figs

plots.py

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+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)
+
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