module plotting.plot_bench_xtime

Short summary

module pymlbenchmark.plotting.plot_bench_xtime

Plotting for benchmarks.

source on GitHub



truncated documentation


Plots benchmark acceleration.


Plotting for benchmarks.

source on GitHub

pymlbenchmark.plotting.plot_bench_xtime.plot_bench_xtime(df, row_cols=None, col_cols=None, hue_cols=None, cmp_col_values=('lib', 'skl'), x_value='mean', y_value='xtime', parallel=(1.0, 0.5), title=None, box_side=6, labelsize=10, fontsize='small', label_fct=None, color_fct=None, ax=None)

Plots benchmark acceleration.

  • df – benchmark results

  • row_cols – dataframe columns for graph rows

  • col_cols – dataframe columns for graph columns

  • hue_cols – dataframe columns for other options

  • cmp_col_values – it can be one column or one tuple (column, baseline name)

  • x_value – value for x-axis

  • y_value – value to plot on y-axis (such as mean, min, …)

  • parallel – lower and upper bounds

  • title – graph title

  • box_side – graph side, the function adjusts the size of the graph

  • labelsize – size of the labels

  • fontsize – font size see Text properties

  • ax – existing axis

  • label_fct – if not None, it is a function which modifies the label before printing it on the graph

  • color_fct – if not None, it is a function which modifies a color based on the label and the previous color


fig, ax

Plot benchmark improvments

from pymlbenchmark.datasets import experiment_results
from pymlbenchmark.plotting import plot_bench_xtime
import matplotlib.pyplot as plt

df = experiment_results('onnxruntime_LogisticRegression')

plot_bench_xtime(df, row_cols='N', col_cols='method',
                 title="LogisticRegression\nAcceleration scikit-learn / onnxruntime")

(png, hires.png, pdf)


source on GitHub