class FastaLookupAnnotator:
def __init__(self):
self.log = Logger().logger
self.manager = CapiceManager()
self.fasta_loc = self.manager.reference_genome
self.fasta = None
self._load_fasta()
def _load_fasta(self):
self.log.info('Loading in Fasta file, this may take a moment.')
self.fasta = pysam.FastaFile(self.fasta_loc)
self.log.info('Succesfully loaded Fasta file at: {}'.format(
self.fasta_loc))
def get_reference_sequence(self, chromosome: str, start: int, end: int):
"""
Function to obtain a sequence from the reference Fasta file.
:param chromosome: string,
chromosome to get the reference sequence from.
:param start: Chromosomal position at what point the sequence
should be obtained.
:param end: Chromosomal position at what point the obtained sequence
should end.
:return: string, obtained reference sequence.
"""
try:
self.log.debug('Obtaining reference sequence for: '
'[Chromosome: {}], [start: {}], [stop: {}]'.format(
chromosome, start, end))
append_ns = False
if start < 0:
append_ns = abs(start)
start = 0
return_sequence = self.fasta.fetch(chromosome, start, end)
if append_ns:
return_sequence = '{}{}'.format('N' * append_ns,
return_sequence)
return return_sequence
except KeyError:
self.log.warning(
'Unable to obtain sequence for: '
'[Chromosome: {}], [start: {}], [stop: {}],'
'did you supply a reference with contigs 1-22 + x,y,mt?'.
format(chromosome, start, end))
return None
def close_connection(self):
"""
Function to tell pysam to close the connection to the Fasta file
"""
if self.fasta:
self.fasta.close()
class ManualAnnotator:
def __init__(self):
self.log = Logger().logger
self.vep_annotators = []
self.location = os.path.join(get_project_root_dir(),
'src',
'main',
'python',
'resources',
'annotaters',
'vep')
self._load_vep_annotators()
def _load_vep_annotators(self):
python_modules = load_modules(self.location)
self._check_n_modules(python_modules)
loaded_python_modules = importer(python_modules, path=self.location)
self._check_n_modules(loaded_python_modules)
for module in loaded_python_modules:
if 'name' in dir(module) and module.usable:
self.vep_annotators.append(module)
self._check_n_modules(self.vep_annotators)
def _check_n_modules(self, modules_list):
if len(modules_list) < 1:
error_message = 'Unable to locate VEP Processors at {}, ' \
'was the directory moved?'.format(self.location)
self.log.critical(error_message)
raise FileNotFoundError(error_message)
def process(self, dataset: pd.DataFrame):
for processor in self.vep_annotators:
if processor.name in dataset.columns and processor.usable:
dataset = processor.process(dataset)
if processor.drop:
dataset.drop(columns=processor.name, inplace=True)
else:
self.log.warning(
'Could not use processor {} on input dataset!'.format(
processor.name
)
)
return dataset
class Exporter:
"""
Class specifically export files and create unique filenames.
"""
def __init__(self, file_path):
self.log = Logger().logger
self.force = CapiceManager().force
self.now = CapiceManager().now
self.capice_filename = CapiceManager().output_filename
self.file_path = file_path
self.export_cols = [
Column.chr_pos_ref_alt.value, Column.GeneName.value,
Column.FeatureID.value, Column.Consequence.value,
Column.probabilities.value
]
def export_capice_prediction(self, datafile: pd.DataFrame):
"""
Function specific to export the dataset created for the prediction
pathway.
:param datafile: prediction pandas DataFrame
"""
filename = self._export_filename_ready(file_name=self.capice_filename,
check_extension=False)
datafile[self.export_cols].to_csv(filename,
sep='\t',
compression='gzip',
index=False)
self.log.info(
'Successfully exported CAPICE datafile to: {}'.format(filename))
def export_capice_training_dataset(self, datafile: pd.DataFrame, name: str,
feature: str):
"""
Function specific to export a (splitted) dataset comming from the
training pathway.
:param datafile: pandas DataFrame
:param name: Name of the export file
:param feature: Name of what is exported
"""
filename = self._export_filename_ready(file_name=name)
datafile.to_csv(filename, sep='\t', compression='gzip', index=False)
self.log.info('Exported {} with shape {} to: {}'.format(
feature, datafile.shape, filename))
def export_capice_model(self, model, model_type):
"""
Function specific to export a newly created CAPICE model
:param model: RandomizedSearchCV or XGBClassifier instance
:param model_type: either "XGBClassifier" or "RandomizedSearchCV"
"""
export_name = ""
if model_type == 'XGBClassifier':
export_name = 'xgb_classifier'
elif model_type == 'RandomizedSearchCV':
export_name = 'randomized_search_cv'
filename_model = '{}_{}'.format(export_name,
self.now.strftime("%H%M%S%f_%d%m%Y"))
filename = self._export_filename_ready(file_name=filename_model,
type_export='model')
with open(filename, 'wb') as model_dump:
pickle.dump(model, model_dump)
def _export_filename_ready(self,
file_name,
type_export='dataset',
check_extension=True):
"""
Function to build an unique filename in case that force is turned off.
:param file_name: Name of the to be created file
:param type_export: "prediction" for the prediction pathway,
"dataset" for the export of datasets or
"model" for the export of models.
:param check_extension: Boolean if the extension should be
checked before exporting.
:return: full export path
"""
path_and_filename = os.path.join(self.file_path, file_name)
types_export_and_extensions = {
'dataset': '.tsv.gz',
'model': '.pickle.dat'
}
if check_extension:
extension = types_export_and_extensions[type_export]
if not file_name.endswith(extension):
file_name = file_name + extension
full_path = os.path.join(self.file_path, file_name)
export_path = None
if not check_file_exists(full_path):
self.log.info(
'No file found at {}, save to create.'.format(full_path))
export_path = full_path
elif self.force and check_file_exists(full_path):
self.log.warning(
'Found existing file at {}, '
'removing file for overwriting.'.format(full_path))
os.remove(full_path)
export_path = full_path
else:
self.log.info('Found existing file at {}, '
'not able to overwrite. '
'Creating new filename.'.format(full_path))
filename, extension = get_filename_and_extension(full_path)
basedir = os.path.dirname(path_and_filename)
export_exists = True
extension_counter = 1
while export_exists:
attempted_file = os.path.join(
basedir,
filename + "_{}.".format(extension_counter) + extension)
if not check_file_exists(attempted_file):
self.log.info('Able to create {}'.format(attempted_file))
export_exists = False
export_path = attempted_file
extension_counter += 1
return export_path
class CapiceImputing:
"""
Class to dynamically load in all imputing files and identify the file
suitable for the run's use case.
"""
def __init__(self):
self.manager = CapiceManager()
self.vep_version = self.manager.vep_version
self.grch_build = self.manager.grch_build
self.log = Logger().logger
self.log.info('Imputer started.')
self.overrule = self.manager.overwrite_impute
self.modules = []
self.module = None
self._load_modules()
self._is_correct_datafile_present()
self._check_if_imputer_is_applied()
self.columns = []
self.annotation_columns_present = []
self.impute_values = {}
self.pre_dtypes = {}
self.dtypes = {}
def _load_modules(self):
"""
Method to dynamically load in all python files containing a class that
contains the properties
name and
_json_name.
If at the end of this function, the list of impute files is empty,
will throw the module not found error.
"""
self.log.info('Identifying imputing files.')
directory = os.path.join(get_project_root_dir(), 'src', 'main',
'python', 'resources', 'data_files',
'imputing')
usable_modules = load_modules(directory)
if len(usable_modules) < 1:
self._raise_no_module_found_error()
loaded_modules = importer(usable_modules=usable_modules,
path=directory)
for module in loaded_modules:
if "name" in dir(module) and "_json_name" in dir(module):
self.modules.append(module)
if len(self.modules) < 1:
self._raise_no_module_found_error()
self.log.info(
'Identified {} files available for usage in imputing.'.format(
len(self.modules)))
def _raise_no_module_found_error(self):
"""
Function to raise when no suitable impute files are found.
Put into a function since 2 other functions within this module will use
it.
"""
error_message = 'No usable python files are found ' \
'within the imputing directory!'
self.log.critical(error_message)
raise FileNotFoundError(error_message)
def _is_correct_datafile_present(self):
"""
Function to check the VEP version and GRCh build
(or --overwrite_impute_file)
match the impute file.
"""
for module in self.modules:
if self.overrule and module.name == self.overrule:
self.log.info(
'Overrule successful for: {} , located at: {}'.format(
self.overrule, inspect.getfile(module.__class__)))
self.module = module
break
else:
module_vep_version = module.supported_vep_version
module_grch_build = module.supported_grch_build
if module_vep_version == self.vep_version and \
module_grch_build == self.grch_build:
self.log.info('Impute data file successfully found: {} , '
'located at: {}'.format(
module.name,
inspect.getfile(module.__class__)))
self.module = module
break
def _check_if_imputer_is_applied(self):
# Checking if self.data_file is assigned
if self.module is None:
if self.overrule:
error_message = 'No imputing data file found for overrule: ' \
'{}'.format(self.overrule)
else:
error_message = 'No imputing data file found for ' \
'VEP version: {} and ' \
'GRCh build: {}'.format(self.vep_version,
self.grch_build
)
self.log.critical(error_message)
raise FileNotFoundError(error_message)
def _load_values(self, dataset: pd.DataFrame):
"""
Function to be called right when impute() is called,
gets the input datafile features,
imputes values from the impute file and
saves the datafile features to the manager.
"""
self.columns = self.module.annotation_features
for col in self.columns:
if col in dataset.columns:
self.annotation_columns_present.append(col)
self.manager.annotation_features = self.columns
self.impute_values = self.module.impute_values
def impute(self, datafile: pd.DataFrame):
"""
Function to call the CapiceImputing to start imputing.
:return: pandas DataFrame
"""
self._load_values(datafile)
datafile = self._check_chrom_pos(datafile)
self._get_nan_ratio_per_column(dataset=datafile)
self._get_full_nan_row(dataset=datafile)
datafile.dropna(how='all', subset=self.annotation_columns_present)
datafile = datafile[~datafile['CAPICE_drop_out']]
datafile.drop(columns=['CAPICE_drop_out'], inplace=True)
self._correct_dtypes(datafile=datafile)
datafile.fillna(self.impute_values, inplace=True)
datafile = datafile.astype(dtype=self.pre_dtypes, copy=False)
datafile = datafile.astype(dtype=self.dtypes, copy=False)
datafile = self._add_missing_columns(datafile)
self.log.info('Imputing successfully performed.')
return datafile
@deprecated
def _add_missing_columns(self, datafile: pd.DataFrame):
for key, value in self.impute_values.items():
if key not in datafile.columns:
datafile[key] = value
return datafile
def _correct_dtypes(self, datafile: pd.DataFrame):
"""
Function to correct the dtypes that originate from the lookup annotator
according to the dtypes specified within the data json.
"""
for key, item in self.impute_values.items():
if key in datafile.columns:
self._save_dtypes(key=key, item=item)
def _save_dtypes(self, key, item):
if isinstance(item, int):
self.pre_dtypes[key] = float
else:
self.pre_dtypes[key] = type(item)
self.dtypes[key] = type(item)
def _check_chrom_pos(self, dataset: pd.DataFrame):
"""
Function to check if all values of the columns Chr and Pos are present.
:param dataset: not imputed pandas DataFrame
:return: pandas DataFrame
containing no NaN or gaps for Chr and Pos columns.
"""
chrom_is_float = False
if dataset['Chr'].isnull().values.any():
if dataset.dtypes['Chr'] == np.float64:
chrom_is_float = True
n_delete = dataset['Chr'].isnull().values.sum()
self.log.warning('Detected NaN in the Chromosome column! '
'Deleting {} row(s).'.format(n_delete))
dataset = dataset[~dataset['Chr'].isnull()]
if dataset['Pos'].isnull().values.any():
n_delete = dataset['Pos'].isnull().values.sum()
self.log.warning('Detected NaN is the Position column! '
'Deleting {} row(s).'.format(n_delete))
dataset = dataset[~dataset['Pos'].isnull()]
dataset.index = range(0, dataset.shape[0])
if chrom_is_float:
dataset['Chr'] = dataset['Chr'].astype(int)
dataset['Chr'] = dataset['Chr'].astype(str)
dataset['Pos'] = dataset['Pos'].astype(int)
return dataset
def _get_nan_ratio_per_column(self, dataset: pd.DataFrame):
"""
Generic function to get the percentage of gaps per column
:param dataset: not imputed pandas DataFrame
"""
for column in dataset.columns:
series = dataset[column]
self._calculate_percentage_nan(column=series)
def _calculate_percentage_nan(self, column):
n_nan = column.isnull().sum()
if n_nan > 0:
n_samples = column.size
p_nan = round((n_nan / n_samples) * 100, ndigits=2)
self.log.debug(
'NaN detected in column {}, percentage: {}%.'.format(
column.name, p_nan))
def _get_full_nan_row(self, dataset: pd.DataFrame):
"""
Function to get the samples of which absolutely no prediction is
possible due to all non chr pos ref alt rows being gaps.
:param dataset: not imputed pandas DataFrame
"""
n_samples = dataset.shape[0]
dataset.index = range(1, n_samples + 1)
dataset['CAPICE_drop_out'] = dataset[
self.annotation_columns_present].isnull().values.all(axis=1)
samples_dropped_out = dataset[dataset['CAPICE_drop_out']]
if samples_dropped_out.shape[0] > 0:
self.log.warning(
'The following samples are filtered out due to missing values: '
'(indexing is python based, '
'so the index starts at 0). \n {}'.format(samples_dropped_out[[
'Chr', 'Pos', 'Ref', 'Alt', 'FeatureID'
]]))
else:
self.log.info(
'No samples are filtered out due to too many NaN values.')
class TemplateSetup(metaclass=ABCMeta):
"""
Abstract class to act as template for new models that might be
added in future patches of CAPICE.
Contains the necessary steps for preprocessing as well.
"""
def __init__(self, name, usable, vep_version, grch_build):
self.log = Logger().logger
self.property_checker = PropertyCheckerLogger()
self.name = name
self.usable = usable
self.supported_vep_version = vep_version
self.supported_grch_build = grch_build
self.annotation_features = CapiceManager().annotation_features
self.train = False
self.model = None
self.annotation_object = []
self.model_features = None
@property
def name(self):
"""
Property getter name, to get the init defined name of the model module.
:return: str
"""
return self._name
@name.setter
def name(self, value='Template'):
"""
Property setter name, to set a name for a model module.
Raises TypeError if not supplied with a string.
:param value: str
"""
self.property_checker.check_property(value=value, expected_type=str)
self._name = value
@property
def usable(self):
"""
Property getter usable, to get the boolean value of a model module
whenever it can be used for preprocessing and prediction.
:return: bool
"""
return self._usable
@usable.setter
def usable(self, value=False):
"""
Property setter usable, to set the boolean value of a model module
whenever it should be used for preprocessing and prediction.
Raises TypeError if not supplied with a boolean.
:param value: bool
"""
self.property_checker.check_property(value=value, expected_type=bool)
self._usable = value
@property
def supported_vep_version(self):
"""
Property getter supported_annotation_version,
to get the float annotation_version value of a model/prediction file
that is supported within the module.
:return: float or None
"""
return self._vep_version
@supported_vep_version.setter
def supported_vep_version(self, value):
"""
Property setter supported_annotation_version,
to set the float annotation_version value of a model/prediction file
that is supported within the module.
Raises TypeError if not supplied with a float or None.
:param value: float or None
"""
self.property_checker.check_property(
value=value,
expected_type=float,
include_none=True
)
self._vep_version = value
@property
def supported_grch_build(self):
"""
Property getter supported_grch_build,
to get the integer grch_build value that defines what genome build
is supported by the model/prediction module.
:return: integer or None
"""
return self._grch_build
@supported_grch_build.setter
def supported_grch_build(self, value):
"""
Property getter supported_grch_build,
to set the integer value grch_build that defines what genome build
is supported by the model/prediction module.
Raises TypeError if not supplied with an integer or None.
:param value: integer or None
"""
self.property_checker.check_property(
value=value,
expected_type=int,
include_none=True
)
self._grch_build = value
def preprocess(self, dataset: pd.DataFrame, is_train: bool):
"""
Callable function to start the preprocessing of a dataset.
:param dataset: imputed pandas DataFrame
:param is_train: boolean
:return: processed pandas DataFrame
"""
self.train = is_train
self._load_model()
if not self.train:
self._load_model_features()
dataset = self._duplicate_chr_pos_ref_alt(dataset=dataset)
self._get_categorical_columns(dataset=dataset)
processed_dataset = self._process_objects(dataset=dataset)
if not self.train:
processed_dataset = self._check_all_model_features_present(
processed_dataset
)
self.log.info('Successfully preprocessed data.')
return processed_dataset
@deprecated
def _check_all_model_features_present(self, dataset: pd.DataFrame):
for feature in self.model_features:
if feature not in dataset.columns:
dataset[feature] = 0
return dataset
def _get_categorical_columns(self, dataset: pd.DataFrame):
"""
Function to get the categorical columns that are within the supplied
annotation features of the imputing file.
:param dataset: pandas DataFrame
"""
for feature in dataset.select_dtypes(include=["O"]).columns:
if feature in self.annotation_features:
self.annotation_object.append(feature)
self.log.debug(
'Converting the categorical columns: {}.'.format(
", ".join(self.annotation_object)
)
)
@staticmethod
def _duplicate_chr_pos_ref_alt(dataset):
"""
Function to create the chr_pos_ref_alt column so that it doesn't get
lost in preprocessing.
:param dataset: unprocessed pandas DataFrame
:return: unprocessed pandas DataFrame
containing column 'chr_pos_ref_alt'
"""
dataset['chr_pos_ref_alt'] = dataset[
['Chr', 'Pos', 'Ref', 'Alt']].astype(str).agg('_'.join, axis=1)
return dataset
@property
def model_features(self):
return self._model_features
@model_features.setter
def model_features(self, value):
self._model_features = value
def _process_objects(self, dataset: pd.DataFrame):
"""
(If train) will create a dictionary telling the processor how many
categories are within a certain column.
If not train: Will look up each annotation feature from the impute file
within the columns of the datafile (either in full name or the column
starts with the feature from the impute file).
This dictionary is then passed to the actual processor.
:param dataset: unprocessed pandas DataFrame
:return: processed pandas DataFrame
"""
annotation_feats_dict = {}
if self.train:
hardcoded_features = ['Ref', 'Alt', 'Domain']
for feature in hardcoded_features:
annotation_feats_dict[feature] = 5
self.log.info(
'Training protocol, '
'creating new categorical conversion identifiers.'
)
for feat in self.annotation_object:
if feat not in annotation_feats_dict.keys():
annotation_feats_dict[feat] = 5
else:
for feature in self.annotation_object:
annotation_feats_dict = self._process_objects_no_train(
feature=feature,
annotation_features_dict=annotation_feats_dict
)
processed_data = self._process_categorical_vars(
dataset=dataset,
annotation_feats_dict=annotation_feats_dict
)
return processed_data
def _process_objects_no_train(self, feature: str,
annotation_features_dict: dict):
for model_feature in self.model_features:
if model_feature.startswith(feature):
extension = model_feature.split(''.join([feature, '_']))[-1]
if feature in annotation_features_dict.keys():
annotation_features_dict[feature].append(extension)
else:
annotation_features_dict[feature] = [extension]
return annotation_features_dict
def _load_model_features(self):
"""
Function to access the protected member of the XGBoost _Booster class
to get the features that the model is trained on.
:return: list
"""
self.log.info('Using features saved within the model.')
self.model_features = self.model._Booster.feature_names
def _process_categorical_vars(self,
dataset: pd.DataFrame,
annotation_feats_dict: dict):
"""
Processor of categorical columns. Will create new columns based on the
quantity of a value within a column.
:param dataset: unprocessed pandas DataFrame
:param annotation_feats_dict:
dictionary that is to contain the levels for each categorical
feature
:return: processed pandas DataFrame
"""
if self.train:
for annotation_feature in annotation_feats_dict.keys():
feature_names = self._get_top10_or_less_cats(
column=dataset[annotation_feature],
return_num=annotation_feats_dict[annotation_feature]
)
dataset[annotation_feature] = np.where(
dataset[annotation_feature].isin(feature_names),
dataset[annotation_feature],
'other')
else:
for annotation_feature in annotation_feats_dict.keys():
feature_names = annotation_feats_dict[annotation_feature]
self.log.debug('For feature: {} loaded {} levels: {}'.format(
annotation_feature,
len(feature_names),
feature_names
))
dataset[annotation_feature] = np.where(
dataset[annotation_feature].isin(feature_names),
dataset[annotation_feature],
'other'
)
dataset = pd.get_dummies(
dataset,
columns=list(annotation_feats_dict.keys())
)
# Checking if all annotation features are processed.
# If not, add a column containing all "false" (0)
for annotation_feature in annotation_feats_dict.keys():
dataset = self._check_all_annotation_features_processed(
current_annotation_feature=annotation_feature,
dataset=dataset,
annotation_features_dict=annotation_feats_dict
)
return dataset
def _check_all_annotation_features_processed(self,
current_annotation_feature,
dataset: pd.DataFrame,
annotation_features_dict):
if not self.train:
afd = annotation_features_dict
for processed_feature in afd[current_annotation_feature]:
col_be_present = "_".join(
[current_annotation_feature, processed_feature])
if col_be_present not in dataset.columns:
self.log.warning(
'Of annotation feature {},'
' detected {} not present in columns.'.format(
current_annotation_feature, processed_feature))
dataset[col_be_present] = 0
return dataset
def _get_top10_or_less_cats(self, column: pd.Series, return_num: int):
"""
Function for when a training file is preprocessed to get the top
return_num quantity values within a categorical column.
Some converting is done for the logger to be able to print them.
:param column: pandas Series
:param return_num: integer
:return: pandas Series
"""
value_counts = column.value_counts().index[:return_num].values
printable_value_counts = []
for value in value_counts:
if not isinstance(value, str):
value = str(value)
printable_value_counts.append(value)
self.log.info('For feature: {} saved the following values: {}'.format(
column.name,
', '.join(printable_value_counts)
))
return value_counts
# Model stuff
def predict(self, data: pd.DataFrame):
"""
Function to load the model and predict the CAPICE scores.
Can be overwritten in case of legacy support.
:return: pandas DataFrame
"""
self.log.info('Predicting for {} samples.'.format(data.shape[0]))
self._load_model()
self._load_model_features()
data['probabilities'] = self._predict(
self._create_input_matrix(dataset=data))
self.log.info('Predicting successful.')
return data
def _predict(self, predict_data):
"""
Further down defined prediction function, which is different for
XGBoost 0.72.1 and current XGBoost version.
:param predict_data: preprocessed pandas DataFrame
:return: numpy array
"""
return self.model.predict_proba(predict_data)[:, 1]
def _create_input_matrix(self, dataset: pd.DataFrame):
"""
Also a template function, which can be overwritten to be compatible
with first generation CAPICE.
:param dataset: pandas DataFrame
:return: XGBoost workable data
"""
return dataset[self.model_features]
def _load_model(self):
"""
Template method to load in the model once supported values are correct.
:return: pickled model instance
"""
model = None
if not self.train:
with open(self._get_model_loc(), 'rb') as model_file:
model = pickle.load(model_file)
self.log.info('Successfully loaded model at: {}'.format(
self._get_model_loc()))
self.model = model
@staticmethod
@abstractmethod
def _get_model_loc():
"""
Template to mark the directory where the model is located.
Use of os.path.join is required.
You may use the get_project_root_dir() from utilities if the model is
within this project directory.
:return: path-like or None if no model has been created yet.
"""
pass
class InputVersionChecker:
"""
Class to check the given VEP config argument and file VEP version match.
Class is self running.
"""
def __init__(self, config_vep_version: float, file_vep_version: float,
config_grch_build: int, file_grch_build: int):
"""
Class to check the given VEP config argument and
the header of the VEP file match.
:param config_vep_version: float,
config argument for the used VEP version
:param file_vep_version: flaot,
config argument for the used GRCh build
"""
self.config_vep_version = config_vep_version
self.file_vep_version = file_vep_version
self.config_grch_build = config_grch_build
self.file_grch_build = file_grch_build
self.manager = CapiceManager()
self.export_vep_version = None
self.export_grch_build = None
self.check_match = []
self.unable_check = []
self.check_overrule = False
self.log = Logger().logger
self._check_all_present()
if self.check_overrule:
self._check_overrule()
self._check_version_match()
self._set_global_vep_version()
self._set_global_grch_build()
def _set_global_vep_version(self):
"""
Function to provide the CapiceManager with the VEP version to be used
globally later on in CAPICE.
"""
self.manager.vep_version = self.export_vep_version
self.log.info('VEP version set to: {}'.format(self.export_vep_version))
def _set_global_grch_build(self):
"""
Function to provide the CapiceManager with the Genome Build version to
be used globally later on in CAPICE.
"""
self.manager.grch_build = self.export_grch_build
self.log.info('GRCh build set to: {}'.format(self.export_grch_build))
def _check_overrule(self):
"""
Function called when either the
VEP version or
GRCh build
can not be determined.
Overrule must be present for both impute and model,
since it can not determine what file to use without VEP or
GRCh argument.
"""
if self.manager.overwrite_impute is False and \
self.manager.overwrite_model is False:
error_message = """
VEP version or GRCh build not specified and both overwrites are not
set! Not able to find a correct impute or processing file!
""".strip()
self.log.critical(error_message)
raise InputError(error_message)
def _check_all_present(self):
"""
Function to check if both the VEP version and GRCh build are present
within either the config arguments or within the file.
"""
dict_of_all_present = {
'VEP': [self.file_vep_version, self.config_vep_version],
'GRCh': [self.file_grch_build, self.config_grch_build]
}
for type_of_check in dict_of_all_present.keys():
to_check = dict_of_all_present[type_of_check]
self._check_individual_argument(to_check=to_check,
type_of_check=type_of_check)
def _check_individual_argument(self, to_check, type_of_check):
"""
Function belonging to _check_all_present to check if a VEP version and
GRCh build can be set globally.
:param to_check: list
:param type_of_check: string
"""
if False in to_check:
if to_check.count(False) == len(to_check):
self._turn_on_check_overrule(type_of_check=type_of_check)
for argument in to_check:
self._apply_export_version(argument=argument,
type_of_check=type_of_check)
else:
self.check_match.append(type_of_check)
def _turn_on_check_overrule(self, type_of_check):
"""
Function to turn on the overrule check if no VEP or GRCh arguments are
passed.
"""
self.check_overrule = type_of_check
self.log.warning(
'Unable to obtain {} version from file or config file!'.format(
type_of_check))
self.check_overrule = True
def _apply_export_version(self, argument, type_of_check):
"""
Function to set the global VEP version or GRCh build.
:param argument: int or float
"""
if argument is not False:
if type_of_check == 'VEP':
self.export_vep_version = argument
else:
self.export_grch_build = argument
def _check_version_match(self):
"""
Function to check if the Config Argument and the file header specified
VEP versions match.
If not: use the config argument as form of "overwrite" and warn.
"""
if len(self.check_match) > 0:
for check_match in self.check_match:
if check_match == 'VEP':
self._check_vep_match(check_match=check_match)
elif check_match == 'GRCh':
self._check_grch_match(check_match=check_match)
def _check_vep_match(self, check_match):
if self.file_vep_version != self.config_vep_version:
self._raise_version_mismatch(type_of_mismatch=check_match,
version_cla=self.file_vep_version,
version_file=self.config_vep_version)
else:
self._raise_version_mismatch(type_of_mismatch=check_match,
match_successful=True)
self.export_vep_version = self.file_vep_version
def _check_grch_match(self, check_match):
if self.config_grch_build != self.file_grch_build:
self._raise_version_mismatch(type_of_mismatch=check_match,
version_cla=self.config_grch_build,
version_file=self.file_grch_build)
else:
self._raise_version_mismatch(type_of_mismatch=check_match,
match_successful=True)
self.export_grch_build = self.file_grch_build
def _raise_version_mismatch(self,
type_of_mismatch,
version_cla=None,
version_file=None,
match_successful=False):
if match_successful:
self.log.info(
'Successfully matched CLA and file versions for {}.'.format(
type_of_mismatch))
else:
warning_message = """
Warning matching {} versions.
CLA version supplied:
{} does not match file version: {} !""".format(
type_of_mismatch, version_cla, version_file).strip()
warnings.warn(warning_message)
self.log.warning(warning_message)
class InputHeaderParser:
"""
Autonomous class to parse just the header of the input file to get the
amount of comment lines that pandas should skip when reading.
"""
def __init__(self, is_gzipped: bool, input_file_loc: str):
self.log = Logger().logger
self.manager = CapiceManager()
self.log.info('Starting to parse input file header.')
self.is_gzipped = is_gzipped
self.input_file_loc = input_file_loc
self.header = ''
self.header_build = False
self.header_version = False
self.header_present = False
self.file_type = None
self.skip_rows = 0
self._parse_header()
if self.header_present:
self.log.info(
"Input file header successfully identified: {}".format(
self.header.strip()))
self._get_file_type()
else:
self.log.warning(
'Unable to parse input file header, header not located. '
'Does the header start with "##"?')
def _parse_header(self):
"""
Class to see if the first line is present within the input file.
"""
if self.is_gzipped:
file_handle = gzip.open(self.input_file_loc, mode='rt')
else:
file_handle = open(self.input_file_loc, mode='rt')
for line in file_handle:
if line.startswith('##'):
self._check_vep_version(line=line)
self._add_skip_row(line=line)
else:
break
file_handle.close()
def _add_skip_row(self, line):
if self.skip_rows == 0:
self.header_present = True
self.header = line
self.skip_rows += 1
else:
self.skip_rows += 1
def _check_vep_version(self, line):
if line.startswith('##VEP="'):
self._parse_vep_version(line)
def _parse_vep_version(self, line):
for annotation in line.split(' '):
if annotation.startswith('##VEP'):
self.header_version = float(
annotation.split('v')[1].split('"')[0])
self.log.info('Header VEP version identified: {}'.format(
self.header_version))
elif annotation.startswith('assembly'):
self.header_build = int(annotation.split('h')[1].split('.')[0])
self.log.info('Header GRCh build identified: {}'.format(
self.header_build))
def _get_file_type(self):
if not self.header.startswith('## VEP VCF to CAPICE tsv converter'):
warning_message = 'Unable to recognize origin of input file.'
self.log.warning(warning_message)
warnings.warn(warning_message)
def get_skip_rows(self):
"""
Function to return the integer value of how many rows pandas.read_csv()
should skip to reach the data.
:return: int
"""
return self.skip_rows
def get_vep_version(self):
"""
Function to return the float value of the VEP version used to generate
the input file.
:return: float
"""
return self.header_version
def get_grch_build(self):
"""
Function to return the float value of the GRCh build used to generate
the input file.
:return: int
"""
return self.header_build