def main(inputs,
infile_estimator,
outfile_predict,
infile_weights=None,
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
infile_weights : str
File path to weights input
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
with open(infile_estimator, 'rb') as est_handler:
estimator = load_model(est_handler)
main_est = estimator
if isinstance(estimator, Pipeline):
main_est = estimator.steps[-1][-1]
if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'):
if not infile_weights or infile_weights == 'None':
raise ValueError("The selected model skeleton asks for weights, "
"but dataset for weights wan not selected!")
main_est.load_weights(infile_weights)
# 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, 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 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)
# 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
# handle memory
memory = joblib.Memory(location=CACHE_DIR, verbose=0)
# cache iraps_core fits could increase search speed significantly
if estimator.__class__.__name__ == 'IRAPSClassifier':
estimator.set_params(memory=memory)
else:
# For iraps buried in pipeline
new_params = {}
for p, v in estimator_params.items():
if p.endswith('memory'):
# for case of `__irapsclassifier__memory`
if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
# cache iraps_core fits could increase search
# speed significantly
new_params[p] = memory
# security reason, we don't want memory being
# modified unexpectedly
elif v:
new_params[p] = None
# handle n_jobs
elif p.endswith('n_jobs'):
# For now, 1 CPU is suggested for iprasclassifier
if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
new_params[p] = 1
else:
new_params[p] = N_JOBS
# for security reason, types of callback are limited
elif p.endswith('callbacks'):
for cb in v:
cb_type = cb['callback_selection']['callback_type']
if cb_type not in ALLOWED_CALLBACKS:
raise ValueError(
"Prohibited callback type: %s!" % cb_type)
estimator.set_params(**new_params)
# 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, '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'):
scores = estimator.evaluate(X_test, y_test=y_test,
scorer=scorer,
is_multimetric=True)
else:
scores = _score(estimator, X_test, y_test, scorer,
is_multimetric=True)
# 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.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_
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,
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 _handle_X_y(estimator,
params,
infile1,
infile2,
loaded_df={},
ref_seq=None,
intervals=None,
targets=None,
fasta_path=None):
"""read inputs
Params
-------
estimator : estimator object
params : dict
Galaxy tool parameter inputs
infile1 : str
File path to dataset containing features
infile2 : str
File path to dataset containing target values
loaded_df : dict
Contains loaded DataFrame objects with file path as keys
ref_seq : str
File path to dataset containing genome sequence file
interval : 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
Returns
-------
estimator : estimator object after setting new attributes
X : numpy array
y : numpy array
"""
estimator_params = estimator.get_params()
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)
if df_key in loaded_df:
infile1 = loaded_df[df_key]
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
return estimator, X, y
def main(
inputs,
infile_estimator,
outfile_predict,
infile_weights=None,
infile1=None,
fasta_path=None,
ref_seq=None,
vcf_path=None,
):
"""
Parameter
---------
inputs : str
File path to galaxy tool parameter
infile_estimator : strgit
File path to trained estimator input
outfile_predict : str
File path to save the prediction results, tabular
infile_weights : str
File path to weights input
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
with open(infile_estimator, "rb") as est_handler:
estimator = load_model(est_handler)
main_est = estimator
if isinstance(estimator, Pipeline):
main_est = estimator.steps[-1][-1]
if hasattr(main_est, "config") and hasattr(main_est, "load_weights"):
if not infile_weights or infile_weights == "None":
raise ValueError("The selected model skeleton asks for weights, "
"but dataset for weights wan not selected!")
main_est.load_weights(infile_weights)
# 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,
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')
with open(inputs, 'r') as param_handler:
params = json.load(param_handler)
params_builder = params['search_schemes']['search_params_builder']
with open(infile_estimator, 'rb') as estimator_handler:
estimator = load_model(estimator_handler)
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
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)
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()
options['cv_selector']['groups_selector'] = groups
splitter, groups = get_cv(options.pop('cv_selector'))
options['cv'] = splitter
options['n_jobs'] = N_JOBS
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
# del loaded_df
del loaded_df
# handle memory
memory = joblib.Memory(location=CACHE_DIR, verbose=0)
# cache iraps_core fits could increase search speed significantly
if estimator.__class__.__name__ == 'IRAPSClassifier':
estimator.set_params(memory=memory)
else:
# For iraps buried in pipeline
for p, v in estimator_params.items():
if p.endswith('memory'):
# for case of `__irapsclassifier__memory`
if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
# cache iraps_core fits could increase search
# speed significantly
new_params = {p: memory}
estimator.set_params(**new_params)
# security reason, we don't want memory being
# modified unexpectedly
elif v:
new_params = {p, None}
estimator.set_params(**new_params)
# For now, 1 CPU is suggested for iprasclassifier
elif p.endswith('n_jobs'):
new_params = {p: 1}
estimator.set_params(**new_params)
# for security reason, types of callbacks are limited
elif p.endswith('callbacks'):
for cb in v:
cb_type = cb['callback_selection']['callback_type']
if cb_type not in ALLOWED_CALLBACKS:
raise ValueError(
"Prohibited callback type: %s!" % cb_type)
param_grid = _eval_search_params(params_builder)
searcher = optimizer(estimator, param_grid, **options)
# do nested split
split_mode = params['outer_split'].pop('split_mode')
# nested CV, outer cv using cross_validate
if split_mode == 'nested_cv':
outer_cv, _ = get_cv(params['outer_split']['cv_selector'])
if options['error_score'] == 'raise':
rval = cross_validate(
searcher, X, y, scoring=options['scoring'],
cv=outer_cv, n_jobs=N_JOBS, verbose=0,
error_score=options['error_score'])
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=0,
error_score=options['error_score'])
except ValueError:
pass
for warning in w:
print(repr(warning.message))
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)
else:
if split_mode == 'train_test_split':
train_test_split = try_get_attr(
'galaxy_ml.model_validations', 'train_test_split')
# 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)
split_options = params['outer_split']
# splits
if split_options['shuffle'] == 'stratified':
split_options['labels'] = y
X, X_test, y, y_test = train_test_split(X, y, **split_options)
elif split_options['shuffle'] == 'group':
if groups is None:
raise ValueError("No group based CV option was "
"choosen for group shuffle!")
split_options['labels'] = groups
if y is None:
X, X_test, groups, _ =\
train_test_split(X, groups, **split_options)
else:
X, X_test, y, y_test, groups, _ =\
train_test_split(X, y, groups, **split_options)
else:
if split_options['shuffle'] == 'None':
split_options['shuffle'] = None
X, X_test, y, y_test =\
train_test_split(X, y, **split_options)
# end train_test_split
# shared by both train_test_split and non-split
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))
# no outer split
if split_mode == 'no':
# save results
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)
# train_test_split, output test result using best_estimator_
# or rebuild the trained estimator using weights if applicable.
else:
scorer_ = searcher.scorer_
if isinstance(scorer_, collections.Mapping):
is_multimetric = True
else:
is_multimetric = False
best_estimator_ = getattr(searcher, 'best_estimator_', None)
if not best_estimator_:
raise ValueError("GridSearchCV object has no "
"`best_estimator_` when `refit`=False!")
if best_estimator_.__class__.__name__ == 'KerasGBatchClassifier' \
and hasattr(estimator.data_batch_generator, 'target_path'):
test_score = best_estimator_.evaluate(
X_test, scorer=scorer_, is_multimetric=is_multimetric)
else:
test_score = _score(best_estimator_, X_test,
y_test, scorer_,
is_multimetric=is_multimetric)
if not is_multimetric:
test_score = {primary_scoring: test_score}
for key, value in test_score.items():
test_score[key] = [value]
result_df = pd.DataFrame(test_score)
result_df.to_csv(path_or_buf=outfile_result, sep='\t',
header=True, index=False)
memory.clear(warn=False)
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
main_est = best_estimator_
if isinstance(best_estimator_, pipeline.Pipeline):
main_est = best_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_
del main_est.validation_data
if getattr(main_est, 'data_generator_', None):
del main_est.data_generator_
del main_est.data_batch_generator
with open(outfile_object, 'wb') as output_handler:
pickle.dump(best_estimator_, output_handler,
pickle.HIGHEST_PROTOCOL)
def main(inputs,
infile_estimator,
outfile_predict,
infile_weights=None,
infile1=None,
fasta_path=None,
ref_seq=None,
vcf_path=None):
"""
Parameter
---------
inputs : str
File path to galaxy tool parameter
infile_estimator : strgit
File path to trained estimator input
outfile_predict : str
File path to save the prediction results, tabular
infile_weights : str
File path to weights input
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
with open(infile_estimator, 'rb') as est_handler:
estimator = load_model(est_handler)
main_est = estimator
if isinstance(estimator, Pipeline):
main_est = estimator.steps[-1][-1]
if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'):
if not infile_weights or infile_weights == 'None':
raise ValueError("The selected model skeleton asks for weights, "
"but dataset for weights wan not selected!")
main_est.load_weights(infile_weights)
# 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.fit()
preds = estimator.model_.predict_generator(
pred_data_generator.flow(batch_size=32),
workers=N_JOBS,
use_multiprocessing=True)
if preds.min() < 0. or preds.max() > 1.:
warnings.warn('Network returning invalid probability values. '
'The last layer might not normalize predictions '
'into probabilities '
'(like softmax or sigmoid would).')
if params['method'] == 'predict_proba' and preds.shape[1] == 1:
# first column is probability of class 0 and second is of class 1
preds = np.hstack([1 - preds, preds])
elif params['method'] == 'predict':
if preds.shape[-1] > 1:
# if the last activation is `softmax`, the sum of all
# probibilities will 1, the classification is considered as
# multi-class problem, otherwise, we take it as multi-label.
act = getattr(estimator.model_.layers[-1], 'activation', None)
if act and act.__name__ == 'softmax':
classes = preds.argmax(axis=-1)
else:
preds = (preds > 0.5).astype('int32')
else:
classes = (preds > 0.5).astype('int32')
preds = estimator.classes_[classes]
# end input
# output
if input_type == 'variant_effect': # TODO: save in batchs
rval = pd.DataFrame(preds)
meta = pd.DataFrame(
pred_data_generator.variants,
columns=['chrom', 'pos', 'name', 'ref', 'alt', 'strand'])
rval = pd.concat([meta, rval], axis=1)
elif 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)
