1027 lines
31 KiB
Python
1027 lines
31 KiB
Python
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# Authors: Danny Sullivan <dbsullivan23@gmail.com>
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# Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
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#
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# License: BSD 3 clause
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import math
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import re
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import numpy as np
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import pytest
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from scipy.special import logsumexp
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from sklearn._loss.loss import HalfMultinomialLoss
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from sklearn.base import clone
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from sklearn.datasets import load_iris, make_blobs, make_classification
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from sklearn.linear_model import LogisticRegression, Ridge
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from sklearn.linear_model._base import make_dataset
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from sklearn.linear_model._linear_loss import LinearModelLoss
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from sklearn.linear_model._sag import get_auto_step_size
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from sklearn.linear_model._sag_fast import _multinomial_grad_loss_all_samples
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from sklearn.preprocessing import LabelBinarizer, LabelEncoder
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from sklearn.utils import check_random_state, compute_class_weight
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from sklearn.utils._testing import (
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assert_allclose,
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assert_almost_equal,
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assert_array_almost_equal,
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)
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from sklearn.utils.extmath import row_norms
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from sklearn.utils.fixes import CSR_CONTAINERS
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iris = load_iris()
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# this is used for sag classification
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def log_dloss(p, y):
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z = p * y
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# approximately equal and saves the computation of the log
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if z > 18.0:
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return math.exp(-z) * -y
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if z < -18.0:
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return -y
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return -y / (math.exp(z) + 1.0)
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def log_loss(p, y):
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return np.mean(np.log(1.0 + np.exp(-y * p)))
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# this is used for sag regression
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def squared_dloss(p, y):
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return p - y
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def squared_loss(p, y):
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return np.mean(0.5 * (p - y) * (p - y))
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# function for measuring the log loss
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def get_pobj(w, alpha, myX, myy, loss):
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w = w.ravel()
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pred = np.dot(myX, w)
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p = loss(pred, myy)
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p += alpha * w.dot(w) / 2.0
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return p
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def sag(
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X,
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y,
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step_size,
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alpha,
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n_iter=1,
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dloss=None,
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sparse=False,
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sample_weight=None,
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fit_intercept=True,
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saga=False,
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):
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n_samples, n_features = X.shape[0], X.shape[1]
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weights = np.zeros(X.shape[1])
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sum_gradient = np.zeros(X.shape[1])
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gradient_memory = np.zeros((n_samples, n_features))
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intercept = 0.0
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intercept_sum_gradient = 0.0
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intercept_gradient_memory = np.zeros(n_samples)
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rng = np.random.RandomState(77)
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decay = 1.0
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seen = set()
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# sparse data has a fixed decay of .01
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if sparse:
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decay = 0.01
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for epoch in range(n_iter):
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for k in range(n_samples):
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idx = int(rng.rand() * n_samples)
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# idx = k
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entry = X[idx]
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seen.add(idx)
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p = np.dot(entry, weights) + intercept
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gradient = dloss(p, y[idx])
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if sample_weight is not None:
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gradient *= sample_weight[idx]
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update = entry * gradient + alpha * weights
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gradient_correction = update - gradient_memory[idx]
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sum_gradient += gradient_correction
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gradient_memory[idx] = update
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if saga:
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weights -= gradient_correction * step_size * (1 - 1.0 / len(seen))
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if fit_intercept:
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gradient_correction = gradient - intercept_gradient_memory[idx]
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intercept_gradient_memory[idx] = gradient
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intercept_sum_gradient += gradient_correction
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gradient_correction *= step_size * (1.0 - 1.0 / len(seen))
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if saga:
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intercept -= (
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step_size * intercept_sum_gradient / len(seen) * decay
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) + gradient_correction
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else:
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intercept -= step_size * intercept_sum_gradient / len(seen) * decay
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weights -= step_size * sum_gradient / len(seen)
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return weights, intercept
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def sag_sparse(
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X,
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y,
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step_size,
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alpha,
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n_iter=1,
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dloss=None,
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sample_weight=None,
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sparse=False,
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fit_intercept=True,
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saga=False,
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random_state=0,
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):
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if step_size * alpha == 1.0:
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raise ZeroDivisionError(
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"Sparse sag does not handle the case step_size * alpha == 1"
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)
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n_samples, n_features = X.shape[0], X.shape[1]
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weights = np.zeros(n_features)
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sum_gradient = np.zeros(n_features)
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last_updated = np.zeros(n_features, dtype=int)
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gradient_memory = np.zeros(n_samples)
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rng = check_random_state(random_state)
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intercept = 0.0
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intercept_sum_gradient = 0.0
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wscale = 1.0
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decay = 1.0
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seen = set()
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c_sum = np.zeros(n_iter * n_samples)
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# sparse data has a fixed decay of .01
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if sparse:
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decay = 0.01
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counter = 0
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for epoch in range(n_iter):
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for k in range(n_samples):
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# idx = k
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idx = int(rng.rand() * n_samples)
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entry = X[idx]
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seen.add(idx)
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if counter >= 1:
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for j in range(n_features):
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if last_updated[j] == 0:
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weights[j] -= c_sum[counter - 1] * sum_gradient[j]
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else:
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weights[j] -= (
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c_sum[counter - 1] - c_sum[last_updated[j] - 1]
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) * sum_gradient[j]
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last_updated[j] = counter
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p = (wscale * np.dot(entry, weights)) + intercept
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gradient = dloss(p, y[idx])
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if sample_weight is not None:
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gradient *= sample_weight[idx]
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update = entry * gradient
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gradient_correction = update - (gradient_memory[idx] * entry)
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sum_gradient += gradient_correction
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if saga:
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for j in range(n_features):
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weights[j] -= (
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gradient_correction[j]
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* step_size
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* (1 - 1.0 / len(seen))
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/ wscale
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)
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if fit_intercept:
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gradient_correction = gradient - gradient_memory[idx]
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intercept_sum_gradient += gradient_correction
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gradient_correction *= step_size * (1.0 - 1.0 / len(seen))
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if saga:
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intercept -= (
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step_size * intercept_sum_gradient / len(seen) * decay
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) + gradient_correction
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else:
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intercept -= step_size * intercept_sum_gradient / len(seen) * decay
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gradient_memory[idx] = gradient
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wscale *= 1.0 - alpha * step_size
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if counter == 0:
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c_sum[0] = step_size / (wscale * len(seen))
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else:
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c_sum[counter] = c_sum[counter - 1] + step_size / (wscale * len(seen))
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if counter >= 1 and wscale < 1e-9:
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for j in range(n_features):
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if last_updated[j] == 0:
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weights[j] -= c_sum[counter] * sum_gradient[j]
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else:
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weights[j] -= (
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c_sum[counter] - c_sum[last_updated[j] - 1]
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) * sum_gradient[j]
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last_updated[j] = counter + 1
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c_sum[counter] = 0
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weights *= wscale
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wscale = 1.0
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counter += 1
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for j in range(n_features):
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if last_updated[j] == 0:
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weights[j] -= c_sum[counter - 1] * sum_gradient[j]
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else:
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weights[j] -= (
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c_sum[counter - 1] - c_sum[last_updated[j] - 1]
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) * sum_gradient[j]
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weights *= wscale
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return weights, intercept
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def get_step_size(X, alpha, fit_intercept, classification=True):
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if classification:
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return 4.0 / (np.max(np.sum(X * X, axis=1)) + fit_intercept + 4.0 * alpha)
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else:
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return 1.0 / (np.max(np.sum(X * X, axis=1)) + fit_intercept + alpha)
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def test_classifier_matching():
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n_samples = 20
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X, y = make_blobs(n_samples=n_samples, centers=2, random_state=0, cluster_std=0.1)
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y[y == 0] = -1
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alpha = 1.1
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fit_intercept = True
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step_size = get_step_size(X, alpha, fit_intercept)
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for solver in ["sag", "saga"]:
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if solver == "sag":
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n_iter = 80
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else:
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# SAGA variance w.r.t. stream order is higher
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n_iter = 300
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clf = LogisticRegression(
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solver=solver,
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fit_intercept=fit_intercept,
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tol=1e-11,
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C=1.0 / alpha / n_samples,
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max_iter=n_iter,
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random_state=10,
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multi_class="ovr",
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)
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clf.fit(X, y)
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weights, intercept = sag_sparse(
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X,
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y,
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step_size,
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alpha,
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n_iter=n_iter,
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dloss=log_dloss,
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fit_intercept=fit_intercept,
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saga=solver == "saga",
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)
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weights2, intercept2 = sag(
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X,
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y,
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step_size,
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alpha,
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n_iter=n_iter,
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dloss=log_dloss,
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fit_intercept=fit_intercept,
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saga=solver == "saga",
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)
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weights = np.atleast_2d(weights)
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intercept = np.atleast_1d(intercept)
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weights2 = np.atleast_2d(weights2)
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intercept2 = np.atleast_1d(intercept2)
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assert_array_almost_equal(weights, clf.coef_, decimal=9)
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assert_array_almost_equal(intercept, clf.intercept_, decimal=9)
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assert_array_almost_equal(weights2, clf.coef_, decimal=9)
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assert_array_almost_equal(intercept2, clf.intercept_, decimal=9)
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def test_regressor_matching():
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n_samples = 10
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n_features = 5
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rng = np.random.RandomState(10)
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X = rng.normal(size=(n_samples, n_features))
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true_w = rng.normal(size=n_features)
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y = X.dot(true_w)
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alpha = 1.0
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n_iter = 100
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fit_intercept = True
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step_size = get_step_size(X, alpha, fit_intercept, classification=False)
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clf = Ridge(
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fit_intercept=fit_intercept,
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tol=0.00000000001,
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solver="sag",
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alpha=alpha * n_samples,
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max_iter=n_iter,
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)
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clf.fit(X, y)
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weights1, intercept1 = sag_sparse(
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X,
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y,
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step_size,
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alpha,
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n_iter=n_iter,
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dloss=squared_dloss,
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fit_intercept=fit_intercept,
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)
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weights2, intercept2 = sag(
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X,
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y,
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step_size,
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alpha,
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n_iter=n_iter,
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dloss=squared_dloss,
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fit_intercept=fit_intercept,
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)
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assert_allclose(weights1, clf.coef_)
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assert_allclose(intercept1, clf.intercept_)
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assert_allclose(weights2, clf.coef_)
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assert_allclose(intercept2, clf.intercept_)
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@pytest.mark.filterwarnings("ignore:The max_iter was reached")
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_sag_pobj_matches_logistic_regression(csr_container):
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"""tests if the sag pobj matches log reg"""
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n_samples = 100
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alpha = 1.0
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max_iter = 20
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X, y = make_blobs(n_samples=n_samples, centers=2, random_state=0, cluster_std=0.1)
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clf1 = LogisticRegression(
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solver="sag",
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fit_intercept=False,
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tol=0.0000001,
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C=1.0 / alpha / n_samples,
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max_iter=max_iter,
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random_state=10,
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multi_class="ovr",
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)
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clf2 = clone(clf1)
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clf3 = LogisticRegression(
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fit_intercept=False,
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tol=0.0000001,
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C=1.0 / alpha / n_samples,
|
||
|
|
max_iter=max_iter,
|
||
|
|
random_state=10,
|
||
|
|
multi_class="ovr",
|
||
|
|
)
|
||
|
|
|
||
|
|
clf1.fit(X, y)
|
||
|
|
clf2.fit(csr_container(X), y)
|
||
|
|
clf3.fit(X, y)
|
||
|
|
|
||
|
|
pobj1 = get_pobj(clf1.coef_, alpha, X, y, log_loss)
|
||
|
|
pobj2 = get_pobj(clf2.coef_, alpha, X, y, log_loss)
|
||
|
|
pobj3 = get_pobj(clf3.coef_, alpha, X, y, log_loss)
|
||
|
|
|
||
|
|
assert_array_almost_equal(pobj1, pobj2, decimal=4)
|
||
|
|
assert_array_almost_equal(pobj2, pobj3, decimal=4)
|
||
|
|
assert_array_almost_equal(pobj3, pobj1, decimal=4)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings("ignore:The max_iter was reached")
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_sag_pobj_matches_ridge_regression(csr_container):
|
||
|
|
"""tests if the sag pobj matches ridge reg"""
|
||
|
|
n_samples = 100
|
||
|
|
n_features = 10
|
||
|
|
alpha = 1.0
|
||
|
|
n_iter = 100
|
||
|
|
fit_intercept = False
|
||
|
|
rng = np.random.RandomState(10)
|
||
|
|
X = rng.normal(size=(n_samples, n_features))
|
||
|
|
true_w = rng.normal(size=n_features)
|
||
|
|
y = X.dot(true_w)
|
||
|
|
|
||
|
|
clf1 = Ridge(
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
tol=0.00000000001,
|
||
|
|
solver="sag",
|
||
|
|
alpha=alpha,
|
||
|
|
max_iter=n_iter,
|
||
|
|
random_state=42,
|
||
|
|
)
|
||
|
|
clf2 = clone(clf1)
|
||
|
|
clf3 = Ridge(
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
tol=0.00001,
|
||
|
|
solver="lsqr",
|
||
|
|
alpha=alpha,
|
||
|
|
max_iter=n_iter,
|
||
|
|
random_state=42,
|
||
|
|
)
|
||
|
|
|
||
|
|
clf1.fit(X, y)
|
||
|
|
clf2.fit(csr_container(X), y)
|
||
|
|
clf3.fit(X, y)
|
||
|
|
|
||
|
|
pobj1 = get_pobj(clf1.coef_, alpha, X, y, squared_loss)
|
||
|
|
pobj2 = get_pobj(clf2.coef_, alpha, X, y, squared_loss)
|
||
|
|
pobj3 = get_pobj(clf3.coef_, alpha, X, y, squared_loss)
|
||
|
|
|
||
|
|
assert_array_almost_equal(pobj1, pobj2, decimal=4)
|
||
|
|
assert_array_almost_equal(pobj1, pobj3, decimal=4)
|
||
|
|
assert_array_almost_equal(pobj3, pobj2, decimal=4)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings("ignore:The max_iter was reached")
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_sag_regressor_computed_correctly(csr_container):
|
||
|
|
"""tests if the sag regressor is computed correctly"""
|
||
|
|
alpha = 0.1
|
||
|
|
n_features = 10
|
||
|
|
n_samples = 40
|
||
|
|
max_iter = 100
|
||
|
|
tol = 0.000001
|
||
|
|
fit_intercept = True
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.normal(size=(n_samples, n_features))
|
||
|
|
w = rng.normal(size=n_features)
|
||
|
|
y = np.dot(X, w) + 2.0
|
||
|
|
step_size = get_step_size(X, alpha, fit_intercept, classification=False)
|
||
|
|
|
||
|
|
clf1 = Ridge(
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
tol=tol,
|
||
|
|
solver="sag",
|
||
|
|
alpha=alpha * n_samples,
|
||
|
|
max_iter=max_iter,
|
||
|
|
random_state=rng,
|
||
|
|
)
|
||
|
|
clf2 = clone(clf1)
|
||
|
|
|
||
|
|
clf1.fit(X, y)
|
||
|
|
clf2.fit(csr_container(X), y)
|
||
|
|
|
||
|
|
spweights1, spintercept1 = sag_sparse(
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
step_size,
|
||
|
|
alpha,
|
||
|
|
n_iter=max_iter,
|
||
|
|
dloss=squared_dloss,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
random_state=rng,
|
||
|
|
)
|
||
|
|
|
||
|
|
spweights2, spintercept2 = sag_sparse(
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
step_size,
|
||
|
|
alpha,
|
||
|
|
n_iter=max_iter,
|
||
|
|
dloss=squared_dloss,
|
||
|
|
sparse=True,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
random_state=rng,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert_array_almost_equal(clf1.coef_.ravel(), spweights1.ravel(), decimal=3)
|
||
|
|
assert_almost_equal(clf1.intercept_, spintercept1, decimal=1)
|
||
|
|
|
||
|
|
# TODO: uncomment when sparse Ridge with intercept will be fixed (#4710)
|
||
|
|
# assert_array_almost_equal(clf2.coef_.ravel(),
|
||
|
|
# spweights2.ravel(),
|
||
|
|
# decimal=3)
|
||
|
|
# assert_almost_equal(clf2.intercept_, spintercept2, decimal=1)'''
|
||
|
|
|
||
|
|
|
||
|
|
def test_get_auto_step_size():
|
||
|
|
X = np.array([[1, 2, 3], [2, 3, 4], [2, 3, 2]], dtype=np.float64)
|
||
|
|
alpha = 1.2
|
||
|
|
fit_intercept = False
|
||
|
|
# sum the squares of the second sample because that's the largest
|
||
|
|
max_squared_sum = 4 + 9 + 16
|
||
|
|
max_squared_sum_ = row_norms(X, squared=True).max()
|
||
|
|
n_samples = X.shape[0]
|
||
|
|
assert_almost_equal(max_squared_sum, max_squared_sum_, decimal=4)
|
||
|
|
|
||
|
|
for saga in [True, False]:
|
||
|
|
for fit_intercept in (True, False):
|
||
|
|
if saga:
|
||
|
|
L_sqr = max_squared_sum + alpha + int(fit_intercept)
|
||
|
|
L_log = (max_squared_sum + 4.0 * alpha + int(fit_intercept)) / 4.0
|
||
|
|
mun_sqr = min(2 * n_samples * alpha, L_sqr)
|
||
|
|
mun_log = min(2 * n_samples * alpha, L_log)
|
||
|
|
step_size_sqr = 1 / (2 * L_sqr + mun_sqr)
|
||
|
|
step_size_log = 1 / (2 * L_log + mun_log)
|
||
|
|
else:
|
||
|
|
step_size_sqr = 1.0 / (max_squared_sum + alpha + int(fit_intercept))
|
||
|
|
step_size_log = 4.0 / (
|
||
|
|
max_squared_sum + 4.0 * alpha + int(fit_intercept)
|
||
|
|
)
|
||
|
|
|
||
|
|
step_size_sqr_ = get_auto_step_size(
|
||
|
|
max_squared_sum_,
|
||
|
|
alpha,
|
||
|
|
"squared",
|
||
|
|
fit_intercept,
|
||
|
|
n_samples=n_samples,
|
||
|
|
is_saga=saga,
|
||
|
|
)
|
||
|
|
step_size_log_ = get_auto_step_size(
|
||
|
|
max_squared_sum_,
|
||
|
|
alpha,
|
||
|
|
"log",
|
||
|
|
fit_intercept,
|
||
|
|
n_samples=n_samples,
|
||
|
|
is_saga=saga,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert_almost_equal(step_size_sqr, step_size_sqr_, decimal=4)
|
||
|
|
assert_almost_equal(step_size_log, step_size_log_, decimal=4)
|
||
|
|
|
||
|
|
msg = "Unknown loss function for SAG solver, got wrong instead of"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
get_auto_step_size(max_squared_sum_, alpha, "wrong", fit_intercept)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("seed", range(3)) # locally tested with 1000 seeds
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_sag_regressor(seed, csr_container):
|
||
|
|
"""tests if the sag regressor performs well"""
|
||
|
|
xmin, xmax = -5, 5
|
||
|
|
n_samples = 300
|
||
|
|
tol = 0.001
|
||
|
|
max_iter = 100
|
||
|
|
alpha = 0.1
|
||
|
|
rng = np.random.RandomState(seed)
|
||
|
|
X = np.linspace(xmin, xmax, n_samples).reshape(n_samples, 1)
|
||
|
|
|
||
|
|
# simple linear function without noise
|
||
|
|
y = 0.5 * X.ravel()
|
||
|
|
|
||
|
|
clf1 = Ridge(
|
||
|
|
tol=tol,
|
||
|
|
solver="sag",
|
||
|
|
max_iter=max_iter,
|
||
|
|
alpha=alpha * n_samples,
|
||
|
|
random_state=rng,
|
||
|
|
)
|
||
|
|
clf2 = clone(clf1)
|
||
|
|
clf1.fit(X, y)
|
||
|
|
clf2.fit(csr_container(X), y)
|
||
|
|
score1 = clf1.score(X, y)
|
||
|
|
score2 = clf2.score(X, y)
|
||
|
|
assert score1 > 0.98
|
||
|
|
assert score2 > 0.98
|
||
|
|
|
||
|
|
# simple linear function with noise
|
||
|
|
y = 0.5 * X.ravel() + rng.randn(n_samples, 1).ravel()
|
||
|
|
|
||
|
|
clf1 = Ridge(tol=tol, solver="sag", max_iter=max_iter, alpha=alpha * n_samples)
|
||
|
|
clf2 = clone(clf1)
|
||
|
|
clf1.fit(X, y)
|
||
|
|
clf2.fit(csr_container(X), y)
|
||
|
|
score1 = clf1.score(X, y)
|
||
|
|
score2 = clf2.score(X, y)
|
||
|
|
assert score1 > 0.45
|
||
|
|
assert score2 > 0.45
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings("ignore:The max_iter was reached")
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_sag_classifier_computed_correctly(csr_container):
|
||
|
|
"""tests if the binary classifier is computed correctly"""
|
||
|
|
alpha = 0.1
|
||
|
|
n_samples = 50
|
||
|
|
n_iter = 50
|
||
|
|
tol = 0.00001
|
||
|
|
fit_intercept = True
|
||
|
|
X, y = make_blobs(n_samples=n_samples, centers=2, random_state=0, cluster_std=0.1)
|
||
|
|
step_size = get_step_size(X, alpha, fit_intercept, classification=True)
|
||
|
|
classes = np.unique(y)
|
||
|
|
y_tmp = np.ones(n_samples)
|
||
|
|
y_tmp[y != classes[1]] = -1
|
||
|
|
y = y_tmp
|
||
|
|
|
||
|
|
clf1 = LogisticRegression(
|
||
|
|
solver="sag",
|
||
|
|
C=1.0 / alpha / n_samples,
|
||
|
|
max_iter=n_iter,
|
||
|
|
tol=tol,
|
||
|
|
random_state=77,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
multi_class="ovr",
|
||
|
|
)
|
||
|
|
clf2 = clone(clf1)
|
||
|
|
|
||
|
|
clf1.fit(X, y)
|
||
|
|
clf2.fit(csr_container(X), y)
|
||
|
|
|
||
|
|
spweights, spintercept = sag_sparse(
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
step_size,
|
||
|
|
alpha,
|
||
|
|
n_iter=n_iter,
|
||
|
|
dloss=log_dloss,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
)
|
||
|
|
spweights2, spintercept2 = sag_sparse(
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
step_size,
|
||
|
|
alpha,
|
||
|
|
n_iter=n_iter,
|
||
|
|
dloss=log_dloss,
|
||
|
|
sparse=True,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert_array_almost_equal(clf1.coef_.ravel(), spweights.ravel(), decimal=2)
|
||
|
|
assert_almost_equal(clf1.intercept_, spintercept, decimal=1)
|
||
|
|
|
||
|
|
assert_array_almost_equal(clf2.coef_.ravel(), spweights2.ravel(), decimal=2)
|
||
|
|
assert_almost_equal(clf2.intercept_, spintercept2, decimal=1)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings("ignore:The max_iter was reached")
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_sag_multiclass_computed_correctly(csr_container):
|
||
|
|
"""tests if the multiclass classifier is computed correctly"""
|
||
|
|
alpha = 0.1
|
||
|
|
n_samples = 20
|
||
|
|
tol = 0.00001
|
||
|
|
max_iter = 40
|
||
|
|
fit_intercept = True
|
||
|
|
X, y = make_blobs(n_samples=n_samples, centers=3, random_state=0, cluster_std=0.1)
|
||
|
|
step_size = get_step_size(X, alpha, fit_intercept, classification=True)
|
||
|
|
classes = np.unique(y)
|
||
|
|
|
||
|
|
clf1 = LogisticRegression(
|
||
|
|
solver="sag",
|
||
|
|
C=1.0 / alpha / n_samples,
|
||
|
|
max_iter=max_iter,
|
||
|
|
tol=tol,
|
||
|
|
random_state=77,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
multi_class="ovr",
|
||
|
|
)
|
||
|
|
clf2 = clone(clf1)
|
||
|
|
|
||
|
|
clf1.fit(X, y)
|
||
|
|
clf2.fit(csr_container(X), y)
|
||
|
|
|
||
|
|
coef1 = []
|
||
|
|
intercept1 = []
|
||
|
|
coef2 = []
|
||
|
|
intercept2 = []
|
||
|
|
for cl in classes:
|
||
|
|
y_encoded = np.ones(n_samples)
|
||
|
|
y_encoded[y != cl] = -1
|
||
|
|
|
||
|
|
spweights1, spintercept1 = sag_sparse(
|
||
|
|
X,
|
||
|
|
y_encoded,
|
||
|
|
step_size,
|
||
|
|
alpha,
|
||
|
|
dloss=log_dloss,
|
||
|
|
n_iter=max_iter,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
)
|
||
|
|
spweights2, spintercept2 = sag_sparse(
|
||
|
|
X,
|
||
|
|
y_encoded,
|
||
|
|
step_size,
|
||
|
|
alpha,
|
||
|
|
dloss=log_dloss,
|
||
|
|
n_iter=max_iter,
|
||
|
|
sparse=True,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
)
|
||
|
|
coef1.append(spweights1)
|
||
|
|
intercept1.append(spintercept1)
|
||
|
|
|
||
|
|
coef2.append(spweights2)
|
||
|
|
intercept2.append(spintercept2)
|
||
|
|
|
||
|
|
coef1 = np.vstack(coef1)
|
||
|
|
intercept1 = np.array(intercept1)
|
||
|
|
coef2 = np.vstack(coef2)
|
||
|
|
intercept2 = np.array(intercept2)
|
||
|
|
|
||
|
|
for i, cl in enumerate(classes):
|
||
|
|
assert_array_almost_equal(clf1.coef_[i].ravel(), coef1[i].ravel(), decimal=2)
|
||
|
|
assert_almost_equal(clf1.intercept_[i], intercept1[i], decimal=1)
|
||
|
|
|
||
|
|
assert_array_almost_equal(clf2.coef_[i].ravel(), coef2[i].ravel(), decimal=2)
|
||
|
|
assert_almost_equal(clf2.intercept_[i], intercept2[i], decimal=1)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_classifier_results(csr_container):
|
||
|
|
"""tests if classifier results match target"""
|
||
|
|
alpha = 0.1
|
||
|
|
n_features = 20
|
||
|
|
n_samples = 10
|
||
|
|
tol = 0.01
|
||
|
|
max_iter = 200
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
X = rng.normal(size=(n_samples, n_features))
|
||
|
|
w = rng.normal(size=n_features)
|
||
|
|
y = np.dot(X, w)
|
||
|
|
y = np.sign(y)
|
||
|
|
clf1 = LogisticRegression(
|
||
|
|
solver="sag",
|
||
|
|
C=1.0 / alpha / n_samples,
|
||
|
|
max_iter=max_iter,
|
||
|
|
tol=tol,
|
||
|
|
random_state=77,
|
||
|
|
)
|
||
|
|
clf2 = clone(clf1)
|
||
|
|
|
||
|
|
clf1.fit(X, y)
|
||
|
|
clf2.fit(csr_container(X), y)
|
||
|
|
pred1 = clf1.predict(X)
|
||
|
|
pred2 = clf2.predict(X)
|
||
|
|
assert_almost_equal(pred1, y, decimal=12)
|
||
|
|
assert_almost_equal(pred2, y, decimal=12)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings("ignore:The max_iter was reached")
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_binary_classifier_class_weight(csr_container):
|
||
|
|
"""tests binary classifier with classweights for each class"""
|
||
|
|
alpha = 0.1
|
||
|
|
n_samples = 50
|
||
|
|
n_iter = 20
|
||
|
|
tol = 0.00001
|
||
|
|
fit_intercept = True
|
||
|
|
X, y = make_blobs(n_samples=n_samples, centers=2, random_state=10, cluster_std=0.1)
|
||
|
|
step_size = get_step_size(X, alpha, fit_intercept, classification=True)
|
||
|
|
classes = np.unique(y)
|
||
|
|
y_tmp = np.ones(n_samples)
|
||
|
|
y_tmp[y != classes[1]] = -1
|
||
|
|
y = y_tmp
|
||
|
|
|
||
|
|
class_weight = {1: 0.45, -1: 0.55}
|
||
|
|
clf1 = LogisticRegression(
|
||
|
|
solver="sag",
|
||
|
|
C=1.0 / alpha / n_samples,
|
||
|
|
max_iter=n_iter,
|
||
|
|
tol=tol,
|
||
|
|
random_state=77,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
multi_class="ovr",
|
||
|
|
class_weight=class_weight,
|
||
|
|
)
|
||
|
|
clf2 = clone(clf1)
|
||
|
|
|
||
|
|
clf1.fit(X, y)
|
||
|
|
clf2.fit(csr_container(X), y)
|
||
|
|
|
||
|
|
le = LabelEncoder()
|
||
|
|
class_weight_ = compute_class_weight(class_weight, classes=np.unique(y), y=y)
|
||
|
|
sample_weight = class_weight_[le.fit_transform(y)]
|
||
|
|
spweights, spintercept = sag_sparse(
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
step_size,
|
||
|
|
alpha,
|
||
|
|
n_iter=n_iter,
|
||
|
|
dloss=log_dloss,
|
||
|
|
sample_weight=sample_weight,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
)
|
||
|
|
spweights2, spintercept2 = sag_sparse(
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
step_size,
|
||
|
|
alpha,
|
||
|
|
n_iter=n_iter,
|
||
|
|
dloss=log_dloss,
|
||
|
|
sparse=True,
|
||
|
|
sample_weight=sample_weight,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert_array_almost_equal(clf1.coef_.ravel(), spweights.ravel(), decimal=2)
|
||
|
|
assert_almost_equal(clf1.intercept_, spintercept, decimal=1)
|
||
|
|
|
||
|
|
assert_array_almost_equal(clf2.coef_.ravel(), spweights2.ravel(), decimal=2)
|
||
|
|
assert_almost_equal(clf2.intercept_, spintercept2, decimal=1)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings("ignore:The max_iter was reached")
|
||
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
||
|
|
def test_multiclass_classifier_class_weight(csr_container):
|
||
|
|
"""tests multiclass with classweights for each class"""
|
||
|
|
alpha = 0.1
|
||
|
|
n_samples = 20
|
||
|
|
tol = 0.00001
|
||
|
|
max_iter = 50
|
||
|
|
class_weight = {0: 0.45, 1: 0.55, 2: 0.75}
|
||
|
|
fit_intercept = True
|
||
|
|
X, y = make_blobs(n_samples=n_samples, centers=3, random_state=0, cluster_std=0.1)
|
||
|
|
step_size = get_step_size(X, alpha, fit_intercept, classification=True)
|
||
|
|
classes = np.unique(y)
|
||
|
|
|
||
|
|
clf1 = LogisticRegression(
|
||
|
|
solver="sag",
|
||
|
|
C=1.0 / alpha / n_samples,
|
||
|
|
max_iter=max_iter,
|
||
|
|
tol=tol,
|
||
|
|
random_state=77,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
multi_class="ovr",
|
||
|
|
class_weight=class_weight,
|
||
|
|
)
|
||
|
|
clf2 = clone(clf1)
|
||
|
|
clf1.fit(X, y)
|
||
|
|
clf2.fit(csr_container(X), y)
|
||
|
|
|
||
|
|
le = LabelEncoder()
|
||
|
|
class_weight_ = compute_class_weight(class_weight, classes=np.unique(y), y=y)
|
||
|
|
sample_weight = class_weight_[le.fit_transform(y)]
|
||
|
|
|
||
|
|
coef1 = []
|
||
|
|
intercept1 = []
|
||
|
|
coef2 = []
|
||
|
|
intercept2 = []
|
||
|
|
for cl in classes:
|
||
|
|
y_encoded = np.ones(n_samples)
|
||
|
|
y_encoded[y != cl] = -1
|
||
|
|
|
||
|
|
spweights1, spintercept1 = sag_sparse(
|
||
|
|
X,
|
||
|
|
y_encoded,
|
||
|
|
step_size,
|
||
|
|
alpha,
|
||
|
|
n_iter=max_iter,
|
||
|
|
dloss=log_dloss,
|
||
|
|
sample_weight=sample_weight,
|
||
|
|
)
|
||
|
|
spweights2, spintercept2 = sag_sparse(
|
||
|
|
X,
|
||
|
|
y_encoded,
|
||
|
|
step_size,
|
||
|
|
alpha,
|
||
|
|
n_iter=max_iter,
|
||
|
|
dloss=log_dloss,
|
||
|
|
sample_weight=sample_weight,
|
||
|
|
sparse=True,
|
||
|
|
)
|
||
|
|
coef1.append(spweights1)
|
||
|
|
intercept1.append(spintercept1)
|
||
|
|
coef2.append(spweights2)
|
||
|
|
intercept2.append(spintercept2)
|
||
|
|
|
||
|
|
coef1 = np.vstack(coef1)
|
||
|
|
intercept1 = np.array(intercept1)
|
||
|
|
coef2 = np.vstack(coef2)
|
||
|
|
intercept2 = np.array(intercept2)
|
||
|
|
|
||
|
|
for i, cl in enumerate(classes):
|
||
|
|
assert_array_almost_equal(clf1.coef_[i].ravel(), coef1[i].ravel(), decimal=2)
|
||
|
|
assert_almost_equal(clf1.intercept_[i], intercept1[i], decimal=1)
|
||
|
|
|
||
|
|
assert_array_almost_equal(clf2.coef_[i].ravel(), coef2[i].ravel(), decimal=2)
|
||
|
|
assert_almost_equal(clf2.intercept_[i], intercept2[i], decimal=1)
|
||
|
|
|
||
|
|
|
||
|
|
def test_classifier_single_class():
|
||
|
|
"""tests if ValueError is thrown with only one class"""
|
||
|
|
X = [[1, 2], [3, 4]]
|
||
|
|
y = [1, 1]
|
||
|
|
|
||
|
|
msg = "This solver needs samples of at least 2 classes in the data"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
LogisticRegression(solver="sag").fit(X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_step_size_alpha_error():
|
||
|
|
X = [[0, 0], [0, 0]]
|
||
|
|
y = [1, -1]
|
||
|
|
fit_intercept = False
|
||
|
|
alpha = 1.0
|
||
|
|
msg = re.escape(
|
||
|
|
"Current sag implementation does not handle the case"
|
||
|
|
" step_size * alpha_scaled == 1"
|
||
|
|
)
|
||
|
|
|
||
|
|
clf1 = LogisticRegression(solver="sag", C=1.0 / alpha, fit_intercept=fit_intercept)
|
||
|
|
with pytest.raises(ZeroDivisionError, match=msg):
|
||
|
|
clf1.fit(X, y)
|
||
|
|
|
||
|
|
clf2 = Ridge(fit_intercept=fit_intercept, solver="sag", alpha=alpha)
|
||
|
|
with pytest.raises(ZeroDivisionError, match=msg):
|
||
|
|
clf2.fit(X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multinomial_loss():
|
||
|
|
# test if the multinomial loss and gradient computations are consistent
|
||
|
|
X, y = iris.data, iris.target.astype(np.float64)
|
||
|
|
n_samples, n_features = X.shape
|
||
|
|
n_classes = len(np.unique(y))
|
||
|
|
|
||
|
|
rng = check_random_state(42)
|
||
|
|
weights = rng.randn(n_features, n_classes)
|
||
|
|
intercept = rng.randn(n_classes)
|
||
|
|
sample_weights = np.abs(rng.randn(n_samples))
|
||
|
|
|
||
|
|
# compute loss and gradient like in multinomial SAG
|
||
|
|
dataset, _ = make_dataset(X, y, sample_weights, random_state=42)
|
||
|
|
loss_1, grad_1 = _multinomial_grad_loss_all_samples(
|
||
|
|
dataset, weights, intercept, n_samples, n_features, n_classes
|
||
|
|
)
|
||
|
|
# compute loss and gradient like in multinomial LogisticRegression
|
||
|
|
loss = LinearModelLoss(
|
||
|
|
base_loss=HalfMultinomialLoss(n_classes=n_classes),
|
||
|
|
fit_intercept=True,
|
||
|
|
)
|
||
|
|
weights_intercept = np.vstack((weights, intercept)).T
|
||
|
|
loss_2, grad_2 = loss.loss_gradient(
|
||
|
|
weights_intercept, X, y, l2_reg_strength=0.0, sample_weight=sample_weights
|
||
|
|
)
|
||
|
|
grad_2 = grad_2[:, :-1].T
|
||
|
|
# convert to same convention, i.e. LinearModelLoss uses average(loss, weight=sw)
|
||
|
|
loss_2 *= np.sum(sample_weights)
|
||
|
|
grad_2 *= np.sum(sample_weights)
|
||
|
|
|
||
|
|
# comparison
|
||
|
|
assert_array_almost_equal(grad_1, grad_2)
|
||
|
|
assert_almost_equal(loss_1, loss_2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multinomial_loss_ground_truth():
|
||
|
|
# n_samples, n_features, n_classes = 4, 2, 3
|
||
|
|
n_classes = 3
|
||
|
|
X = np.array([[1.1, 2.2], [2.2, -4.4], [3.3, -2.2], [1.1, 1.1]])
|
||
|
|
y = np.array([0, 1, 2, 0], dtype=np.float64)
|
||
|
|
lbin = LabelBinarizer()
|
||
|
|
Y_bin = lbin.fit_transform(y)
|
||
|
|
|
||
|
|
weights = np.array([[0.1, 0.2, 0.3], [1.1, 1.2, -1.3]])
|
||
|
|
intercept = np.array([1.0, 0, -0.2])
|
||
|
|
sample_weights = np.array([0.8, 1, 1, 0.8])
|
||
|
|
|
||
|
|
prediction = np.dot(X, weights) + intercept
|
||
|
|
logsumexp_prediction = logsumexp(prediction, axis=1)
|
||
|
|
p = prediction - logsumexp_prediction[:, np.newaxis]
|
||
|
|
loss_1 = -(sample_weights[:, np.newaxis] * p * Y_bin).sum()
|
||
|
|
diff = sample_weights[:, np.newaxis] * (np.exp(p) - Y_bin)
|
||
|
|
grad_1 = np.dot(X.T, diff)
|
||
|
|
|
||
|
|
loss = LinearModelLoss(
|
||
|
|
base_loss=HalfMultinomialLoss(n_classes=n_classes),
|
||
|
|
fit_intercept=True,
|
||
|
|
)
|
||
|
|
weights_intercept = np.vstack((weights, intercept)).T
|
||
|
|
loss_2, grad_2 = loss.loss_gradient(
|
||
|
|
weights_intercept, X, y, l2_reg_strength=0.0, sample_weight=sample_weights
|
||
|
|
)
|
||
|
|
grad_2 = grad_2[:, :-1].T
|
||
|
|
# convert to same convention, i.e. LinearModelLoss uses average(loss, weight=sw)
|
||
|
|
loss_2 *= np.sum(sample_weights)
|
||
|
|
grad_2 *= np.sum(sample_weights)
|
||
|
|
|
||
|
|
assert_almost_equal(loss_1, loss_2)
|
||
|
|
assert_array_almost_equal(grad_1, grad_2)
|
||
|
|
|
||
|
|
# ground truth
|
||
|
|
loss_gt = 11.680360354325961
|
||
|
|
grad_gt = np.array(
|
||
|
|
[[-0.557487, -1.619151, +2.176638], [-0.903942, +5.258745, -4.354803]]
|
||
|
|
)
|
||
|
|
assert_almost_equal(loss_1, loss_gt)
|
||
|
|
assert_array_almost_equal(grad_1, grad_gt)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("solver", ["sag", "saga"])
|
||
|
|
def test_sag_classifier_raises_error(solver):
|
||
|
|
# Following #13316, the error handling behavior changed in cython sag. This
|
||
|
|
# is simply a non-regression test to make sure numerical errors are
|
||
|
|
# properly raised.
|
||
|
|
|
||
|
|
# Train a classifier on a simple problem
|
||
|
|
rng = np.random.RandomState(42)
|
||
|
|
X, y = make_classification(random_state=rng)
|
||
|
|
clf = LogisticRegression(solver=solver, random_state=rng, warm_start=True)
|
||
|
|
clf.fit(X, y)
|
||
|
|
|
||
|
|
# Trigger a numerical error by:
|
||
|
|
# - corrupting the fitted coefficients of the classifier
|
||
|
|
# - fit it again starting from its current state thanks to warm_start
|
||
|
|
clf.coef_[:] = np.nan
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match="Floating-point under-/overflow"):
|
||
|
|
clf.fit(X, y)
|