def load(self,
filename=None,
fields=None,
csv_header=True,
file_format=None,
delimiter=None,
convert_numeric_fields=True):
"""Load file
Parameters
----------
filename : str, optional
File path
Default value filename given to class constructor
fields : list of str, optional
List of column names
csv_header : bool, optional
Read field names from first line (header). Used only for CSV formatted files.
Default value True
file_format : FileFormat, optional
Forced file format, use this when there is a miss-match between file extension and file format.
delimiter : str, optional
Forced data delimiter for csv format. If None given, automatic delimiter sniffer used. Use this when sniffer does not work.
convert_numeric_fields : bool, optional
Convert int and float fields to correct type.
Default value True
Raises
------
IOError:
File does not exists or has unknown file format
ValueError:
No fields or csv_header set for CSV formatted file.
Returns
-------
self
"""
if filename:
self.filename = filename
if not file_format:
self.detect_file_format()
self.validate_format()
if file_format and FileFormat.validate_label(label=file_format):
self.format = file_format
if self.exists():
from dcase_util.files import Serializer
if self.format == FileFormat.CSV:
if fields is None and csv_header is None:
message = '{name}: Parameters fields or csv_header has to be set for CSV files.'.format(
name=self.__class__.__name__)
self.logger.exception(message)
raise ValueError(message)
data = []
if not delimiter:
delimiter = self.delimiter()
with open(self.filename, 'r') as f:
csv_reader = csv.reader(f, delimiter=delimiter)
if csv_header:
csv_fields = next(csv_reader)
if fields is None:
fields = csv_fields
for row in csv_reader:
if convert_numeric_fields:
for cell_id, cell_data in enumerate(row):
if is_int(cell_data):
row[cell_id] = int(cell_data)
elif is_float(cell_data):
row[cell_id] = float(cell_data)
data.append(dict(zip(fields, row)))
list.__init__(self, data)
elif self.format == FileFormat.YAML:
data = Serializer.load_yaml(filename=self.filename)
if isinstance(data, list):
list.__init__(self, data)
else:
message = '{name}: YAML data is not in list format.'.format(
name=self.__class__.__name__)
self.logger.exception(message)
raise ImportError(message)
elif self.format == FileFormat.CPICKLE:
list.__init__(self,
Serializer.load_cpickle(filename=self.filename))
else:
message = '{name}: Unknown format [{format}]'.format(
name=self.__class__.__name__, format=self.filename)
self.logger.exception(message)
raise IOError(message)
else:
message = '{name}: File does not exists [{file}]'.format(
name=self.__class__.__name__, file=self.filename)
self.logger.exception(message)
raise IOError(message)
# Check if after load function is defined, call if found
if hasattr(self, '_after_load'):
self._after_load()
return self
def load(self,
filename=None,
fields=None,
csv_header=True,
file_format=None,
delimiter=None,
decimal='point'):
"""Load probability list from file
Preferred delimiter is tab, however, other delimiters are supported automatically
(they are sniffed automatically).
Supported input formats:
- [file(string)][label(string)][probability(float)]
Parameters
----------
filename : str
Path to the probability list in text format (csv). If none given, one given for class constructor is used.
Default value None
fields : list of str, optional
List of column names. Used only for CSV formatted files.
Default value None
csv_header : bool, optional
Read field names from first line (header). Used only for CSV formatted files.
Default value True
file_format : FileFormat, optional
Forced file format, use this when there is a miss-match between file extension and file format.
Default value None
delimiter : str, optional
Forced data delimiter for csv format. If None given, automatic delimiter sniffer used.
Use this when sniffer does not work.
Default value None
decimal : str
Decimal 'point' or 'comma'
Default value 'point'
Returns
-------
data : list of probability item dicts
List containing probability item dicts
"""
def validate(row_format, valid_formats):
for valid_format in valid_formats:
if row_format == valid_format:
return True
return False
if filename:
self.filename = filename
if not file_format:
self.detect_file_format()
self.validate_format()
if file_format and FileFormat.validate_label(label=file_format):
self.format = file_format
if self.exists():
if self.format in [FileFormat.TXT]:
if decimal == 'comma':
delimiter = self.delimiter(exclude_delimiters=[','])
else:
delimiter = self.delimiter()
data = []
field_validator = FieldValidator()
f = io.open(self.filename, 'rt')
try:
for row in csv.reader(f, delimiter=delimiter):
if row:
row_format = []
for item in row:
row_format.append(
field_validator.process(item))
for item_id, item in enumerate(row):
if row_format[
item_id] == FieldValidator.NUMBER:
# Translate decimal comma into decimal point
row[item_id] = float(row[item_id].replace(
',', '.'))
elif row_format[item_id] in [
FieldValidator.AUDIOFILE,
FieldValidator.DATAFILE,
FieldValidator.STRING,
FieldValidator.ALPHA1,
FieldValidator.ALPHA2,
FieldValidator.LIST
]:
row[item_id] = row[item_id].strip()
if validate(row_format=row_format,
valid_formats=[
[
FieldValidator.AUDIOFILE,
FieldValidator.STRING,
FieldValidator.NUMBER
],
[
FieldValidator.AUDIOFILE,
FieldValidator.ALPHA1,
FieldValidator.NUMBER
],
[
FieldValidator.AUDIOFILE,
FieldValidator.ALPHA2,
FieldValidator.NUMBER
],
[
FieldValidator.DATAFILE,
FieldValidator.STRING,
FieldValidator.NUMBER
],
[
FieldValidator.DATAFILE,
FieldValidator.ALPHA1,
FieldValidator.NUMBER
],
[
FieldValidator.DATAFILE,
FieldValidator.ALPHA2,
FieldValidator.NUMBER
]
]):
# Format: [file label probability]
data.append(
self.item_class({
'filename': row[0],
'label': row[1],
'probability': row[2],
}))
elif validate(row_format=row_format,
valid_formats=[
[
FieldValidator.AUDIOFILE,
FieldValidator.STRING,
FieldValidator.NUMBER,
FieldValidator.NUMBER
],
[
FieldValidator.AUDIOFILE,
FieldValidator.ALPHA1,
FieldValidator.NUMBER,
FieldValidator.NUMBER
],
[
FieldValidator.AUDIOFILE,
FieldValidator.ALPHA2,
FieldValidator.NUMBER,
FieldValidator.NUMBER
],
[
FieldValidator.DATAFILE,
FieldValidator.STRING,
FieldValidator.NUMBER,
FieldValidator.NUMBER
],
[
FieldValidator.DATAFILE,
FieldValidator.ALPHA1,
FieldValidator.NUMBER,
FieldValidator.NUMBER
],
[
FieldValidator.DATAFILE,
FieldValidator.ALPHA2,
FieldValidator.NUMBER,
FieldValidator.NUMBER
]
]):
# Format: [file label probability index]
data.append(
self.item_class({
'filename': row[0],
'label': row[1],
'probability': row[2],
'index': row[3]
}))
else:
message = '{name}: Unknown row format [{row}] [{row_format}]'.format(
name=self.__class__.__name__,
row=row,
row_format=row_format)
self.logger.exception(message)
raise IOError(message)
finally:
f.close()
self.update(data=data)
elif self.format == FileFormat.CSV:
if fields is None and csv_header is None:
message = '{name}: Parameters fields or csv_header has to be set for CSV files.'.format(
name=self.__class__.__name__)
self.logger.exception(message)
raise ValueError(message)
if not delimiter:
if decimal == 'comma':
delimiter = self.delimiter(exclude_delimiters=[','])
else:
delimiter = self.delimiter()
data = []
with open(self.filename, 'r') as f:
csv_reader = csv.reader(f, delimiter=delimiter)
if csv_header:
csv_fields = next(csv_reader)
if fields is None:
fields = csv_fields
for row in csv_reader:
if row:
for cell_id, cell_data in enumerate(row):
if decimal == 'comma':
# Translate decimal comma into decimal point
cell_data = float(
cell_data.replace(',', '.'))
if is_int(cell_data):
row[cell_id] = int(cell_data)
elif is_float(cell_data):
row[cell_id] = float(cell_data)
data.append(dict(zip(fields, row)))
self.update(data=data)
elif self.format == FileFormat.CPICKLE:
from dcase_util.files import Serializer
self.update(data=Serializer.load_cpickle(
filename=self.filename))
else:
message = '{name}: File not found [{file}]'.format(
name=self.__class__.__name__, file=self.filename)
self.logger.exception(message)
raise IOError(message)
return self
def formatted_value(self, value, data_type='auto'):
"""Format value into string.
Valid data_type parameters:
- auto
- str or stf (fixed width string, padded with white space)
- bool
- float1, float2, float3, float4
- float1_percentage, float2_percentage, float3_percentage, float4_percentage
- float1_percentage+ci, float2_percentage+ci, float3_percentage+ci, float4_percentage+ci
- float1_ci, float2_ci, float3_ci, float4_ci
- float1_ci_bracket, float2_ci_bracket, float3_ci_bracket, float4_ci_bracket
Parameters
----------
value :
data_type : str
Data type in format [type label][length], e.g. for floats with 4 decimals use 'float4',
strings with fixed length 10 use 'str10'. For automatic value formatting use 'auto'.
Default value 'auto'
Returns
-------
str
"""
if value is None:
value = "None"
if data_type == 'auto':
if isinstance(value, bool):
data_type = 'bool'
elif isinstance(value, int):
data_type = 'int'
elif isinstance(value, float):
data_type = 'float2'
else:
data_type = 'str'
# Float
if data_type == 'float1' and is_float(value):
value = '{:.1f}'.format(float(value))
elif data_type == 'float2' and is_float(value):
value = '{:.2f}'.format(float(value))
elif data_type == 'float3' and is_float(value):
value = '{:.3f}'.format(float(value))
elif data_type == 'float4' and is_float(value):
value = '{:.4f}'.format(float(value))
elif data_type == 'int' and is_int(value):
value = '{:d}'.format(int(value))
# Float (percentage)
elif data_type == 'float1_percentage' and is_float(value):
value = '{:3.1f}%'.format(float(value))
elif data_type == 'float2_percentage' and is_float(value):
value = '{:3.2f}%'.format(float(value))
elif data_type == 'float3_percentage' and is_float(value):
value = '{:3.3f}%'.format(float(value))
elif data_type == 'float4_percentage' and is_float(value):
value = '{:3.4f}%'.format(float(value))
# Float (percentage) + confidence interval
elif data_type == 'float1_percentage+ci' and isinstance(value, tuple):
value = '{:3.1f}% (+/-{:3.1f})'.format(float(value[0]),
float(value[1]))
elif data_type == 'float2_percentage+ci' and isinstance(value, tuple):
value = '{:3.2f}% (+/-{:3.2f})'.format(float(value[0]),
float(value[1]))
elif data_type == 'float3_percentage+ci' and isinstance(value, tuple):
value = '{:3.3f}% (+/-{:3.3f})'.format(float(value[0]),
float(value[1]))
elif data_type == 'float4_percentage+ci' and isinstance(value, tuple):
value = '{:3.4f}% (+/-{:3.4f})'.format(float(value[0]),
float(value[1]))
# Float + confidence interval
elif data_type == 'float1+ci' and isinstance(value, tuple):
value = '{:3.1f} (+/-{:3.1f})'.format(float(value[0]),
float(value[1]))
elif data_type == 'float2+ci' and isinstance(value, tuple):
value = '{:3.2f} (+/-{:3.2f})'.format(float(value[0]),
float(value[1]))
elif data_type == 'float3+ci' and isinstance(value, tuple):
value = '{:3.3f} (+/-{:3.3f})'.format(float(value[0]),
float(value[1]))
elif data_type == 'float4+ci' and isinstance(value, tuple):
value = '{:3.4f} (+/-{:3.4f})'.format(float(value[0]),
float(value[1]))
# Float confidence interval
elif data_type == 'float1_ci':
value = '+/-{:3.1f}'.format(float(value))
elif data_type == 'float2_ci':
value = '+/-{:3.2f}'.format(float(value))
elif data_type == 'float3_ci':
value = '+/-{:3.3f}'.format(float(value))
elif data_type == 'float4_ci':
value = '+/-{:3.4f}'.format(float(value))
# Float confidence interval bracket
elif data_type == 'float1_ci_bracket' and isinstance(value, tuple):
value = '{:3.1f}-{:3.1f}'.format(float(value[0]), float(value[1]))
elif data_type == 'float2_ci_bracket' and isinstance(value, tuple):
value = '{:3.2f}-{:3.2f}'.format(float(value[0]), float(value[1]))
elif data_type == 'float3_ci_bracket' and isinstance(value, tuple):
value = '{:3.3f}-{:3.3f}'.format(float(value[0]), float(value[1]))
elif data_type == 'float4_ci_bracket' and isinstance(value, tuple):
value = '{:3.4f}-{:3.4f}'.format(float(value[0]), float(value[1]))
elif isinstance(value, numpy.ndarray):
shape = value.shape
if len(shape) == 1:
value = 'array ({0:d},)'.format(shape[0])
elif len(shape) == 2:
value = 'matrix ({0:d},{1:d})'.format(shape[0], shape[1])
elif len(shape) == 3:
value = 'matrix ({0:d},{1:d},{2:d})'.format(
shape[0], shape[1], shape[2])
elif len(shape) == 4:
value = 'matrix ({0:d},{1:d},{2:d},{3:d})'.format(
shape[0], shape[1], shape[2], shape[3])
elif data_type == 'bool':
if value:
value = 'True'
else:
value = 'False'
elif data_type.startswith('str'):
value = str(value)
if len(data_type) > 3:
value_width = int(data_type[3:])
if value and len(value) > value_width:
value = value[0:value_width - 2] + '..'
elif data_type.startswith('stf'):
value = str(value)
if len(data_type) > 3:
value_width = int(data_type[3:])
if value and len(value) > value_width:
value = value[0:value_width - 2] + '..'
elif value and len(value) < value_width:
value = value.ljust(value_width)
return value
def table(self,
cell_data=None,
column_headers=None,
column_types=None,
column_separators=None,
row_separators=None,
indent=0):
"""Data table
Parameters
----------
cell_data : list of list
Cell data in format [ [cell(col1,row1), cell(col1,row2), cell(col1,row3)],
[cell(col2,row1), cell(col2,row2), cell(col2,row3)] ]
Default value None
column_headers : list of str
Column headers in list, if None given column numbers are used
Default value None
column_types : list of str
Column data types, if None given type is determined automatically.
Possible values: ['int', 'float1', 'float2', 'float3', 'float4', 'str10', 'str20']]
Default value None
column_separators : list of int
Column ids where to place separation lines. Line is placed on the right of the indicated column.
Default value None
row_separators : list of int
Row ids where to place separation lines. Line is place after indicated row.
Default value None
indent : int
Amount of indention used for the line
Default value 0
Returns
-------
str
"""
if cell_data is None:
cell_data = []
if column_headers is None:
column_headers = []
if column_types is None:
column_types = []
if column_separators is None:
column_separators = []
if row_separators is None:
row_separators = []
if len(cell_data) != len(column_headers):
# Generate generic column titles
for column_id, column_data in enumerate(cell_data):
if column_id >= len(column_headers):
column_headers.append('Col #{:d}'.format(column_id))
if len(cell_data) != len(column_types):
# Generate generic column types
for column_id, column_data in enumerate(cell_data):
if column_id >= len(
column_types) or column_types[column_id] == 'auto':
row_data = cell_data[column_id]
if all(isinstance(x, int) for x in row_data):
data_type = 'int'
elif all(isinstance(x, float) for x in row_data):
data_type = 'float2'
elif all(
isinstance(x, six.string_types) for x in row_data):
data_type = 'str20'
else:
data_type = 'str20'
column_types.append(data_type)
line_template = ""
sep_column = []
for column_id, (data, header, data_type) in enumerate(
zip(cell_data, column_headers, column_types)):
if data_type.startswith('str'):
if len(data_type) > 3:
column_width = int(data_type[3:])
else:
column_width = 10
line_template += '{' + str(column_id) + ':<' + str(
column_width) + 's} '
elif data_type.startswith('float') or data_type.startswith('int'):
column_width = 6
if len(column_headers[column_id]) > column_width:
column_width = len(column_headers[column_id])
line_template += '{' + str(column_id) + ':>' + str(
column_width) + 's} '
else:
message = '{name}: Unknown column type [{data_type}].'.format(
name=self.__class__.__name__, data_type=data_type)
self.logger.exception(message)
raise ValueError(message)
if column_id in column_separators:
line_template += '| '
else:
line_template += ' '
sep_column.append('-' * column_width)
output = ''
output += ' ' * indent + line_template.format(*column_headers) + '\n'
output += ' ' * indent + line_template.format(*sep_column) + '\n'
for row_id, tmp in enumerate(cell_data[0]):
row_data = []
for column_id, (column_data, data_type) in enumerate(
zip(cell_data, column_types)):
cell_value = column_data[row_id]
if data_type == 'auto':
if isinstance(cell_value, int):
data_type = 'int'
elif isinstance(cell_value, float):
data_type = 'float2'
elif isinstance(cell_value, six.string_types):
data_type = 'str10'
else:
data_type = 'str10'
if data_type == 'float1' and is_float(cell_value):
row_data.append('{:6.1f}'.format(float(cell_value)))
elif data_type == 'float2' and is_float(cell_value):
row_data.append('{:6.2f}'.format(float(cell_value)))
elif data_type == 'float3' and is_float(cell_value):
row_data.append('{:6.3f}'.format(float(cell_value)))
elif data_type == 'float4' and is_float(cell_value):
row_data.append('{:6.4f}'.format(float(cell_value)))
elif data_type == 'int' and is_int(cell_value):
row_data.append('{:d}'.format(int(cell_value)))
elif data_type.startswith('str'):
if len(data_type) > 3:
column_width = int(data_type[3:])
else:
column_width = 10
if cell_value is None:
cell_value = '-'
if cell_value and len(cell_value) > column_width:
cell_value = cell_value[0:column_width - 2] + '..'
row_data.append(cell_value)
elif cell_value is None:
row_data.append('-')
if row_id in row_separators:
output += ' ' * indent + line_template.format(
*sep_column) + '\n'
output += ' ' * indent + line_template.format(*row_data) + '\n'
return output
def formatted_value(self, value, data_type='auto'):
"""Format value into string.
Parameters
----------
value :
data_type : str
Data type in format [type label][length], e.g. for floats with 4 decimals use 'float4',
strings with fixed length 10 use 'str10'. For automatic value formatting use 'auto'.
Default value 'auto'
Returns
-------
str
"""
if value is None:
value = "None"
if data_type == 'auto':
if isinstance(value, bool):
data_type = 'bool'
elif isinstance(value, int):
data_type = 'int'
elif isinstance(value, float):
data_type = 'float2'
else:
data_type = 'str'
if data_type == 'float1' and is_float(value):
value = '{:.1f}'.format(float(value))
elif data_type == 'float2' and is_float(value):
value = '{:.2f}'.format(float(value))
elif data_type == 'float3' and is_float(value):
value = '{:.3f}'.format(float(value))
elif data_type == 'float4' and is_float(value):
value = '{:.4f}'.format(float(value))
elif data_type == 'int' and is_int(value):
value = '{:d}'.format(int(value))
elif data_type == 'float1_percentage' and is_float(value):
value = '{:3.1f}%'.format(float(value))
elif data_type == 'float2_percentage' and is_float(value):
value = '{:3.2f}%'.format(float(value))
elif isinstance(value, numpy.ndarray):
shape = value.shape
if len(shape) == 1:
value = 'array ({0:d},)'.format(shape[0])
elif len(shape) == 2:
value = 'matrix ({0:d},{1:d})'.format(shape[0], shape[1])
elif len(shape) == 3:
value = 'matrix ({0:d},{1:d},{2:d})'.format(
shape[0], shape[1], shape[2])
elif len(shape) == 4:
value = 'matrix ({0:d},{1:d},{2:d},{3:d})'.format(
shape[0], shape[1], shape[2], shape[3])
elif data_type == 'bool':
if value:
value = 'True'
else:
value = 'False'
elif data_type.startswith('str'):
value = str(value)
if len(data_type) > 3:
value_width = int(data_type[3:])
if value and len(value) > value_width:
value = value[0:value_width - 2] + '..'
value = value
return value
