Tutorial

Basic usage:

import pandas as pd
from mlsampler import RandomSampler, HybridSampler

Random Sampling

def create_demo_df():
    data = {
        "is_active": [1, 0, 1, 1, 0],
        "is_negative": [0, 1, 0, 0, 1],
        "score": [10, 0, 50, 100, 5],
        "temperature": [-5.5, 20.0, 36.6, -1.2, 15.8],
        "category": ["A", "B", "C", "D", "100"],
        "city": ["Tokyo", "Osaka", "Nagoya", "Fukuoka", "Sapporo"],
        "cost": [100]*5
    }
    df = pd.DataFrame(data)
    return df

train = create_demo_df()
sampler = RandomSampler.setup(train.values)
print(type(sampler).__qualname__)

sampler.reset_constraints()
sampler.set_constraints('multihot', cols=[0,1])
sampler.set_constraints('random', cols=[2,3], max_used=1)
sampler.set_constraints(
    'categories',
    cols=[4, 5],
    values=train[['category', 'city']].to_numpy(),
    strength='soft'
)

result = sampler.sample(20)
print(pd.DataFrame(result, columns=train.columns))

HyperGrid Sampling

df = pd.DataFrame([
        [0.1, 0.9, 1, 0, "A"],
        [0.5, 0.4, 2, 1, "B"],
        [0.9, 0.75, 3, 0, "AB"],
        [0.2, 0.8, 4, 1, "O"],
    ],
    columns=['ratio1', 'ratio2', 'rank', 'isOk', 'bloodType']
)

sampler = HyperGridSampler.setup(df.values, random_state=42)

print("\n=== dtype check ===")
for i, f in enumerate(sampler.config.features):
    print(f"col {i}: {f.dtype}")

samples = sampler.sample(1000)

print(pd.DataFrame(samples, columns=df.columns))

Notes

• RandomSampler is recommended when: - You have multiple constraints to satisfy simultaneously - The relationships between variables are complex or hard to express analytically

• HybridSampler is recommended when:

  • You want better coverage of continuous feature space

  • You are performing design of experiments (DoE)