def __init__(self, pmml):
PMMLBaseRegressor.__init__(self, pmml)
OneHotEncodingMixin.__init__(self)
# Import coefficients and intercepts
model = self.root.find('RegressionModel')
if model is None:
raise Exception('PMML model does not contain RegressionModel.')
tables = model.findall('RegressionTable')
self.coef_ = np.array([
_get_coefficients(self, table)
for table in tables
])
self.intercept_ = np.array([
float(table.get('intercept'))
for table in tables
])
if self.coef_.shape[0] == 1:
self.coef_ = self.coef_[0]
if self.intercept_.shape[0] == 1:
self.intercept_ = self.intercept_[0]
def __init__(self, pmml):
PMMLBaseClassifier.__init__(self, pmml)
OneHotEncodingMixin.__init__(self)
LinearSVC.__init__(self)
# Import coefficients and intercepts
model = self.root.find('RegressionModel')
if model is None:
raise Exception('PMML model does not contain RegressionModel.')
tables = [
table for table in model.findall('RegressionTable')
if table.find('NumericPredictor') is not None
]
self.coef_ = [
_linear_get_coefficients(self, table) for table in tables
]
self.intercept_ = [float(table.get('intercept')) for table in tables]
if len(self.coef_) == 1:
self.coef_ = [self.coef_[0]]
if len(self.intercept_) == 1:
self.intercept_ = [self.intercept_[0]]
self.coef_ = np.array(self.coef_)
self.intercept_ = np.array(self.intercept_)
def __init__(self, pmml):
PMMLBaseClassifier.__init__(self, pmml)
OneHotEncodingMixin.__init__(self)
LogisticRegression.__init__(self)
# Import coefficients and intercepts
model = self.root.find('RegressionModel')
mining_model = self.root.find('MiningModel')
tables = []
if mining_model is not None and self.n_classes_ > 2:
self.multi_class = 'ovr'
segmentation = mining_model.find('Segmentation')
if segmentation.get('multipleModelMethod') not in ['modelChain']:
raise Exception('PMML model for multi-class logistic regression should use modelChain method.')
# Parse segments
segments = segmentation.findall('Segment')
valid_segments = [segment for segment in segments if segment.find('True') is not None]
models = [segment.find('RegressionModel') for segment in valid_segments]
tables = [
models[i].find('RegressionTable') for i in range(self.n_classes_)
]
elif model is not None:
self.multi_class = 'auto'
tables = [
table for table in model.findall('RegressionTable')
if table.find('NumericPredictor') is not None
]
else:
raise Exception('PMML model does not contain RegressionModel or Segmentation.')
self.coef_ = [
_get_coefficients(self, table)
for table in tables
]
self.intercept_ = [
float(table.get('intercept'))
for table in tables
]
if len(self.coef_) == 1:
self.coef_ = [self.coef_[0]]
if len(self.intercept_) == 1:
self.intercept_ = [self.intercept_[0]]
self.coef_ = np.array(self.coef_)
self.intercept_ = np.array(self.intercept_)
self.solver = 'lbfgs'
def __init__(self, pmml):
PMMLBaseClassifier.__init__(self, pmml)
OneHotEncodingMixin.__init__(self)
# Import coefficients and intercepts
model = self.root.find('GeneralRegressionModel')
if model is None:
raise Exception(
'PMML model does not contain GeneralRegressionModel.')
self.coef_ = np.array([_get_coefficients(self, model)])
self.intercept_ = _get_intercept(model)
def __init__(self, pmml):
PMMLBaseClassifier.__init__(self, pmml)
OneHotEncodingMixin.__init__(self)
model = self.root.find('NaiveBayesModel')
if model is None:
raise Exception('PMML model does not contain NaiveBayesModel.')
inputs = model.find('BayesInputs')
target_values = {
target: self._get_target_values(inputs, target)
for target in self.classes_
}
try:
outputs = model.find('BayesOutput').find(
'TargetValueCounts').findall('TargetValueCount')
counts = [int(x.get('count')) for x in outputs]
self.class_prior_ = np.array([x / np.sum(counts) for x in counts])
except AttributeError:
self.class_prior_ = np.array(
[1 / len(self.classes_) for _ in self.classes_])
self.theta_ = np.array(
[[float(value.get('mean', 0)) for value in target_values[target]]
for target in self.classes_])
try:
self.sigma_ = np.array([[
float(value.get('variance', 0))
for value in target_values[target]
] for target in self.classes_])
except AttributeError:
self.var_ = np.array([[
float(value.get('variance', 0))
for value in target_values[target]
] for target in self.classes_])
def __init__(self, pmml):
PMMLBaseRegressor.__init__(self, pmml)
OneHotEncodingMixin.__init__(self)
SVR.__init__(self)
PMMLBaseSVM.__init__(self)
def __init__(self, pmml):
PMMLBaseClassifier.__init__(self, pmml)
OneHotEncodingMixin.__init__(self)
SVC.__init__(self)
PMMLBaseSVM.__init__(self)