plot_prplot_script with examples#
An example showing the prplot function
used by a scikit-learn regressor.
9 # Authors: The scikit-plots developers
10 # SPDX-License-Identifier: BSD-3-Clause
Import scikit-plot
14 import scikitplot.snsx as sp
18 ax = sp.prplot(
19 x=[0, 1, 1, 0, 1, 0, 1, 1, 0, 1],
20 y=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 , 0.9, 1.0],
21 )
24 import numpy as np; np.random.seed(0) # reproducibility
25 import pandas as pd
26 df = pd.DataFrame({
27 "true": np.random.normal(0.5, 0.1, 100).round(),
28 "prob": np.random.normal(0.5, 0.1, 100),
29 "group": ["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"]*10
30 })
PR Curve
35 ax = sp.prplot(x=df.true, y=df.prob, hue=df.group)
36 # ax = sp.rocplot(df, x="true", y="prob", label=f"classA")
40 for i in range(10):
41 np.random.seed(i) # reproducibility
42 df = pd.DataFrame({
43 "true": np.random.normal(0.5, 0.1, 100).round(),
44 "prob": np.random.normal(0.5, 0.1, 100),
45 "group": ["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"]*10
46 })
47 ax = sp.prplot(df, x="true", y="prob", label=f"{i}")
48
49 # ax = sp.rocplot(
50 # x=np.random.normal(0.5, 0.1, 100).round(),
51 # y=np.random.normal(0.5, 0.1, 100),
52 # label=f"{i}",
53 # )
54
55 # --- Collect unique handles and labels ---
56 handles, labels = ax.get_legend_handles_labels()
57 by_label = dict(zip(labels, handles)) # deduplicate
58
59 # Override legend
60 ax.legend(by_label.values(), by_label.keys(), title="Legend")
Total running time of the script: (0 minutes 0.455 seconds)
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