156 lines
5.0 KiB
Python
156 lines
5.0 KiB
Python
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# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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#
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# Implementation of the logistic regression algorithm for classification.
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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# License: BSD 3 clause
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import numpy as np
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from time import time
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from .._base import _BaseModel
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from .._base import _IterativeModel
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from .._base import _Classifier
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class LogisticRegression(_BaseModel, _IterativeModel, _Classifier):
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"""Logistic regression classifier.
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Note that this implementation of Logistic Regression
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expects binary class labels in {0, 1}.
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Parameters
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------------
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eta : float (default: 0.01)
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Learning rate (between 0.0 and 1.0)
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epochs : int (default: 50)
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Passes over the training dataset.
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Prior to each epoch, the dataset is shuffled
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if `minibatches > 1` to prevent cycles in stochastic gradient descent.
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l2_lambda : float
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Regularization parameter for L2 regularization.
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No regularization if l2_lambda=0.0.
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minibatches : int (default: 1)
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The number of minibatches for gradient-based optimization.
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If 1: Gradient Descent learning
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If len(y): Stochastic Gradient Descent (SGD) online learning
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If 1 < minibatches < len(y): SGD Minibatch learning
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random_seed : int (default: None)
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Set random state for shuffling and initializing the weights.
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print_progress : int (default: 0)
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Prints progress in fitting to stderr.
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0: No output
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1: Epochs elapsed and cost
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2: 1 plus time elapsed
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3: 2 plus estimated time until completion
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Attributes
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-----------
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w_ : 2d-array, shape={n_features, 1}
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Model weights after fitting.
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b_ : 1d-array, shape={1,}
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Bias unit after fitting.
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cost_ : list
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List of floats with cross_entropy cost (sgd or gd) for every
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epoch.
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Examples
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-----------
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For usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/classifier/LogisticRegression/
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"""
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def __init__(self, eta=0.01, epochs=50,
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l2_lambda=0.0, minibatches=1,
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random_seed=None,
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print_progress=0):
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_BaseModel.__init__(self)
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_IterativeModel.__init__(self)
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_Classifier.__init__(self)
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self.eta = eta
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self.epochs = epochs
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self.l2_lambda = l2_lambda
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self.minibatches = minibatches
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self.random_seed = random_seed
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self.print_progress = print_progress
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self._is_fitted = False
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def _fit(self, X, y, init_params=True):
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self._check_target_array(y, allowed={(0, 1)})
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if init_params:
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self.b_, self.w_ = self._init_params(
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weights_shape=(X.shape[1], 1),
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bias_shape=(1,),
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random_seed=self.random_seed)
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self.cost_ = []
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self.init_time_ = time()
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rgen = np.random.RandomState(self.random_seed)
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for i in range(self.epochs):
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for idx in self._yield_minibatches_idx(
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rgen=rgen,
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n_batches=self.minibatches,
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data_ary=y,
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shuffle=True):
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y_val = self._activation(X[idx])
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errors = (y[idx] - y_val)
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neg_grad = X[idx].T.dot(errors).reshape(self.w_.shape)
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l2_reg = self.l2_lambda * self.w_
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self.w_ += self.eta * (neg_grad - l2_reg)
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self.b_ += self.eta * errors.sum()
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cost = self._logit_cost(y, self._activation(X))
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self.cost_.append(cost)
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if self.print_progress:
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self._print_progress(iteration=(i + 1),
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n_iter=self.epochs,
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cost=cost)
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return self
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def _predict(self, X):
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# equivalent to np.where(self._activation(X) < 0.5, 0, 1)
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return np.where(self._net_input(X) < 0.0, 0, 1)
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def _net_input(self, X):
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"""Compute the linear net input."""
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return (X.dot(self.w_) + self.b_).flatten()
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def _activation(self, X):
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""" Compute sigmoid activation."""
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z = self._net_input(X)
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return self._sigmoid(z)
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def predict_proba(self, X):
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"""Predict class probabilities of X from the net input.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Training vectors, where n_samples is the number of samples and
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n_features is the number of features.
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Returns
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----------
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Class 1 probability : float
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"""
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return self._activation(X)
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def _logit_cost(self, y, y_val):
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logit = -y.dot(np.log(y_val)) - ((1 - y).dot(np.log(1 - y_val)))
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if self.l2_lambda:
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l2 = self.l2_lambda / 2.0 * np.sum(self.w_**2)
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logit += l2
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return logit
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def _sigmoid(self, z):
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"""Compute the output of the logistic sigmoid function."""
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return 1.0 / (1.0 + np.exp(-z))
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