ModelRender.py

# Import necessary libraries
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import warnings
import matplotlib.lines as mlines
import matplotlib.transforms as mtransforms
from matplotlib.patches import Rectangle
import warnings

# Ignore warnings
warnings.filterwarnings('ignore')

# Define the list of models
modelset = ('DTR', 'GPR', 'KRR', 'MLPR', 'OLS', 'RFR', 'RidgeCV', 'SVR')


# Function to plot the predicted vs actual data for each model
def plotdata(model):
    # Read test and train data from CSV files
    testdata = pd.read_csv('Models/' + model + '/TestArray.csv')
    traindata = pd.read_csv('Models/' + model + '/TrainArray.csv')

    # Create a scatter plot for test and train data
    fig, ax = plt.subplots()

    ax.scatter([testdata.iloc[:, 0]], [testdata.iloc[:, 1]], color='blue', marker='o', edgecolors='white', alpha=0.8)
    ax.scatter([traindata.iloc[:, 0]], [traindata.iloc[:, 1]], color='green', marker='s', edgecolors='white', alpha=0.8)
    ax.legend(['Testing Data', 'Training Data'])
    plt.axis([-1.5, 0.5, -1.5, 0.5])
    plt.title(model + " Pred ΔG vs Actual ΔG", pad=15)
    plt.xlabel("Predicted ΔG")
    plt.ylabel("Actual ΔG")
    line = mlines.Line2D([0, 1], [0, 1], color='red', linestyle='dashed')
    transform = ax.transAxes
    line.set_transform(transform)
    ax.add_line(line)
    circlehighlight = plt.Circle((0, 0), 0.15, color='r', fill=False)
    plt.gca().add_patch(circlehighlight)
    plt.savefig('Models/' + model + '/TrainVSTest.png')
    plt.show()

# Function to plot the RMSE values for each model
def plotrmse(model):
    # Read RMSE values from CSV file
    rmse = pd.read_csv('Models/' + model + '/RMSEArray.csv')

    # Create a bar plot for RMSE values
    plt.bar([rmse.columns[0]], [rmse.iloc[0, 0]])
    plt.bar([rmse.columns[1]], [rmse.iloc[1, 0]])
    plt.ylim(0, 0.5)
    plt.title(model + " Training RMSE vs Testing RMSE", pad=15)
    plt.ylabel("Root Mean Squared Error")
    plt.savefig('Models/' + model + '/RMSEBar.png')
    plt.show()


def autormse():
    for i, modelstring in enumerate(modelset):
        rmse = pd.read_csv('Models/' + modelstring + '/RMSEArray.csv')
        plt.bar([rmse.columns[0]], [rmse.iloc[0, 0]])
        plt.bar([rmse.columns[1]], [rmse.iloc[1, 0]])
        continue
    plt.ylim(0, 0.5)
    plt.title("Training RMSE vs Testing RMSE", pad=15)
    plt.ylabel("Root Mean Squared Error")
    plt.savefig('Models/' + modelstring + '/RMSEBar.png')
    plt.show()

# Function to plot the predicted values for material generation
def plotpred(model):
    # Read predicted data from CSV file
    preddata = pd.read_csv('Models/' + model + '/MatGenArray.csv')

    # Create a scatter plot for predicted values
    fig, ax = plt.subplots()

    x = preddata.iloc[:, 0]
    y = preddata.iloc[:, 1]
    # ax.scatter([preddata.iloc[:, 0]], [preddata.iloc[:, 1]], color='blue', edgecolors='white', alpha=0.8)
    ax.scatter(x, y, edgecolors='white', alpha=0.8)
    plt.xlabel("Material Number")
    plt.ylabel("Predicted ΔG")
    plt.title(model + " Material Number vs Predicted ΔG", pad=15)
    ax.add_patch(Rectangle(xy=(0, -0.15), width=300, height=0.30, linewidth=1, color='red', fill=False))
    fig.canvas.draw()
    plt.show()


# Calling functions portion of code. This should eventually be automated to request a model list
# and iterate through that list with the functions you'd like.

# Call the functions for each model in the modelset
for model in modelset:
    plotdata(model)
    plotrmse(model)

# Call the plotpred function for a specific model ('RFR' in this case)
plotpred('RFR')