Create SpecialSingleEvalTests.py for MAE prints with and without TZVP of non-nested o-MFML

Author: vivinvinod

SpecialSingleEvalTests.py

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+import numpy as np
+from sklearn.utils import shuffle
+from sklearn.model_selection import train_test_split
+from tqdm.notebook import tqdm
+from TrueNonNested_Model_MFML import ModelMFML as MF
+
+def main_withTZVP():
+    prop='SCF'
+    rep='CM'
+    indexes = np.load('CheMFi/raws/indexes.npy',allow_pickle=True) #STO3G first
+    X_train = np.load(f'CheMFi/raws/X_train_{rep}.npy')
+    X_test = np.load(f'CheMFi/raws/X_test_{rep}.npy')
+    X_val = np.load(f'CheMFi/raws/X_val_{rep}.npy')
+    energies = np.load(f'CheMFi/raws/energies_{prop}.npy',allow_pickle=True) #STO3G first
+    for i in range(5):
+        avg=np.mean(energies[i])
+        energies[i] = energies[i] - avg
+    #energies = energies[:-1]
+    y_test = np.load(f'CheMFi/raws/y_test_{prop}.npy') - avg
+    y_val = np.load(f'CheMFi/raws/y_val_{prop}.npy') - avg
+    
+    
+    nfids = indexes.shape[0]
+    regs = np.full(2*nfids-1,1e-10)
+    sigmas = np.full(2*nfids-1,150.0)
+    kernels = np.full(2*nfids-1,'matern')
+    
+    n_trains = np.asarray([2**(13), 2**(12), 2**(11), 2**(10),2**(9)])
+    #init
+    model = MF(reg=regs, kernel=kernels, sigma=sigmas,
+               order=1, metric='l2', gammas=None, 
+               p_bar=True)
+    #train models
+    model.train(X_train_parent=X_train, y_trains=energies, 
+            indexes=indexes, 
+            shuffle=True, n_trains=n_trains, 
+            seed=42)
+    _ = model.predict(X_test=X_test, X_val=X_val,
+                      y_test=y_test, y_val=y_val, 
+                      optimiser='OLS')
+    print('MAE for non-nested o-MFML on **including** TZVP fidelity in ground state energy training data: ',model.mae)
+
+def main_noTZVP():
+    prop='SCF'
+    rep='CM'
+    indexes = np.load('CheMFi/raws/indexes.npy',allow_pickle=True)[:-1] #STO3G first
+    X_train = np.load(f'CheMFi/raws/X_train_{rep}.npy')
+    X_test = np.load(f'CheMFi/raws/X_test_{rep}.npy')
+    X_val = np.load(f'CheMFi/raws/X_val_{rep}.npy')
+    energies = np.load(f'CheMFi/raws/energies_{prop}.npy',allow_pickle=True)[:-1] #STO3G first
+    for i in range(4):
+        avg=np.mean(energies[i])
+        energies[i] = energies[i] - avg
+    #energies = energies[:-1]
+    y_test = np.load(f'CheMFi/raws/y_test_{prop}.npy') - avg
+    y_val = np.load(f'CheMFi/raws/y_val_{prop}.npy') - avg
+    
+    
+    nfids = indexes.shape[0]
+    regs = np.full(2*nfids-1,1e-10)
+    sigmas = np.full(2*nfids-1,150.0)
+    kernels = np.full(2*nfids-1,'matern')
+    
+    n_trains = np.asarray([2**(13), 2**(12), 2**(11), 2**(10)])#[5-nfids:]
+    #init
+    model = MF(reg=regs, kernel=kernels, sigma=sigmas,
+               order=1, metric='l2', gammas=None, 
+               p_bar=True)
+    #train models
+    model.train(X_train_parent=X_train, y_trains=energies, 
+            indexes=indexes, 
+            shuffle=True, n_trains=n_trains, 
+            seed=42)
+    _ = model.predict(X_test=X_test, X_val=X_val,
+                      y_test=y_test, y_val=y_val, 
+                      optimiser='OLS')
+    print('MAE for non-nested o-MFML **without** TZVP fidelity in ground state energy  training data: ',model.mae)
+    
+    
+main_withTZVP()
+main_noTZVP()