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#!/usr/bin/env python3
import os
import sys
import argparse
import zipfile
import shutil
import tempfile
import xml.etree.ElementTree as ET

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function ff() {
    if [ -z $1 ]
    then
        search_dir="."
    else
        search_dir="$1"
    fi
    ag --nobreak --noheading . $search_dir | \
        fzf --preview='cat "$(echo {} | cut -d: -f1)"' --preview-window=right
}

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def benchmark_pandas():
    import timerit
    import pandas as pd
    import numpy as np
    column_data = {
        key: np.random.rand(1000)
        for key in map(chr, range(ord('a'), ord('z')))
    }
    data_frame = pd.DataFrame(column_data)

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from time import perf_counter

from cuml import using_output_type
from cuml.datasets import make_classification
from cuml.ensemble import RandomForestClassifier
from cuml.metrics import accuracy_score

with using_output_type('cupy'):
    data, labels = make_classification(
        n_samples=int(1e5),

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import cupy
from cupyx.time import repeat



cupy.random.seed(0)
for size in (5, 50, 500, 5000, 50000, int(5e6)):
    a = cupy.random.rand(size)
    q = cupy.linspace(0, 100, 3)
    def f():

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compute-sanitizer pytest python/cuml/test

========= COMPUTE-SANITIZER
============================= test session starts ==============================
platform linux -- Python 3.8.6, pytest-6.2.2, py-1.10.0, pluggy-0.13.1
rapids_pytest_benchmark: 0.0.13
benchmark: 3.2.3 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
rootdir: /home/whicks/proj_cuml/cuml/python, configfile: pytest.ini
plugins: hypothesis-6.1.1, cov-2.11.1, rapids-pytest-benchmark-0.0.13, benchmark-3.2.3, timeout-1.4.2, repeat-0.8.0, forked-1.3.0, asyncio-0.12.0, xdist-2.2.0
collected 27716 items

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tree
version=v3
num_class=1
num_tree_per_iteration=1
label_index=0
max_feature_idx=31
objective=binary sigmoid:1
feature_names=0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
feature_infos=[-6.5546088218688965:6.2278804779052734] -1:1:0 [-4.965395450592041:6.5972399711608887] [-4.6478652954101562:6.5585684776306152] [-8.2879343032836914:7.7419576644897461] [-6.8546819686889648:7.0196652412414551] [-8.7973241806030273:5.5609464645385742] [-6.5725240707397461:7.2596492767333984] -1:0:1 [-6.8401269912719727:6.9245181083679199] [-11.817563056945801:12.412569046020508] [-14.371319770812988:12.11441707611084] [-2.6720693111419678:3.1814792156219482] [-3.1954507827758789:3.3344674110412598] [-3.2284896373748779:3.2031958103179932] [-4.0048222541809082:3.3795340061187744] [-3.2715401649475098:2.9391093254089355] [-3.5124735832214355:2.8368351459503174] [-2.8929731845855713:3.4158065319061279] [-3.7133853435516357:3.583622932434082] [-2.697335958480835:3.3189976215362549] [-2.9512

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import cupy as cp
import os
import numpy as np
import treelite
import treelite_runtime
import xgboost as xgb
from time import perf_counter
from cuml.common.device_selection import using_device_type
from cuml.experimental import ForestInference