0.14.50

get core model for river / sklearn

Author: bartzbeielstein

notebooks/00_spotPython_tests.ipynb

@@ -4409,6 +4409,41 @@
         "data.frame.to_csv('~/data.csv', index=False)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Moons Data Set\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.datasets import make_moons, make_circles, make_classification\n",
+        "n_features = 2\n",
+        "n_samples = 500\n",
+        "target_column = \"y\"\n",
+        "ds =  make_moons(n_samples, noise=0.5, random_state=0)\n",
+        "X, y = ds\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X, y, test_size=0.3, random_state=42\n",
+        ")\n",
+        "train = pd.DataFrame(np.hstack((X_train, y_train.reshape(-1, 1))))\n",
+        "test = pd.DataFrame(np.hstack((X_test, y_test.reshape(-1, 1))))\n",
+        "train.columns = [f\"x{i}\" for i in range(1, n_features+1)] + [target_column]\n",
+        "test.columns = [f\"x{i}\" for i in range(1, n_features+1)] + [target_column]\n",
+        "train.head()\n",
+        "# combine the training and test data and save to a csv file\n",
+        "data = pd.concat([train, test])\n",
+        "data.to_csv('moon.csv', index=False)"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.14.48"
+version = "0.14.50"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/fun/hypersklearn.py

@@ -6,7 +6,7 @@
 from spotPython.hyperparameters.values import (
     generate_one_config_from_var_dict,
 )
-from spotPython.sklearn.traintest import evaluate_cv, evaluate_model_oob, evaluate_hold_out
+from spotPython.sklearn.traintest import evaluate_cv, evaluate_model_oob, evaluate_hold_out, evaluate_model
 import logging
 from sklearn.metrics import mean_absolute_error
 
@@ -139,7 +139,9 @@ def fun_sklearn(self, X: np.ndarray, fun_control: dict = None) -> np.ndarray:
                 model = self.fun_control["core_model"](**config)
             try:
                 eval_type = fun_control["eval"]
-                if eval_type == "eval_oob_score":
+                if eval_type == "eval_test":
+                    df_eval, _ = evaluate_model(model, self.fun_control)
+                elif eval_type == "eval_oob_score":
                     df_eval, _ = evaluate_model_oob(model, self.fun_control)
                 elif eval_type == "train_cv":
                     df_eval, _ = evaluate_cv(model, self.fun_control)