def __init__(self,
model=HoeffdingTreeClassifier(grace_period=50,
split_confidence=0.01),
n_models: int = 100,
subspace_size: typing.Union[int, float, str] = .6,
training_method: str = "patches",
lam: float = 6.0,
drift_detector: typing.Union[base.DriftDetector,
None] = ADWIN(delta=1e-5),
warning_detector: base.DriftDetector = ADWIN(delta=1e-4),
disable_weighted_vote: bool = False,
nominal_attributes=None,
seed=None,
metric: MultiClassMetric = Accuracy()):
super().__init__([None
]) # List of models is properly initialized later
self.models = []
self.model = model # Not restricted to a specific base estimator.
self.n_models = n_models
self.subspace_size = subspace_size
self.training_method = training_method
self.lam = lam
self.drift_detector = drift_detector
self.warning_detector = warning_detector
self.disable_weighted_vote = disable_weighted_vote
self.metric = metric
self.nominal_attributes = nominal_attributes if nominal_attributes else []
self.seed = seed
self._rng = check_random_state(self.seed)
self._n_samples_seen = 0
self._subspaces = None
self._base_learner_class = StreamingRandomPatchesBaseLearner
def __init__(
self,
model: base.Estimator = None,
n_models: int = 10,
subspace_size: typing.Union[int, float, str] = 0.6,
training_method: str = "patches",
lam: float = 6.0,
drift_detector: base.DriftDetector = None,
warning_detector: base.DriftDetector = None,
disable_detector: str = "off",
disable_weighted_vote: bool = False,
seed=None,
metric: Metric = None,
):
if model is None:
model = HoeffdingTreeClassifier(grace_period=50,
split_confidence=0.01)
if drift_detector is None:
drift_detector = ADWIN(delta=1e-5)
if warning_detector is None:
warning_detector = ADWIN(delta=1e-4)
if disable_detector == "off":
pass
elif disable_detector == "drift":
drift_detector = None
warning_detector = None
elif disable_detector == "warning":
warning_detector = None
else:
raise AttributeError(
f"{disable_detector} is not a valid value for disable_detector.\n"
f"Valid options are: 'off', 'drift', 'warning'")
if metric is None:
metric = Accuracy()
super().__init__(
model=model,
n_models=n_models,
subspace_size=subspace_size,
training_method=training_method,
lam=lam,
drift_detector=drift_detector,
warning_detector=warning_detector,
disable_detector=disable_detector,
disable_weighted_vote=disable_weighted_vote,
seed=seed,
metric=metric,
)
self._base_learner_class = BaseSRPClassifier
def __init__(
self,
model: base.Classifier = None,
n_models: int = 100,
subspace_size: typing.Union[int, float, str] = 0.6,
training_method: str = "patches",
lam: float = 6.0,
drift_detector: base.DriftDetector = None,
warning_detector: base.DriftDetector = None,
disable_detector: str = "off",
disable_weighted_vote: bool = False,
nominal_attributes=None,
seed=None,
metric: MultiClassMetric = None,
):
if model is None:
model = HoeffdingTreeClassifier(grace_period=50,
split_confidence=0.01)
if drift_detector is None:
drift_detector = ADWIN(delta=1e-5)
if warning_detector is None:
warning_detector = ADWIN(delta=1e-4)
if disable_detector == "off":
pass
elif disable_detector == "drift":
drift_detector = None
warning_detector = None
elif disable_detector == "warning":
warning_detector = None
else:
raise AttributeError(
f"{disable_detector} is not a valid value for disable_detector.\n"
f"Valid options are: 'off', 'drift', 'warning'")
self.disable_detector = disable_detector
if metric is None:
metric = Accuracy()
super().__init__([None
]) # List of models is properly initialized later
self.models = []
self.model = model # Not restricted to a specific base estimator.
self.n_models = n_models
self.subspace_size = subspace_size
self.training_method = training_method
self.lam = lam
self.drift_detector = drift_detector
self.warning_detector = warning_detector
self.disable_weighted_vote = disable_weighted_vote
self.metric = metric
self.nominal_attributes = nominal_attributes if nominal_attributes else []
self.seed = seed
self._rng = np.random.default_rng(self.seed)
self._n_samples_seen = 0
self._subspaces = None
self._base_learner_class = StreamingRandomPatchesBaseLearner
dataset = stream.shuffle(dataset, buffer_size=50)
# construct our pipeline
"""
model = StandardScaler() | HoeffdingTreeClassifier(
grace_period=100, split_confidence=1e-5)
model = StandardScaler() | OneVsRestClassifier(classifier=LogisticRegression())
"""
filename = 'model.pkl'
base_path = os.path.join('benchmark', 'tree', model_name)
model = HoeffdingTreeClassifier()
#model = pickle.load(open(os.path.join('benchmark', 'tree', 'resnet50', 'model2.pkl'), 'rb'))
metric = Accuracy()
acc_history = list()
start = time.time()
#for j in range(0,5):
for (i, (X, y)) in enumerate(dataset):
preds = model.predict_one(X)
model = model.learn_one(X, y)
metric = metric.update(y, preds)