Update docs.

HideakiImamura · Feb 15, 2024 · 4ce4c6c · 4ce4c6c
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Showing 5 changed files with 5 additions and 35 deletions.
diff --git a/.github/workflows/sphinx-build.yml b/.github/workflows/sphinx-build.yml
@@ -38,7 +38,6 @@ jobs:
     - name: Install Dependencies
       run: |
         python -m pip install -U pip
-        pip install git+https://github.com/optuna/optuna-integration@main
         pip install --progress-bar off -U .[document] --extra-index-url https://download.pytorch.org/whl/cpu
 
     - name: Output installed packages
@@ -92,7 +91,6 @@ jobs:
     - name: Install Dependencies
       run: |
         python -m pip install -U pip
-        pip install git+https://github.com/optuna/optuna-integration@main
         pip install --progress-bar off -U .[document] --extra-index-url https://download.pytorch.org/whl/cpu
 
     - name: Output installed packages

diff --git a/.readthedocs.yml b/.readthedocs.yml
@@ -9,9 +9,6 @@ build:
   os: ubuntu-22.04
   tools:
     python: "3.11"
-  jobs:
-    post_install:
-      - pip install git+https://github.com/optuna/optuna-integration@main
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:

diff --git a/tutorial/10_key_features/003_efficient_optimization_algorithms.py b/tutorial/10_key_features/003_efficient_optimization_algorithms.py
@@ -181,9 +181,9 @@ def objective(trial):
 # For example, `optuna_integration.LightGBMPruningCallback <https://optuna-integration.readthedocs.io/en/stable/reference/generated/optuna_integration.LightGBMPruningCallback.html>`_ introduces pruning without directly changing the logic of training iteration.
 # (See also `example <https://github.com/optuna/optuna-examples/blob/main/lightgbm/lightgbm_integration.py>`_ for the entire script.)
 #
-# .. code-block:: python
+# .. code-block:: text
 #
-#         import optuna_integration
+#         import optuna.integration
 #
-#         pruning_callback = optuna_integration.LightGBMPruningCallback(trial, 'validation-error')
+#         pruning_callback = optuna.integration.LightGBMPruningCallback(trial, 'validation-error')
 #         gbm = lgb.train(param, dtrain, valid_sets=[dvalid], callbacks=[pruning_callback])
diff --git a/tutorial/10_key_features/005_visualization.py b/tutorial/10_key_features/005_visualization.py
@@ -53,8 +53,6 @@
 from optuna.visualization import plot_slice
 from optuna.visualization import plot_timeline
 
-import optuna_integration
-
 
 SEED = 42
 
@@ -80,8 +78,7 @@ def objective(trial):
     }
 
     # Add a callback for pruning.
-    pruning_callback = optuna_integration.LightGBMPruningCallback(trial, "auc")
-    gbm = lgb.train(param, dtrain, valid_sets=[dvalid], callbacks=[pruning_callback])
+    gbm = lgb.train(param, dtrain, valid_sets=[dvalid])
 
     preds = gbm.predict(valid_x)
     pred_labels = np.rint(preds)
@@ -103,10 +100,6 @@ def objective(trial):
 # Visualize the optimization history. See :func:`~optuna.visualization.plot_optimization_history` for the details.
 plot_optimization_history(study)
 
-###################################################################################################
-# Visualize the learning curves of the trials. See :func:`~optuna.visualization.plot_intermediate_values` for the details.
-plot_intermediate_values(study)
-
 ###################################################################################################
 # Visualize high-dimensional parameter relationships. See :func:`~optuna.visualization.plot_parallel_coordinate` for the details.
 plot_parallel_coordinate(study)
@@ -152,18 +145,3 @@ def objective(trial):
 ###################################################################################################
 # Visualize the optimization timeline of performed trials. See :func:`~optuna.visualization.plot_timeline` for the details.
 plot_timeline(study)
-
-###################################################################################################
-# Customize generated figures
-# ---------------------------
-# In :mod:`optuna.visualization` and :mod:`optuna.visualization.matplotlib`, a function returns an editable figure object:
-# :class:`plotly.graph_objects.Figure` or :class:`matplotlib.axes.Axes` depending on the module.
-# This allows users to modify the generated figure for their demand by using API of the visualization library.
-# The following example replaces figure titles drawn by Plotly-based :func:`~optuna.visualization.plot_intermediate_values` manually.
-fig = plot_intermediate_values(study)
-
-fig.update_layout(
-    title="Hyperparameter optimization for GBDT-based binary classification",
-    xaxis_title="Iteration",
-    yaxis_title="Validation AUC",
-)
diff --git a/tutorial/20_recipes/008_specify_params.py b/tutorial/20_recipes/008_specify_params.py
@@ -37,8 +37,6 @@
 
 import optuna
 
-import optuna_integration
-
 
 ###################################################################################################
 # Define the objective function.
@@ -59,8 +57,7 @@ def objective(trial):
     }
 
     # Add a callback for pruning.
-    pruning_callback = optuna_integration.LightGBMPruningCallback(trial, "auc")
-    gbm = lgb.train(param, dtrain, valid_sets=[dvalid], callbacks=[pruning_callback])
+    gbm = lgb.train(param, dtrain, valid_sets=[dvalid])
 
     preds = gbm.predict(valid_x)
     pred_labels = np.rint(preds)