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[WIP] Change print statements to logging #17808

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[WIP] Change print statements to logging #17808

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fb1a1ec
replace print with logging
rubywerman Jul 1, 2020
3e1a864
replace print with logging
rubywerman Jul 1, 2020
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28 changes: 15 additions & 13 deletions sklearn/cluster/_kmeans.py
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Expand Up @@ -14,6 +14,7 @@
import warnings

import numpy as np
import logging
import scipy.sparse as sp
from threadpoolctl import threadpool_limits

Expand All @@ -39,6 +40,7 @@
from ._k_means_elkan import elkan_iter_chunked_dense
from ._k_means_elkan import elkan_iter_chunked_sparse

logger = logging.getLogger(__name__)
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I think they want a global logger, like

logging.getLogger('sklearn')


###############################################################################
# Initialization heuristic
Expand Down Expand Up @@ -406,7 +408,7 @@ def _kmeans_single_elkan(X, sample_weight, n_clusters, max_iter=300,
x_squared_norms=x_squared_norms)

if verbose:
print('Initialization complete')
logger.info('Initialization complete')

n_samples = X.shape[0]

Expand Down Expand Up @@ -446,12 +448,12 @@ def _kmeans_single_elkan(X, sample_weight, n_clusters, max_iter=300,

if verbose:
inertia = _inertia(X, sample_weight, centers, labels)
print("Iteration {0}, inertia {1}" .format(i, inertia))
logger.info("Iteration {0}, inertia {1}" .format(i, inertia))

center_shift_tot = (center_shift**2).sum()
if center_shift_tot <= tol:
if verbose:
print("Converged at iteration {0}: "
logger.info("Converged at iteration {0}: "
"center shift {1} within tolerance {2}"
.format(i, center_shift_tot, tol))
break
Expand Down Expand Up @@ -553,7 +555,7 @@ def _kmeans_single_lloyd(X, sample_weight, n_clusters, max_iter=300,
x_squared_norms=x_squared_norms)

if verbose:
print("Initialization complete")
logger.info("Initialization complete")

centers_new = np.zeros_like(centers)
labels = np.full(X.shape[0], -1, dtype=np.int32)
Expand All @@ -576,12 +578,12 @@ def _kmeans_single_lloyd(X, sample_weight, n_clusters, max_iter=300,

if verbose:
inertia = _inertia(X, sample_weight, centers, labels)
print("Iteration {0}, inertia {1}" .format(i, inertia))
logger.info("Iteration {0}, inertia {1}" .format(i, inertia))

center_shift_tot = (center_shift**2).sum()
if center_shift_tot <= tol:
if verbose:
print("Converged at iteration {0}: "
logger.info("Converged at iteration {0}: "
"center shift {1} within tolerance {2}"
.format(i, center_shift_tot, tol))
break
Expand Down Expand Up @@ -1309,7 +1311,7 @@ def _mini_batch_step(X, sample_weight, x_squared_norms, centers, weight_sums,
new_centers = random_state.choice(X.shape[0], replace=False,
size=n_reassigns)
if verbose:
print("[MiniBatchKMeans] Reassigning %i cluster centers."
logger.info("[MiniBatchKMeans] Reassigning %i cluster centers."
% n_reassigns)

if sp.issparse(X) and not sp.issparse(centers):
Expand Down Expand Up @@ -1398,13 +1400,13 @@ def _mini_batch_convergence(model, iteration_idx, n_iter, tol,
' mean batch inertia: %f, ewa inertia: %f ' % (
iteration_idx + 1, n_iter, batch_inertia,
ewa_inertia))
print(progress_msg)
logger.info(progress_msg)

# Early stopping based on absolute tolerance on squared change of
# centers position (using EWA smoothing)
if tol > 0.0 and ewa_diff <= tol:
if verbose:
print('Converged (small centers change) at iteration %d/%d'
logger.info('Converged (small centers change) at iteration %d/%d'
% (iteration_idx + 1, n_iter))
return True

Expand All @@ -1420,7 +1422,7 @@ def _mini_batch_convergence(model, iteration_idx, n_iter, tol,
if (model.max_no_improvement is not None
and no_improvement >= model.max_no_improvement):
if verbose:
print('Converged (lack of improvement in inertia)'
logger.info('Converged (lack of improvement in inertia)'
' at iteration %d/%d'
% (iteration_idx + 1, n_iter))
return True
Expand Down Expand Up @@ -1668,7 +1670,7 @@ def fit(self, X, y=None, sample_weight=None):
best_inertia = None
for init_idx in range(n_init):
if self.verbose:
print("Init %d/%d with method: %s"
logger.info("Init %d/%d with method: %s"
% (init_idx + 1, n_init, self.init))
weight_sums = np.zeros(self.n_clusters, dtype=sample_weight.dtype)

Expand Down Expand Up @@ -1696,7 +1698,7 @@ def fit(self, X, y=None, sample_weight=None):
x_squared_norms_valid,
cluster_centers)
if self.verbose:
print("Inertia for init %d/%d: %f"
logger.info("Inertia for init %d/%d: %f"
% (init_idx + 1, n_init, inertia))
if best_inertia is None or inertia < best_inertia:
self.cluster_centers_ = cluster_centers
Expand Down Expand Up @@ -1768,7 +1770,7 @@ def _labels_inertia_minibatch(self, X, sample_weight):
Sum of squared distances of points to nearest cluster.
"""
if self.verbose:
print('Computing label assignment and total inertia')
logger.info('Computing label assignment and total inertia')
sample_weight = _check_normalize_sample_weight(sample_weight, X)
x_squared_norms = row_norms(X, squared=True)
slices = gen_batches(X.shape[0], self.batch_size)
Expand Down