sklearn--感知机Perceptron

Perceptron(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)

参数说明

penalty : None, ‘l2’ or ‘l1’ or ‘elasticnet’

The penalty (aka regularization term) to be used. Defaults to None.

正则项，l2、l1或弹性网络。L1、L2正则化参考。

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.

是否需要计算截距。

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.

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.

迭代停机的值。

shuffle : bool, optional, default True

Whether or not the training data should be shuffled after each epoch.

verbose : integer, optional

The verbosity level冗余

eta0 : double

Constant by which the updates are multiplied. Defaults to 1.

n_jobs : int or None, optional (default=None)

The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

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.伪随机数种子。

early_stopping : bool, default=False

Whether to use early stopping to terminate training when validation. score is not improving. If set to True, it will automatically set aside a fraction of training data as validation and terminate training when validation score is not improving by at least tol for n_iter_no_change consecutive epochs.New in version 0.20.

validation_fraction : float, default=0.1

The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True.New in version 0.20.训练集中用于验证集的比率。

n_iter_no_change : int, default=5

Number of iterations with no improvement to wait before early stopping.New in version 0.20.

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. See the Glossary.

n_iter : int, optional

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

属性

coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes, n_features]

Weights assigned to the features.权重

intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]

Constants in decision function.截距（b)

n_iter_ : int

The actual number of iterations to reach the stopping criterion. For multiclass fits, it is the maximum over every binary fit.

代码示例

import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn.linear_model import Perceptron
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['label'] = iris.target
df.columns=['sepal length', 'sepal width', 'petal length', 'petal width', 'label']
#print(df.label.value_counts())data = np.array(df.iloc[:100, [0, 1, -1]])
X, y = data[:, :-1], data[:, -1]
y = np.array([1 if i == 1 else -1 for i in y])
perceptron = Model()
perceptron.fit(X, y)
print(perceptron.w)
print(perceptron.b)
#sklearnclf = Perceptron(fit_intercept=False, max_iter=1000, shuffle=False)
clf.fit(X, y)
print(clf.coef_)
print(clf.intercept_)