094fix

.readthedocs.yaml

@@ -16,5 +16,4 @@ python:
     - requirements: requirements_docs.txt
 
 formats:
-  - pdf
   - epub

docs/sections/Advanced/logging.rst

@@ -0,0 +1,147 @@
+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.
+
+Using the verbose Parameter (Recommended)
+-----------------------------------------
+
+The easiest way to enable logging is using the ``verbose`` parameter in ``GeneticSearch``:
+
+.. code-block:: python
+
+    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.
+
+Basic Configuration
+-------------------
+
+To enable logging output, configure Python's logging module before running your optimization:
+
+.. code-block:: python
+
+    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.
+
+Configuring mloptimizer's Logger Only
+-------------------------------------
+
+To enable logging specifically for mloptimizer without affecting other libraries:
+
+.. code-block:: python
+
+    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)
+
+Logging to a File
+-----------------
+
+To save logs to a file for later analysis:
+
+.. code-block:: python
+
+    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:
+
+.. code-block:: python
+
+    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)
+
+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:
+
+.. code-block:: python
+
+    import logging
+    logging.getLogger("mloptimizer").setLevel(logging.DEBUG)
+
+Silencing Logs
+--------------
+
+To completely suppress mloptimizer's log output:
+
+.. code-block:: python
+
+    import logging
+    logging.getLogger("mloptimizer").setLevel(logging.CRITICAL)
+
+Or to only see warnings and errors:
+
+.. code-block:: python
+
+    import logging
+    logging.getLogger("mloptimizer").setLevel(logging.WARNING)
+
+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:
+
+.. code-block:: python
+
+    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

mloptimizer/VERSION

@@ -1 +1 @@
-0.9.4
+0.9.4.1