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main40.py
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main40.py
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import logging
import glob
import numpy as np
import random
import math
import time
import operator
import matplotlib.pyplot as plt
import global_flags_constanst as gfc
import support_functions as sf
from models import BaseModel
from models import PolynomialModel
from models import XGBRegressorModel
from models import RidgeRegressionModel
from models import KernelRidgeRegressionModel
logger = logging.getLogger(__name__)
file_handler = logging.FileHandler("logs.log")
file_handler.setLevel(gfc.LOGGING_LEVEL)
handler = logging.StreamHandler()
formatter = logging.Formatter("%(asctime)s %(name)-12s %(levelname)-8s %(message)s")
handler.setFormatter(formatter)
file_handler.setFormatter(formatter)
logger.addHandler(handler)
logger.addHandler(file_handler)
logger.setLevel(gfc.LOGGING_LEVEL)
if __name__ == "__main__":
# Separate models will be fit for the
# specimen with the following number of atoms.
# number_of_total_atoms: rank
# noa = 40 and noa = 80 not included
# Simple models do not work for them.
additional_feature_list = ["rho_data",
"percentage_atom_data",
"unit_cell_data",
"nn_bond_parameters_data",
"angles_and_rs_data",
"ewald_sum_data"]
#"preliminary_predictions_data"]
seed = int(random.randint(1, 2**16 - 1))
colsample_bytree = random.random()
subsample = random.random()
model_parameters = {"max_depth": 4,
"learning_rate": 0.1,
"n_estimators": 600,
"silent": True,
"objective": 'reg:linear',
"booster": 'gbtree',
"n_jobs": 1,
"nthread": None,
"gamma": 0.0,
"min_child_weight": 5,
"max_delta_step": 0,
"subsample": subsample,
"colsample_bytree": colsample_bytree,
"colsample_bylevel": 1,
"reg_alpha": 0,
"reg_lambda": 1,
"scale_pos_weight": 1,
"base_score": 0.5,
"random_state": seed + 1,
"seed": seed,
"missing": None}
# model_parameters = {"alpha": 0.5,
# "kernel": "chi2",
# "gamma": 0.1,
# "degree": 10,
# "coef0": 1,
# "n_features": None,
# "max_features": None,
# "validation_data": None}
# bg_general_model, _ = sf.get_model_for_noa(40,
# additional_feature_list,
# model_class=XGBRegressorModel,
# model_parameters=model_parameters,
# y_type="band_gap")
# fe_general_model = get_model_for_noa(-1,
# additional_feature_list,
# model_class=XGBRegressorModel,
# model_parameters=xgb_regressor_model_parameters,
# y_type="formation_energy")
noa = 40
x, y, ids = sf.prepare_data_for_model(noa,
additional_feature_list,
data_type="train",
y_type="band_gap")
print("x space groups: {0}".format(np.unique(x[:, 0])))
plt.figure()
for sg in [33, 227]:
xf, yf = sf.feature_split(x,
y,
feature_index=0,
feature_value=sg,
op=operator.eq)
plt.scatter(xf[:, -7], yf,
label=str(sg))
plt.legend(ncol=3)
plt.show()
xt, yt, idst = sf.prepare_data_for_model(noa,
additional_feature_list,
data_type="test",
y_type="band_gap")
print("xt space groups: {0}".format(np.unique(xt[:, 0])))