import json
import lightgbm as lgb
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
try:
import cPickle as pickle
except BaseException:
import pickle
print('Loading data...')
# load or create your dataset
df_train = pd.read_csv('../binary_classification/binary.train', header=None, sep='\t')
df_test = pd.read_csv('../binary_classification/binary.test', header=None, sep='\t')
W_train = pd.read_csv('../binary_classification/binary.train.weight', header=None)[0]
W_test = pd.read_csv('../binary_classification/binary.test.weight', header=None)[0]
y_train = df_train[0]
y_test = df_test[0]
X_train = df_train.drop(0, axis=1)
X_test = df_test.drop(0, axis=1)
num_train, num_feature = X_train.shape
# create dataset for lightgbm
# if you want to re-use data, remember to set free_raw_data=False
lgb_train = lgb.Dataset(X_train, y_train,
weight=W_train, free_raw_data=False)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train,
weight=W_test, free_raw_data=False)
# specify your configurations as a dict
params = {
'boosting_type': 'gbdt',
'objective': 'binary',
'metric': 'binary_logloss',
'num_leaves': 31,
'learning_rate': 0.05,
'feature_fraction': 0.9,
'bagging_fraction': 0.8,
'bagging_freq': 5,
'verbose': 0
}
# generate feature names
feature_name = ['feature_' + str(col) for col in range(num_feature)]
print('Starting training...')
# feature_name and categorical_feature
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
valid_sets=lgb_train, # eval training data
feature_name=feature_name,
categorical_feature=[21])
print('Finished first 10 rounds...')
# check feature name
print('7th feature name is:', lgb_train.feature_name[6])
print('Saving model...')
# save model to file
gbm.save_model('model.txt')
print('Dumping model to JSON...')
# dump model to JSON (and save to file)
model_json = gbm.dump_model()
with open('model.json', 'w+') as f:
json.dump(model_json, f, indent=4)
# feature names
print('Feature names:', gbm.feature_name())
# feature importances
print('Feature importances:', list(gbm.feature_importance()))
print('Loading model to predict...')
# load model to predict
bst = lgb.Booster(model_file='model.txt')
# can only predict with the best iteration (or the saving iteration)
y_pred = bst.predict(X_test)
# eval with loaded model
print("The rmse of loaded model's prediction is:", mean_squared_error(y_test, y_pred) ** 0.5)
print('Dumping and loading model with pickle...')
# dump model with pickle
with open('model.pkl', 'wb') as fout:
pickle.dump(gbm, fout)
# load model with pickle to predict
with open('model.pkl', 'rb') as fin:
pkl_bst = pickle.load(fin)
# can predict with any iteration when loaded in pickle way
y_pred = pkl_bst.predict(X_test, num_iteration=7)
# eval with loaded model
print("The rmse of pickled model's prediction is:", mean_squared_error(y_test, y_pred) ** 0.5)
# continue training
# init_model accepts:
# 1. model file name
# 2. Booster()
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
init_model='model.txt',
valid_sets=lgb_eval)
print('Finished 10 - 20 rounds with model file...')
# decay learning rates
# learning_rates accepts:
# 1. list/tuple with length = num_boost_round
# 2. function(curr_iter)
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
init_model=gbm,
learning_rates=lambda iter: 0.05 * (0.99 ** iter),
valid_sets=lgb_eval)
print('Finished 20 - 30 rounds with decay learning rates...')
# change other parameters during training
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
init_model=gbm,
valid_sets=lgb_eval,
callbacks=[lgb.reset_parameter(bagging_fraction=[0.7] * 5 + [0.6] * 5)])
print('Finished 30 - 40 rounds with changing bagging_fraction...')
# self-defined objective function
# f(preds: array, train_data: Dataset) -> grad: array, hess: array
# log likelihood loss
def loglikelihood(preds, train_data):
labels = train_data.get_label()
preds = 1. / (1. + np.exp(-preds))
grad = preds - labels
hess = preds * (1. - preds)
return grad, hess
# self-defined eval metric
# f(preds: array, train_data: Dataset) -> name: string, eval_result: float, is_higher_better: bool
# binary error
def binary_error(preds, train_data):
labels = train_data.get_label()
return 'error', np.mean(labels != (preds > 0.5)), False
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
init_model=gbm,
fobj=loglikelihood,
feval=binary_error,
valid_sets=lgb_eval)
print('Finished 40 - 50 rounds with self-defined objective function and eval metric...')
# another self-defined eval metric
# f(preds: array, train_data: Dataset) -> name: string, eval_result: float, is_higher_better: bool
# accuracy
def accuracy(preds, train_data):
labels = train_data.get_label()
return 'accuracy', np.mean(labels == (preds > 0.5)), True
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
init_model=gbm,
fobj=loglikelihood,
feval=lambda preds, train_data: [binary_error(preds, train_data),
accuracy(preds, train_data)],
valid_sets=lgb_eval)
print('Finished 50 - 60 rounds with self-defined objective function '
'and multiple self-defined eval metrics...')
print('Starting a new training job...')
# callback
def reset_metrics():
def callback(env):
lgb_eval_new = lgb.Dataset(X_test, y_test, reference=lgb_train)
if env.iteration - env.begin_iteration == 5:
print('Add a new valid dataset at iteration 5...')
env.model.add_valid(lgb_eval_new, 'new_valid')
callback.before_iteration = True
callback.order = 0
return callback
gbm = lgb.train(params,
lgb_train,
num_boost_round=10,
valid_sets=lgb_train,
callbacks=[reset_metrics()])
print('Finished first 10 rounds with callback function...')
