8000 DOC: use notebook-style in plot_prediction_latency.py by ss-is-master-chief · Pull Request #22418 · scikit-learn/scikit-learn · GitHub
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DOC: use notebook-style in plot_prediction_latency.py #22418
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Merged
merged 3 commits into from
Feb 14, 2022
Merged

DOC: use notebook-style in plot_prediction_latency.py #22418

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838f1e2
fix blocksplitter in plot_prediction_latency.py
Feb 8, 2022
1010f53
added header suggestions
Feb 9, 2022
5038157
split plots into separate cells
Feb 12, 2022
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27 changes: 16 additions & 11 deletions examples/applications/plot_prediction_latency.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,11 @@ def _not_in_sphinx():
return "__file__" in globals()


# %%
# Benchmark and plot helper functions
# -----------------------------------


def atomic_benchmark_estimator(estimator, X_test, verbose=False):
"""Measure runtime prediction of each instance."""
n_instances = X_test.shape[0]
Expand Down Expand Up @@ -289,13 +294,10 @@ def plot_benchmark_throughput(throughputs, configuration):
plt.show()


# #############################################################################
# Main code

start_time = time.time()

# #############################################################################
# %%
# Benchmark bulk/atomic prediction speed for various regressors
# -------------------------------------------------------------

configuration = {
"n_train": int(1e3),
"n_test": int(1e2),
Expand Down Expand Up @@ -325,7 +327,10 @@ def plot_benchmark_throughput(throughputs, configuration):
}
benchmark(configuration)

# benchmark n_features influence on prediction speed
# %%
# Benchmark n_features influence on prediction speed
# --------------------------------------------------

percentile = 90
percentiles = n_feature_influence(
{"ridge": Ridge()},
Expand All @@ -336,9 +341,9 @@ def plot_benchmark_throughput(throughputs, configuration):
)
plot_n_features_influence(percentiles, percentile)

# benchmark throughput
# %%
# Benchmark throughput
# --------------------

throughputs = benchmark_throughputs(configuration)
plot_benchmark_throughput(throughputs, configuration)

stop_time = time.time()
print("example run in %.2fs" % (stop_time - start_time))
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