| """Instructions | |
| 1. Build this PR and run: | |
| ```bash | |
| python bench_missing_rf.py bench ~/bench_results_rf pr | |
| ``` | |
| 2. On main run: | |
| ```bash |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import seaborn as sns | |
| results = pd.read_csv("results_backend.csv") | |
| sns.set_theme(context="paper", font_scale=1.4) | |
| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), constrained_layout=True, sharey=True) | |
| sns.barplot(y="backend", x="duration", data=results[results["method"] == "fit"], ax=ax1) | |
| ax1.set_xlabel("duration (sec)") |
| """Benchmark for early stopping with predefined metric strings. | |
| ```python | |
| python bench_hist_early_stopping.py --problem classification | |
| python bench_hist_early_stopping.py --problem regression | |
| ``` | |
| """ | |
| import argparse | |
| from time import perf_counter |
| """Instructions | |
| 1. Build this PR and run: | |
| ```bash | |
| python bench_missing.py bench ~/bench_results pr | |
| ``` | |
| 2. On main run: | |
| ```bash |
https://scikit-learn.org/dev/auto_examples/tree/plot_iris_dtc.html
https://scikit-learn.org/dev/auto_examples/ensemble/plot_adaboost_twoclass.html
https://scikit-learn.org/dev/auto_examples/calibration/plot_compare_calibration.html
https://scikit-learn.org/dev/auto_examples/linear_model/plot_logistic_multinomial.html
https://scikit-learn.org/dev/auto_examples/linear_model/plot_sgd_iris.html