Micky774

sudo apt update
sudo apt-get install git -y

"${SHELL}" <(curl -L micro.mamba.pm/install.sh) << START_ARGS

y
y

START_ARGS

Micky774

# %%
results_path = 'local_artifacts/benchmarks/simd'
results_path += '/' if results_path[-1] != '/' else ''
branch = "PR"

# %%
from sklearn.metrics import DistanceMetric
from time import perf_counter
from functools import partial
from itertools import product

Micky774

from sklearn.metrics.pairwise import pairwise_distances
from scipy.sparse import csr_matrix
from sklearn.metrics import DistanceMetric
import numpy as np

N_FEATURES = 10
N_SAMPLES = 10_000

def _generate_PWD_data(n_samples_X, n_samples_Y, n_features, n_classes, n_outs=1, random_state=0):
    rng = np.random.RandomState(random_state)

Micky774

# %%
results_path = 'local_artifacts/benchmarks/float32'
results_path += '/' if results_path[-1] != '/' else ''
branch = "main"

# %%
from sklearn.metrics import DistanceMetric
from scipy.sparse import csr_matrix
from time import perf_counter
from functools import partial

Micky774

# %%
from os import mkdir
from pathlib import Path
from os.path import join

branch = "PR"
path = Path(join("data", f"{branch}.csv"))
if not path.parent.exists():
    mkdir(path.parent)

Micky774

# %%
results_path = 'local_artifacts/benchmarks/dist'
results_path += '/' if results_path[-1] != '/' else ''

# %%
from sklearn.metrics._pairwise_distances_reduction import ArgKmin
from scipy.sparse import csr_matrix
from statistics import mean, stdev
from time import perf_counter
from functools import partial

Micky774

# %%
from pathlib import Path

results_path = 'local_artifacts/benchmarks/KNR/'
results_path += '/' if results_path[-1] != '/' else ''
Path(results_path).mkdir(parents=True, exist_ok=True)
results_path += "data.csv"

# %%
from slsdm import get_distance_metric

Micky774

results_path = "local_artifacts/benchmarks/native/quick/manhattan"
results_path += "/" if results_path[-1] != "/" else ""

from distance_metrics import get_distance_metric

from sklearn.metrics._dist_metrics import ManhattanDistance, ManhattanDistance32
from statistics import mean, stdev
from time import perf_counter
from functools import partial
from itertools import product

Micky774

from sklearn.metrics._dist_metrics import DistanceMetric, DistanceMetric32
from statistics import mean, stdev
from time import perf_counter
from functools import partial
from itertools import product
from pathlib import Path
import numpy as np
import csv

results_path = 'local_artifacts/benchmarks/native/quick'

Micky774

import numpy as np
from scipy import sparse as sp

def generate_data(n_samples, n_features, n_classes=2, X_density=1, y_sparse=False, dtype=np.float64, random_state=None):
    rng = np.random.RandomState(random_state)
    if X_density < 1:
        X = sp.random(n_samples, n_features, format="csr", density=X_density, random_state=rng)
    else:
        X = np.round(rng.rand(n_samples,n_features)*50).astype(dtype)
    y = np.round(rng.randint(n_classes,size=(n_samples,))).astype(dtype)