4.4. Logging Configuration#

mloptimizer follows the standard Python library logging pattern, giving you full control over log output. By default, the library uses a NullHandler, meaning no log messages are displayed unless you explicitly configure logging.

4.4.1. Using the verbose Parameter (Recommended)#

The easiest way to enable logging is using the verbose parameter in GeneticSearch:

from mloptimizer.interfaces import GeneticSearch

# Silent (default)
opt = GeneticSearch(estimator_class=..., hyperparam_space=..., verbose=0)

# Info level - shows optimization lifecycle
opt = GeneticSearch(estimator_class=..., hyperparam_space=..., verbose=1)

# Debug level - shows detailed evaluation info
opt = GeneticSearch(estimator_class=..., hyperparam_space=..., verbose=2)

This is similar to how scikit-learn and XGBoost handle verbosity.

4.4.2. Basic Configuration#

To enable logging output, configure Python’s logging module before running your optimization:

import logging

# Enable INFO level logging globally
logging.basicConfig(level=logging.INFO)

# Now use mloptimizer
from mloptimizer.interfaces import GeneticSearch
# ... your optimization code

This will display informative messages about the optimization process, including start/end summaries and progress updates.

4.4.3. Configuring mloptimizer’s Logger Only#

To enable logging specifically for mloptimizer without affecting other libraries:

import logging

# Get mloptimizer's logger
mlopt_logger = logging.getLogger("mloptimizer")
mlopt_logger.setLevel(logging.INFO)

# Add a handler to output to console
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter("%(asctime)s [%(levelname)s]: %(message)s"))
mlopt_logger.addHandler(handler)

4.4.4. Logging to a File#

To save logs to a file for later analysis:

import logging

# Configure logging to file
logging.basicConfig(
    filename='optimization.log',
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s]: %(message)s"
)

Or configure both console and file output:

import logging

# Create logger
logger = logging.getLogger("mloptimizer")
logger.setLevel(logging.INFO)

# Console handler
console_handler = logging.StreamHandler()
console_handler.setFormatter(logging.Formatter("%(asctime)s [%(levelname)s]: %(message)s"))
logger.addHandler(console_handler)

# File handler
file_handler = logging.FileHandler("optimization.log")
file_handler.setFormatter(logging.Formatter("%(asctime)s [%(levelname)s]: %(message)s"))
logger.addHandler(file_handler)

4.4.5. Log Levels#

mloptimizer uses standard Python logging levels:

DEBUG: Detailed information for diagnosing problems (individual evaluations, internal state)
INFO: Confirmation that things are working as expected (optimization start/end, generation summaries)
WARNING: Indication of potential issues (deprecated parameters, suboptimal configurations)
ERROR: Serious problems that prevent operation

Example with DEBUG level:

import logging
logging.getLogger("mloptimizer").setLevel(logging.DEBUG)

4.4.6. Silencing Logs#

To completely suppress mloptimizer’s log output:

import logging
logging.getLogger("mloptimizer").setLevel(logging.CRITICAL)

Or to only see warnings and errors:

import logging
logging.getLogger("mloptimizer").setLevel(logging.WARNING)

4.4.7. Integration with scikit-learn#

Since mloptimizer follows the same logging pattern as scikit-learn and other major Python libraries, you can configure logging for your entire ML pipeline consistently:

import logging

# Configure logging for all ML libraries
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(name)s] %(levelname)s: %(message)s"
)

# Now all libraries (mloptimizer, sklearn, etc.) will log consistently