Generalized additive models with structured interactions
The following environments are required:
- Python 3.7 (anaconda is preferable)
- tensorflow 2.0
pip install git+https://github.com/SelfExplainML/gaminet.gitImport library
import os
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from gaminet import GAMINet
from gaminet.utils import local_visualize
from gaminet.utils import global_visualize_density
from gaminet.utils import feature_importance_visualize
from gaminet.utils import plot_trajectory
from gaminet.utils import plot_regularizationLoad data
def metric_wrapper(metric, scaler):
def wrapper(label, pred):
return metric(label, pred, scaler=scaler)
return wrapper
def rmse(label, pred, scaler):
pred = scaler.inverse_transform(pred.reshape([-1, 1]))
label = scaler.inverse_transform(label.reshape([-1, 1]))
return np.sqrt(np.mean((pred - label)**2))
def data_generator1(datanum, random_state=0):
np.random.seed(random_state)
x = np.zeros((datanum, 10))
for i in range(10):
x[:, i:i+1] = np.random.uniform(0, 1,[datanum,1])
x1, x2, x3, x4, x5, x6, x7, x8, x9, x10 = [x[:, [i]] for i in range(10)]
def cliff(x1, x2):
# x1: -20,20
# x2: -10,5
x1 = (2 * x1 - 1) * 20
x2 = (2 * x2 - 1) * 7.5 - 2.5
term1 = -0.5 * x1 ** 2 / 100
term2 = -0.5 * (x2 + 0.03 * x1 ** 2 - 3) ** 2
y = 10 * np.exp(term1 + term2)
return y
y = (8 * (x1 - 0.5) ** 2
+ 0.1 * np.exp(-8 * x2 + 4)
+ 3 * np.sin(2 * np.pi * x3 * x4)
+ cliff(x5, x6)).reshape([-1,1]) + 1 * np.random.normal(0, 1, [datanum, 1])
task_type = "Regression"
meta_info = {"X1":{"type":'continuous'},
'X2':{'type':'continuous'},
'X3':{'type':'continuous'},
'X4':{'type':'continuous'},
'X5':{'type':'continuous'},
'X6':{'type':'continuous'},
'X7':{'type':'continuous'},
'X8':{'type':'continuous'},
'X9':{'type':'continuous'},
'X10':{'type':'continuous'},
'Y':{'type':'target'}}
for i, (key, item) in enumerate(meta_info.items()):
if item['type'] == 'target':
sy = MinMaxScaler((0, 1))
y = sy.fit_transform(y)
meta_info[key]['scaler'] = sy
else:
sx = MinMaxScaler((0, 1))
sx.fit([[0], [1]])
x[:,[i]] = sx.transform(x[:,[i]])
meta_info[key]['scaler'] = sx
train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2, random_state=random_state)
return train_x, test_x, train_y, test_y, task_type, meta_info, metric_wrapper(rmse, sy)
train_x, test_x, train_y, test_y, task_type, meta_info, get_metric = data_generator1(5000, 0)Run GAMI-Net
## Note the current GAMINet API requires input features being normalized within 0 to 1.
model = GAMINet(meta_info=meta_info, interact_num=20, interact_arch=[20, 10], subnet_arch=[20, 10],
task_type=task_type, activation_func=tf.tanh, main_grid_size=41, interact_grid_size=41,
batch_size=500, lr_bp=0.001, main_effect_epochs=2000,
interaction_epochs=2000, tuning_epochs=50, loss_threshold=0.01,
verbose=True, val_ratio=0.2, early_stop_thres=100)
model.fit(train_x, train_y)
val_x = train_x[model.val_idx, :]
val_y = train_y[model.val_idx, :]
tr_x = train_x[model.tr_idx, :]
tr_y = train_y[model.tr_idx, :]
pred_train = model.predict(tr_x)
pred_val = model.predict(val_x)
pred_test = model.predict(test_x)
gaminet_stat = np.hstack([np.round(get_metric(tr_y, pred_train),5),
np.round(get_metric(val_y, pred_val),5),
np.round(get_metric(test_y, pred_test),5)])
print(gaminet_stat)Training Logs
simu_dir = "./results/"
if not os.path.exists(simu_dir):
os.makedirs(simu_dir)
data_dict_logs = model.summary_logs(save_dict=False)
plot_trajectory(data_dict_logs, folder=simu_dir, name="s1_traj_plot", log_scale=True, save_png=True)
plot_regularization(data_dict_logs, folder=simu_dir, name="s1_regu_plot", log_scale=True, save_png=True)
Global Visualization
data_dict = model.global_explain(save_dict=False)
global_visualize_density(data_dict, save_png=True, folder=simu_dir, name='s1_global')
Feature Importance
feature_importance_visualize(data_dict, save_png=True, folder=simu_dir, name='s1_feature')
Local Visualization
data_dict_local = model.local_explain(train_x[[0]], train_y[[0]], save_dict=False)
local_visualize(data_dict_local, save_png=True, folder=simu_dir, name='s1_local')
Yang, Z., Zhang, A. and Sudjianto, A., 2020. GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions. arXiv:2003.0713
@article{yang2020gami,
title={GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions},
author={Yang, Zebin and Zhang, Aijun and Sudjianto, Agus},
journal={arXiv preprint arXiv:2003.07132},
year={2020}
}