| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import seaborn as sns | |
| sns.set_theme() | |
| def read_results(filename): | |
| with open(filename, "r") as f: | |
| lines = f.readlines() | |
| preds_values = [] | |
| actual_values = [] | |
| mae_values = [] | |
| for line in lines: | |
| if line.startswith("Preds:"): | |
| preds = line.replace("[", "") | |
| preds = preds.replace("]", "") | |
| preds = preds.strip("Preds:") | |
| preds = preds.strip() | |
| preds = preds.split(",") | |
| preds = [p.strip() for p in preds] | |
| preds = np.asarray([float(p) for p in preds]) | |
| preds_values.append(preds) | |
| if line.startswith("Actual:"): | |
| actual = line.replace("[", "") | |
| actual = actual.replace("]", "") | |
| actual = actual.strip("Actual values:") | |
| actual = actual.strip() | |
| actual = actual.split(",") | |
| actual = [a.strip() for a in actual] | |
| actual = np.asarray([float(a) for a in actual]) | |
| actual_values.append(actual) | |
| if line.startswith("MAE"): | |
| mae = float(line.split()[-1]) | |
| mae_values.append(mae) | |
| return preds_values, actual_values, mae_values | |
| def plot_distribution(preds_values, actual_values, mae_values, model_name, threshold, oversampled): | |
| for i in range(2): | |
| if i == 0: | |
| input_type = "BoW" | |
| else: | |
| input_type = "TF-IDF" | |
| preds = preds_values[i] | |
| actual = actual_values[i] | |
| mae = mae_values[i] | |
| res = pd.DataFrame() | |
| res["Prediction"] = preds | |
| res["Actual"] = actual | |
| sns.displot(res, kind="kde") | |
| plt.xlabel("Home standard score") | |
| plt.title(f"Model: {model_name}, Input type: {input_type}, MAE: {mae}, Threshold:{threshold}", | |
| fontsize = 10) | |
| plt.ylim(-0.03, 2.5) | |
| plt.tight_layout() | |
| plt.savefig(f"figs/{model_name}_{input_type}_{threshold[0]}_{threshold[1]}_{oversampled}.png") | |
| plt.close() | |
| if __name__ == "__main__": | |
| preds_values, actual_values, mae_values = read_results("linear_models/lasso_0.01_0.99.txt") | |
| plot_distribution(preds_values, actual_values, mae_values, "Lasso", [0.01, 0.99], False) | |
| preds_values, actual_values, mae_values = read_results("linear_models/lin_reg_0.01_0.99.txt") | |
| plot_distribution(preds_values, actual_values, mae_values, "Linear regression", [0.01, 0.99], False) | |
| preds_values, actual_values, mae_values = read_results("linear_models/sgd_reg_0.01_0.99.txt") | |
| plot_distribution(preds_values, actual_values, mae_values, "SGD Regressor", [0.01, 0.99], False) | |
| preds_values, actual_values, mae_values = read_results("oversampled_False_catboost_reg_0.01_0.99.txt") | |
| plot_distribution(preds_values, actual_values, mae_values, "CatBoostRegressor", [0.01, 0.99], False) | |
| preds_values, actual_values, mae_values = read_results("linear_models/lasso_0.2_0.8.txt") | |
| plot_distribution(preds_values, actual_values, mae_values, "Lasso", [0.2, 0.8], False) | |
| preds_values, actual_values, mae_values = read_results("linear_models/oversampled_lin_reg_0.01_0.99.txt") | |
| plot_distribution(preds_values, actual_values, mae_values, "Linear regression", [0.01, 0.99], True) | |
| preds_values, actual_values, mae_values = read_results("linear_models/oversampled_lasso_0.01_0.99.txt") | |
| plot_distribution(preds_values, actual_values, mae_values, "Lasso", [0.01, 0.99], True) | |
| preds_values, actual_values, mae_values = read_results("linear_models/oversampled_sgd_reg_0.01_0.99.txt") | |
| plot_distribution(preds_values, actual_values, mae_values, "SGD Regressor", [0.01, 0.99], True) | |
| preds_values, actual_values, mae_values = read_results("oversampled_True_catboost_reg_0.01_0.99.txt") | |
| plot_distribution(preds_values, actual_values, mae_values, "CatBoostRegressor", [0.01, 0.99], True) | |
| preds_values, actual_values, mae_values = read_results("linear_models/oversampled_lasso_0.15_0.85.txt") | |
| plot_distribution(preds_values, actual_values, mae_values, "Lasso", [0.15, 0.85], False) | |