clean up

Author: trrt-good

CleanData.py

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,8 +56,8 @@
     "    main_ax.set(xlim=(0, 12), ylim=(0, 12))\n",
     "    main_ax.grid(True)\n",
     "    main_ax.plot([0, 12], [0, 12], 'k--')\n",
-    "    main_ax.set_xlabel('Predicted pKa', fontsize=12)\n",
-    "    main_ax.set_ylabel('Experimental pKa', fontsize=12)\n",
+    "    main_ax.set_xlabel('Predicted pKd', fontsize=12)\n",
+    "    main_ax.set_ylabel('Experimental pKd', fontsize=12)\n",
     "    main_ax.legend(loc='upper left')  # Adding the legend\n",
     "\n",
     "    x_hist_ax.hist(df.iloc[:, 0], bins=40, color=color, alpha=0.7)\n",

Data-Preprocess.Py

@@ -324,44 +324,6 @@
     "    json.dump(experimental_pKd, file)\n",
     "plapt.clear_cache()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
-    "from scipy.stats import pearsonr\n",
-    "\n",
-    "# Plotting the straight line test\n",
-    "plt.scatter(experimental_pKd, predicted_pKd, color='blue')\n",
-    "plt.plot([min(experimental_pKd), max(experimental_pKd)], [min(predicted_pKd), max(predicted_pKd)], color='red') # Line\n",
-    "plt.xlabel('Experimental pKd')\n",
-    "plt.ylabel('Predicted pKd')\n",
-    "plt.title('Straight Line Test')\n",
-    "plt.show()\n",
-    "\n",
-    "# Calculating Metrics\n",
-    "mse = mean_squared_error(experimental_pKd, predicted_pKd)\n",
-    "mae = mean_absolute_error(experimental_pKd, predicted_pKd)\n",
-    "rmse = np.sqrt(mse)\n",
-    "pearson_corr, _ = pearsonr(experimental_pKd, predicted_pKd)\n",
-    "\n",
-    "print(\"Mean Squared Error (MSE):\", mse)\n",
-    "print(\"Mean Absolute Error (MAE):\", mae)\n",
-    "print(\"Root Mean Squared Error (RMSE):\", rmse)\n",
-    "print(\"Pearson's Correlation Coefficient:\", pearson_corr)\n"
-   ]
   }
  ],
  "metadata": {