Logger example (#1158)

* loggign info

* Try order

* Try other location

* Log example debug (#15)

* Show only ensemble builder msg

* Fix title

autosklearn/util/logging_.py

@@ -27,15 +27,38 @@ def setup_logger(
         with open(os.path.join(os.path.dirname(__file__), 'logging.yaml'), 'r') as fh:
             logging_config = yaml.safe_load(fh)
 
+    # Make sure we have a filename handler
+    if 'handlers' not in logging_config:
+        logging_config['handlers'] = {}
+    if 'file_handler' not in logging_config['handlers']:
+        logging_config['handlers']['file_handler'] = {
+            'class': 'logging.FileHandler',
+            'level': 'DEBUG',
+            'filename': 'autosklearn.log'
+        }
+    if 'distributed_logfile' not in logging_config['handlers']:
+        # We have to create a file handler
+        logging_config['handlers']['distributed_logfile'] = {
+            'class': 'logging.FileHandler',
+            'level': 'DEBUG',
+            'filename': 'distributed.log'
+        }
+
     if filename is None:
         filename = logging_config['handlers']['file_handler']['filename']
-    logging_config['handlers']['file_handler']['filename'] = os.path.join(
-        output_dir, filename
-    )
 
     if distributedlog_filename is None:
         distributedlog_filename = logging_config['handlers']['distributed_logfile']['filename']
-    logging_config['handlers']['distributed_logfile']['filename'] = os.path.join(
+
+    # Make path absolute only if required
+    # This is needed because this function might be called multiple times with the same
+    # dict, and we don't want /path/path/<name>.log but rather just /path/<name>.log
+    if os.path.sep not in logging_config['handlers']['file_handler']['filename']:
+        logging_config['handlers']['file_handler']['filename'] = os.path.join(
+            output_dir, filename
+        )
+    if os.path.sep not in logging_config['handlers']['distributed_logfile']['filename']:
+        logging_config['handlers']['distributed_logfile']['filename'] = os.path.join(
             output_dir, distributedlog_filename
         )
     logging.config.dictConfig(logging_config)

examples/40_advanced/example_debug_logging.py

@@ -0,0 +1,105 @@
+# -*- encoding: utf-8 -*-
+"""
+=====================
+Logging and debugging
+=====================
+
+This example shows how to provide a custom logging configuration to *auto-sklearn*.
+We will be fitting 2 pipelines and showing any INFO-level msg on console.
+Even if you do not provide a logging_configuration, autosklearn creates a log file
+in the temporal working directory. This directory can be specified via the `tmp_folder`
+as exemplified below.
+
+This example also highlights additional information about *auto-sklearn* internal
+directory structure.
+"""
+import pathlib
+
+import sklearn.datasets
+import sklearn.metrics
+import sklearn.model_selection
+
+import autosklearn.classification
+
+
+############################################################################
+# Data Loading
+# ============
+# Load kr-vs-kp dataset from https://www.openml.org/d/3
+X, y = data = sklearn.datasets.fetch_openml(data_id=3, return_X_y=True, as_frame=True)
+
+X_train, X_test, y_train, y_test = \
+     sklearn.model_selection.train_test_split(X, y, random_state=1)
+
+
+############################################################################
+# Create a logging config
+# =======================
+# *auto-sklearn* uses a default
+# `logging config <https://github.com/automl/auto-sklearn/blob/master/autosklearn/util/logging.yaml>`_
+# We will instead create a custom one as follows:
+
+logging_config = {
+    'version': 1,
+    'disable_existing_loggers': True,
+    'formatters': {
+        'custom': {
+            # More format options are available in the official
+            # `documentation <https://docs.python.org/3/howto/logging-cookbook.html>`_
+            'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+        }
+    },
+
+    # Any INFO level msg will be printed to the console
+    'handlers': {
+        'console': {
+            'level': 'INFO',
+            'formatter': 'custom',
+            'class': 'logging.StreamHandler',
+            'stream': 'ext://sys.stdout',
+        },
+    },
+
+    'loggers': {
+        '': {  # root logger
+            'level': 'DEBUG',
+        },
+        'Client-EnsembleBuilder': {
+            'level': 'DEBUG',
+            'handlers': ['console'],
+        },
+    },
+}
+
+
+############################################################################
+# Build and fit a classifier
+# ==========================
+cls = autosklearn.classification.AutoSklearnClassifier(
+    time_left_for_this_task=30,
+    # Bellow two flags are provided to speed up calculations
+    # Not recommended for a real implementation
+    initial_configurations_via_metalearning=0,
+    smac_scenario_args={'runcount_limit': 2},
+    # Pass the config file we created
+    logging_config=logging_config,
+    # *auto-sklearn* generates temporal files under tmp_folder
+    tmp_folder='./tmp_folder',
+    # By default tmp_folder is deleted. We will preserve it
+    # for debug purposes
+    delete_tmp_folder_after_terminate=False,
+)
+cls.fit(X_train, y_train, X_test, y_test)
+
+# *auto-sklearn* generates intermediate files which can be of interest
+# Dask multiprocessing information. Useful on multi-core runs:
+#   * tmp_folder/distributed.log
+# The individual fitted estimators are written to disk on:
+#   * tmp_folder/.auto-sklearn/runs
+# SMAC output is stored in this directory.
+# For more info, you can check the `SMAC documentation <https://github.com/automl/SMAC3>`_
+#   * tmp_folder/smac3-output
+# Auto-sklearn always outputs to this log file
+# tmp_folder/AutoML*.log
+for filename in pathlib.Path('./tmp_folder').glob('*'):
+    print(filename)