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[WIP] replaced n_iter by max_iter and added deprecation #7761

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3ec39b9
replaced n_iter by max_iter and added deprecation
arnaudbenard Oct 26, 2016
0d19b05
added warnings in the models and fixed tests
arnaudbenard Oct 28, 2016
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41 changes: 25 additions & 16 deletions sklearn/linear_model/bayes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,11 +9,13 @@
from math import log
import numpy as np
from scipy import linalg
import warnings

from .base import LinearModel
from ..base import RegressorMixin
from ..utils.extmath import fast_logdet, pinvh
from ..utils import check_X_y
from ..utils import deprecated


###############################################################################
Expand All @@ -29,7 +31,7 @@ class BayesianRidge(LinearModel, RegressorMixin):

Parameters
----------
n_iter : int, optional
max_iter : int, optional
Maximum number of iterations. Default is 300.

tol : float, optional
Expand Down Expand Up @@ -102,7 +104,7 @@ class BayesianRidge(LinearModel, RegressorMixin):
... # doctest: +NORMALIZE_WHITESPACE
BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False,
copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06,
n_iter=300, normalize=False, tol=0.001, verbose=False)
max_iter=300, normalize=False, tol=0.001, verbose=False)
>>> clf.predict([[1, 1]])
array([ 1.])

Expand All @@ -111,11 +113,14 @@ class BayesianRidge(LinearModel, RegressorMixin):
See examples/linear_model/plot_bayesian_ridge.py for an example.
"""

def __init__(self, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6,
lambda_1=1.e-6, lambda_2=1.e-6, compute_score=False,
fit_intercept=True, normalize=False, copy_X=True,
verbose=False):
self.n_iter = n_iter
@deprecated("Attribute n_iter was deprecated. Use 'max_iter' instead")
def __init__(self, n_iter=None, max_iter=300, tol=1.e-3, alpha_1=1.e-6,
alpha_2=1.e-6, lambda_1=1.e-6, lambda_2=1.e-6,
compute_score=False, fit_intercept=True, normalize=False,
copy_X=True, verbose=False):
if n_iter is not None:
warnings.warn("'n_iter' was deprecated. Use 'max_iter' instead.")
self.max_iter = max_iter
self.tol = tol
self.alpha_1 = alpha_1
self.alpha_2 = alpha_2
Expand Down Expand Up @@ -164,7 +169,7 @@ def fit(self, X, y):
eigen_vals_ = S ** 2

# Convergence loop of the bayesian ridge regression
for iter_ in range(self.n_iter):
for iter_ in range(self.max_iter):

# Compute mu and sigma
# sigma_ = lambda_ / alpha_ * np.eye(n_features) + np.dot(X.T, X)
Expand Down Expand Up @@ -238,7 +243,7 @@ class ARDRegression(LinearModel, RegressorMixin):

Parameters
----------
n_iter : int, optional
max_iter : int, optional
Maximum number of iterations. Default is 300

tol : float, optional
Expand Down Expand Up @@ -315,7 +320,7 @@ class ARDRegression(LinearModel, RegressorMixin):
... # doctest: +NORMALIZE_WHITESPACE
ARDRegression(alpha_1=1e-06, alpha_2=1e-06, compute_score=False,
copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06,
n_iter=300, normalize=False, threshold_lambda=10000.0, tol=0.001,
max_iter=300, normalize=False, threshold_lambda=10000.0, tol=0.001,
verbose=False)
>>> clf.predict([[1, 1]])
array([ 1.])
Expand All @@ -325,11 +330,15 @@ class ARDRegression(LinearModel, RegressorMixin):
See examples/linear_model/plot_ard.py for an example.
"""

def __init__(self, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6,
lambda_1=1.e-6, lambda_2=1.e-6, compute_score=False,
threshold_lambda=1.e+4, fit_intercept=True, normalize=False,
copy_X=True, verbose=False):
self.n_iter = n_iter
@deprecated("Attribute n_iter was deprecated. Use 'max_iter' instead")
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you need to remove this line (same in other classes)

def __init__(self, n_iter=None, max_iter=300, tol=1.e-3, alpha_1=1.e-6,
alpha_2=1.e-6, lambda_1=1.e-6, lambda_2=1.e-6,
compute_score=False, threshold_lambda=1.e+4,
fit_intercept=True, normalize=False, copy_X=True,
verbose=False):
if n_iter is not None:
warnings.warn("'n_iter' was deprecated. Use 'max_iter' instead.")
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@TomDLT TomDLT Oct 28, 2016 •
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Please raise a DeprecationWarning
You also need to use this value if provided, so that the result of `ARDRegression(n_iter=10).fit(...)``is not modify by this PR:

self.n_iter = n_iter
if n_iter is not None:
    warnings.warn(...)
    self.max_iter = n_iter
else:
    self.max_iter = max_iter

(same in other classes)

self.max_iter = max_iter
self.tol = tol
self.fit_intercept = fit_intercept
self.normalize = normalize
Expand Down Expand Up @@ -385,7 +394,7 @@ def fit(self, X, y):
coef_old_ = None

# Iterative procedure of ARDRegression
for iter_ in range(self.n_iter):
for iter_ in range(self.max_iter):
# Compute mu and sigma (using Woodbury matrix identity)
sigma_ = pinvh(np.eye(n_samples) / alpha_ +
np.dot(X[:, keep_lambda] *
Expand Down
29 changes: 17 additions & 12 deletions sklearn/manifold/t_sne.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,10 +13,12 @@
import scipy.sparse as sp
from scipy.spatial.distance import pdist
from scipy.spatial.distance import squareform
import warnings
from ..neighbors import BallTree
from ..base import BaseEstimator
from ..utils import check_array
from ..utils import check_random_state
from ..utils import deprecated
from ..utils.extmath import _ravel
from ..decomposition import PCA
from ..metrics.pairwise import pairwise_distances
Expand Down Expand Up @@ -295,7 +297,7 @@ def _kl_divergence_bh(params, P, neighbors, degrees_of_freedom, n_samples,
return error, grad


def _gradient_descent(objective, p0, it, n_iter, objective_error=None,
def _gradient_descent(objective, p0, it, max_iter, objective_error=None,
n_iter_check=1, n_iter_without_progress=50,
momentum=0.5, learning_rate=1000.0, min_gain=0.01,
min_grad_norm=1e-7, min_error_diff=1e-7, verbose=0,
Expand All @@ -317,7 +319,7 @@ def _gradient_descent(objective, p0, it, n_iter, objective_error=None,
Current number of iterations (this function will be called more than
once during the optimization).

n_iter : int
max_iter : int
Maximum number of gradient descent iterations.

n_iter_check : int
Expand Down Expand Up @@ -383,7 +385,7 @@ def _gradient_descent(objective, p0, it, n_iter, objective_error=None,
best_error = np.finfo(np.float).max
best_iter = 0

for i in range(it, n_iter):
for i in range(it, max_iter):
new_error, grad = objective(p, *args, **kwargs)
grad_norm = linalg.norm(grad)

Expand Down Expand Up @@ -541,7 +543,7 @@ class TSNE(BaseEstimator):
might be too high. If the cost function gets stuck in a bad local
minimum increasing the learning rate helps sometimes.

n_iter : int, optional (default: 1000)
max_iter : int, optional (default: 1000)
Maximum number of iterations for the optimization. Should be at
least 200.

Expand Down Expand Up @@ -644,21 +646,24 @@ class TSNE(BaseEstimator):
http://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf
"""

@deprecated("Attribute n_iter was deprecated. Use 'max_iter' instead")
def __init__(self, n_components=2, perplexity=30.0,
early_exaggeration=4.0, learning_rate=1000.0, n_iter=1000,
n_iter_without_progress=30, min_grad_norm=1e-7,
early_exaggeration=4.0, learning_rate=1000.0, n_iter=None,
max_iter=1000, n_iter_without_progress=30, min_grad_norm=1e-7,
metric="euclidean", init="random", verbose=0,
random_state=None, method='barnes_hut', angle=0.5):
if not ((isinstance(init, string_types) and
init in ["pca", "random"]) or
isinstance(init, np.ndarray)):
msg = "'init' must be 'pca', 'random', or a numpy array"
raise ValueError(msg)
if n_iter is not None:
warnings.warn("'n_iter' was deprecated. Use 'max_iter' instead.")
self.n_components = n_components
self.perplexity = perplexity
self.early_exaggeration = early_exaggeration
self.learning_rate = learning_rate
self.n_iter = n_iter
self.max_iter = max_iter
self.n_iter_without_progress = n_iter_without_progress
self.min_grad_norm = min_grad_norm
self.metric = metric
Expand Down Expand Up @@ -711,8 +716,8 @@ def _fit(self, X, skip_num_points=0):
raise ValueError("early_exaggeration must be at least 1, but is "
"%f" % self.early_exaggeration)

if self.n_iter < 200:
raise ValueError("n_iter should be at least 200")
if self.max_iter < 200:
raise ValueError("max_iter should be at least 200")

if self.metric == "precomputed":
if isinstance(self.init, string_types) and self.init == 'pca':
Expand Down Expand Up @@ -806,7 +811,7 @@ def _tsne(self, P, degrees_of_freedom, n_samples, random_state,
self.n_components)
params = X_embedded.ravel()

opt_args = {"n_iter": 50, "momentum": 0.5, "it": 0,
opt_args = {"max_iter": 50, "momentum": 0.5, "it": 0,
"learning_rate": self.learning_rate,
"n_iter_without_progress": self.n_iter_without_progress,
"verbose": self.verbose, "n_iter_check": 25,
Expand Down Expand Up @@ -840,7 +845,7 @@ def _tsne(self, P, degrees_of_freedom, n_samples, random_state,

params, kl_divergence, it = _gradient_descent(obj_func, params,
**opt_args)
opt_args['n_iter'] = 100
opt_args['max_iter'] = 100
opt_args['momentum'] = 0.8
opt_args['it'] = it + 1
params, kl_divergence, it = _gradient_descent(obj_func, params,
Expand All @@ -853,7 +858,7 @@ def _tsne(self, P, degrees_of_freedom, n_samples, random_state,

# Final optimization
P /= self.early_exaggeration
opt_args['n_iter'] = self.n_iter
opt_args['max_iter'] = self.max_iter
opt_args['it'] = it + 1
params, error, it = _gradient_descent(obj_func, params, **opt_args)

Expand Down
18 changes: 9 additions & 9 deletions sklearn/manifold/tests/test_t_sne.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@ def flat_function(_):
sys.stdout = StringIO()
try:
_, error, it = _gradient_descent(
ObjectiveSmallGradient(), np.zeros(1), 0, n_iter=100,
ObjectiveSmallGradient(), np.zeros(1), 0, max_iter=100,
n_iter_without_progress=100, momentum=0.0, learning_rate=0.0,
min_gain=0.0, min_grad_norm=1e-5, min_error_diff=0.0, verbose=2)
finally:
Expand All @@ -63,7 +63,7 @@ def flat_function(_):
sys.stdout = StringIO()
try:
_, error, it = _gradient_descent(
ObjectiveSmallGradient(), np.zeros(1), 0, n_iter=100,
ObjectiveSmallGradient(), np.zeros(1), 0, max_iter=100,
n_iter_without_progress=100, momentum=0.0, learning_rate=0.0,
min_gain=0.0, min_grad_norm=0.0, min_error_diff=0.2, verbose=2)
finally:
Expand All @@ -79,7 +79,7 @@ def flat_function(_):
sys.stdout = StringIO()
try:
_, error, it = _gradient_descent(
flat_function, np.zeros(1), 0, n_iter=100,
flat_function, np.zeros(1), 0, max_iter=100,
n_iter_without_progress=10, momentum=0.0, learning_rate=0.0,
min_gain=0.0, min_grad_norm=0.0, min_error_diff=-1.0, verbose=2)
finally:
Expand All @@ -95,7 +95,7 @@ def flat_function(_):
sys.stdout = StringIO()
try:
_, error, it = _gradient_descent(
ObjectiveSmallGradient(), np.zeros(1), 0, n_iter=11,
ObjectiveSmallGradient(), np.zeros(1), 0, max_iter=11,
n_iter_without_progress=100, momentum=0.0, learning_rate=0.0,
min_gain=0.0, min_grad_norm=0.0, min_error_diff=0.0, verbose=2)
finally:
Expand Down Expand Up @@ -254,9 +254,9 @@ def test_optimization_minimizes_kl_divergence():
random_state = check_random_state(0)
X, _ = make_blobs(n_features=3, random_state=random_state)
kl_divergences = []
for n_iter in [200, 250, 300]:
for max_iter in [200, 250, 300]:
tsne = TSNE(n_components=2, perplexity=10, learning_rate=100.0,
n_iter=n_iter, random_state=0)
max_iter=max_iter, random_state=0)
tsne.fit_transform(X)
kl_divergences.append(tsne.kl_divergence_)
assert_less_equal(kl_divergences[1], kl_divergences[0])
Expand Down Expand Up @@ -297,8 +297,8 @@ def test_early_exaggeration_too_small():

def test_too_few_iterations():
# Number of gradient descent iterations must be at least 200.
tsne = TSNE(n_iter=199)
assert_raises_regexp(ValueError, "n_iter .*", tsne.fit_transform,
tsne = TSNE(max_iter=199)
assert_raises_regexp(ValueError, "max_iter .*", tsne.fit_transform,
np.array([[0.0]]))


Expand Down Expand Up @@ -469,7 +469,7 @@ def test_no_sparse_on_barnes_hut():
X = random_state.randn(100, 2)
X[(np.random.randint(0, 100, 50), np.random.randint(0, 2, 50))] = 0.0
X_csr = sp.csr_matrix(X)
tsne = TSNE(n_iter=199, method='barnes_hut')
tsne = TSNE(max_iter=199, method='barnes_hut')
assert_raises_regexp(TypeError, "A sparse matrix was.*",
tsne.fit_transform, X_csr)

Expand Down
16 changes: 11 additions & 5 deletions sklearn/neural_network/rbm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,12 +11,14 @@

import numpy as np
import scipy.sparse as sp
import warnings

from ..base import BaseEstimator
from ..base import TransformerMixin
from ..externals.six.moves import xrange
from ..utils import check_array
from ..utils import check_random_state
from ..utils import deprecated
from ..utils import gen_even_slices
from ..utils import issparse
from ..utils.extmath import safe_sparse_dot
Expand Down Expand Up @@ -51,7 +53,7 @@ class BernoulliRBM(BaseEstimator, TransformerMixin):
batch_size : int, optional
Number of examples per minibatch.

n_iter : int, optional
max_iter : int, optional
Number of iterations/sweeps over the training dataset to perform
during training.

Expand Down Expand Up @@ -83,7 +85,7 @@ class BernoulliRBM(BaseEstimator, TransformerMixin):
>>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
>>> model = BernoulliRBM(n_components=2)
>>> model.fit(X)
BernoulliRBM(batch_size=10, learning_rate=0.1, n_components=2, n_iter=10,
BernoulliRBM(batch_size=10, learning_rate=0.1, n_components=2, max_iter=10,
random_state=None, verbose=0)

References
Expand All @@ -97,12 +99,16 @@ class BernoulliRBM(BaseEstimator, TransformerMixin):
Approximations to the Likelihood Gradient. International Conference
on Machine Learning (ICML) 2008
"""

@deprecated("Attribute n_iter was deprecated. Use 'max_iter' instead")
def __init__(self, n_components=256, learning_rate=0.1, batch_size=10,
n_iter=10, verbose=0, random_state=None):
n_iter=None, max_iter=10, verbose=0, random_state=None):
if n_iter is not None:
warnings.warn("'n_iter' was deprecated. Use 'max_iter' instead.")
self.n_components = n_components
self.learning_rate = learning_rate
self.batch_size = batch_size
self.n_iter = n_iter
self.max_iter = max_iter
self.verbose = verbose
self.random_state = random_state

Expand Down Expand Up @@ -350,7 +356,7 @@ def fit(self, X, y=None):
n_batches, n_samples))
verbose = self.verbose
begin = time.time()
for iteration in xrange(1, self.n_iter + 1):
for iteration in xrange(1, self.max_iter + 1):
for batch_slice in batch_slices:
self._fit(X[batch_slice], rng)

Expand Down
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