File size: 4,222 Bytes
c52228e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
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)
|