSClassifier':
main_est.set_params(memory=memory)
# handle scorer, convert to scorer dict
scoring = params['experiment_schemes']['metrics']['scoring']
scorer = get_scoring(scoring)
scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
# handle test (first) split
test_split_options = (
params['experiment_schemes']['test_split']['split_algos'])
if test_split_options['shuffle'] == 'group':
test_split_options['labels'] = groups
if test_split_options['shuffle'] == 'stratified':
if y is not None:
test_split_options['labels'] = y
else:
raise ValueError("Stratified shuffle split is not "
"applicable on empty target values!")
X_train, X_test, y_train, y_test, groups_train, groups_test = \
train_test_split_none(X, y, groups, **test_split_options)
exp_scheme = params['experiment_schemes']['selected_exp_scheme']
# handle validation (second) split
if exp_scheme == 'train_val_test':
val_split_options = (
params['experiment_schemes']['val_split']['split_algos'])
if val_split_options['shuffle'] == 'group':
val_split_options['labels'] = groups_train
if val_split_options['shuffle'] == 'stratified':
if y_train is not None:
val_split_options['labels'] = y_train
else:
raise ValueError("Stratified shuffle split is not "
"applicable on empty target values!")
X_train, X_val, y_train, y_val, groups_train, groups_val = \
train_test_split_none(X_train, y_train, groups_train,
**val_split_options)
# train and eval
if hasattr(estimator, 'config') and hasattr(estimator, 'model_type'):
if exp_scheme == 'train_val_test':
estimator.fit(X_train, y_train, validation_data=(X_val, y_val))
else:
estimator.fit(X_train, y_train, validation_data=(X_test, y_test))
else:
estimator.fit(X_train, y_train)
if isinstance(estimator, KerasGBatchClassifier):
scores = {}
steps = estimator.prediction_steps
batch_size = estimator.batch_size
data_generator = estimator.data_generator_
scores, predictions, y_true = _evaluate_keras_and_sklearn_scores(
estimator,
data_generator,
X_test,
y=y_test,
sk_scoring=sk_scoring,
steps=steps,
batch_size=batch_size,
return_predictions=bool(outfile_y_true))
else:
scores = {}
if hasattr(estimator, 'model_') \
and hasattr(estimator.model_, 'metrics_names'):
batch_size = estimator.batch_size
score_results = estimator.model_.evaluate(X_test,
y=y_test,
batch_size=batch_size,
verbose=0)
metrics_names = estimator.model_.metrics_names
if not isinstance(metrics_names, list):
scores[metrics_names] = score_results
else:
scores = dict(zip(metrics_names, score_results))
if hasattr(estimator, 'predict_proba'):
predictions = estimator.predict_proba(X_test)
else:
predictions = estimator.predict(X_test)
y_true = y_test
sk_scores = _score(estimator,
X_test,
y_test,
scorer,
is_multimetric=True)
scores.update(sk_scores)
# handle output
if outfile_y_true:
try:
pd.DataFrame(y_true).to_csv(outfile_y_true, sep='\t', index=False)
pd.DataFrame(predictions).astype(np.float32).to_csv(
outfile_y_preds,
sep='\t',
index=False,
float_format='%g',
chunksize=10000)
except Exception as e:
print("Error in saving predictions: %s" % e)
# handle output
for name, score in scores.items():
scores[name] = [score]
df = pd.DataFrame(scores)
df = df[sorted(df.columns)]
df.to_csv(path_or_buf=outfile_result, sep='\t', header=True, index=False)
memory.clear(warn=False)
if outfile_object:
main_est = estimator
if isinstance(estimator, Pipeline):
main_est = estimator.steps[-1][-1]
if hasattr(main_est, 'model_') \
and hasattr(main_est, 'save_weights'):
if outfile_weights:
main_est.save_weights(outfile_weights)
del main_est.model_
del main_est.fit_params
del main_est.model_class_
main_est.callbacks = []
if getattr(main_est, 'data_generator_', None):
del main_est.data_generator_
with open(outfile_object, 'wb') as output_handler:
pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)
def main(inputs,
infile_estimator,
infile1,
infile2,
outfile_result,
outfile_object=None,
outfile_weights=None,
groups=None,
ref_seq=None,
intervals=None,
targets=None,
fasta_path=None):
"""
Parameter
---------
inputs : str
File path to galaxy tool parameter
infile_estimator : str
File path to estimator
infile1 : str
File path to dataset containing features
infile2 : str
File path to dataset containing target values
outfile_result : str
File path to save the results, either cv_results or test result
outfile_object : str, optional
File path to save searchCV object
outfile_weights : str, optional
File path to save model weights
groups : str
File path to dataset containing groups labels
ref_seq : str
File path to dataset containing genome sequence file
intervals : str
File path to dataset containing interval file
targets : str
File path to dataset compressed target bed file
fasta_path : str
File path to dataset containing fasta file
"""
warnings.simplefilter('ignore')
# store read dataframe object
loaded_df = {}
with open(inputs, 'r') as param_handler:
params = json.load(param_handler)
# Override the refit parameter
params['search_schemes']['options']['refit'] = True \
if params['save'] != 'nope' else False
with open(infile_estimator, 'rb') as estimator_handler:
estimator = load_model(estimator_handler)
optimizer = params['search_schemes']['selected_search_scheme']
optimizer = getattr(model_selection, optimizer)
# handle gridsearchcv options
options = params['search_schemes']['options']
if groups:
header = 'infer' if (
options['cv_selector']['groups_selector']['header_g']) else None
column_option = (
options['cv_selector']['groups_selector']
['column_selector_options_g']['selected_column_selector_option_g'])
if column_option in [
'by_index_number', 'all_but_by_index_number', 'by_header_name',
'all_but_by_header_name'
]:
c = (options['cv_selector']['groups_selector']
['column_selector_options_g']['col_g'])
else:
c = None
df_key = groups + repr(header)
groups = pd.read_csv(groups, sep='\t', header=header, parse_dates=True)
loaded_df[df_key] = groups
groups = read_columns(groups,
c=c,
c_option=column_option,
sep='\t',
header=header,
parse_dates=True)
groups = groups.ravel()
options['cv_selector']['groups_selector'] = groups
splitter, groups = get_cv(options.pop('cv_selector'))
options['cv'] = splitter
primary_scoring = options['scoring']['primary_scoring']
options['scoring'] = get_scoring(options['scoring'])
if options['error_score']:
options['error_score'] = 'raise'
else:
options['error_score'] = np.NaN
if options['refit'] and isinstance(options['scoring'], dict):
options['refit'] = primary_scoring
if 'pre_dispatch' in options and options['pre_dispatch'] == '':
options['pre_dispatch'] = None
params_builder = params['search_schemes']['search_params_builder']
param_grid = _eval_search_params(params_builder)
estimator = clean_params(estimator)
# save the SearchCV object without fit
if params['save'] == 'save_no_fit':
searcher = optimizer(estimator, param_grid, **options)
print(searcher)
with open(outfile_object, 'wb') as output_handler:
pickle.dump(searcher, output_handler, pickle.HIGHEST_PROTOCOL)
return 0
# read inputs and loads new attributes, like paths
estimator, X, y = _handle_X_y(estimator,
params,
infile1,
infile2,
loaded_df=loaded_df,
ref_seq=ref_seq,
intervals=intervals,
targets=targets,
fasta_path=fasta_path)
# cache iraps_core fits could increase search speed significantly
memory = joblib.Memory(location=CACHE_DIR, verbose=0)
main_est = get_main_estimator(estimator)
if main_est.__class__.__name__ == 'IRAPSClassifier':
main_est.set_params(memory=memory)
searcher = optimizer(estimator, param_grid, **options)
split_mode = params['outer_split'].pop('split_mode')
if split_mode == 'nested_cv':
# make sure refit is choosen
# this could be True for sklearn models, but not the case for
# deep learning models
if not options['refit'] and \
not all(hasattr(estimator, attr)
for attr in ('config', 'model_type')):
warnings.warn("Refit is change to `True` for nested validation!")
setattr(searcher, 'refit', True)
outer_cv, _ = get_cv(params['outer_split']['cv_selector'])
# nested CV, outer cv using cross_validate
if options['error_score'] == 'raise':
rval = cross_validate(
searcher,
X,
y,
scoring=options['scoring'],
cv=outer_cv,
n_jobs=N_JOBS,
verbose=options['verbose'],
return_estimator=(params['save'] == 'save_estimator'),
error_score=options['error_score'],
return_train_score=True)
else:
warnings.simplefilter('always', FitFailedWarning)
with warnings.catch_warnings(record=True) as w:
try:
rval = cross_validate(
searcher,
X,
y,
scoring=options['scoring'],
cv=outer_cv,
n_jobs=N_JOBS,
verbose=options['verbose'],
return_estimator=(params['save'] == 'save_estimator'),
error_score=options['error_score'],
return_train_score=True)
except ValueError:
pass
for warning in w:
print(repr(warning.message))
fitted_searchers = rval.pop('estimator', [])
if fitted_searchers:
import os
pwd = os.getcwd()
save_dir = os.path.join(pwd, 'cv_results_in_folds')
try:
os.mkdir(save_dir)
for idx, obj in enumerate(fitted_searchers):
target_name = 'cv_results_' + '_' + 'split%d' % idx
target_path = os.path.join(pwd, save_dir, target_name)
cv_results_ = getattr(obj, 'cv_results_', None)
if not cv_results_:
print("%s is not available" % target_name)
continue
cv_results_ = pd.DataFrame(cv_results_)
cv_results_ = cv_results_[sorted(cv_results_.columns)]
cv_results_.to_csv(target_path,
sep='\t',
header=True,
index=False)
except Exception as e:
print(e)
finally:
del os
keys = list(rval.keys())
for k in keys:
if k.startswith('test'):
rval['mean_' + k] = np.mean(rval[k])
rval['std_' + k] = np.std(rval[k])
if k.endswith('time'):
rval.pop(k)
rval = pd.DataFrame(rval)
rval = rval[sorted(rval.columns)]
rval.to_csv(path_or_buf=outfile_result,
sep='\t',
header=True,
index=False)
return 0
# deprecate train test split mode
"""searcher = _do_train_test_split_val(
searcher, X, y, params,
primary_scoring=primary_scoring,
error_score=options['error_score'],
groups=groups,
outfile=outfile_result)"""
# no outer split
else:
searcher.set_params(n_jobs=N_JOBS)
if options['error_score'] == 'raise':
searcher.fit(X, y, groups=groups)
else:
warnings.simplefilter('always', FitFailedWarning)
with warnings.catch_warnings(record=True) as w:
try:
searcher.fit(X, y, groups=groups)
except ValueError:
pass
for warning in w:
print(repr(warning.message))
cv_results = pd.DataFrame(searcher.cv_results_)
cv_results = cv_results[sorted(cv_results.columns)]
cv_results.to_csv(path_or_buf=outfile_result,
sep='\t',
header=True,
index=False)
memory.clear(warn=False)
# output best estimator, and weights if applicable
if outfile_object:
best_estimator_ = getattr(searcher, 'best_estimator_', None)
if not best_estimator_:
warnings.warn("GridSearchCV object has no attribute "
"'best_estimator_', because either it's "
"nested gridsearch or `refit` is False!")
return
# clean prams
best_estimator_ = clean_params(best_estimator_)
main_est = get_main_estimator(best_estimator_)
if hasattr(main_est, 'model_') \
and hasattr(main_est, 'save_weights'):
if outfile_weights:
main_est.save_weights(outfile_weights)
del main_est.model_
del main_est.fit_params
del main_est.model_class_
del main_est.validation_data
if getattr(main_est, 'data_generator_', None):
del main_est.data_generator_
with open(outfile_object, 'wb') as output_handler:
print("Best estimator is saved: %s " % repr(best_estimator_))
pickle.dump(best_estimator_, output_handler,
pickle.HIGHEST_PROTOCOL)
def main(
inputs,
infile_estimator,
infile1,
infile2,
outfile_result,
outfile_object=None,
outfile_weights=None,
outfile_y_true=None,
outfile_y_preds=None,
groups=None,
ref_seq=None,
intervals=None,
targets=None,
fasta_path=None,
):
"""
Parameter
---------
inputs : str
File path to galaxy tool parameter
infile_estimator : str
File path to estimator
infile1 : str
File path to dataset containing features
infile2 : str
File path to dataset containing target values
outfile_result : str
File path to save the results, either cv_results or test result
outfile_object : str, optional
File path to save searchCV object
outfile_weights : str, optional
File path to save deep learning model weights
outfile_y_true : str, optional
File path to target values for prediction
outfile_y_preds : str, optional
File path to save deep learning model weights
groups : str
File path to dataset containing groups labels
ref_seq : str
File path to dataset containing genome sequence file
intervals : str
File path to dataset containing interval file
targets : str
File path to dataset compressed target bed file
fasta_path : str
File path to dataset containing fasta file
"""
warnings.simplefilter("ignore")
with open(inputs, "r") as param_handler:
params = json.load(param_handler)
# load estimator
with open(infile_estimator, "rb") as estimator_handler:
estimator = load_model(estimator_handler)
estimator = clean_params(estimator)
# swap hyperparameter
swapping = params["experiment_schemes"]["hyperparams_swapping"]
swap_params = _eval_swap_params(swapping)
estimator.set_params(**swap_params)
estimator_params = estimator.get_params()
# store read dataframe object
loaded_df = {}
input_type = params["input_options"]["selected_input"]
# tabular input
if input_type == "tabular":
header = "infer" if params["input_options"]["header1"] else None
column_option = params["input_options"]["column_selector_options_1"][
"selected_column_selector_option"
]
if column_option in [
"by_index_number",
"all_but_by_index_number",
"by_header_name",
"all_but_by_header_name",
]:
c = params["input_options"]["column_selector_options_1"]["col1"]
else:
c = None
df_key = infile1 + repr(header)
df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
loaded_df[df_key] = df
X = read_columns(df, c=c, c_option=column_option).astype(float)
# sparse input
elif input_type == "sparse":
X = mmread(open(infile1, "r"))
# fasta_file input
elif input_type == "seq_fasta":
pyfaidx = get_module("pyfaidx")
sequences = pyfaidx.Fasta(fasta_path)
n_seqs = len(sequences.keys())
X = np.arange(n_seqs)[:, np.newaxis]
for param in estimator_params.keys():
if param.endswith("fasta_path"):
estimator.set_params(**{param: fasta_path})
break
else:
raise ValueError(
"The selected estimator doesn't support "
"fasta file input! Please consider using "
"KerasGBatchClassifier with "
"FastaDNABatchGenerator/FastaProteinBatchGenerator "
"or having GenomeOneHotEncoder/ProteinOneHotEncoder "
"in pipeline!"
)
elif input_type == "refseq_and_interval":
path_params = {
"data_batch_generator__ref_genome_path": ref_seq,
"data_batch_generator__intervals_path": intervals,
"data_batch_generator__target_path": targets,
}
estimator.set_params(**path_params)
n_intervals = sum(1 for line in open(intervals))
X = np.arange(n_intervals)[:, np.newaxis]
# Get target y
header = "infer" if params["input_options"]["header2"] else None
column_option = params["input_options"]["column_selector_options_2"][
"selected_column_selector_option2"
]
if column_option in [
"by_index_number",
"all_but_by_index_number",
"by_header_name",
"all_but_by_header_name",
]:
c = params["input_options"]["column_selector_options_2"]["col2"]
else:
c = None
df_key = infile2 + repr(header)
if df_key in loaded_df:
infile2 = loaded_df[df_key]
else:
infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
loaded_df[df_key] = infile2
y = read_columns(
infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True
)
if len(y.shape) == 2 and y.shape[1] == 1:
y = y.ravel()
if input_type == "refseq_and_interval":
estimator.set_params(data_batch_generator__features=y.ravel().tolist())
y = None
# end y
# load groups
if groups:
groups_selector = (
params["experiment_schemes"]["test_split"]["split_algos"]
).pop("groups_selector")
header = "infer" if groups_selector["header_g"] else None
column_option = groups_selector["column_selector_options_g"][
"selected_column_selector_option_g"
]
if column_option in [
"by_index_number",
"all_but_by_index_number",
"by_header_name",
"all_but_by_header_name",
]:
c = groups_selector["column_selector_options_g"]["col_g"]
else:
c = None
df_key = groups + repr(header)
if df_key in loaded_df:
groups = loaded_df[df_key]
groups = read_columns(
groups,
c=c,
c_option=column_option,
sep="\t",
header=header,
parse_dates=True,
)
groups = groups.ravel()
# del loaded_df
del loaded_df
# cache iraps_core fits could increase search speed significantly
memory = joblib.Memory(location=CACHE_DIR, verbose=0)
main_est = get_main_estimator(estimator)
if main_est.__class__.__name__ == "IRAPSClassifier":
main_est.set_params(memory=memory)
# handle scorer, convert to scorer dict
scoring = params["experiment_schemes"]["metrics"]["scoring"]
if scoring is not None:
# get_scoring() expects secondary_scoring to be a comma separated string (not a list)
# Check if secondary_scoring is specified
secondary_scoring = scoring.get("secondary_scoring", None)
if secondary_scoring is not None:
# If secondary_scoring is specified, convert the list into comman separated string
scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"])
scorer = get_scoring(scoring)
scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
# handle test (first) split
test_split_options = params["experiment_schemes"]["test_split"]["split_algos"]
if test_split_options["shuffle"] == "group":
test_split_options["labels"] = groups
if test_split_options["shuffle"] == "stratified":
if y is not None:
test_split_options["labels"] = y
else:
raise ValueError(
"Stratified shuffle split is not " "applicable on empty target values!"
)
(
X_train,
X_test,
y_train,
y_test,
groups_train,
_groups_test,
) = train_test_split_none(X, y, groups, **test_split_options)
exp_scheme = params["experiment_schemes"]["selected_exp_scheme"]
# handle validation (second) split
if exp_scheme == "train_val_test":
val_split_options = params["experiment_schemes"]["val_split"]["split_algos"]
if val_split_options["shuffle"] == "group":
val_split_options["labels"] = groups_train
if val_split_options["shuffle"] == "stratified":
if y_train is not None:
val_split_options["labels"] = y_train
else:
raise ValueError(
"Stratified shuffle split is not "
"applicable on empty target values!"
)
(
X_train,
X_val,
y_train,
y_val,
groups_train,
_groups_val,
) = train_test_split_none(X_train, y_train, groups_train, **val_split_options)
# train and eval
if hasattr(estimator, "validation_data"):
if exp_scheme == "train_val_test":
estimator.fit(X_train, y_train, validation_data=(X_val, y_val))
else:
estimator.fit(X_train, y_train, validation_data=(X_test, y_test))
else:
estimator.fit(X_train, y_train)
if hasattr(estimator, "evaluate"):
steps = estimator.prediction_steps
batch_size = estimator.batch_size
generator = estimator.data_generator_.flow(
X_test, y=y_test, batch_size=batch_size
)
predictions, y_true = _predict_generator(
estimator.model_, generator, steps=steps
)
scores = _evaluate(y_true, predictions, scorer, is_multimetric=True)
else:
if hasattr(estimator, "predict_proba"):
predictions = estimator.predict_proba(X_test)
else:
predictions = estimator.predict(X_test)
y_true = y_test
scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True)
if outfile_y_true:
try:
pd.DataFrame(y_true).to_csv(outfile_y_true, sep="\t", index=False)
pd.DataFrame(predictions).astype(np.float32).to_csv(
outfile_y_preds,
sep="\t",
index=False,
float_format="%g",
chunksize=10000,
)
except Exception as e:
print("Error in saving predictions: %s" % e)
# handle output
for name, score in scores.items():
scores[name] = [score]
df = pd.DataFrame(scores)
df = df[sorted(df.columns)]
df.to_csv(path_or_buf=outfile_result, sep="\t", header=True, index=False)
memory.clear(warn=False)
if outfile_object:
main_est = estimator
if isinstance(estimator, Pipeline):
main_est = estimator.steps[-1][-1]
if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"):
if outfile_weights:
main_est.save_weights(outfile_weights)
del main_est.model_
del main_est.fit_params
del main_est.model_class_
if getattr(main_est, "validation_data", None):
del main_est.validation_data
if getattr(main_est, "data_generator_", None):
del main_est.data_generator_
with open(outfile_object, "wb") as output_handler:
pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)
def main(
inputs,
infile_estimator,
infile1,
infile2,
outfile_result,
outfile_object=None,
outfile_weights=None,
groups=None,
ref_seq=None,
intervals=None,
targets=None,
fasta_path=None,
):
"""
Parameter
---------
inputs : str
File path to galaxy tool parameter
infile_estimator : str
File path to estimator
infile1 : str
File path to dataset containing features
infile2 : str
File path to dataset containing target values
outfile_result : str
File path to save the results, either cv_results or test result
outfile_object : str, optional
File path to save searchCV object
outfile_weights : str, optional
File path to save model weights
groups : str
File path to dataset containing groups labels
ref_seq : str
File path to dataset containing genome sequence file
intervals : str
File path to dataset containing interval file
targets : str
File path to dataset compressed target bed file
fasta_path : str
File path to dataset containing fasta file
"""
warnings.simplefilter("ignore")
# store read dataframe object
loaded_df = {}
with open(inputs, "r") as param_handler:
params = json.load(param_handler)
# Override the refit parameter
params["search_schemes"]["options"]["refit"] = (
True if params["save"] != "nope" else False
)
with open(infile_estimator, "rb") as estimator_handler:
estimator = load_model(estimator_handler)
optimizer = params["search_schemes"]["selected_search_scheme"]
optimizer = getattr(model_selection, optimizer)
# handle gridsearchcv options
options = params["search_schemes"]["options"]
if groups:
header = (
"infer" if (options["cv_selector"]["groups_selector"]["header_g"]) else None
)
column_option = options["cv_selector"]["groups_selector"][
"column_selector_options_g"
]["selected_column_selector_option_g"]
if column_option in [
"by_index_number",
"all_but_by_index_number",
"by_header_name",
"all_but_by_header_name",
]:
c = options["cv_selector"]["groups_selector"]["column_selector_options_g"][
"col_g"
]
else:
c = None
df_key = groups + repr(header)
groups = pd.read_csv(groups, sep="\t", header=header, parse_dates=True)
loaded_df[df_key] = groups
groups = read_columns(
groups,
c=c,
c_option=column_option,
sep="\t",
header=header,
parse_dates=True,
)
groups = groups.ravel()
options["cv_selector"]["groups_selector"] = groups
splitter, groups = get_cv(options.pop("cv_selector"))
options["cv"] = splitter
primary_scoring = options["scoring"]["primary_scoring"]
# get_scoring() expects secondary_scoring to be a comma separated string (not a list)
# Check if secondary_scoring is specified
secondary_scoring = options["scoring"].get("secondary_scoring", None)
if secondary_scoring is not None:
# If secondary_scoring is specified, convert the list into comman separated string
options["scoring"]["secondary_scoring"] = ",".join(
options["scoring"]["secondary_scoring"]
)
options["scoring"] = get_scoring(options["scoring"])
if options["error_score"]:
options["error_score"] = "raise"
else:
options["error_score"] = np.nan
if options["refit"] and isinstance(options["scoring"], dict):
options["refit"] = primary_scoring
if "pre_dispatch" in options and options["pre_dispatch"] == "":
options["pre_dispatch"] = None
params_builder = params["search_schemes"]["search_params_builder"]
param_grid = _eval_search_params(params_builder)
estimator = clean_params(estimator)
# save the SearchCV object without fit
if params["save"] == "save_no_fit":
searcher = optimizer(estimator, param_grid, **options)
print(searcher)
with open(outfile_object, "wb") as output_handler:
pickle.dump(searcher, output_handler, pickle.HIGHEST_PROTOCOL)
return 0
# read inputs and loads new attributes, like paths
estimator, X, y = _handle_X_y(
estimator,
params,
infile1,
infile2,
loaded_df=loaded_df,
ref_seq=ref_seq,
intervals=intervals,
targets=targets,
fasta_path=fasta_path,
)
# cache iraps_core fits could increase search speed significantly
memory = joblib.Memory(location=CACHE_DIR, verbose=0)
main_est = get_main_estimator(estimator)
if main_est.__class__.__name__ == "IRAPSClassifier":
main_est.set_params(memory=memory)
searcher = optimizer(estimator, param_grid, **options)
split_mode = params["outer_split"].pop("split_mode")
if split_mode == "nested_cv":
# make sure refit is choosen
# this could be True for sklearn models, but not the case for
# deep learning models
if not options["refit"] and not all(
hasattr(estimator, attr) for attr in ("config", "model_type")
):
warnings.warn("Refit is change to `True` for nested validation!")
setattr(searcher, "refit", True)
outer_cv, _ = get_cv(params["outer_split"]["cv_selector"])
# nested CV, outer cv using cross_validate
if options["error_score"] == "raise":
rval = cross_validate(
searcher,
X,
y,
scoring=options["scoring"],
cv=outer_cv,
n_jobs=N_JOBS,
verbose=options["verbose"],
return_estimator=(params["save"] == "save_estimator"),
error_score=options["error_score"],
return_train_score=True,
)
else:
warnings.simplefilter("always", FitFailedWarning)
with warnings.catch_warnings(record=True) as w:
try:
rval = cross_validate(
searcher,
X,
y,
scoring=options["scoring"],
cv=outer_cv,
n_jobs=N_JOBS,
verbose=options["verbose"],
return_estimator=(params["save"] == "save_estimator"),
error_score=options["error_score"],
return_train_score=True,
)
except ValueError:
pass
for warning in w:
print(repr(warning.message))
fitted_searchers = rval.pop("estimator", [])
if fitted_searchers:
import os
pwd = os.getcwd()
save_dir = os.path.join(pwd, "cv_results_in_folds")
try:
os.mkdir(save_dir)
for idx, obj in enumerate(fitted_searchers):
target_name = "cv_results_" + "_" + "split%d" % idx
target_path = os.path.join(pwd, save_dir, target_name)
cv_results_ = getattr(obj, "cv_results_", None)
if not cv_results_:
print("%s is not available" % target_name)
continue
cv_results_ = pd.DataFrame(cv_results_)
cv_results_ = cv_results_[sorted(cv_results_.columns)]
cv_results_.to_csv(target_path, sep="\t", header=True, index=False)
except Exception as e:
print(e)
finally:
del os
keys = list(rval.keys())
for k in keys:
if k.startswith("test"):
rval["mean_" + k] = np.mean(rval[k])
rval["std_" + k] = np.std(rval[k])
if k.endswith("time"):
rval.pop(k)
rval = pd.DataFrame(rval)
rval = rval[sorted(rval.columns)]
rval.to_csv(path_or_buf=outfile_result, sep="\t", header=True, index=False)
# deprecate train test split mode
"""searcher = _do_train_test_split_val(
searcher, X, y, params,
primary_scoring=primary_scoring,
error_score=options['error_score'],
groups=groups,
outfile=outfile_result)"""
return 0
# no outer split
else:
searcher.set_params(n_jobs=N_JOBS)
if options["error_score"] == "raise":
searcher.fit(X, y, groups=groups)
else:
warnings.simplefilter("always", FitFailedWarning)
with warnings.catch_warnings(record=True) as w:
try:
searcher.fit(X, y, groups=groups)
except ValueError:
pass
for warning in w:
print(repr(warning.message))
cv_results = pd.DataFrame(searcher.cv_results_)
cv_results = cv_results[sorted(cv_results.columns)]
cv_results.to_csv(
path_or_buf=outfile_result, sep="\t", header=True, index=False
)
memory.clear(warn=False)
# output best estimator, and weights if applicable
if outfile_object:
best_estimator_ = getattr(searcher, "best_estimator_", None)
if not best_estimator_:
warnings.warn(
"GridSearchCV object has no attribute "
"'best_estimator_', because either it's "
"nested gridsearch or `refit` is False!"
)
return
# clean prams
best_estimator_ = clean_params(best_estimator_)
main_est = get_main_estimator(best_estimator_)
if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"):
if outfile_weights:
main_est.save_weights(outfile_weights)
del main_est.model_
del main_est.fit_params
del main_est.model_class_
del main_est.validation_data
if getattr(main_est, "data_generator_", None):
del main_est.data_generator_
with open(outfile_object, "wb") as output_handler:
print("Best estimator is saved: %s " % repr(best_estimator_))
pickle.dump(best_estimator_, output_handler, pickle.HIGHEST_PROTOCOL)
def main(
inputs,
infile_estimator,
outfile_predict,
infile1=None,
fasta_path=None,
ref_seq=None,
vcf_path=None
):
"""
Parameter
---------
inputs : str
File path to galaxy tool parameter
infile_estimator : str
File path to trained estimator input
outfile_predict : str
File path to save the prediction results, tabular
infile1 : str
File path to dataset containing features
fasta_path : str
File path to dataset containing fasta file
ref_seq : str
File path to dataset containing the reference genome sequence.
vcf_path : str
File path to dataset containing variants info.
"""
warnings.filterwarnings('ignore')
with open(inputs, 'r') as param_handler:
params = json.load(param_handler)
# load model
estimator = load_model_from_h5(infile_estimator)
estimator = clean_params(estimator)
# handle data input
input_type = params['input_options']['selected_input']
# tabular input
if input_type == 'tabular':
header = 'infer' if params['input_options']['header1'] else None
column_option = (params['input_options']
['column_selector_options_1']
['selected_column_selector_option'])
if column_option in ['by_index_number', 'all_but_by_index_number',
'by_header_name', 'all_but_by_header_name']:
c = params['input_options']['column_selector_options_1']['col1']
else:
c = None
df = pd.read_csv(infile1, sep='\t', header=header, parse_dates=True)
X = read_columns(df, c=c, c_option=column_option).astype(float)
if params['method'] == 'predict':
preds = estimator.predict(X)
else:
preds = estimator.predict_proba(X)
# sparse input
elif input_type == 'sparse':
X = mmread(open(infile1, 'r'))
if params['method'] == 'predict':
preds = estimator.predict(X)
else:
preds = estimator.predict_proba(X)
# fasta input
elif input_type == 'seq_fasta':
if not hasattr(estimator, 'data_batch_generator'):
raise ValueError(
"To do prediction on sequences in fasta input, "
"the estimator must be a `KerasGBatchClassifier`"
"equipped with data_batch_generator!")
pyfaidx = get_module('pyfaidx')
sequences = pyfaidx.Fasta(fasta_path)
n_seqs = len(sequences.keys())
X = np.arange(n_seqs)[:, np.newaxis]
seq_length = estimator.data_batch_generator.seq_length
batch_size = getattr(estimator, 'batch_size', 32)
steps = (n_seqs + batch_size - 1) // batch_size
seq_type = params['input_options']['seq_type']
klass = try_get_attr(
'galaxy_ml.preprocessors', seq_type)
pred_data_generator = klass(
fasta_path, seq_length=seq_length)
if params['method'] == 'predict':
preds = estimator.predict(
X, data_generator=pred_data_generator, steps=steps)
else:
preds = estimator.predict_proba(
X, data_generator=pred_data_generator, steps=steps)
# vcf input
elif input_type == 'variant_effect':
klass = try_get_attr('galaxy_ml.preprocessors',
'GenomicVariantBatchGenerator')
options = params['input_options']
options.pop('selected_input')
if options['blacklist_regions'] == 'none':
options['blacklist_regions'] = None
pred_data_generator = klass(
ref_genome_path=ref_seq, vcf_path=vcf_path, **options)
pred_data_generator.set_processing_attrs()
variants = pred_data_generator.variants
# predict 1600 sample at once then write to file
gen_flow = pred_data_generator.flow(batch_size=1600)
file_writer = open(outfile_predict, 'w')
header_row = '\t'.join(['chrom', 'pos', 'name', 'ref',
'alt', 'strand'])
file_writer.write(header_row)
header_done = False
steps_done = 0
# TODO: multiple threading
try:
while steps_done < len(gen_flow):
index_array = next(gen_flow.index_generator)
batch_X = gen_flow._get_batches_of_transformed_samples(
index_array)
if params['method'] == 'predict':
batch_preds = estimator.predict(
batch_X,
# The presence of `pred_data_generator` below is to
# override model carrying data_generator if there
# is any.
data_generator=pred_data_generator)
else:
batch_preds = estimator.predict_proba(
batch_X,
# The presence of `pred_data_generator` below is to
# override model carrying data_generator if there
# is any.
data_generator=pred_data_generator)
if batch_preds.ndim == 1:
batch_preds = batch_preds[:, np.newaxis]
batch_meta = variants[index_array]
batch_out = np.column_stack([batch_meta, batch_preds])
if not header_done:
heads = np.arange(batch_preds.shape[-1]).astype(str)
heads_str = '\t'.join(heads)
file_writer.write("\t%s\n" % heads_str)
header_done = True
for row in batch_out:
row_str = '\t'.join(row)
file_writer.write("%s\n" % row_str)
steps_done += 1
finally:
file_writer.close()
# TODO: make api `pred_data_generator.close()`
pred_data_generator.close()
return 0
# end input
# output
if len(preds.shape) == 1:
rval = pd.DataFrame(preds, columns=['Predicted'])
else:
rval = pd.DataFrame(preds)
rval.to_csv(outfile_predict, sep='\t', header=True, index=False)
def main(inputs,
infile_estimator,
infile_images,
infile_dataframe,
outfile_result,
infile_weights=None,
outfile_object=None,
outfile_weights=None,
outfile_y_true=None,
outfile_y_preds=None,
groups=None):
"""
Parameter
---------
inputs : str
File path to galaxy tool parameter
infile_estimator : str
File path to estimator
infile_images : str
File path to datasets containing images
infile_dataframe : str
File path to tabular dataset containing image information
outfile_result : str
File path to save the results, either cv_results or test result
infile_weights : str
File path to input model weights, used in evaluation and
prediction mode.
outfile_object : str, optional
File path to save searchCV object
outfile_weights : str, optional
File path to save deep learning model weights
outfile_y_true : str, optional
File path to target values for prediction
outfile_y_preds : str, optional
File path to save deep learning model weights
groups : str
File path to dataset containing groups labels
"""
warnings.simplefilter('ignore')
with open(inputs, 'r') as param_handler:
params = json.load(param_handler)
# load estimator
with open(infile_estimator, 'rb') as estimator_handler:
estimator = load_model(estimator_handler)
estimator = clean_params(estimator)
if not isinstance(estimator, KerasGBatchClassifier):
raise ValueError(
"Only `galaxy_ml.keras_galaxy_models.KerasGBatchClassifier` "
"is supported!")
# read DataFrame for images
data_frame = pd.read_csv(infile_dataframe, sep='\t', header='infer')
kwargs = _handle_image_generator_params(params['input_options'],
data_frame)
# build data generator
image_generator = ImageDataFrameBatchGenerator(dataframe=data_frame,
directory=IMAGES_DIR,
**kwargs)
estimator.set_params(data_batch_generator=image_generator)
steps = estimator.prediction_steps
batch_size = estimator.batch_size
# Get X and y
X = np.arange(data_frame.shape[0])[:, np.newaxis]
if isinstance(kwargs['y_col'], list):
y = None
else:
y = data_frame[kwargs['y_col']].ravel()
# load groups
if groups:
groups_selector = (params['experiment_schemes']['test_split']
['split_algos']).pop('groups_selector')
header = 'infer' if groups_selector['header_g'] else None
column_option = \
(groups_selector['column_selector_options_g']
['selected_column_selector_option_g'])
if column_option in [
'by_index_number', 'all_but_by_index_number', 'by_header_name',
'all_but_by_header_name'
]:
c = groups_selector['column_selector_options_g']['col_g']
else:
c = None
if groups == infile_dataframe:
groups = data_frame
groups = read_columns(groups,
c=c,
c_option=column_option,
sep='\t',
header=header,
parse_dates=True)
groups = groups.ravel()
exp_scheme = params['experiment_schemes']['selected_exp_scheme']
# Model Predictions
if exp_scheme == 'model_predict':
estimator.load_weights(infile_weights)
steps = params['experiment_schemes']['pred_steps']
generator = image_generator.flow(X, y=None, batch_size=batch_size)
predictions = estimator.model_.predict_generator(generator,
steps=steps)
try:
pd.DataFrame(predictions).astype(np.float32).to_csv(
outfile_result,
sep='\t',
index=False,
float_format='%g',
chunksize=10000)
except Exception as e:
print("Error in saving predictions: %s" % e)
return 0
# Model Evaluation
if exp_scheme == 'model_eval':
estimator.load_weights(infile_weights)
# compile model
estimator.model_.compile(loss=estimator.loss,
optimizer=estimator._optimizer,
metrics=estimator.metrics)
steps = params['experiment_schemes']['eval_steps']
sk_scoring = params['experiment_schemes']['metrics']['scoring']
scores, predictions, y_true = _evaluate_keras_and_sklearn_scores(
estimator,
image_generator,
X,
sk_scoring=sk_scoring,
steps=steps,
batch_size=batch_size,
return_predictions=bool(outfile_y_true))
# for other two modes, train/val and train/val/test
else:
# swap hyperparameter
swapping = params['experiment_schemes']['hyperparams_swapping']
swap_params = _eval_swap_params(swapping)
estimator.set_params(**swap_params)
# handle test (first) split
test_split_options = (
params['experiment_schemes']['test_split']['split_algos'])
if test_split_options['shuffle'] == 'group':
test_split_options['labels'] = groups
if test_split_options['shuffle'] == 'stratified':
if y is not None:
test_split_options['labels'] = y
else:
raise ValueError("Stratified shuffle split is not "
"applicable on empty target values or "
"multiple output targets!")
X_train, X_test, y_train, y_test, groups_train, groups_test = \
train_test_split_none(X, y, groups, **test_split_options)
# handle validation (second) split
if exp_scheme == 'train_val_test':
val_split_options = (
params['experiment_schemes']['val_split']['split_algos'])
if val_split_options['shuffle'] == 'group':
val_split_options['labels'] = groups_train
if val_split_options['shuffle'] == 'stratified':
if y_train is not None:
val_split_options['labels'] = y_train
else:
raise ValueError("Stratified shuffle split is not "
"applicable on empty target values!")
X_train, X_val, y_train, y_val, groups_train, groups_val = \
train_test_split_none(X_train, y_train, groups_train,
**val_split_options)
# In image data generator, `y_val` must be None
# labels will be retrived in generator.
estimator.fit(X_train, y_train, validation_data=(X_val, ))
else:
estimator.fit(X_train, y_train, validation_data=(X_test, ))
data_generator = estimator.data_generator_
sk_scoring = params['experiment_schemes']['metrics']['scoring']
steps = estimator.prediction_steps
scores, predictions, y_true = _evaluate_keras_and_sklearn_scores(
estimator,
data_generator,
X_test,
sk_scoring=sk_scoring,
steps=steps,
batch_size=batch_size,
return_predictions=bool(outfile_y_true))
# handle output
if outfile_y_true:
try:
pd.DataFrame(y_true).to_csv(outfile_y_true, sep='\t', index=False)
pd.DataFrame(predictions).astype(np.float32).to_csv(
outfile_y_preds,
sep='\t',
index=False,
float_format='%g',
chunksize=10000)
except Exception as e:
print("Error in saving predictions: %s" % e)
# handle output
for name, score in scores.items():
scores[name] = [score]
df = pd.DataFrame(scores)
df = df[sorted(df.columns)]
df.to_csv(path_or_buf=outfile_result, sep='\t', header=True, index=False)
if outfile_object:
main_est = get_main_estimator(estimator)
if hasattr(main_est, 'model_') \
and hasattr(main_est, 'save_weights'):
if outfile_weights:
main_est.save_weights(outfile_weights)
del main_est.model_
del main_est.fit_params
del main_est.model_class_
main_est.data_batch_generator.dataframe = None
main_est.callbacks = []
if getattr(main_est, 'data_generator_', None):
del main_est.data_generator_
with open(outfile_object, 'wb') as output_handler:
pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)
def main(inputs,
infile_estimator,
infile1,
infile2,
outfile_result,
outfile_object=None,
groups=None,
ref_seq=None,
intervals=None,
targets=None,
fasta_path=None):
"""
Parameter
---------
inputs : str
File path to galaxy tool parameter.
infile_estimator : str
File path to estimator.
infile1 : str
File path to dataset containing features
infile2 : str
File path to dataset containing target values
outfile_result : str
File path to save the results, either cv_results or test result
outfile_object : str, optional
File path to save searchCV object
groups : str
File path to dataset containing groups labels
ref_seq : str
File path to dataset containing genome sequence file
intervals : str
File path to dataset containing interval file
targets : str
File path to dataset compressed target bed file
fasta_path : str
File path to dataset containing fasta file
"""
warnings.simplefilter('ignore')
# store read dataframe object
loaded_df = {}
with open(inputs, 'r') as param_handler:
params = json.load(param_handler)
# Override the refit parameter
params['search_schemes']['options']['refit'] = True \
if (params['save'] != 'nope' or
params['outer_split']['split_mode'] == 'nested_cv') else False
estimator = load_model_from_h5(infile_estimator)
estimator = clean_params(estimator)
if estimator.__class__.__name__ == 'KerasGBatchClassifier':
_fit_and_score = try_get_attr('galaxy_ml.model_validations',
'_fit_and_score')
setattr(_search, '_fit_and_score', _fit_and_score)
setattr(_validation, '_fit_and_score', _fit_and_score)
optimizer = params['search_schemes']['selected_search_scheme']
if optimizer == 'skopt.BayesSearchCV':
optimizer = BayesSearchCV
else:
optimizer = getattr(model_selection, optimizer)
# handle gridsearchcv options
options = params['search_schemes']['options']
if groups:
header = 'infer' if (
options['cv_selector']['groups_selector']['header_g']) else None
column_option = (
options['cv_selector']['groups_selector']
['column_selector_options_g']['selected_column_selector_option_g'])
if column_option in [
'by_index_number', 'all_but_by_index_number', 'by_header_name',
'all_but_by_header_name'
]:
c = (options['cv_selector']['groups_selector']
['column_selector_options_g']['col_g'])
else:
c = None
df_key = groups + repr(header)
groups = pd.read_csv(groups, sep='\t', header=header, parse_dates=True)
loaded_df[df_key] = groups
groups = read_columns(groups,
c=c,
c_option=column_option,
sep='\t',
header=header,
parse_dates=True)
groups = groups.ravel()
options['cv_selector']['groups_selector'] = groups
cv_selector = options.pop('cv_selector')
if Version(galaxy_ml_version) < Version('0.8.3'):
cv_selector.pop('n_stratification_bins', None)
splitter, groups = get_cv(cv_selector)
options['cv'] = splitter
primary_scoring = options['scoring']['primary_scoring']
options['scoring'] = get_scoring(options['scoring'])
# TODO make BayesSearchCV support multiple scoring
if optimizer == 'skopt.BayesSearchCV' and \
isinstance(options['scoring'], dict):
options['scoring'] = options['scoring'][primary_scoring]
warnings.warn("BayesSearchCV doesn't support multiple "
"scorings! Primary scoring is used.")
if options['error_score']:
options['error_score'] = 'raise'
else:
options['error_score'] = np.NaN
if options['refit'] and isinstance(options['scoring'], dict):
options['refit'] = primary_scoring
if 'pre_dispatch' in options and options['pre_dispatch'] == '':
options['pre_dispatch'] = None
params_builder = params['search_schemes']['search_params_builder']
param_grid = _eval_search_params(params_builder)
# save the SearchCV object without fit
if params['save'] == 'save_no_fit':
searcher = optimizer(estimator, param_grid, **options)
dump_model_to_h5(searcher, outfile_object)
return 0
# read inputs and loads new attributes, like paths
estimator, X, y = _handle_X_y(estimator,
params,
infile1,
infile2,
loaded_df=loaded_df,
ref_seq=ref_seq,
intervals=intervals,
targets=targets,
fasta_path=fasta_path)
# cache iraps_core fits could increase search speed significantly
memory = joblib.Memory(location=CACHE_DIR, verbose=0)
estimator = _set_memory(estimator, memory)
searcher = optimizer(estimator, param_grid, **options)
split_mode = params['outer_split'].pop('split_mode')
# Nested CV
if split_mode == 'nested_cv':
cv_selector = params['outer_split']['cv_selector']
if Version(galaxy_ml_version) < Version('0.8.3'):
cv_selector.pop('n_stratification_bins', None)
outer_cv, _ = get_cv(cv_selector)
# nested CV, outer cv using cross_validate
if options['error_score'] == 'raise':
rval = cross_validate(
searcher,
X,
y,
groups=groups,
scoring=options['scoring'],
cv=outer_cv,
n_jobs=N_JOBS,
verbose=options['verbose'],
fit_params={'groups': groups},
return_estimator=(params['save'] == 'save_estimator'),
error_score=options['error_score'],
return_train_score=True)
else:
warnings.simplefilter('always', FitFailedWarning)
with warnings.catch_warnings(record=True) as w:
try:
rval = cross_validate(
searcher,
X,
y,
groups=groups,
scoring=options['scoring'],
cv=outer_cv,
n_jobs=N_JOBS,
verbose=options['verbose'],
fit_params={'groups': groups},
return_estimator=(params['save'] == 'save_estimator'),
error_score=options['error_score'],
return_train_score=True)
except ValueError:
pass
for warning in w:
print(repr(warning.message))
fitted_searchers = rval.pop('estimator', [])
if fitted_searchers:
import os
pwd = os.getcwd()
save_dir = os.path.join(pwd, 'cv_results_in_folds')
try:
os.mkdir(save_dir)
for idx, obj in enumerate(fitted_searchers):
target_name = 'cv_results_' + '_' + 'split%d' % idx
target_path = os.path.join(pwd, save_dir, target_name)
cv_results_ = getattr(obj, 'cv_results_', None)
if not cv_results_:
print("%s is not available" % target_name)
continue
cv_results_ = pd.DataFrame(cv_results_)
cv_results_ = cv_results_[sorted(cv_results_.columns)]
cv_results_.to_csv(target_path,
sep='\t',
header=True,
index=False)
except Exception as e:
print(e)
finally:
del os
keys = list(rval.keys())
for k in keys:
if k.startswith('test'):
rval['mean_' + k] = np.mean(rval[k])
rval['std_' + k] = np.std(rval[k])
if k.endswith('time'):
rval.pop(k)
rval = pd.DataFrame(rval)
rval = rval[sorted(rval.columns)]
rval.to_csv(path_or_buf=outfile_result,
sep='\t',
header=True,
index=False)
return 0
# deprecate train test split mode
"""searcher = _do_train_test_split_val(
searcher, X, y, params,
primary_scoring=primary_scoring,
error_score=options['error_score'],
groups=groups,
outfile=outfile_result)"""
# no outer split
else:
searcher.set_params(n_jobs=N_JOBS)
if options['error_score'] == 'raise':
searcher.fit(X, y, groups=groups)
else:
warnings.simplefilter('always', FitFailedWarning)
with warnings.catch_warnings(record=True) as w:
try:
searcher.fit(X, y, groups=groups)
except ValueError:
pass
for warning in w:
print(repr(warning.message))
cv_results = pd.DataFrame(searcher.cv_results_)
cv_results = cv_results[sorted(cv_results.columns)]
cv_results.to_csv(path_or_buf=outfile_result,
sep='\t',
header=True,
index=False)
memory.clear(warn=False)
# output best estimator, and weights if applicable
if outfile_object:
best_estimator_ = getattr(searcher, 'best_estimator_', None)
if not best_estimator_:
warnings.warn("GridSearchCV object has no attribute "
"'best_estimator_', because either it's "
"nested gridsearch or `refit` is False!")
return
print("Saving best estimator: %s " % repr(best_estimator_))
dump_model_to_h5(best_estimator_, outfile_object)