class PerceptronMask(BaseSKMObject, ClassifierMixin):
""" Mask for sklearn.linear_model.Perceptron.
scikit-multiflow requires a few interfaces, not present in scikit-learn,
This mask serves as a wrapper for the Perceptron classifier.
"""
def __init__(self,
penalty=None,
alpha=0.0001,
fit_intercept=True,
max_iter=None,
tol=None,
shuffle=True,
verbose=0,
eta0=1.0,
n_jobs=None,
random_state=0,
early_stopping=False,
validation_fraction=0.1,
n_iter_no_change=5,
class_weight=None,
warm_start=False,
n_iter=None):
self.penalty = penalty
self.alpha = alpha
self.fit_intercept = fit_intercept
self.max_iter = max_iter
self.tol = tol
self.shuffle = shuffle
self.verbose = verbose
self.eta0 = eta0
self.n_jobs = n_jobs
self.random_state = random_state
self.early_stopping = early_stopping
self.validation_fraction = validation_fraction
self.n_iter_no_change = n_iter_no_change
self.class_weight = class_weight
self.warm_start = warm_start
self.n_iter = n_iter
super().__init__()
self.classifier = Perceptron(
penalty=self.penalty,
alpha=self.alpha,
fit_intercept=self.fit_intercept,
max_iter=self.max_iter,
tol=self.tol,
shuffle=self.shuffle,
verbose=self.verbose,
eta0=self.eta0,
n_jobs=self.n_jobs,
random_state=self.random_state,
early_stopping=self.early_stopping,
validation_fraction=self.validation_fraction,
n_iter_no_change=self.n_iter_no_change,
class_weight=self.class_weight,
warm_start=self.warm_start)
def fit(self, X, y, classes=None, sample_weight=None):
""" Calls the Perceptron fit function from sklearn.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
y: Array-like
The class labels for all samples in X.
classes: Not used.
sample_weight:
Samples weight. If not provided, uniform weights are assumed.
Returns
-------
PerceptronMask
self
"""
self.classifier.fit(X=X, y=y, sample_weight=sample_weight)
return self
def partial_fit(self, X, y, classes=None, sample_weight=None):
""" partial_fit
Calls the Perceptron partial_fit from sklearn.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
y: Array-like
The class labels for all samples in X.
classes: Not used.
sample_weight:
Samples weight. If not provided, uniform weights are assumed.
Returns
-------
PerceptronMask
self
"""
self.classifier.partial_fit(X=X,
y=y,
classes=classes,
sample_weight=sample_weight)
return self
def predict(self, X):
""" predict
Uses the current model to predict samples in X.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
Returns
-------
numpy.ndarray
A numpy.ndarray containing the predicted labels for all instances in X.
"""
return np.asarray(self.classifier.predict(X))
def predict_proba(self, X):
""" Predicts the probability of each sample belonging to each one of the known classes.
Parameters
----------
X: Numpy.ndarray of shape (n_samples, n_features)
A matrix of the samples we want to predict.
Returns
-------
numpy.ndarray
An array of shape (n_samples, n_features), in which each outer entry is
associated with the X entry of the same index. And where the list in
index [i] contains len(self.target_values) elements, each of which represents
the probability that the i-th sample of X belongs to a certain label.
"""
return self.classifier._predict_proba_lr(X)
svm = SVC(kernel='rbf', C=1.0, random_state=1, gamma=1, probability=True)
svm.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std,
y_combined,
classifier=svm,
test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
#plt.savefig('images/03_11.png', dpi=300)
test = [-2, -2], [0, 0.5], [2, 2]
print(ppn._predict_proba_lr(test))
print(lr.predict_proba(test))
print(svm.predict_proba(test))
# # Solving non-linear problems using a kernel SVM
plt.figure(4)
np.random.seed(1)
X_xor = np.random.randn(200, 2)
y_xor = np.logical_xor(X_xor[:, 0] > 0, X_xor[:, 1] > 0)
y_xor = np.where(y_xor, 1, -1)
plt.scatter(X_xor[y_xor == 1, 0],
X_xor[y_xor == 1, 1],
c='b',
class PerceptronMask(BaseSKMObject, ClassifierMixin):
""" Mask for sklearn.linear_model.Perceptron.
scikit-multiflow requires a few interfaces, not present in scikit-learn,
This mask serves as a wrapper for the Perceptron classifier.
Examples
--------
>>> # Imports
>>> from skmultiflow.neural_networks import PerceptronMask
>>> from skmultiflow.data import SEAGenerator
>>>
>>> # Setup a data stream
>>> stream = SEAGenerator(random_state=1)
>>>
>>> # Setup the Perceptron Mask
>>> perceptron = PerceptronMask()
>>>
>>> n_samples = 0
>>> correct_cnt = 0
>>> while n_samples < 5000 and stream.has_more_samples():
>>> X, y = stream.next_sample()
>>> my_pred = perceptron.predict(X)
>>> if y[0] == my_pred[0]:
>>> correct_cnt += 1
>>> perceptron.partial_fit(X, y, classes=stream.target_values)
>>> n_samples += 1
>>>
>>> # Display the results
>>> print('Perceptron Mask usage example')
>>> print('{} samples analyzed'.format(n_samples))
>>> print("Perceptron's performance: {}".format(correct_cnt / n_samples))
"""
def __init__(self,
penalty=None,
alpha=0.0001,
fit_intercept=True,
max_iter=1000,
tol=0.001,
shuffle=True,
verbose=0,
eta0=1.0,
n_jobs=None,
random_state=0,
early_stopping=False,
validation_fraction=0.1,
n_iter_no_change=5,
class_weight=None,
warm_start=False):
self.penalty = penalty
self.alpha = alpha
self.fit_intercept = fit_intercept
self.max_iter = max_iter
self.tol = tol
self.shuffle = shuffle
self.verbose = verbose
self.eta0 = eta0
self.n_jobs = n_jobs
self.random_state = random_state
self.early_stopping = early_stopping
self.validation_fraction = validation_fraction
self.n_iter_no_change = n_iter_no_change
self.class_weight = class_weight
self.warm_start = warm_start
super().__init__()
self.classifier = Perceptron(
penalty=self.penalty,
alpha=self.alpha,
fit_intercept=self.fit_intercept,
max_iter=self.max_iter,
tol=self.tol,
shuffle=self.shuffle,
verbose=self.verbose,
eta0=self.eta0,
n_jobs=self.n_jobs,
random_state=self.random_state,
early_stopping=self.early_stopping,
validation_fraction=self.validation_fraction,
n_iter_no_change=self.n_iter_no_change,
class_weight=self.class_weight,
warm_start=self.warm_start)
def fit(self, X, y, classes=None, sample_weight=None):
""" Calls the Perceptron fit function from sklearn.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
y: Array-like
The class labels for all samples in X.
classes: Not used.
sample_weight:
Samples weight. If not provided, uniform weights are assumed.
Returns
-------
PerceptronMask
self
"""
self.classifier.fit(X=X, y=y, sample_weight=sample_weight)
return self
def partial_fit(self, X, y, classes=None, sample_weight=None):
""" partial_fit
Calls the Perceptron partial_fit from sklearn.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
y: Array-like
The class labels for all samples in X.
classes: Not used.
sample_weight:
Samples weight. If not provided, uniform weights are assumed.
Returns
-------
PerceptronMask
self
"""
self.classifier.partial_fit(X=X,
y=y,
classes=classes,
sample_weight=sample_weight)
return self
def predict(self, X):
""" predict
Uses the current model to predict samples in X.
Parameters
----------
X: numpy.ndarray of shape (n_samples, n_features)
The feature's matrix.
Returns
-------
numpy.ndarray
A numpy.ndarray containing the predicted labels for all instances in X.
"""
return np.asarray(self.classifier.predict(X))
def predict_proba(self, X):
""" Predicts the probability of each sample belonging to each one of the known classes.
Parameters
----------
X: Numpy.ndarray of shape (n_samples, n_features)
A matrix of the samples we want to predict.
Returns
-------
numpy.ndarray
An array of shape (n_samples, n_features), in which each outer entry is
associated with the X entry of the same index. And where the list in
index [i] contains len(self.target_values) elements, each of which represents
the probability that the i-th sample of X belongs to a certain label.
"""
return self.classifier._predict_proba_lr(X)
