Uv workflow improvements (#531)

* Uv workflow improvements

* Uv workflow improvements

* linter improvements

* pytproject.toml fixes

* pytproject.toml fixes

* pytproject.toml fixes

* pytproject.toml fixes

* pytproject.toml fixes

* pytproject.toml fixes

* windows fixes

* windows fixes

* windows fixes

* windows fixes

* windows fixes

* windows fixes

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

* win32 fix

ch06/01_main-chapter-code/ch06.ipynb

@@ -79,28 +79,6 @@
     "<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch06_compressed/chapter-overview.webp\" width=500px>"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "946c3e56-b04b-4b0f-b35f-b485ce5b28df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Utility to prevent certain cells from being executed twice\n",
-    "\n",
-    "from IPython.core.magic import register_line_cell_magic\n",
-    "\n",
-    "executed_cells = set()\n",
-    "\n",
-    "@register_line_cell_magic\n",
-    "def run_once(line, cell):\n",
-    "    if line not in executed_cells:\n",
-    "        get_ipython().run_cell(cell)\n",
-    "        executed_cells.add(line)\n",
-    "    else:\n",
-    "        print(f\"Cell '{line}' has already been executed.\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "3a84cf35-b37f-4c15-8972-dfafc9fadc1c",
@@ -450,9 +428,6 @@
     }
    ],
    "source": [
-    "%%run_once balance_df\n",
-    "\n",
-    "\n",
     "def create_balanced_dataset(df):\n",
     "    \n",
     "    # Count the instances of \"spam\"\n",
@@ -490,7 +465,6 @@
    },
    "outputs": [],
    "source": [
-    "%%run_once label_mapping\n",
     "balanced_df[\"Label\"] = balanced_df[\"Label\"].map({\"ham\": 0, \"spam\": 1})    "
    ]
   },

ch06/03_bonus_imdb-classification/sklearn-baseline.ipynb

@@ -190,13 +190,13 @@
     "    \n",
     "    # Calculating accuracy and balanced accuracy\n",
     "    accuracy_train = accuracy_score(y_train, y_pred_train)\n",
-    "    balanced_accuracy_train = balanced_accuracy_score(y_train, y_pred_train)\n",
+    "    # balanced_accuracy_train = balanced_accuracy_score(y_train, y_pred_train)\n",
     "    \n",
     "    accuracy_val = accuracy_score(y_val, y_pred_val)\n",
-    "    balanced_accuracy_val = balanced_accuracy_score(y_val, y_pred_val)\n",
+    "    # balanced_accuracy_val = balanced_accuracy_score(y_val, y_pred_val)\n",
     "\n",
     "    accuracy_test = accuracy_score(y_test, y_pred_test)\n",
-    "    balanced_accuracy_test = balanced_accuracy_score(y_test, y_pred_test)\n",
+    "    # balanced_accuracy_test = balanced_accuracy_score(y_test, y_pred_test)\n",
     "    \n",
     "    # Printing the results\n",
     "    print(f\"Training Accuracy: {accuracy_train*100:.2f}%\")\n",
@@ -269,7 +269,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,