bayesian ridge grid search added

ruxailab · Aug 10, 2024 · 065438e · 065438e
1 parent 5235cec
commit 065438e
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diff --git a/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb b/app/services/calib_validation/test/bayesian_regression/test_bayesian_ridge_regression.ipynb
@@ -1196,11 +1196,11 @@
     "    \"\"\"\n",
     "    Calculate the root mean square error between the predicted X and true X values.\n",
     "\n",
-    "    Parameters:\n",
-    "    group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n",
+    "    Args:\n",
+    "        group (pandas.DataFrame): A DataFrame containing the predicted X and true X values.\n",
     "\n",
     "    Returns:\n",
-    "    float: The root mean square error between the predicted X and true X values.\n",
+    "        float: The root mean square error between the predicted X and true X values.\n",
     "    \"\"\"\n",
     "    return np.sqrt(np.sum(np.square([group[\"Predicted X\"], group[\"True X\"]])))\n",
     "\n",
@@ -1209,13 +1209,11 @@
     "    \"\"\"\n",
     "    Calculate the root mean square error between the predicted Y values and the true Y values.\n",
     "\n",
-    "    Parameters:\n",
-    "    - group: pandas.DataFrame\n",
-    "        A DataFrame containing the predicted Y values and the true Y values.\n",
+    "    Args:\n",
+    "        group (pandas.DataFrame): A DataFrame containing the predicted Y values and the true Y values.\n",
     "\n",
     "    Returns:\n",
-    "    - float\n",
-    "        The root mean square error between the predicted Y values and the true Y values.\n",
+    "        float: The root mean square error between the predicted Y values and the true Y values.\n",
     "    \"\"\"\n",
     "    return np.sqrt(np.sum(np.square([group[\"Predicted Y\"], group[\"True Y\"]])))\n",
     "\n",
@@ -1389,18 +1387,16 @@
     "    \"\"\"\n",
     "    Plots the true and predicted points for X and Y coordinates.\n",
     "\n",
-    "    Parameters:\n",
-    "    - ax (matplotlib.axes.Axes): The axes object to plot on.\n",
-    "    - y_test_x (list or numpy.ndarray): The true X coordinates.\n",
-    "    - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n",
-    "    - y_test_y (list or numpy.ndarray): The true Y coordinates.\n",
-    "    - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n",
-    "    - title (str, optional): The title of the plot. Defaults to None.\n",
+    "    Args:\n",
+    "        - ax (matplotlib.axes.Axes): The axes object to plot on.\n",
+    "        - y_test_x (list or numpy.ndarray): The true X coordinates.\n",
+    "        - y_pred_x (list or numpy.ndarray): The predicted X coordinates.\n",
+    "        - y_test_y (list or numpy.ndarray): The true Y coordinates.\n",
+    "        - y_pred_y (list or numpy.ndarray): The predicted Y coordinates.\n",
+    "        - title (str, optional): The title of the plot. Defaults to None.\n",
     "\n",
-    "    Returns:\n",
-    "    - None\n",
+    "    Returns: None\n",
     "    \"\"\"\n",
-    "\n",
     "    # Convert the data to numpy arrays\n",
     "    y_test_x = np.array(y_test_x)\n",
     "    y_test_y = np.array(y_test_y)\n",
@@ -1505,18 +1501,13 @@
     "    \"\"\"\n",
     "    Perform analysis on the given DataFrame.\n",
     "\n",
-    "    Parameters:\n",
-    "    - df: DataFrame\n",
-    "        The input DataFrame containing the data for analysis.\n",
-    "    - ax: AxesSubplot, optional\n",
-    "        The subplot to plot the analysis results on.\n",
-    "    - title: str, optional\n",
-    "        The title of the plot.\n",
+    "    Args:\n",
+    "        - df (DataFrame): The input DataFrame containing the data for analysis.\n",
+    "        - ax (AxesSubplot, optional): The subplot to plot the analysis results on.\n",
+    "        - title (str, optional): The title of the plot.\n",
     "\n",
-    "    Returns:\n",
-    "    None\n",
+    "    Returns: None\n",
     "    \"\"\"\n",
-    "\n",
     "    # Initialize the StandardScaler and BayesianRidge model\n",
     "    # with 2-degree polynomial features\n",
     "    sc = StandardScaler()\n",