predictor = TabularPredictor(label="target").fit(train_data)
predictions = predictor.predict(test_data)

pip install autogluon

from sklearn import datasets
from sklearn.model_selection import train_test_split
import pandas as pd

dataset = datasets.load_iris()

df = pd.DataFrame(data=dataset.data, columns=dataset.feature_names)
df["target"] = dataset.target

train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)

display(train_df.head())
display(test_df.head())

from autogluon.tabular import TabularDataset, TabularPredictor

train_data = TabularDataset(train_df)
test_data = TabularDataset(test_df)

predictor = TabularPredictor(label="target").fit(train_data)
predictions = predictor.predict(test_data)

=================== System Info ===================
[省略]
===================================================
[省略]
Train Data Rows:    120
Train Data Columns: 4
Label Column:       target
AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == int, but few unique label-values observed).
	3 unique label values:  [np.int64(0), np.int64(1), np.int64(2)]
	If 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])
Problem Type:       multiclass # <- multiclassになっています。
Preprocessing data ...
[省略]

predictions[:5]

ret = predictor.evaluate(test_data, detailed_report=True)

ret_df = pd.DataFrame(ret["classification_report"])
display(ret_df)

predictor.leaderboard(test_data, extra_metrics=["f1_macro"])

dataset = datasets.fetch_california_housing()

df = pd.DataFrame(data=dataset.data, columns=dataset.feature_names)
df["target"] = dataset.target

train_df, test_df = train_test_split(df[:1000], test_size=0.2, random_state=42)

display(train_df.head())
display(test_df.head())

train_data = TabularDataset(train_df)
test_data = TabularDataset(test_df)

predictor = TabularPredictor(label="target").fit(train_data)
predictions = predictor.predict(test_data)

=================== System Info ===================
[省略]
===================================================
[省略]
Train Data Rows:    800
Train Data Columns: 8
Label Column:       target
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
	Label info (max, min, mean, stddev): (5.00001, 0.675, 2.12968, 0.89625)
	If 'regression' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])
Problem Type:       regression # <- regressionになっています。
Preprocessing data ...
[省略]

ret = predictor.evaluate(test_data, detailed_report=True)

ret_df = pd.DataFrame([ret])
display(ret_df)

predictor.leaderboard(test_data, extra_metrics=["r2"])

# OpenMLから取得したデータセットだとdatetimeがないため、Kaggleのデータセットを使用します。事前にダウンロードが必要です。

df = pd.read_csv("data/bike-sharing-demand/train.csv", parse_dates=["datetime"])

df["item_id"] = 1  # 単一の時系列データとして扱うためのID列を追加

train_df = df[(df["datetime"] >= "2011-01-01") & (df["datetime"] <= "2012-09-30")]
test_df = df[(df["datetime"] >= "2012-10-01") & (df["datetime"] <= "2012-12-31")]

display(train_df.head())
display(test_df.head())

from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor

train_data = TimeSeriesDataFrame.from_data_frame(
    train_df, id_column="item_id", timestamp_column="datetime"
)
test_data = TimeSeriesDataFrame.from_data_frame(
    test_df, id_column="item_id", timestamp_column="datetime"
)

display(train_data.head())
display(test_data.head())

predictor = TimeSeriesPredictor(
    prediction_length=48,  # 48時間先まで予測
    freq="H",              # 1時間単位のデータ
    target="count",        # 予測する列
    eval_metric="MASE",    # 平均絶対スケール誤差（スケール不変・季節性考慮）
)
predictor.fit(
    train_data,
    presets="medium_quality",  # 速度重視（検証目的）
    time_limit=60,             # 最大60秒で学習を打ち切る
)

backtest_window = 100

predictions = predictor.predict(test_data[:-backtest_window])

display(predictions.head())
display(predictions.tail())

history_length = 200
fig = predictor.plot(
    test_data[:-backtest_window],
    predictions,
    quantile_levels=[0.1, 0.9],
    max_history_length=history_length,
)
ax = fig.axes[0] if hasattr(fig, "axes") and fig.axes else fig
item_id = test_data.item_ids[0]
actual_series = test_data.loc[item_id]["count"]
actual_series = actual_series[len(actual_series) - (history_length + backtest_window):]
actual_series = actual_series.reindex(predictions.index.get_level_values("timestamp"))
ax.plot(
    actual_series.index,
    actual_series.values,
    linestyle="--",
    color="black",
    label="Actual",
)
ax.legend()

fig

predictor.leaderboard(test_data)

from PIL import Image
from autogluon.multimodal.utils import download
import os

dataset = datasets.load_digits()

train_data_size = 100
test_data_size = 10
train_images = dataset.images[:train_data_size].astype("uint8")
for i in range(train_images.shape[0]):
    train_images[i] = (train_images[i] / train_images[i].max()) * 255
train_labels = dataset.target[:train_data_size]
test_images = dataset.images[train_data_size : train_data_size + test_data_size].astype(
    "uint8"
)
for i in range(test_images.shape[0]):
    test_images[i] = (test_images[i] / test_images[i].max()) * 255
test_labels = dataset.target[train_data_size : train_data_size + test_data_size]

os.makedirs("tmp/train", exist_ok=True)
os.makedirs("tmp/test", exist_ok=True)

train_data = []
for i in range(len(train_images)):
    Image.fromarray(train_images[i]).save(f"tmp/train/digit_train_{i}.png")
    train_data.append(
        {
            "image": f"tmp/train/digit_train_{i}.png",
            "label": int(train_labels[i]),
        }
    )
test_data = []
for i in range(len(test_images)):
    Image.fromarray(test_images[i]).save(f"tmp/test/digit_test_{i}.png")
    test_data.append(
        {
            "image": f"tmp/test/digit_test_{i}.png",
            "label": int(test_labels[i]),
        }
    )

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 10, figsize=(8, 8))
for i, ax in enumerate(axes.flat):
    ax.imshow(train_images[i], cmap="gray")
    ax.axis("off")
plt.show()

train_df = pd.DataFrame(train_data)
test_df = pd.DataFrame(test_data)

display(train_df.head())
display(test_df.head())

from autogluon.multimodal import MultiModalPredictor
import uuid

model_path = f"./tmp/{uuid.uuid4().hex}-automm_mnist"
predictor = MultiModalPredictor(label="label", path=model_path)
predictor.fit(
    train_data=train_df,
    time_limit=120, # seconds
)

=================== System Info ===================
[省略]
===================================================
AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == int, but few unique label-values observed).
	10 unique label values:  [np.int64(0), np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5), np.int64(6), np.int64(7), np.int64(8), np.int64(9)]
	If 'multiclass' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])

AutoMM starts to create your model. ✨✨✨
[省略]
INFO:timm.models._builder:Loading pretrained weights from Hugging Face hub (timm/caformer_b36.sail_in22k_ft_in1k)
INFO:timm.models._hub:[timm/caformer_b36.sail_in22k_ft_in1k] Safe alternative available for 'pytorch_model.bin' (as 'model.safetensors'). Loading weights using safetensors.
INFO:timm.models._builder:Missing keys (head.fc.fc2.weight, head.fc.fc2.bias) discovered while loading pretrained weights. This is expected if model is being adapted.
[省略]

probs = predictor.predict_proba(
    {"image": [image_path for image_path in test_df["image"].tolist()]}
)

pd.DataFrame(
    {
        "pred": probs.argmax(axis=1),
        "prob": probs.max(axis=1),
        "actual": test_df["label"].tolist(),
    }
)

scores = predictor.evaluate(test_df, metrics=["accuracy"])
print("Top-1 test acc: %.3f" % scores["accuracy"])

Top-1 test acc: 0.800