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performance.py
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performance.py
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import numpy as np
from time import time
import os
from distribution import TestDistribution
from RTER import RegressionTree
from ensemble import RegressionTreeBoosting, RegressionTreeEnsemble
from sklearn.metrics import mean_squared_error as MSE
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
distribution_index_vec=[1,2,3,4,5,6,7,8,9,10]
repeat_time=5
log_file_dir = "./results/performace/"
for distribution_iter,distribution_index in enumerate(distribution_index_vec):
for iterate in range(repeat_time):
np.random.seed(iterate+256)
# generate distribution
sample_generator=TestDistribution(distribution_index).returnDistribution()
n_test, n_train = 5000,1000
X_train, Y_train = sample_generator.generate(n_train)
X_test, Y_test = sample_generator.generate(n_test)
# single tree
parameters={"min_samples_split":[5,15], "max_depth":[0,1,2,3,4,5,6,7],
"splitter":["maxedge"],"estimator":["naive_estimator"]}
cv_model_tree=GridSearchCV(estimator=RegressionTree(),param_grid=parameters, cv=3, n_jobs=-1)
cv_model_tree.fit(X_train, Y_train)
tree_model = cv_model_tree.best_estimator_
time_start=time()
tree_model.fit(X_train, Y_train)
mse_score=-tree_model.score(X_test, Y_test)
time_end=time()
log_file_name = "{}.csv".format("tree")
log_file_path = os.path.join(log_file_dir, log_file_name)
with open(log_file_path, "a") as f:
logs= "{},{},{},{},{},{}\n".format(distribution_index,
mse_score, time_end-time_start,
iterate,n_train,n_test)
f.writelines(logs)
# RTER
parameters={"min_samples_split":[10,30], "max_depth":[1,2,3,4,5],
"order":[0,1,2,3,6],"splitter":["maxedge"],
"estimator":["pointwise_extrapolation_estimator"],
"r_range_low":[0,0.1],"r_range_up":[0.6,0.8,1],
"lamda":[0.0001,0.001,0.01,0.1,1,5],"V":[3,5,7,9,12,15,20]}
cv_model_RTER=GridSearchCV(estimator=RegressionTree(),param_grid=parameters, cv=3, n_jobs=-1)
cv_model_RTER.fit(X_train, Y_train)
RTER_model = cv_model_RTER.best_estimator_
RTER_model.parallel_jobs = "auto"
time_start=time()
RTER_model.fit(X_train, Y_train)
mse_score=-RTER_model.score(X_test, Y_test)
time_end=time()
log_file_name = "{}.csv".format("RTER")
log_file_path = os.path.join(log_file_dir, log_file_name)
with open(log_file_path, "a") as f:
logs= "{},{},{},{},{},{}\n".format(distribution_index,
mse_score, time_end-time_start,
iterate,n_train,n_test)
f.writelines(logs)
# boosting
parameters= {"min_samples_split":[10], "max_depth":[1,2,3,4,5],
"splitter":["maxedge"], "estimator":["naive_estimator"],
"n_estimators":[100,150,200,300],"rho":[0.1]}
cv_model_boosting=GridSearchCV(estimator=RegressionTreeBoosting(),param_grid=parameters, cv=10, n_jobs=-1)
cv_model_boosting.fit(X_train, Y_train)
boosting_model = cv_model_boosting.best_estimator_
time_start=time()
boosting_model.fit(X_train, Y_train)
mse_score= - boosting_model.score(X_test, Y_test)
time_end=time()
log_file_name = "{}.csv".format("boosting")
log_file_path = os.path.join(log_file_dir, log_file_name)
with open(log_file_path, "a") as f:
logs= "{},{},{},{},{},{}\n".format(distribution_index,
mse_score, time_end-time_start,
iterate,n_train,n_test)
f.writelines(logs)
# ensemble
parameters= {"min_samples_split":[10], "max_depth":[1,2,3,4,5],
"splitter":["maxedge"], "estimator":["naive_estimator"],
"n_estimators":[200,300,400,700]}
cv_model_ensemble=GridSearchCV(estimator=RegressionTreeEnsemble(),param_grid=parameters, cv=5, n_jobs=-1)
cv_model_ensemble.fit(X_train, Y_train)
ensemble_model = cv_model_ensemble.best_estimator_
time_start=time()
ensemble_model.ensemble_parallel = 1
ensemble_model.fit(X_train, Y_train)
mse_score= - ensemble_model.score(X_test, Y_test)
time_end=time()
log_file_name = "{}.csv".format("ensemble")
log_file_path = os.path.join(log_file_dir, log_file_name)
with open(log_file_path, "a") as f:
logs= "{},{},{},{},{},{}\n".format(distribution_index,
mse_score, time_end-time_start,
iterate,n_train,n_test)
f.writelines(logs)
# GBRT
parameters= {"n_estimators":[500,1000,2000], "learning_rate":[0.01,0.05]}
cv_model_GBRT=GridSearchCV(estimator=GradientBoostingRegressor(),param_grid=parameters, cv=10, n_jobs=-1)
cv_model_GBRT.fit(X_train, Y_train)
model_GBRT = cv_model_GBRT.best_estimator_
time_start=time()
model_GBRT.fit(X_train, Y_train)
mse_score = model_GBRT.score(X_test,Y_test)
time_end=time()
log_file_name = "{}.csv".format("GBRT")
log_file_path = os.path.join(log_file_dir, log_file_name)
with open(log_file_path, "a") as f:
logs= "{},{},{},{},{},{}\n".format(distribution_index,
mse_score, time_end-time_start,
iterate,n_train,n_test)
f.writelines(logs)
# RF
parameters = {"n_estimators":[10,100,200]}
cv_model_RFR = GridSearchCV(estimator=RandomForestRegressor(),param_grid=parameters, cv=10, n_jobs=-1)
cv_model_RFR.fit(X_train, Y_train)
model_RFR = cv_model_RFR.best_estimator_
time_start=time()
model_RFR.fit(X_train, Y_train)
mse_score = model_RFR.score(X_test,Y_test)
time_end=time()
log_file_name = "{}.csv".format("RFR")
log_file_path = os.path.join(log_file_dir, log_file_name)
with open(log_file_path, "a") as f:
logs= "{},{},{},{},{},{}\n".format(distribution_index,
mse_score, time_end-time_start,
iterate,n_train,n_test)
f.writelines(logs)
# DT
parameters = {"max_depth":[2,5,8]}
cv_model_DT = GridSearchCV(estimator=DecisionTreeRegressor(),param_grid=parameters, cv=5, n_jobs=-1)
cv_model_DT.fit(X_train, Y_train)
model_DT = cv_model_DT.best_estimator_
time_start=time()
model_DT.fit(X_train, Y_train)
mse_score=model_DT.score(X_test)
time_end=time()
log_file_name = "{}.csv".format("DT")
log_file_path = os.path.join(log_file_dir, log_file_name)
with open(log_file_path, "a") as f:
logs= "{},{},{},{},{},{}\n".format(distribution_index,
mse_score, time_end-time_start,
iterate,n_train,n_test)
f.writelines(logs)