From b0f60b950c6752e89228f47e8f5a0078cea8d438 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jochen=20Wersd=C3=B6rfer?= <jochen@wersdoerfer.de>
Date: Mon, 30 Oct 2023 09:13:12 +0100
Subject: [PATCH] remove obsolete mlb stuff

---
 01_text_classification.ipynb | 79 ++----------------------------------
 1 file changed, 4 insertions(+), 75 deletions(-)

diff --git a/01_text_classification.ipynb b/01_text_classification.ipynb
index b935d48..79a9096 100644
--- a/01_text_classification.ipynb
+++ b/01_text_classification.ipynb
@@ -684,38 +684,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "        earn      0.990     0.980     0.985      1087\n",
-      "         acq      0.980     0.949     0.964       719\n",
-      "    money-fx      0.799     0.777     0.788       179\n",
-      "       grain      0.977     0.852     0.910       149\n",
-      "       crude      0.899     0.852     0.875       189\n",
-      "       trade      0.827     0.692     0.753       117\n",
-      "    interest      0.880     0.672     0.762       131\n",
-      "        ship      0.919     0.640     0.755        89\n",
-      "       wheat      0.917     0.775     0.840        71\n",
-      "        corn      0.957     0.786     0.863        56\n",
-      "\n",
-      "   micro avg      0.953     0.897     0.924      2787\n",
-      "   macro avg      0.914     0.797     0.849      2787\n",
-      "weighted avg      0.951     0.897     0.922      2787\n",
-      " samples avg      0.770     0.768     0.766      2787\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#|notest\n",
-    "from sklearn.preprocessing import MultiLabelBinarizer\n",
-    "mlb = MultiLabelBinarizer()\n",
-    "y_transformed = mlb.fit_transform(df_train.label)\n",
-    "y_test_transformed = mlb.transform(df_test.label)\n",
     "pipeline.fit(df_train, y_train)\n",
     "y_pred = pipeline.predict(df_test)\n",
     "print(classification_report(y_test, y_pred, target_names=top_ten_names, labels=top_ten_ids, digits=3, zero_division=0.0))"
@@ -739,15 +710,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average precision score, micro-averaged over all classes: 0.93\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#|notest\n",
     "from sklearn.metrics import precision_recall_curve\n",
@@ -770,15 +733,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "265509 0.8760683760683761 0.8760683760683761\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#|notest\n",
     "# precision recall breakeven point\n",
@@ -793,33 +748,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Average precision score, micro-averaged over all classes: AP=0.93')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 851,
-       "width": 1239
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#|notest\n",
     "plt.figure(figsize=(15, 10))\n",