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DOC Change print format in TSNE example (scikit-learn#25569)
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Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
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@ArturoAmorQ @ogrisel
ArturoAmorQ and ogrisel authored Feb 9, 2023
1 parent 584d413 commit 57bb1ac
Showing 1 changed file with 30 additions and 35 deletions.
65 changes: 30 additions & 35 deletions examples/neighbors/approximate_nearest_neighbors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -156,54 +156,50 @@ def load_mnist(n_samples):

for dataset_name, (X, y) in datasets:

msg = "Benchmarking on %s:" % dataset_name
print("\n%s\n%s" % (msg, "-" * len(msg)))
msg = f"Benchmarking on {dataset_name}:"
print(f"\n{msg}\n" + str("-" * len(msg)))

for transformer_name, transformer in transformers:
longest = np.max([len(name) for name, model in transformers])
whitespaces = " " * (longest - len(transformer_name))
start = time.time()
transformer.fit(X)
fit_duration = time.time() - start
print("%s: %s%.3f sec (fit)" % (transformer_name, whitespaces, fit_duration))
print(f"{transformer_name:<{longest}} {fit_duration:.3f} sec (fit)")
start = time.time()
Xt = transformer.transform(X)
transform_duration = time.time() - start
print(
"%s: %s%.3f sec (transform)"
% (transformer_name, whitespaces, transform_duration)
)
print(f"{transformer_name:<{longest}} {transform_duration:.3f} sec (transform)")
if transformer_name == "PyNNDescentTransformer":
start = time.time()
Xt = transformer.transform(X)
transform_duration = time.time() - start
print(
"%s: %s%.3f sec (transform)"
% (transformer_name, whitespaces, transform_duration)
f"{transformer_name:<{longest}} {transform_duration:.3f} sec"
" (transform)"
)

# %%
# Sample output::
#
# Benchmarking on MNIST_10000:
# ----------------------------
# KNeighborsTransformer: 0.007 sec (fit)
# KNeighborsTransformer: 1.139 sec (transform)
# NMSlibTransformer: 0.208 sec (fit)
# NMSlibTransformer: 0.315 sec (transform)
# PyNNDescentTransformer: 4.823 sec (fit)
# PyNNDescentTransformer: 4.884 sec (transform)
# PyNNDescentTransformer: 0.744 sec (transform)
# KNeighborsTransformer 0.007 sec (fit)
# KNeighborsTransformer 1.139 sec (transform)
# NMSlibTransformer 0.208 sec (fit)
# NMSlibTransformer 0.315 sec (transform)
# PyNNDescentTransformer 4.823 sec (fit)
# PyNNDescentTransformer 4.884 sec (transform)
# PyNNDescentTransformer 0.744 sec (transform)
#
# Benchmarking on MNIST_20000:
# ----------------------------
# KNeighborsTransformer: 0.011 sec (fit)
# KNeighborsTransformer: 5.769 sec (transform)
# NMSlibTransformer: 0.733 sec (fit)
# NMSlibTransformer: 1.077 sec (transform)
# PyNNDescentTransformer: 14.448 sec (fit)
# PyNNDescentTransformer: 7.103 sec (transform)
# PyNNDescentTransformer: 1.759 sec (transform)
# KNeighborsTransformer 0.011 sec (fit)
# KNeighborsTransformer 5.769 sec (transform)
# NMSlibTransformer 0.733 sec (fit)
# NMSlibTransformer 1.077 sec (transform)
# PyNNDescentTransformer 14.448 sec (fit)
# PyNNDescentTransformer 7.103 sec (transform)
# PyNNDescentTransformer 1.759 sec (transform)
#
# Notice that the `PyNNDescentTransformer` takes more time during the first
# `fit` and the first `transform` due to the overhead of the numba just in time
Expand Down Expand Up @@ -248,18 +244,17 @@ def load_mnist(n_samples):

for dataset_name, (X, y) in datasets:

msg = "Benchmarking on %s:" % dataset_name
print("\n%s\n%s" % (msg, "-" * len(msg)))
msg = f"Benchmarking on {dataset_name}:"
print(f"\n{msg}\n" + str("-" * len(msg)))

for transformer_name, transformer in transformers:
longest = np.max([len(name) for name, model in transformers])
whitespaces = " " * (longest - len(transformer_name))
start = time.time()
Xt = transformer.fit_transform(X)
transform_duration = time.time() - start
print(
"%s: %s%.3f sec (fit_transform)"
% (transformer_name, whitespaces, transform_duration)
f"{transformer_name:<{longest}} {transform_duration:.3f} sec"
" (fit_transform)"
)

# plot TSNE embedding which should be very similar across methods
Expand All @@ -284,15 +279,15 @@ def load_mnist(n_samples):
#
# Benchmarking on MNIST_10000:
# ----------------------------
# TSNE with internal NearestNeighbors: 24.828 sec (fit_transform)
# TSNE with KNeighborsTransformer: 20.111 sec (fit_transform)
# TSNE with NMSlibTransformer: 21.757 sec (fit_transform)
# TSNE with internal NearestNeighbors 24.828 sec (fit_transform)
# TSNE with KNeighborsTransformer 20.111 sec (fit_transform)
# TSNE with NMSlibTransformer 21.757 sec (fit_transform)
#
# Benchmarking on MNIST_20000:
# ----------------------------
# TSNE with internal NearestNeighbors: 51.955 sec (fit_transform)
# TSNE with KNeighborsTransformer: 50.994 sec (fit_transform)
# TSNE with NMSlibTransformer: 43.536 sec (fit_transform)
# TSNE with internal NearestNeighbors 51.955 sec (fit_transform)
# TSNE with KNeighborsTransformer 50.994 sec (fit_transform)
# TSNE with NMSlibTransformer 43.536 sec (fit_transform)
#
# We can observe that the default :class:`~sklearn.manifold.TSNE` estimator with
# its internal :class:`~sklearn.neighbors.NearestNeighbors` implementation is
Expand Down

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