Simplify logging behavior (#2645)

* Remove file writing part of get_logger

* Replace all calls to get_logger with logging.getLogger

* Add verbose flag to AutoMLSearch

* Allow for verbose=False after verbose=True

* Include plotting suppression when verbose=False

* Update log_error_callback to use error/warning instead of info/debug

docs/source/demos/cost_benefit_matrix.ipynb

@@ -123,7 +123,7 @@
    "source": [
     "from evalml import AutoMLSearch\n",
     "automl = AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary', objective='log loss binary',\n",
-    "                      max_iterations=5)\n",
+    "                      max_iterations=5, verbose=True)\n",
     "automl.search()\n",
     "\n",
     "ll_pipeline = automl.best_pipeline\n",
@@ -174,7 +174,7 @@
    "outputs": [],
    "source": [
     "automl = AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary', objective=cost_benefit_matrix,\n",
-    "                      max_iterations=5)\n",
+    "                      max_iterations=5, verbose=True)\n",
     "automl.search()\n",
     "\n",
     "cbm_pipeline = automl.best_pipeline"
@@ -274,4 +274,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/demos/fraud.ipynb

@@ -112,7 +112,8 @@
     "                      additional_objectives=['auc', 'f1', 'precision'],\n",
     "                      allowed_model_families=[\"random_forest\", \"linear_model\"],\n",
     "                      max_batches=1,\n",
-    "                      optimize_thresholds=True)\n",
+    "                      optimize_thresholds=True,\n",
+    "                      verbose=True)\n",
     "\n",
     "automl.search()"
    ]
@@ -215,7 +216,8 @@
     "                          additional_objectives=['f1', 'precision'],\n",
     "                          max_batches=1,\n",
     "                          allowed_model_families=[\"random_forest\", \"linear_model\"],\n",
-    "                          optimize_thresholds=True)\n",
+    "                          optimize_thresholds=True,\n",
+    "                          verbose=True)\n",
     "\n",
     "automl_auc.search()"
    ]
@@ -298,4 +300,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/demos/lead_scoring.ipynb

@@ -147,7 +147,8 @@
     "                      objective=lead_scoring_objective,\n",
     "                      additional_objectives=['auc'],\n",
     "                      allowed_model_families=[\"catboost\", \"random_forest\", \"linear_model\"],\n",
-    "                      max_batches=1)\n",
+    "                      max_batches=1,\n",
+    "                      verbose=True)\n",
     "\n",
     "automl.search()"
    ]
@@ -243,7 +244,8 @@
     "                                 objective='auc',\n",
     "                                 additional_objectives=[lead_scoring_objective],\n",
     "                                 allowed_model_families=[\"catboost\", \"random_forest\", \"linear_model\"],\n",
-    "                                 max_batches=1)\n",
+    "                                 max_batches=1,\n",
+    "                                 verbose=True)\n",
     "\n",
     "automl_auc.search()"
    ]
@@ -327,4 +329,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/demos/text_input.ipynb

@@ -133,7 +133,8 @@
     "automl = AutoMLSearch(X_train=X_train, y_train=y_train,\n",
     "                      problem_type='binary',\n",
     "                      max_batches=1,\n",
-    "                      optimize_thresholds=True)\n",
+    "                      optimize_thresholds=True,\n",
+    "                      verbose=True)\n",
     "\n",
     "automl.search()"
    ]
@@ -354,7 +355,8 @@
     "automl_no_text = AutoMLSearch(X_train=X_train, y_train=y_train,\n",
     "                              problem_type='binary',\n",
     "                              max_batches=1,\n",
-    "                              optimize_thresholds=True)\n",
+    "                              optimize_thresholds=True,\n",
+    "                              verbose=True)\n",
     "\n",
     "automl_no_text.search()"
    ]

docs/source/release_notes.rst

@@ -2,13 +2,15 @@ Release Notes
 -------------
 **Future Releases**
     * Enhancements
+        * Added ``verbose`` flag to ``AutoMLSearch`` to run search in silent mode by default :pr:`2645`
         * Added label encoder to ``XGBoostClassifier`` to remove the warning :pr:`2701`
         * Set ``eval_metric`` to ``logloss`` for ``XGBoostClassifier`` :pr:`2741`
         * Added support for ``woodwork`` versions ``0.7.0`` and ``0.7.1`` :pr:`2743`
     * Fixes
         * Fixed bug where ``Imputer.transform`` would erase ww typing information prior to handing data to the ``SimpleImputer`` :pr:`2752`
         * Fixed bug where ``Oversampler`` could not be copied :pr:`2755`
     * Changes
+        * Removed default logging setup and debugging log file :pr:`2645`
     * Documentation Changes
         * Specified installation steps for Prophet :pr:`2713`
         * Added documentation for data exploration on data check actions :pr:`2696` 
@@ -18,6 +20,7 @@ Release Notes
 .. warning::
 
     **Breaking Changes**
+        * Removed default logging setup and debugging log file :pr:`2645`
 
 **v0.32.0 Aug. 31, 2021**
     * Enhancements

docs/source/start.ipynb

@@ -114,7 +114,8 @@
    "outputs": [],
    "source": [
     "automl = AutoMLSearch(X_train=X_train, y_train=y_train,\n",
-    "                      problem_type='binary', objective='f1', max_batches=1)"
+    "                      problem_type='binary', objective='f1', \n",
+    "                      max_batches=1, verbose=True)"
    ]
   },
   {
@@ -135,6 +136,25 @@
     "automl.search()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you would like to suppress stdout output, set `verbose=False`. This is also the default behavior for `AutoMLSearch` if `verbose` is not specified."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl = AutoMLSearch(X_train=X_train, y_train=y_train,\n",
+    "                      problem_type='binary', objective='f1', \n",
+    "                      max_batches=1, verbose=False)\n",
+    "automl.search()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -246,4 +266,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/user_guide/automl.ipynb

@@ -38,7 +38,7 @@
     "\n",
     "In its simplest usage, the AutoML search interface requires only the input data, the target data and a `problem_type` specifying what kind of supervised ML problem to model.\n",
     "\n",
-    "** Graphing methods, like AutoMLSearch, on Jupyter Notebook and Jupyter Lab require [ipywidgets](https://ipywidgets.readthedocs.io/en/latest/user_install.html) to be installed.\n",
+    "** Graphing methods, like verbose AutoMLSearch, on Jupyter Notebook and Jupyter Lab require [ipywidgets](https://ipywidgets.readthedocs.io/en/latest/user_install.html) to be installed.\n",
     "\n",
     "** If graphing on Jupyter Lab, [jupyterlab-plotly](https://plotly.com/python/getting-started/#jupyterlab-support-python-35) required. To download this, make sure you have [npm](https://nodejs.org/en/download/) installed."
    ]
@@ -135,15 +135,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "automl = evalml.automl.AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary')\n",
+    "automl = evalml.automl.AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary', verbose=True)\n",
     "automl.search()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The AutoML search will log its progress, reporting each pipeline and parameter set evaluated during the search.\n",
+    "With the `verbose` argument set to True, the AutoML search will log its progress, reporting each pipeline and parameter set evaluated during the search.\n",
     "\n",
     "There are a number of mechanisms to control the AutoML search time. One way is to set the `max_batches` parameter which controls the maximum number of rounds of AutoML to evaluate, where each round may train and score a variable number of pipelines. Another way is to set the `max_iterations` parameter which controls the maximum number of candidate models to be evaluated during AutoML. By default, AutoML will search for a single batch. The first pipeline to be evaluated will always be a baseline model representing a trivial solution. "
    ]
@@ -234,11 +234,13 @@
     "from evalml.pipelines import MulticlassClassificationPipeline\n",
     "\n",
     "\n",
-    "automl_custom = evalml.automl.AutoMLSearch(X_train=X_train,\n",
-    "                                           y_train=y_train,\n",
-    "                                           problem_type='multiclass',\n",
-    "                                           allowed_component_graphs={\"My_pipeline\": ['Simple Imputer', 'Random Forest Classifier'],\n",
-    "                                                                     \"My_other_pipeline\": ['One Hot Encoder', 'Random Forest Classifier']})"
+    "automl_custom = evalml.automl.AutoMLSearch(\n",
+    "    X_train=X_train,\n",
+    "    y_train=y_train,\n",
+    "    problem_type='multiclass',\n",
+    "    verbose=True,\n",
+    "    allowed_component_graphs={\"My_pipeline\": ['Simple Imputer', 'Random Forest Classifier'],\n",
+    "                              \"My_other_pipeline\": ['One Hot Encoder', 'Random Forest Classifier']})"
    ]
   },
   {
@@ -515,8 +517,10 @@
     "# using this custom hyperparameter means that our Imputer components in these pipelines will only search through\n",
     "# 'median' and 'most_frequent' strategies for 'numeric_impute_strategy', and the initial batch parameter will be\n",
     "# set to 'median'\n",
-    "automl_constrained = AutoMLSearch(X_train=X, y_train=y, problem_type='binary', pipeline_parameters=pipeline_parameters,\n",
-    "                                  custom_hyperparameters=custom_hyperparameters)"
+    "automl_constrained = AutoMLSearch(X_train=X, y_train=y, problem_type='binary', \n",
+    "                                  pipeline_parameters=pipeline_parameters,\n",
+    "                                  custom_hyperparameters=custom_hyperparameters, \n",
+    "                                  verbose=True)"
    ]
   },
   {
@@ -616,7 +620,8 @@
     "                                      problem_type=\"binary\",\n",
     "                                      allowed_model_families=[ModelFamily.LINEAR_MODEL],\n",
     "                                      max_batches=4,\n",
-    "                                      ensembling=True)\n",
+    "                                      ensembling=True,\n",
+    "                                      verbose=True)\n",
     "automl_with_ensembling.search()"
    ]
   },
@@ -803,7 +808,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -822,4 +827,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/user_guide/utilities.ipynb

@@ -13,9 +13,9 @@
    "source": [
     "## Configuring Logging\n",
     "\n",
-    "EvalML uses [the standard python logging package](https://docs.python.org/3/library/logging.html). By default, EvalML will log `INFO`-level logs and higher (warnings, errors and critical) to stdout, and will log everything to `evalml_debug.log` in the current working directory.\n",
+    "EvalML uses [the standard Python logging package](https://docs.python.org/3/library/logging.html). Default logging behavior prints WARNING level logs and above (ERROR and CRITICAL) to stdout. To configure different behavior, please refer to the Python logging documentation.\n",
     "\n",
-    "If you want to change the location of the logfile, before import, set the `EVALML_LOG_FILE` environment variable to specify a filename within an existing directory in which you have write permission. If you want to disable logging to the logfile, set `EVALML_LOG_FILE` to be empty. If the environment variable is set to an invalid location, EvalML will print a warning message to stdout and will not create a log file."
+    "To see up-to-date feedback as `AutoMLSearch` runs, use the argument `verbose=True` when instantiating the object. This will temporarily set up a logging object to print INFO level logs and above to stdout, as well as display a graph of the best score over pipeline iterations."
    ]
   },
   {