<jupyter_start><jupyter_text>Install<jupyter_code>!git clone https://github.com/wangcongcong123/mts.git
%cd mts<jupyter_output>Cloning into 'mts'...
remote: Enumerating objects: 55, done.[K
remote: Counting objects: 100% (55/55), done.[K
remote: Compressing objects: 100% (48/48), done.[K
remote: Total 55 (delta 11), reused 44 (delta 5), pack-reused 0[K
Unpacking objects: 100% (55/55), done.
/content/mts<jupyter_text>Dependencies<jupyter_code>!pip install -r requirements.txt<jupyter_output>Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (0.22.2.post1)
Requirement already satisfied: torch==1.6.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 2)) (1.6.0+cu101)
Requirement already satisfied: tensorboard in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 3)) (2.3.0)
Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 4)) (1.18.5)
Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 5)) (1.0.5)
Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->-r requirements.txt (line 1)) (1.4.1)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->-r requirements.txt (line 1)) (0.16.0)
Requirement already satisfied: future in /usr/local/lib/pyth[...]<jupyter_text>Prepare data<jupyter_code>!python obtain_split_data.py --dataset_name iris --test_size 0.1<jupyter_output>numeric_feature_names:
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
done writing iris to data/iris<jupyter_text>Train and evaluate<jupyter_code>!python run.py --dataset_name iris --task cls --train_epochs 100 --train_batch_size 130 --device cpu --lr 0.03 --do_train --do_eval<jupyter_output>[1;30;43mStreaming output truncated to the last 5000 lines.[0m
  "save_path": "tmp/mlp_iris",
  "eval_on": "accuracy",
  "eval_every": 2,
  "use_wandb": false,
  "loss_fn": "ce",
  "keep_ck_num": 3,
  "lr": 0.03
}

  0% 0/2 [00:00<?, ?it/s][A
training epoch 48/100 iter 0: train loss 0.14703. lr 3.000000e-02:   0% 0/2 [00:00<?, ?it/s][A
training epoch 48/100 iter 1: train loss 0.02886. lr 3.000000e-02:   0% 0/2 [00:00<?, ?it/s][AINFO:trainer:
*****************[epoch: 48, global step: 96] eval training set based on eval_every=2***************
INFO:trainer:              precision    recall  f1-score   support

           0       1.00      1.00      1.00        42
           1       1.00      0.84      0.91        49
           2       0.85      1.00      0.92        44

    accuracy                           0.94       135
   macro avg       0.95      0.95      0.94       135
weighted avg       0.95      0.94      0.94       135

INFO:trainer:{
  "train_accuracy_score": 0.94074074074[...]
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