polish others buggy and fixed versions

benchmark/lightgbm_1/lightgbm_1_reproduced.ipynb

@@ -161,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-12T18:02:09.014720Z",
@@ -171,25 +171,7 @@
      "shell.execute_reply.started": "2023-02-12T18:02:09.014686Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "quality\n",
-       "5    1322\n",
-       "6    1240\n",
-       "7     476\n",
-       "4      88\n",
-       "8      55\n",
-       "3      18\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train['quality'].value_counts()"
    ]
@@ -205,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-12T18:02:09.030956Z",
@@ -222,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-12T18:02:09.313121Z",
@@ -255,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-12T18:02:09.320779Z",
@@ -275,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-12T18:02:09.335217Z",
@@ -293,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-12T18:02:09.355693Z",
@@ -303,18 +285,7 @@
      "shell.execute_reply.started": "2023-02-12T18:02:09.355637Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fe_mi = train2.drop(columns=[conf.target]).columns.to_list()\n",
     "mi = mutual_info_classif(train2[fe_mi], train2[[conf.target]]['quality'].values, \n",
@@ -355,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -373,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-12T18:02:10.073706Z",
@@ -400,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -416,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-12T18:53:59.234069Z",
@@ -444,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-12T18:13:26.570515Z",
@@ -468,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-12T18:13:27.553853Z",
@@ -538,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-11T16:05:27.831081Z",
@@ -562,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-11T16:06:59.796494Z",
@@ -606,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-11T11:34:02.522323Z",
@@ -632,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-11T11:34:02.538553Z",
@@ -661,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-11T11:34:04.059715Z",
@@ -686,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-11T11:34:04.069026Z",
@@ -774,7 +745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 20,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-11T11:34:04.089989Z",
@@ -815,7 +786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 21,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-11T11:34:04.104662Z",
@@ -830,12 +801,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[I 2025-04-02 11:10:24,134] A new study created in memory with name: no-name-c228e547-f301-4137-b5bb-b4c3c568e9ef\n",
-      "[W 2025-04-02 11:10:24,138] Trial 0 failed with parameters: {'random_state': 1736, 'n_splits': 17} because of the following error: ValueError('Input y contains NaN.').\n",
+      "[I 2025-06-24 13:55:55,049] A new study created in memory with name: no-name-ee6f19f2-768c-437e-afb5-2e59bcd4ab3e\n",
+      "[W 2025-06-24 13:55:55,053] Trial 0 failed with parameters: {'random_state': 1298, 'n_splits': 15} because of the following error: ValueError('Input y contains NaN.').\n",
       "Traceback (most recent call last):\n",
       "  File \"/usr/local/lib/python3.10/dist-packages/optuna/study/_optimize.py\", line 197, in _run_trial\n",
       "    value_or_values = func(trial)\n",
-      "  File \"<ipython-input-30-d0efff53950a>\", line 10, in find_out_params_model\n",
+      "  File \"<ipython-input-20-d0efff53950a>\", line 10, in find_out_params_model\n",
       "    for fold, (train_idx, valid_idx) in enumerate(cv.split(X, y)):\n",
       "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py\", line 771, in split\n",
       "    y = check_array(y, input_name=\"y\", ensure_2d=False, dtype=None)\n",
@@ -844,7 +815,7 @@
       "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\", line 161, in _assert_all_finite\n",
       "    raise ValueError(msg_err)\n",
       "ValueError: Input y contains NaN.\n",
-      "[W 2025-04-02 11:10:24,166] Trial 0 failed with value None.\n"
+      "[W 2025-06-24 13:55:55,060] Trial 0 failed with value None.\n"
      ]
     },
     {
@@ -854,13 +825,13 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-31-0a4981d346d5>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mstudy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_study\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"maximize\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfind_out_params_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-21-0a4981d346d5>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mstudy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_study\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"maximize\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfind_out_params_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/optuna/study/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m    473\u001b[0m                 \u001b[0mIf\u001b[0m \u001b[0mnested\u001b[0m \u001b[0minvocation\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0moccurs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    474\u001b[0m         \"\"\"\n\u001b[0;32m--> 475\u001b[0;31m         _optimize(\n\u001b[0m\u001b[1;32m    476\u001b[0m             \u001b[0mstudy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    477\u001b[0m             \u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_optimize\u001b[0;34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m             _optimize_sequential(\n\u001b[0m\u001b[1;32m     64\u001b[0m                 \u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m                 \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_optimize_sequential\u001b[0;34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m             \u001b[0mfrozen_trial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_run_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0;31m# The following line mitigates memory problems that can be occurred in some\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    246\u001b[0m         \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m     ):\n\u001b[0;32m--> 248\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mfunc_err\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    249\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfrozen_trial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    195\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mget_heartbeat_thread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trial_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m             \u001b[0mvalue_or_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialPruned\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m             \u001b[0;31m# TODO(mamu): Handle multi-objective cases.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-30-d0efff53950a>\u001b[0m in \u001b[0;36mfind_out_params_model\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     )\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mfold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_idx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0;31m#print(fold, end=' ')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_valid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalid_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-20-d0efff53950a>\u001b[0m in \u001b[0;36mfind_out_params_model\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     )\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mfold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_idx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0;31m#print(fold, end=' ')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_valid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalid_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36msplit\u001b[0;34m(self, X, y, groups)\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mto\u001b[0m \u001b[0man\u001b[0m \u001b[0minteger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    770\u001b[0m         \"\"\"\n\u001b[0;32m--> 771\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    772\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    773\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    920\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 921\u001b[0;31m             _assert_all_finite(\n\u001b[0m\u001b[1;32m    922\u001b[0m                 \u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m                 \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    159\u001b[0m                 \u001b[0;34m\"#estimators-that-handle-nan-values\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m             )\n\u001b[0;32m--> 161\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",

benchmark/statsmodels_1/statsmodels_1_fixed.ipynb

@@ -501,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-14T09:31:12.770619Z",
@@ -531,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-14T09:31:31.348831Z",
@@ -561,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-14T09:31:33.105215Z",
@@ -591,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-14T09:31:33.649089Z",
@@ -621,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-14T09:31:34.265843Z",

benchmark/statsmodels_1/statsmodels_1_reproduced.ipynb

@@ -543,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-14T09:31:31.348831Z",
@@ -553,39 +553,14 @@
      "shell.execute_reply.started": "2023-04-14T09:31:31.348798Z"
     }
    },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "45 option for line not understood",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-1c3a8c52f37c>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqqplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'osmo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m45\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36mqqplot\u001b[0;34m(data, dist, distargs, a, loc, scale, fit, line, ax, **plotkwargs)\u001b[0m\n\u001b[1;32m    689\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdistargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m     )\n\u001b[0;32m--> 691\u001b[0;31m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprobplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqqplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplotkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    692\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36mqqplot\u001b[0;34m(self, xlabel, ylabel, line, other, ax, swap, **plotkwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             fig, ax = _do_plot(\n\u001b[0m\u001b[1;32m    478\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtheoretical_quantiles\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_quantiles\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36m_do_plot\u001b[0;34m(x, y, dist, line, ax, fmt, step, **kwargs)\u001b[0m\n\u001b[1;32m   1047\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"q\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"45\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"s\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"%s option for line not understood\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1049\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1050\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1051\u001b[0m         \u001b[0mqqline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: 45 option for line not understood"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = sm.qqplot(train['osmo'],line=45,fit=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-14T09:31:33.105215Z",
@@ -595,39 +570,14 @@
      "shell.execute_reply.started": "2023-04-14T09:31:33.105161Z"
     }
    },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "45 option for line not understood",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-9-9ab9d9320dbf>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqqplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'urea'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m45\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36mqqplot\u001b[0;34m(data, dist, distargs, a, loc, scale, fit, line, ax, **plotkwargs)\u001b[0m\n\u001b[1;32m    689\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdistargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m     )\n\u001b[0;32m--> 691\u001b[0;31m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprobplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqqplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplotkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    692\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36mqqplot\u001b[0;34m(self, xlabel, ylabel, line, other, ax, swap, **plotkwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             fig, ax = _do_plot(\n\u001b[0m\u001b[1;32m    478\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtheoretical_quantiles\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_quantiles\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36m_do_plot\u001b[0;34m(x, y, dist, line, ax, fmt, step, **kwargs)\u001b[0m\n\u001b[1;32m   1047\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"q\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"45\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"s\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"%s option for line not understood\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1049\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1050\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1051\u001b[0m         \u001b[0mqqline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: 45 option for line not understood"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = sm.qqplot(train['urea'],line=45,fit=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-14T09:31:33.649089Z",
@@ -637,39 +587,14 @@
      "shell.execute_reply.started": "2023-04-14T09:31:33.649049Z"
     }
    },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "45 option for line not understood",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-e9ea6ed4326d>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqqplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'calc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m45\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36mqqplot\u001b[0;34m(data, dist, distargs, a, loc, scale, fit, line, ax, **plotkwargs)\u001b[0m\n\u001b[1;32m    689\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdistargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m     )\n\u001b[0;32m--> 691\u001b[0;31m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprobplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqqplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplotkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    692\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36mqqplot\u001b[0;34m(self, xlabel, ylabel, line, other, ax, swap, **plotkwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             fig, ax = _do_plot(\n\u001b[0m\u001b[1;32m    478\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtheoretical_quantiles\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_quantiles\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36m_do_plot\u001b[0;34m(x, y, dist, line, ax, fmt, step, **kwargs)\u001b[0m\n\u001b[1;32m   1047\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"q\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"45\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"s\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"%s option for line not understood\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1049\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1050\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1051\u001b[0m         \u001b[0mqqline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: 45 option for line not understood"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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bWRpEurq6NHv2bG3evNnK0wBAxqut9qm8xGPKXP/0p7N19WcmcjkGacHSu2ZuuOEG3XDDDVaeAgCyxp/OrdK/vnQw4ePHFeWpgZbuSDNpdftub2+vens/3YwVCoVsrAYA0kNjs1/rf/amusORuI7LdUmXTx2n2mofT9hF2kqrINLQ0KD6+nq7ywCAtBFr75ArppbqotLCgafrEjzgFGkVRDZu3Kj169cPfB0KhVRVVWVjRQCQepGoodfeO6Wf7zuiX77VFtMxxzp69MRtCwkecJy0CiIej0cejzmbsQDAiRK9DMPzYuBUaRVEACCbJdvCnefFwIksDSKffPKJDh78dId3a2urDhw4IJ/PpylTplh5agBwlEjUUN0zzUnNwfNi4ESWBpE33nhD11577cDX/fs/Vq1apUceecTKUwOAozS1BnSiM5zw8TwvBk5laRC55pprZBiGlacAgLQVPhPVo6+1qqk1oK6ePsnlUs+ZiDw5brlcLp3uO6PwGUOeXLc6e88kdS6eFwOnYo8IAFig4fkWPbi7VVb/U6wgz61N3/w8TcrgWAQRADBZw/MtemB3q+XnGVeYq//666XKz+X5pXAu/vYCgInCZ6J6MAUhRJIavj6LEALH428wAJjosT2HLb8cM64oT1tXzuFyDDICl2YAwEQfBLotm3vppWW6eVE1rduRUQgiAGCi7iTvfhnJ966aTudUZBwuzQCASSJRQ6+8d9KSuekTgkxFEAEAE0Sihh7e/X5STclGQp8QZCouzQBAkhJ9UF0sxnhy9E8rZrMxFRmLIAIASUjkQXU15WNVWpR/XmfVgrwcjR+Tr4+6wuqNRFU1rkhfn1OphRdPYCUEGY0gAgAJSuRBdRXeAu3488WEC+AP2CMCAAlK5EF1i2Zw6y1wLoIIACSovbMn7mOKPCxEA+ciiABAgsqKC+I+ZqqvyIJKAOciiABAgmqrfZpUnB/zeLdLumnBNOsKAhyIIAIACcpxu1T/1Zkxj7/16moeUgcMwk8EACRh2cwKbV05R0X5OcOOcbmk2xZXa+OXalJYGeAM7JoCgCQtm1mhpTXleu29U3py/1EdDXQrHDH+0JZ9vFYtnMZKCDAMgggAJCkSNdTUGlDgdFjfqp2q2moft+gCMSKIAEASGpv9qt/RIn/w01t5K7wFqlteQ1t2IAasFQJAghqb/Vq9ff95IUSS2oI9Wr19vxqb/TZVBjgHQQQAEhCJGqrf0SJjiO/1v1a/o0WR6FAjAPQjiABAAppaAxeshJzLkOQP9qipNZC6ogAHIogAQAJibe+eSBt4IJsQRAAgAbG2d0+kDTyQTQgiAJCA2mqfKrwFGu4mXZf0hz4ivlSWBTgOQQQAEpDjdqlu+dlOqYPDSP/Xdctr6CcCjIIgAgAJWjazQltWzlG59/zLL+XeAm1ZOYc+IkAMaGgGAEnob+/e1BpQe2ePyooL6KwKxIEgAgBJynG7tGDGeLvLAByJIAIASYhEDe099JH2vH9K0tlAcuX08ayIADEiiABAgna8eVz/98k31dMXHXjt/pcOqrQoT/d87TL2iAAxYLMqACTg1p+8rnX/8d/nhZB+Hd19up1nzQAxIYgAQIwiUUOvvndKX7lvt15saR91PM+aAUbHpRkAiEFjs18bfvGWOrr7Yj6m/1kzbGQFhkcQAYBRNDb7dfv2/Qkdy7NmgJFxaQYARhCJGrrr2bcTPp5nzQAjI4gAwAiaWgNqC/UmdCzPmgFGRxABgBE89MqhhI/lWTPA6AgiADCMhudbtPOdk3Efl5fj0laeNQPEhM2qADCE8JmoHtzdGvdx+TkuNdcvU34u/84DYsFPCgAM4bE9h5VIB5B//fblhBAgDvy0AMAQPgh0xzXek+vmcgyQAC7NAMAQpvqK4hr/76vmadElEyyqBshcrIgAwBBuWjBNsd7vUuEt0JV0TwUSQhABgCHk57q1pKYsprHcpgskjiACAENobPbr16M82G5cUR77QoAksUcEAAaJRA3V72gZ8a6ZcUV5+q8fLuEOGSBJ/AQBwCBNrQH5gyM/rO7j7j7t++DjFFUEZC6CCAAMEusTc3myLpA8gggADBLrE3N5si6QPIIIAAxSW+1Thbdg2Nt3XeLJuoBZCCIAMIQVcyqH3KzaH064ZRcwB3fNAMA5Gpv9Wv+zN9Udjgw9wCV9/+pqbtkFTMKKCAD8QWOzX7dv3z98CJFkGNIDu1vV2OxPYWVA5iKIAIDO9g6pe6Y55vH1O1oUiSbyfF4A5yKIAIDO9g450RmOebw/2KOm1oCFFQHZgSACAJLaQvH3BKGPCJA8NqsCyDiRqKHX3juln+87ohZ/SKf7IirMdSs/N0d9kagMGcrP+fT3PX2GPuyIP1TQRwRIHkEEQEbZ8eZxrf/ZAfVFrN2/QR8RwBwEEQAZ49afvK4XR3lirlnoIwKYgz0iADLC3b98OyUhZFxRnraunEMfEcAkrIgAcLzwmageeuWw5edZcmmZHrjpClZCABOxIgLA8R7bc3jIduxmWzB9PCEEMBlBBIDjfRDotvwcbpd004Jplp8HyDZcmgHgWP236e47/JHl57r16mrl5/JvN8BsKfmp2rx5s6ZNm6aCggLNnz9fTU1NqTgtgAzW2OzXZXe9oJu2Nelt/yeWnccl6bbF1dr4pRrLzgFkM8tXRJ544gmtX79eW7du1fz587Vp0yZdf/31evfdd1VWVmb16QFkoP6H01lpUrFHt1xVrZsXsRICWMllGIale7zmz5+vefPm6f7775ckRaNRVVVVad26ddqwYcOIx4ZCIXm9XgWDQZWUlFhZJgCHiEQNLWz4dVzPhZGk6eMLVZA3dGfV/JwcnYkaKinM03U15YQPIEnxfH5buiISDoe1b98+bdy4ceA1t9utJUuWaM+ePReM7+3tVW9v78DXoVDIyvIAOFC8D6fr92dXTtMtV0+3oCIAybA08p86dUqRSESTJk067/VJkyapra3tgvENDQ3yer0Dv6qqqqwsD4DDRKKGXvn9yYSOTcWdNQDil1Zrjxs3blQwGBz4dfToUbtLApAmnv+tX5+7s1E/3nUooeOn+opMrgiAGSy9NDNhwgTl5OToxIkT571+4sQJlZeXXzDe4/HI4/FYWRIAB2p4vkUP7G5N+Hh6gADpy9IVkfz8fM2dO1c7d+4ceC0ajWrnzp1asGCBlacGkCGe/+3xpEKIRA8QIJ1Zfvvu+vXrtWrVKl1xxRWqra3Vpk2b1NXVpe9+97tWnxqAw0Wihv766eaEj3e5pO9fTQ8QIJ1ZHkS++c1v6uTJk7rzzjvV1tamz3/+82psbLxgAysADNbUGtDH3X0JHXvj7Mn6xxWzWQkB0lxKWryvXbtWa9euTcWpAGSQ9s6ehI/9Zu0UQgjgAPyUAkhbZcUFCR1X4S1QbbXP5GoAWIEgAiBt1Vb7VF4S/510dctrlON2WVARALMRRACkrRy3S3d95XMxjx9XlKetK+do2cwKC6sCYKaU7BEBgEQtm1mhrSvnaMMv3lLHoI2rLkmfmTRGX/xsuRZdMkFXTh/PSgjgMAQRAGktEjVU7MnTt+ZV6rdHg+qNRFU1rkhfn1OphRdPIHgADkcQAZBWIlFDew99pFcOtus/f9eugye7FB30jPB9H3To5d+f1D1fu4zLMIDDEUQApI3GZv+Ql2CG0tHdp9u372dPCOBwbFYFkBYam/26ffv+mELIuep3tCgyeMkEgGOwIgLANuEzUT36Wqv2Hjqll39/KqE5/MEeNbUGtGDGeJOrA5AKBBEAtrj7ly36t1eSe5hdv2Q6sAKwF0EEQMrd+pPX9WJLu2nzJdqBFYD92CMCIKWeO3DM1BBCO3fA2QgiAFImEjW08em3TJ2Tdu6AsxFEAKRMU2tAnT0RU+Ya48nh1l0gA7BHBEDK/LqlLek5ct3S2msv1rovfoaVECADEEQAWC4SNfQvL/5eD796OOE5PDkuPfidK3TVJRMJIEAGIYgAMF1/f5D/ev8jvdPWqQ87kr+99l++fbn++I/KTKgOQDohiAAwVcPzLXpwd6vM6nU6xpOjf1oxm70gQIYiiAAwTcPzLXpgd/JNyiYVe3Tl9PH6xlyesAtkOoIIAFOEz0T1oAkhRJI2fetyWrYDWYLbdwGY4rE9h025HEODMiC7EEQAmOKDQLcp89CgDMguBBEAppjqK0rqeE+umwZlQBYiiAAwxU0LpinRdYwvzZyklr9bRggBshBBBIAp8nPd+v7i6riOyXO79OP/ebl+vPIKLscAWYq7ZgAkLBI19Np7p/TzfUf0u7ZOnezsjem4HJe05poZ+oulf0QAAbIcQQRAQhqb/Vr/szfVHY7tIXbjCvO0+DMT6Q0C4DwEEQBxa2z26/bt++M6JtjTp3tXzFZ+LleEAXyK/yMAiEskaqjumea4j4saZ3uNAMC5WBEBMKz+PSBP7j+qo4Fu9fRF1NEd1onOcELzmdVrBEDmIIgAGFK8e0BikWyvEQCZhyAC4AKJ7AEZjUtne40AwLkIIgAGhM9E9fBvDulHL/ze9Lm/d1U1G1UBXIAgAmShofZ+HA/2qOP0GUvON6uyRH/75RpL5gbgbAQRIMtYsfdjJLdcNVV/++WZKTkXAOchiAAZbPDKR3tnrz7s6LH8vLku6S+XfVY3L+JyDICREUSADBM+E9W2V9/XE68fVeupbhk21PCv356jL83iAXYARkcQATJEJGroL/7jv/XcW35b67htcTUhBEDMCCJABmhs9uvPf3pA4TNR22oYk+/Wvd+YrS/NmmxbDQCchyACOJwVPT9icfGEIk3yFmp2ZakWXTJBV04fz4PsAMSNIAI4WCRq6K/+329Tft6tK+do2UwuvwBIHtvZAQe7/z/fU9Ci3h9DGePJIYQAMBUrIoBDRaKG/v3VVkvPUVnq0aSSQlX5ivT1OZVaePEELr8AMBVBBHCoptaA6ashk0s8mlc9Xt+YS+gAkBoEEcChHnrlkGlzjSvKU8PXLuOSC4CUI4gADhQ+E9V/vnMyoWPLi/NVXJirsmLueAFgP4II4ECP7Tkcc8dUt6Tv//F0XX3JRAIHgLRDEAEc6INAd8xjf8xdLgDSGLfvAg401VcU07hvzLmIEAIgrRFEAAe6acE0jXaFxSXp7782KyX1AECiCCKAA+XnunXr1dUjjvn+4mrl5/IjDiC9sUcESGORqKHX3julJ/cf1dFAt3rPRJWf41I4Yqg7fEYu6YJNqy6X9P2rq7XxSzV2lAwAcSGIAGnq+d/6tf5nB9QT5xN1DUO6fMo4i6oCAHMRRAAbhc9E9ehrrWpqDairp09yuXS674yOfHRaH3X3JTxv/Y4WLa0p51ZdAGmPIAKkWH/42L73A30QOG3JOfzBHjW1BrRgxnhL5gcAsxBEgBS6+5ct+rdXrH1QXb/2zp6UnAcAkkEQAVLkfz36un79u/aUna+suCBl5wKARBFEgCRFoob2HvpIrxxs12+PBtVzJqKC3ByNH5Ovj7rC6jkT0bFAt058kviej3iVFOSqttqXsvMBQKIIIkASGpv92vCLt9SRxMZSK9z9J5exURWAIxBEgFH0r3i8euikjn18WoZh6NQnYflDPWo9FfszX1JlaU2Zls+ebHcZABATgggy2uAQEa/jHad14MOg+iKxPuvWXrdePU1//T8+Z3cZABAzgggyVrpeNknWNF+BxnjylJ/jUl9UqvAWqLZ6vFYtnEZLdwCOQxBBRhi88nG847Re/6DD7rJMVVqYq3u+Poun6QLIKAQRpJX+QLHn/VOKGpK3ME+hnj4Zf/h9x+mw/B09qigtUGlhvjpOh7Xv8Md688OgeuNshZ5uXJIuKSvSGE+uwmcMeXLdKszP1ezKUi26ZIKunD6eDagAMg5BBGkjUy+ljGZWZYn+atmlBA0AWYkggrTQ2OzX7dv3211Gyi25dKIeWlVrdxkAYBvLdrbdfffdWrhwoYqKilRaWmrVaZABIlFDdz37tt1lpNytV08jhADIepatiITDYa1YsUILFizQww8/bNVpkAGaWgNqC/XaXYap/qisSJ+ZVDLQWdWT45bb7VaRJ0e107jDBQD6WRZE6uvrJUmPPPKIVadAGopEjbPBInhaga6wSovyFejqVcfpsxtOxxXlyzcmXx3dn36v6XDA7rJNM64oTw1fu4w7WwAgRmm1R6S3t1e9vZ/+yzgUCtlYDYYzVNjo6A7rw47TeubAcQW6wnaXaJlclzS7yitPbs7AM2UmFheo0leohTO4swUA4pVWQaShoWFgJQXpqbHZr/odLfIHnfmI+Sumluqi0sK4jnG5XLpoHEEDAKwQVxDZsGGD/uEf/mHEMb/73e/02c9+NqFiNm7cqPXr1w98HQqFVFVVldBcMF9js1+rt++XM5qdn49LJgCQnuIKIj/4wQ908803jzhm+vTpCRfj8Xjk8XgSPh6J67/c0t7ZowljPJJLOvVJ78Dv2zpO667nWtI+hHhyXJpV6dXk0kJWMgDAAeIKIhMnTtTEiROtqgU2SffLLUsvLdMlk4qH7azq7+ghcACAQ1m2R+TIkSMKBAI6cuSIIpGIDhw4IEm6+OKLNXbsWKtOm3XOXckoKy5QbbVPkoZc3SgrLtDcqeO074OPB773+uGANu18z+Y/xci+d9V0LZgx3u4yAAAWsCyI3HnnnXr00UcHvr788sslSS+99JKuueYaq06bVYZaySgtypOkYduku11SNN2vr/yBS1K599NwBQDIPC7DMNL2YykUCsnr9SoYDKqkpMTuctKKkzeOxsolacvKOWwwBQCHiefzO61u30VsIlFD9TvSf+NoMiq8BapbXkMIAYAMRxBxoKbWQNpuLJUk35g8fXX2ZFWOK4qps+q535tQ7FF5ydnLMWw6BYDMRxBxoPbO9AkhxQU5qvvy5xQ83SffWEIEACA+BBEHKisusLsESWf3cNz7jdlcPgEAJIwgkqaGui03x+1SJGooGjVUWpinjtND3xmTCuzhAACYgSCShoa6LbfCW6CvzK7Qs2/6Ld8f8hdfvFi11ePP66zaHupRoCvM5RcAgKkcH0SGWzlI1XyxjB9pzODvfdwV1prHL7wt1x/s0QO7W0etP5k+IqxyAABSzdFBZLiVg0Q/TOOdL5bxI42RdMH33C4ldFtuaWGeNv/ZHF05/WwH0lg7q577PVY5AACp5tiGZsM19Or/GI23EVa888UyXtKwY6x40//j1itphQ4AsF08Dc3cKarJVCM19Op/rX5HiyIx9jKPd75Yxt/17Nu669mRx5gtnW7rBQAgFo4MIqM19DJ0dk9FU2vAkvliGd8W6lVbKLXBIF1u6wUAIFaO3CMS67/8rRqXbisPPBwOAOBUjlwRifVf/laNS6eVh/49KXXLa9hoCgBwHEcGkdpqnyq8BRruY9els3emxLpCEO98sYwvLznbbyPRaDA4U1R4C3Tb4mpVeM8PQeXeAp5QCwBwLEdemslxu1S3vEart++/4A6URFYI4p0vlvF3feVzkjTsGGOI3597/P3fvlzjxngu6D3yl8suNbVvCgAAdnLs7btSZvYRoakYAMDp4rl919FBRMq8zqqscAAAnC6rgggAAEgvGd/QDAAAZAaCCAAAsA1BBAAA2IYgAgAAbEMQAQAAtiGIAAAA2xBEAACAbQgiAADANgQRAABgm7R+6F1/09dQKGRzJQAAIFb9n9uxNG9P6yDS2dkpSaqqqrK5EgAAEK/Ozk55vd4Rx6T1s2ai0aiOHz+u4uJiuVw8CC5WoVBIVVVVOnr0KM/oMQnvqfl4T63B+2o+3tP4GYahzs5OTZ48WW73yLtA0npFxO12q7Ky0u4yHKukpIQfGpPxnpqP99QavK/m4z2Nz2grIf3YrAoAAGxDEAEAALYhiGQgj8ejuro6eTweu0vJGLyn5uM9tQbvq/l4T62V1ptVAQBAZmNFBAAA2IYgAgAAbEMQAQAAtiGIAAAA2xBEMtjhw4d1yy23qLq6WoWFhZoxY4bq6uoUDoftLs3R7r77bi1cuFBFRUUqLS21uxzH2rx5s6ZNm6aCggLNnz9fTU1NdpfkaLt379by5cs1efJkuVwuPf3003aX5HgNDQ2aN2+eiouLVVZWphtvvFHvvvuu3WVlHIJIBnvnnXcUjUb1wAMP6O2339Y///M/a+vWrfrhD39od2mOFg6HtWLFCq1evdruUhzriSee0Pr161VXV6f9+/dr9uzZuv7669Xe3m53aY7V1dWl2bNna/PmzXaXkjF27dqlNWvWaO/evXrxxRfV19en6667Tl1dXXaXllG4fTfL3HvvvdqyZYvef/99u0txvEceeUR33HGHOjo67C7FcebPn6958+bp/vvvl3T2uVJVVVVat26dNmzYYHN1zudyufTUU0/pxhtvtLuUjHLy5EmVlZVp165dWrx4sd3lZAxWRLJMMBiUz+ezuwxksXA4rH379mnJkiUDr7ndbi1ZskR79uyxsTJgZMFgUJL4f6jJCCJZ5ODBg7rvvvt022232V0KstipU6cUiUQ0adKk816fNGmS2trabKoKGFk0GtUdd9yhRYsWaebMmXaXk1EIIg60YcMGuVyuEX+988475x1z7NgxLVu2TCtWrNCtt95qU+XpK5H3FED2WLNmjZqbm/XTn/7U7lIyTq7dBSB+P/jBD3TzzTePOGb69OkDvz9+/LiuvfZaLVy4UA8++KDF1TlTvO8pEjdhwgTl5OToxIkT571+4sQJlZeX21QVMLy1a9fqueee0+7du1VZWWl3ORmHIOJAEydO1MSJE2Mae+zYMV177bWaO3eutm3bJrebRbChxPOeIjn5+fmaO3eudu7cObCZMhqNaufOnVq7dq29xQHnMAxD69at01NPPaWXX35Z1dXVdpeUkQgiGezYsWO65pprNHXqVP3oRz/SyZMnB77HvzwTd+TIEQUCAR05ckSRSEQHDhyQJF188cUaO3asvcU5xPr167Vq1SpdccUVqq2t1aZNm9TV1aXvfve7dpfmWJ988okOHjw48HVra6sOHDggn8+nKVOm2FiZc61Zs0aPP/64nnnmGRUXFw/sYfJ6vSosLLS5ugxiIGNt27bNkDTkLyRu1apVQ76nL730kt2lOcp9991nTJkyxcjPzzdqa2uNvXv32l2So7300ktD/r1ctWqV3aU51nD//9y2bZvdpWUU+ogAAADbsGEAAADYhiACAABsQxABAAC2IYgAAADbEEQAAIBtCCIAAMA2BBEAAGAbgggAALANQQQAANiGIAIAAGxDEAEAALYhiAAAANv8f0nwyEGprUZOAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = sm.qqplot(train['calc'],line=45,fit=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-14T09:31:34.265843Z",
@@ -679,32 +604,7 @@
      "shell.execute_reply.started": "2023-04-14T09:31:34.265810Z"
     }
    },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "45 option for line not understood",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-dc34b990139b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqqplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cond'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m45\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36mqqplot\u001b[0;34m(data, dist, distargs, a, loc, scale, fit, line, ax, **plotkwargs)\u001b[0m\n\u001b[1;32m    689\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdistargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m     )\n\u001b[0;32m--> 691\u001b[0;31m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprobplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqqplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplotkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    692\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36mqqplot\u001b[0;34m(self, xlabel, ylabel, line, other, ax, swap, **plotkwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             fig, ax = _do_plot(\n\u001b[0m\u001b[1;32m    478\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtheoretical_quantiles\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_quantiles\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/gofplots.py\u001b[0m in \u001b[0;36m_do_plot\u001b[0;34m(x, y, dist, line, ax, fmt, step, **kwargs)\u001b[0m\n\u001b[1;32m   1047\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"q\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"45\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"s\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"%s option for line not understood\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1049\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1050\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1051\u001b[0m         \u001b[0mqqline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: 45 option for line not understood"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = sm.qqplot(train['cond'],line=45,fit=True)"
    ]

benchmark/statsmodels_2/statsmodels_2_fixed.ipynb

@@ -846,7 +846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-11-01T03:07:01.152865Z",
@@ -900,15 +900,17 @@
     "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
     "\n",
     "# fix ------------- Ensure lags do not exceed available data length\n",
+    "# plot_acf(residuals, lags=180, title='ACF of Residuals')\n",
+    "\n",
     "max_acf_lags = min(len(residuals) - 1, 180)  # ACF limit\n",
     "max_pacf_lags = min(len(residuals) // 2 - 1, 180)  # PACF limit (50% rule)\n",
     "\n",
     "plt.figure(figsize=(12, 6))\n",
-    "plot_acf(residuals, lags=max_acf_lags, title='ACF of Residuals')\n",
+    "plot_acf(residuals, lags=max_acf_lags, title='ACF of Residuals') # fix ------------- replace lags\n",
     "plt.show()\n",
     "\n",
     "plt.figure(figsize=(12, 6))\n",
-    "plot_pacf(residuals, lags=max_pacf_lags, title='PACF of Residuals')\n",
+    "plot_pacf(residuals, lags=max_pacf_lags, title='PACF of Residuals') # fix ------------- replace lags\n",
     "plt.show()\n",
     "\n",
     "# Ideal: no significant autocorrelation\n",