metatreelib 0.1.0

PyTorch Implementation for MetaTree: Learning a Decision Tree Algorithm with Transformers

🌲 MetaTree 🌲

Learning a Decision Tree Algorithm with Transformers

MetaTree is a transformer-based decision tree algorithm. It learns from classical decision tree algorithms (greedy algorithm CART, optimal algorithm GOSDT), for better generalization capabilities.

Quickstart -- use MetaTree to generate decision tree models

Model is avaliable at https://huggingface.co/yzhuang/MetaTree

1. Install metatreelib:

pip install metatreelib
# Alternatively,  
# clone then pip install -e .
# pip install git+https://github.com/EvanZhuang/MetaTree

2. Use MetaTree on your datasets to generate a decision tree model

from metatree.model_metatree import LlamaForMetaTree as MetaTree
from metatree.decision_tree_class import DecisionTree, DecisionTreeForest
from metatree.run_train import preprocess_dimension_patch
from transformers import AutoConfig
import imodels # pip install imodels 

# Initialize Model
model_name_or_path = "yzhuang/MetaTree"

config = AutoConfig.from_pretrained(model_name_or_path)
model = MetaTree.from_pretrained(
    model_name_or_path,
    config=config,
)   

X, y, feature_names = imodels.get_clean_dataset('fico', data_source='imodels')

print("Dataset Shapes X={}, y={}, Num of Classes={}".format(X.shape, y.shape, len(set(y))))

train_idx, test_idx = sklearn.model_selection.train_test_split(range(X.shape[0]), test_size=0.3, random_state=seed)

# Dimension Subsampling
feature_idx = np.random.choice(X.shape[1], 10, replace=False)
X = X[:, feature_idx]

test_X, test_y = X[test_idx], y[test_idx]

# Sample Train and Test Data
subset_idx = random.sample(train_idx, 256)
train_X, train_y = X[subset_idx], y[subset_idx]

input_x = torch.tensor(train_X, dtype=torch.float32)
input_y = torch.nn.functional.one_hot(torch.tensor(train_y)).float()

batch = {"input_x": input_x, "input_y": input_y, "input_y_clean": input_y}
batch = preprocess_dimension_patch(batch, n_feature=10, n_class=10)
model.depth = 2
outputs = model.generate_decision_tree(batch['input_x'], batch['input_y'], depth=model.depth)
decision_tree_forest.add_tree(DecisionTree(auto_dims=outputs.metatree_dimensions, auto_thresholds=outputs.tentative_splits, input_x=batch['input_x'], input_y=batch['input_y'], depth=model.depth))

print("Decision Tree Features: ", [x.argmax(dim=-1) for x in outputs.metatree_dimensions])
print("Decision Tree Threasholds: ", outputs.tentative_splits)

3. Inference with the decision tree model

tree_pred = decision_tree_forest.predict(torch.tensor(test_X, dtype=torch.float32))

accuracy = accuracy_score(test_y, tree_pred.argmax(dim=-1).squeeze(0))
print("MetaTree Test Accuracy: ", accuracy)

To reproduce the evaluation results

Datasets used in evaluation are avaliable on huggingface

for dataset_name in metatree_mfeat_fourier metatree_mfeat_zernike metatree_mfeat_morphological metatree_mfeat_karhunen metatree_page_blocks metatree_optdigits metatree_pendigits metatree_waveform_5000 metatree_Hyperplane_10_1E_3 metatree_Hyperplane_10_1E_4 metatree_pokerhand metatree_RandomRBF_0_0 metatree_RandomRBF_10_1E_3 metatree_RandomRBF_50_1E_3 metatree_RandomRBF_10_1E_4 metatree_RandomRBF_50_1E_4 metatree_SEA_50_ metatree_SEA_50000_ metatree_satimage metatree_BNG_labor_ metatree_BNG_breast_w_ metatree_BNG_mfeat_karhunen_ metatree_BNG_bridges_version1_ metatree_BNG_mfeat_zernike_ metatree_BNG_cmc_ metatree_BNG_colic_ORIG_ metatree_BNG_colic_ metatree_BNG_credit_a_ metatree_BNG_page_blocks_ metatree_BNG_credit_g_ metatree_BNG_pendigits_ metatree_BNG_cylinder_bands_ metatree_BNG_dermatology_ metatree_BNG_sonar_ metatree_BNG_glass_ metatree_BNG_heart_c_ metatree_BNG_heart_statlog_ metatree_BNG_vehicle_ metatree_BNG_hepatitis_ metatree_BNG_waveform_5000_ metatree_BNG_zoo_ metatree_vehicle_sensIT metatree_UNIX_user_data metatree_fri_c3_1000_25 metatree_rmftsa_sleepdata metatree_JapaneseVowels metatree_fri_c4_1000_100 metatree_abalone metatree_fri_c4_1000_25 metatree_bank8FM metatree_analcatdata_supreme metatree_ailerons metatree_cpu_small metatree_space_ga metatree_fri_c1_1000_5 metatree_puma32H metatree_fri_c3_1000_10 metatree_cpu_act metatree_fri_c4_1000_10 metatree_quake metatree_fri_c4_1000_50 metatree_fri_c0_1000_5 metatree_delta_ailerons metatree_fri_c3_1000_50 metatree_kin8nm metatree_fri_c3_1000_5 metatree_puma8NH metatree_delta_elevators metatree_houses metatree_bank32nh metatree_fri_c1_1000_50 metatree_house_8L metatree_fri_c0_1000_10 metatree_elevators metatree_wind metatree_fri_c0_1000_25 metatree_fri_c2_1000_50 metatree_pollen metatree_mv metatree_fried metatree_fri_c2_1000_25 metatree_fri_c0_1000_50 metatree_fri_c1_1000_10 metatree_fri_c2_1000_5 metatree_fri_c2_1000_10 metatree_fri_c1_1000_25 metatree_visualizing_soil metatree_socmob metatree_mozilla4 metatree_pc3 metatree_pc1
do
for tree_size in 1 5 10 20 30 40 50 60 70 80 90 100
do
PYTHONPATH="./metatree":"$PYTHONPATH" python ./metatree/eval_generalization.py \
    --model_name_or_path "yzhuang/MetaTree" \
    --normalize \
    --dataset_name yzhuang/$dataset_name \
    --max_train_steps $tree_size \
    --backward_window 1 \
    --n_feature 10 \
    --n_class 10 \
    --block_size 256 \
    --depth 2 \
    --per_device_train_batch_size 256 \
    --per_device_eval_batch_size 512 \
    --preprocessing_num_workers 8 \
    --inference_only \
    #--with_tracking \ 
    #--report_to "wandb" # In case you want to use wandb to log the results 
done
done