parallel-grid-search

name	parallel-grid-search
description	Parallelize hyperparameter grid searches using joblib for CPU-bound tasks like DBSCAN clustering evaluation loops.

Parallel Grid Search with joblib

Overview

joblib provides easy parallelism via Parallel and delayed. Ideal for embarrassingly parallel grid searches where each parameter combination is independent.

Installation

pip install joblib

Basic Pattern

from joblib import Parallel, delayed
import itertools

def evaluate_params(param_combo, data):
    """Evaluate one parameter combination. Must be picklable (no lambdas)."""
    epsilon, min_samples, shape_weight = param_combo
    # ... run DBSCAN, compute metrics ...
    return {'epsilon': epsilon, 'min_samples': min_samples,
            'shape_weight': shape_weight, 'F1': f1, 'delta': delta}

# Define search space
epsilons = range(4, 25, 2)       # 4,6,8,...,24
min_samples_range = range(3, 10) # 3,4,...,9
shape_weights = [round(0.9 + 0.1*i, 1) for i in range(11)]  # 0.9..1.9

all_combos = list(itertools.product(epsilons, min_samples_range, shape_weights))

# Run in parallel (n_jobs=-1 uses all available cores)
results = Parallel(n_jobs=-1, verbose=1)(
    delayed(evaluate_params)(combo, data)
    for combo in all_combos
)

import pandas as pd
results_df = pd.DataFrame(results)

Avoiding Pickling Issues

joblib pickles arguments, so avoid:

• Lambda functions as metric arguments
• Local closures with complex state

Good: use module-level functions or classes with __call__:

# BAD: lambda not picklable across processes
metric = lambda a, b: np.linalg.norm(a - b)

# GOOD: named function
def euclidean(a, b):
    return np.linalg.norm(a - b)

# GOOD: class instance
class WeightedMetric:
    def __init__(self, w):
        self.w = w
    def __call__(self, a, b):
        dx, dy = a[0]-b[0], a[1]-b[1]
        return np.sqrt((self.w*dx)**2 + ((2-self.w)*dy)**2)

Precomputing Shared Data

Pass shared read-only data as arguments (joblib uses copy-on-write with fork):

def evaluate_one(combo, citsci_groups, expert_groups, all_images):
    eps, ms, sw = combo
    # citsci_groups and expert_groups are dicts: {file_rad -> np.array}
    ...

# Pre-group data outside parallel loop
citsci_groups = {k: v[['x','y']].values for k, v in citsci.groupby('file_rad')}
expert_groups = {k: v[['x','y']].values for k, v in expert.groupby('file_rad')}
all_images = expert['file_rad'].unique()

results = Parallel(n_jobs=-1)(
    delayed(evaluate_one)(combo, citsci_groups, expert_groups, all_images)
    for combo in all_combos
)

Choosing n_jobs

• n_jobs=-1: use all CPU cores
• n_jobs=-2: use all but one core (leave one for OS)
• n_jobs=4: use exactly 4 cores

Progress Tracking

from joblib import Parallel, delayed
from tqdm import tqdm

# With tqdm progress bar
results = Parallel(n_jobs=-1)(
    delayed(evaluate_one)(combo, data)
    for combo in tqdm(all_combos)
)

Backend Options

# Default 'loky' backend: robust, works across platforms
Parallel(n_jobs=-1, backend='loky')(...)

# 'multiprocessing': use Python's multiprocessing
Parallel(n_jobs=-1, backend='multiprocessing')(...)

# 'threading': for I/O-bound tasks (GIL-free libraries like numpy)
Parallel(n_jobs=-1, backend='threading')(...)

Full Grid Search Template

import numpy as np
import pandas as pd
import itertools
from joblib import Parallel, delayed
from sklearn.cluster import DBSCAN
from scipy.spatial.distance import cdist


def make_weighted_metric(w):
    class M:
        def __init__(self, w): self.w = w
        def __call__(self, a, b):
            return np.sqrt((self.w*(a[0]-b[0]))**2 + ((2-self.w)*(a[1]-b[1]))**2)
    return M(w)


def evaluate_combo(combo, citsci_groups, expert_groups, all_images):
    eps, ms, sw = combo
    metric = make_weighted_metric(sw)

    f1_list, delta_list = [], []
    for img in all_images:
        cit = citsci_groups.get(img, np.empty((0,2)))
        exp = expert_groups.get(img, np.empty((0,2)))

        if len(cit) == 0:
            f1_list.append(0.0); delta_list.append(np.nan); continue

        labels = DBSCAN(eps=eps, min_samples=ms, metric=metric).fit_predict(cit)
        unique = (labels) - {-}
          unique:
            f1_list.append(); delta_list.append(np.nan); 

        centroids = np.array([cit[labels==l].mean(axis=)  l  unique])
        
        f1_list.append(f1); delta_list.append(delta)

    avg_f1 = np.mean(f1_list)
    valid_d = [d  d  delta_list   np.isnan(d)]
    avg_delta = np.mean(valid_d)  valid_d  np.nan
     {: avg_f1, : avg_delta, : eps,
            : ms, : sw}



combos = (itertools.product((,,), (,),
                                 [(+*i,)  i  ()]))
results = Parallel(n_jobs=-)(
    delayed(evaluate_combo)(c, citsci_g, expert_g, images)  c  combos
)
df = pd.DataFrame(results)
df = df[df[] > ]