Merge pull request #236 from Alnusjaponica/update-black

Apply Black 2024 to codebase

.github/workflows/checks.yml

@@ -23,9 +23,7 @@ jobs:
     - name: Install
       run: |
         python -m pip install -U pip
-        pip install --progress-bar off -U hacking isort
-        # TODO(Shinichi): Remove version specification after black 24.x becomes stable.
-        pip install --progress-bar off -U "black<24.0.0"
+        pip install --progress-bar off -U black hacking isort
 
     - name: black
       run: black . --check --diff

chainer/chainermn_simple.py

@@ -11,6 +11,7 @@
     $ mpirun -n 2 -- python chainermn_simple.py $STUDY_NAME $STORAGE_URL
 
 """
+
 import sys
 
 import chainermn

hiplot/plot_study.ipynb

@@ -43,7 +43,7 @@
     "from sklearn.datasets import fetch_openml\n",
     "from sklearn.model_selection import train_test_split\n",
     "\n",
-    "mnist = fetch_openml(name='Fashion-MNIST', version=1)\n",
+    "mnist = fetch_openml(name=\"Fashion-MNIST\", version=1)\n",
     "classes = list(set(mnist.target))\n",
     "\n",
     "# For demonstrational purpose, only use a subset of the dataset.\n",
@@ -69,23 +69,26 @@
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "\n",
+    "\n",
     "def objective(trial):\n",
-    "    \n",
+    "\n",
     "    clf = MLPClassifier(\n",
-    "        hidden_layer_sizes=tuple([trial.suggest_int('n_units_l{}'.format(i), 32, 64) for i in range(3)]),\n",
-    "        learning_rate_init=trial.suggest_float('lr_init', 1e-5, 1e-1, log=True),\n",
+    "        hidden_layer_sizes=tuple(\n",
+    "            [trial.suggest_int(\"n_units_l{}\".format(i), 32, 64) for i in range(3)]\n",
+    "        ),\n",
+    "        learning_rate_init=trial.suggest_float(\"lr_init\", 1e-5, 1e-1, log=True),\n",
     "    )\n",
     "\n",
     "    for step in range(100):\n",
     "        clf.partial_fit(x_train, y_train, classes=classes)\n",
-    "        value = clf.score(x_valid, y_valid)  \n",
-    "        \n",
+    "        value = clf.score(x_valid, y_valid)\n",
+    "\n",
     "        # Report intermediate objective value.\n",
     "        trial.report(value, step)\n",
     "\n",
     "        # Handle pruning based on the intermediate value.\n",
     "        if trial.should_prune():\n",
-    "            raise optuna.TrialPruned()  \n",
+    "            raise optuna.TrialPruned()\n",
     "\n",
     "    return value"
    ]
@@ -105,9 +108,11 @@
    "source": [
     "import optuna\n",
     "\n",
-    "optuna.logging.set_verbosity(optuna.logging.WARNING)  # This verbosity change is just to simplify the notebook output.\n",
+    "optuna.logging.set_verbosity(\n",
+    "    optuna.logging.WARNING\n",
+    ")  # This verbosity change is just to simplify the notebook output.\n",
     "\n",
-    "study = optuna.create_study(direction='maximize', pruner=optuna.pruners.MedianPruner())\n",
+    "study = optuna.create_study(direction=\"maximize\", pruner=optuna.pruners.MedianPruner())\n",
     "study.optimize(objective, n_trials=100)"
    ]
   },

keras/keras_integration.py

@@ -14,6 +14,7 @@
     https://github.com/optuna/optuna-examples/blob/main/mlflow/keras_mlflow.py
 
 """
+
 import urllib
 import warnings
 

keras/keras_simple.py

@@ -6,6 +6,7 @@
 Keras. We optimize the filter and kernel size, kernel stride and layer activation.
 
 """
+
 import urllib
 import warnings
 

kubernetes/simple/sklearn_distributed.py

@@ -10,6 +10,7 @@
 2 - It sets the storage address to the postgres pod deployed with the workers.
 
 """
+
 import os
 
 import optuna

lightgbm/lightgbm_integration.py

@@ -9,6 +9,7 @@
     $ python lightgbm_integration.py
 
 """
+
 import numpy as np
 import optuna
 

lightgbm/lightgbm_tuner_cv.py

@@ -4,6 +4,7 @@
 In this example, we optimize the cross-validated log loss of cancer detection.
 
 """
+
 import optuna.integration.lightgbm as lgb
 
 from lightgbm import early_stopping

pytorch/pytorch_distributed_spawn.py

@@ -21,6 +21,7 @@
 If you wish to use optuna < 3.1.0, you would need to pass
 `device=device_id` in TorchDistributedTrial.
 """
+
 import argparse
 from functools import partial
 import os

pytorch/pytorch_lightning_ddp.py

@@ -11,6 +11,7 @@
     $ python pytorch/pytorch_lightning_ddp.py [--pruning]
 
 """
+
 import argparse
 import os
 from typing import List

pytorch/pytorch_lightning_simple.py

@@ -10,6 +10,7 @@
     $ python pytorch_lightning_simple.py [--pruning]
 
 """
+
 import argparse
 import os
 from typing import List

quickstart.ipynb

@@ -67,16 +67,18 @@
     "import sklearn.ensemble\n",
     "import sklearn.model_selection\n",
     "\n",
+    "\n",
     "def objective():\n",
     "    iris = sklearn.datasets.load_iris()  # Prepare the data.\n",
-    "    \n",
-    "    clf = sklearn.ensemble.RandomForestClassifier(    \n",
-    "        n_estimators=5, max_depth=3)  # Define the model.\n",
-    "    \n",
+    "\n",
+    "    clf = sklearn.ensemble.RandomForestClassifier(n_estimators=5, max_depth=3)  # Define the model.\n",
+    "\n",
     "    return sklearn.model_selection.cross_val_score(\n",
-    "        clf, iris.data, iris.target, n_jobs=-1, cv=3).mean()  # Train and evaluate the model.\n",
+    "        clf, iris.data, iris.target, n_jobs=-1, cv=3\n",
+    "    ).mean()  # Train and evaluate the model.\n",
+    "\n",
     "\n",
-    "print('Accuracy: {}'.format(objective()))"
+    "print(\"Accuracy: {}\".format(objective()))"
    ]
   },
   {
@@ -96,24 +98,26 @@
    "source": [
     "import optuna\n",
     "\n",
+    "\n",
     "def objective(trial):\n",
     "    iris = sklearn.datasets.load_iris()\n",
-    "    \n",
-    "    n_estimators = trial.suggest_int('n_estimators', 2, 20)\n",
-    "    max_depth = int(trial.suggest_float('max_depth', 1, 32, log=True))\n",
-    "    \n",
-    "    clf = sklearn.ensemble.RandomForestClassifier(\n",
-    "        n_estimators=n_estimators, max_depth=max_depth)\n",
-    "    \n",
+    "\n",
+    "    n_estimators = trial.suggest_int(\"n_estimators\", 2, 20)\n",
+    "    max_depth = int(trial.suggest_float(\"max_depth\", 1, 32, log=True))\n",
+    "\n",
+    "    clf = sklearn.ensemble.RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth)\n",
+    "\n",
     "    return sklearn.model_selection.cross_val_score(\n",
-    "        clf, iris.data, iris.target, n_jobs=-1, cv=3).mean()\n",
+    "        clf, iris.data, iris.target, n_jobs=-1, cv=3\n",
+    "    ).mean()\n",
+    "\n",
     "\n",
-    "study = optuna.create_study(direction='maximize')\n",
+    "study = optuna.create_study(direction=\"maximize\")\n",
     "study.optimize(objective, n_trials=100)\n",
     "\n",
     "trial = study.best_trial\n",
     "\n",
-    "print('Accuracy: {}'.format(trial.value))\n",
+    "print(\"Accuracy: {}\".format(trial.value))\n",
     "print(\"Best hyperparameters: {}\".format(trial.params))"
    ]
   },
@@ -132,31 +136,35 @@
    "source": [
     "import sklearn.svm\n",
     "\n",
+    "\n",
     "def objective(trial):\n",
     "    iris = sklearn.datasets.load_iris()\n",
     "\n",
-    "    classifier = trial.suggest_categorical('classifier', ['RandomForest', 'SVC'])\n",
-    "    \n",
-    "    if classifier == 'RandomForest':\n",
-    "        n_estimators = trial.suggest_int('n_estimators', 2, 20)\n",
-    "        max_depth = int(trial.suggest_float('max_depth', 1, 32, log=True))\n",
+    "    classifier = trial.suggest_categorical(\"classifier\", [\"RandomForest\", \"SVC\"])\n",
+    "\n",
+    "    if classifier == \"RandomForest\":\n",
+    "        n_estimators = trial.suggest_int(\"n_estimators\", 2, 20)\n",
+    "        max_depth = int(trial.suggest_float(\"max_depth\", 1, 32, log=True))\n",
     "\n",
     "        clf = sklearn.ensemble.RandomForestClassifier(\n",
-    "            n_estimators=n_estimators, max_depth=max_depth)\n",
+    "            n_estimators=n_estimators, max_depth=max_depth\n",
+    "        )\n",
     "    else:\n",
-    "        c = trial.suggest_float('svc_c', 1e-10, 1e10, log=True)\n",
-    "        \n",
-    "        clf = sklearn.svm.SVC(C=c, gamma='auto')\n",
+    "        c = trial.suggest_float(\"svc_c\", 1e-10, 1e10, log=True)\n",
+    "\n",
+    "        clf = sklearn.svm.SVC(C=c, gamma=\"auto\")\n",
     "\n",
     "    return sklearn.model_selection.cross_val_score(\n",
-    "        clf, iris.data, iris.target, n_jobs=-1, cv=3).mean()\n",
+    "        clf, iris.data, iris.target, n_jobs=-1, cv=3\n",
+    "    ).mean()\n",
+    "\n",
     "\n",
-    "study = optuna.create_study(direction='maximize')\n",
+    "study = optuna.create_study(direction=\"maximize\")\n",
     "study.optimize(objective, n_trials=100)\n",
     "\n",
     "trial = study.best_trial\n",
     "\n",
-    "print('Accuracy: {}'.format(trial.value))\n",
+    "print(\"Accuracy: {}\".format(trial.value))\n",
     "print(\"Best hyperparameters: {}\".format(trial.params))"
    ]
   },
@@ -207,7 +215,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "optuna.visualization.plot_contour(study, params=['n_estimators', 'max_depth'])"
+    "optuna.visualization.plot_contour(study, params=[\"n_estimators\", \"max_depth\"])"
    ]
   }
  ],

rapids_simple.py

@@ -11,6 +11,7 @@
 
 Learn more about rapids: https://rapids.ai/
 """
+
 import cudf
 from cuml.ensemble import RandomForestClassifier
 from cuml.metrics import accuracy_score

ray/ray_joblib.py

@@ -10,6 +10,7 @@
     $ python ray_joblib.py
 
 """
+
 import joblib
 import optuna
 

rl/sb3_simple.py

@@ -8,6 +8,7 @@
     $ python sb3_simple.py
 
 """
+
 from typing import Any
 from typing import Dict
 

terminator/terminator_improvement_plot.py

@@ -14,7 +14,6 @@
 
 """
 
-
 import optuna
 from optuna.terminator.erroreval import report_cross_validation_scores
 from optuna.visualization import plot_terminator_improvement

visualization/plot_study.ipynb

@@ -45,7 +45,7 @@
     "from sklearn.datasets import fetch_openml\n",
     "from sklearn.model_selection import train_test_split\n",
     "\n",
-    "mnist = fetch_openml(name='Fashion-MNIST', version=1)\n",
+    "mnist = fetch_openml(name=\"Fashion-MNIST\", version=1)\n",
     "classes = list(set(mnist.target))\n",
     "\n",
     "# For demonstrational purpose, only use a subset of the dataset.\n",
@@ -71,23 +71,26 @@
    "source": [
     "from sklearn.neural_network import MLPClassifier\n",
     "\n",
+    "\n",
     "def objective(trial):\n",
-    "    \n",
+    "\n",
     "    clf = MLPClassifier(\n",
-    "        hidden_layer_sizes=tuple([trial.suggest_int('n_units_l{}'.format(i), 32, 64) for i in range(3)]),\n",
-    "        learning_rate_init=trial.suggest_float('lr_init', 1e-5, 1e-1, log=True),\n",
+    "        hidden_layer_sizes=tuple(\n",
+    "            [trial.suggest_int(\"n_units_l{}\".format(i), 32, 64) for i in range(3)]\n",
+    "        ),\n",
+    "        learning_rate_init=trial.suggest_float(\"lr_init\", 1e-5, 1e-1, log=True),\n",
     "    )\n",
     "\n",
     "    for step in range(100):\n",
     "        clf.partial_fit(x_train, y_train, classes=classes)\n",
-    "        value = clf.score(x_valid, y_valid)  \n",
-    "        \n",
+    "        value = clf.score(x_valid, y_valid)\n",
+    "\n",
     "        # Report intermediate objective value.\n",
     "        trial.report(value, step)\n",
     "\n",
     "        # Handle pruning based on the intermediate value.\n",
     "        if trial.should_prune():\n",
-    "            raise optuna.TrialPruned()  \n",
+    "            raise optuna.TrialPruned()\n",
     "\n",
     "    return value"
    ]
@@ -107,9 +110,11 @@
    "source": [
     "import optuna\n",
     "\n",
-    "optuna.logging.set_verbosity(optuna.logging.WARNING)  # This verbosity change is just to simplify the notebook output.\n",
+    "optuna.logging.set_verbosity(\n",
+    "    optuna.logging.WARNING\n",
+    ")  # This verbosity change is just to simplify the notebook output.\n",
     "\n",
-    "study = optuna.create_study(direction='maximize', pruner=optuna.pruners.MedianPruner())\n",
+    "study = optuna.create_study(direction=\"maximize\", pruner=optuna.pruners.MedianPruner())\n",
     "study.optimize(objective, n_trials=100)"
    ]
   },
@@ -180,7 +185,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_parallel_coordinate(study, params=['lr_init', 'n_units_l0'])"
+    "plot_parallel_coordinate(study, params=[\"lr_init\", \"n_units_l0\"])"
    ]
   },
   {
@@ -214,7 +219,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_contour(study, params=['n_units_l0', 'n_units_l1'])"
+    "plot_contour(study, params=[\"n_units_l0\", \"n_units_l1\"])"
    ]
   },
   {
@@ -248,7 +253,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_slice(study, params=['n_units_l0', 'n_units_l1'])"
+    "plot_slice(study, params=[\"n_units_l0\", \"n_units_l1\"])"
    ]
   },
   {