1692 iterative graph not showing in docs (#3592)

* Shows two graphs, one empty one filled

* Made it only print out one graph

* Updated docstring

* test to see if .show() makes tests slow

* release notes update

* testing to see if returning the figure works

* Trying to make the parameter false.

* show_iteration_plot is now default false

* Added testing and removed redundant code

* Added Becca's changes

* Added check so that tests don't take hours

* Edited some ipynb to reflect change

* Documentation fixes

* Deleted un-needed test

Author: MichaelFu512

docs/source/demos/cost_benefit_matrix.ipynb

@@ -124,7 +124,7 @@
     "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, verbose=True)\n",
-    "automl.search()\n",
+    "automl.search(interactive_plot=False)\n",
     "\n",
     "ll_pipeline = automl.best_pipeline\n",
     "ll_pipeline.score(X_holdout, y_holdout, ['log loss binary'])"
@@ -175,7 +175,7 @@
    "source": [
     "automl = AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary', objective=cost_benefit_matrix,\n",
     "                      max_iterations=5, verbose=True)\n",
-    "automl.search()\n",
+    "automl.search(interactive_plot=False)\n",
     "\n",
     "cbm_pipeline = automl.best_pipeline"
    ]
@@ -274,4 +274,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/demos/fraud.ipynb

@@ -115,7 +115,7 @@
     "                      optimize_thresholds=True,\n",
     "                      verbose=True)\n",
     "\n",
-    "automl.search()"
+    "automl.search(interactive_plot=False)"
    ]
   },
   {
@@ -219,7 +219,7 @@
     "                          optimize_thresholds=True,\n",
     "                          verbose=True)\n",
     "\n",
-    "automl_auc.search()"
+    "automl_auc.search(interactive_plot=False)"
    ]
   },
   {

docs/source/demos/lead_scoring.ipynb

@@ -150,7 +150,7 @@
     "                      max_batches=3,\n",
     "                      verbose=True)\n",
     "\n",
-    "automl.search()"
+    "automl.search(interactive_plot=False)"
    ]
   },
   {
@@ -247,7 +247,7 @@
     "                                 max_batches=3,\n",
     "                                 verbose=True)\n",
     "\n",
-    "automl_auc.search()"
+    "automl_auc.search(interactive_plot=False)"
    ]
   },
   {
@@ -329,4 +329,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/demos/text_input.ipynb

@@ -136,7 +136,7 @@
     "                      optimize_thresholds=True,\n",
     "                      verbose=True)\n",
     "\n",
-    "automl.search()"
+    "automl.search(interactive_plot=False)"
    ]
   },
   {
@@ -362,7 +362,7 @@
     "                              optimize_thresholds=True,\n",
     "                              verbose=True)\n",
     "\n",
-    "automl_no_text.search()"
+    "automl_no_text.search(interactive_plot=False)"
    ]
   },
   {
@@ -455,4 +455,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/release_notes.rst

@@ -3,6 +3,7 @@ Release Notes
 **Future Releases**
     * Enhancements
     * Fixes
+        * Fixed iterative graphs not appearing in documentation :pr:`3592`
         * Updated the `load_diabetes()` method to account for scikit-learn 1.1.1 changes to the dataset :pr:`3591`
     * Changes
     * Documentation Changes

docs/source/start.ipynb

@@ -133,14 +133,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "automl.search()"
+    "automl.search(interactive_plot=False)"
    ]
   },
   {
    "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."
+    "If you would like to suppress stdout output, set `verbose=False`. This is also the default behavior for `AutoMLSearch` if `verbose` is not specified. \n",
+    "\n",
+    "Also, if you would like to see the interactive plot update dynamically over time as the search progresses, either remove the parameter or set `interactive_plot=True`. This is the default setting for `search()` if `interactive_plot` is not specified (it is set to False here due to documentation workaround)."
    ]
   },
   {
@@ -266,4 +268,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/user_guide/automl.ipynb

@@ -136,7 +136,7 @@
    "outputs": [],
    "source": [
     "automl = evalml.automl.AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary', verbose=True)\n",
-    "automl.search()"
+    "automl.search(interactive_plot=False)"
    ]
   },
   {
@@ -645,7 +645,7 @@
     "                                      ensembling=True,\n",
     "                                      automl_algorithm='iterative',\n",
     "                                      verbose=True)\n",
-    "automl_with_ensembling.search()"
+    "automl_with_ensembling.search(interactive_plot=False)"
    ]
   },
   {
@@ -716,7 +716,7 @@
    "source": [
     "from evalml import AutoMLSearch\n",
     "auto_iterative = AutoMLSearch(X_train=X, y_train=y, problem_type='binary', automl_algorithm='iterative', verbose=True)\n",
-    "auto_iterative.search()"
+    "auto_iterative.search(interactive_plot=False)"
    ]
   },
   {
@@ -780,7 +780,7 @@
    "source": [
     "from evalml import AutoMLSearch\n",
     "auto_default = AutoMLSearch(X_train=X, y_train=y, problem_type='binary', automl_algorithm='default', ensembling=True, verbose=True)\n",
-    "auto_default.search()"
+    "auto_default.search(interactive_plot=False)"
    ]
   },
   {
@@ -858,7 +858,7 @@
     "                                  problem_type=\"binary\",\n",
     "                                  allowed_model_families=[ModelFamily.LINEAR_MODEL],\n",
     "                                  engine=\"cf_threaded\")\n",
-    "automl_cf_threaded.search(show_iteration_plot = False)\n",
+    "automl_cf_threaded.search(interactive_plot = False)\n",
     "automl_cf_threaded.close_engine()"
    ]
   },
@@ -888,7 +888,7 @@
     "                                  problem_type=\"binary\",\n",
     "                                  allowed_model_families=[ModelFamily.LINEAR_MODEL],\n",
     "                                  engine=cf_engine)\n",
-    "automl_cf_threaded.search(show_iteration_plot = False)\n",
+    "automl_cf_threaded.search(interactive_plot = False)\n",
     "automl_cf_threaded.close_engine()"
    ]
   },
@@ -906,7 +906,7 @@
     "automl_cf_process = AutoMLSearch(X_train=X, y_train=y,\n",
     "                                 problem_type=\"binary\",\n",
     "                                 engine=\"cf_process\")\n",
-    "automl_cf_process.search(show_iteration_plot = False)\n",
+    "automl_cf_process.search(interactive_plot = False)\n",
     "automl_cf_process.close_engine()\n",
     "```"
    ]
@@ -935,7 +935,7 @@
     "                              problem_type=\"binary\",\n",
     "                              allowed_model_families=[ModelFamily.LINEAR_MODEL],\n",
     "                              engine=dask_engine_p2)\n",
-    "automl_dask_p2.search(show_iteration_plot = False)\n",
+    "automl_dask_p2.search(interactive_plot = False)\n",
     "\n",
     "# Explicitly shutdown the automl object's LocalCluster\n",
     "automl_dask_p2.close_engine()"
@@ -953,7 +953,7 @@
     "                              problem_type=\"binary\",\n",
     "                              allowed_model_families=[ModelFamily.LINEAR_MODEL],\n",
     "                              engine=dask_engine_t4)\n",
-    "automl_dask_t4.search(show_iteration_plot = False)\n",
+    "automl_dask_t4.search(interactive_plot = False)\n",
     "automl_dask_t4.close_engine()"
    ]
   },

evalml/automl/automl_search.py

@@ -13,6 +13,7 @@
 import numpy as np
 import pandas as pd
 from dask import distributed as dd
+from plotly import io as pio
 from sklearn.model_selection import BaseCrossValidator
 from skopt.space import Categorical
 
@@ -857,11 +858,11 @@ def _handle_keyboard_interrupt(self):
             else:
                 leading_char = ""
 
-    def search(self, show_iteration_plot=True):
+    def search(self, interactive_plot=True):
         """Find the best pipeline for the data set.
 
         Args:
-            show_iteration_plot (boolean, True): Shows an iteration vs. score plot in Jupyter notebook.
+            interactive_plot (boolean, True): Shows an iteration vs. score plot in Jupyter notebook.
                 Disabled by default in non-Jupyter enviroments.
 
         Raises:
@@ -874,11 +875,11 @@ def search(self, show_iteration_plot=True):
             return
 
         # don't show iteration plot outside of a jupyter notebook
-        if show_iteration_plot:
+        if interactive_plot:
             try:
                 get_ipython
             except NameError:
-                show_iteration_plot = False
+                interactive_plot = False
 
         log_title(self.logger, "Beginning pipeline search")
         self.logger.info("Optimizing for %s. " % self.objective.name)
@@ -909,7 +910,7 @@ def search(self, show_iteration_plot=True):
         self.search_iteration_plot = None
         if self.plot and self.verbose:
             self.search_iteration_plot = self.plot.search_iteration_plot(
-                interactive_plot=show_iteration_plot
+                interactive_plot=interactive_plot
             )
 
         self._start = time.time()
@@ -1006,6 +1007,13 @@ def search(self, show_iteration_plot=True):
                 f"Best pipeline {self.objective.name}: {best_pipeline['validation_score']:3f}"
             )
         self._searched = True
+        if self.search_iteration_plot is not None:
+            if self.verbose and not interactive_plot:
+                self.search_iteration_plot = self.plot.search_iteration_plot(
+                    interactive_plot=interactive_plot
+                )
+                if pio.renderers.default != "browser":
+                    self.search_iteration_plot.show()
 
     def _find_best_pipeline(self):
         """Finds the best pipeline in the rankings If self._best_pipeline already exists, check to make sure it is different from the current best pipeline before training and thresholding."""