本文整理汇总了Python中weka.classifiers.Classifier.predict方法的典型用法代码示例。如果您正苦于以下问题：Python Classifier.predict方法的具体用法？Python Classifier.predict怎么用？Python Classifier.predict使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类weka.classifiers.Classifier的用法示例。
在下文中一共展示了Classifier.predict方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: WekaRandomForestClassifier
# 需要导入模块: from weka.classifiers import Classifier [as 别名]
# 或者: from weka.classifiers.Classifier import predict [as 别名]
class WekaRandomForestClassifier(BaseEstimator, ClassifierMixin):
def __init__(self, n_estimators=10,
max_depth=None,
max_features="auto",
random_state=None):
self.n_estimators = n_estimators
self.max_depth = max_depth
self.max_features = max_features
self.random_state = random_state
def fit(self, X, y):
# Check params
self.n_features_ = X.shape[1]
random_state = check_random_state(self.random_state)
if isinstance(self.max_features, str):
if self.max_features == "auto":
max_features = max(1, int(np.sqrt(self.n_features_)))
elif self.max_features == "sqrt":
max_features = max(1, int(np.sqrt(self.n_features_)))
elif self.max_features == "log2":
max_features = max(1, int(np.log2(self.n_features_)))
else:
raise ValueError(
'Invalid value for max_features. Allowed string '
'values are "auto", "sqrt" or "log2".')
elif self.max_features is None:
max_features = self.n_features_
elif isinstance(self.max_features, (numbers.Integral, np.integer)):
max_features = self.max_features
else: # float
max_features = int(self.max_features * self.n_features_)
params = {}
params["-I"] = self.n_estimators
params["-K"] = max_features
params["-depth"] = 0 if self.max_depth is None else self.max_depth
params["-no-cv"] = None
params["-s"] = random_state.randint(1000000)
# Convert data
self.classes_ = np.unique(y)
self.n_classes_ = len(self.classes_)
y = np.searchsorted(self.classes_, y)
tf = tempfile.NamedTemporaryFile(mode="w", suffix=".arff", dir="/dev/shm", delete=False)
to_arff(X, y, self.n_classes_, tf)
tf.close()
# Run
self.model_ = Classifier(name="weka.classifiers.trees.RandomForest", ckargs=params)
self.model_.train(tf.name)
os.remove(tf.name)
return self
def predict(self, X):
tf = tempfile.NamedTemporaryFile(mode="w", suffix=".arff", dir="/dev/shm", delete=False)
to_arff(X, None, self.n_classes_, tf)
tf.close()
pred = np.zeros(len(X), dtype=np.int32)
for i, r in enumerate(self.model_.predict(tf.name)):
pred[i] = int(r.predicted[5])
os.remove(tf.name)
return self.classes_[pred]示例2: test_IBk
# 需要导入模块: from weka.classifiers import Classifier [as 别名]
# 或者: from weka.classifiers.Classifier import predict [as 别名]
def test_IBk(self):
# Train a classifier.
print('Training IBk classifier...')
c = Classifier(name='weka.classifiers.lazy.IBk', ckargs={'-K':1})
training_fn = os.path.join(BP, 'fixtures/abalone-train.arff')
c.train(training_fn, verbose=1)
self.assertTrue(c._model_data)
# Make a valid query.
print('Using IBk classifier...')
query_fn = os.path.join(BP, 'fixtures/abalone-query.arff')
predictions = list(c.predict(query_fn, verbose=1, cleanup=0))
pred0 = predictions[0]
print('pred0:', pred0)
pred1 = PredictionResult(actual=None, predicted=7, probability=None)
print('pred1:', pred1)
self.assertEqual(pred0, pred1)
# Make a valid query.
with self.assertRaises(PredictionError):
query_fn = os.path.join(BP, 'fixtures/abalone-query-bad.arff')
predictions = list(c.predict(query_fn, verbose=1, cleanup=0))
# Make a valid query manually.
query = arff.ArffFile(relation='test', schema=[
('Sex', ('M', 'F', 'I')),
('Length', 'numeric'),
('Diameter', 'numeric'),
('Height', 'numeric'),
('Whole weight', 'numeric'),
('Shucked weight', 'numeric'),
('Viscera weight', 'numeric'),
('Shell weight', 'numeric'),
('Class_Rings', 'integer'),
])
query.append(['M', 0.35, 0.265, 0.09, 0.2255, 0.0995, 0.0485, 0.07, '?'])
data_str0 = """%
@relation test
@attribute 'Sex' {F,I,M}
@attribute 'Length' numeric
@attribute 'Diameter' numeric
@attribute 'Height' numeric
@attribute 'Whole weight' numeric
@attribute 'Shucked weight' numeric
@attribute 'Viscera weight' numeric
@attribute 'Shell weight' numeric
@attribute 'Class_Rings' integer
@data
M,0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,?
"""
data_str1 = query.write(fmt=DENSE)
# print(data_str0
# print(data_str1
self.assertEqual(data_str0, data_str1)
predictions = list(c.predict(query, verbose=1, cleanup=0))
self.assertEqual(predictions[0],
PredictionResult(actual=None, predicted=7, probability=None))
# Test pickling.
fn = os.path.join(BP, 'fixtures/IBk.pkl')
c.save(fn)
c = Classifier.load(fn)
predictions = list(c.predict(query, verbose=1, cleanup=0))
self.assertEqual(predictions[0],
PredictionResult(actual=None, predicted=7, probability=None))
#print('Pickle verified.')
# Make a valid dict query manually.
query = arff.ArffFile(relation='test', schema=[
('Sex', ('M', 'F', 'I')),
('Length', 'numeric'),
('Diameter', 'numeric'),
('Height', 'numeric'),
('Whole weight', 'numeric'),
('Shucked weight', 'numeric'),
('Viscera weight', 'numeric'),
('Shell weight', 'numeric'),
('Class_Rings', 'integer'),
])
query.append({
'Sex': 'M',
'Length': 0.35,
'Diameter': 0.265,
'Height': 0.09,
'Whole weight': 0.2255,
'Shucked weight': 0.0995,
'Viscera weight': 0.0485,
'Shell weight': 0.07,
'Class_Rings': arff.MISSING,
})
predictions = list(c.predict(query, verbose=1, cleanup=0))
self.assertEqual(predictions[0],
PredictionResult(actual=None, predicted=7, probability=None))