import prop
import fold
import fitRF
fOut = open("CM-RF.txt", "w")
# 1. CM vector parsing
CM_Path = "/Volumes/Macintosh HD 2/FSU/2018Fall/SeminarJose/Seminar2/FinalProject/Supplementary_Materials/CM"
time0 = time.time()
CM_vec = feature.parse_feature_CM(CM_Path)
# print(CM_vec[1])
time1 = time.time()
fOut.write("Execuation time 01 - CM vector parsing: %s sec.\n" % (time1 - time0))
# 2. Property parsing
QM9_Prop, QM9_Index = prop.parse_prop("/Volumes/Macintosh HD 2/FSU/2018Fall/SeminarJose/Seminar2/FinalProject/Supplementary_Materials/qm9-mol-info-standardized-v1")
time2 = time.time()
fOut.write("Execuation time 02 - Property parsing: %s sec.\n" % (time2 - time1))
# 3. Fold parsing
Predefined_Split, Train_Index, Test_Index = fold.predefined_fold()
time3 = time.time()
fOut.write("Execuation time 03 - Fold parsing: %s sec. \n" % (time3 - time2))
# 4. Reindex to training and testing set
Train_X = np.zeros([len(Train_Index[0]), CM_vec.shape[1]])
Train_Y = np.zeros([13, len(Train_Index[0])])
Test_X = np.zeros([len(Test_Index[0]), CM_vec.shape[1]])
Test_Y = np.zeros([13, len(Test_Index[0])])
Predict_Y = np.zeros([13, len(Test_Index[0])])
for Indi, i in enumerate(Train_Index[0]):
def mainprog():
#-- Test functions
#-- Test of parse_feature_CM
# import numpy as np
# import time
# CM_Path = "/share/home/zyzhu/Documents-Graduate/Faber-Lilienfeld.JCTC.2017.ASAP/SI_Source/Feature_Files/CM"
# time0 = time.time()
# CM_vec = feature.parse_feature_CM(CM_Path)
# print(CM_vec[1])
# print("Execuation time: %s" % (time.time() - time0))
#-- Test of parse_prop
# import numpy as np
# QM9_Prop, QM9_Index = parse_prop("/share/home/zyzhu/Documents-Graduate/Faber-Lilienfeld.JCTC.2017.ASAP/SI_Source/Prop_Files/qm9-mol-info-standardized-v1")
# print(QM9_Index[:100])
# print(QM9_Prop[:5])
#-- Test of predefined_fold
# Predefined_Split, Train_Index, Test_Index = fold.predefined_fold()
import numpy as np
import time
# 0. output file
FOut = open("CM-BR-EN-RF.txt", "w")
time0 = time.time()
# 1. CM vector parsing
CM_Path = "/share/home/zyzhu/Documents-Graduate/Faber-Lilienfeld.JCTC.2017.ASAP/SI_Source/Feature_Files/CM"
CM_vec = feature.parse_feature_CM(CM_Path)
# print(CM_vec[1])
time1 = time.time()
FOut.write("Execuation time 01 - CM vector parsing: %s \n" %
(time1 - time0))
# 2. Property parsing
QM9_Prop, QM9_Index = prop.parse_prop(
"/share/home/zyzhu/Documents-Graduate/Faber-Lilienfeld.JCTC.2017.ASAP/SI_Source/Prop_Files/qm9-mol-info-standardized-v1"
)
time2 = time.time()
FOut.write("Execuation time 02 - Property parsing: %s \n" %
(time2 - time1))
# 3. Fold parsing
Predefined_Split, Train_Index, Test_Index = fold.predefined_fold()
time3 = time.time()
FOut.write("Execuation time 03 - Fold parsing: %s \n" % (time3 - time2))
# 4. Reindex to training and testing set
# Only test 10% testing set. The result should be able to be compared to Table 3 in
# Faber et al. (JCTC2017)
# For this file, all the properties are tested.
# ! Note that CM_vec is 133885 length, so no reindex needed to this vector.
# However, some rows in CM_vec is empty.
# QM9_Prop as well.
# ! The 10% training and testing set ([0]) should be created with reindex process.
# ! *_Y as property arrays are indexed as [Prop, reindexed-Id].
# - Train_X
# - Train_Y
# - Test_X
# - Test_Y
# - Temp_prop_U: Temporary extraction of the property U0
Train_X = np.zeros([len(Train_Index[0]), CM_vec.shape[1]])
Train_Y = np.zeros([13, len(Train_Index[0])])
Test_X = np.zeros([len(Test_Index[0]), CM_vec.shape[1]])
Test_Y = np.zeros([13, len(Test_Index[0])])
for Indi, i in enumerate(Train_Index[0]):
Train_X[Indi] = CM_vec[i - 1]
Train_Y[:, Indi] = QM9_Prop[i - 1, :]
for Indi, i in enumerate(Test_Index[0]):
Test_X[Indi] = CM_vec[i - 1]
Test_Y[:, Indi] = QM9_Prop[i - 1, :]
time4 = time.time()
FOut.write("Execuation time 04 - Reindex: %s \n" % (time4 - time3))
# 5. Training and Testing - BR
FOut.write("\n")
FOut.write("--- Training and Testing BR ---\n")
FOut.write("\n")
FOut.write(" Prop_Id" + " Err_MAD" + " Err_RMSD" +
" Time_Train" + " Time_Test" + "\n")
for Indi in range(13):
(Err_MAD, Err_RMSD, Time_Train, Time_Test) = \
fit.fit_BR(Train_X, Train_Y[Indi], Test_X, Test_Y[Indi], Predefined_Split[0])
FOut.write("{:8}{:14.8f}{:14.8f}{:11.2f}{:11.2f}\n".format(
Indi, Err_MAD, Err_RMSD, Time_Train, Time_Test))
# 6. Training and Testing - EN
FOut.write("\n")
FOut.write("--- Training and Testing EN ---\n")
FOut.write("\n")
FOut.write(" Prop_Id" + " Err_MAD" + " Err_RMSD" +
" Time_Train" + " Time_Test" + "\n")
for Indi in range(13):
(Err_MAD, Err_RMSD, Time_Train, Time_Test) = \
fit.fit_EN(Train_X, Train_Y[Indi], Test_X, Test_Y[Indi], Predefined_Split[0])
FOut.write("{:8}{:14.8f}{:14.8f}{:11.2f}{:11.2f}\n".format(
Indi, Err_MAD, Err_RMSD, Time_Train, Time_Test))
FOut.close()
# 6. Training and Testing - RF
FOut.write("\n")
FOut.write("--- Training and Testing RF ---\n")
FOut.write("\n")
FOut.write(" Prop_Id" + " Err_MAD" + " Err_RMSD" +
" Time_Train" + " Time_Test" + "\n")
for Indi in range(13):
(Err_MAD, Err_RMSD, Time_Train, Time_Test) = \
fit.fit_RF(Train_X, Train_Y[Indi], Test_X, Test_Y[Indi], Predefined_Split[0])
FOut.write("{:8}{:14.8f}{:14.8f}{:11.2f}{:11.2f}\n".format(
Indi, Err_MAD, Err_RMSD, Time_Train, Time_Test))
FOut.close()
import folding
import fitElasticNet
fOut = open("CM-EN.txt", "w")
# 1. CM vector parsing
CM_Path = "SupplementaryMaterials/CM"
time0 = time.time()
CM_vec = featureCM.parse_feature_CM(CM_Path)
# print(CM_vec[1])
time1 = time.time()
fOut.write("Execuation time 01 - CM vector parsing: %s sec.\n" %
(time1 - time0))
# 2. Property parsing
QM9_Prop, QM9_Index = prop.parse_prop(
"SupplementaryMaterials/qm9-mol-info-standardized-v1")
time2 = time.time()
fOut.write("Execuation time 02 - Property parsing: %s sec.\n" %
(time2 - time1))
# 3. Fold parsing
Predefined_Split, Train_Index, Test_Index = folding.predef_Fold()
time3 = time.time()
fOut.write("Execuation time 03 - Fold parsing: %s sec. \n" % (time3 - time2))
# 4. Reindex to training and testing set
Train_X = np.zeros([len(Train_Index[0]), CM_vec.shape[1]])
Train_Y = np.zeros([13, len(Train_Index[0])])
Test_X = np.zeros([len(Test_Index[0]), CM_vec.shape[1]])
Test_Y = np.zeros([13, len(Test_Index[0])])
Predict_Y = np.zeros([13, len(Test_Index[0])])
