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# Natural Language Toolkit - Instance
# Understands the various operations that can be preformed on an instance
# Each Instance inheriting from the main Instance is capable of operations
# it can logically perform on that instance value eg: Test instance can
# set the Class where as the Training instance cannot
#
# Author: Sumukh Ghodke <sumukh dot ghodke at gmail dot com>
#
# URL: <http://www.nltk.org/>
# This software is distributed under GPL, for license information see LICENSE.TXT
from nltk_contrib.classifier.exceptions import systemerror as system, invaliddataerror as inv
import item, copy
class Instance:
def __init__(self):
self.klass_value, self.attrs, self.classified_klass = None, None, None
def is_valid(self, klass, attributes):
"""
Verifies if the instance contains valid attribute and class values
"""
return attributes.has_values(self.attrs)
def value(self, attribute):
"""
Returns the value corresponding to @param:attribute
"""
if attribute.is_continuous():
return float(self.attrs[attribute.index])
return self.attrs[attribute.index]
def values(self, attributes):
"""
Returns a list of attribute values corresponding to @param:attributes
"""
return [self.attrs[attribute.index] for attribute in attributes]
def discretise(self, discretised_attributes):
"""
Set discretised values for continuous attributes
"""
for discretised_attribute in discretised_attributes:
index = discretised_attribute.index
self.attrs[index] = discretised_attribute.mapping(float(self.attrs[index]))
def remove_attributes(self, attributes):
"""
Used when selecting features and @param:attributes are removed from instances
@param:attributes is an array of attributes
"""
to_be_removed = [attribute.index for attribute in attributes]
to_be_removed.sort()
to_be_removed.reverse()
for r in to_be_removed:
self.attrs.__delitem__(r)
def convert_to_float(self, indices):
"""
Converts attribute values at @param:indices to floats from numeric data represented as strings
Will throw a value error if an attempt is made to convert anything other than numeric data
"""
for index in indices:
self.attrs[index] = float(self.attrs[index])
def __eq__(self, other):
if other is None: return False
if self.__class__ != other.__class__: return False
if self.klass_value == other.klass_value and self.attrs == other.attrs and self.classified_klass == other.classified_klass: return True
return False
def str_klassified_klass(self):
"""
Returns the classified class as a string, will return <whitespace> if instance is not classified
"""
return self.__check_none(self.classified_klass)
def __check_none(self, var):
if var is None:
return ' '
return var.__str__()
def str_class(self):
"""
Returns the class as a string, will return <whitespace> in the case of a test instance
"""
return self.__check_none(self.klass_value)
def str_attrs(self):
"""
Returns the a comma separated string of attribute values
"""
return ','.join([self.__check_none(each) for each in self.attrs])
def __str__(self):
return '[' + ';'.join(self.as_str()) + ']'
def as_str(self):
"""
Helper method for __str__(self)
"""
return [self.str_attrs()]
class TrainingInstance(Instance):
def __init__(self, attr_values, klass_value):
Instance.__init__(self)
self.klass_value, self.attrs = klass_value, attr_values
def is_valid(self, klass, attributes):
"""
Verifies if the instance contains valid attribute and class values
"""
return Instance.is_valid(self, klass, attributes) and klass.__contains__(self.klass_value)
def as_gold(self):
"""
Converts the training instance into a Gold instance(used in cross validation)
"""
return GoldInstance(copy.copy(self.attrs), self.klass_value)
def as_str(self):
"""
Helper method for __str__(self)
"""
_attrs = Instance.as_str(self)
_attrs.append(self.str_class())
return _attrs
class TestInstance(Instance):
def __init__(self, attr_values):
Instance.__init__(self)
self.attrs = attr_values
def set_klass(self, klass):
self.classified_klass = klass
def as_str(self):
"""
Helper method for __str__(self)
"""
_attrs = Instance.as_str(self)
_attrs.append(self.str_klassified_klass())
return _attrs
class GoldInstance(TrainingInstance, TestInstance):
def __init__(self, attr_values, klass_value):
TrainingInstance.__init__(self, attr_values, klass_value)
def is_valid(self, klass, attributes):
"""
Verifies if the instance contains valid attribute and class values
"""
return TrainingInstance.is_valid(self, klass, attributes)
def as_str(self):
"""
Helper method for __str__(self)
"""
_attrs = Instance.as_str(self)
_attrs.append(self.str_class())
_attrs.append(self.str_klassified_klass())
return _attrs
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