Update wordifier_nb.ipynb

notebooks/wordifier_nb.ipynb

@@ -93,12 +93,12 @@
      "output_type": "stream",
      "name": "stderr",
      "text": [
-      "2021-05-10 14:30:04.984 WARNING root: \n",
+      "2021-05-10 18:34:49.425 WARNING root: \n",
       "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
       "  command:\n",
       "\n",
       "    streamlit run /Users/49796/miniconda3/envs/py38/lib/python3.8/site-packages/ipykernel_launcher.py [ARGUMENTS]\n",
-      "100%|██████████| 6269/6269 [00:02<00:00, 2793.61it/s]\n"
+      "100%|██████████| 6269/6269 [00:02<00:00, 2750.45it/s]\n"
      ]
     }
    ],
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,14 +117,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
     "clf = LogisticRegression(\n",
     "    penalty=\"l1\",\n",
     "    C=0.05,#ModelConfigs.PENALTIES.value[np.random.randint(len(ModelConfigs.PENALTIES.value))],\n",
-    "    solver=\"saga\",\n",
+    "    solver=\"liblinear\",\n",
     "    multi_class=\"auto\",\n",
     "    max_iter=500,\n",
     "    class_weight=\"balanced\",\n",
@@ -133,17 +133,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "CPU times: user 1min 23s, sys: 138 ms, total: 1min 23s\n",
-      "Wall time: 1min 24s\n",
-      "/Users/49796/miniconda3/envs/py38/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
-      "  warnings.warn(\"The max_iter was reached which means \"\n"
+      "CPU times: user 1.45 s, sys: 10.6 ms, total: 1.46 s\nWall time: 1.46 s\n"
      ]
     },
     {
@@ -151,11 +148,11 @@
      "data": {
       "text/plain": [
        "LogisticRegression(C=0.05, class_weight='balanced', max_iter=500, penalty='l1',\n",
-       "                   solver='saga')"
+       "                   solver='liblinear')"
       ]
      },
      "metadata": {},
-     "execution_count": 12
+     "execution_count": 22
     }
    ],
    "source": [
@@ -163,6 +160,13 @@
     "clf.fit(X, y)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 14,