AutoGluon处理多模态数据方法及案例——Multimodal Data Tables: Tabular, Text, and Image
多模式数据表：表格、文本和图像
注意：本教程需要 GPU 才能训练图像和文本模型。此外，具有适当 CUDA 版本的 MXNet 和 Torch 需要安装 GPU。
PetFinder 数据集
我们将使用PetFinder 数据集。PetFinder 数据集提供有关收容所动物的信息，这些信息出现在其收养档案中，目的是预测动物的收养率。最终目标是让救援避难所使用预测的收养率来识别可以改善其档案的动物，以便他们找到一个家。
每只动物的收养档案都包含各种信息，例如动物的图片、动物的文字描述以及各种表格特征，例如年龄、品种、名称、颜色等。
首先，我们首先需要下载数据集。包含图像的数据集需要的不仅仅是 CSV 文件，因此数据集在 S3 中打包在一个 zip 文件中。我们将首先下载它并解压缩内容：
download_dir = './ag_petfinder_tutorial'
zip_file = 'https://automl-mm-bench.s3.amazonaws.com/petfinder_kaggle.zip'
from autogluon.core.utils.loaders import load_zip
load_zip.unzip(zip_file, unzip_dir=download_dir)现在数据已经下载并解压，我们来看看内容：
import os
os.listdir(download_dir)
['petfinder_processed', 'file.zip']“file.zip”是我们下载的原始 zip 文件，“petfinder_processed”是包含数据集文件的目录。
dataset_path = download_dir + '/petfinder_processed'
os.listdir(dataset_path)
['test.csv', 'dev.csv', 'test_images', 'train_images', 'train.csv']在这里，我们可以看到 train、test 和 dev CSV 文件，以及两个目录：“test_images”和“train_images”，其中包含图像 JPG 文件。
注意：我们将使用 dev 数据作为测试数据，因为 dev 包含用于显示分数的基本事实标签predictor.leaderboard。让我们看一下“train_images”目录中的前 10 个文件：
os.listdir(dataset_path + '/train_images')[:10]
['ca587cb42-1.jpg',
'ae00eded4-4.jpg',
'6e3457b81-2.jpg',
'acb248693-1.jpg',
'0bd867d1b-1.jpg',
'fa53dd6cd-1.jpg',
'9726ab93e-1.jpg',
'39818f12c-2.jpg',
'90ce48a71-2.jpg',
'2ece6b26b-1.jpg']接下来，我们将加载 train 和 dev CSV 文件：
import pandas as pd
train_data = pd.read_csv(f'{dataset_path}/train.csv', index_col=0)
test_data = pd.read_csv(f'{dataset_path}/dev.csv', index_col=0)train_data.head(3) Type Name Age Breed1 Breed2 Gender Color1 Color2 Color3 MaturitySize ... Quantity Fee State RescuerID VideoAmt Description PetID PhotoAmt AdoptionSpeed Images
10721 1 Elbi 2 307 307 2 5 0 0 3 ... 1 0 41336 e9a86209c54f589ba72c345364cf01aa 0 I'm looking for people to adopt my dog e4b90955c 4.0 4 train_images/e4b90955c-1.jpg;train_images/e4b9...
13114 2 Darling 4 266 0 1 1 0 0 2 ... 1 0 41401 01f954cdf61526daf3fbeb8a074be742 0 Darling was born at the back lane of Jalan Alo... a0c1384d1 5.0 3 train_images/a0c1384d1-1.jpg;train_images/a0c1...
13194 1 Wolf 3 307 0 1 1 2 0 2 ... 1 0 41332 6e19409f2847326ce3b6d0cec7e42f81 0 I found Wolf about a month ago stuck in a drai... cf357f057 7.0 4 train_images/cf357f057-1.jpg;train_images/cf35..3行×25列
查看前 3 个示例，我们可以看出有多种表格特征、文本描述（'Description'）和图像路径（'Images'）。
对于 PetFinder 数据集，我们将尝试预测动物的收养速度（“AdoptionSpeed”），分为 5 个类别。这意味着我们正在处理一个多类分类问题。
label = 'AdoptionSpeed'
image_col = 'Images'让我们看一下图像列中的值是什么样的：
train_data[image_col].iloc[0]
'train_images/e4b90955c-1.jpg;train_images/e4b90955c-2.jpg;train_images/e4b90955c-3.jpg;train_images/e4b90955c-4.jpg'目前，AutoGluon 仅支持每行一张图像。由于 PetFinder 数据集每行包含一个或多个图像，我们首先需要对图像列进行预处理，使其仅包含每行的第一个图像。
train_data[image_col] = train_data[image_col].apply(lambda ele: ele.split(';')[0])
test_data[image_col] = test_data[image_col].apply(lambda ele: ele.split(';')[0])
train_data[image_col].iloc[0]
'train_images/e4b90955c-1.jpg'AutoGluon 根据图像列提供的文件路径加载图像。
在这里，我们更新路径以指向磁盘上的正确位置：
def path_expander(path, base_folder):
path_l = path.split(';')
return ';'.join([os.path.abspath(os.path.join(base_folder, path)) for path in path_l])
train_data[image_col] = train_data[image_col].apply(lambda ele: path_expander(ele, base_folder=dataset_path))
test_data[image_col] = test_data[image_col].apply(lambda ele: path_expander(ele, base_folder=dataset_path))
train_data[image_col].iloc[0]
'/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/ag_petfinder_tutorial/petfinder_processed/train_images/e4b90955c-1.jpg'example_row = train_data.iloc[1]
example_row
Type 2
Name Darling
Age 4
Breed1 266
Breed2 0
Gender 1
Color1 1
Color2 0
Color3 0
MaturitySize 2
FurLength 1
Vaccinated 2
Dewormed 2
Sterilized 2
Health 1
Quantity 1
Fee 0
State 41401
RescuerID 01f954cdf61526daf3fbeb8a074be742
VideoAmt 0
Description Darling was born at the back lane of Jalan Alo...
PetID a0c1384d1
PhotoAmt 5.0
AdoptionSpeed 3
Images /var/lib/jenkins/workspace/workspace/autogluon...
Name: 13114, dtype: objectexample_row['Description']
'Darling was born at the back lane of Jalan Alor and was foster by a feeder. All his siblings had died of accident. His mother and grandmother had just been spayed. Darling make a great condo/apartment cat. He love to play a lot. He would make a great companion for someone looking for a cat to love.'example_image = example_row['Images']
from IPython.display import Image, display
pil_img = Image(filename=example_image)
display(pil_img)
PetFinder 数据集相当大。出于本教程的目的，我们将采样 500 行进行训练。
在大型多模态数据集上进行训练可能需要非常密集的计算，尤其是在使用best_quality的AutoGluon 中的预设时。在进行原型设计时，建议对数据进行采样以了解哪些模型值得训练，然后像使用任何其他机器学习算法一样，逐渐使用大量数据和更长的时间限制进行训练。
train_data = train_data.sample(500, random_state=0)构建特征元数据
接下来，让我们看看 AutoGluon 通过从训练数据中构造一个 FeatureMetadata 对象来推断特征类型是什么：
from autogluon.tabular import FeatureMetadata
feature_metadata = FeatureMetadata.from_df(train_data)
print(feature_metadata)
('float', []) : 1 | ['PhotoAmt']
('int', []) : 19 | ['Type', 'Age', 'Breed1', 'Breed2', 'Gender', ...]
('object', []) : 4 | ['Name', 'RescuerID', 'PetID', 'Images']
('object', ['text']) : 1 | ['Description']注意，FeatureMetadata 自动将“描述”列识别为文本，因此我们不需要手动指定它是文本。
为了利用图像，我们需要告诉 AutoGluon 哪一列包含图像路径。我们可以通过指定FeatureMetadata 对象并将 'image_path' 特殊类型添加到图像列来做到这一点。我们稍后将此自定义 FeatureMetadata 传递给 TabularPredictor.fit。
feature_metadata = feature_metadata.add_special_types({image_col: ['image_path']})
print(feature_metadata)
('float', []) : 1 | ['PhotoAmt']
('int', []) : 19 | ['Type', 'Age', 'Breed1', 'Breed2', 'Gender', ...]
('object', []) : 3 | ['Name', 'RescuerID', 'PetID']
('object', ['image_path']) : 1 | ['Images']
('object', ['text']) : 1 | ['Description']指定超参数
接下来，我们需要指定我们想要训练的模型。这是通过hyperparametersTabularPredictor.fit 的参数完成的。
AutoGluon 有一个预定义的配置，适用于称为“多模式”的多模式数据集。我们可以通过以下方式访问它：
from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
hyperparameters = get_hyperparameter_config('multimodal')
hyperparameters
{'NN_TORCH': {},
'GBM': [{},
{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}},
'GBMLarge'],
'CAT': {},
'XGB': {},
'AG_TEXT_NN': {'presets': 'medium_quality_faster_train'},
'AG_IMAGE_NN': {},
'VW': {}}此超参数配置将训练各种表格模型以及微调 Electra BERT 文本模型和 ResNet 图像模型。
用 TabularPredictor 拟合
现在我们将使用我们之前定义的特征元数据和超参数在数据集上训练一个 TabularPredictor。此 TabularPredictor 将同时利用表格、文本和图像功能。
from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label=label).fit(
train_data=train_data,
hyperparameters=hyperparameters,
feature_metadata=feature_metadata,
time_limit=900,
)No path specified. Models will be saved in: "AutogluonModels/ag-20220315_003808/"
Beginning AutoGluon training ... Time limit = 900s
AutoGluon will save models to "AutogluonModels/ag-20220315_003808/"
AutoGluon Version: 0.4.0b20220315
Python Version: 3.9.10
Operating System: Linux
Train Data Rows: 500
Train Data Columns: 24
Label Column: AdoptionSpeed
Preprocessing data ...
AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == int, but few unique label-values observed).
5 unique label values: [2, 3, 4, 0, 1]
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'])
Train Data Class Count: 5
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 22403.51 MB
Train Data (Original) Memory Usage: 0.51 MB (0.0% of available memory)
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting IdentityFeatureGenerator...
Fitting RenameFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Fitting TextSpecialFeatureGenerator...
Fitting BinnedFeatureGenerator...
Fitting DropDuplicatesFeatureGenerator...
Fitting TextNgramFeatureGenerator...
Fitting CountVectorizer for text features: ['Description']
CountVectorizer fit with vocabulary size = 170
Fitting IdentityFeatureGenerator...
Fitting IsNanFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Unused Original Features (Count: 1): ['PetID']
These features were not used to generate any of the output features. Add a feature generator compatible with these features to utilize them.
Features can also be unused if they carry very little information, such as being categorical but having almost entirely unique values or being duplicates of other features.
These features do not need to be present at inference time.
('object', []) : 1 | ['PetID']
Types of features in original data (raw dtype, special dtypes):
('float', []) : 1 | ['PhotoAmt']
('int', []) : 18 | ['Type', 'Age', 'Breed1', 'Breed2', 'Gender', ...]
('object', []) : 2 | ['Name', 'RescuerID']
('object', ['image_path']) : 1 | ['Images']
('object', ['text']) : 1 | ['Description']
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 2 | ['Name', 'RescuerID']
('category', ['text_as_category']) : 1 | ['Description']
('float', []) : 1 | ['PhotoAmt']
('int', []) : 17 | ['Age', 'Breed1', 'Breed2', 'Gender', 'Color1', ...]
('int', ['binned', 'text_special']) : 24 | ['Description.char_count', 'Description.word_count', 'Description.capital_ratio', 'Description.lower_ratio', 'Description.digit_ratio', ...]
('int', ['bool']) : 1 | ['Type']
('int', ['text_ngram']) : 171 | ['__nlp__.about', '__nlp__.active', '__nlp__.active and', '__nlp__.adopt', '__nlp__.adopted', ...]
('object', ['image_path']) : 1 | ['Images']
('object', ['text']) : 1 | ['Description_raw_text']
0.5s = Fit runtime
23 features in original data used to generate 219 features in processed data.
Train Data (Processed) Memory Usage: 0.58 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.57s ...
AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 400, Val Rows: 100
Fitting 9 L1 models ...
Fitting model: LightGBM ... Training model for up to 899.43s of the 899.43s of remaining time.
0.34 = Validation score (accuracy)
1.29s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBMXT ... Training model for up to 898.13s of the 898.13s of remaining time.
0.34 = Validation score (accuracy)
0.82s = Training runtime
0.01s = Validation runtime
Fitting model: CatBoost ... Training model for up to 897.28s of the 897.28s of remaining time.
0.3 = Validation score (accuracy)
2.88s = Training runtime
0.01s = Validation runtime
Fitting model: XGBoost ... Training model for up to 894.38s of the 894.38s of remaining time.
0.35 = Validation score (accuracy)
1.64s = Training runtime
0.01s = Validation runtime
Fitting model: NeuralNetTorch ... Training model for up to 892.73s of the 892.72s of remaining time.
0.35 = Validation score (accuracy)
1.8s = Training runtime
0.02s = Validation runtime
Fitting model: VowpalWabbit ... Training model for up to 890.9s of the 890.9s of remaining time.
0.24 = Validation score (accuracy)
0.73s = Training runtime
0.03s = Validation runtime
Fitting model: LightGBMLarge ... Training model for up to 889.81s of the 889.81s of remaining time.
0.37 = Validation score (accuracy)
2.48s = Training runtime
0.01s = Validation runtime
Fitting model: TextPredictor ... Training model for up to 887.3s of the 887.3s of remaining time.
Global seed set to 0
Using 16bit native Automatic Mixed Precision (AMP)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params
----------------------------------------------------------
0 | model | MultimodalFusionMLP | 13.7 M
1 | validation_metric | Accuracy | 0
2 | loss_func | CrossEntropyLoss | 0
----------------------------------------------------------
13.7 M Trainable params
0 Non-trainable params
13.7 M Total params
27.305 Total estimated model params size (MB)
Global seed set to 0
Epoch 0, global step 1: val_accuracy reached 0.24000 (best 0.24000), saving model to "/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220315_003808/models/TextPredictor/epoch=0-step=1.ckpt" as top 3
Epoch 0, global step 3: val_accuracy reached 0.28000 (best 0.28000), saving model to "/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220315_003808/models/TextPredictor/epoch=0-step=3.ckpt" as top 3
Epoch 1, global step 5: val_accuracy reached 0.25000 (best 0.28000), saving model to "/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220315_003808/models/TextPredictor/epoch=1-step=5.ckpt" as top 3
Epoch 1, global step 7: val_accuracy reached 0.27000 (best 0.28000), saving model to "/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220315_003808/models/TextPredictor/epoch=1-step=7.ckpt" as top 3
Epoch 2, global step 9: val_accuracy reached 0.30000 (best 0.30000), saving model to "/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220315_003808/models/TextPredictor/epoch=2-step=9.ckpt" as top 3
Epoch 2, global step 11: val_accuracy reached 0.28000 (best 0.30000), saving model to "/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220315_003808/models/TextPredictor/epoch=2-step=11.ckpt" as top 3
Epoch 3, global step 13: val_accuracy was not in top 3
Epoch 3, global step 15: val_accuracy was not in top 3
Epoch 4, global step 17: val_accuracy was not in top 3
Epoch 4, global step 19: val_accuracy was not in top 3
Epoch 5, global step 21: val_accuracy reached 0.30000 (best 0.30000), saving model to "/var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220315_003808/models/TextPredictor/epoch=5-step=21.ckpt" as top 3
Epoch 5, global step 23: val_accuracy was not in top 3
Epoch 6, global step 25: val_accuracy was not in top 3
Epoch 6, global step 27: val_accuracy was not in top 3
Epoch 7, global step 29: val_accuracy was not in top 3
0.25 = Validation score (accuracy)
52.65s = Training runtime
0.62s = Validation runtime
Fitting model: ImagePredictor ... Training model for up to 833.92s of the 833.92s of remaining time.
ImagePredictor sets accuracy as default eval_metric for classification problems.
The number of requested GPUs is greater than the number of available GPUs.Reduce the number to 1
modified configs( != ): {
root.misc.seed 42 != 716
root.misc.num_workers 4 != 8
root.train.epochs 200 != 15
root.train.early_stop_max_value 1.0 != inf
root.train.batch_size 32 != 16
root.train.early_stop_baseline 0.0 != -inf
root.train.early_stop_patience -1 != 10
root.img_cls.model resnet101 != resnet50
}
Saved config to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220315_003808/models/ImagePredictor/78a64d2a/.trial_0/config.yaml
Model resnet50 created, param count: 23518277
AMP not enabled. Training in float32.
Disable EMA as it is not supported for now.
Start training from [Epoch 0]
[Epoch 0] training: accuracy=0.182500
[Epoch 0] speed: 84 samples/sec time cost: 4.555561
[Epoch 0] validation: top1=0.230000 top5=1.000000
[Epoch 0] Current best top-1: 0.230000 vs previous -inf, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220315_003808/models/ImagePredictor/78a64d2a/.trial_0/best_checkpoint.pkl
[Epoch 1] training: accuracy=0.280000
[Epoch 1] speed: 93 samples/sec time cost: 4.089249
[Epoch 1] validation: top1=0.220000 top5=1.000000
[Epoch 2] training: accuracy=0.310000
[Epoch 2] speed: 94 samples/sec time cost: 4.081534
[Epoch 2] validation: top1=0.220000 top5=1.000000
[Epoch 3] training: accuracy=0.332500
[Epoch 3] speed: 94 samples/sec time cost: 4.083061
[Epoch 3] validation: top1=0.230000 top5=1.000000
[Epoch 4] training: accuracy=0.330000
[Epoch 4] speed: 93 samples/sec time cost: 4.095332
[Epoch 4] validation: top1=0.280000 top5=1.000000
[Epoch 4] Current best top-1: 0.280000 vs previous 0.230000, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-tabular-v3/docs/_build/eval/tutorials/tabular_prediction/AutogluonModels/ag-20220315_003808/models/ImagePredictor/78a64d2a/.trial_0/best_checkpoint.pkl
[Epoch 5] training: accuracy=0.355000
[Epoch 5] speed: 93 samples/sec time cost: 4.091876
[Epoch 5] validation: top1=0.270000 top5=1.000000
[Epoch 6] training: accuracy=0.377500
[Epoch 6] speed: 93 samples/sec time cost: 4.109509
[Epoch 6] validation: top1=0.280000 top5=1.000000
[Epoch 7] training: accuracy=0.370000
[Epoch 7] speed: 93 samples/sec time cost: 4.090519
[Epoch 7] validation: top1=0.230000 top5=1.000000
[Epoch 8] training: accuracy=0.390000
[Epoch 8] speed: 93 samples/sec time cost: 4.102693
[Epoch 8] validation: top1=0.260000 top5=1.000000
[Epoch 9] training: accuracy=0.400000
[Epoch 9] speed: 93 samples/sec time cost: 4.096608
[Epoch 9] validation: top1=0.240000 top5=1.000000
[Epoch 10] training: accuracy=0.365000
[Epoch 10] speed: 93 samples/sec time cost: 4.091880
[Epoch 10] validation: top1=0.240000 top5=1.000000
[Epoch 11] training: accuracy=0.417500
[Epoch 11] speed: 93 samples/sec time cost: 4.099170
[Epoch 11] validation: top1=0.230000 top5=1.000000
[Epoch 12] training: accuracy=0.417500
[Epoch 12] speed: 93 samples/sec time cost: 4.108267
[Epoch 12] validation: top1=0.190000 top5=1.000000
[Epoch 13] training: accuracy=0.430000
[Epoch 13] speed: 93 samples/sec time cost: 4.096501
[Epoch 13] validation: top1=0.260000 top5=1.000000
[Epoch 14] training: accuracy=0.447500
[Epoch 14] speed: 93 samples/sec time cost: 4.105119
[Epoch 14] validation: top1=0.240000 top5=1.000000
Applying the state from the best checkpoint...
0.28 = Validation score (accuracy)
72.02s = Training runtime
0.93s = Validation runtime
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 756.98s of remaining time.
0.37 = Validation score (accuracy)
0.21s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 143.24s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20220315_003808/") 预测器拟合后，我们可以看看排行榜，看看各种模型的表现：
leaderboard = predictor.leaderboard(test_data)
model score_test score_val pred_time_test pred_time_val fit_time pred_time_test_marginal pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 LightGBMLarge 0.323775 0.37 0.016176 0.009152 2.483743 0.016176 0.009152 2.483743 1 True 7
1 WeightedEnsemble_L2 0.323775 0.37 0.418150 0.009574 2.690200 0.401975 0.000422 0.206457 2 True 10
2 NeuralNetTorch 0.319773 0.35 0.067532 0.020741 1.798623 0.067532 0.020741 1.798623 1 True 5
3 CatBoost 0.319106 0.30 0.020695 0.012886 2.882545 0.020695 0.012886 2.882545 1 True 3
4 LightGBMXT 0.315772 0.34 0.040475 0.007016 0.820213 0.040475 0.007016 0.820213 1 True 2
5 XGBoost 0.292431 0.35 0.044200 0.007139 1.641712 0.044200 0.007139 1.641712 1 True 4
6 LightGBM 0.289763 0.34 0.023030 0.006547 1.285891 0.023030 0.006547 1.285891 1 True 1
7 TextPredictor 0.285428 0.25 11.946029 0.617158 52.650396 11.946029 0.617158 52.650396 1 True 8
8 VowpalWabbit 0.278760 0.24 0.823174 0.033238 0.729900 0.823174 0.033238 0.729900 1 True 6
9 ImagePredictor 0.271757 0.28 10.863207 0.932101 72.022450 10.863207 0.932101 72.022450 1 True 9