Merge pull request #6 from sdpython/doc

Updates one documentation example

Author: sdpython

_doc/examples/plot_benchmark_rf.py

@@ -81,6 +81,7 @@ def skl2onnx_convert_lightgbm(scope, operator, container):
 ###############################################
 # Or with the following command.
 out, err = run_cmd("cat /proc/cpuinfo")
+print(out)
 
 ###############################################
 # Fonction to measure inference time
@@ -124,8 +125,6 @@ def measure_inference(fct, X, repeat, max_time=5, quantile=1):
 # model for a random forest and onnxruntime after it was converted
 # into ONNX and for the following configurations.
 
-legend = "parallel-batch-4096-block"
-
 small = cpu_count() < 12
 if small:
     N = 1000
@@ -142,6 +141,8 @@ def measure_inference(fct, X, repeat, max_time=5, quantile=1):
     depth = [6, 8, 10, 12, 14]
     Regressor = RandomForestRegressor
 
+legend = f"parallel-nf-{n_features}-"
+
 # avoid duplicates on machine with 1 or 2 cores.
 n_jobs = list(sorted(set(n_jobs), reverse=True))
 
@@ -175,7 +176,7 @@ def measure_inference(fct, X, repeat, max_time=5, quantile=1):
 
     # parallelization
     cache_name = os.path.join(
-        cache_dir, f"rf-J-{n_j}-E-{n_estimators}-D-{max_depth}.pkl"
+        cache_dir, f"nf-{X.shape[1]}-rf-J-{n_j}-E-{n_estimators}-D-{max_depth}.pkl"
     )
     if os.path.exists(cache_name):
         with open(cache_name, "rb") as f:
@@ -196,7 +197,7 @@ def measure_inference(fct, X, repeat, max_time=5, quantile=1):
     so = SessionOptions()
     so.intra_op_num_threads = n_j
     cache_name = os.path.join(
-        cache_dir, f"rf-J-{n_j}-E-{n_estimators}-D-{max_depth}.onnx"
+        cache_dir, f"nf-{X.shape[1]}-rf-J-{n_j}-E-{n_estimators}-D-{max_depth}.onnx"
     )
     if os.path.exists(cache_name):
         sess = InferenceSession(cache_name, so)
@@ -268,7 +269,7 @@ def measure_inference(fct, X, repeat, max_time=5, quantile=1):
 
 
 fig, axes = plt.subplots(n_rows, n_cols, figsize=(4 * n_cols, 4 * n_rows))
-fig.suptitle(f"{rf.__class__.__name__}")
+fig.suptitle(f"{rf.__class__.__name__}\nX.shape={X.shape}")
 
 for n_j, n_estimators in tqdm(product(n_jobs, n_ests)):
     i = n_jobs.index(n_j)

setup.cfg

@@ -0,0 +1,5 @@
+[options]
+packages = find:
+
+[options.packages.find]
+include = onnx_array_api*

setup.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 import os
 
-from setuptools import find_packages, setup
+from setuptools import setup
 
 ######################
 # beginning of setup
@@ -11,8 +11,6 @@
 here = os.path.dirname(__file__)
 if here == "":
     here = "."
-packages = find_packages(where=here)
-package_dir = {k: os.path.join(here, k.replace(".", "/")) for k in packages}
 package_data = {}
 
 try:
@@ -48,8 +46,6 @@
     author="Xavier Dupré",
     author_email="[email protected]",
     url="https://github.com/sdpython/onnx-array-api",
-    packages=packages,
-    package_dir=package_dir,
     package_data=package_data,
     setup_requires=["numpy", "scipy"],
     install_requires=requirements,