近年来，机器学习在各个领域都取得了重大突破，在生命科学、医学领域应用的也越来越多。但想要真正建立一个模型仍费时费力，要花相当一段时间去学习（可参考我之前写的相关笔记）。而且即使是高水平的人工智能专家，在大数据智能分析机器学习建模时，也主要依靠人工经验，建模过程费时费力，缺少有效方法。
为了解决这一突出问题，自动机器学习（AutoML）应运而生，即是将机器学习应用于现实问题的端到端过程自动化的过程。AutoML 使机器学习真正意义上成为可能，即使对于在该领域没有专业知识的人也是如此。
本文介绍的 AutoGluon 是去年亚马逊开源在 GitHub 上的 AutoML 解决方案，它以三年前亚马逊联合微软推出的 Gluon 深度学习库为基础，通过自动调整限制条件内的选择来实现决策的自动化，利用现有资源找到最优的模型。
❝GitHub 地址：https://github.com/awslabs/autogluon ❞
AutoGluon 兼具易用和扩展性，并专注于涵盖图像、文本或表格数据的深度学习和实际应用。AutoGluon 适用于机器学习初学者和专家，能够使他们：
- 通过几行代码即可快速地为数据构建深度学习原型方案；
- 利用自动超参数微调、模型选择/架构搜索和数据处理；
- 无需专家知识即可自动使用深度学习 SOTA 方法；
- 轻松地提升现有定制模型和数据管道，或者根据用例自定义 AutoGluon。
现在 AutoGluon 已经支持了以下一些应用：
- 表格预测：基于数据表中一些列的值预测其他列的值；
- 图像分类：识别图像中的主要对象；
- 对象检测：借助图像中的边界框检测多个对象；
- 文本分类：基于文本内容做出预测。
怎样使用 AutoGluon 呢？官方文档中提供了各种应用的示例代码，下面我节选了最常用的 Tablular Prediction。
❝官方文档：https://auto.gluon.ai/stable/index.html ❞
安装
Linux
- CPU
python3 -m pip install -U pip
python3 -m pip install -U setuptools wheel
python3 -m pip install -U "mxnet<2.0.0"
python3 -m pip install autogluon
- GPU
python3 -m pip install -U pip
python3 -m pip install -U setuptools wheel
# Here we assume CUDA 10.1 is installed. You should change the number
# according to your own CUDA version (e.g. mxnet_cu100 for CUDA 10.0).
python3 -m pip install -U "mxnet_cu101<2.0.0"
python3 -m pip install autogluon
Mac
❝
需先安装 XCode，Homebrew，LibOMP。若已经安装了 Homebrew，则可通过以下命令安装 LibOMP：
代码语言：javascript复制brew install libomp
❞
代码语言：javascript复制python3 -m pip install -U pip
python3 -m pip install -U setuptools wheel
python3 -m pip install -U "mxnet<2.0.0"
python3 -m pip install autogluon
三行代码构建一个绝佳的模型
代码语言：javascript复制# 载入包
from autogluon.tabular import TabularDataset, TabularPredictor
# 载入训练数据
train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
# 建模
predictor = TabularPredictor(label='class').fit(train_data, time_limit=120) # Fit models for 120s
# 载入测试数据
test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')
# 查看模型性能排名
leaderboard = predictor.leaderboard(test_data)
基于表格数据预测
通过一个简单的 fit() 函数，AutoGluon 即可生成高度精确的模型，本例演示了如何使用 AutoGluon 生成一个分类模型来预测一个人的收入是否超过 50,000 美元。
首先，导入 AutoGluon 的 TabularPredictor 和 TabularDataset 类:
代码语言：javascript复制from autogluon.tabular import TabularDataset, TabularPredictor
将训练数据从 CSV 文件加载到 AutoGluon Dataset 对象中。这个对象实质上等同于 Pandas DataFrame。
代码语言：javascript复制train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
subsample_size = 500 # subsample subset of data for faster demo, try setting this to much larger values
train_data = train_data.sample(n=subsample_size, random_state=0)
train_data.head()
train_data 中的每一行都对应一个样本，每一列包含各种特征，我们将使用这些特征来预测收入。首先指定 'class' 列为标签：
label = 'class'
print("Summary of class variable: n", train_data[label].describe())
Summary of class variable:
count 500
unique 2
top <=50K
freq 365
Name: class, dtype: object
接下来使用 AutoGluon 自动训练模型：
代码语言：javascript复制# 指定储存模型的文件夹
save_path = 'agModels-predictClass'
predictor = TabularPredictor(label=label, path=save_path).fit(train_data)
Beginning AutoGluon training ...
AutoGluon will save models to "agModels-predictClass/"
AutoGluon Version: 0.2.0b20210429
Train Data Rows: 500
Train Data Columns: 14
Preprocessing data ...
AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).
2 unique label values: [' >50K', ' <=50K']
If 'binary' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Selected class <--> label mapping: class 1 = >50K, class 0 = <=50K
Note: For your binary classification, AutoGluon arbitrarily selected which label-value represents positive ( >50K) vs negative ( <=50K) class.
To explicitly set the positive_class, either rename classes to 1 and 0, or specify positive_class in Predictor init.
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 26825.14 MB
Train Data (Original) Memory Usage: 0.29 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('int', []) : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
('object', []) : 8 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 8 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]
('int', []) : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
0.1s = Fit runtime
14 features in original data used to generate 14 features in processed data.
Train Data (Processed) Memory Usage: 0.03 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.07s ...
AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
To change this, specify the eval_metric argument of fit()
Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 400, Val Rows: 100
Fitting model: KNeighborsUnif ...
0.73 = Validation accuracy score
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: KNeighborsDist ...
0.65 = Validation accuracy score
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: LightGBMXT ...
/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/venv/lib/python3.7/site-packages/fsspec/__init__.py:47: DeprecationWarning: SelectableGroups dict interface is deprecated. Use select.
for spec in entry_points.get("fsspec.specs", []):
0.83 = Validation accuracy score
0.77s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBM ...
0.85 = Validation accuracy score
0.16s = Training runtime
0.01s = Validation runtime
Fitting model: RandomForestGini ...
0.84 = Validation accuracy score
0.51s = Training runtime
0.11s = Validation runtime
Fitting model: RandomForestEntr ...
0.83 = Validation accuracy score
0.5s = Training runtime
0.11s = Validation runtime
Fitting model: CatBoost ...
0.84 = Validation accuracy score
0.39s = Training runtime
0.01s = Validation runtime
Fitting model: ExtraTreesGini ...
0.82 = Validation accuracy score
0.5s = Training runtime
0.11s = Validation runtime
Fitting model: ExtraTreesEntr ...
0.82 = Validation accuracy score
0.5s = Training runtime
0.11s = Validation runtime
Fitting model: NeuralNetFastAI ...
/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/venv/lib/python3.7/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 10010). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.)
return torch._C._cuda_getDeviceCount() > 0
No improvement since epoch 7: early stopping
0.83 = Validation accuracy score
1.15s = Training runtime
0.03s = Validation runtime
Fitting model: XGBoost ...
0.85 = Validation accuracy score
0.17s = Training runtime
0.01s = Validation runtime
Fitting model: NeuralNetMXNet ...
0.84 = Validation accuracy score
5.57s = Training runtime
0.02s = Validation runtime
Fitting model: LightGBMLarge ...
0.83 = Validation accuracy score
0.37s = Training runtime
0.01s = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
0.85 = Validation accuracy score
0.35s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 12.68s ...
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("agModels-predictClass/")
下一步，加载独立测试数据集：
代码语言：javascript复制test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')
y_test = test_data[label] # 提取标签列
test_data_nolab = test_data.drop(columns=[label]) # 删除标签列
test_data_nolab.head()
Loaded data from: https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv | Columns = 15 / 15 | Rows = 9769 -> 9769
用训练好的模型进行预测，评估模型性能：
代码语言：javascript复制predictor = TabularPredictor.load(save_path) # 非必要步骤，载入之前训练好的模型
y_pred = predictor.predict(test_data_nolab)
print("Predictions: n", y_pred)
perf = predictor.evaluate_predictions(y_true=y_test, y_pred=y_pred, auxiliary_metrics=True)
Evaluation: accuracy on test data: 0.8397993653393387
Evaluations on test data:
{
"accuracy": 0.8397993653393387,
"balanced_accuracy": 0.7437076677780596,
"mcc": 0.5295565206264157,
"f1": 0.6242496998799519,
"precision": 0.7038440714672441,
"recall": 0.5608283002588438
}
Predictions:
0 <=50K
1 <=50K
2 >50K
3 <=50K
4 <=50K
...
9764 <=50K
9765 <=50K
9766 <=50K
9767 <=50K
9768 <=50K
Name: class, Length: 9769, dtype: object
我们也可以输出所有模型在测试集中的表现：
代码语言：javascript复制predictor.leaderboard(test_data, silent=True)
总之，如果想在自己的数据集上使用 AutoGluon，训练一个牛逼的模型实际只需两行代码：
代码语言：javascript复制from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label=<variable-name>).fit(train_data=<file-name>)
❝上面的例子仅仅使用了
fit()的默认参数，在下一节中将演示如何通过调整fit()参数来最大化预测性能。 ❞
最大限度地提高预测性能
如果你正在对 AutoGluon-Tabular 进行基准测试或希望最大限度地提高模型准确性，其实不应使用 fit() 的默认参数。为了获得 AutoGluon 的最佳预测精度，你通常应该使用下面的几行命令：
time_limit = 60 # 这里设置 60 秒仅用于快速演示代码，在实际中应该设置为你愿意等待的最长时间（以秒为单位）
metric = 'roc_auc' # 指定模型性能指标，这里为 AUC
predictor = TabularPredictor(label, eval_metric=metric).fit(train_data, time_limit=time_limit, presets='best_quality')
predictor.leaderboard(test_data, silent=True)
No path specified. Models will be saved in: "AutogluonModels/ag-20210429_010727/"
Presets specified: ['best_quality']
Beginning AutoGluon training ... Time limit = 60s
AutoGluon will save models to "AutogluonModels/ag-20210429_010727/"
AutoGluon Version: 0.2.0b20210429
Train Data Rows: 500
Train Data Columns: 14
Preprocessing data ...
AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).
2 unique label values: [' >50K', ' <=50K']
If 'binary' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Selected class <--> label mapping: class 1 = >50K, class 0 = <=50K
Note: For your binary classification, AutoGluon arbitrarily selected which label-value represents positive ( >50K) vs negative ( <=50K) class.
To explicitly set the positive_class, either rename classes to 1 and 0, or specify positive_class in Predictor init.
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 26582.18 MB
Train Data (Original) Memory Usage: 0.29 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('int', []) : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
('object', []) : 8 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 8 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]
('int', []) : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
0.1s = Fit runtime
14 features in original data used to generate 14 features in processed data.
Train Data (Processed) Memory Usage: 0.03 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.07s ...
AutoGluon will gauge predictive performance using evaluation metric: 'roc_auc'
This metric expects predicted probabilities rather than predicted class labels, so you'll need to use predict_proba() instead of predict()
To change this, specify the eval_metric argument of fit()
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 59.93s of the 59.92s of remaining time.
0.5196 = Validation roc_auc score
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 59.82s of the 59.82s of remaining time.
0.537 = Validation roc_auc score
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 59.71s of the 59.71s of remaining time.
0.8814 = Validation roc_auc score
0.9s = Training runtime
0.05s = Validation runtime
Fitting model: LightGBM_BAG_L1 ... Training model for up to 58.74s of the 58.73s of remaining time.
0.867 = Validation roc_auc score
0.91s = Training runtime
0.05s = Validation runtime
Fitting model: RandomForestGini_BAG_L1 ... Training model for up to 57.75s of the 57.75s of remaining time.
0.8847 = Validation roc_auc score
0.5s = Training runtime
0.09s = Validation runtime
Fitting model: RandomForestEntr_BAG_L1 ... Training model for up to 57.15s of the 57.14s of remaining time.
0.8863 = Validation roc_auc score
0.5s = Training runtime
0.09s = Validation runtime
Fitting model: CatBoost_BAG_L1 ... Training model for up to 56.54s of the 56.54s of remaining time.
0.8875 = Validation roc_auc score
2.93s = Training runtime
0.04s = Validation runtime
Fitting model: ExtraTreesGini_BAG_L1 ... Training model for up to 53.55s of the 53.55s of remaining time.
0.8929 = Validation roc_auc score
0.6s = Training runtime
0.09s = Validation runtime
Fitting model: ExtraTreesEntr_BAG_L1 ... Training model for up to 52.83s of the 52.83s of remaining time.
0.8939 = Validation roc_auc score
0.5s = Training runtime
0.09s = Validation runtime
Fitting model: NeuralNetFastAI_BAG_L1 ... Training model for up to 52.22s of the 52.22s of remaining time.
0.8653 = Validation roc_auc score
4.69s = Training runtime
0.13s = Validation runtime
Fitting model: XGBoost_BAG_L1 ... Training model for up to 47.34s of the 47.33s of remaining time.
0.8666 = Validation roc_auc score
1.84s = Training runtime
0.03s = Validation runtime
Fitting model: NeuralNetMXNet_BAG_L1 ... Training model for up to 45.41s of the 45.41s of remaining time.
Ran out of time, stopping training early. (Stopping on epoch 95)
0.8331 = Validation roc_auc score
31.16s = Training runtime
0.18s = Validation runtime
Fitting model: LightGBMLarge_BAG_L1 ... Training model for up to 14.02s of the 14.02s of remaining time.
0.8417 = Validation roc_auc score
1.7s = Training runtime
0.05s = Validation runtime
Completed 1/20 k-fold bagging repeats ...
Fitting model: WeightedEnsemble_L2 ... Training model for up to 59.93s of the 12.24s of remaining time.
0.9003 = Validation roc_auc score
1.26s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 49.04s ...
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20210429_010727/")
这行命令正是用了下面的策略以最大限度地提高模型准确性：
- 指定参数
presets='best_quality'，这使 AutoGluon 可以基于 stacking/bagging 自动构建功能强大的集成模型，如果有足够的训练时间这将大大提高预测性能。这个参数的默认值为'medium_quality_faster_train'，它产生的模型准确度较低，但可以更快地建立一个模型，查看分类性能。总之，使用presets参数，我们可以根据用途灵活地建立模型。比如，如果你只是希望快速构建一个基本模型，并不非常注重预测性能，则可以考虑使用：presets=['good_quality_faster_inference_only_refit', 'optimize_for_deployment']。 eval_metric参数用于设置模型评估指标，例如：'f1'（二分类），'roc_auc'（二分类），'log_loss'（分类），'mean_absolute_error'（回归），'median_absolute_error'（回归）。除此之外，我们自定义指标函数，具体参见文件夹中的示例：autogluon/core/metrics/- 将所有数据都包含在
train_data中，不额外分配tuning_data（AutoGluon 会更智能地拆分数据以满足其需求）。 - 不指定
hyperparameter_tune_kwargs参数（与直觉相反，超参数调整并非是在有限时间内训练模型的最佳方法，集成模型一般会更好）。如果你只是想部署单个模型而不是集成模型，则建议仅使用hyperparameter_tune_kwargs。 - 不指定
hyperparameters参数（AutoGluon 将自适应选择要使用的模型和超参数）。 - 将
time_limit设置的尽可能长。
回归（预测连续性变量）
为了演示 fit() 也可以自动处理回归任务，我们现在尝试根据其他特性预测同一数据集中的年龄变量：
age_column = 'age'
print("Summary of age variable: n", train_data[age_column].describe())
Summary of age variable:
count 500.00000
mean 39.65200
std 13.52393
min 17.00000
25% 29.00000
50% 38.00000
75% 49.00000
max 85.00000
Name: age, dtype: float64
我们再次调用 fit()，这次也加了一个时间限制：
predictor_age = TabularPredictor(label=age_column, path="agModels-predictAge").fit(train_data, time_limit=60)
performance = predictor_age.evaluate(test_data)
Beginning AutoGluon training ... Time limit = 60s
AutoGluon will save models to "agModels-predictAge/"
AutoGluon Version: 0.2.0b20210429
Train Data Rows: 500
Train Data Columns: 14
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).
Label info (max, min, mean, stddev): (85, 17, 39.652, 13.52393)
If 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 26519.77 MB
Train Data (Original) Memory Usage: 0.32 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('int', []) : 5 | ['fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']
('object', []) : 9 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 9 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]
('int', []) : 5 | ['fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']
0.1s = Fit runtime
14 features in original data used to generate 14 features in processed data.
Train Data (Processed) Memory Usage: 0.03 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.07s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
To change this, specify the eval_metric argument of fit()
Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 400, Val Rows: 100
Fitting model: KNeighborsUnif ... Training model for up to 59.93s of the 59.93s of remaining time.
-15.6869 = Validation root_mean_squared_error score
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: KNeighborsDist ... Training model for up to 59.82s of the 59.82s of remaining time.
-15.1801 = Validation root_mean_squared_error score
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: LightGBMXT ... Training model for up to 59.72s of the 59.72s of remaining time.
-11.8147 = Validation root_mean_squared_error score
0.21s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBM ... Training model for up to 59.49s of the 59.48s of remaining time.
-11.9295 = Validation root_mean_squared_error score
0.19s = Training runtime
0.01s = Validation runtime
Fitting model: RandomForestMSE ... Training model for up to 59.28s of the 59.28s of remaining time.
-11.6028 = Validation root_mean_squared_error score
0.5s = Training runtime
0.11s = Validation runtime
Fitting model: CatBoost ... Training model for up to 58.66s of the 58.66s of remaining time.
-11.7448 = Validation root_mean_squared_error score
0.34s = Training runtime
0.01s = Validation runtime
Fitting model: ExtraTreesMSE ... Training model for up to 58.31s of the 58.31s of remaining time.
-11.4808 = Validation root_mean_squared_error score
0.5s = Training runtime
0.11s = Validation runtime
Fitting model: NeuralNetFastAI ... Training model for up to 57.69s of the 57.68s of remaining time.
-40.9923 = Validation root_mean_squared_error score
0.97s = Training runtime
0.03s = Validation runtime
Fitting model: XGBoost ... Training model for up to 56.68s of the 56.68s of remaining time.
-12.1743 = Validation root_mean_squared_error score
0.53s = Training runtime
0.01s = Validation runtime
Fitting model: NeuralNetMXNet ... Training model for up to 56.12s of the 56.12s of remaining time.
-12.8081 = Validation root_mean_squared_error score
6.81s = Training runtime
0.03s = Validation runtime
Fitting model: LightGBMLarge ... Training model for up to 49.27s of the 49.27s of remaining time.
-12.1676 = Validation root_mean_squared_error score
0.51s = Training runtime
0.01s = Validation runtime
Fitting model: WeightedEnsemble_L2 ... Training model for up to 59.93s of the 48.14s of remaining time.
-11.3341 = Validation root_mean_squared_error score
0.42s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 12.3s ...
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("agModels-predictAge/")
Evaluation: root_mean_squared_error on test data: -10.459079691277005
Note: Scores are always higher_is_better. This metric score can be multiplied by -1 to get the metric value.
Evaluations on test data:
{
"root_mean_squared_error": -10.459079691277005,
"mean_squared_error": -109.39234798848308,
"mean_absolute_error": -8.194572974525316,
"r2": 0.4152766647850106,
"pearsonr": 0.6454293362505439,
"median_absolute_error": -6.8994293212890625
}
这里我们不需要告诉 AutoGluon 这是一个回归问题，它会从数据中自动进行推断，并报告适当的性能指标（默认情况下为 RMSE）。
查看每个模型的性能：
