catboost回归自动调参

import os

import time

import optuna

import pandas as pd

from catboost import CatBoostRegressor

from sklearn.metrics import r2_score, mean_squared_error

from sklearn.model_selection import train_test_split

X_train = data.drop(['label', 'b1', 'b2'], axis=1).values

y_train = data['label'].values

X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2, random_state=42)

def epoch_time(start_time, end_time):

elapsed_secs = end_time - start_time

elapsed_mins = elapsed_secs / 60

return elapsed_mins, elapsed_secs

def objective(trial):

自定义的参数空间

depth = trial.suggest_int('depth', 1, 16)

border_count = trial.suggest_int('border_count', 1, 222)

l2_leaf_reg = trial.suggest_int('l2_leaf_reg', 1, 222)

learning_rate = trial.suggest_uniform('learning_rate', 0.001, 0.9)

iterations = trial.suggest_int('iterations', 1, 100)

estimator = CatBoostRegressor(loss_function='RMSE', random_seed=22, learning_rate=learning_rate,

iterations=iterations, l2_leaf_reg=l2_leaf_reg,

border_count=border_count,

depth=depth, verbose=0)

estimator.fit(X_train, y_train)

val_pred = estimator.predict(X_test)

mse = mean_squared_error(y_test, val_pred)

return mse

""" Run optimize.

Set n_trials and/or timeout (in sec) for optimization by Optuna

"""

study = optuna.create_study(sampler=optuna.samplers.TPESampler(), direction='minimize')

study = optuna.create_study(sampler=optuna.samplers.RandomSampler(), direction='minimize')

start_time = time.time()

study.optimize(objective, n_trials=10)

end_time = time.time()

elapsed_mins, elapsed_secs = epoch_time(start_time, end_time)

print('elapsed_secs:', elapsed_secs)

print('Best value:', study.best_trial.value)

import os

import time

import pandas as pd

from catboost import CatBoostRegressor

from hyperopt import fmin, hp, partial, Trials, tpe,rand

from sklearn.metrics import r2_score, mean_squared_error

from sklearn.model_selection import train_test_split

自定义hyperopt的参数空间

space = {"iterations": hp.choice("iterations", range(1, 100)),

"depth": hp.randint("depth", 16),

"l2_leaf_reg": hp.randint("l2_leaf_reg", 222),

"border_count": hp.randint("border_count", 222),

'learning_rate': hp.uniform('learning_rate', 0.001, 0.9),

}

X_train = data.drop(['label', 'b1', 'b2'], axis=1).values

y_train = data['label'].values

X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2, random_state=42)

def epoch_time(start_time, end_time):

elapsed_secs = end_time - start_time

elapsed_mins = elapsed_secs / 60

return elapsed_mins, elapsed_secs

自动化调参并训练

def cat_factory(argsDict):

estimator = CatBoostRegressor(loss_function='RMSE', random_seed=22, learning_rate=argsDict['learning_rate'],

iterations=argsDict['iterations'], l2_leaf_reg=argsDict['l2_leaf_reg'],

border_count=argsDict['border_count'],

depth=argsDict['depth'], verbose=0)

estimator.fit(X_train, y_train)

val_pred = estimator.predict(X_test)

mse = mean_squared_error(y_test, val_pred)

return mse

算法选择 tpe

algo = partial(tpe.suggest)

随机搜索

algo = partial(rand.suggest)

初始化每次尝试

trials = Trials()

开始自动参数寻优

start_time = time.time()

best = fmin(cat_factory, space, algo=algo, max_evals=10, trials=trials)

end_time = time.time()

elapsed_mins, elapsed_secs = epoch_time(start_time, end_time)

print('elapsed_secs:', elapsed_secs)

all = []

遍历每一次的寻参结果

for one in trials:

str_re = str(one)

argsDict = one['misc']['vals']

value = one['result']['loss']

learning_rate = argsDict["learning_rate"][0]

iterations = argsDict["iterations"][0]

depth = argsDict["depth"][0]

l2_leaf_reg = argsDict["l2_leaf_reg"][0]

border_count = argsDict["border_count"][0]

finish = [value, learning_rate, iterations, depth, l2_leaf_reg, border_count]

all.append(finish)

parameters = pd.DataFrame(all, columns=['value', 'learning_rate', 'iterations', 'depth', 'l2_leaf_reg', 'border_count'])

从寻参结果中找到r2最大的

best = parameters.loc[abs(parameters['value']).idxmin()]

print("best: {}".format(best))