14wk-62: NLP with Disaster Tweets (Text) / 자료분석(Autogluon)

Author

최규빈

Published

December 1, 2023

1. 강의영상

2. Imports

#!pip install autogluon.multimodal 
import numpy as np
import pandas as pd
#---#
from autogluon.multimodal import MultiModalPredictor # from autogluon.tabular import TabularPredictor
#---#
import warnings
warnings.filterwarnings('ignore')

3. Data

!kaggle competitions download -c nlp-getting-started
Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'
Downloading nlp-getting-started.zip to /root/Dropbox/MP
100%|████████████████████████████████████████| 593k/593k [00:00<00:00, 1.09MB/s]
100%|████████████████████████████████████████| 593k/593k [00:00<00:00, 1.09MB/s]
!unzip nlp-getting-started.zip -d data 
Archive:  nlp-getting-started.zip
  inflating: data/sample_submission.csv  
  inflating: data/test.csv           
  inflating: data/train.csv          
df_train = pd.read_csv('data/train.csv')
df_test = pd.read_csv('data/test.csv')
sample_submission = pd.read_csv('data/sample_submission.csv')
!rm -rf data
!rm nlp-getting-started.zip

4. 분석

df_train.head()
id keyword location text target
0 1 NaN NaN Our Deeds are the Reason of this #earthquake M... 1
1 4 NaN NaN Forest fire near La Ronge Sask. Canada 1
2 5 NaN NaN All residents asked to 'shelter in place' are ... 1
3 6 NaN NaN 13,000 people receive #wildfires evacuation or... 1
4 7 NaN NaN Just got sent this photo from Ruby #Alaska as ... 1 
df_test.head()
id keyword location text
0 0 NaN NaN Just happened a terrible car crash
1 2 NaN NaN Heard about #earthquake is different cities, s...
2 3 NaN NaN there is a forest fire at spot pond, geese are...
3 9 NaN NaN Apocalypse lighting. #Spokane #wildfires
4 11 NaN NaN Typhoon Soudelor kills 28 in China and Taiwan 
# step1 -- pass
# step2 
predictr = MultiModalPredictor(label = 'target')
# step3
predictr.fit(df_train)
# step4 
yhat = predictr.predict(df_test) 
No path specified. Models will be saved in: "AutogluonModels/ag-20231207_033914/"
AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).
    2 unique label values:  [1, 0]
    If 'binary' 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'])
Global seed set to 0
AutoMM starts to create your model. ✨

- AutoGluon version is 0.8.2.

- Pytorch version is 1.13.1.post200.

- Model will be saved to "/root/Dropbox/MP/AutogluonModels/ag-20231207_033914".

- Validation metric is "roc_auc".

- To track the learning progress, you can open a terminal and launch Tensorboard:
    ```shell
    # Assume you have installed tensorboard
    tensorboard --logdir /root/Dropbox/MP/AutogluonModels/ag-20231207_033914
    ```

Enjoy your coffee, and let AutoMM do the job ☕☕☕ Learn more at https://auto.gluon.ai

1 GPUs are detected, and 1 GPUs will be used.
   - GPU 0 name: NVIDIA A100-SXM4-80GB MIG 7g.80gb
   - GPU 0 memory: 84.37GB/85.20GB (Free/Total)
CUDA version is 11.2.

Using 16bit None Automatic Mixed Precision (AMP)
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
You are using a CUDA device ('NVIDIA A100-SXM4-80GB MIG 7g.80gb') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

  | Name              | Type                | Params
----------------------------------------------------------
0 | model             | MultimodalFusionMLP | 109 M 
1 | validation_metric | BinaryAUROC         | 0     
2 | loss_func         | CrossEntropyLoss    | 0     
----------------------------------------------------------
109 M     Trainable params
0         Non-trainable params
109 M     Total params
219.567   Total estimated model params size (MB)
Epoch 0, global step 26: 'val_roc_auc' reached 0.81683 (best 0.81683), saving model to '/root/Dropbox/MP/AutogluonModels/ag-20231207_033914/epoch=0-step=26.ckpt' as top 3
Epoch 0, global step 53: 'val_roc_auc' reached 0.88538 (best 0.88538), saving model to '/root/Dropbox/MP/AutogluonModels/ag-20231207_033914/epoch=0-step=53.ckpt' as top 3
Epoch 1, global step 80: 'val_roc_auc' reached 0.88780 (best 0.88780), saving model to '/root/Dropbox/MP/AutogluonModels/ag-20231207_033914/epoch=1-step=80.ckpt' as top 3
Epoch 1, global step 107: 'val_roc_auc' reached 0.87404 (best 0.88780), saving model to '/root/Dropbox/MP/AutogluonModels/ag-20231207_033914/epoch=1-step=107.ckpt' as top 3
Epoch 2, global step 134: 'val_roc_auc' reached 0.89212 (best 0.89212), saving model to '/root/Dropbox/MP/AutogluonModels/ag-20231207_033914/epoch=2-step=134.ckpt' as top 3
Epoch 2, global step 161: 'val_roc_auc' reached 0.89695 (best 0.89695), saving model to '/root/Dropbox/MP/AutogluonModels/ag-20231207_033914/epoch=2-step=161.ckpt' as top 3
Epoch 3, global step 188: 'val_roc_auc' reached 0.89258 (best 0.89695), saving model to '/root/Dropbox/MP/AutogluonModels/ag-20231207_033914/epoch=3-step=188.ckpt' as top 3
Epoch 3, global step 215: 'val_roc_auc' reached 0.89418 (best 0.89695), saving model to '/root/Dropbox/MP/AutogluonModels/ag-20231207_033914/epoch=3-step=215.ckpt' as top 3
Epoch 4, global step 242: 'val_roc_auc' was not in top 3
Epoch 4, global step 269: 'val_roc_auc' was not in top 3
Epoch 5, global step 296: 'val_roc_auc' was not in top 3
Epoch 5, global step 323: 'val_roc_auc' was not in top 3
Epoch 6, global step 350: 'val_roc_auc' was not in top 3
Epoch 6, global step 377: 'val_roc_auc' was not in top 3
Epoch 7, global step 404: 'val_roc_auc' was not in top 3
Epoch 7, global step 431: 'val_roc_auc' was not in top 3
Start to fuse 3 checkpoints via the greedy soup algorithm.
You are using a CUDA device ('NVIDIA A100-SXM4-80GB MIG 7g.80gb') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
You are using a CUDA device ('NVIDIA A100-SXM4-80GB MIG 7g.80gb') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
You are using a CUDA device ('NVIDIA A100-SXM4-80GB MIG 7g.80gb') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
AutoMM has created your model 🎉🎉🎉

- To load the model, use the code below:
    ```python
    from autogluon.multimodal import MultiModalPredictor
    predictor = MultiModalPredictor.load("/root/Dropbox/MP/AutogluonModels/ag-20231207_033914")
    ```

- You can open a terminal and launch Tensorboard to visualize the training log:
    ```shell
    # Assume you have installed tensorboard
    tensorboard --logdir /root/Dropbox/MP/AutogluonModels/ag-20231207_033914
    ```

- If you are not satisfied with the model, try to increase the training time, 
adjust the hyperparameters (https://auto.gluon.ai/stable/tutorials/multimodal/advanced_topics/customization.html),
or post issues on GitHub: https://github.com/autogluon/autogluon


You are using a CUDA device ('NVIDIA A100-SXM4-80GB MIG 7g.80gb') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision

5. 제출

sample_submission
id target
0 0 0
1 2 0
2 3 0
3 9 0
4 11 0
... ... ...
3258 10861 0
3259 10865 0
3260 10868 0
3261 10874 0
3262 10875 0

3263 rows × 2 columns

sample_submission['target'] = yhat 
sample_submission.to_csv("submission.csv",index=False)
!kaggle competitions submit -c nlp-getting-started -f submission.csv -m "오토글루온, MultiModalPredictor"
Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'
100%|██████████████████████████████████████| 22.2k/22.2k [00:01<00:00, 12.4kB/s]
Successfully submitted to Natural Language Processing with Disaster Tweets

250/1094
0.22851919561243145

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