# Path: molml/features.py



from sklearn.linear_model import Ridge
from sklearn.metrics import mean_absolute_error as MAE
from molml.features import EncodedBond, BagOfBonds, Connectivity, CoulombMatrix
from utils import load_qm7
from __future__ import print_function





if __name__ == "__main__":
    # This is just boiler plate code to load the data
    Xin_train, Xin_test, y_train, y_test = load_qm7()

    # Change this to make the tranformations parallel
    # Values less than 1 will set to the number of cores the CPU has
    N_JOBS = 1

    # Just a few examples of different features
    tfs = [
        EncodedBond(n_jobs=N_JOBS),
        EncodedBond(spacing="inverse", n_jobs=N_JOBS),
        BagOfBonds(n_jobs=N_JOBS),
        CoulombMatrix(n_jobs=N_JOBS),
        Connectivity(depth=1, n_jobs=N_JOBS),
        Connectivity(depth=2, use_bond_order=True, n_jobs=N_JOBS),

# Path: molml/features.py



# Path: examples/qm7.py
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_absolute_error as MAE
from molml.features import EncodedBond, BagOfBonds, Connectivity, CoulombMatrix
from utils import load_qm7

from __future__ import print_function





if __name__ == "__main__":
    # This is just boiler plate code to load the data
    Xin_train, Xin_test, y_train, y_test = load_qm7()

    # Change this to make the tranformations parallel
    # Values less than 1 will set to the number of cores the CPU has
    N_JOBS = 1

    # Just a few examples of different features
    tfs = [
        EncodedBond(n_jobs=N_JOBS),
        EncodedBond(spacing="inverse", n_jobs=N_JOBS),
        BagOfBonds(n_jobs=N_JOBS),
        CoulombMatrix(n_jobs=N_JOBS),
        Connectivity(depth=1, n_jobs=N_JOBS),
        Connectivity(depth=2, use_bond_order=True, n_jobs=N_JOBS),

        Connectivity(depth=3, use_coordination=True, n_jobs=N_JOBS),