'Ti', 'Ta', 'Hf', 'Re', 'V', 'B', 'N', 'O', 'S', 'Zr'
]
df[ele] = df[ele].fillna(0)
df = df.dropna(subset=[
'CT_RT', 'CT_CS', 'CT_EL', 'CT_RA', 'CT_Temp', 'Normal', 'Temper1',
'AGS No.', 'CT_MCR'
])
df['log_CT_CS'] = np.log(df['CT_CS'])
df['log_CT_MCR'] = np.log(df['CT_MCR'])
features = [i for i in df.columns if i not in ['CT_CS', 'CT_MCR', 'ID']]
X = df[features].to_numpy(np.float32)
#print(X.shape)
y = df['CT_RT'].to_numpy(np.float32)
pdata = ProcessData(X=X, y=y, features=features)
#pdata.clean_data(scale_strategy={'strategy': 'power_transform',
# 'method': 'yeo-johnson'})
pdata.clean_data(scale_strategy={'strategy': 'MinMaxScaler'})
data = pdata.get_data()
scale = pdata.scale
#X = scale.inverse_transform(data['X'])
X = data['X']
features = data['features']
del data, pdata, df, y, ID, ele, model
ae = AutoEncoder(arch=[33, 22, 11], X=X, loss='mse', epochs=250)
ae.build_model()
X_pred = ae.predict(X)
print(scale.inverse_transform(X)[0], scale.inverse_transform(X_pred)[0])
df = df.dropna(subset=['CT_RT', 'CT_CS', 'CT_EL', 'CT_RA', 'CT_Temp',
'Normal', 'Temper1', 'AGS No.', 'CT_MCR'])
df['log_CT_CS'] = np.log(df['CT_CS'])
df['log_CT_MCR'] = np.log(df['CT_MCR'])
df['LMP_Model'] = df.apply(lambda x:
1e-3 * x['CT_Temp'] * (np.log(x['CT_RT']) + 25), axis=1)
features = [i for i in df.columns if i not in ['CT_RT', 'CT_Temp',
'ID', 'CT_CS', 'LMP_Model', 'CT_MCR']]
df = df[df['CT_RT'] < 200000]
X = df[features].to_numpy(np.float32)
y = df['LMP_Model'].to_numpy(np.float32)
y2 = df[['ID', 'CT_RT', 'CT_Temp', 'CT_CS']].values.tolist()
pdata = ProcessData(X=X, y=y, y2=y2, features=features)
pdata.clean_data()
data = pdata.get_data()
scale = pdata.scale
del pdata
CT_RT = np.array([i[1] for i in data['y2']])
CT_Temp = np.array([i[2] for i in data['y2']])
CT_CS = np.array([i[3] for i in data['y2']])
ID = [i[0] for i in data['y2']]
C = np.array([25 for i in ID])
skreg = SKREG(X=data['X'],
y=data['y'],
estimator="LR",
validation="3-Fold",
import seaborn as sns
from xmat_pnnl_code import ProcessData
from xmat_pnnl_code import SKGP
from sklearn.gaussian_process.kernels import (RBF, WhiteKernel, DotProduct,
Matern)
#Load the features
df = pd.read_csv('../../9_12_Cr.csv')
df = df.dropna(how='all')
df = df.dropna(subset=['RT', 'CS', 'CT Temp'])
df['log_CS'] = np.log(df['CS'])
X = df[[i for i in df.columns
if i not in ['ID', 'RT', 'CS']]].to_numpy(dtype=np.float)
y = df['RT'].to_numpy()
pd = ProcessData(
X=X, y=y, metadata=[i for i in df.columns if i not in ['ID', 'RT', 'CS']])
pd.clean_data()
X, y, metadata = pd.get_data()
del pd
kernel = 1.0 * RBF(length_scale=1.0) + WhiteKernel(noise_level=1.0)
kernel += 1.0 * DotProduct(sigma_0=1.0) + 1.0 * Matern(length_scale=1.0)
skgp = SKGP(X=X, y=y, kernel=kernel, validation='5-Fold')
skgp.run_GP()
skgp.__dict__['features'] = metadata
print(skgp.__dict__)
np.save('gp_run.npy', skgp.__dict__)
skgp.plot_parity(data='train', err_bar=True).savefig('train_parity_plot.png')
skgp.plot_parity(data='test', err_bar=True).savefig('test_parity_plot.png')
df = pd.read_csv(path)
df.replace('ND', np.nan, inplace=True)
ID = [1, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
77, 78, 79, 82]
ID = ['9Cr-{}'.format(str(i).zfill(3)) for i in ID]
df = df[df.ID.isin(ID)]
df['log_CT_CS'] = np.log(df['CT_CS'])
X = df[[i for i in df.columns if i not in ['ID', 'CT_RT',
'CT_Temp', 'CT_CS']]].to_numpy(dtype=np.float)
df['LMP'] = df.apply(lambda x: 1e-3*(x['CT_Temp'])*(np.log(x['CT_RT'])
+data[x['ID']]['C']), axis=1)
y = df['LMP'].to_numpy()
dprocess = ProcessData(X=X, y=y, metadata=[i for i in df.columns
if i not in ['ID', 'CT_RT', 'CT_Temp', 'CT_CS']])
dprocess.clean_data()
X, y, metadata = dprocess.get_data()
del dprocess
df = pd.DataFrame(X, columns=metadata)
df['LMP'] = y
#Initialize the model
bframe = BayesFrame(df=df, target="LMP", val_scheme='5-Fold',
bic_scheme="per_n", model_scheme=["selection"])
#Print the best model
print(bframe.zoo)
np.save('best_model.npy', bframe.zoo)
df = pd.read_csv(path + '/Cleaned_data.csv')
df = df[df.ID.isin(ID)]
df['log_CT_CS'] = np.log(df['CT_CS'])
df['LMP_Model'] = df.apply(lambda x: 1e-3 * x['CT_Temp'] *
(np.log(x['CT_RT']) + 25),
axis=1)
features = [
i for i in df.columns
if i not in ['CT_RT', 'CT_Temp', 'ID', 'CT_CS', 'LMP_Model']
]
X = df[features].to_numpy(np.float32)
y = df['LMP_Model'].to_numpy(np.float32)
pd = ProcessData(X=X, y=y, metadata=features)
pd.clean_data()
X, y, metadata = pd.get_data()
del pd
param_space = {
'max_iter': [5000],
'activation': ['relu'],
'solver': ['lbfgs'],
'alpha': [
0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.01, 0.02, 0.03, 0.04, 0.05,
0.06
],
'learning_rate': ['constant']
}
df = pd.read_csv(path + '/Cleaned_data.csv')
ele = ["Fe", "C", "Cr", "Mn", "Si", "Ni", "Co", "Mo", "W", "Nb", "Al", "P",
"Cu", "Ti", "V", "B", "N", "S"]
df[ele] = df[ele].fillna(0)
df = df.dropna(subset=['CT_RT', 'CT_CS', 'CT_EL', 'CT_RA', 'CT_Temp',
'AGS No.'])
df['log_CT_CS'] = np.log(df['CT_CS'])
df['log_CT_MCR'] = np.log(df['CT_MCR'])
df['LMP_Model'] = df.apply(lambda x:
1e-3 * x['CT_Temp'] * (np.log(x['CT_RT']) + 25), axis=1)
features = [i for i in df.columns if i not in ['CT_RT',
'CT_CS', 'ID', 'CT_MCR', 'LMP_Model']]
X = df[features].to_numpy(np.float64)
y = df['CT_RT'].to_numpy(np.float64)
y2 = df[['ID', 'CT_RT', 'CT_Temp', 'CT_CS']].values.tolist()
pdata = ProcessData(X=X, y=y, y2=y2, features=features)
pdata.clean_data(scale_strategy={'strategy': 'StandardScaler'})
data = pdata.get_data()
del pdata
'''
parameters_grid = {'boosting_type': ['gbdt', 'goss'],
'num_leaves': [100, 200],
'max_depth': [-1],
'learning_rate': [0.01],
'n_estimators': [100, 200],
'subsample_for_bin': [200000],
'objective': [None],
'class_weight': [None],
'min_split_gain': [0.0],
'min_child_weight': [0.001],
'Fe', 'C', 'Cr', 'Mn', 'Si', 'Ni', 'Co', 'Mo', 'W', 'Nb', 'Al', 'P', 'Cu',
'Ti', 'Ta', 'Hf', 'Re', 'V', 'B', 'N', 'O', 'S', 'Zr'
]
df[ele] = df[ele].fillna(0)
df['LMP_Model'] = df.apply(lambda x: 1e-3 * x['CT_Temp'] *
(np.log(x['CT_RT']) + C_data[x['ID']]),
axis=1)
features = [
i for i in df.columns
if i not in ['CT_RT', 'CT_Temp', 'ID', 'ID_2', 'CT_CS', 'LMP_Model']
]
X = df[features].to_numpy(np.float32)
y = df['LMP_Model'].to_numpy(np.float32)
metadata = df[['ID', 'ID_2', 'CT_RT']].values.tolist()
pdata = ProcessData(X=X, y=y, features=features, metadata=metadata)
pdata.clean_data()
data = pdata.get_data()
del pdata
'''
parameters_grid = {'boosting_type': ['gbdt', 'goss'],
'num_leaves': [100, 200],
'max_depth': [-1],
'learning_rate': [0.01],
'n_estimators': [100, 200],
'subsample_for_bin': [200000],
'objective': [None],
'class_weight': [None],
'min_split_gain': [0.0],
'min_child_weight': [0.001],
'min_child_samples': [20],
