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Running a multi-parameter grid search efficiently using parallel processing to find optimal hyperparameters.
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Handles reading, populating, and saving .docx files using the python-docx library. Use this skill for any tasks involving template filling or modifying Word documents.
Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
This skill includes search capability in 13F, such as fuzzy search a fund information using possibly inaccurate name, or fuzzy search a stock cusip info using its name.
基于 SOC 职业分类
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| name | grid-search-parallelization |
| description | Running a multi-parameter grid search efficiently using parallel processing to find optimal hyperparameters. |
When searching a large hyperparameter space (like combinations of min_samples, epsilon, and shape_weight), joblib or multiprocessing can significantly reduce execution time.
Workflow:
itertools.product.from itertools import product
from joblib import Parallel, delayed
def evaluate_params(params):
min_s, eps, w = params
# ... logic to group images, cluster, match, and calculate averages ...
return {'F1': avg_f1, 'delta': avg_delta, 'min_samples': min_s, 'epsilon': eps, 'shape_weight': w}
# Define ranges
space = list(product(range(3, 10), range(4, 25, 2), np.arange(0.9, 2.0, 0.1)))
results = Parallel(n_jobs=-1)(delayed(evaluate_params)(p) for p in space)