FullFit.py

import matplotlib.pyplot as plt
import data_parser
import numpy as np
from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics import mean_squared_error


def fullfit(model=KernelRidge(alpha=.00139, coef0=1, degree=3, gamma=.518, kernel='rbf', kernel_params=None),
            datapath="../../DBTT_Data.csv", savepath='../../{}.png',
            X=["N(Cu)", "N(Ni)", "N(Mn)", "N(P)", "N(Si)", "N( C )", "N(log(fluence)", "N(log(flux)", "N(Temp)"],
            Y="delta sigma"):

    data = data_parser.parse(datapath)
    data.set_x_features(X)
    data.set_y_feature(Y)

    Ydata = np.asarray(data.get_y_data()).ravel()
    #Ydata_norm = (Ydata - np.mean(Ydata)) / np.std(Ydata)

    IVARindices = np.linspace(0, 1463, 1464).astype(int)
    IVARplusindices = np.linspace(1464, 1505, 43).astype(int)

    model = model

    # Train the model using the training sets
    model.fit(data.get_x_data(), Ydata)

    Ypredict = model.predict(data.get_x_data())
    #Ypredict = Ypredict_norm * np.std(Ydata) + np.mean(Ydata)

    # calculate rms
    rms = np.sqrt(mean_squared_error(Ypredict, Ydata))
    IVAR_rms = np.sqrt(mean_squared_error(Ypredict[IVARindices], Ydata[IVARindices]))
    IVARplus_rms = np.sqrt(mean_squared_error(Ypredict[IVARplusindices], Ydata[IVARplusindices]))
    print('RMS: %.5f, IVAR RMS: %.5f, IVAR+ RMS: %.5f' % (rms, IVAR_rms, IVARplus_rms))

    # graph outputs
    plt.figure(1)
    plt.scatter(Ydata[IVARindices], Ypredict[IVARindices], s=10, color='black', label='IVAR')
    plt.legend(loc=4)
    plt.scatter(Ydata[IVARplusindices], Ypredict[IVARplusindices], s=10, color='red', label='IVAR+')
    plt.legend(loc=4)
    plt.plot(plt.gca().get_ylim(), plt.gca().get_ylim(), ls="--", c=".3")
    plt.xlabel('Measured (MPa)')
    plt.ylabel('Predicted (MPa)')
    plt.title('Full Fit')
    plt.figtext(.15, .83, 'Overall RMS: %.4f' % (rms), fontsize=14)
    plt.figtext(.15, .77, 'IVAR RMS: %.4f' % (IVAR_rms), fontsize=14)
    plt.figtext(.15, .71, 'IVAR+ RMS: %.4f' % (IVARplus_rms), fontsize=14)
    plt.savefig(savepath.format(plt.gca().get_title()), dpi=200, bbox_inches='tight')

    '''
    plt.figure(2)
    plt.scatter(Ydata, Ypredict-Ydata, s=10, color='black')
    plt.xlabel('Measured (MPa)')
    plt.ylabel('Predicted - Measured (MPa)')
    plt.title('Error vs Actual')
    plt.savefig(savepath.format("error_vs_actual"), dpi=200, bbox_inches='tight')'''

    plt.show()
    plt.close()