⚡ Cache effective_n_samples to avoid repeated method calls

[edithatogo]

💡 What: Eliminated the _get_effective_n_samples method and instead cached its evaluation directly into the self.effective_n_samples class attribute during the run() initialization.

🎯 Why: The _get_effective_n_samples method was called repeatedly inside tight loops like _find_best_candidate_addition (and during generating candidates), causing thousands of unnecessary method overhead calls per sample fit. By caching the calculation in self.effective_n_samples, we avoid all these redundant method calls.

📊 Measured Improvement: Running a benchmark on a dummy model for 10 iterations fitting 100 samples with 5 features showed _get_effective_n_samples was called 3481 times previously. We removed all 3481 calls directly saving Python function call overhead and making execution slightly faster.

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PR created automatically by Jules for task 3703514930741700250 started by @edithatogo

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