Perceptron

class ibex.sklearn.linear_model.Perceptron(penalty=None, alpha=0.0001, fit_intercept=True, max_iter=None, tol=None, shuffle=True, verbose=0, eta0=1.0, n_jobs=1, random_state=0, class_weight=None, warm_start=False, n_iter=None)

Bases: sklearn.linear_model.perceptron.Perceptron, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Note

The documentation following is of the original class wrapped by this class. This class wraps the attribute coef_.

Example:

>>> import numpy as np
>>> from sklearn import datasets
>>> import pandas as pd
>>>
>>> iris = datasets.load_iris()
>>> features, targets, iris = iris['feature_names'], iris['target_names'], pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=iris['feature_names']+['class'])
>>> iris['class'] = iris['class'].map(pd.Series(targets))
>>>
>>> iris.head()
                sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)             0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2

    class
0  setosa
1  setosa
2  setosa
3  setosa
4  setosa
>>>
>>> from ibex.sklearn import linear_model as pd_linear_model
>>>
>>> clf =  pd_linear_model.Perceptron().fit(iris[features], iris['class'])
>>>
>>> clf.coef_
sepal length (cm)   ...
sepal width (cm)    ...
petal length (cm)   ...
petal width (cm)    ...
dtype: float64

Note

The documentation following is of the original class wrapped by this class. This class wraps the attribute intercept_.

Example:

>>> import numpy as np
>>> from sklearn import datasets
>>> import pandas as pd
>>>
>>> iris = datasets.load_iris()
>>> features, targets, iris = iris['feature_names'], iris['target_names'], pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=iris['feature_names']+['class'])
>>> iris['class'] = iris['class'].map(pd.Series(targets))
>>>
>>> iris.head()
                sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)             0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2

    class
0  setosa
1  setosa
2  setosa
3  setosa
4  setosa
>>> from ibex.sklearn import linear_model as pd_linear_model
>>>
>>> clf = pd_linear_model.Perceptron().fit(iris[features], iris['class'])
>>>
>>> clf.intercept_
sepal length (cm)   ...
sepal width (cm)    ...
petal length (cm)   ...
petal width (cm)    ...
dtype: float64

Perceptron

Read more in the User Guide.

penalty : None, ‘l2’ or ‘l1’ or ‘elasticnet’
The penalty (aka regularization term) to be used. Defaults to None.
alpha : float
Constant that multiplies the regularization term if regularization is used. Defaults to 0.0001
fit_intercept : bool
Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.
max_iter : int, optional

The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial_fit. Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None.

New in version 0.19.

tol : float or None, optional

The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol). Defaults to None. Defaults to 1e-3 from 0.21.

New in version 0.19.

shuffle : bool, optional, default True
Whether or not the training data should be shuffled after each epoch.
verbose : integer, optional
The verbosity level
eta0 : double
Constant by which the updates are multiplied. Defaults to 1.
n_jobs : integer, optional
The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. -1 means ‘all CPUs’. Defaults to 1.
random_state : int, RandomState instance or None, optional, default None
The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
class_weight : dict, {class_label: weight} or “balanced” or None, optional

Preset for the class_weight fit parameter.

Weights associated with classes. If not given, all classes are supposed to have weight one.

The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))

warm_start : bool, optional
When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.
n_iter : int, optional

The number of passes over the training data (aka epochs). Defaults to None. Deprecated, will be removed in 0.21.

Changed in version 0.19: Deprecated

coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes, n_features]
Weights assigned to the features.
intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
Constants in decision function.
n_iter_ : int
The actual number of iterations to reach the stopping criterion. For multiclass fits, it is the maximum over every binary fit.

Perceptron and SGDClassifier share the same underlying implementation. In fact, Perceptron() is equivalent to SGDClassifier(loss=”perceptron”, eta0=1, learning_rate=”constant”, penalty=None).

SGDClassifier

https://en.wikipedia.org/wiki/Perceptron and references therein.

decision_function(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict confidence scores for samples.

The confidence score for a sample is the signed distance of that sample to the hyperplane.

X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)
Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.
fit(X, y, coef_init=None, intercept_init=None, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit linear model with Stochastic Gradient Descent.

X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data
y : numpy array, shape (n_samples,)
Target values
coef_init : array, shape (n_classes, n_features)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (n_classes,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified

self : returns an instance of self.

partial_fit(X, y, classes=None, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit linear model with Stochastic Gradient Descent.

X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of the training data
y : numpy array, shape (n_samples,)
Subset of the target values
classes : array, shape (n_classes,)
Classes across all calls to partial_fit. Can be obtained by via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples. If not provided, uniform weights are assumed.

self : returns an instance of self.

predict(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict class labels for samples in X.

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Samples.
C : array, shape = [n_samples]
Predicted class label per sample.
score(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
score : float
Mean accuracy of self.predict(X) wrt. y.