/
__init__.py
250 lines (221 loc) · 8.66 KB
/
__init__.py
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# -*- coding: utf-8 -*-
from ..Classifier import Classifier
class SVC(Classifier):
"""
See also
--------
sklearn.svm.SVC
http://scikit-learn.org/0.18/modules/generated/sklearn.svm.SVC.html
"""
SUPPORTED_METHODS = ['predict']
# @formatter:off
TEMPLATES = {
'c': {
'type': '{0}',
'arr': '{{{0}}}',
'arr[]': '{type} {name}[] = {{{values}}};',
'arr[][]': '{type} {name}[{n}][{m}] = {{{values}}};',
'indent': ' ',
},
'java': {
'type': '{0}',
'arr': '{{{0}}}',
'arr[]': '{type}[] {name} = {{{values}}};',
'arr[][]': '{type}[][] {name} = {{{values}}};',
'indent': ' ',
},
'js': {
'type': '{0}',
'arr': '[{0}]',
'arr[]': 'var {name} = [{values}];',
'arr[][]': 'var {name} = [{values}];',
'indent': ' ',
},
'php': {
'type': '{0}',
'arr': '[{0}]',
'arr[]': '${name} = [{values}];',
'arr[][]': '${name} = [{values}];',
'indent': ' ',
},
'ruby': {
'type': '{0}',
'arr': '[{0}]',
'arr[]': '{name} = [{values}]',
'arr[][]': '{name} = [{values}]',
'indent': ' ',
},
}
# @formatter:on
def __init__(self, estimator, target_language='java',
target_method='predict', **kwargs):
"""
Port a trained estimator to the syntax of a chosen programming language.
Parameters
----------
:param estimator : AdaBoostClassifier
An instance of a trained SVC estimator.
:param target_language : string
The target programming language.
:param target_method : string
The target method of the estimator.
"""
super(SVC, self).__init__(estimator, target_language=target_language,
target_method=target_method, **kwargs)
self.estimator = estimator
params = estimator.get_params()
# Check kernel type:
kernels = ['linear', 'rbf', 'poly', 'sigmoid']
if params['kernel'] not in kernels:
msg = 'The kernel type is not supported.'
raise ValueError(msg)
# Check rbf gamma value:
if params['kernel'] == 'rbf' and params['gamma'] == 'auto':
msg = ('The classifier gamma value have to '
'be set (currently it is \'auto\').')
raise ValueError(msg)
self.params = params
self.svs = estimator.support_vectors_
self.n_svs = len(estimator.support_vectors_[0])
self.svs_rows = estimator.n_support_
self.n_svs_rows = len(estimator.n_support_)
self.coeffs = estimator.dual_coef_
self.inters = estimator._intercept_ # pylint: disable=W0212
self.n_inters = len(estimator._intercept_) # pylint: disable=W0212
self.classes = estimator.classes_
self.n_classes = len(estimator.classes_)
self.is_binary = self.n_classes == 2
self.prefix = 'binary' if self.is_binary else 'multi'
def export(self, class_name, method_name, use_repr=True, use_file=False):
"""
Port a trained estimator to the syntax of a chosen programming language.
Parameters
----------
:param class_name: string, default: 'Brain'
The name of the class in the returned result.
:param method_name: string, default: 'predict'
The name of the method in the returned result.
:param use_repr : bool, default True
Whether to use repr() for floating-point values or not.
:param: use_file : bool, default False
Whether to store the estimator data in a separate file or not.
Returns
-------
:return : string
The transpiled algorithm with the defined placeholders.
"""
self.class_name = class_name
self.method_name = method_name
self.use_repr = use_repr
self.use_file = use_file
if self.target_method == 'predict':
return self.predict()
def predict(self):
"""
Transpile the predict method.
Returns
-------
:return : string
The transpiled predict method as string.
"""
return self.create_class(self.create_method())
def create_method(self):
"""
Build the estimator method or function.
Returns
-------
:return out : string
The built method as string.
"""
temp_type = self.temp('type')
temp_arr = self.temp('arr')
temp_arr_ = self.temp('arr[]')
temp_arr__ = self.temp('arr[][]')
out = '\n'
# Number of support vectors:
n_svs = [temp_type.format(self.repr(v)) for v in self.svs_rows]
n_svs = ', '.join(n_svs)
out += temp_arr_.format(type='int', name='n_svs', values=n_svs)
out += '\n'
# Support vectors:
vectors = []
for vector in self.svs:
_vectors = [temp_type.format(self.repr(v)) for v in vector]
_vectors = temp_arr.format(', '.join(_vectors))
vectors.append(_vectors)
vectors = ', '.join(vectors)
out += self.temp('arr[][]', skipping=True).format(
type='double', name='svs', values=vectors,
n=len(self.svs), m=self.n_svs)
out += '\n'
# Coefficients:
coeffs = []
for coeff in self.coeffs:
_coeffs = [temp_type.format(self.repr(c)) for c in coeff]
_coeffs = temp_arr.format(', '.join(_coeffs))
coeffs.append(_coeffs)
coeffs = ', '.join(coeffs)
out += temp_arr__.format(type='double', name='coeffs', values=coeffs,
n=len(self.coeffs), m=len(self.coeffs[0]))
out += '\n'
# Interceptions:
inters = [temp_type.format(self.repr(i)) for i in self.inters]
inters = ', '.join(inters)
out += temp_arr_.format(type='double', name='inters', values=inters)
out += '\n'
# Classes:
if not self.is_binary:
classes = [temp_type.format(self.repr(c)) for c in self.classes]
classes = ', '.join(classes)
out += temp_arr_.format(type='int', name='classes', values=classes)
out += '\n'
# Kernels:
if self.params['kernel'] == 'rbf':
name = self.prefix + '.kernel.rbf'
out += self.temp(name).format(
len(self.svs), self.n_svs,
self.repr(self.params['gamma']))
elif self.params['kernel'] == 'poly':
name = self.prefix + '.kernel.poly'
out += self.temp(name).format(
len(self.svs), self.n_svs,
self.repr(self.params['gamma']),
self.repr(self.params['coef0']),
self.repr(self.params['degree']))
elif self.params['kernel'] == 'sigmoid':
name = self.prefix + '.kernel.sigmoid'
out += self.temp(name).format(
len(self.svs), self.n_svs,
self.repr(self.params['gamma']),
self.repr(self.params['coef0']),
self.repr(self.params['degree']))
elif self.params['kernel'] == 'linear':
name = self.prefix + '.kernel.linear'
out += self.temp(name).format(
len(self.svs), self.n_svs)
out += '\n'
# Decicion:
out += self.temp('starts').format(self.n_svs_rows)
out += self.temp('ends').format(self.n_svs_rows)
name = self.prefix + '.decisions'
out += self.temp(name).format(self.n_inters, self.n_svs_rows)
name = self.prefix + '.classes'
out += self.temp(name).format(self.n_inters, self.n_classes)
n_indents = 0 if self.target_language in ['java', 'js', 'php', 'ruby'] else 1
out = self.indent(out, n_indents=2-n_indents)
temp_method = self.temp('method', n_indents=1-n_indents, skipping=True)
out = temp_method.format(method_name=self.method_name, decicion=out)
return out
def create_class(self, method):
"""
Build the estimator class.
Returns
-------
:return out : string
The built class as string.
"""
temp_class = self.temp('class')
out = temp_class.format(class_name=self.class_name,
method_name=self.method_name, method=method,
n_features=self.n_svs)
return out