Import examples from github.com/optuna/optuna-dashboard at e0c89e2f34…

…cfb4dc5302b0d910165c5fe0f228f1.

dashboard/hitl/main.py

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+import os
+import textwrap
+import time
+from typing import NoReturn
+
+import optuna
+from optuna.artifacts import FileSystemArtifactStore
+from optuna.artifacts import upload_artifact
+from optuna.trial import TrialState
+from optuna_dashboard import ChoiceWidget
+from optuna_dashboard import register_objective_form_widgets
+from optuna_dashboard import save_note
+from optuna_dashboard.artifact import get_artifact_path
+from PIL import Image
+
+
+def suggest_and_generate_image(
+    study: optuna.Study, artifact_store: FileSystemArtifactStore
+) -> None:
+    # 1. Ask new parameters
+    trial = study.ask()
+    r = trial.suggest_int("r", 0, 255)
+    g = trial.suggest_int("g", 0, 255)
+    b = trial.suggest_int("b", 0, 255)
+
+    # 2. Generate image
+    image_path = f"tmp/sample-{trial.number}.png"
+    image = Image.new("RGB", (320, 240), color=(r, g, b))
+    image.save(image_path)
+
+    # 3. Upload Artifact
+    artifact_id = upload_artifact(trial, image_path, artifact_store)
+    artifact_path = get_artifact_path(trial, artifact_id)
+
+    # 4. Save Note
+    note = textwrap.dedent(
+        f"""\
+    ## Trial {trial.number}
+
+    ![generated-image]({artifact_path})
+    """
+    )
+    save_note(trial, note)
+
+
+def start_optimization(artifact_store: FileSystemArtifactStore) -> NoReturn:
+    # 1. Create Study
+    study = optuna.create_study(
+        study_name="Human-in-the-loop Optimization",
+        storage="sqlite:///db.sqlite3",
+        sampler=optuna.samplers.TPESampler(constant_liar=True, n_startup_trials=5),
+        load_if_exists=True,
+    )
+
+    # 2. Set an objective name
+    study.set_metric_names(["Looks like sunset color?"])
+
+    # 3. Register ChoiceWidget
+    register_objective_form_widgets(
+        study,
+        widgets=[
+            ChoiceWidget(
+                choices=["Good 👍", "So-so👌", "Bad 👎"],
+                values=[-1, 0, 1],
+                description="Please input your score!",
+            ),
+        ],
+    )
+
+    # 4. Start Human-in-the-loop Optimization
+    n_batch = 4
+    while True:
+        running_trials = study.get_trials(deepcopy=False, states=(TrialState.RUNNING,))
+        if len(running_trials) >= n_batch:
+            time.sleep(1)  # Avoid busy-loop
+            continue
+        suggest_and_generate_image(study, artifact_store)
+
+
+def main() -> NoReturn:
+    tmp_path = os.path.join(os.path.dirname(__file__), "tmp")
+
+    # 1. Create Artifact Store
+    artifact_path = os.path.join(os.path.dirname(__file__), "artifact")
+    artifact_store = FileSystemArtifactStore(artifact_path)
+
+    if not os.path.exists(artifact_path):
+        os.mkdir(artifact_path)
+
+    if not os.path.exists(tmp_path):
+        os.mkdir(tmp_path)
+
+    # 2. Run optimize loop
+    start_optimization(artifact_store)
+
+
+if __name__ == "__main__":
+    main()

dashboard/preferential-optimization/evaluator.sh

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+#!/usr/bin/env sh
+optuna-dashboard sqlite:///example.db --artifact-dir ./artifact

dashboard/preferential-optimization/generator.py

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+from __future__ import annotations
+
+import os
+import tempfile
+import time
+from typing import NoReturn
+
+from optuna.artifacts import FileSystemArtifactStore
+from optuna.artifacts import upload_artifact
+from optuna_dashboard import register_preference_feedback_component
+from optuna_dashboard.preferential import create_study
+from optuna_dashboard.preferential.samplers.gp import PreferentialGPSampler
+from PIL import Image
+
+
+STORAGE_URL = "sqlite:///example.db"
+artifact_path = os.path.join(os.path.dirname(__file__), "artifact")
+artifact_store = FileSystemArtifactStore(base_path=artifact_path)
+os.makedirs(artifact_path, exist_ok=True)
+
+
+def main() -> NoReturn:
+    study = create_study(
+        n_generate=4,
+        study_name="Preferential Optimization",
+        storage=STORAGE_URL,
+        sampler=PreferentialGPSampler(),
+        load_if_exists=True,
+    )
+    # Change the component, displayed on the human feedback pages.
+    # By default (component_type="note"), the Trial's Markdown note is displayed.
+    user_attr_key = "rgb_image"
+    register_preference_feedback_component(study, "artifact", user_attr_key)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        while True:
+            # If study.should_generate() returns False,
+            # the generator waits for human evaluation.
+            if not study.should_generate():
+                time.sleep(0.1)  # Avoid busy-loop
+                continue
+
+            trial = study.ask()
+            # 1. Ask new parameters
+            r = trial.suggest_int("r", 0, 255)
+            g = trial.suggest_int("g", 0, 255)
+            b = trial.suggest_int("b", 0, 255)
+
+            # 2. Generate image
+            image_path = os.path.join(tmpdir, f"sample-{trial.number}.png")
+            image = Image.new("RGB", (320, 240), color=(r, g, b))
+            image.save(image_path)
+
+            # 3. Upload Artifact and set artifact_id to trial.user_attrs["rgb_image"].
+            artifact_id = upload_artifact(trial, image_path, artifact_store)
+            trial.set_user_attr(user_attr_key, artifact_id)
+
+
+if __name__ == "__main__":
+    main()

dashboard/streamlit_plugin/rgb_evaluator.py

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+from __future__ import annotations
+
+import os
+import shutil
+import tempfile
+import uuid
+
+import optuna
+from optuna.trial import TrialState
+from optuna_dashboard.artifact.file_system import FileSystemBackend
+from optuna_dashboard.streamlit import render_objective_form_widgets
+from optuna_dashboard.streamlit import render_trial_note
+
+import streamlit as st
+
+
+artifact_path = os.path.join(os.path.dirname(__file__), "artifact")
+artifact_backend = FileSystemBackend(base_path=artifact_path)
+
+
+def get_tmp_dir() -> str:
+    if "tmp_dir" not in st.session_state:
+        tmp_dir_name = str(uuid.uuid4())
+        tmp_dir_path = os.path.join(tempfile.gettempdir(), tmp_dir_name)
+        os.makedirs(tmp_dir_path, exist_ok=True)
+        st.session_state.tmp_dir = tmp_dir_path
+
+    return st.session_state.tmp_dir
+
+
+def start_streamlit() -> None:
+    tmpdir = get_tmp_dir()
+    study = optuna.load_study(
+        storage="sqlite:///streamlit-db.sqlite3", study_name="Human-in-the-loop Optimization"
+    )
+    selected_trial = st.sidebar.selectbox("Trial", study.trials, format_func=lambda t: t.number)
+
+    if selected_trial is None:
+        return
+    render_trial_note(study, selected_trial)
+    artifact_id = selected_trial.user_attrs.get("artifact_id")
+    if artifact_id:
+        with artifact_backend.open(artifact_id) as fsrc:
+            tmp_img_path = os.path.join(tmpdir, artifact_id + ".png")
+            with open(tmp_img_path, "wb") as fdst:
+                shutil.copyfileobj(fsrc, fdst)
+        st.image(tmp_img_path, caption="Image")
+
+    if selected_trial.state == TrialState.RUNNING:
+        render_objective_form_widgets(study, selected_trial)
+
+
+if __name__ == "__main__":
+    start_streamlit()

dashboard/streamlit_plugin/rgb_generator.py

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+from __future__ import annotations
+
+import os
+import tempfile
+import time
+from typing import NoReturn
+
+import optuna
+from optuna.trial import TrialState
+from optuna_dashboard import ChoiceWidget
+from optuna_dashboard import register_objective_form_widgets
+from optuna_dashboard import save_note
+from optuna_dashboard.artifact import upload_artifact
+from optuna_dashboard.artifact.file_system import FileSystemBackend
+from PIL import Image
+
+
+def suggest_and_generate_image(
+    study: optuna.Study, artifact_backend: FileSystemBackend, tmpdir: str
+) -> None:
+    # 1. Ask new parameters
+    trial = study.ask()
+    r = trial.suggest_int("r", 0, 255)
+    g = trial.suggest_int("g", 0, 255)
+    b = trial.suggest_int("b", 0, 255)
+
+    # 2. Generate image
+    image_path = os.path.join(tmpdir, f"sample-{trial.number}.png")
+    image = Image.new("RGB", (320, 240), color=(r, g, b))
+    image.save(image_path)
+
+    # 3. Upload Artifact
+    artifact_id = upload_artifact(artifact_backend, trial, image_path)
+    trial.set_user_attr("artifact_id", artifact_id)
+
+    # 4. Save Note
+    save_note(trial, f"## Trial {trial.number}")
+
+
+def main() -> NoReturn:
+    # 1. Create Artifact Store
+    artifact_path = os.path.join(os.path.dirname(__file__), "artifact")
+    artifact_backend = FileSystemBackend(base_path=artifact_path)
+
+    if not os.path.exists(artifact_path):
+        os.mkdir(artifact_path)
+
+    # 2. Create Study
+    study = optuna.create_study(
+        study_name="Human-in-the-loop Optimization",
+        storage="sqlite:///streamlit-db.sqlite3",
+        sampler=optuna.samplers.TPESampler(constant_liar=True, n_startup_trials=5),
+        load_if_exists=True,
+    )
+    study.set_metric_names(["Looks like sunset color?"])
+
+    # 4. Register ChoiceWidget
+    register_objective_form_widgets(
+        study,
+        widgets=[
+            ChoiceWidget(
+                choices=["Good 👍", "So-so👌", "Bad 👎"],
+                values=[-1, 0, 1],
+                description="Please input your score!",
+            ),
+        ],
+    )
+
+    # 5. Start Human-in-the-loop Optimization
+    n_batch = 4
+    with tempfile.TemporaryDirectory() as tmpdir:
+        while True:
+            running_trials = study.get_trials(deepcopy=False, states=(TrialState.RUNNING,))
+            if len(running_trials) >= n_batch:
+                time.sleep(1)  # Avoid busy-loop
+                continue
+            suggest_and_generate_image(study, artifact_backend, tmpdir)
+
+
+if __name__ == "__main__":
+    main()