polish nbspecific buggy and fixed versions

benchmark/NBspecific_1/NBspecific_1_fixed.ipynb

@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:01:40.837603Z",
@@ -145,7 +145,7 @@
        "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:01:46.978097Z",
@@ -221,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:01:49.177674Z",
@@ -250,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:01:52.159195Z",
@@ -279,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:01:54.689274Z",
@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:02:12.057700Z",
@@ -499,18 +499,7 @@
      "shell.execute_reply.started": "2023-02-28T09:02:12.057663Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"nltk.download('stopwords')\""
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import the library and download the stop words:\n",
     "from nltk.corpus import stopwords\n",
@@ -522,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:02:14.667306Z",
@@ -551,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:02:16.297289Z",
@@ -579,7 +568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:02:43.119351Z",
@@ -604,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:02:45.558806Z",
@@ -614,18 +603,7 @@
      "shell.execute_reply.started": "2023-02-28T09:02:45.558761Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hello, world ,,   '"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "emoji_text = \"hello, world 😀,😃,😄  😈 😀\"\n",
     "remove_emoji(emoji_text)"
@@ -756,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:03:36.468767Z",
@@ -784,7 +762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:04:35.737577Z",
@@ -802,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:04:46.312604Z",
@@ -812,35 +790,15 @@
      "shell.execute_reply.started": "2023-02-28T09:04:46.312561Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0        one review mention watch 1 oz episod youll hoo...\n",
-       "1        wonder littl product film techniqu unassum old...\n",
-       "2        thought wonder way spend time hot summer weeke...\n",
-       "3        basic there famili littl boy jake think there ...\n",
-       "4        petter mattei love time money visual stun film...\n",
-       "                               ...                        \n",
-       "49995    thought movi right good job wasnt creativ orig...\n",
-       "49996    bad plot bad dialogu bad act idiot direct anno...\n",
-       "49997    cathol taught parochi elementari school nun ta...\n",
-       "49998    im go disagre previou comment side maltin one ...\n",
-       "49999    one expect star trek movi high art fan expect ...\n",
-       "Name: review, Length: 50000, dtype: object"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Function for applying stemming function\n",
     "def stem_words(text):\n",
-    "    return \" \".join([ps.stem(word) for word in text])\n",
+    "    # fix 1 --- split the string into words before stemming, rather than looping characters\n",
+    "#     return \" \".join([ps.stem(word) for word in text])\n",
+    "    return \" \".join([ps.stem(word) for word in text.split()])\n",
     "\n",
-    "df['review'].apply(stem_words)"
+    "# df['review'].apply(stem_words)"
    ]
   },
   {
@@ -875,7 +833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:12:39.848502Z",
@@ -887,7 +845,10 @@
    },
    "outputs": [],
    "source": [
-    "df['lemma_review'] = df['review'].astype(str) #.apply(stem_words) # fix ---- already stemed previously, and need to be list of string"
+    "# fix for faster reproducing and fixing process, use a random 10% of the data:\n",
+    "df = df.sample(frac=0.1, random_state=42).copy()\n",
+    "\n",
+    "df['lemma_review'] = df['review'].apply(stem_words)"
    ]
   },
   {
@@ -1003,7 +964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:17:00.313046Z",
@@ -1015,6 +976,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix 2 --- execute this cell\n",
     "from sklearn.feature_extraction.text import TfidfVectorizer\n",
     "tfidf = TfidfVectorizer()\n",
     "tf_idf = tfidf.fit_transform(df['lemma_review'])"
@@ -1022,7 +984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:16:54.789060Z",
@@ -1045,7 +1007,7 @@
        "       [0., 0., 0., ..., 0., 0., 0.]])"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }

benchmark/NBspecific_1/NBspecific_1_reproduced.ipynb

@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:02:12.057700Z",
@@ -499,18 +499,7 @@
      "shell.execute_reply.started": "2023-02-28T09:02:12.057663Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"nltk.download('stopwords')\""
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import the library and download the stop words:\n",
     "from nltk.corpus import stopwords\n",
@@ -522,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:02:14.667306Z",
@@ -551,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:02:16.297289Z",
@@ -579,7 +568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:02:43.119351Z",
@@ -604,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:02:45.558806Z",
@@ -614,18 +603,7 @@
      "shell.execute_reply.started": "2023-02-28T09:02:45.558761Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hello, world ,,   '"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "emoji_text = \"hello, world 😀,😃,😄  😈 😀\"\n",
     "remove_emoji(emoji_text)"
@@ -756,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:03:36.468767Z",
@@ -784,7 +762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:04:35.737577Z",
@@ -802,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:04:46.312604Z",
@@ -818,7 +796,7 @@
     "def stem_words(text):\n",
     "    return \" \".join([ps.stem(word) for word in text])\n",
     "\n",
-    "df['review'].apply(stem_words)"
+    "# df['review'].apply(stem_words)"
    ]
   },
   {
@@ -853,7 +831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:12:39.848502Z",
@@ -865,6 +843,9 @@
    },
    "outputs": [],
    "source": [
+    "# fix for faster reproducing and fixing process, use a random 10% of the data:\n",
+    "df = df.sample(frac=0.1, random_state=42).copy()\n",
+    "\n",
     "df['lemma_review'] = df['review'].apply(stem_words)"
    ]
   },
@@ -1000,7 +981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-28T09:16:54.789060Z",
@@ -1018,7 +999,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-3425eba7af87>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf_idf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\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[0;32m<ipython-input-11-3425eba7af87>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf_idf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\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[0;31mNameError\u001b[0m: name 'tf_idf' is not defined"
      ]
     }

benchmark/NBspecific_10/NBspecific_10_fixed.ipynb

@@ -3461,6 +3461,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix -- not execute this cell beforehand\n",
     "Uni=Final.ActivePower"
    ]
   },
@@ -3526,6 +3527,7 @@
     }
    ],
    "source": [
+    "# fix -- not execute this cell beforehand\n",
     "# train is from 0 to 44582 test is from 44582 to 49536\n",
     "  \n",
     "train, test = Uni[0:44582], Uni[44582:]\n",
@@ -3547,6 +3549,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix -- not execute this cell beforehand\n",
     "from sklearn.preprocessing import MinMaxScaler\n",
     "\n",
     "scaler = MinMaxScaler(feature_range=(0,1))\n",

benchmark/NBspecific_10/NBspecific_10_reproduced.ipynb

@@ -2271,7 +2271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 26,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2295,7 +2295,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-31-25e1234a523b>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstattools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0madfuller\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mcheck_stationarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ActivePower'\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[0;32m<ipython-input-26-25e1234a523b>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstattools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0madfuller\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mcheck_stationarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ActivePower'\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[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
      ]
     }
@@ -3431,7 +3431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 23,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-01T08:55:10.327344Z",
@@ -3485,7 +3485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 24,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-01T09:19:10.143687Z",
@@ -3503,7 +3503,7 @@
        "((44582,), (4958,))"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3517,7 +3517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 25,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-01T09:19:15.961670Z",

benchmark/NBspecific_11/NBspecific_11_reproduced.ipynb

@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:08:48.236386Z",
@@ -116,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:10:11.562488Z",
@@ -153,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:11:40.531442Z",
@@ -171,9 +171,7 @@
       "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
       "  warnings.warn(\n",
       "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
-      "  warnings.warn(msg)\n",
-      "Downloading: \"https://download.pytorch.org/models/alexnet-owt-7be5be79.pth\" to /root/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth\n",
-      "100%|██████████| 233M/233M [00:06<00:00, 40.0MB/s] \n"
+      "  warnings.warn(msg)\n"
      ]
     }
    ],
@@ -184,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:12:39.020940Z",
@@ -219,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:14:18.682336Z",
@@ -238,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:15:33.227206Z",
@@ -248,18 +246,7 @@
      "shell.execute_reply.started": "2023-06-09T02:15:33.227171Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1000,)"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 下面是调试内容\n",
     "np.eye(num_labels)[target_label].shape"
@@ -267,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:16:32.401599Z",
@@ -277,18 +264,7 @@
      "shell.execute_reply.started": "2023-06-09T02:16:32.401569Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1000])"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# print(tlab)\n",
     "tlab.shape"
@@ -296,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:34:21.875455Z",
@@ -313,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:34:24.649587Z",
@@ -338,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:48:29.739149Z",
@@ -470,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:56:04.716191Z",
@@ -480,16 +456,7 @@
      "shell.execute_reply.started": "2023-06-09T02:56:04.716162Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1, 3, 224, 224)\n",
-      "(1, 3, 224, 224)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(o_bestattack.shape)\n",
     "print(img.shape)"
@@ -497,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T02:59:51.731662Z",
@@ -543,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T03:05:08.845633Z",
@@ -608,7 +575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-09T03:05:11.739431Z",
@@ -626,7 +593,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-57-5bc605183c9d>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madv\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-12-5bc605183c9d>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madv\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/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[1;32m   2693\u001b[0m         \u001b[0minterpolation_stage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2694\u001b[0m         resample=None, url=None, data=None, **kwargs):\n\u001b[0;32m-> 2695\u001b[0;31m     __ret = gca().imshow(\n\u001b[0m\u001b[1;32m   2696\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maspect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2697\u001b[0m         \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\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/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1440\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1441\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\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[0;32m-> 1442\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   5663\u001b[0m                               **kwargs)\n\u001b[1;32m   5664\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5665\u001b[0;31m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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   5666\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5667\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\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",

benchmark/NBspecific_12/NBspecific_12_fixed.ipynb

@@ -26,6 +26,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix 1 --- execute this cell\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "%matplotlib inline\n"
@@ -418,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-24T05:45:47.424378Z",
@@ -435,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-24T05:45:55.533625Z",
@@ -498,7 +499,7 @@
     "\n",
     "fig, axs = plt.subplots(6, 3, figsize=(7, 17))\n",
     "\n",
-    "# fix ----- an additional wrong variable name error\n",
+    "# fix 2 ----- an additional wrong variable name error\n",
     "# for col,ax in zip(numvars,axs.ravel()):\n",
     "for col,ax in zip(num,axs.ravel()):\n",
     "    if pp.train[col].dtype == float or pp.train[col].dtype == int:\n",
@@ -514,6 +515,13 @@
     "fig.suptitle('Feature distributions', y=1.02, fontsize=20)\n",
     "plt.tight_layout()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

benchmark/NBspecific_12/NBspecific_12_reproduced.ipynb

@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {
     "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
     "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-24T05:45:44.800959Z",
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-24T05:45:45.203530Z",
@@ -405,7 +405,7 @@
        "[5 rows x 24 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -418,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-24T05:45:47.424378Z",
@@ -435,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-24T05:45:55.533625Z",
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-24T05:46:36.494735Z",
@@ -486,7 +486,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-ff56c1381872>\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[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17\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      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumvars\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistplot\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[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\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-4-ff56c1381872>\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[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17\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      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumvars\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistplot\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[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
      ]
     }

benchmark/NBspecific_13/NBspecific_13_fixed.ipynb

@@ -467,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-11T14:20:38.616852Z",
@@ -477,162 +477,14 @@
      "shell.execute_reply.started": "2023-12-11T14:20:38.616816Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>cp</th>\n",
-       "      <th>trtbps</th>\n",
-       "      <th>chol</th>\n",
-       "      <th>fbs</th>\n",
-       "      <th>restecg</th>\n",
-       "      <th>thalachh</th>\n",
-       "      <th>exng</th>\n",
-       "      <th>oldpeak</th>\n",
-       "      <th>slp</th>\n",
-       "      <th>caa</th>\n",
-       "      <th>thall</th>\n",
-       "      <th>output</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>63</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>145</td>\n",
-       "      <td>233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>150</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>37</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>130</td>\n",
-       "      <td>250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>187</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>41</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>130</td>\n",
-       "      <td>204</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>172</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>56</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>120</td>\n",
-       "      <td>236</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>178</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>57</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>120</td>\n",
-       "      <td>354</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>163</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
-       "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
-       "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
-       "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
-       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
-       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
-       "\n",
-       "   caa  thall  output  \n",
-       "0    0      1       1  \n",
-       "1    0      2       1  \n",
-       "2    0      2       1  \n",
-       "3    0      2       1  \n",
-       "4    0      2       1  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-12T08:18:23.920518Z",
@@ -799,7 +651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-12T08:35:48.471762Z",
@@ -814,11 +666,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 0.75\n"
+      "Accuracy: 0.6973684210526315\n"
      ]
     }
    ],
    "source": [
+    "# fix --- execute this cell beforehand\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
     "from sklearn.metrics import accuracy_score\n",

benchmark/NBspecific_13/NBspecific_13_reproduced.ipynb

@@ -450,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-12T08:22:09.636349Z",
@@ -467,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-11T14:20:38.616852Z",
@@ -477,162 +477,14 @@
      "shell.execute_reply.started": "2023-12-11T14:20:38.616816Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>cp</th>\n",
-       "      <th>trtbps</th>\n",
-       "      <th>chol</th>\n",
-       "      <th>fbs</th>\n",
-       "      <th>restecg</th>\n",
-       "      <th>thalachh</th>\n",
-       "      <th>exng</th>\n",
-       "      <th>oldpeak</th>\n",
-       "      <th>slp</th>\n",
-       "      <th>caa</th>\n",
-       "      <th>thall</th>\n",
-       "      <th>output</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>63</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>145</td>\n",
-       "      <td>233</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>150</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>37</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>130</td>\n",
-       "      <td>250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>187</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>41</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>130</td>\n",
-       "      <td>204</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>172</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>56</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>120</td>\n",
-       "      <td>236</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>178</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>57</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>120</td>\n",
-       "      <td>354</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>163</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
-       "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
-       "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
-       "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
-       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
-       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
-       "\n",
-       "   caa  thall  output  \n",
-       "0    0      1       1  \n",
-       "1    0      2       1  \n",
-       "2    0      2       1  \n",
-       "3    0      2       1  \n",
-       "4    0      2       1  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-12-12T08:18:23.920518Z",
@@ -650,7 +502,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-5-522783fb824e>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeClassifier\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[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfit\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[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0my_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_hat\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[0m\n",
+      "\u001b[0;32m<ipython-input-3-522783fb824e>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeClassifier\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[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfit\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[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0my_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_hat\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[0m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
      ]
     }

benchmark/NBspecific_14/NBspecific_14_fixed.ipynb

@@ -201,6 +201,7 @@
     }
    ],
    "source": [
+    "# fix --- execute this cell\n",
     "train_data = pd.read_csv(\"data/train.csv\")\n",
     "train_data.head()"
    ]
@@ -349,13 +350,14 @@
     }
    ],
    "source": [
+    "# fix --- execute this cell\n",
     "test_data = pd.read_csv(\"data/test.csv\")\n",
     "test_data.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-14T22:30:13.299598Z",
@@ -365,15 +367,7 @@
      "shell.execute_reply.started": "2023-08-14T22:30:13.299556Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "% of women who survived: 0.7420382165605095\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "women = train_data.loc[train_data.Sex == 'female'][\"Survived\"]\n",
     "rate_women = sum(women)/len(women)\n",
@@ -383,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-14T22:30:33.452100Z",
@@ -393,15 +387,7 @@
      "shell.execute_reply.started": "2023-08-14T22:30:33.452066Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "% of men who survived: 0.18890814558058924\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "men = train_data.loc[train_data.Sex == 'male'][\"Survived\"]\n",
     "rate_men = sum(men)/len(men)\n",
@@ -411,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-16T22:27:02.446192Z",

benchmark/NBspecific_16/NBspecific_16_fixed.ipynb

@@ -193,6 +193,7 @@
     }
    ],
    "source": [
+    "# fix --- execute this cell\n",
     "base_skin_dir = os.path.join('data_small', 'skin-cancer-mnist-ham10000')\n",
     "skin_df = pd.read_csv(os.path.join(base_skin_dir, 'HAM10000_metadata.csv'))\n",
     "\n",
@@ -219,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
     "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
@@ -241,20 +242,7 @@
      "shell.execute_reply.started": "2024-01-01T11:59:57.382255Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "image\n",
-       "(224, 224, 3)    48\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from PIL import Image\n",
     "skin_df['image'] = skin_df['path'].map(lambda x: np.asarray(Image.open(x).resize((HEIGHT, WIDTH))))\n",
@@ -263,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-01-01T18:58:20.307523Z",
@@ -305,115 +293,95 @@
        "      <th>path</th>\n",
        "      <th>cell_type</th>\n",
        "      <th>cell_type_idx</th>\n",
-       "      <th>image</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>HAM_0002921</td>\n",
-       "      <td>ISIC_0029308</td>\n",
+       "      <th>0</th>\n",
+       "      <td>HAM_0000118</td>\n",
+       "      <td>ISIC_0027419</td>\n",
        "      <td>bkl</td>\n",
        "      <td>histo</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>female</td>\n",
-       "      <td>face</td>\n",
-       "      <td>data_small/skin-cancer-mnist-ham10000/HAM10000...</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>male</td>\n",
+       "      <td>scalp</td>\n",
+       "      <td>None</td>\n",
        "      <td>Benign keratosis-like lesions</td>\n",
        "      <td>2</td>\n",
-       "      <td>[[[25, 12, 18], [25, 12, 19], [25, 14, 23], [2...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>342</th>\n",
-       "      <td>HAM_0005157</td>\n",
-       "      <td>ISIC_0029340</td>\n",
+       "      <th>1</th>\n",
+       "      <td>HAM_0000118</td>\n",
+       "      <td>ISIC_0025030</td>\n",
        "      <td>bkl</td>\n",
        "      <td>histo</td>\n",
-       "      <td>40.0</td>\n",
+       "      <td>80.0</td>\n",
        "      <td>male</td>\n",
-       "      <td>chest</td>\n",
-       "      <td>data_small/skin-cancer-mnist-ham10000/HAM10000...</td>\n",
+       "      <td>scalp</td>\n",
+       "      <td>None</td>\n",
        "      <td>Benign keratosis-like lesions</td>\n",
        "      <td>2</td>\n",
-       "      <td>[[[179, 158, 162], [192, 171, 172], [191, 170,...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>526</th>\n",
-       "      <td>HAM_0002592</td>\n",
-       "      <td>ISIC_0029311</td>\n",
+       "      <th>2</th>\n",
+       "      <td>HAM_0002730</td>\n",
+       "      <td>ISIC_0026769</td>\n",
        "      <td>bkl</td>\n",
        "      <td>histo</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>female</td>\n",
-       "      <td>lower extremity</td>\n",
-       "      <td>data_small/skin-cancer-mnist-ham10000/HAM10000...</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>male</td>\n",
+       "      <td>scalp</td>\n",
+       "      <td>None</td>\n",
        "      <td>Benign keratosis-like lesions</td>\n",
        "      <td>2</td>\n",
-       "      <td>[[[135, 121, 134], [135, 121, 134], [136, 122,...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>732</th>\n",
-       "      <td>HAM_0002844</td>\n",
-       "      <td>ISIC_0029329</td>\n",
+       "      <th>3</th>\n",
+       "      <td>HAM_0002730</td>\n",
+       "      <td>ISIC_0025661</td>\n",
        "      <td>bkl</td>\n",
        "      <td>histo</td>\n",
-       "      <td>85.0</td>\n",
+       "      <td>80.0</td>\n",
        "      <td>male</td>\n",
-       "      <td>back</td>\n",
-       "      <td>data_small/skin-cancer-mnist-ham10000/HAM10000...</td>\n",
+       "      <td>scalp</td>\n",
+       "      <td>None</td>\n",
        "      <td>Benign keratosis-like lesions</td>\n",
        "      <td>2</td>\n",
-       "      <td>[[[148, 107, 103], [147, 106, 103], [145, 102,...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>865</th>\n",
-       "      <td>HAM_0006186</td>\n",
-       "      <td>ISIC_0029320</td>\n",
+       "      <th>4</th>\n",
+       "      <td>HAM_0001466</td>\n",
+       "      <td>ISIC_0031633</td>\n",
        "      <td>bkl</td>\n",
-       "      <td>consensus</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>histo</td>\n",
+       "      <td>75.0</td>\n",
        "      <td>male</td>\n",
-       "      <td>lower extremity</td>\n",
-       "      <td>data_small/skin-cancer-mnist-ham10000/HAM10000...</td>\n",
+       "      <td>ear</td>\n",
+       "      <td>None</td>\n",
        "      <td>Benign keratosis-like lesions</td>\n",
        "      <td>2</td>\n",
-       "      <td>[[[67, 47, 56], [72, 51, 62], [86, 60, 70], [9...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       lesion_id      image_id   dx    dx_type   age     sex     localization  \\\n",
-       "90   HAM_0002921  ISIC_0029308  bkl      histo  60.0  female             face   \n",
-       "342  HAM_0005157  ISIC_0029340  bkl      histo  40.0    male            chest   \n",
-       "526  HAM_0002592  ISIC_0029311  bkl      histo  60.0  female  lower extremity   \n",
-       "732  HAM_0002844  ISIC_0029329  bkl      histo  85.0    male             back   \n",
-       "865  HAM_0006186  ISIC_0029320  bkl  consensus   0.0    male  lower extremity   \n",
-       "\n",
-       "                                                  path  \\\n",
-       "90   data_small/skin-cancer-mnist-ham10000/HAM10000...   \n",
-       "342  data_small/skin-cancer-mnist-ham10000/HAM10000...   \n",
-       "526  data_small/skin-cancer-mnist-ham10000/HAM10000...   \n",
-       "732  data_small/skin-cancer-mnist-ham10000/HAM10000...   \n",
-       "865  data_small/skin-cancer-mnist-ham10000/HAM10000...   \n",
-       "\n",
-       "                          cell_type  cell_type_idx  \\\n",
-       "90   Benign keratosis-like lesions               2   \n",
-       "342  Benign keratosis-like lesions               2   \n",
-       "526  Benign keratosis-like lesions               2   \n",
-       "732  Benign keratosis-like lesions               2   \n",
-       "865  Benign keratosis-like lesions               2   \n",
+       "     lesion_id      image_id   dx dx_type   age   sex localization  path  \\\n",
+       "0  HAM_0000118  ISIC_0027419  bkl   histo  80.0  male        scalp  None   \n",
+       "1  HAM_0000118  ISIC_0025030  bkl   histo  80.0  male        scalp  None   \n",
+       "2  HAM_0002730  ISIC_0026769  bkl   histo  80.0  male        scalp  None   \n",
+       "3  HAM_0002730  ISIC_0025661  bkl   histo  80.0  male        scalp  None   \n",
+       "4  HAM_0001466  ISIC_0031633  bkl   histo  75.0  male          ear  None   \n",
        "\n",
-       "                                                 image  \n",
-       "90   [[[25, 12, 18], [25, 12, 19], [25, 14, 23], [2...  \n",
-       "342  [[[179, 158, 162], [192, 171, 172], [191, 170,...  \n",
-       "526  [[[135, 121, 134], [135, 121, 134], [136, 122,...  \n",
-       "732  [[[148, 107, 103], [147, 106, 103], [145, 102,...  \n",
-       "865  [[[67, 47, 56], [72, 51, 62], [86, 60, 70], [9...  "
+       "                        cell_type  cell_type_idx  \n",
+       "0  Benign keratosis-like lesions               2  \n",
+       "1  Benign keratosis-like lesions               2  \n",
+       "2  Benign keratosis-like lesions               2  \n",
+       "3  Benign keratosis-like lesions               2  \n",
+       "4  Benign keratosis-like lesions               2  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -438,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-01-01T12:03:24.917862Z",
@@ -464,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-01-01T12:03:25.242919Z",
@@ -488,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-01-01T12:03:25.264419Z",
@@ -516,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-01-01T12:03:27.601346Z",
@@ -546,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2024-01-01T12:03:31.275322Z",
@@ -556,20 +524,7 @@
      "shell.execute_reply.started": "2024-01-01T12:03:31.275264Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/3\n",
-      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 2s/step - accuracy: 0.1716 - loss: 1.9351 - val_accuracy: 0.4000 - val_loss: 2.0258\n",
-      "Epoch 2/3\n",
-      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 258ms/step - accuracy: 0.6294 - loss: 1.4507 - val_accuracy: 0.4000 - val_loss: 2.1167\n",
-      "Epoch 3/3\n",
-      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 290ms/step - accuracy: 0.6294 - loss: 1.5548 - val_accuracy: 0.4000 - val_loss: 1.7907\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "base_model.trainable = False\n",
     "model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n",

benchmark/NBspecific_17/NBspecific_17_fixed.ipynb

@@ -67,1403 +67,237 @@
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-       "      <td>Normal</td>\n",
-       "      <td>147500</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1460 rows × 80 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
-       "Id                                                                      \n",
-       "1             60       RL         65.0     8450   Pave   NaN      Reg   \n",
-       "2             20       RL         80.0     9600   Pave   NaN      Reg   \n",
-       "3             60       RL         68.0    11250   Pave   NaN      IR1   \n",
-       "4             70       RL         60.0     9550   Pave   NaN      IR1   \n",
-       "5             60       RL         84.0    14260   Pave   NaN      IR1   \n",
-       "...          ...      ...          ...      ...    ...   ...      ...   \n",
-       "1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
-       "1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
-       "1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
-       "1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
-       "1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
-       "\n",
-       "     LandContour Utilities LotConfig  ... PoolArea PoolQC  Fence MiscFeature  \\\n",
-       "Id                                    ...                                      \n",
-       "1            Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "2            Lvl    AllPub       FR2  ...        0    NaN    NaN         NaN   \n",
-       "3            Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "4            Lvl    AllPub    Corner  ...        0    NaN    NaN         NaN   \n",
-       "5            Lvl    AllPub       FR2  ...        0    NaN    NaN         NaN   \n",
-       "...          ...       ...       ...  ...      ...    ...    ...         ...   \n",
-       "1456         Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "1457         Lvl    AllPub    Inside  ...        0    NaN  MnPrv         NaN   \n",
-       "1458         Lvl    AllPub    Inside  ...        0    NaN  GdPrv        Shed   \n",
-       "1459         Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "1460         Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "\n",
-       "     MiscVal MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
-       "Id                                                               \n",
-       "1          0      2    2008        WD         Normal     208500  \n",
-       "2          0      5    2007        WD         Normal     181500  \n",
-       "3          0      9    2008        WD         Normal     223500  \n",
-       "4          0      2    2006        WD        Abnorml     140000  \n",
-       "5          0     12    2008        WD         Normal     250000  \n",
-       "...      ...    ...     ...       ...            ...        ...  \n",
-       "1456       0      8    2007        WD         Normal     175000  \n",
-       "1457       0      2    2010        WD         Normal     210000  \n",
-       "1458    2500      5    2010        WD         Normal     266500  \n",
-       "1459       0      4    2010        WD         Normal     142125  \n",
-       "1460       0      6    2008        WD         Normal     147500  \n",
-       "\n",
-       "[1460 rows x 80 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_df"
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-   "cell_type": "code",
-   "execution_count": null,
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-     "shell.execute_reply.started": "2023-06-25T13:23:10.039067Z"
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-   "outputs": [],
-   "source": [
-    "train_df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
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-     "shell.execute_reply.started": "2023-06-25T13:23:10.620726Z"
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-       "      <th>1461</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RH</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>11622</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>120</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MnPrv</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1462</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>14267</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Corner</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Gar2</td>\n",
-       "      <td>12500</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1463</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>13830</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MnPrv</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1464</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>9978</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1465</th>\n",
-       "      <td>120</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>5005</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>HLS</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>144</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2915</th>\n",
-       "      <td>160</td>\n",
-       "      <td>RM</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1936</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2916</th>\n",
-       "      <td>160</td>\n",
-       "      <td>RM</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1894</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Abnorml</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2917</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>20000</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Abnorml</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2918</th>\n",
-       "      <td>85</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>10441</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MnPrv</td>\n",
-       "      <td>Shed</td>\n",
-       "      <td>700</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2919</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>9627</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1459 rows × 79 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
-       "Id                                                                      \n",
-       "1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
-       "1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
-       "1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
-       "1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
-       "1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
-       "...          ...      ...          ...      ...    ...   ...      ...   \n",
-       "2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
-       "2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
-       "2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
-       "2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
-       "2919          60       RL         74.0     9627   Pave   NaN      Reg   \n",
-       "\n",
-       "     LandContour Utilities LotConfig  ... ScreenPorch PoolArea PoolQC  Fence  \\\n",
-       "Id                                    ...                                      \n",
-       "1461         Lvl    AllPub    Inside  ...         120        0    NaN  MnPrv   \n",
-       "1462         Lvl    AllPub    Corner  ...           0        0    NaN    NaN   \n",
-       "1463         Lvl    AllPub    Inside  ...           0        0    NaN  MnPrv   \n",
-       "1464         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
-       "1465         HLS    AllPub    Inside  ...         144        0    NaN    NaN   \n",
-       "...          ...       ...       ...  ...         ...      ...    ...    ...   \n",
-       "2915         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
-       "2916         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
-       "2917         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
-       "2918         Lvl    AllPub    Inside  ...           0        0    NaN  MnPrv   \n",
-       "2919         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
-       "\n",
-       "     MiscFeature MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
-       "Id                                                                 \n",
-       "1461         NaN       0       6    2010        WD         Normal  \n",
-       "1462        Gar2   12500       6    2010        WD         Normal  \n",
-       "1463         NaN       0       3    2010        WD         Normal  \n",
-       "1464         NaN       0       6    2010        WD         Normal  \n",
-       "1465         NaN       0       1    2010        WD         Normal  \n",
-       "...          ...     ...     ...     ...       ...            ...  \n",
-       "2915         NaN       0       6    2006        WD         Normal  \n",
-       "2916         NaN       0       4    2006        WD        Abnorml  \n",
-       "2917         NaN       0       9    2006        WD        Abnorml  \n",
-       "2918        Shed     700       7    2006        WD         Normal  \n",
-       "2919         NaN       0      11    2006        WD         Normal  \n",
-       "\n",
-       "[1459 rows x 79 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-06-25T13:23:11.131462Z",
-     "iopub.status.busy": "2023-06-25T13:23:11.130679Z",
-     "iopub.status.idle": "2023-06-25T13:23:11.150802Z",
-     "shell.execute_reply": "2023-06-25T13:23:11.149870Z",
-     "shell.execute_reply.started": "2023-06-25T13:23:11.131424Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "test_df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1_1. 통계적 분석"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-06-25T13:23:11.907074Z",
-     "iopub.status.busy": "2023-06-25T13:23:11.906709Z",
-     "iopub.status.idle": "2023-06-25T13:23:12.010254Z",
-     "shell.execute_reply": "2023-06-25T13:23:12.009287Z",
-     "shell.execute_reply.started": "2023-06-25T13:23:11.907046Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "train_df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-06-25T13:23:12.176455Z",
-     "iopub.status.busy": "2023-06-25T13:23:12.176129Z",
-     "iopub.status.idle": "2023-06-25T13:23:12.276238Z",
-     "shell.execute_reply": "2023-06-25T13:23:12.275426Z",
-     "shell.execute_reply.started": "2023-06-25T13:23:12.176427Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "test_df.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1_2. 상관관계 분석"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-06-25T13:23:12.942491Z",
-     "iopub.status.busy": "2023-06-25T13:23:12.942135Z",
-     "iopub.status.idle": "2023-06-25T13:23:13.004797Z",
-     "shell.execute_reply": "2023-06-25T13:23:13.004032Z",
-     "shell.execute_reply.started": "2023-06-25T13:23:12.942462Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "train_df.select_dtypes(include='number').corr()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-06-25T13:23:13.452652Z",
-     "iopub.status.busy": "2023-06-25T13:23:13.452258Z",
-     "iopub.status.idle": "2023-06-25T13:23:17.052104Z",
-     "shell.execute_reply": "2023-06-25T13:23:17.051263Z",
-     "shell.execute_reply.started": "2023-06-25T13:23:13.452618Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(20,20))\n",
-    "sns.heatmap(data=train_df.select_dtypes(include='number').corr(), vmin=abs(0.5), annot=True, fmt = '.2f')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-06-25T13:23:17.053953Z",
-     "iopub.status.busy": "2023-06-25T13:23:17.053665Z",
-     "iopub.status.idle": "2023-06-25T13:23:17.058931Z",
-     "shell.execute_reply": "2023-06-25T13:23:17.058050Z",
-     "shell.execute_reply.started": "2023-06-25T13:23:17.053927Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "num_cols = train_df.select_dtypes(include='number')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-06-25T13:23:17.060402Z",
-     "iopub.status.busy": "2023-06-25T13:23:17.060101Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "for col in num_cols:\n",
-    "    fig, ax = plt.subplots(1, 3, figsize=(20,3))\n",
-    "    sns.boxplot(data=train_df,  x=col, ax=ax[0] )\n",
-    "    sns.histplot(data=train_df, x=col, bins=40, ax=ax[1])\n",
-    "    sns.kdeplot(data=train_df,  x=col, ax=ax[2])\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 2. Data preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2_1. 결측치 처리"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-06-25T13:22:25.832475Z",
-     "iopub.status.busy": "2023-06-25T13:22:25.832137Z",
-     "iopub.status.idle": "2023-06-25T13:22:25.845482Z",
-     "shell.execute_reply": "2023-06-25T13:22:25.844584Z",
-     "shell.execute_reply.started": "2023-06-25T13:22:25.832443Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_df = pd.concat([train_df, test_df], axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-06-25T13:22:25.846913Z",
-     "iopub.status.busy": "2023-06-25T13:22:25.846612Z",
-     "iopub.status.idle": "2023-06-25T13:22:25.883701Z",
-     "shell.execute_reply": "2023-06-25T13:22:25.882835Z",
-     "shell.execute_reply.started": "2023-06-25T13:22:25.846886Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MSSubClass</th>\n",
-       "      <th>MSZoning</th>\n",
-       "      <th>LotFrontage</th>\n",
-       "      <th>LotArea</th>\n",
-       "      <th>Street</th>\n",
-       "      <th>Alley</th>\n",
-       "      <th>LotShape</th>\n",
-       "      <th>LandContour</th>\n",
-       "      <th>Utilities</th>\n",
-       "      <th>LotConfig</th>\n",
-       "      <th>...</th>\n",
-       "      <th>PoolArea</th>\n",
-       "      <th>PoolQC</th>\n",
-       "      <th>Fence</th>\n",
-       "      <th>MiscFeature</th>\n",
-       "      <th>MiscVal</th>\n",
-       "      <th>MoSold</th>\n",
-       "      <th>YrSold</th>\n",
-       "      <th>SaleType</th>\n",
-       "      <th>SaleCondition</th>\n",
-       "      <th>SalePrice</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>8450</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>208500.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>9600</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>FR2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>181500.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>11250</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>223500.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>70</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>9550</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Corner</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Abnorml</td>\n",
-       "      <td>140000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>14260</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>FR2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>250000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2915</th>\n",
-       "      <td>160</td>\n",
-       "      <td>RM</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1936</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2916</th>\n",
-       "      <td>160</td>\n",
-       "      <td>RM</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1894</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Abnorml</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2917</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>20000</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Abnorml</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2918</th>\n",
-       "      <td>85</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>10441</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MnPrv</td>\n",
-       "      <td>Shed</td>\n",
-       "      <td>700</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2919</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>9627</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2919 rows × 80 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
-       "Id                                                                      \n",
-       "1             60       RL         65.0     8450   Pave   NaN      Reg   \n",
-       "2             20       RL         80.0     9600   Pave   NaN      Reg   \n",
-       "3             60       RL         68.0    11250   Pave   NaN      IR1   \n",
-       "4             70       RL         60.0     9550   Pave   NaN      IR1   \n",
-       "5             60       RL         84.0    14260   Pave   NaN      IR1   \n",
-       "...          ...      ...          ...      ...    ...   ...      ...   \n",
-       "2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
-       "2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
-       "2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
-       "2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
-       "2919          60       RL         74.0     9627   Pave   NaN      Reg   \n",
-       "\n",
-       "     LandContour Utilities LotConfig  ... PoolArea PoolQC  Fence MiscFeature  \\\n",
-       "Id                                    ...                                      \n",
-       "1            Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "2            Lvl    AllPub       FR2  ...        0    NaN    NaN         NaN   \n",
-       "3            Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "4            Lvl    AllPub    Corner  ...        0    NaN    NaN         NaN   \n",
-       "5            Lvl    AllPub       FR2  ...        0    NaN    NaN         NaN   \n",
-       "...          ...       ...       ...  ...      ...    ...    ...         ...   \n",
-       "2915         Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "2916         Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "2917         Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "2918         Lvl    AllPub    Inside  ...        0    NaN  MnPrv        Shed   \n",
-       "2919         Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "\n",
-       "     MiscVal MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
-       "Id                                                               \n",
-       "1          0      2    2008        WD         Normal   208500.0  \n",
-       "2          0      5    2007        WD         Normal   181500.0  \n",
-       "3          0      9    2008        WD         Normal   223500.0  \n",
-       "4          0      2    2006        WD        Abnorml   140000.0  \n",
-       "5          0     12    2008        WD         Normal   250000.0  \n",
-       "...      ...    ...     ...       ...            ...        ...  \n",
-       "2915       0      6    2006        WD         Normal        NaN  \n",
-       "2916       0      4    2006        WD        Abnorml        NaN  \n",
-       "2917       0      9    2006        WD        Abnorml        NaN  \n",
-       "2918     700      7    2006        WD         Normal        NaN  \n",
-       "2919       0     11    2006        WD         Normal        NaN  \n",
-       "\n",
-       "[2919 rows x 80 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:23:09.551735Z",
+     "iopub.status.busy": "2023-06-25T13:23:09.551039Z",
+     "iopub.status.idle": "2023-06-25T13:23:09.584973Z",
+     "shell.execute_reply": "2023-06-25T13:23:09.584115Z",
+     "shell.execute_reply.started": "2023-06-25T13:23:09.551700Z"
     }
-   ],
+   },
+   "outputs": [],
+   "source": [
+    "train_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:23:10.039100Z",
+     "iopub.status.busy": "2023-06-25T13:23:10.038687Z",
+     "iopub.status.idle": "2023-06-25T13:23:10.057291Z",
+     "shell.execute_reply": "2023-06-25T13:23:10.056458Z",
+     "shell.execute_reply.started": "2023-06-25T13:23:10.039067Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "train_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:23:10.620763Z",
+     "iopub.status.busy": "2023-06-25T13:23:10.620023Z",
+     "iopub.status.idle": "2023-06-25T13:23:10.654397Z",
+     "shell.execute_reply": "2023-06-25T13:23:10.653445Z",
+     "shell.execute_reply.started": "2023-06-25T13:23:10.620726Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "test_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:23:11.131462Z",
+     "iopub.status.busy": "2023-06-25T13:23:11.130679Z",
+     "iopub.status.idle": "2023-06-25T13:23:11.150802Z",
+     "shell.execute_reply": "2023-06-25T13:23:11.149870Z",
+     "shell.execute_reply.started": "2023-06-25T13:23:11.131424Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "test_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1_1. 통계적 분석"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:23:11.907074Z",
+     "iopub.status.busy": "2023-06-25T13:23:11.906709Z",
+     "iopub.status.idle": "2023-06-25T13:23:12.010254Z",
+     "shell.execute_reply": "2023-06-25T13:23:12.009287Z",
+     "shell.execute_reply.started": "2023-06-25T13:23:11.907046Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "train_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:23:12.176455Z",
+     "iopub.status.busy": "2023-06-25T13:23:12.176129Z",
+     "iopub.status.idle": "2023-06-25T13:23:12.276238Z",
+     "shell.execute_reply": "2023-06-25T13:23:12.275426Z",
+     "shell.execute_reply.started": "2023-06-25T13:23:12.176427Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "test_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1_2. 상관관계 분석"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:23:12.942491Z",
+     "iopub.status.busy": "2023-06-25T13:23:12.942135Z",
+     "iopub.status.idle": "2023-06-25T13:23:13.004797Z",
+     "shell.execute_reply": "2023-06-25T13:23:13.004032Z",
+     "shell.execute_reply.started": "2023-06-25T13:23:12.942462Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "train_df.select_dtypes(include='number').corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:23:13.452652Z",
+     "iopub.status.busy": "2023-06-25T13:23:13.452258Z",
+     "iopub.status.idle": "2023-06-25T13:23:17.052104Z",
+     "shell.execute_reply": "2023-06-25T13:23:17.051263Z",
+     "shell.execute_reply.started": "2023-06-25T13:23:13.452618Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20,20))\n",
+    "sns.heatmap(data=train_df.select_dtypes(include='number').corr(), vmin=abs(0.5), annot=True, fmt = '.2f')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:23:17.053953Z",
+     "iopub.status.busy": "2023-06-25T13:23:17.053665Z",
+     "iopub.status.idle": "2023-06-25T13:23:17.058931Z",
+     "shell.execute_reply": "2023-06-25T13:23:17.058050Z",
+     "shell.execute_reply.started": "2023-06-25T13:23:17.053927Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "num_cols = train_df.select_dtypes(include='number')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:23:17.060402Z",
+     "iopub.status.busy": "2023-06-25T13:23:17.060101Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "for col in num_cols:\n",
+    "    fig, ax = plt.subplots(1, 3, figsize=(20,3))\n",
+    "    sns.boxplot(data=train_df,  x=col, ax=ax[0] )\n",
+    "    sns.histplot(data=train_df, x=col, bins=40, ax=ax[1])\n",
+    "    sns.kdeplot(data=train_df,  x=col, ax=ax[2])\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Data preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2_1. 결측치 처리"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:22:25.832475Z",
+     "iopub.status.busy": "2023-06-25T13:22:25.832137Z",
+     "iopub.status.idle": "2023-06-25T13:22:25.845482Z",
+     "shell.execute_reply": "2023-06-25T13:22:25.844584Z",
+     "shell.execute_reply.started": "2023-06-25T13:22:25.832443Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# fix --- execute this cell\n",
+    "data_df = pd.concat([train_df, test_df], axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-06-25T13:22:25.846913Z",
+     "iopub.status.busy": "2023-06-25T13:22:25.846612Z",
+     "iopub.status.idle": "2023-06-25T13:22:25.883701Z",
+     "shell.execute_reply": "2023-06-25T13:22:25.882835Z",
+     "shell.execute_reply.started": "2023-06-25T13:22:25.846886Z"
+    }
+   },
+   "outputs": [],
    "source": [
     "data_df"
    ]
@@ -1519,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-06-25T13:22:25.918575Z",
@@ -1927,19 +761,20 @@
        "[2919 rows x 37 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# fix --- execute this cell\n",
     "nume_cols = data_df.select_dtypes(include='number')\n",
     "nume_cols"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-06-25T13:22:25.920075Z",
@@ -1950,6 +785,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix --- execute this cell\n",
     "num_filled = nume_cols.fillna(nume_cols.mean())"
    ]
   },
@@ -1985,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-06-25T13:22:25.924104Z",
@@ -2379,19 +1215,20 @@
        "[2919 rows x 43 columns]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# fix --- execute this cell\n",
     "cat_cols = data_df.select_dtypes(include='object')\n",
     "cat_cols"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-06-25T13:22:25.925561Z",
@@ -2402,6 +1239,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix --- execute this cell\n",
     "cat_filled = cat_cols.fillna(cat_cols.mode().iloc[0])"
    ]
   },
@@ -2430,7 +1268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-06-25T13:22:25.928626Z",
@@ -2441,6 +1279,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix --- execute this cell\n",
     "cat_enc = pd.get_dummies(cat_filled)"
    ]
   },
@@ -2485,7 +1324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-06-25T13:22:25.933152Z",
@@ -2496,6 +1335,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix --- execute this cell\n",
     "from sklearn.preprocessing import MinMaxScaler\n",
     "\n",
     "columns_to_scale = num_filled.drop('SalePrice', axis=1).columns\n",
@@ -2534,7 +1374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-06-25T13:22:25.937170Z",
@@ -2545,6 +1385,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix --- execute this cell\n",
     "data = pd.concat([final_df.reset_index(drop=True), cat_enc.reset_index(drop=True)], axis = 1)"
    ]
   },
@@ -2573,7 +1414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-06-25T13:22:25.940252Z",
@@ -2584,13 +1425,14 @@
    },
    "outputs": [],
    "source": [
+    "# fix --- execute this cell\n",
     "target = data['SalePrice']\n",
     "features = data.drop('SalePrice', axis=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-06-25T13:22:25.941942Z",
@@ -2601,6 +1443,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix --- execute this cell\n",
     "X      = features[:len(train_df)]\n",
     "X_test = features[len(train_df):]\n",
     "\n",
@@ -2633,7 +1476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-25T13:22:51.312853Z",
@@ -2670,7 +1513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-25T13:22:52.925171Z",
@@ -2687,7 +1530,7 @@
        "((1460, 287), (1460, 288))"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }

benchmark/NBspecific_17/NBspecific_17_reproduced.ipynb

@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-25T13:23:09.551735Z",
@@ -77,396 +77,7 @@
      "shell.execute_reply.started": "2023-06-25T13:23:09.551700Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MSSubClass</th>\n",
-       "      <th>MSZoning</th>\n",
-       "      <th>LotFrontage</th>\n",
-       "      <th>LotArea</th>\n",
-       "      <th>Street</th>\n",
-       "      <th>Alley</th>\n",
-       "      <th>LotShape</th>\n",
-       "      <th>LandContour</th>\n",
-       "      <th>Utilities</th>\n",
-       "      <th>LotConfig</th>\n",
-       "      <th>...</th>\n",
-       "      <th>PoolArea</th>\n",
-       "      <th>PoolQC</th>\n",
-       "      <th>Fence</th>\n",
-       "      <th>MiscFeature</th>\n",
-       "      <th>MiscVal</th>\n",
-       "      <th>MoSold</th>\n",
-       "      <th>YrSold</th>\n",
-       "      <th>SaleType</th>\n",
-       "      <th>SaleCondition</th>\n",
-       "      <th>SalePrice</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>8450</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>208500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>9600</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>FR2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>181500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>11250</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>223500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>70</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>9550</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Corner</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Abnorml</td>\n",
-       "      <td>140000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>14260</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>FR2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>12</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>250000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1456</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>7917</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>175000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1457</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>13175</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MnPrv</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>210000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1458</th>\n",
-       "      <td>70</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>9042</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>GdPrv</td>\n",
-       "      <td>Shed</td>\n",
-       "      <td>2500</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>266500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1459</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>9717</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>142125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1460</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>9937</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "      <td>147500</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1460 rows × 80 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
-       "Id                                                                      \n",
-       "1             60       RL         65.0     8450   Pave   NaN      Reg   \n",
-       "2             20       RL         80.0     9600   Pave   NaN      Reg   \n",
-       "3             60       RL         68.0    11250   Pave   NaN      IR1   \n",
-       "4             70       RL         60.0     9550   Pave   NaN      IR1   \n",
-       "5             60       RL         84.0    14260   Pave   NaN      IR1   \n",
-       "...          ...      ...          ...      ...    ...   ...      ...   \n",
-       "1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
-       "1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
-       "1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
-       "1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
-       "1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
-       "\n",
-       "     LandContour Utilities LotConfig  ... PoolArea PoolQC  Fence MiscFeature  \\\n",
-       "Id                                    ...                                      \n",
-       "1            Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "2            Lvl    AllPub       FR2  ...        0    NaN    NaN         NaN   \n",
-       "3            Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "4            Lvl    AllPub    Corner  ...        0    NaN    NaN         NaN   \n",
-       "5            Lvl    AllPub       FR2  ...        0    NaN    NaN         NaN   \n",
-       "...          ...       ...       ...  ...      ...    ...    ...         ...   \n",
-       "1456         Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "1457         Lvl    AllPub    Inside  ...        0    NaN  MnPrv         NaN   \n",
-       "1458         Lvl    AllPub    Inside  ...        0    NaN  GdPrv        Shed   \n",
-       "1459         Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "1460         Lvl    AllPub    Inside  ...        0    NaN    NaN         NaN   \n",
-       "\n",
-       "     MiscVal MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
-       "Id                                                               \n",
-       "1          0      2    2008        WD         Normal     208500  \n",
-       "2          0      5    2007        WD         Normal     181500  \n",
-       "3          0      9    2008        WD         Normal     223500  \n",
-       "4          0      2    2006        WD        Abnorml     140000  \n",
-       "5          0     12    2008        WD         Normal     250000  \n",
-       "...      ...    ...     ...       ...            ...        ...  \n",
-       "1456       0      8    2007        WD         Normal     175000  \n",
-       "1457       0      2    2010        WD         Normal     210000  \n",
-       "1458    2500      5    2010        WD         Normal     266500  \n",
-       "1459       0      4    2010        WD         Normal     142125  \n",
-       "1460       0      6    2008        WD         Normal     147500  \n",
-       "\n",
-       "[1460 rows x 80 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_df"
    ]
@@ -490,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-25T13:23:10.620763Z",
@@ -500,396 +111,7 @@
      "shell.execute_reply.started": "2023-06-25T13:23:10.620726Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MSSubClass</th>\n",
-       "      <th>MSZoning</th>\n",
-       "      <th>LotFrontage</th>\n",
-       "      <th>LotArea</th>\n",
-       "      <th>Street</th>\n",
-       "      <th>Alley</th>\n",
-       "      <th>LotShape</th>\n",
-       "      <th>LandContour</th>\n",
-       "      <th>Utilities</th>\n",
-       "      <th>LotConfig</th>\n",
-       "      <th>...</th>\n",
-       "      <th>ScreenPorch</th>\n",
-       "      <th>PoolArea</th>\n",
-       "      <th>PoolQC</th>\n",
-       "      <th>Fence</th>\n",
-       "      <th>MiscFeature</th>\n",
-       "      <th>MiscVal</th>\n",
-       "      <th>MoSold</th>\n",
-       "      <th>YrSold</th>\n",
-       "      <th>SaleType</th>\n",
-       "      <th>SaleCondition</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1461</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RH</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>11622</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>120</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MnPrv</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1462</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>14267</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Corner</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Gar2</td>\n",
-       "      <td>12500</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1463</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>13830</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MnPrv</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1464</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>9978</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1465</th>\n",
-       "      <td>120</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>5005</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>IR1</td>\n",
-       "      <td>HLS</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>144</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2915</th>\n",
-       "      <td>160</td>\n",
-       "      <td>RM</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1936</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2916</th>\n",
-       "      <td>160</td>\n",
-       "      <td>RM</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>1894</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Abnorml</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2917</th>\n",
-       "      <td>20</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>160.0</td>\n",
-       "      <td>20000</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Abnorml</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2918</th>\n",
-       "      <td>85</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>10441</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MnPrv</td>\n",
-       "      <td>Shed</td>\n",
-       "      <td>700</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2919</th>\n",
-       "      <td>60</td>\n",
-       "      <td>RL</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>9627</td>\n",
-       "      <td>Pave</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Reg</td>\n",
-       "      <td>Lvl</td>\n",
-       "      <td>AllPub</td>\n",
-       "      <td>Inside</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>WD</td>\n",
-       "      <td>Normal</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1459 rows × 79 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
-       "Id                                                                      \n",
-       "1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
-       "1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
-       "1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
-       "1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
-       "1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
-       "...          ...      ...          ...      ...    ...   ...      ...   \n",
-       "2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
-       "2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
-       "2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
-       "2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
-       "2919          60       RL         74.0     9627   Pave   NaN      Reg   \n",
-       "\n",
-       "     LandContour Utilities LotConfig  ... ScreenPorch PoolArea PoolQC  Fence  \\\n",
-       "Id                                    ...                                      \n",
-       "1461         Lvl    AllPub    Inside  ...         120        0    NaN  MnPrv   \n",
-       "1462         Lvl    AllPub    Corner  ...           0        0    NaN    NaN   \n",
-       "1463         Lvl    AllPub    Inside  ...           0        0    NaN  MnPrv   \n",
-       "1464         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
-       "1465         HLS    AllPub    Inside  ...         144        0    NaN    NaN   \n",
-       "...          ...       ...       ...  ...         ...      ...    ...    ...   \n",
-       "2915         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
-       "2916         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
-       "2917         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
-       "2918         Lvl    AllPub    Inside  ...           0        0    NaN  MnPrv   \n",
-       "2919         Lvl    AllPub    Inside  ...           0        0    NaN    NaN   \n",
-       "\n",
-       "     MiscFeature MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
-       "Id                                                                 \n",
-       "1461         NaN       0       6    2010        WD         Normal  \n",
-       "1462        Gar2   12500       6    2010        WD         Normal  \n",
-       "1463         NaN       0       3    2010        WD         Normal  \n",
-       "1464         NaN       0       6    2010        WD         Normal  \n",
-       "1465         NaN       0       1    2010        WD         Normal  \n",
-       "...          ...     ...     ...     ...       ...            ...  \n",
-       "2915         NaN       0       6    2006        WD         Normal  \n",
-       "2916         NaN       0       4    2006        WD        Abnorml  \n",
-       "2917         NaN       0       9    2006        WD        Abnorml  \n",
-       "2918        Shed     700       7    2006        WD         Normal  \n",
-       "2919         NaN       0      11    2006        WD         Normal  \n",
-       "\n",
-       "[1459 rows x 79 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_df"
    ]
@@ -1452,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-25T13:22:51.312853Z",
@@ -1489,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-06-25T13:22:52.925171Z",
@@ -1507,7 +729,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-6-050313322d03>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# removes squared and higher-order terms for reproducing and fixing purposes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpoly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPolynomialFeatures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdegree\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minteraction_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# PolynomialFeatures(degree=3)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpoly_X\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpoly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpoly_X\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-4-050313322d03>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# removes squared and higher-order terms for reproducing and fixing purposes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpoly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPolynomialFeatures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdegree\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minteraction_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# PolynomialFeatures(degree=3)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpoly_X\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpoly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpoly_X\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
      ]
     }

benchmark/NBspecific_18/NBspecific_18_fixed.ipynb

@@ -212,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.724205Z",
@@ -222,38 +222,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:34.724137Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Name                    0\n",
-       "Location                0\n",
-       "Year                    0\n",
-       "Kilometers_Driven       0\n",
-       "Fuel_Type               0\n",
-       "Transmission            0\n",
-       "Owner_Type              0\n",
-       "Mileage                 2\n",
-       "Engine                 36\n",
-       "Power                  36\n",
-       "Seats                  38\n",
-       "New_Price            5032\n",
-       "Price                   0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.isna().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.747718Z",
@@ -270,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.759838Z",
@@ -419,7 +395,7 @@
        "6   3.50  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -431,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.797282Z",
@@ -449,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.811483Z",
@@ -459,157 +435,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:34.811433Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Location</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Kilometers_Driven</th>\n",
-       "      <th>Fuel_Type</th>\n",
-       "      <th>Transmission</th>\n",
-       "      <th>Owner_Type</th>\n",
-       "      <th>Mileage</th>\n",
-       "      <th>Engine</th>\n",
-       "      <th>Power</th>\n",
-       "      <th>Seats</th>\n",
-       "      <th>Price</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
-       "      <td>Pune</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>41000</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
-       "      <td>First</td>\n",
-       "      <td>19.67 kmpl</td>\n",
-       "      <td>1582.0</td>\n",
-       "      <td>126.2 bhp</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>12.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Honda Jazz V</td>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>46000</td>\n",
-       "      <td>Petrol</td>\n",
-       "      <td>Manual</td>\n",
-       "      <td>First</td>\n",
-       "      <td>13 km/kg</td>\n",
-       "      <td>1199.0</td>\n",
-       "      <td>88.7 bhp</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Maruti Ertiga VDI</td>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>87000</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
-       "      <td>First</td>\n",
-       "      <td>20.77 kmpl</td>\n",
-       "      <td>1248.0</td>\n",
-       "      <td>88.76 bhp</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>6.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
-       "      <td>Coimbatore</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>40670</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Automatic</td>\n",
-       "      <td>Second</td>\n",
-       "      <td>15.2 kmpl</td>\n",
-       "      <td>1968.0</td>\n",
-       "      <td>140.8 bhp</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>17.74</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Nissan Micra Diesel XV</td>\n",
-       "      <td>Jaipur</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>86999</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
-       "      <td>First</td>\n",
-       "      <td>23.08 kmpl</td>\n",
-       "      <td>1461.0</td>\n",
-       "      <td>63.1 bhp</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.50</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Name    Location  Year  Kilometers_Driven  \\\n",
-       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
-       "2                      Honda Jazz V     Chennai  2011              46000   \n",
-       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
-       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
-       "6            Nissan Micra Diesel XV      Jaipur  2013              86999   \n",
-       "\n",
-       "  Fuel_Type Transmission Owner_Type     Mileage  Engine      Power  Seats  \\\n",
-       "1    Diesel       Manual      First  19.67 kmpl  1582.0  126.2 bhp    5.0   \n",
-       "2    Petrol       Manual      First    13 km/kg  1199.0   88.7 bhp    5.0   \n",
-       "3    Diesel       Manual      First  20.77 kmpl  1248.0  88.76 bhp    7.0   \n",
-       "4    Diesel    Automatic     Second   15.2 kmpl  1968.0  140.8 bhp    5.0   \n",
-       "6    Diesel       Manual      First  23.08 kmpl  1461.0   63.1 bhp    5.0   \n",
-       "\n",
-       "   Price  \n",
-       "1  12.50  \n",
-       "2   4.50  \n",
-       "3   6.00  \n",
-       "4  17.74  \n",
-       "6   3.50  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.845806Z",
@@ -751,7 +584,7 @@
        "6    Diesel       Manual      First    23.08  1461.0   63.1 bhp    5.0   3.50  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -772,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.884529Z",
@@ -782,30 +615,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:34.884478Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Owner_Type\n",
-       "First             4786\n",
-       "Second             913\n",
-       "Third              101\n",
-       "Fourth & Above       7\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Owner_Type.value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.901170Z",
@@ -960,7 +777,7 @@
        "6           1.0  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -976,7 +793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.933045Z",
@@ -1125,7 +942,7 @@
        "6           1.0  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1137,7 +954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.960194Z",
@@ -1155,7 +972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.974289Z",
@@ -1165,6 +982,40 @@
      "shell.execute_reply.started": "2023-10-25T11:24:34.974246Z"
     }
    },
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-25T11:24:34.997968Z",
+     "iopub.status.busy": "2023-10-25T11:24:34.997587Z",
+     "iopub.status.idle": "2023-10-25T11:24:35.008472Z",
+     "shell.execute_reply": "2023-10-25T11:24:35.006926Z",
+     "shell.execute_reply.started": "2023-10-25T11:24:34.997937Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df.Transmission.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-25T11:24:35.010363Z",
+     "iopub.status.busy": "2023-10-25T11:24:35.009965Z",
+     "iopub.status.idle": "2023-10-25T11:24:35.050378Z",
+     "shell.execute_reply": "2023-10-25T11:24:35.048962Z",
+     "shell.execute_reply.started": "2023-10-25T11:24:35.010325Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1191,14 +1042,16 @@
        "      <th>Location</th>\n",
        "      <th>Year</th>\n",
        "      <th>Kilometers_Driven</th>\n",
-       "      <th>Fuel_Type</th>\n",
-       "      <th>Transmission</th>\n",
        "      <th>Mileage</th>\n",
        "      <th>Engine</th>\n",
        "      <th>Power</th>\n",
        "      <th>Seats</th>\n",
        "      <th>Price</th>\n",
        "      <th>No_of_owners</th>\n",
+       "      <th>Transmission_Automatic</th>\n",
+       "      <th>Transmission_Manual</th>\n",
+       "      <th>Fuel_Type_Diesel</th>\n",
+       "      <th>Fuel_Type_Petrol</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1208,14 +1061,16 @@
        "      <td>Pune</td>\n",
        "      <td>2015</td>\n",
        "      <td>41000</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
        "      <td>19.67</td>\n",
        "      <td>1582.0</td>\n",
        "      <td>126.20</td>\n",
        "      <td>5.0</td>\n",
        "      <td>12.50</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1223,14 +1078,16 @@
        "      <td>Chennai</td>\n",
        "      <td>2011</td>\n",
        "      <td>46000</td>\n",
-       "      <td>Petrol</td>\n",
-       "      <td>Manual</td>\n",
        "      <td>18.20</td>\n",
        "      <td>1199.0</td>\n",
        "      <td>88.70</td>\n",
        "      <td>5.0</td>\n",
        "      <td>4.50</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1238,14 +1095,16 @@
        "      <td>Chennai</td>\n",
        "      <td>2012</td>\n",
        "      <td>87000</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
        "      <td>20.77</td>\n",
        "      <td>1248.0</td>\n",
        "      <td>88.76</td>\n",
        "      <td>7.0</td>\n",
        "      <td>6.00</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1253,208 +1112,16 @@
        "      <td>Coimbatore</td>\n",
        "      <td>2013</td>\n",
        "      <td>40670</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Automatic</td>\n",
        "      <td>15.20</td>\n",
        "      <td>1968.0</td>\n",
        "      <td>140.80</td>\n",
        "      <td>5.0</td>\n",
        "      <td>17.74</td>\n",
        "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Nissan Micra Diesel XV</td>\n",
-       "      <td>Jaipur</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>86999</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
-       "      <td>23.08</td>\n",
-       "      <td>1461.0</td>\n",
-       "      <td>63.10</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Name    Location  Year  Kilometers_Driven  \\\n",
-       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
-       "2                      Honda Jazz V     Chennai  2011              46000   \n",
-       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
-       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
-       "6            Nissan Micra Diesel XV      Jaipur  2013              86999   \n",
-       "\n",
-       "  Fuel_Type Transmission  Mileage  Engine   Power  Seats  Price  No_of_owners  \n",
-       "1    Diesel       Manual    19.67  1582.0  126.20    5.0  12.50           1.0  \n",
-       "2    Petrol       Manual    18.20  1199.0   88.70    5.0   4.50           1.0  \n",
-       "3    Diesel       Manual    20.77  1248.0   88.76    7.0   6.00           1.0  \n",
-       "4    Diesel    Automatic    15.20  1968.0  140.80    5.0  17.74           2.0  \n",
-       "6    Diesel       Manual    23.08  1461.0   63.10    5.0   3.50           1.0  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-10-25T11:24:34.997968Z",
-     "iopub.status.busy": "2023-10-25T11:24:34.997587Z",
-     "iopub.status.idle": "2023-10-25T11:24:35.008472Z",
-     "shell.execute_reply": "2023-10-25T11:24:35.006926Z",
-     "shell.execute_reply.started": "2023-10-25T11:24:34.997937Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Transmission\n",
-       "Manual       4105\n",
-       "Automatic    1702\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.Transmission.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-10-25T11:24:35.010363Z",
-     "iopub.status.busy": "2023-10-25T11:24:35.009965Z",
-     "iopub.status.idle": "2023-10-25T11:24:35.050378Z",
-     "shell.execute_reply": "2023-10-25T11:24:35.048962Z",
-     "shell.execute_reply.started": "2023-10-25T11:24:35.010325Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Location</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Kilometers_Driven</th>\n",
-       "      <th>Mileage</th>\n",
-       "      <th>Engine</th>\n",
-       "      <th>Power</th>\n",
-       "      <th>Seats</th>\n",
-       "      <th>Price</th>\n",
-       "      <th>No_of_owners</th>\n",
-       "      <th>Transmission_Automatic</th>\n",
-       "      <th>Transmission_Manual</th>\n",
-       "      <th>Fuel_Type_Diesel</th>\n",
-       "      <th>Fuel_Type_Petrol</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
-       "      <td>Pune</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>41000</td>\n",
-       "      <td>19.67</td>\n",
-       "      <td>1582.0</td>\n",
-       "      <td>126.20</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>12.50</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Honda Jazz V</td>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>46000</td>\n",
-       "      <td>18.20</td>\n",
-       "      <td>1199.0</td>\n",
-       "      <td>88.70</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Maruti Ertiga VDI</td>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>87000</td>\n",
-       "      <td>20.77</td>\n",
-       "      <td>1248.0</td>\n",
-       "      <td>88.76</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>6.00</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
-       "      <td>Coimbatore</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>40670</td>\n",
-       "      <td>15.20</td>\n",
-       "      <td>1968.0</td>\n",
-       "      <td>140.80</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>17.74</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -1507,7 +1174,7 @@
        "6             False  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1519,7 +1186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.052577Z",
@@ -1680,7 +1347,7 @@
        "6                 True              True             False   10  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1693,7 +1360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.092736Z",
@@ -1703,37 +1370,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:35.092700Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Name\n",
-       "Mahindra XUV500 W8 2WD                 49\n",
-       "Maruti Swift VDI                       45\n",
-       "Honda City 1.5 S MT                    34\n",
-       "Maruti Swift Dzire VDI                 34\n",
-       "Hyundai i10 Sportz                     30\n",
-       "                                       ..\n",
-       "Skoda Rapid 1.6 MPI AT Ambition         1\n",
-       "Hyundai i20 2015-2017 1.4 Magna ABS     1\n",
-       "Ford EcoSport 1.5 Petrol Trend          1\n",
-       "Volvo XC90 2007-2015 D5 AWD             1\n",
-       "Mahindra Xylo D4 BSIV                   1\n",
-       "Name: count, Length: 1785, dtype: int64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Name.value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.109327Z",
@@ -1751,7 +1395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.119797Z",
@@ -1761,163 +1405,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:35.119754Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Location</th>\n",
-       "      <th>Kilometers_Driven</th>\n",
-       "      <th>Mileage</th>\n",
-       "      <th>Engine</th>\n",
-       "      <th>Power</th>\n",
-       "      <th>Seats</th>\n",
-       "      <th>Price</th>\n",
-       "      <th>No_of_owners</th>\n",
-       "      <th>Transmission_Automatic</th>\n",
-       "      <th>Transmission_Manual</th>\n",
-       "      <th>Fuel_Type_Diesel</th>\n",
-       "      <th>Fuel_Type_Petrol</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Pune</td>\n",
-       "      <td>41000</td>\n",
-       "      <td>19.67</td>\n",
-       "      <td>1582.0</td>\n",
-       "      <td>126.20</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>12.50</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>46000</td>\n",
-       "      <td>18.20</td>\n",
-       "      <td>1199.0</td>\n",
-       "      <td>88.70</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>87000</td>\n",
-       "      <td>20.77</td>\n",
-       "      <td>1248.0</td>\n",
-       "      <td>88.76</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>6.00</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Coimbatore</td>\n",
-       "      <td>40670</td>\n",
-       "      <td>15.20</td>\n",
-       "      <td>1968.0</td>\n",
-       "      <td>140.80</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>17.74</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Jaipur</td>\n",
-       "      <td>86999</td>\n",
-       "      <td>23.08</td>\n",
-       "      <td>1461.0</td>\n",
-       "      <td>63.10</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Location  Kilometers_Driven  Mileage  Engine   Power  Seats  Price  \\\n",
-       "1        Pune              41000    19.67  1582.0  126.20    5.0  12.50   \n",
-       "2     Chennai              46000    18.20  1199.0   88.70    5.0   4.50   \n",
-       "3     Chennai              87000    20.77  1248.0   88.76    7.0   6.00   \n",
-       "4  Coimbatore              40670    15.20  1968.0  140.80    5.0  17.74   \n",
-       "6      Jaipur              86999    23.08  1461.0   63.10    5.0   3.50   \n",
-       "\n",
-       "   No_of_owners  Transmission_Automatic  Transmission_Manual  \\\n",
-       "1           1.0                   False                 True   \n",
-       "2           1.0                   False                 True   \n",
-       "3           1.0                   False                 True   \n",
-       "4           2.0                    True                False   \n",
-       "6           1.0                   False                 True   \n",
-       "\n",
-       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Age  \n",
-       "1              True             False    8  \n",
-       "2             False              True   12  \n",
-       "3              True             False   11  \n",
-       "4              True             False   10  \n",
-       "6              True             False   10  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.156223Z",
@@ -1927,37 +1422,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:35.156187Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Location\n",
-       "Mumbai        757\n",
-       "Hyderabad     709\n",
-       "Kochi         637\n",
-       "Coimbatore    629\n",
-       "Pune          581\n",
-       "Delhi         535\n",
-       "Kolkata       521\n",
-       "Chennai       474\n",
-       "Jaipur        400\n",
-       "Bangalore     347\n",
-       "Ahmedabad     217\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Location.value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.169687Z",
@@ -2177,7 +1649,7 @@
        "[5 rows x 23 columns]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2190,7 +1662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.219105Z",
@@ -2208,7 +1680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:36.364741Z",
@@ -2218,25 +1690,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:36.364696Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5807, 23)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:36.376612Z",
@@ -2254,7 +1715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:36.403139Z",
@@ -2272,7 +1733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:36.423402Z",
@@ -2292,7 +1753,7 @@
        "DecisionTreeRegressor()"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2303,26 +1764,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 6.    5.1  11.52 ...  4.3  29.    2.6 ]\n"
+      "[39.45  2.5   4.9  ...  8.3   4.   27.  ]\n"
      ]
     }
    ],
    "source": [
-    "# fix --------- changed the format of this cell from NBConvert to Code\n",
+    "# fix --------- changed the format of this cell from NBConvert to Code, and execute it\n",
     "y_pred = model.predict(X_test)\n",
     "print(y_pred)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 19,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:36.479677Z",
@@ -2336,10 +1797,10 @@
     {
      "data": {
       "text/plain": [
-       "1.9310499139414803"
+       "1.8813339070567983"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }

benchmark/NBspecific_18/NBspecific_18_reproduced.ipynb

@@ -212,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.724205Z",
@@ -222,38 +222,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:34.724137Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Name                    0\n",
-       "Location                0\n",
-       "Year                    0\n",
-       "Kilometers_Driven       0\n",
-       "Fuel_Type               0\n",
-       "Transmission            0\n",
-       "Owner_Type              0\n",
-       "Mileage                 2\n",
-       "Engine                 36\n",
-       "Power                  36\n",
-       "Seats                  38\n",
-       "New_Price            5032\n",
-       "Price                   0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.isna().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.747718Z",
@@ -270,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.759838Z",
@@ -419,7 +395,7 @@
        "6   3.50  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -431,7 +407,7 @@
   },
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    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.797282Z",
@@ -449,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.811483Z",
@@ -459,157 +435,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:34.811433Z"
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-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Location</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Kilometers_Driven</th>\n",
-       "      <th>Fuel_Type</th>\n",
-       "      <th>Transmission</th>\n",
-       "      <th>Owner_Type</th>\n",
-       "      <th>Mileage</th>\n",
-       "      <th>Engine</th>\n",
-       "      <th>Power</th>\n",
-       "      <th>Seats</th>\n",
-       "      <th>Price</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
-       "      <td>Pune</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>41000</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
-       "      <td>First</td>\n",
-       "      <td>19.67 kmpl</td>\n",
-       "      <td>1582.0</td>\n",
-       "      <td>126.2 bhp</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>12.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Honda Jazz V</td>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>46000</td>\n",
-       "      <td>Petrol</td>\n",
-       "      <td>Manual</td>\n",
-       "      <td>First</td>\n",
-       "      <td>13 km/kg</td>\n",
-       "      <td>1199.0</td>\n",
-       "      <td>88.7 bhp</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Maruti Ertiga VDI</td>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>87000</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
-       "      <td>First</td>\n",
-       "      <td>20.77 kmpl</td>\n",
-       "      <td>1248.0</td>\n",
-       "      <td>88.76 bhp</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>6.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
-       "      <td>Coimbatore</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>40670</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Automatic</td>\n",
-       "      <td>Second</td>\n",
-       "      <td>15.2 kmpl</td>\n",
-       "      <td>1968.0</td>\n",
-       "      <td>140.8 bhp</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>17.74</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Nissan Micra Diesel XV</td>\n",
-       "      <td>Jaipur</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>86999</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
-       "      <td>First</td>\n",
-       "      <td>23.08 kmpl</td>\n",
-       "      <td>1461.0</td>\n",
-       "      <td>63.1 bhp</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.50</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Name    Location  Year  Kilometers_Driven  \\\n",
-       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
-       "2                      Honda Jazz V     Chennai  2011              46000   \n",
-       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
-       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
-       "6            Nissan Micra Diesel XV      Jaipur  2013              86999   \n",
-       "\n",
-       "  Fuel_Type Transmission Owner_Type     Mileage  Engine      Power  Seats  \\\n",
-       "1    Diesel       Manual      First  19.67 kmpl  1582.0  126.2 bhp    5.0   \n",
-       "2    Petrol       Manual      First    13 km/kg  1199.0   88.7 bhp    5.0   \n",
-       "3    Diesel       Manual      First  20.77 kmpl  1248.0  88.76 bhp    7.0   \n",
-       "4    Diesel    Automatic     Second   15.2 kmpl  1968.0  140.8 bhp    5.0   \n",
-       "6    Diesel       Manual      First  23.08 kmpl  1461.0   63.1 bhp    5.0   \n",
-       "\n",
-       "   Price  \n",
-       "1  12.50  \n",
-       "2   4.50  \n",
-       "3   6.00  \n",
-       "4  17.74  \n",
-       "6   3.50  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.845806Z",
@@ -751,7 +584,7 @@
        "6    Diesel       Manual      First    23.08  1461.0   63.1 bhp    5.0   3.50  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -772,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.884529Z",
@@ -782,30 +615,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:34.884478Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Owner_Type\n",
-       "First             4786\n",
-       "Second             913\n",
-       "Third              101\n",
-       "Fourth & Above       7\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Owner_Type.value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.901170Z",
@@ -960,7 +777,7 @@
        "6           1.0  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -976,7 +793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.933045Z",
@@ -1125,7 +942,7 @@
        "6           1.0  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1137,7 +954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.960194Z",
@@ -1155,7 +972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:34.974289Z",
@@ -1165,6 +982,40 @@
      "shell.execute_reply.started": "2023-10-25T11:24:34.974246Z"
     }
    },
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-25T11:24:34.997968Z",
+     "iopub.status.busy": "2023-10-25T11:24:34.997587Z",
+     "iopub.status.idle": "2023-10-25T11:24:35.008472Z",
+     "shell.execute_reply": "2023-10-25T11:24:35.006926Z",
+     "shell.execute_reply.started": "2023-10-25T11:24:34.997937Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df.Transmission.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-25T11:24:35.010363Z",
+     "iopub.status.busy": "2023-10-25T11:24:35.009965Z",
+     "iopub.status.idle": "2023-10-25T11:24:35.050378Z",
+     "shell.execute_reply": "2023-10-25T11:24:35.048962Z",
+     "shell.execute_reply.started": "2023-10-25T11:24:35.010325Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1191,14 +1042,16 @@
        "      <th>Location</th>\n",
        "      <th>Year</th>\n",
        "      <th>Kilometers_Driven</th>\n",
-       "      <th>Fuel_Type</th>\n",
-       "      <th>Transmission</th>\n",
        "      <th>Mileage</th>\n",
        "      <th>Engine</th>\n",
        "      <th>Power</th>\n",
        "      <th>Seats</th>\n",
        "      <th>Price</th>\n",
        "      <th>No_of_owners</th>\n",
+       "      <th>Transmission_Automatic</th>\n",
+       "      <th>Transmission_Manual</th>\n",
+       "      <th>Fuel_Type_Diesel</th>\n",
+       "      <th>Fuel_Type_Petrol</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1208,14 +1061,16 @@
        "      <td>Pune</td>\n",
        "      <td>2015</td>\n",
        "      <td>41000</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
        "      <td>19.67</td>\n",
        "      <td>1582.0</td>\n",
        "      <td>126.20</td>\n",
        "      <td>5.0</td>\n",
        "      <td>12.50</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1223,14 +1078,16 @@
        "      <td>Chennai</td>\n",
        "      <td>2011</td>\n",
        "      <td>46000</td>\n",
-       "      <td>Petrol</td>\n",
-       "      <td>Manual</td>\n",
        "      <td>18.20</td>\n",
        "      <td>1199.0</td>\n",
        "      <td>88.70</td>\n",
        "      <td>5.0</td>\n",
        "      <td>4.50</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1238,14 +1095,16 @@
        "      <td>Chennai</td>\n",
        "      <td>2012</td>\n",
        "      <td>87000</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
        "      <td>20.77</td>\n",
        "      <td>1248.0</td>\n",
        "      <td>88.76</td>\n",
        "      <td>7.0</td>\n",
        "      <td>6.00</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1253,208 +1112,16 @@
        "      <td>Coimbatore</td>\n",
        "      <td>2013</td>\n",
        "      <td>40670</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Automatic</td>\n",
        "      <td>15.20</td>\n",
        "      <td>1968.0</td>\n",
        "      <td>140.80</td>\n",
        "      <td>5.0</td>\n",
        "      <td>17.74</td>\n",
        "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Nissan Micra Diesel XV</td>\n",
-       "      <td>Jaipur</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>86999</td>\n",
-       "      <td>Diesel</td>\n",
-       "      <td>Manual</td>\n",
-       "      <td>23.08</td>\n",
-       "      <td>1461.0</td>\n",
-       "      <td>63.10</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               Name    Location  Year  Kilometers_Driven  \\\n",
-       "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
-       "2                      Honda Jazz V     Chennai  2011              46000   \n",
-       "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
-       "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
-       "6            Nissan Micra Diesel XV      Jaipur  2013              86999   \n",
-       "\n",
-       "  Fuel_Type Transmission  Mileage  Engine   Power  Seats  Price  No_of_owners  \n",
-       "1    Diesel       Manual    19.67  1582.0  126.20    5.0  12.50           1.0  \n",
-       "2    Petrol       Manual    18.20  1199.0   88.70    5.0   4.50           1.0  \n",
-       "3    Diesel       Manual    20.77  1248.0   88.76    7.0   6.00           1.0  \n",
-       "4    Diesel    Automatic    15.20  1968.0  140.80    5.0  17.74           2.0  \n",
-       "6    Diesel       Manual    23.08  1461.0   63.10    5.0   3.50           1.0  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-10-25T11:24:34.997968Z",
-     "iopub.status.busy": "2023-10-25T11:24:34.997587Z",
-     "iopub.status.idle": "2023-10-25T11:24:35.008472Z",
-     "shell.execute_reply": "2023-10-25T11:24:35.006926Z",
-     "shell.execute_reply.started": "2023-10-25T11:24:34.997937Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Transmission\n",
-       "Manual       4105\n",
-       "Automatic    1702\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.Transmission.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-10-25T11:24:35.010363Z",
-     "iopub.status.busy": "2023-10-25T11:24:35.009965Z",
-     "iopub.status.idle": "2023-10-25T11:24:35.050378Z",
-     "shell.execute_reply": "2023-10-25T11:24:35.048962Z",
-     "shell.execute_reply.started": "2023-10-25T11:24:35.010325Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Location</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Kilometers_Driven</th>\n",
-       "      <th>Mileage</th>\n",
-       "      <th>Engine</th>\n",
-       "      <th>Power</th>\n",
-       "      <th>Seats</th>\n",
-       "      <th>Price</th>\n",
-       "      <th>No_of_owners</th>\n",
-       "      <th>Transmission_Automatic</th>\n",
-       "      <th>Transmission_Manual</th>\n",
-       "      <th>Fuel_Type_Diesel</th>\n",
-       "      <th>Fuel_Type_Petrol</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
-       "      <td>Pune</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>41000</td>\n",
-       "      <td>19.67</td>\n",
-       "      <td>1582.0</td>\n",
-       "      <td>126.20</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>12.50</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Honda Jazz V</td>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>46000</td>\n",
-       "      <td>18.20</td>\n",
-       "      <td>1199.0</td>\n",
-       "      <td>88.70</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Maruti Ertiga VDI</td>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>87000</td>\n",
-       "      <td>20.77</td>\n",
-       "      <td>1248.0</td>\n",
-       "      <td>88.76</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>6.00</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
-       "      <td>Coimbatore</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>40670</td>\n",
-       "      <td>15.20</td>\n",
-       "      <td>1968.0</td>\n",
-       "      <td>140.80</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>17.74</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -1507,7 +1174,7 @@
        "6             False  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1519,7 +1186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.052577Z",
@@ -1680,7 +1347,7 @@
        "6                 True              True             False   10  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1693,7 +1360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.092736Z",
@@ -1703,37 +1370,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:35.092700Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Name\n",
-       "Mahindra XUV500 W8 2WD                 49\n",
-       "Maruti Swift VDI                       45\n",
-       "Honda City 1.5 S MT                    34\n",
-       "Maruti Swift Dzire VDI                 34\n",
-       "Hyundai i10 Sportz                     30\n",
-       "                                       ..\n",
-       "Skoda Rapid 1.6 MPI AT Ambition         1\n",
-       "Hyundai i20 2015-2017 1.4 Magna ABS     1\n",
-       "Ford EcoSport 1.5 Petrol Trend          1\n",
-       "Volvo XC90 2007-2015 D5 AWD             1\n",
-       "Mahindra Xylo D4 BSIV                   1\n",
-       "Name: count, Length: 1785, dtype: int64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Name.value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.109327Z",
@@ -1751,7 +1395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.119797Z",
@@ -1761,163 +1405,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:35.119754Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Location</th>\n",
-       "      <th>Kilometers_Driven</th>\n",
-       "      <th>Mileage</th>\n",
-       "      <th>Engine</th>\n",
-       "      <th>Power</th>\n",
-       "      <th>Seats</th>\n",
-       "      <th>Price</th>\n",
-       "      <th>No_of_owners</th>\n",
-       "      <th>Transmission_Automatic</th>\n",
-       "      <th>Transmission_Manual</th>\n",
-       "      <th>Fuel_Type_Diesel</th>\n",
-       "      <th>Fuel_Type_Petrol</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Pune</td>\n",
-       "      <td>41000</td>\n",
-       "      <td>19.67</td>\n",
-       "      <td>1582.0</td>\n",
-       "      <td>126.20</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>12.50</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>46000</td>\n",
-       "      <td>18.20</td>\n",
-       "      <td>1199.0</td>\n",
-       "      <td>88.70</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.50</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Chennai</td>\n",
-       "      <td>87000</td>\n",
-       "      <td>20.77</td>\n",
-       "      <td>1248.0</td>\n",
-       "      <td>88.76</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>6.00</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Coimbatore</td>\n",
-       "      <td>40670</td>\n",
-       "      <td>15.20</td>\n",
-       "      <td>1968.0</td>\n",
-       "      <td>140.80</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>17.74</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Jaipur</td>\n",
-       "      <td>86999</td>\n",
-       "      <td>23.08</td>\n",
-       "      <td>1461.0</td>\n",
-       "      <td>63.10</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Location  Kilometers_Driven  Mileage  Engine   Power  Seats  Price  \\\n",
-       "1        Pune              41000    19.67  1582.0  126.20    5.0  12.50   \n",
-       "2     Chennai              46000    18.20  1199.0   88.70    5.0   4.50   \n",
-       "3     Chennai              87000    20.77  1248.0   88.76    7.0   6.00   \n",
-       "4  Coimbatore              40670    15.20  1968.0  140.80    5.0  17.74   \n",
-       "6      Jaipur              86999    23.08  1461.0   63.10    5.0   3.50   \n",
-       "\n",
-       "   No_of_owners  Transmission_Automatic  Transmission_Manual  \\\n",
-       "1           1.0                   False                 True   \n",
-       "2           1.0                   False                 True   \n",
-       "3           1.0                   False                 True   \n",
-       "4           2.0                    True                False   \n",
-       "6           1.0                   False                 True   \n",
-       "\n",
-       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Age  \n",
-       "1              True             False    8  \n",
-       "2             False              True   12  \n",
-       "3              True             False   11  \n",
-       "4              True             False   10  \n",
-       "6              True             False   10  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.156223Z",
@@ -1927,37 +1422,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:35.156187Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Location\n",
-       "Mumbai        757\n",
-       "Hyderabad     709\n",
-       "Kochi         637\n",
-       "Coimbatore    629\n",
-       "Pune          581\n",
-       "Delhi         535\n",
-       "Kolkata       521\n",
-       "Chennai       474\n",
-       "Jaipur        400\n",
-       "Bangalore     347\n",
-       "Ahmedabad     217\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.Location.value_counts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.169687Z",
@@ -2177,7 +1649,7 @@
        "[5 rows x 23 columns]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2190,7 +1662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 14,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:35.219105Z",
@@ -2208,7 +1680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:36.364741Z",
@@ -2218,25 +1690,14 @@
      "shell.execute_reply.started": "2023-10-25T11:24:36.364696Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5807, 23)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 15,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:36.376612Z",
@@ -2254,7 +1715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 16,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:36.403139Z",
@@ -2272,7 +1733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 17,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:36.423402Z",
@@ -2292,7 +1753,7 @@
        "DecisionTreeRegressor()"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2311,7 +1772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 18,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-10-25T11:24:36.479677Z",
@@ -2329,7 +1790,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-27-f778b0ff9eb9>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmae\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\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      3\u001b[0m \u001b[0mmae\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-18-f778b0ff9eb9>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmae\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\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      3\u001b[0m \u001b[0mmae\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'y_pred' is not defined"
      ]
     }

benchmark/NBspecific_19/NBspecific_19_fixed.ipynb

@@ -14,8 +14,173 @@
      "shell.execute_reply.started": "2023-10-03T10:17:38.056796Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data_small/trainLabels.csv\n",
+      "data_small/test/1.png\n",
+      "data_small/test/10.png\n",
+      "data_small/test/100.png\n",
+      "data_small/test/1000.png\n",
+      "data_small/test/10000.png\n",
+      "data_small/test/100000.png\n",
+      "data_small/test/100001.png\n",
+      "data_small/test/100002.png\n",
+      "data_small/test/100003.png\n",
+      "data_small/test/100004.png\n",
+      "data_small/test/100005.png\n",
+      "data_small/test/100006.png\n",
+      "data_small/test/100007.png\n",
+      "data_small/test/100008.png\n",
+      "data_small/test/100009.png\n",
+      "data_small/test/10001.png\n",
+      "data_small/test/100010.png\n",
+      "data_small/test/100011.png\n",
+      "data_small/test/100012.png\n",
+      "data_small/test/100013.png\n",
+      "data_small/test/100014.png\n",
+      "data_small/test/100015.png\n",
+      "data_small/test/100016.png\n",
+      "data_small/test/100017.png\n",
+      "data_small/test/100018.png\n",
+      "data_small/test/100019.png\n",
+      "data_small/test/10002.png\n",
+      "data_small/test/100020.png\n",
+      "data_small/test/100021.png\n",
+      "data_small/test/100022.png\n",
+      "data_small/test/100023.png\n",
+      "data_small/test/100024.png\n",
+      "data_small/test/100025.png\n",
+      "data_small/train/1.png\n",
+      "data_small/train/10.png\n",
+      "data_small/train/100.png\n",
+      "data_small/train/1000.png\n",
+      "data_small/train/10000.png\n",
+      "data_small/train/10001.png\n",
+      "data_small/train/10002.png\n",
+      "data_small/train/10003.png\n",
+      "data_small/train/10004.png\n",
+      "data_small/train/10005.png\n",
+      "data_small/train/10006.png\n",
+      "data_small/train/10007.png\n",
+      "data_small/train/10008.png\n",
+      "data_small/train/10009.png\n",
+      "data_small/train/1001.png\n",
+      "data_small/train/10010.png\n",
+      "data_small/train/10011.png\n",
+      "data_small/train/10012.png\n",
+      "data_small/train/10013.png\n",
+      "data_small/train/10014.png\n",
+      "data_small/train/10015.png\n",
+      "data_small/train/10016.png\n",
+      "data_small/train/10017.png\n",
+      "data_small/train/10018.png\n",
+      "data_small/train/10019.png\n",
+      "data_small/train/1002.png\n",
+      "data_small/train/10020.png\n",
+      "data_small/train/10021.png\n",
+      "data_small/train/10022.png\n",
+      "data_small/train/10023.png\n",
+      "data_small/train/10024.png\n",
+      "data_small/train/10025.png\n",
+      "data_small/train/10026.png\n",
+      "data_small/train/10027.png\n",
+      "data_small/train/10028.png\n",
+      "data_small/train/10029.png\n",
+      "data_small/train/1003.png\n",
+      "data_small/train/10030.png\n",
+      "data_small/train/10031.png\n",
+      "data_small/train/10032.png\n",
+      "data_small/train/10033.png\n",
+      "data_small/train/10034.png\n",
+      "data_small/train/10035.png\n",
+      "data_small/train/10036.png\n",
+      "data_small/train/10037.png\n",
+      "data_small/train/10038.png\n",
+      "data_small/train/10039.png\n",
+      "data_small/train/1004.png\n",
+      "data_small/train/10040.png\n",
+      "data_small/train/10041.png\n",
+      "data_small/train/10042.png\n",
+      "data_small/train/10043.png\n",
+      "data_small/train/10044.png\n",
+      "data_small/train/10045.png\n",
+      "data_small/train/10046.png\n",
+      "data_small/train/10047.png\n",
+      "data_small/train/10048.png\n",
+      "data_small/train/10049.png\n",
+      "data_small/train/1005.png\n",
+      "data_small/train/10050.png\n",
+      "data_small/train/10051.png\n",
+      "data_small/train/10052.png\n",
+      "data_small/train/10053.png\n",
+      "data_small/train/10054.png\n",
+      "data_small/train/10055.png\n",
+      "data_small/train/10056.png\n",
+      "data_small/train/10057.png\n",
+      "data_small/train/10058.png\n",
+      "data_small/train/10059.png\n",
+      "data_small/train/1006.png\n",
+      "data_small/train/10060.png\n",
+      "data_small/train/10061.png\n",
+      "data_small/train/10062.png\n",
+      "data_small/train/10063.png\n",
+      "data_small/train/10064.png\n",
+      "data_small/train/10065.png\n",
+      "data_small/train/10066.png\n",
+      "data_small/train/10067.png\n",
+      "data_small/train/10068.png\n",
+      "data_small/train/10069.png\n",
+      "data_small/train/1007.png\n",
+      "data_small/train/10070.png\n",
+      "data_small/train/10071.png\n",
+      "data_small/train/10072.png\n",
+      "data_small/train/10073.png\n",
+      "data_small/train/10074.png\n",
+      "data_small/train/10075.png\n",
+      "data_small/train/10076.png\n",
+      "data_small/train/10077.png\n",
+      "data_small/train/10078.png\n",
+      "data_small/train/10079.png\n",
+      "data_small/train/1008.png\n",
+      "data_small/train/10080.png\n",
+      "data_small/train/10081.png\n",
+      "data_small/train/10082.png\n",
+      "data_small/train/10083.png\n",
+      "data_small/train/10084.png\n",
+      "data_small/train/10085.png\n",
+      "data_small/train/10086.png\n",
+      "data_small/train/10087.png\n",
+      "data_small/train/10088.png\n",
+      "data_small/train/10089.png\n",
+      "data_small/train/1009.png\n",
+      "data_small/train/10090.png\n",
+      "data_small/train/10091.png\n",
+      "data_small/train/10092.png\n",
+      "data_small/train/10093.png\n",
+      "data_small/train/10094.png\n",
+      "data_small/train/10095.png\n",
+      "data_small/train/10096.png\n",
+      "data_small/train/10097.png\n",
+      "data_small/train/10098.png\n",
+      "data_small/train/10099.png\n",
+      "data_small/train/101.png\n",
+      "data_small/train/1010.png\n",
+      "data_small/train/10100.png\n",
+      "data_small/train/10101.png\n",
+      "data_small/train/10102.png\n",
+      "data_small/train/10103.png\n",
+      "data_small/train/10104.png\n",
+      "data_small/train/10105.png\n",
+      "data_small/train/10106.png\n",
+      "data_small/train/10107.png\n"
+     ]
+    }
+   ],
    "source": [
+    "# fix --- execute this cell\n",
     "# This Python 3 environment comes with many helpful analytics libraries installed\n",
     "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
     "# For example, here's several helpful packages to load\n",
@@ -198,6 +363,7 @@
     }
    ],
    "source": [
+    "# fix --- execute this cell\n",
     "# Load train labels\n",
     "train_labels_df = pd.read_csv('data_small/trainLabels.csv')\n",
     "train_labels_df"
@@ -226,6 +392,7 @@
     }
    ],
    "source": [
+    "# fix --- execute this cell\n",
     "# Preprocess the Data\n",
     "from sklearn.preprocessing import OneHotEncoder\n",
     "from PIL import Image\n",
@@ -286,6 +453,7 @@
     }
    ],
    "source": [
+    "# fix --- execute this cell\n",
     "# Build the MLP Model in PyTorch\n",
     "import torch\n",
     "import torch.nn as nn\n",
@@ -326,7 +494,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -338,11 +506,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch [20/20], Loss: 2.1422\n"
+      "Epoch [20/20], Loss: 2.1659\n"
      ]
     }
    ],
    "source": [
+    "# fix --- execute this cell\n",
     "import matplotlib.pyplot as plt\n",
     "from IPython import display\n",
     "\n",

benchmark/NBspecific_2/NBspecific_2_fixed.ipynb

@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:20.618616Z",
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:20.631972Z",
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:20.642705Z",
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:25.915500Z",
@@ -182,214 +182,14 @@
      "shell.execute_reply.started": "2023-03-16T09:27:25.915472Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>label</th>\n",
-       "      <th>pixel1</th>\n",
-       "      <th>pixel2</th>\n",
-       "      <th>pixel3</th>\n",
-       "      <th>pixel4</th>\n",
-       "      <th>pixel5</th>\n",
-       "      <th>pixel6</th>\n",
-       "      <th>pixel7</th>\n",
-       "      <th>pixel8</th>\n",
-       "      <th>pixel9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>pixel775</th>\n",
-       "      <th>pixel776</th>\n",
-       "      <th>pixel777</th>\n",
-       "      <th>pixel778</th>\n",
-       "      <th>pixel779</th>\n",
-       "      <th>pixel780</th>\n",
-       "      <th>pixel781</th>\n",
-       "      <th>pixel782</th>\n",
-       "      <th>pixel783</th>\n",
-       "      <th>pixel784</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>9</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>43</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 785 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
-       "0      2       0       0       0       0       0       0       0       0   \n",
-       "1      9       0       0       0       0       0       0       0       0   \n",
-       "2      6       0       0       0       0       0       0       0       5   \n",
-       "3      0       0       0       0       1       2       0       0       0   \n",
-       "4      3       0       0       0       0       0       0       0       0   \n",
-       "\n",
-       "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
-       "0       0  ...         0         0         0         0         0         0   \n",
-       "1       0  ...         0         0         0         0         0         0   \n",
-       "2       0  ...         0         0         0        30        43         0   \n",
-       "3       0  ...         3         0         0         0         0         1   \n",
-       "4       0  ...         0         0         0         0         0         0   \n",
-       "\n",
-       "   pixel781  pixel782  pixel783  pixel784  \n",
-       "0         0         0         0         0  \n",
-       "1         0         0         0         0  \n",
-       "2         0         0         0         0  \n",
-       "3         0         0         0         0  \n",
-       "4         0         0         0         0  \n",
-       "\n",
-       "[5 rows x 785 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:25.945744Z",
@@ -414,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:25.952561Z",
@@ -424,15 +224,7 @@
      "shell.execute_reply.started": "2023-03-16T09:27:25.952452Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pullover\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(labels[df.label[0]])"
    ]
@@ -494,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:27.779136Z",
@@ -512,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:27.895472Z",
@@ -529,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:28.047020Z",
@@ -546,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:28.053553Z",
@@ -558,6 +350,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix --- execute this cell\n",
     "from sklearn.model_selection import train_test_split\n",
     "\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
@@ -715,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:30:30.982098Z",
@@ -732,7 +525,7 @@
        "0.7139166666666666"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -740,7 +533,7 @@
    "source": [
     "from xgboost import XGBClassifier\n",
     "\n",
-    "# fix -------- using cpu instead (and change settings) for reproducing and fixing purposes\n",
+    "# fix for reproducing and fixing purposes, using cpu instead (and change settings)\n",
     "# classifier = XGBClassifier(tree_method='gpu_hist', gpu_id=0)\n",
     "classifier = XGBClassifier(tree_method=\"hist\", n_estimators=2, max_depth=2)\n",
     "\n",

benchmark/NBspecific_2/NBspecific_2_reproduced.ipynb

@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:20.618616Z",
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:20.631972Z",
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:20.642705Z",
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:25.915500Z",
@@ -182,214 +182,14 @@
      "shell.execute_reply.started": "2023-03-16T09:27:25.915472Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>label</th>\n",
-       "      <th>pixel1</th>\n",
-       "      <th>pixel2</th>\n",
-       "      <th>pixel3</th>\n",
-       "      <th>pixel4</th>\n",
-       "      <th>pixel5</th>\n",
-       "      <th>pixel6</th>\n",
-       "      <th>pixel7</th>\n",
-       "      <th>pixel8</th>\n",
-       "      <th>pixel9</th>\n",
-       "      <th>...</th>\n",
-       "      <th>pixel775</th>\n",
-       "      <th>pixel776</th>\n",
-       "      <th>pixel777</th>\n",
-       "      <th>pixel778</th>\n",
-       "      <th>pixel779</th>\n",
-       "      <th>pixel780</th>\n",
-       "      <th>pixel781</th>\n",
-       "      <th>pixel782</th>\n",
-       "      <th>pixel783</th>\n",
-       "      <th>pixel784</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>9</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>43</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 785 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
-       "0      2       0       0       0       0       0       0       0       0   \n",
-       "1      9       0       0       0       0       0       0       0       0   \n",
-       "2      6       0       0       0       0       0       0       0       5   \n",
-       "3      0       0       0       0       1       2       0       0       0   \n",
-       "4      3       0       0       0       0       0       0       0       0   \n",
-       "\n",
-       "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
-       "0       0  ...         0         0         0         0         0         0   \n",
-       "1       0  ...         0         0         0         0         0         0   \n",
-       "2       0  ...         0         0         0        30        43         0   \n",
-       "3       0  ...         3         0         0         0         0         1   \n",
-       "4       0  ...         0         0         0         0         0         0   \n",
-       "\n",
-       "   pixel781  pixel782  pixel783  pixel784  \n",
-       "0         0         0         0         0  \n",
-       "1         0         0         0         0  \n",
-       "2         0         0         0         0  \n",
-       "3         0         0         0         0  \n",
-       "4         0         0         0         0  \n",
-       "\n",
-       "[5 rows x 785 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:25.945744Z",
@@ -414,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:25.952561Z",
@@ -424,15 +224,7 @@
      "shell.execute_reply.started": "2023-03-16T09:27:25.952452Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pullover\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(labels[df.label[0]])"
    ]
@@ -494,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:27.779136Z",
@@ -512,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:27:27.895472Z",
@@ -715,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-03-16T09:30:30.982098Z",
@@ -733,7 +525,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-fbd99a22b7bd>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mclassifier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtree_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gpu_hist'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpu_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\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      6\u001b[0m \u001b[0my_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_hat\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-6-fbd99a22b7bd>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mclassifier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtree_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gpu_hist'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpu_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\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      6\u001b[0m \u001b[0my_hat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_hat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
      ]
     }

benchmark/NBspecific_20/NBspecific_20_fixed.ipynb

@@ -221,7 +221,7 @@
     {
      "data": {
       "text/plain": [
-       "<statsmodels.tsa.seasonal.DecomposeResult at 0x7fe82a688cd0>"
+       "<statsmodels.tsa.seasonal.DecomposeResult at 0x72fad5090fd0>"
       ]
      },
      "execution_count": 7,
@@ -268,6 +268,7 @@
     }
    ],
    "source": [
+    "# fix --- execute this cell beforehand\n",
     "multiplicative_model, additive_model = decompose(data, 'Open')"
    ]
   },

benchmark/NBspecific_3/NBspecific_3_fixed.ipynb

@@ -1172,7 +1172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-19T06:46:16.944366Z",
@@ -1182,16 +1182,7 @@
      "shell.execute_reply.started": "2023-02-19T06:46:16.944335Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(272, 6)\n",
-      "(68, 6)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(X_train.shape)\n",
     "print(X_test.shape)"
@@ -1199,7 +1190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-19T06:46:17.964019Z",
@@ -1209,15 +1200,7 @@
      "shell.execute_reply.started": "2023-02-19T06:46:17.963980Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(272,) (272, 6)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "X_train=X_train\n",
     "print(y_train.shape,X_train.shape)"
@@ -1225,7 +1208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-19T06:46:18.564017Z",
@@ -1245,7 +1228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-19T06:49:05.697057Z",
@@ -1285,7 +1268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-19T06:51:37.728006Z",
@@ -1295,27 +1278,7 @@
      "shell.execute_reply.started": "2023-02-19T06:51:37.727965Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "F统计量：2.2858\n",
-      "F检验的P值：3.6126e-02\n",
-      "R2：0.0492\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "m = X_train.shape[1]\n",
     "n = X_train.shape[0]\n",
@@ -1341,7 +1304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-18T13:58:40.224476Z",
@@ -1366,6 +1329,7 @@
     }
    ],
    "source": [
+    "# fix --- execute this cell\n",
     "model2 = LinearRegression() # 定义模型\n",
     "model2.fit(X_train, y_train) # 训练模型\n",
     "yFit = model2.predict(X_test)  # 用回归模型来预测输出\n",
@@ -1381,7 +1345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-18T14:04:46.849588Z",

benchmark/NBspecific_3/NBspecific_3_reproduced.ipynb

@@ -1172,7 +1172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-19T06:46:16.944366Z",
@@ -1182,16 +1182,7 @@
      "shell.execute_reply.started": "2023-02-19T06:46:16.944335Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(272, 6)\n",
-      "(68, 6)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(X_train.shape)\n",
     "print(X_test.shape)"
@@ -1199,7 +1190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-19T06:46:17.964019Z",
@@ -1209,15 +1200,7 @@
      "shell.execute_reply.started": "2023-02-19T06:46:17.963980Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(272,) (272, 6)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "X_train=X_train\n",
     "print(y_train.shape,X_train.shape)"
@@ -1225,7 +1208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-19T06:46:18.564017Z",
@@ -1245,7 +1228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-19T06:49:05.697057Z",
@@ -1285,7 +1268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-19T06:51:37.728006Z",
@@ -1295,27 +1278,7 @@
      "shell.execute_reply.started": "2023-02-19T06:51:37.727965Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "F统计量：2.2858\n",
-      "F检验的P值：3.6126e-02\n",
-      "R2：0.0492\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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e3t6oVq0aVqxYgUqVKiE4OBg1atRAjRo1UL9+fQDA4MGDERERAVdXV9P+Z5f69eujV69emDZtGu7evWsKS3b27FkAuWNVzowuXbrg888/R//+/dG0aVMcP34cS5cu1XQsBCh83syZM9G7d28MGTIExYoVw9KlS+Hl5aWpu4uLC+bPn4+OHTuievXq6N+/P0qUKIEbN25g69atCAgIwO+///7I95Nh8hIseBmmAKO3XgrUSRWyy5w5c1C/fn3MnTsXH3/8Mdzc3BAeHo6XX34ZTz75JADqoLNu3Tq89957+PTTT1GoUCG8/PLLaNu2rckylxvYUzcA+Oqrr+Dj44N58+Zh06ZNaNKkCf7++280a9bMJDoy48UXX0Tx4sXx5Zdf4uuvv0ZKSgpKlCiB5s2bmyJOlCpVCr1798bmzZuxZMkSuLm5oUqVKvj555/Rq1cvACTUDxw4gOXLlyMyMhKBgYFo2LAhli5datYhS0316tWxc+dOjBw5EhMnToTRaESjRo3w008/mcXgfVTMnz8fgwYNwrBhw5CamorRo0ejRo0a6NmzJwYNGoTly5fjp59+gqIoORa8APDjjz+iaNGi+N///odVq1ahXbt2WLFiBSpXrmz3eXQkH3/8MRISErBs2TKsWLEC9erVwx9//IERI0ZolhNxqQcNGoTp06fDz88Pffr0QdOmTdGrVy9N3Vu1aoW9e/di3LhxmDlzJuLj41G0aFE0atQIb7755qPeRYbJcxiUvNoTgWEYJg9y//59FCpUCOPHj8cnn3zi7Oow2eTo0aOoW7cufvrpJ1MGvseFadOmYdiwYbh+/TpKlCjh7OowzGMB+/AyDMNYISkpyWzetGnTAJBFjXk8sHYeXVxcNJ0l8yL6uicnJ2Pu3LmoWLEii12GyQLs0sAwDGOFFStWYNGiRejUqRP8/Pywa9cu/O9//0P79u01rg9M3mbSpEk4dOgQWrduDTc3N6xfvx7r16/HG2+88cijVGSVnj17onTp0qhTpw5iY2Px008/4fTp01bD6jEMYxkWvAzDMFaoVasW3NzcMGnSJMTFxZk6so0fP97ZVWOyQNOmTbFx40aMGzcO8fHxKF26NMaMGfNYuKRERERg/vz5WLp0KdLT01GtWjUsX74czz//vLOrxjCPFezDyzAMwzAMw+Rr2IeXYRiGYRiGydew4GUYhmEYhmHyNezDawGj0YibN2/C39/fKUHJGYZhGIZhGNsoioIHDx6gePHicHGxbcNlwWuBmzdv5vmeuwzDMAzDMAxw7do1lCxZ0uYyLHgt4O/vD4AO4KPOM88wDMMwDMNkTlxcHEqVKmXSbbZgwWsB4cYQEBDAgpdhGIZhGCYPY4/7KXdaYxiGYRiGYfI1LHgZhmEYhmGYfA0LXoZhGIZhGCZfwz68DMMwDMMwGSiKgocPHyI9Pd3ZVSnwuLq6ws3NzSEhYlnwMgzDMAzDAEhNTcWtW7eQmJjo7KowGfj4+KBYsWLw8PDIUTkseBmGYRiGKfAYjUZcunQJrq6uKF68ODw8PDj5lBNRFAWpqamIjo7GpUuXULFixUyTS9iCBS/DMAzDMAWe1NRUGI1GlCpVCj4+Ps6uDgPA29sb7u7uuHLlClJTU+Hl5ZXtsrjTGsMwDMMwTAY5sSIyjsdR54PPKsMwDMMwDJOvYcHLMAzDMAzD5GtY8DIMwzAMwzC5gsFgwOrVq51dDRa8DMMwDMMwjzPR0dF4++23Ubp0aXh6eqJo0aKIiIjA7t27nV21PANHaWAYhmEYhnmM6dWrF1JTU7F48WKUK1cOkZGR2Lx5M+7evevsquUZ2MLLMAzDMAxjg4SErP8ePpTrP3xI85KSMi83q9y/fx87d+7EV199hdatW6NMmTJo2LAhRo4cia5duwIApkyZgpo1a8LX1xelSpXCO++8g/j4eFMZixYtQlBQENatW4fKlSvDx8cHzzzzDBITE7F48WKEh4ejUKFCGDx4sCYDXXh4OMaNG4fevXvD19cXJUqUwKxZs2zW99q1a3juuecQFBSE4OBgdOvWDZcvX876jmcRFrwMwzAMwzA28PPL+m/VKrn+qlU0r2NHbbnh4ebrZb1ufvDz88Pq1auRkpJicRkXFxd8++23OHnyJBYvXowtW7bgww8/1CyTmJiIb7/9FsuXL8eGDRuwbds29OjRA3/++Sf+/PNPLFmyBHPnzsUvv/yiWe/rr79G7dq1ceTIEYwYMQJDhgzBxo0bLdYjLS0NERER8Pf3x86dO7F79274+fmhQ4cOSE1NzfrOZwF2aWAYhmEYhnlMcXNzw6JFizBgwADMmTMH9erVQ8uWLfHCCy+gVq1aAIChQ4ealg8PD8f48ePx1ltv4bvvvjPNT0tLw+zZs1G+fHkAwDPPPIMlS5YgMjISfn5+qFatGlq3bo2tW7fi+eefN6335JNPYsSIEQCASpUqYffu3Zg6dSqeeuops7quWLECRqMR8+fPN2WxW7hwIYKCgrBt2za0b9/e4cdHwIKXYRiGYRjGBqrWf7vx9JTjPXpQGfocCo5qye/Vqxc6d+6MnTt3Yt++fVi/fj0mTZqE+fPno1+/fti0aRMmTpyI06dPIy4uDg8fPkRycjISExNNWeV8fHxMYhcAihQpgvDwcPipzM5FihRBVFSUZttNmjQxm542bZrFeh47dgznz5+Hv7+/Zn5ycjIuXLiQk0OQKSx4GYZhGIZhbODrm7P13dzo5+hy1Xh5eeGpp57CU089hc8++wyvv/46Ro8ejVatWqFLly54++23MWHCBAQHB2PXrl147bXXkJqaahK87u7umvIMBoPFeUajMdt1jI+PR/369bF06VKz/0JDQ7Ndrj2wDy+Tr5k6FShdGvj0U2fXhGEYhmEeHdWqVUNCQgIOHToEo9GIyZMno3HjxqhUqRJu3rzpsO3s27fPbLpq1aoWl61Xrx7OnTuHsLAwVKhQQfMLDAx0WJ0swYKXydfExADXrgGxsc6uCcMwDMM4nrt376JNmzb46aef8O+//+LSpUtYuXIlJk2ahG7duqFChQpIS0vDjBkzcPHiRSxZsgRz5sxx2PZ3796NSZMm4ezZs5g1axZWrlyJIUOGWFz2pZdeQkhICLp164adO3fi0qVL2LZtGwYPHozr1687rE6WYJcGJl9z+jQN9+93bj0YhmEYJjfw8/NDo0aNMHXqVFy4cAFpaWkoVaoUBgwYgI8//hje3t6YMmUKvvrqK4wcORItWrTAxIkT0adPH4ds/7333sM///yDsWPHIiAgAFOmTEFERITFZX18fLBjxw589NFH6NmzJx48eIASJUqgbdu2CAgIcEh9rGFQFEXJ1S08hsTFxSEwMBCxsbG5fgKY3MXPT8Y15CudYRiGsUZycjIuXbqEsmXLwsvLy9nVeSwIDw/H0KFDNVEgHI2t85IVvcYuDUy+pmhRZ9eAYRiGYRhnw4KXydeIVpVRo5xbD4ZhGIZhnAf78DL5GpHa0VI4GIZhGIZhss+jSAnsKNjCy+RrhODVhRJkGIZhGKYAwYKXydesWUPDkSOdWw+GYRiGYZwHC14mX5OS4uwaMAzDMAzjbFjwMgzDMAzDMPkaFrwMwzAMwzBMvoYFL5OvMRicXQOGYRiGYZwNC14mX/PWW86uAcMwDMM4h1atWmU7C1p4eDimTZvm0Po4Exa8TL7m2WdpWKqUc+vBMAzDMLlFv379YDAYzH6TJk3CuHHjTMtZErGLFi1CUFCQWZkHDx7EG2+8kcs1f3RwOH4mXyPi76alObceDMMwDJObdOjQAQsXLtTMCw0Nhaura7bKCw0NdUS18gxs4WXyNf/9R8PoaOfWg2EYhnkMURQgIeHR/xQly1X19PRE0aJFNb+2bduaXBpatWqFK1euYNiwYSYL8LZt29C/f3/Exsaa5o0ZMwaAuTXYYDBg/vz56NGjB3x8fFCxYkWsXbtWU4e1a9eiYsWK8PLyQuvWrbF48WIYDAbcv38/myfAcbCFl8nXCB/e9HTn1oNhGIZ5DElMBPz8Hv124+MBX1+HFvnbb7+hdu3aeOONNzBgwAAAQHBwMKZNm4ZRo0bhzJkzAAA/G/s7duxYTJo0CV9//TVmzJiBl156CVeuXEFwcDAuXbqEZ555BkOGDMHrr7+OI0eO4P3333foPuQEtvAy+ZrwcGfXgGEYhmFyn3Xr1sHPz8/0e1Z0YskgODgYrq6u8Pf3N1mAPTw8EBgYCIPBYJpnS/D269cPvXv3RoUKFfDFF18gPj4eBw4cAADMnTsXlStXxtdff43KlSvjhRdeQL9+/XJzl7MEW3iZfM26dUDp0tKXl2EYhmHsxseHrK3O2G4Wad26NWbPnm2a9vX1Re/evR1ZK9SqVUtTfkBAAKKiogAAZ86cQYMGDTTLN2zY0KHbzwkseJl8jbrTmqJwXF6GYRgmCxgMDnctyC18fX1RoUKFXN2Gu856ZDAYYDQac3WbjoIFL5OvCQwEZs1iCy/DMAzDeHh4IF3XqcXSvOxQuXJl/Pnnn5p5Bw8ezHG5joJ9eJl8Tc+ewA8/AE2bsnWXYRiGKdiEh4djx44duHHjBu7cuWOaFx8fj82bN+POnTtITEzMVtlvvvkmTp8+jY8++ghnz57Fzz//jEWLFgEgS7CzYcHL5GuOHwcOHwZSUpxdE4ZhGIZxLp9//jkuX76M8uXLm+LsNm3aFG+99Raef/55hIaGYtKkSdkqu2zZsvjll1/w22+/oVatWpg9ezY++eQTABQyzdkYFCUbwd7yOXFxcQgMDERsbCwCAgKcXR0mB4SFUQzeb74BBg0CPDycXSOGYRgmL5KcnIxLly6hbNmy8PLycnZ18gUTJkzAnDlzcO3atWyXYeu8ZEWvsYWXydc8fEjD998H7t1zbl0YhmEYJj/z3Xff4eDBg7h48SKWLFmCr7/+Gn379nV2tQBwpzUmnyP88H192YeXYRiGYXKTc+fOYfz48YiJiUHp0qXx3nvvYeTIkc6uFgAWvEw+Jy2NhidOkHsDwzAMwzC5w9SpUzF16lRnV8Mi7NLA5GuES4Mbf9oxDMMwTIGFBS+Tb1EUaeHlOLwMwzCMPXBf/ryFo84HC14m36JO/lKlCvDff86rC8MwDJO3EVnEshuHlskdxPnQZ3nLKtzQy+RbhDsDANy/D/AzjGEYhrGGq6srgoKCEBUVBQDw8fHJEwkTCiqKoiAxMRFRUVEICgqCq6trjspjwcvkW4Q7g7VphmEYhlFTtGhRADCJXsb5BAUFmc5LTmDBy+Rb1BZegAUvwzAMYxuDwYBixYohLCwMafzScDru7u45tuwKWPAy+Ra94NVPMwzDMIwlXF1dHSa0mLwBd1pj8i1eXsDHH8tp/lhnGIZhmIIJC14m3+LnB0yYANSvT9MseBmGYRimYMKCl8n3iEgmLHgZhmEYpmDCPrxMviUlBbhyBbh1i6bZh5dhGIZhCiYseJl8y4ULQPXqcpotvAzDMAxTMGGXBibfoihAQICcZsHLMAzDMAUTFrxMvqV6dSA2FujcmaZZ8DIMwzBMwcTpgnfWrFkIDw+Hl5cXGjVqhAMHDlhd9uTJk+jVqxfCw8NhMBgwbdo0s2UmTpyIBg0awN/fH2FhYejevTvOnDmTi3vA5HVEpzX24WUYhmGYgolTBe+KFSswfPhwjB49GocPH0bt2rURERFhNaVfYmIiypUrhy+//NJqmrnt27fj3Xffxb59+7Bx40akpaWhffv2SEhIyM1dYfIwL74IjB8PNGjg7JowDMMwDOMMDIqiKM7aeKNGjdCgQQPMnDkTAGA0GlGqVCkMGjQII0aMsLlueHg4hg4diqFDh9pcLjo6GmFhYdi+fTtatGhhV73i4uIQGBiI2NhYBKidQJnHiqNHgQ8/BMqXB2bPdnZtGIZhGIZxJFnRa06L0pCamopDhw5h5MiRpnkuLi5o164d9u7d67DtxMbGAgCCg4OtLpOSkoKUlBTTdFxcnMO2zziP6Ghg40bASoMBwzAMwzAFBKe5NNy5cwfp6ekoUqSIZn6RIkVw+/Zth2zDaDRi6NChePLJJ1GjRg2ry02cOBGBgYGmX6lSpRyyfca5iE5qUVFk7RXxeBmGYRiGKVg4vdNabvLuu+/ixIkTWL58uc3lRo4cidjYWNPv2rVrj6iGTG4iOqndugXUrQt8/71z68MwDMMwjHNwmktDSEgIXF1dERkZqZkfGRlptUNaVhg4cCDWrVuHHTt2oGTJkjaX9fT0hKenZ463yeQt1FEZihUDfHycVxeGYRiGYZyH0yy8Hh4eqF+/PjZv3myaZzQasXnzZjRp0iTb5SqKgoEDB2LVqlXYsmULypYt64jqMo8hwqWhVSvg5k3ggw+cWh2GYRiGYZyEU1MLDx8+HH379sUTTzyBhg0bYtq0aUhISED//v0BAH369EGJEiUwceJEANTR7dSpU6bxGzdu4OjRo/Dz80OFChUAkBvDsmXLsGbNGvj7+5v8gQMDA+Ht7e2EvWSchbDwunECbYZhGIYp0DhVCjz//POIjo7GqFGjcPv2bdSpUwcbNmwwdWS7evUqXFykEfrmzZuoW7euafqbb77BN998g5YtW2Lbtm0AgNkZ8adatWql2dbChQvRr1+/XN0fJm8hLLwi8QTDMAzDMAUTp8bhzatwHN78wfz5wIABNN60KdC1K/DRR86tE8MwDMMwjiErei1fR2lgCjbCwgsAe/YA5845ry4MwzAMwzgPFrxMvkUdpQHQCmCGYRiGYQoOLHiZfAsLXoZhGIZhABa8TD5GL3D1AphhGIZhmIIBC14m31KzJvDaa0DDhjTNFl6GYRiGKZiw4GXyLR07UqSGN9+kaRa8DMMwDFMwYcHL5HtEHF4WvAzDMAxTMOEcVEy+JTGRRK7RSNMseBmGYRimYMIWXibfMmoUEBQEjB1L09xpjWEYhmEKJix4mXyLELje3jRkCy/DMAzDFExY8DL5lilTgJQU4IsvaJoFL8MwDMMUTFjwMvkWFxfAwwPw8aFpFrwMwzAMUzDhTmtMvqdMGWDwYKBECWfXhGEYhmEYZ8CCl8m3fPcdsHs38PLLwPTpzq4NwzAMwzDOgl0amHzLrl3AsmXAmTPOrgnDMAzDMM6EBS+TbxFRGgwG4NYt4MYN59aHYRiGYRjnwC4NTL5FdFI7fx4oXhwICwMiI51bJ4ZhGIZhHj1s4WXyLeo4vC4u9GMYhmEYpuDBFl4m3yIsvDVqAOnpzq0LwzAMwzDOg21eTL5FWHjd+LOOYRiGYQo0LHiZfIsQvO7uzq0HwzAMwzDOhQUvk28RLg1JSUCPHsDTTwNGo3PrxDAMwzDMo4cbe5l8i7DwurgAq1fLeR4eTqsSwzAMwzBOgC28TL5FWHi9veU8IYIZ5xMX5+waMAzDMAUFFrxMvkUdlkwgRDDjXL77DggMBBYvdnZNGIZhmIIAC14m32LJwsuCN2/w7rs07NfPqdVgGIZhCgjsw8vkW9q2BSpWBIoUIT9eo5EFL8MwDMMURFjwMvmWmTPluLs7kJLCgpdhGIZhCiLs0sAUCEQsXu60xjAMwzAFDxa8TIFAZFtjCy/DMAzDFDxY8DL5ltBQirl7+rS08LLgzRuMGEHDwEDn1oNhGIYpGLDgZfItqakkcF1dWfDmNTp3pmFoqHPrwTAMwxQMuNMak285c4YEbtGiLHjzGqGhQKNGQJkyzq4JwzAMUxBgwcvkW4oWlePcaS1vcf48uTW0aOHsmjAMwzAFARa8TIHg6aeBqCigcGFn14QBgHfeAa5eBfbvBxo2dHZtGIZhmPwOC14mX5KeDgwbRtEZxo0Dpkxxdo0YNU88AZQoAQQFObsmDMMwTEHAoCiK4uxK5DXi4uIQGBiI2NhYBAQEOLs6TDZISQG8vGj8/n2OBpAXqVgRuHMHOHGCxC/DMAzDZIWs6DWO0sDkS9Sd09zcyOKbnMw+vHmJmBj6GImLc3ZNGIZhmPwOC14mX6IWtu7uQKtWgLc3sGaN06rE6NiyBfjvP6BcOWfXhGEYhsnvsA8vky9RC143N5lpjS28zicpCShVilxOzpwBPD2dXSOGYRgmv8OCl8mXCJcGFxf6Ccuuj4/z6sQQycnA3bs0zmKXYRiGeRSwSwOTLxGWXGHZDQignxt/4jmd5GQ5PnYscPCg8+rCMAzDFAxY8DL5Er3gZfIOasE7fjxw4IDz6sIwDMMUDFjwMvkS4dIgMqx9/z3w6qvApk3OqxNDJCVppx88cE49GIZhmIIDC14mX6K38G7dCixcCJw86bw6MYTawguw4GUYhmFyHxa8TL5Eb+EVQ3V8XsY5sOBlGIZhHjUseJl8id7Cy4I378CCl2EYhnnUsOBl8iVC8LKFN+/BgpdhGIZ51HAfdiZf4uMDPPkkUKwYTbPgzTuw4GUYhmEeNSx4mXxJzZrArl1ymjOt5R1Y8DIMwzCPGnZpYAoEbOHNOwjB6+1NQxa8DMMwTG7DgpcpEDhS8A4fDnzwAXDlSs7LyimpqZS84dAhZ9fEfoTgDQ2lIQtehmEYJrdhwcvkS/76i/x3u3alaUcJXkUB5s0DvvkGSEjIWVmOYOpU4LPPgCeecHZN7Kd5c2DmTGDwYJpmwcswDMPkNix4mXxJQgJw+zYQE0PTjhK8Dx8C774L+PkBhw/nrCxHoPZTzi5RUcDKlY/O3aNmTTqGzz9P0yx4GYZhmNyGBS+TL2nbFjh2DFiwgKYd1WnN3R1o1AiIjycrpbNJTMx5GW3bAs89R1brR0lAAFC5MlC7NvtWMwzDMLkLC14mXxIYCNSqBVSqRNOO9OEtXpyGN2/mvKyc0qdPzss4cYKGS5bkvCx7OHMG2L4duHcPOH0aOHhQnh+GYRiGyQ1Y8DIFgrJlgfbtgRo1clbOgQPAtWs0fusWYDTmvG45oW1bGrq5kX9xTnhUPskzZwKtWgHz5z+a7TEMwzAMC14mX3LoEDBuHPDLLzTdowd1ZBsxImfljhoFPPssjT98CNy5k7Py7GXNGqBECWDTJu38gABZl5SUnG3DEe4R9hAaClSpAhQt+mi2xzAMwzBOF7yzZs1CeHg4vLy80KhRIxw4cMDqsidPnkSvXr0QHh4Og8GAadOm5bhMJn9y8CCJ0//9z7HlnjmjnX5Ubg3du9O2Bg3Szt++XY7HxWWv7LffpmHhwtlbP6uMGgX89x91XHv6abK+79//aLbNMAzDFEycKnhXrFiB4cOHY/To0Th8+DBq166NiIgIREVFWVw+MTER5cqVw5dffomiVsxDWS2TyRvk1DqpR/jquulyCeak01pSkoy9W7IkDW/cyH559qL2O/bwkOPp6WS5FmRX8PbuLctTExVFocOOH89eufZw8yZw+bKMpsEwDMMwuYFTBe+UKVMwYMAA9O/fH9WqVcOcOXPg4+ODBaJrvY4GDRrg66+/xgsvvABPT0+HlMk4n6NHqWn+008dV6YQtkLwnj9PFswiRbJf5rlz5CdbqBBFFgC0Ft7jx8k3tXPn7G/DEgcPyvFixeR4QgLQsKGczq7gFd+Ot29r57/9NjBjBlCvXvbKtYe5c4G9e4GmTXNvGwzDMAzjNMGbmpqKQ4cOoV27drIyLi5o164d9u7d+0jLTElJQVxcnObHPDoGDKCMYRMmOK5MIXhF7/9ChciKGBND28oOwp2hcmXypwW0gjciglwM/vwze+VbY+tWOa62hAYEAHv2kD8skD3Bm5ICbNhA4/Hx2o5r4pbJaSg3PX37UufBDRsoYUbjxhRVg2EYhmFyC6cJ3jt37iA9PR1FdCa3IkWK4Lbe1JTLZU6cOBGBgYGmX6lSpbK1fSZ7XL3q+DL1Lg3BwRR+Kzo6+yGwTp+mYeXKlkOTiQQKM2bkPGKCmi1baNirF/Djj+b/P/EE0KIF4O2d9bLv3pUZzwAgMlKOO3If1Jw/D5w8+eg6yTEMwzCM0zut5QVGjhyJ2NhY0++aiDvFZIstW4DPP7c/ZFduxH/VuzQYDED16kBICI1nB2HhrVLFXPA+eEAWUgB45ZXsb0NPcjJZcQGKOiGsuWqWLCHLcqNGWS/fYAA6dJDT6u/C1q1pOHly1su1RXIyDb28gJ07qXx15zuGYRiGcTROE7whISFwdXVFpNqkBCAyMtJqh7TcKtPT0xMBAQGaH0MWvux0JmvbFhg92v4ICSI2rouL4+LaCguvoxIaJCYCGzfSeI0a5oL30iUaBgc7tnl+3z4SiEWLmovdqVOB8HBg7Njsl1+sGLB+PaVJPnMGqF9f/idcJIKCsl++JdSCd80a4P33gXXrHLsNhmEYhlHjNMHr4eGB+vXrY/PmzaZ5RqMRmzdvRpMmTfJMmQWZpUspW9kvv5A/Z1azlB07Zt9yIhyW0QjExmZtG9bQW3gBYPlyYOBArU+svcybR1ELwsPJV1f48IooDRcv0jAmBvjuO22YLUUBFi/OXugtUdfWrYEffgC++AK4f5/mXb1KUSMc4RpQty5lpVP3BRXbyS3B6+1NvtUA8O+/jt0GwzAMw6hxqkvD8OHDMW/ePCxevBj//fcf3n77bSQkJKB///4AgD59+mDkyJGm5VNTU3H06FEcPXoUqampuHHjBo4ePYrz58/bXSZjH4pCTc1nz1KihaZNyfcyM0SzPiCFjS02biQRJ7h7N+t1tYS+0xpAnaRmzcq68ExOBr76isZHjqQyixcn94hixUioC8ELUHzZ33+X07t3A/36UcxZSyiK9f2+dw/w8QGaNaOkGZ98Aly/Tv8J94N588gCnBNLryWEZ0+vXuYhy3KC2sL77LP0UfL33/RjGIZhmNzALfNFco/nn38e0dHRGDVqFG7fvo06depgw4YNpk5nV69ehYuL1OQ3b95E3bp1TdPffPMNvvnmG7Rs2RLbtm2zq8yCzvHjJPhefZVcCKxhMADbtgHffgv8+itZa//7D6ha1Xb5ah9Qe7KQrVlDIlRw9y5QoULm62VGUhIN1XFrxSWg83jJlAULKI1wqVIUYQAAwsKoA5xALXgBbUc8sT/R0RQhQl0no5GSSWzYQH6sIr6v4NtvgWnTSHAeP04uJj4+9J841oUK0fazk/Vt6VLgzTfp2Lz8Mll5X3qJ/lNbjh88cJylVy14K1Wi/Z86FRg2jK4zfexkhmEYhskxCmNGbGysAkCJjY11dlUczpNPKgqgKEuW2L9Onz60zoQJmS+7YwctCyhK48aZL//LL4rSv79c548/7K+XLbp1o/K++07OmzyZ5vXunbWyatSg9WbOtL5Mx460TKtWcigwGhXFy4vmX7igXW/QIJpvMCjK8uVZq1eVKrTujz8qyrFjihIZaXm5GzcU5eBBRYmPN/9v1ix57AE6bgJfX5o3bpyiJCZmrW628PGhci9epOl79xSlcGGaN2uW47bDMAzD5G+yotc4SkMB49w5Gi5caH0ZfTgq0Vnqv/8yL19t4RUduWzRqxdZUEXoZEe5NFy+TMMyZeS87Fp4334beO014JlnrC8jLLwtW9Lw6lXal61byVpeujTNF5naAHIRmTGD/l+0CHj++azVSxzrJ54gX+uwMMvLNW0KNGhgOWOaCKVWpQrw1lvS7eLhQxmT9+23sxfyzBKKorXwAmQ5Hj2axufNc8x2GIZhGEYNC94CRGoqdbwCKHSYWnypWbAAaNIEWLaMpoXgFXFobdGihQwzFhlpf4cq0XFNL3gVJXuJD0qXpl/ZsnJedgXvO+8A8+ebZ2kbOxaoVg2YM0eK+1ataHjtGnWSa9MGeO89KbyFEAeoTIA6wZUqRaL/nXfk/+vWkZD95BOaVhQSoQ8ekGgUncqsBSBRFEobLM6zpVDUQvC2awfMnk3CHtB2HnRk1ImHD2UkDiF4Aaqniwtl3VMfI4ZhGIZxBCx48wB//EGWzrVrqZPSsGGUrcvRGa5u3dJOW0piAJDP7r59UngIv93TpzNPRlCkCPmA1q0LtG9vO/vXtWvks5maSp2/ihUDXF3l/0lJJLa9vIDy5em42JsMYe1aEnpqn+PsCl5rGI1k9f7f/2gfXF0pa5iLC0W0OHqU/HWLFpWCV4jP1FRpZX/zTdrXzZupg5vg+HH6CX/gTz4B/PzIGir2wcODhO/48eTvq+boURLdAv35B+T50UfiCw6mcn/9lTrgOSo5iPCtBrRW45AQoHlzGl+71jHbYug6ER81DMMwBRkWvHmA7duB334jcbJlC3VS6tyZmqizmwbXEiKElmDRInMBGRdHwgsAevSgYfny1JEoPl5GCLCFwUBxXf/6y7r1ESBLcJ06lFp46lSKaTtokPz/4EGKEpGeTi4D06YB//yT+fatIQTv3bv0MbF8ueVwWIcPy2QPBw8CR45Yjkc8ZAjw/ffAmDE0XaYMiXMRo3fAAOqo9sYb5oJ39WqythcvDnTpQm4JCxdSeYJ+/ehjaOBAmhYhvO7ckdbaokVJjH72GYVDU1OyJB1XgS0Lr78/CdzTp0msGwxk2Z09G+jZ03GJIdSRO9Qh0ACge3carl7tmG0VdGJi6PoQ7jQMwzAFGRa8eYCePWm4bp02nNW9eyS+HEWRImQl/PhjshRevEhiSi2E168nkV2pknRlcHeXkQYyc2tYuZKErDrlrjWEG0C5cpb/F/vevj3QrRuN2/I9zozChcn6qijkTtC7N1C7tnaZ1FRyQ2jViizQH34I1KtnOYlGcDCJWiFixX6IzNRXr5LlNDDQXPAKYfvaa/QxERZGAledLa1YMaBTJzkvJISGd+9qBa+wzuqt6aGhwNChUpDbErwBAVTHqlW1vtciMoOj4iMLwevpaZ6NTpzjHTsc58tdkBFW+fv3Hd9axDAM87jBgjcP0LAhJTJ48AD46SeaN3gwZb7KTrrYhATLFsny5anpe8IEEkIAWQUrV5ad2bZsoWGXLlpBYm/HtfHjgT59pOX04UMSmJbiuAphFR5uuSwheJs1o9i2AAnPzOL7zpxJovOzz7TzXV1JBALaEF4xMXL8zBkSd2lpZGksVIhEn14Yq2ndmkT+kCE0LSxq6gzVasEbHS2t6K+/bntf1Ag/5zt3pKXdluAVCCt7ZhZeYQG/fZtcWgYMoKQjgOMEr78/XSOffmr+X9my5LOcnk6WbTWKQhZ+a244jDm1a9OzQFE41BvDMAwL3jyAi4t0H0hPJ4vqhAlkZdVbwdRER1OT+ODB2vlffUUW2ZUrra/7+efkcvDZZySeKlak+Xv30vDJJ7XL6zuuxcaS0HvmGa1bRIsWlFp4/34Sly+8AIwbR039wk1AIARv2bIkbps310ZCcHMjgVSvHlldS5Yka9WaNdb3CyDL9fXrWn9RgRB1DRrQBwAAHDok/z9xQo7/9hv9YmLI9cIaZcpQDNsuXWhaCMzhw6XfrBD1165JP91q1bTNzUePAnPn0rGLiaFOcepzKCy8d+7QsgClORaCNyGBrh9FoYQOixbRB0exYvS/LR9ef3+tMD55UnaqA2QHuZxSqBC1MlgSvIC08maE1Tbxxx/kw923r30JTRh6doiYzQzDMAUdFrx5BOHWAJDY9PPLfJ2kJMDXl0SSSPsbG0udl65fN08sceYMcOECWX8NBnIV+Pxz2WwdFycFnz4Ts+j8deAANfunppIo+fVXrdV3xgxg0yYSqXfukJD96isSXmp3hPR02eRatiz9v2sX+cwKFiwgoRURQdZZkfQhM7eGTz8l0fjWW+b/qTuuNWhA4+ptqkN37dhBHxUGg+0PDz0iCoF6e8WL0z6kpdExA8w/KhYsoDr/9hudhzFjgI8+kv+rI1kIkV6/vrbDWXw8WaZ/+YXcVe7ds8/CGxCgXa5ePbo2KlemeY6y8GbGhx/SdbhggXa+WuTaE+4uK9y4ATz1lAwpl18QrSsMwzAMC948Q/PmUtC0b0/DBQuA557TWh3VCItnhQrSZ3bGDClOrl0jn1zBm2/SsqtWWS7vwAF6QYaHS6ugoEkTEmyHDlEzfGgoheNatEhah9W0aEFWyN69ZWiy9evlC/jGDXohizS9lSoBP/8sQ5oJXFxkc2y/fjT8+2/L1kpBcDC5iVjK2CYE6IED1FEM0HaEU4fEMhqzFzFg+HCyHk+fLj863NxkFjUR7q1pU+164jiePy8t6cKyDkgLb2ws8PXXwDffUBmenjKF8v370pVj6FA6T2ohqxdAapcG0dnuxg2KsvHZZzJMmqMEb1wcReawJlr9/Kguep55Rh4Le1JcZwV3d/pI27kzf/m6Tp1K15/BQK05DMMwBRkWvHkENzdqwq5fn3xgAWrOXrlS+nvqcXEh4XDyJDWrJyTIXvm1a1MT8NKlcnl3d3ItKFFCW86cOeQn+/77NK237gIkxn79ldYVVsc33ySrqxBbaWkyqkRgIDXZq30ub9yQ4l0InjJlSEgHBVEzfIsWNF9tJRVUqEBC1mAgH9PsUK8eDWfNki4WasG7bBlZf997j6Zff11aZO0lPJxEmd7VRPjxin3TC14h0M+dI2s8IC2sAB0jIaCrV6c6CjErrLw7d9L14Ocnz6cQ+WlpZPFVo3ZpsBQrWMTgdZTg3beP3EPULRr2UqMGDS9c0M6fPp0SZmQ3/JbYX0UxPz6PM2rfdHtiaDMMw+RnWPDmId59l8SXEKRt2tDQmnXm1VfJmijE48qV9JIrV46sfz16aEXVxo1kbdU3pZ85Q36lx47RtCXBC5B/5cWLJLYs8ddfZG1s25amf/uNXCvCwsjfFwA2bKCh2n/XEgMGkNVX74c8bx5ZKoXPs574eDom06dbFs3DhlHIN1dXsl4aDGQJV8fmDQuTCRgAx3X4WbKEjsd331GoMr1lXEyfOyet8GoLr6urDE2mj2IgBK8Q8fXrS1cVT0+yegNay7iiaF0ahJ/xlSt0TRw/Ll05HCV4DQZqPRCdBy0xZgy1chw8SC4l27fTdS0+CPQW3pMnqfVA3zpgL9Ony3F1Z8bHHbV4FwlnGIZhCiosePMwHTvScOtW8w5YRiNFLJg3j8ZTUmRa1tdeo8xZv/2mzdwFkODQ+/a+/LK2g5Le8qjGw0M7vX07xc7dulWKKZFQ4MgRGr70koyxKlwsLEVoWLeO/JEjI0n4nztnvr1atWyLpcuXycr9+efm+6nezp07FHpMCEp9fN+qVUm4Fy3qON/O0qXpY+btt2k/9X7BwtqdnEzHp3RpSkiiRrg1fPut1idXCF5h+dZ/lFjy41UUssDPmUOCWG3h/eADOtbiA8VRgvepp8j95u+/rS+zZw99nJ08Sa0brVpRh0AhePUW3tKlySdcRBrJKupY0fkpHBoLXoZhGAkHq8nDVK9O4bWuXSMBUKMGWbcOHiTf2ORkEoTvvy+D9bu4SF9Xe6lfX/bm9vYmoWMvK1ZQcgKjUTadC//fiAgS6mPHktAaMoQ6pj14YNnC+/77ZFmsXJmEzuHD0s/WEkajuagVzdNCvFlDWD8bNKBOdwcPUl0XLaLm9ldfJbFnMGizv+Um7u50PM6fp/pv2yYFriAkhI7R99/TR82zz9J8IXjFR0a1atr1ihYFTp3SCl4XF+DFF+W0OGaRkebRJbIapcFopG25u8v4x/by7rt0fbdoQS0P4eF0TYqoGnoL7xtv0DUvfKSzyuTJJLL37ctfFl61SwMLXoZhCjps4c3DGAzSyvvRR/TCj4igKAQiNFeFCtoYt506yc5HikLWtJgYssQ+8YSMv6tHhCNr0ED65NqDqN/69VIkCcHbujU1F/v7U3N9+fLkR7p5s2XBq45CEBJCzdqiKV7Nrl1k9RO+zmpEYgdrsX31qDuu7d5N4a+En7Gbm2PF7tWr5FvbvLlldwuAzm3XrnS+LO2DOEYAfagI9KmB9YL3hx/oWnj+eev1Cw6W0UFEHGUhgrNq4X3jDbJmh4VphZc9dOsG9O9PrjmvvELXynffSQvv5cuyc9l//9FH18WLWduGHnXIt/yC2sIbHe28ejAMw+QFWPDmcTp1oqG608lLL1HKXYD8XNUuCGrf0969SXQsXUpWsUOHZGcoPS++SEJy3Lis1a91a+oId+mS7CBnLZ3w88+Tb27VqtISa03w2sLNjQTh77+TlXv1auqEBthv4RWI0GSbN5M1/bvvtLGAHUlKCnUs3LXLcmIQgDoBrlljvf5CmE2YoM1Qp49soHdpEJE31P7Id+/SsRNxgQ0GuV3R+VBMJybK0Hf2MG+ejBahTiG8fDl1kJwwwf6yALIQFy9OZT18KEPabdtGnQMnT9bWO6uok3o4m1u3LCdqySrs0sAwDCNhwZvHadNGa3Hdv5+ysQnLY+XK1AkLIDEjxgEpJs+ckemD9REaBF5e1JlNREmwFz8/YNIkGhcdoPQhzQQTJlBTfNmy0hpsSfC++SaJJWs95hs2JIF77Bgdjx49yAJ+44bs5Kbu7GWLhg2BDh1IOMfHk3+tLR/mnFCxIgnB1auln3NWGTKEsuGJmMQCtfgtXNi2n7PgxAk6duqPJL1VWSTGcHUlMfjOO9r019YwGMiPOj1dG1P6yhUS2LZCi6Wk0AfI4sXaMGouLtKtQfjxig8cHx86j6VLZ02Y79tHx2rxYprOTPBu20YuOtkV1pnx8CF9eHl6Wo6bnBXYpYFhGEbCgjeP4+8vRWiFCtIiKSy8lSsDNWtSp7FTp7TiWIS0On1axum1JnhzwsCB5PMqsCZ4BR4eZOk8c0YrzNTN9ZMnay2DalxcSHiFh9OxqVWLymvZkgRV6dLUEc8eXFxIgC5YYJ6KODd4/XWZTSw71KpFVnX9eZwwQaalrl7dvEPcqVMUvUJtWfXyAho31maR01uWQ0LoQyAtjeIkz55NLhdCiO7apU3WkZamTRIh4sDevEnnSHS+tCX4k5LIP7lfPxKyajcMvR+vcI154glqwYiMlOmQ7eHqVa3Izax14Z13KIrEDz/Yv42sIBKdANbdXuwhPV3rhvLgAWeoYximYMOC9zFAiMmhQ+llaDRSJzaAXBoA8mnVh7lSpwPOzMKbEwwGcgXo1Im2WbOm9WUVhZI+jBlD9VULM7Xg7djRvrSoBoP0SxZWv+nTKQOdvXh6ks9oVrKp5UVOnqSh3n8XIIv61Kky6QUANGpEvtvLl8t5H31E50cQGEjH0mDQZtRLTKTj3aoVlSM+wH77ja4xtbAWrjVr10rR5eVlfT+CgqTvdnKy1tJfoQJZm4X1Ulh4y5eXmfXeftt2Nrbz5+ljacYM6hCqxpaFNzlZugR9+23uZDErVow6UKan03HNLrGx5vVjP16GYQoyLHgfA158kV7wIsSYurlenZhAj/jvxg0ZBio3BC9AonHdOrIk2hKbiYkU7uvLLyl1rxq14M1KYoIXX5QRIjp3zpkF9XHm1CkaWhK8lSpRqLFBg8iqqQ/DJihdWvoJe3trreyiM9+PP9I5XrWKhFlSEnUuu3+fRGRMjNZHWbhF/PGHfYIX0LpoqD+gxo6l7X3yCU0LwRseDowaRRbr2FjghRek28GNGxT5QXRoXLCAxiMjpS+w+HC0JXhPn5ZW19OnKTtbblChAn1gXL+efbcG8ZHg6yvveXZrYBimIMOC9zGhUCFpgVS7LejDVunXEVai1FTy8bVlfc0pBkPmVlJfX+oUVq6cefOxuglX7YucGZ6eZGHu2pWa3B93S212WLtWRu5o1878/1KlyNfa3Z0s6w0bWs9WJ0KQidBtX3xBHxGic5twqxGh8ACyCpcsScu4u5PFXCDO5YYN5NYAZF/w+vvL6z8+Xlotw8Np/vLlVO8DByjEHECRLw4ckB8Evr7kSuPvLwVv3bo0tCV4hQVdoE5Y4Uj8/KhjJ0BuGtlBCN5ChShSBsCCl2GYgg0L3seQZs3sX3bnTrLM7dpFTdLC2uZMvv2WetbrO8h17Upi5a23pNiyl549SfCVKuWoWj5eiI5azZpJsWSJqlXp40dRSBiXLEmdywSpqdJFRLiU7NhBghqg8GcVK5LPuBDAX31Fw4QEEp7r1mk7IzZtSufz7l2ZDS0rglekFNYjLLZBQfJ6KVMGGDGCxlesoE5ga9aQRVu0PHzyiUwfLVwaWraka1K4RVhCCF6RAfGPP2x3vssOU6aQdVoI+ewKXuHyERzMgpdhGAbgxBOPJd98Q8LEno5Z7u7WUwE7C39/ijagp3hxelHnhm9kfqdFCwrVJsSNNZo2JXeSESNIoCYkkJuJwN1dupqIqBtvvUWWz0OHgHr1yGVC+Ow+8QS5Svj6UlkDB5r7Xru5USi9WbOkq4Pe31yP2i1G7aKhKBTj98QJikkNmKenfvZZ2r9t28jSuXMniXB1Su07d8j9QgjKJk3I99cWQvB270779PffZOV+/33b62WFbdsoCkbVqiR6rbmeZEadOuRy4uFBorxsWRnHmGEYpiDCgvcxxN8fWLjQ2bXIHexxi2DMCQ21LxQZQAJt+nQZGk4dw9dgIKvv9esyc1n37pRY5NAhcoU4eZIEb6dOsrPfu+/a3uaMGWRBjYkhFxR1ZAhLqLP9qQW0wUB+wPv2SZcMfSi1cuVImB8+TKLvjTdo/oIFsnOnq6s25rQ9LR9C8FavTr7C48fTdhyJsDj37Ekd/7IreMPCZDrvR8GdO8DRo2RRrlVLG++ZYRgmL8AuDQxTwHB1pWZzgT5L23vv0VB05AIocgBAgnfuXOo49scf9ifpMBiovMaNyV82s4+ap58GZs6kOMt63nuPfHUHDqRpSxnpRMplEZd5xAgSvs89R+J26lRtB85ChajJ/+RJ6WesZ+5cWq9OHfJjbtDA8WmnhU9x164U0u3WLRlSMC+zdy/w1FOU/U8dpo5hGCavwIKXYQogL74ox/VZ2t54g6IffP01TV+6BBw5QuMNGpBQzMwHN6cIq3HDhub/1alDsXmFENS7NABS8G7aRJZdkUL66FGyoiYkUKIK9faaNSN/YWt+s23bkn+zpXTXjiA+XvreVqkiXZFEfOWssGcPhaA7fZrcQO7dk6EJcwO18I+MzL3tMAzDZBcWvAxTAKlfX4atEyHdBD4+1PlLuDSMGiX/y0udAtUhyfSIBBUAdaoTkSUEtWpR1IoOHYD582leWBiFxhPJMTLj4EFKJPLFF1mtuWWEO0NAAP0GDaLpzz4joS64dYuE9/Xr1sv64Qfym/7tN7LEBwfnrotDp05A+/Y0zoKXYZi8CAtehimAGAwUxWDSJClUrNG1Kw0tZXBzJiI8mbXOWL/9RsJ3yRJzwVu7NonA9etlauWdO8kXVXSGU7NhA7B0qXQ5AChKxA8/ZM8Cq+bmTRK7omzhTyyy8qWmUvIO0bmwc2fywe7d23qZVapQRr6KFWVHRnXnRDVJSeTvnFPEhxMLXoZh8iLctYBhCii1a9MvM3r1ItcAEas2rzBqFFk5rYVh69GDfoIyZUikurlZTs5hS8zPnEmW0u++k9Ec2raldM3t25PbQHY+BtLSSIxXr07HGZCC12Ag6/OBA+SasHAhuXkI95Jdu6yX+8EH9AMoNFtSkmU3lLQ0cg/ZtImEe4kSFEmjefOs7w8LXoZh8jJs4WUYxiYuLiTucst3Nbt06iQjMNiD8OOtUkWbQc4e6tcnEagW/YUKAZMnk0VYLw5v3SIhuX277XJPnSIL75Ej0kVBHTEiJAQYNozG166VsYcF6mQt1nBzs+5z/fChzJbXqxd1KmzZMutW62eeoXCJAAtehmHyJix4GYYpEDRvTkORfVDP2rWUjGP0aPP/xo6l+MSNG9u3rUmTgJ9/1ib1sERAAEWQ6N9f+vDqQ6R1704h3UaMoIQfavQCWGBPLGujkYT6qlXk1uHpKTswqrPo2cPp03KcE1wwDJMXYZcGhmEKBO+8Q2l7n37a8v/R0cDmzdIaGhtLP1sxeqOiKCSXuztZnAGymC5fTuOHD9t2dyhbFpg4kcaF9VjfMbBiRZnGWGTBE/z7r+UoFYUKUZ0OHaL6/+9/5B7RpYu0GF+5QjGLixQBbt8G5s2jMHBNmgBbttB+2Bt2TS1y2cLLMExehC28eZmNG6ktNbv5RRlGTd++FJ7AWqDZfI67O1kyrWWjCwmh4Z07NPzpJ/L7tZWBbc8essCOGSPnbd1KAhIA7t8HLl7MvG4HD8pIDLYiYUydCly4INMb//uv+TKpqSTU79whgQ+Qi8WWLTKLHiCFqRD4BgO5fQQGUr3tfeykp8tjpi6XYRgmL8GCNy+zbBmZiPJrWrVHyeXLZLr67besrzt3LvV+sjde1aPk4UMyEYpu9ikpVFdL7fK//kpZAWz1dsoPrFlDKdWstfdbwZLgBbQJOPTUvrQa+9EQnudOmOYtWwaUwWXsRDN0w2qrwvHhQ0pPfOcOdR4sVYoEao0a5sump5OQ/vhjEuHCmmxJ8N67R0ODgcQrQN/NgErERkWhkf8p3Lun9TN2c5NieuNG6/ut5s4drQtFdDTVt8Bw4wZdLA8fPrptnj5NH7C2YtM5ih07KKXg7t25v63HnbQ04MwZ+3yKmEePwpgRGxurAFBiY2OdW5GuXRUFUJSmTZ1bj/zAxx/TsWzbNuvrlihB6/7+u+PrlVN++onqVrcuTa9fT9MeHory8KFcLi2N5gOKMnasc+r6qOjUifZzzJgsrXbmDK3m6akoGzfSuIuLoty8aX2dtEZNFQVQVqOrEhenKElJihIQoCif41NFAZR9aKh8+KFc3mhUlAULFGXrVkX591/aRkCAoqSnK8rdu4py/brl7aSmKkpgIC2/Z4+i/P03jVeqJJe5elVRwsMVpWhR+i8oSP53/748/VFRiqLUrEk7d+2a2bZmzaLlWrWy77iJ/QgKktuIjLRv3XzBCy/QTv/446Pf5uDBub+t5557dNt63Bk+nI7VunXOrkmBISt6jS28eRmRduno0QJmMskFdu6kYVYtIgkJMkXVuXOOrZMj2LOHhkePkmlP7GdqquwFBQAPHsjxAwceWfWcwv37NFS339tBhQqU2S0lBejYkea1bQsUK2ZlhaQkuB2mnMtP43fc2HoWf/4JxMUBLTzpGDdwPYyPB8ebVlm4EHj1VbLQrl1L8+rWpUgYwcHadMdq3N3JaF2mDMXTrVWL1nvySWlMWr6cGjKEO4U6IUdgIPkCA8Dp9ZfI0m80Wrymn3qKhrt32+f9IlwYSpSgxB1AAeu4JgIob9v26La5bx8NLeXediSKIu8j0XTAWEc0uXB+7TwJC968jBC8iYnA2bPOrcvjTHKyfDFcu5a15qbz5+V4XhS84sWnKCR21SJPXd/YWDl+4ED+bnIT+7p3Lwl/O3FxIY+XYsVk6/RLL9lYYf9+asIE4AIFnnOmY+NGwAAjGoAEr0v6QwT+R+foxg2K2wuQd4zw+61Xz776zZ8PfPQRidwiRciLZcEC2SFOuCAMGgR8/z11VFMj3Bri1mwxzZs3IQqbN2uXq1CBhHVamvZyun/fchg0IW7DwgpoLF7xnBYfn7lNZKRMM3jkCH2h5Rbnz8svKLGfjHXu3tUOmTwFC968wIMHFMNIj/qLWkSbf5wxGp1j+jl4UAqfxMSsWSrUovFRCN64OPs/bpKStE6cGzZorbfq+sbFyfHo6Cz7t1rl5s1H/3CPipIvYUsIwatOIWYtzZiOEiXIBdjbmyyupsQVMTHmvc8yrOm3fcsBAEpuWojzB2JQEefgk3LfbLmBA6lqIjOcENVCiGZGSAh1oLMUUzc5WRr333oLGDCA4g2rEcI64KAUvP9ujsLJk9rlDAYKzwZIo+XmzXQ81GmmBWrB++23lMTCXhGfp4iLsy+wsR4hBE+fdrwoTEw0v+7UVt3UVG3eaTXnz2tbdrKDuKiAx1/wpqWR5TU3P/bFMXoUx8popGsjPxsvHAwLXmeTlERxhZ5/XpvfU1G0N40jcn9aYtEioGhRoE+f7HdKSEy078E6cyaZgJYsyd52sov6oQ1om/oz41EKXkWhPL5Vq8LM7GaJw4e1HWUWLdJaNK0JXsByU+ixY9Tebq8YjomhlGVNmjy6DjsJCdSWX726NjSAGrU1e+dOMo36+tIwM2Jj0aBGEk6dosMbEAB6UT75JJ2X//6Ty2aYP/c1HY4jqAP3tCSs7/E9/hytdRk5t3Antm2jDmf161OM2xdekP/bK3gtER9Plt1du0j0Fi9uPfMcbUdB5ZtS8IYhymSVVTNsGF0iX3xB04sX0+U5a5a5QVFYc4sUIReQtm0pLJpd5BURdeUK7YCtfM2WUBQo6n1wtIvBs8+SL4r6uhOtOtamAeDkSfriiYjImSBSm/gfd5eGsWPJF2jFitzbxqO08H7+OeVOz05H7KySmEg+WI+yY2YuwILX2Xh7U25SAJg9W85PStK+WXLDwpuaSm/hyEgSoc2a0RstK9y9S+KjfPnMhZIQ1F9++Wi/SvWCNyt+vGrRePUqqQpHMngw0LMnne99+6jLvNFIWQYyO0biRdewIQ1FFAk3N/O6q0UgYO7HqyiUtmzhQuD11+07P/v3U7nnztnfpT+nrFhBVuWYGFJheoxG7cfXunV0LAFg/HjbvvD379PHZ7NmCA+nZn0A9CFx+jTdL999R/PS0shlAkBa4xaYjiEAALcf5qJ8FM1Hhw4AgBLX9mHjH6lo0IAaG6pXJyHp70/bEL61WeX6dbIWP/008MMPNO+pp6zH/K1bF6iC0whLl9Zxa4K3enW6rFxd6ZBu2CAP0R9/aJdVW3izxOTJ5PS7alUWV8wFjh6le/v3301uKnYRHw+DWgRkXBMO4/BhOgHqjCNCVAsnbQsiO2V9RiDlvXuls3h2UAvevPJxkl1y2782NZW+QIFHI3iPHaPhP/84tlz9M1JRyCDXrRswcqRjt/WIYcGbFxCBPpctk8JE/3AREewdyW+/UYDOokWlyWnUKHNxZA1FIXF0+TI1k7/yim1BER1Nw1OnHl1orPR0KbRFBoHsWngVxb6gqvaybRswYwa98CdMoCCrgn/+yfxFJV503btrY2d16UJDWxZeveDds0fO27TJXNVYQv0R9uOPmS/vCL7/Xo7PnWt+T+hbGnbskC+fK1ds79fu3WTFUlvOU1KAcePkMj/+SFbmI0doWKgQAptWxwo8j1iXILoXhBDv2xcpASHwQRK6lqB4YEKMli1LRruDB+1P7qCnRAky4pUsKRNdiA5nlihUCHg2eItmnjXBq+bIEXnrAuYNNELwFilCl9y8eaQbM0Xcl7/8YsfCuYywXiYlZU0Q6Z/TjvTjffhQms9FC196urxPBw6koQXBe2OdqkVwzJjsvTuuXwcuXZLTMTGPd/O5uIhzy61OLXIfheAV1+zNm44r8/XX6UYWHTEBuuHXraPxb7/VXhOPGSx48wLNmpFJJTFRCgfxIA0Koi7a9+/LjgqO4ttvafj22xRHskoV2u60adrlzp+XX65qvv+e2mfd3SmA6M6dZL21hvqtOWdOTmtvmaQkekOLB/OxYySCAgJk13u94L182XpznRCNwnHSXreGyEjLlqJLl+RLTJ2tYNIk2TTVvTsNP/vMtk+heNE1bgy0bCnnv/qq3JYQbuIjRjh2HjqkbZ6aMoWGInDre+9RPMl//rHeKUbtZrN6tbmozg6KQpYYS2UdO0b77OZG19u5c+a5dsV+imtSINKY2WrBUH8ECHeJ+fPpeilenNKSxcWRuhStBs2aoVwFFyTDGwuM/Wie8Bdu3BiebZoBABrdWk0fS6pjXqIEEBpqoR5padr7LSWFsk3oMBjoG/nkSerMVqcO0L7KVZv+yh09yFXmdknyo7AoeDPunWPH6P33xBM0u1o1Gv7xh/Z9rly7Dj88QFgYfV+88Yadt7e4D3bscL6Qyq5bQsZ6RhjkuvZE1FEUOk+3blm3KN++LY+L+Lg8dYquDV9foF8/mnfxovbZCsD/vOpj9OhROiG7dmVNiIlrXPjIPHwor8vLl7XPhXPnHHP/5yZ5RfCmpNh+rt+/b9950gteRaGWqJxEdPr7b9r2r7/KsodQ6xWCgmSr8OPKIwiT9tjhlDi8M2dS/L6qVSlY57ZtNF25sqLUq0fjv/5qf3kxMYoyY4aiNGxIcXzv3NH+/88/VKa7u6LcukXzVqyQgUHF8t98Q/MKF1aUSZMUJTGR5j94oCi+vvTf5MmKsngxjbu6Ksq+fZbrVKSIDNTp4aEo0dHyv/h4CkaqKDScNElR/vjDvIyEBNpW69a0X6KM1FRFmTNHUYoXp/I7dVKUkycVJSKCpjt2VJSJE2m8Tx9a59QpRXnmGZpXu7bcRno6BVSNjZX17dCBhl9/Tf/rj6eagwdp2bfflvOuXVOUvn0VxWBQFH9/RfnoI3kcWrSQ22nbls6dCLr655/m5d+8qSjHjtH/BoOixMUpytKlMhhqaioFkwUU5cIFWuerr2j65Zfp/AKK0rOnouzdqygHDlA5IshrWJisD6AopUtT8Nhr1+i8C8qVk/sA0DKClBQ6hgKjkc6xnnXrFKVBA0V5911FWbOG9h9QlOBgRZkyhYLjnjqlKOfOKcqrr9J/zz5Lx1ac1+XLFWXDBipPBIUNDVWU9u1p/Ikn6DiIfTxzxvJ5E9cKQOUoiqKUKUPTs2bRNSmOR0iI6XpISaGiK+G0XD80lPZ5yhTzYzl9uvZYJCYqyunTtJ+TJtHxDwhQlEuX6H+xr3PnWq634NAhRXFzU5QXX5TzYmMVJTnZdA4SvIMVBVDmVaZ9OYsKpttOURS6hwoVUpR9+0yxiMVvzhxFqVNHUbyRoAxof5munWefVRRAuVGyoXLpkqLs3KkoXbooyrhxCl2HcXHW61u+vKnwy9sy9vXWLUW5fNn2/WWBBw/kY0zsqxITY77gP/8oSv36irJpk3b+J5/IHRXPhwzSE5KUtIQUbdni2t60SVEA5SSqKgmufrT+sWNyWUvvkHv35L0jrpUJE2i+mv37tc/L1FRFmTdPGyS5ShXzGOHJycpDFzdFAZTFeEV7EsuXp/qrMRrpuapn4EBaZ+hQ+Ty5fNm0z0rJknR9d+kig0Lr9yEpSb4zFIXigef0vXrvHh0LPYmJ2v1IT6cA1ALx3Gvc2L7tGI30rE1JyXxZRZHvbIDuQ/1xVhS6/jw8FGXYMDkvLk7GTE9Lo2DaxYtbvnfUN2upUlIzKIqMya6Osx4ZqSj//acoFy9q17WGeKdHRNB0377yGfrPP/IZun9/5mU9IrKi11jwWsApgjc2Vl5su3crym+/0XiTJory2ms0/skn9pWVmkovVvWDrmNHuqnmz1eUJ59UFB8fmv/SS3K99HQSfkIAigee+terFy27YwdNlyhB6xmNivL88zSvXDlFuX1bUQYMUJSKFRXlyhVaxtWV/hc36uTJVNbhw/TfwIE0Lfbd19f8pq9fX1uf116jskWSDks/Dw9KyCAeCK1bk5gQx1sIdbEf9erRPmzdSv+FhckX4htv0MPKYJAiS8/06bSsnx89LG/cIAFnqW5vv03HR9RFBCzPEBLKtGnasn/9Vbt+zZo0PyGBxPt339F0tWr0v6ijSLwxaJAU/vpfhw607P/+Rw9sPz/5klD/Bg3SZjIQwdZbt6b1jUYa9/BQlPHjSVDXq0cvzZ075TJTpsgHaFZ+mzYpytGj5vNPnKDyAUWpUIH2vUED+gBRFPlithRA32gkoafeRny8nL53jzI2CHEP0MdoVJSiKPTuBxTln8DWNNKlC5V78ya9LMLCpGgAFKV5c9pmSopG+Gl+kyfTPVu4ME37+CjK2bOWrzlFkddo2bI0HRtL190TT8jpjLKfcDmkKIASawjQltGxIy3z0UdKYiJd6m5utNuXLyvKwveOKwnwNq+ru7v5C7VJE9p+xj08bRrd+r6+9F320MfPtH5fw2LlyMgV2jI//9z6vqpITKTL3dubHiVKQoKitGtHiTWOHNEu/OGHVHa/ftr54qNCnNcM0pNSlAteVZUr7uWl6H3mGfogvX1bSV36s6IAyja0UI6GZnysiQ+TP/6g61s85wR//WX5fFeooD2Gq1Zp/z96lOoNKMqIEbSMmP70U7neITq3d1FIKYS7SnrdetqPWP177Zln6DyJDyxBt260/OzZMpvJkSPyw8/Sr3t3rdCrXZtO+Kef0rundGlF8fIy39aVK3T93r2r2CQqip5Lbdpo56elUfKdsDApcvv2pevyzBn66BN1LFfO9jYSE+ljWTyTn3iC9slopGdHv36Wxax4b4mfWmwL3n2X/vPyov8vXKBj37Ej/X/zplx/0SLtuu+9R8+CK1doWtQvMJCmxfu6e3eaPn5cvnMBeqfYQn2MvLzoWHtn3Ou7d8tjCihK7940HRlJ18kzz9A7xpoxIRdhwZtDnJZpTbyQ586lhwOgKJ07K8q332ovZEvcuCFvsMOH5Qvy88/p4gVIfKpvyMKFpSVLsGOHVgiKh+mcOfLFlpREby+AhKbg3j0ptIWgBhRl4UJ6kInpCRNo2LMnrTduHE27uZEVUVhTAbJoCOLi5PzBg+W4yDrk5UVi8+BB+uoFFKV6dXpRKIqibN8uXyxqi7ooJzqa9kFMN2pEwyefpIcPQAJTiJ527SyfCyEuAUqJJQRmpUr04BDCxNub0mMpCs1ftEg+SMX+ffSRtuxhw7TnZsIEy3UQL6sZM2haPAzFR9O//1IGpcBAEgY+PmTdFYgXb2IiWflLl6bzI64BIbzLlCElJOpz7JhiZhpU/556ispVWz5ffpkeoMWKUZ3OnaPzXqEC1S84mIS3qyutL+rWrx+9iMW1tmYNiQyAPoz0iPRkfn7mliiRZk38/vc/ehmJ8yTOy8SJVPa8eRorU+vWdIj2T9tDVjdLrRNJSXQfifvx4EFFWb2axl1dSXDXqkX3hThWBw5o69WoEb3cLdGkCS3j6Un1VVsIk5LI0gMoSV6ByqhBMdr/BHXq0Dzx8aOquqIoSurndC2nu7gqxuBg5XqTXko6Mj5aVOnVNqxNMc1PPfSv8ssv2t3wRoJmxjy8phxHdXksAG2GyYQEqxYqtXH26/HJ0rIPKMr332sX7t+f5nfqpJ0vPtbFL8M6HD1vlWnejQ3/0geIOH9//qnETKTn4m/orqwPf4vmjx5NZY4aZf6MVBT5LGnXjj54liyR21ULPvGMEr/Zs2U6O2GhFi1wQoAoiundsQltTLpYURT58Xr6tLY+olVs1Cjt/CefpPm//CI/oDdvls+gBg3oHn32WWodFM/Fr7+m9R88sP4cWL1au623Mo7dW2/pT68WYV3Wf2D9/LMse/NmmidE/pIllMJQ/O/nZ3sblp5fe/aYPiQUQFHOnzdfT1jfxe/iRe3/RqM09oh3mxDAok6i5Q4wzwoq3t8rVtCzR72t+HjSCuIZoShSQ7i40DAkhOanp9OzpVcvrXBXi23xXAZou2I58Uzx9aV7csQI7TotWtg+trkAC94c4jTBK276zz6TX9GvvCJvcnUeUTU3b9JLX7wghDgVgmzhQnlBenrSS/u//6w3cURH08OvbFkSo+LrVjxA9uyhZj/APH3rjh3yBhO/8ePpIQtQc77YnwoVaB1hzQRIvKqtfuLmVRSyLqhvXPHyEj91k3pCAj241C9zIWC8vGS6zM8/l5a9kydNokDz69eP9tnSg9vSg09Y5AGyhtaoIR9wguPHqQnbGnr3C4F4MX/zje3m4vffp+WENVOcr0mTzJc1GrVpiK1hNJIVBZBNqeIjTBzPbt3kB0uLFtKy3aWLvC42bJAfVRMmWLaWZAWRSviHHxRl2TIa11uARP3FudAfB7XoAOgjc9cuGs/MIqTY11poQnygDRokxe1778n/xTXo4SFfKE8+Kd1cFi40LzMuTmvNuXNH2xpw4YK876pWVRb8YFRSkfEBIz66FEXe48WLW667cAH68ktFUehU30bGOipr6vgBl03bfqncHsXPT+7y0aOKUjvgouZ4pxjI+p3i6SfTY5cuTYXdukXPjR49ZD3u3FGUmBjl+HH5HTZwoCKfoeKn/yB8+mmar/8geuop7XoZLSNRzXua5l2cuJzud5VguTHwC0UBlPl4VdneNMNNaehQKnPIEJquV0+7LXFv9+2rKApp6/sgMfrwpEqMqj+cAXoeA9SqJu7X5ctpXvPmcr0MEfU13lMmTlRtt1IlWnbrVm19hBAuV057L4rlt21TlGbNaHzlSnn9TpmiLWf2bJofHEzT4lh5etJ9FxIiBd/8+dp1W7eW69pyIfjxR3k81GmxxcceQEaPqCg5/eWX8t0hfpZcOATCPa9VK3IPAqgFQIhTcUz0fPmldhuiZUmgr0OdOlrDkHhniWmDQbuP4pk5cyZ9XKrLOntWfpSI++bzz2m6Rw+5XFKSfA8CWrefEycsv+PULS1GI7lcAGQUEJZ/YayrWNH6cc0lOLXw40rJkjS8fl12oggOlr1Fzp+33IHo6FHqALFnD3WwEZ1vRLiqfv0oCkDXrtRZacQI6rzkYuX0h4RQzMKLF4FPP6XeMQYD0KgR/b9vn+ywpI8w37w5hXF69VWgf3+ad/Om7DAQFkaxEAHqiBMfr+0VvXw53Wb161PnpP37gRMn5PIAhUADgK++Ikd6gCLti+0BgI8PRc9XR+kXeVuTk2WcpebNtemhLKWIqljRPHaUyKE6b5758uoyFi+m+nt4AM88I+fXqGE9YCog89neuqWdLzoolCpFca2sIeorOtmJzlwBAebLGgz2hQowGIC+fWn89Gkais5gY8fS9bRmDR1bg4HSgJ07R9fK778DvXrRst26UYSDJk0ozI21OFr2Inp+3bmT+X6KVGfffqvtLKTvqKROblG0aKZVsHYrWUQcw6VLZTiDPn3k/5UrU7yy1FTZsbRPH7mMOiarYOdObWeVmze14feuX5fXTvHiiIwyIAoZccTEvZmWJsdv3rQc5/gQRZsQwYPnzQPueGTcVyIFN4AW5eS2Iy/GIz6eUiNPmQLUrg2MepPukRhDMBSDAR4KPdd2VHyd7g1RB6ORQlk8eCBjU6ekANWqQalVC28PeIiHD6mf54wZAP76i5YRz0zVPqSlAVcP0f4lXNZ1XBIdgIKDabh/P3DvHoL3rjMtkn78FHUaE9y8iZRb9Jx2DQlGiy6BNF9cg2KoD4MozkPGPX7uHHAHIVTOvTvmy4l9Eb3jX3pJ3q/inaE69qKD2xHU1d4Glp4piiLzR1+8qI0yIa6F0FB5XGJi5Pr6nNsiLWFMDD3Xxf1TsiR1RI2MpCDN6rIFIvpNTIx8NltCHZFAdOLeu1cbDu7kSWiyqajfP/p9s4Q4luHhsmPg8uV0vwoshbbUdzTTT69ZQ8PGjemBId7bgqgobb0UhXqlAnT9i/N05455J+sbN+TxEJ0dxX7UqEHhT8Vy6k7b6o7w1jrKvfyyHDcYgOeeo/Hhw2lbYWEyI01OE53kMix48xKWBG+hQvTCDQykh7+lLFzqC3jzZnPBC1DPyjVrKBpEdmncmIZbtsgHv6WUSq+8QoFBGzSg6Rs3tA/P0FDaJ0WhKABin9TCYsQIEuiADDIqHopC8IaGUuiu8eOlMLCFp6cUt3Fx1JO/YUOt4LWUwatiRRK4QlyXLCljJi9caJ6+Vi14Rc/lLl3k+vZgTfCKh1jx4rbXF+m8RGpkUQ8RhSG7vPiijPMLSMFbpQqdd0GPHnSegoPlMu+9R0Px0TZ9es7FLkAfaABdY0JkWNvPF1+k8339OrBypZwvBK+IYhEdnSXBmyXataMyY2JIhdWpIz8CATomGTF8TS/EiAhZD0sv6y3acGO2BG9CoRKYPh1S8Ipe65GRdE8K1Fn8AHrJCtGVcd+XLAlUe6q43GYGzcvKbfd8Kh7t25NmEJdOtyZ0j3jXqABDzZoAgIdwxeSHQ2g/XVyAhw+hREZh7YyM+N5xcfS7fBmIioLh+nXc3ncJfn4ZYheAknHvJTVsoTlWUVEUyCT1Jk173NPta8bzVsmIiR792w5gxQq4PpT3tts5neC9cQOl/Wm9Tq8Ey2tOL3ijorSGCnFPZ9zDZ88C0QjV1FeUD0CGGRSo7zPxEX/9Ou1Perop89oR1MW776oiQFp6pqSkaD+URMy5hw+lqAoJsU/w+vtT9AixDXH/FClC17SLi7xX1R9Tqanad9hPP8EqasErrkURzlE8x0+ckEYSsY4+MoOtSA2qD0O0aUPDe/coeoIgO4JXhJl84w3L8QOjomS9PD1p+OOPdF7V+x0dbS541eI5NZXOk1inRAmttlCHHFPHzheao0IF+Vxu1oxiKKp5/nkaiuugXz95fViK5pSHYMGbl1B/rastDgaD/MpXP3AF6ofFmjXy61YteB2BELzr15P4DguzLbzEw1j9hS2sceIFv3w5lRUSIsOdhIWR2H39dZr+6Se66YWFt1w5uY3mzYFPPrGcb9US4hgDZKXy8ZER89WCt2VLssoCZIk1GKRFduBAMikVLUoPKH3QUSF41ZH4hfXDXqxZY9QPY1sIC++lS/RCs2X5zAqhoUDnznJaiFkAGD2aPiIAKW7VNGpEGcsAekiKD6Kcon6JZiZ4PT2Bd9+lcZF5LTlZpmcV4iIqyvqLPae4uWmvB7V1VyAEL0AivEwZee9YelkLwSssfzdvaq1+N26Yrp3/7hfH7dsWBK/+40oEtheIVp2yZeULDoBBfZ+rt5fB2y8/wF9/QRP+zDU6Q/CGFwFatwYA/IJn8PfZcMQnu5nE/dYl13Fmo3wpn996DemX5Au7Ck5j9OiM2zo+HoaMl/6OuxlW4gxhNWECGQFDQc8hd2OqNoxWxsv+3zL0kR16bDOUQYMAAJtAVkm/q6fMLIeu92m9sMo2BK/+2OiuqzNnpIVXIwTFOq1by+dbnTrSAg7I50BqKq177hyQmIhkF2+cQWUAKt1l6ZkirIaCn38mESxWMhjoY1+kzrt3z8xCrUG9DfEcVH8whloQ9lev0jtAiKy1a63Hgldf05cvk8AS4RxFaMUTJ7SthmqDi0B8hKxapRWygPYZ6+pKH8kCIeizKnivX6f7x2Cg56ewHJcoId+FkZGyns8+S8/SkydpP9XXz5075vGf9VlSb93S7oe4R21ZeEWZlSrJZ7NojVJTt640OgHAa6/JEJDx8dlLz/2IYMGbl7Dm0gBIwfvff3RBLV0qv870gldRqCxHv6gbNKAbVlgE6tWzbaETD2O9hReQN/nPP8vpN9+k2LP/+x+JzXbtSKDcuUPWXb1LQ3YoVUqON29OQ/EmVjdj165NsQhnzpQvmKlTSdQNGUIPox49aL46042iyAe9eFgEBgKdOmWtnuLc3b0rLcj378tMb5kJ3mLF6Nw8fEjH3lEWXkDuV5Ei8kEKkBD6/Xdqhmva1PK6ixdTSkx9rOeckBXBC8j0sbt2URPcgQNkaQ0JkR+JuWnhBeQxdHPTvlAFbdpIc2hEBA3FB5T+5R0TIwW7WPbGDXMLb4ZYCK5B146Z4NUHsNcLXp07gwn1fa7ensCS1Uedj/izz4CJEzGh2CwYjRm3U8azsGah63iyhBS8g7pfw6ppcrp56GkMHpwxkXG+4uGL627hNC/jWB08CHggBYFQiVxRh/R0k+jZ4d4WwzEZD+BHGdQMBuzrPB4AUCj6rPaYiIx/AAZ8FIxnXrMheNXHQycYz5yRFt7/dkSbL1e6tPywVFt3AXpOiufX9eum+p3xqAUj6OPH1MpsSfCKc+PhIS2ZO3ZI4R0cTKJPvIeuXpXrZCZ41RZegSULr2i5q1qV3nMpKZRUxhJ6C+/Jk3T+ihYllzE3N3rWCdcWsY4lwTtvHmW5VMdDB8xb0dQfpCJJlCXBK97Zwt1NLXiFm0aTJnQfP/ccxUZet06+99UW3ooV5Xvu4kXtObNk4bUleO218Ir6BgfTc3r2bK2boMBgkImqWrQggax2r7MRB9zZsODNSwjxIJrtAHPBe+oU+ci+/DKQYYHQCF7RTOdo6y5AF7XaumDJnUGN2J/bt+XDTwje2rVpKG7cWrXoofv55/SyB0hUCmF86JC5S0N2sCV41T68RYqQtU9YAwGyUI4ZI60tInOb+iEcFyebL997j77Up0+33wItKFxYWkvFsRPbCQ7OvDw3NymQbt1ynIUXIB/cSZPooaj/4ImIkILSEuXLk8BxhPAWqF+i9gj7ChXol5ZGllERZL1TJ3nMsujDm2Vq1qQPg1WrYDHVWUAA1cdgkL7flqxjgEzcULWqvCdtuDSUa14CigK8MtyK4BXXlt6lITPBq74PsiJ4CxcGRoxA5aYkFPbvh+kFHZpyHU1VgrckruG/v+T0i/VOmxpiRJm+5YrgtY+0wmrDBmDXaq0FTrmdUQeVha/X64UwFcNRHhfwb88xwJIlcG3aCA/gB1fjQ+0xuXED9y6QyLkYG4zIFBuCV4goRbFp4U24kiEEk5Lks7F4cXLZGjUKeOcd/ZHUipkM/+7/XKTrmk3BKyy8/v7y2rl0SV5j4t4S7yFh4fb1tdyHQFwLWbHwqp/rAwbQ+EcfkY+oOjkOYO7DKyy54v0hMk6qhZw1lwZxPYuhfhvi/VWzJiVZ+vFHEniA5WydQjCK1jW1FVb43YtWUhcXMvDUqaN9/6gNQ8KV4NIlcwuvXvDqW2euXZPHv3hx64LXkoW3cGFqVXrrLet9Oz76iPr3CHdDb2/ZkSEP+/Gy4M1L+PnJF7V4CFgSvMLHSWTfERew+gEkOpg5GnHDApkL3rAwumGMRulTpbfwCvTT+m3s3y8fYjkRvGqXBmGFtOTSYI/IUT/cBeIh4+dHD7Kff7bcLJQZBoOsgyjfXv9dgfoF50gLr4sL8MEH0probLJq4QWky8Cff8rUts8+q30hi2vB0S0lgt69zf0z1SxZQvdNM8rWphHjasS91bChtulSb3HVu8PoyxPXWatWNDx5Utuxz5rgteTSkBXBm4HYzRkzgKQQyy/oTjWu4YlQlQCOU3XgyyjTUKSImbAKCAAahGs/FBJFxzXxovf3R/Ey7hg8GIhGGBaWHg289BLCihhwCtXM9yEqCoYM14yIF4Lx8wY7LLyxsbKVplgxGI3khSAsvJWCMwSvOJY+PnQtP/EEdQy19KGrFjNnzgAATqRXMf1tJnjV/RTEufH11ZYjLLDiOIr3UEb5Vu8Jey28lgRvuXJkxBGubVOn0j4L9L6sly7JD5AMP3CNQUaItbQ02clWuCRERcl9EUOA3lU6H2sAJMRfeUV7jPQIwStEt9rCK/pSWHp3qe9DcS+GhWkFrzULr7V+IUeP0r64utI5VNc7s05rKnclq/j7U8p10VfEYNC6NeRRWPDmNcSFKSy1wndKCN7Tp4Ft22j8+nW6uMTNJ5oZgNyx8AJaIZ2Z4HV1laJNNAWKB2iVKtrOT9YEr3i5rlpFTVdeXjmzuImHSPXqsunJkktDVgSv+iFs4UWebfQWGb3lwd711WlAHWHhzWtYEryZ7acQvD/+SMfV3586koiXz7178sWQGxZeewgIkPc9IO+dhARts6F4mVepIq/J48e1HaWuXctc8Ir/mzSh45GaKjuU3r8vXYr0931mLg2WLD4W7pPXXyfj2I0bwJc/0jWecPyCRqD1qH8VHaupLFSnT8tnpdqiKK6JxER5rHSW8bhzGcsL8ZDxrBWplGfPpmzkJ08CVXqqzkO1atT6oigISqUyO74UjGJV7BC84hgHBQHe3rh2LcOY60r1DUiJ1i5XvHjmHTvVfT8yroV/Uyqb/rbLwuvnpxVF1iy8wr0qM8F786ZtC6/apUF0PitXjt4ZEybIjmhq14S7d7UfYNeuSaOPJcFbqZK8xsX7R9xPkZHyvomOlmIvOpqsygaD5We4aCGMjNR2VlYUcwuvWvCKe0cIRDXWLLzh4TRuy8Kr74QuyhIfp8WK0THNSqc18V7MKkLwsoWXsRu1BRKQD5pSpeiCSk/X9i7es0daDISvkcFgboVxFC1akIWveHF5Q9pCiDNxg4oHnoeH7ATm4qJ9sasR+6F+KGYpBpSObt2oqey77+Q8S1Ea7BE56oe7IDcEr96lIasWXrUFw1Yos8cV8VK+f1++SDOz8LZqRf7h4t7p2pWmg4Pl9SWEoLMEr56AANmRUi3exPmtXFleG+J+EW4xt25JsSCuC2sWXnVHGiEURIe1MmXMX4him1FRtI30dMt+omos3Cd+fuTl4eYGnEum56Drgb3a9a5d076k792TxyKjzB1niuDJDv4wutOx+mXOHQwZApzeadvCm+AVjK++kv+npJArxKlTQGBj1fOpRg0zwVezparTWkoKiV21IBKC14I7AwB4FNP5tmalNUe8M65eNRX45aoqpgYsU988Ued79+R1LwSv2sJ744a5hVcYXgQ5tfDGx8s6CAuvOhqA6Bh7/Lh0axDHpHBhug/S02U4MiF41QKwRg15/IQIE/+fOqV1CxAnQjxjw8LkvaOmcGEZQUH93H/wQNZTb+E1Gm33P8nMwnv5svZ+SkuTllm94G3ShIbCp1/sv3gPnz6t7ax57578MNP3G8oq4t3CFl7GbvTWO9FkoY4SoGbTJhoWKUI94EeMoA5BuSVsKlYENm6UsVYzQ//AFg9QQL5UK1aUcQL1VK+uffDkxJ0BoAfl5MnSFwvQujSIB449glXsm/oFkpsW3uy6NIiHuZ+fffF2HzcKFZLXohB6mQleX1/tNfDsszR0cdEKOoNBG23DmRgM5j6QiqIVvPrnR/Xq2pYU9YvcmoW3eHHpYy8saNbcGQASMWp/86gore+lnYIXIOvqF18A10Hiy+vODe16ly9L8Siap4V/ZIbAOnW3CPbsNSDVn8TV3t+j8e23wO0T2rjCaTcy6pDxor+VEowRI6gBTRirhg7N6KdUTWfhVd+Dbm5YuNIPH45TPXP1Pp5WBK8woCshdF4TrugsvPa05gihuncvmYvd3VGtc1mTt4zJ4BYUJMWaEKPi3Pj5aUOcWbPwCqwJXrWblyXjQWCgvB6FqFa7NAjKl6fzm5wsD5I4JiVL0ocXQKJXbTBRW3jVglcgBKIQhAJh7c3suBsM8nirz7EQt15e8n8x79Yt2g83N1lvNeIeuHZNik9bPryAPCZ6wSu+ckSrhth/USdxMQQHy+ec+IDMikuDJdjCy2QZtYVX/XAA5E3t4iK/gIXgLVWKbsaJEyG7LecSbdrIL+rM0D841IJXtB2KoSU8PbXbUj8UHYV44CQnywgU9oicoCDpUydeZI/CpSGrglc8zB3ZUSwv4eoqH95JSTS0Z1+FW4OfH5ARfxWA9tyrxVxewJJV9sEDOgbly9P/6haQ0qW114t6XF2WvjOVuCcPHqShpdjeAhcXea3po0MA5oI3OVm+2C3cJ++/D3z7q66lS1jNLl4kMe3mJp1+xfWdce+5FKMyH3jRs6ZXizsYPhyoXJhE3EMDPVMN0VoLr2exYDzzDF0WwmujTh161E7ZYF3wpgcFY+kyA76e4oo0rwzRq242BswFb8b64lvlgScJSyVa58Nrz70unrFqP1F3d5NXj0l/WOoXYMnCa8uHV5CZhVedJEl9jg0GrR+vOr6t2sLr4iI/ukQrg/qYqFsXK1WSz+Hy5aWor1HD8gcgoG0lBeSJsMeoYMmPV+0OoI/SIM5LmTLa97lA3IfCCuzqSh/xYh9v35biWtzbosyiReX23NzM36Vi/8PCtNsuXVqKbyF4c+rSIIxsLHgZu1ELXn0zkrCItmpFcWIBaYFRRx/IS9iy8L79NsVO/PJL22WofQZzauG1hK+vtBYB9oscg8HcrSEv+vCKB1p+9N8ViJeowB7B+/LLJOBGjdK2MKiv0bziziDQW3jFi7psWXrRq/3mAXqeqK8X9bgoKyWFhIcQ0cWLS2H7zz/0ESiEr7W+AWp/9swEr9iOh4fFTjcGA1Cnk+65oQ9zV6qUFC86wetVhu69GBe6JppWjMbkyUAx14xjVpn8WysHai28pWoFY+VKMvYL3fDPP3RIP51XBnHIeKHXqqU5jufuBpse1cleGdedELzCBeX2bRLqFkKSAUC1lnQu/JR4KEnJWRO8Oje4xNJV8OWXMs+BRn/onymWLLxxcdLqKu6rwEBti15mgle4cwQEmLfeqf14xXaKFqUOemqE4BXWWPXzTy2O1QYRV1fqy1KyJL0j1cfPUiupsErqLby2jrt416qvcyFuCxeWHwdxceR+YMt/F5CCV8SvDQkhYRscLEWkaEEUH37iYyI4WNa1dGlzl0jxn4uL9t4vXVoKauEekVMLL7s0MFlGfcHqL7w33qAerHPnmh7aJvKq4FXfZD4+2oeapycwbJj5TapH3YyaG4IX0Fr1siJy9JEa8qKFV5BfLbxA9gRvWBhF//jgA+18teDNrQgN2UUfi1ftziBQ33MlS2rvL/W14+MjX/jHj5PVy82NjmWVKvRfQgKwfTt9NNnqG6CO1CCEgLjX9RYfdWIWa25RXl7ac1qpkvaeKl1aZsXTCd6ASnT/3k7XdZDKOGZutapr66FPKwwpeGfOJL0/dLgL1vVehriJs7S+0gDiPYJNl1uSh07wlitHxzQ9nbZnxYe3QbtApIEscA8u3ZGWRns+bnXL3A6qgpEjgb//pmm1y6bZM0Vt4fX3l/eNiH4g7gUXF+3HibX7Qu02AVh+DqotvJbcGQR16tBQL3j1Fl59a+OiRWQRLVxYe70HB5s/2zt2pGFWBK8lC69a8KoNVffu2Y7QANDxUN8H4h43GLTC3t/fvN9MoUKyrmXLmp8X9X6onwOlSsmyrlyhljHROsad1phHhi3B6+dHPVgrVHh8BK+lJtSs8igEr/rBnB3Bm9sWXnVHoOwK3oJi4XV1NbcWZYXsfvw8CvTZ1iwJXvX1oQ46r/8PkPsqfHRFWl9XV6n6Zs2iYZUq1q8hdaQGIQREnfQWH3vvEXW9y5TRPuPKlJGCV+fDG1qDyr2WRNfElUPR1GouPhKEn6c4hhkW3jvphUwt3UJrAWR4++IL4MVlXRAw4h3t/gJI8go2acFEtwzBKJqg1Ra469c1gldRgA8/pMSN9eobcNdA9b137k7WPm59fDTvCrfqldG/v+y/ZJeFV7RwiWMuxI/6vlK/j6wJXnWrF2D5/rFk4bVH8KrdDdRC0FqEH8C8RcPLS3sNd+tGwwsXyCptz4dGZoLXzU1+HNy9m7mF181NKzLVH9xqgVusmPY/gASvqGvZsqQRhPDU74f6flK7NFy+LD/6XF2z/55gC2/mzJo1C+Hh4fDy8kKjRo1wQPiKWWHlypWoUqUKvLy8ULNmTfz555+a/+Pj4zFw4ECULFkS3t7eqFatGubMmZObu+BY1BeoraYFYTkQ5FXBa6kJNavUrEkP3pAQ+yJDZAf1yzcrYvVRuDSI6BGig4a9ZetfNgVF8AYE2Neh0hp52aVBb+FVhyQT6K066hed/kUurGOzZ9NQLVaE+8Lq1dppS6gtvEI0iDpZE7yZfQBnJnhF8/SVKyReMzrqlKhH98eFB3QeN/x0h6IvZFh675XIELxxcdRUnCF4R3wdbEq6VbUqsGABZa01JbawtL8AUv2k4H3gqhO8gYHazmAqIWswUF6bGTPoUR/rnpF84vQ12flSLexsoTpWpdtXwYIFMpmhTcGrDkumKweA9l6wR/AC2usvJxbeGjXoeSfCRdpr4bVWF7Ev6n1q00ZGP7p4MWsWXkud1oRwVfvxZmbhBbTHSX1fqM9/8eKWBW9EBLmNCGu1+tzoP34Fagvv5cuy/uoOwFmFLby2WbFiBYYPH47Ro0fj8OHDqF27NiIiIhBlKVc8gD179qB379547bXXcOTIEXTv3h3du3fHCRF4HcDw4cOxYcMG/PTTT/jvv/8wdOhQDBw4EGuFQ1Nep1Ah6fNkS/C6u2tvoLwqeC09cLKKlxd95R85om0ucyR50aVBNPmmp8smxiJFLHd8sISXl7Z5raC4NOR0P9XXQl5zabDHwmuvSwMgM3eJl7L6/wYNaCh8C20JXksW3swEb04svKVL0zkX96owfPj4oGQVP7i5AZHpdE2EIhpPPw3TR0J6eHmkgFTsw5tRJsEbg2BTBl+AsqqK7OFxcfRtYdI4quP0MFAK3jjoXBoCA7UWQZ1Lg5r4jE527ru20j0fEmK/v776WGVcC2XKUIPgp5+qltOHOrRm4RVYsvB6eNh+N2XFwiuea/oWS4As12L+0aNaH97Klen5VqyYbSOIpfePuL8DA6l+atcYR7g0qIdqwWvNwquuk7qegFbwFiumPR8GA+3Dc8+RyOzZUy4nsObSoPbhvXIl5x3WALbwZsaUKVMwYMAA9O/f32SJ9fHxwYIFCywuP336dHTo0AEffPABqlatinHjxqFevXqYOXOmaZk9e/agb9++aNWqFcLDw/HGG2+gdu3amVqO8wzqsCf6Tmt61A+JvCp4AwNl83J2BS9g3jTraBzh0hAfL8PBOELwurnJYyaanO11ZxCoH34FxcKbU8H7uFh4k5Nlh5PsujQ89ZTWOmzJwisQAtgSasErVKEo98EDba94ewWvEHtublQvvYUXkJEaRHrookXh5kbvcpG9rEmFO2ja2GgSJYUqhSI5gI6j8VYk0u9IwWstQeX48WT1FfkQ1ELUECwF7z2jBQtvxvFP/fe0tKgWK4aNG6kvoOjflexH13Dw4Y00o25d+61t4hyHhSHevRASEuhS+fhjSuhhIisWXm9vbWdeIXKLFrVdL/U1ZMvCe/Om7HRt7WNKdFz75x9tqtxChejg7dljOy57aKg0EOgFb5UqtB/i3jl+XNtx0xriOrx9W8a21sewVWemE07Utqz12bHwBgbKfVeHmxTH39tb63dtzaXhzh35HMluhzWALby2SE1NxaFDh9CuXTtZGRcXtGvXDntFMGkde/fu1SwPABEREZrlmzZtirVr1+LGjRtQFAVbt27F2bNn0V4ddkhHSkoK4uLiND+nIi7MzC4+8UJRhwXKaxgM8uGRE8Gb22RX8FrKLKTuDJRTRPmOELxs4bWPvCx41Rbe8+dJSAYFaV+S4hoJCtKGmwLMLYYGA6Vz1a8L0Itd3BceHvb5Sp4+LZupxfPJaCRxfvUqxQkXqZzttfCWKkUv9NKl5X/iZf3kkzQUPbQyymzcGLgDuiaKuUWTIMmwVLsWCUFgRVrO434U0qMywpIVDbb6GNUHx4C/P5LdSAy6hqoEb3rGR6WIQxwYiBsutB8xP/5uWhd+fnj3XdJ5W7bQ7LQg2kjhmxktlmpzc2aIY1WlCkaNosfPiBEWlsvMh1d9feg7ggoDTGbvGnstvNu3U7SB4GDrzf3Cj3fhQm2qXIBcHjJzcVO/G/WCVwhdcZ0uX05Dd3fzfVcTEkL3g6JI9x3RMVJv4RUtyyVLWo81r66Tup6Adv+KF7fuU61G7K8+S5+4RsQxCQyU4v2PP7T1zg4clsw6d+7cQXp6OoroHnpFihTBbXWubxW3b9/OdPkZM2agWrVqKFmyJDw8PNChQwfMmjULLdRB5nVMnDgRgYGBpl8pZ1tL27enG0ofikePuGGLF7e/mdsZiIdoXha86gdOVqyzapcGteUqJz6kasTDa906GtrbxKlfH2ALr73kZZcGtYVX+O9Wrqy93p54gpYTsbpLlCBL2RNPWH6R9+kjj5l6fw0GaXmrU8e2O1G5clpLcc2a2hBQ8fFkavzqK/o4tOf51rgxiY9WrWha/VwW48LCK8ykGffunDnA97+pVKpQqkFBVKYq2YzrA+qwU7a+dQODPvwxDAbc8aB736OojNIQnaa79gIDEdCpOVLhjqIPM8RRsWJIS6MW7sKF5SFWCuvOjQXBm5xMqY7NaNuWPrR79DAZFAMCyBNgxw4Z1cp0fqOiyG3CloVX/7wWAisrgteWhVfEYm7Y0Przsnt3EoriI0qkys0KeoPLU09RmaLDWpcuVOapU3IbtqzGLi7y2t61iz5uRNg+4bbQujUNd+/WzreGPRZefac1ay3AasGrRmQIbNdO6oU2bWgoBK8jLLzs0vDomDFjBvbt24e1a9fi0KFDmDx5Mt59911sEgkaLDBy5EjExsaaftf0WXIeNSNGUDNIZi+EFi3oxWFDzOcJhBXGVpOos8mpS8O9ezLerSPcGQSNG9NQWIzEtL0UFAuv+kWQU2H/OERpSEwEtm6lcX1a7sKFyfK0ZAlNu7pSauD9+y2/yP38qK2+cWNKsaxGtKjpWtbM8PAATpwgS2pMDDVVu7tLy2F8vBQtX3xBTf7W/AcEVaqQUJ0/X+5nUBBFbRHWsjp1tM3uGfeery9QsUmGsIqJkR+jGccv3o+Wu3PgAlzTqVm62pPWXcj0rtMAsMetBVLhDveGdU0W3qgU7T2mBATCr3UD9Kr6H5biRSgGA9CkCdzdye04OlpqDJcwneBVh4rI4NNP6btF6CgTTZuSgBw61CR4AwPpe6BlS2mIRGgoiUujkayStnx49R9H4nw1b25WLw1qoWXLwqsv1xKVK9N1JTIhqmOy20uLFnTdiy+LZ5/V+rzWqUOuERUr0rQ9yY1EGru1a2ndmBg6keL5/Oqr1BtRCMvMBK81C6+/v7S66n14rQnepk1pf/W6wN+fXBfWr5fzxH0tXPHyuYXXaWbBkJAQuLq6IlI8iDKIjIxEUSsvmaJFi9pcPikpCR9//DFWrVqFzhnWjVq1auHo0aP45ptvzNwhBJ6envDMrc5Q2cWe+lSoQE/g3Eoj7CjGj6d4u7aaiZxNdgVvYCB1nkhOltmoHCl4R42iTgnJyXSeM3tw6mELb9YpXJjiRbm55b2PBD8/eb39+CPNEy9fNfoWH1sWK4B6aPXvbz7/3XfJmpWZyAFkhih9fRMSSFiJZ/czz9gfolB9/AMDKXqByKoF0H42aiT9AtT3nnh5K4pMxZohJvZeKIKnANzedhohAFLggfrNrYey0wfHiI8HXoibhwB8g3Odg2Rm8STt9XItLhBPFAGio8tjHZbi+KvT8eVceYzUhk2PEiqh4+MjBZiKJUvo1JtcK9RknHO1hbdiRdIyIoEkXF1JmN29S4I3KxbeLl3ow95CshAN9lp4BbY6QwIkQH/+mcRadj5Av/qKnJnV9dZbiRs2pI+0RYukddYWXbtSj8ANG2SdOneW953BQPHm6talsH5qtyFLWLPwAmQA27qV4syJ8wVYF7zNmpEAt/S814ccERZeQU4svPm101q5cuVwV/RKVHH//n2UszP1q4eHB+rXr4/Nmzeb5hmNRmzevBlNRABBHU2aNNEsDwAbN240LZ+Wloa0tDS46B7urq6uMIqexvkNteN6XkWdTjKvUrIkWTlCQ7P2lav2UZ47l4aOtGSL7EB162Zd7AIFx8LrSMEL0EtywoScl+NoDAYpROLj6ZoV4YhyA1dXagJWi8ysIIRUVJR8Eebkg1Cdzlsg3BoArSByd5ciRzRXZxw7QzFarsRZspLHIBj16lt3Q9JnYT55ElBggHfRIISGqsKupmuvvV3HAzXidP3BEMDV1dSar8antOoarl3bYtP9Z5/Rd4kF468JteA9coT6TYkEXQC0YcH0Ft6gINnJ2NIzOygIKSlkdBcNWmaULEnvJHd3xwheQXh49q5DgyFzkQ7QMXj3XfMWE0s88QRdaw8ekP8MYN46AlDr5rJltv3fAesWXoBybf/xBxnBgoLkdWGrU7s+M541ihfX7i93WjPn8uXLSDd9MkpSUlJww9R2kjnDhw/HvHnzsHjxYvz33394++23kZCQgP4ZloY+ffpg5MiRpuWHDBmCDRs2YPLkyTh9+jTGjBmDf/75BwMHDgQABAQEoGXLlvjggw+wbds2XLp0CYsWLcKPP/6IHiK+DMNYws+Pmnwz6/VrCSF4k5LoYZ7Z1/yjpKBYeP39ZTro/CzsAe3LsWtX251hnI2w+ojg+56ejm+RUgtevcAS4kEkp8gQWyWGPosoQxEUUsh/N9Er2OZhFMWkpZHngIimJXSMnx+J0GQP7bXnGhyIPXuAVato+vhxMr6VLUv97oSXBwD4lZVC55hLXQwfLvM/CAYOpPjAZcrAomgG5Hyrt7s6LJjewquOEmSlz0XPnsCAAcBHH1kpPziYKrlkieWWSg8PWbny5fO+McQSLi6gWHegi8LDg+LhZhdxzH18bItzFxdpkMksipO9tG0rx/N5WLIsuTSoY9n+9ddfCFS9WNLT07F582aEZyExwPPPP4/o6GiMGjUKt2/fRp06dbBhwwZTx7SrV69qrLVNmzbFsmXL8Omnn+Ljjz9GxYoVsXr1atQQmXMALF++HCNHjsRLL72EmJgYlClTBhMmTMBbb72VlV1lCiLVq2dvPbWoHDUqbwnLgmLhFa0It27l7/0EtELkueecVw97EEJKKDtb6YSzS+PGJASMRnPBGxICnDsn/Z0zPhaqtiuB1GPbkdSqDbxjbiKksu0Xvbc3XVaxsbQror+gELwGA1lTfxweCEyV6206GIh5qkR1p0+Twe7ePXI1UHsQlG0ghd/VkLqYOhW4f5+0o5qDB4F+/egxYymgkdrCaxG1hVedWlhQqhS5gFhwO9myRYY8XrFCBjYwo29fK39kEBpKFc3Mjzsv07UrMG8ejbdunbMPuRIlyFJsz/0RGkpNDTmxxqpp1478jQHHWXgVxfH3uSNQsoDBYFAMBoPi4uJiGhc/Dw8PpVKlSsrvv/+elSLzJLGxsQoAJTY21tlVYR4HhgxRFEBRypZVlJQUZ9dGS1wc1Q1QlBs3nF2b3KVmTdrPZcucXZPcpU8f2k8/P0VJSnJ2bWzToQPV9dlnafjEE7mznX79FKVSJUV58EA7v2tXef2XLq0o//6r/f/cOUXp2VNR1qzJdBOdO1MxEycqitGoKNeuKcr169plXm97UW4PUMq5XFKio+m/N97Q/KX07avbQHKy6c+j8w8qrq6Kcvq0/PvMGUU5cUJRLl9WFBcXWvTiRfN6BgTQf2fOKMpHHylKrVqKsny5upKv0wKjRsnK3L1r+nvrR+uVe216KMZbt83K/vdfuUqVKpkeMus0bkyFTJuWg0KcTGKionh7037MmvXottuyJW3z++8dU979+/KCOnQoZ+WIiyM52TF1s4Os6LUstd0ajUYYjUaULl0aUVFRpmmj0YiUlBScOXMGXSx1oGCY/Mwrr5CVacECK3lInYi/P4WD6tUr74XYcjQtW1ITanZ6cj9OCLNgt27Z9619VAirj3BpsLezWlZZuJDMp/r41/36kf/79OlktdSnoa1QgZJWWPK/1CFcpdevly3/+iiBQ0ZpWxdijIEYMIDGW7aU8z/4QAafMOHpSREEmjVD7T61kZyszSfy1VcUWWrhQhmpTYQ0FhiN0oUyIICSgf37rypKAyBbCNROuBkW3sREoNO3HVBoy284EW3uf1uzpgwJLrrxXL5MBkJh+bWLAQPIupvXWyhs4e1NTtUtWgC9ez+67Q4YQH7POXGhUBMYSP4pHTrYTtOcGepWgrzqx/sIBPhjB1t4GeYxxGhUlPh4Z9ci97l5U1FGjFCUW7ecXZPM6dePLD6BgTTs18/ZNco2Fy7QLri6kjFLT58+ilI8LE1jxnXBQ+WFF+j/lBRFGTlSUdaty/q209OlUXTFCkX59lsab9tWu5y6QSchQVHeeovGx4xRLTR5Ms0UlkI3N7p3FLJad+umKNWqmWaZcf26PA5Go6L88gtNN22a9f1i8hnC4m2p6SGXyIpey3ZYss2bN2Pz5s0mS68aa6mBGYZhcg2DQWtlyK8UKwZMnOjsWtiH8GsUPakcGbLvEVOuHEU7OHuW+hV99x3w9tvy/7g44GaUG9I8feGekgDFzw/L5rtCJPn08KAQxFnh2jXqA3v+PFlrAepUL3yHd+2ijm2iw53w33V1pXni8GuShwoLr0gn6+tr8rcsWRJYvZr6YYm+WIING6hPk4iWlp5O5YokYyEhVOTnn5P1Oq8HD2JyAT8/uiDzaMe1bF2SY8eORfv27bF582bcuXMH9+7d0/wYhmEYxszFILdcGh4R6ghwO3dq//vyS8ps5hpMbg2GwEA8/3zOOtMHB5PQPHmSvhlcXEhwVq5M4jQlRdbjwAGZR0BEpbKYC0B0WhMJlnTn6JNPSOiqAiQBAF5+mVrSL1+WAjsmRptVt00bcrn4/ffs7zPzGJPHk09ky8I7Z84cLFq0CK+88oqj68MwDMPkF/KZ4B01ilz2d+0yzwth8rcNCgRu3XRItBBfX4oaJfxjK1SQkb6eeorE5d9/k9Ds3VsGwxARGsTQouAVLbMZrSLXrpGAFuWrjXRJSeTeeeYM7XdwMPkFx8TIJBhhYcALL5DPcOnSOd515nEkj4cmy5bgTU1NRdPM0t4yDMMwBRu94H2MXRoAEnrBwZTd2CpC6DooPN7TT0vBW7WqnC8E7/ffU46Ub76h5HvPPCP7pwr9IaywAMzi61656wdcobTFP/0ko0mpNYu3t4zsBsjs1SJhmyh22DB2ZSjQ5PHkE9m6NF9//XUsW7bM0XVhGIZh8hP5zMJrFw4WvOrAR+qkWCJfwIMHFKwjOJgSXLz0kswYKwTvpk1SON930yZ6uHLXF+HhJHYBua46i60eEa5VbeENCcl9sasolNDy5Zcp38jYsbm7PSaL5EcLb3JyMr7//nts2rQJtWrVgrvIcJTBlClTHFI5hmEY5jFGH4yfBW+WKVmSLMqHDlHGYYH+UFrKm6M+/OvW0e+vDb5o5+EJl9QUAMBDLz8gmcp+4QVKHLl5s1azpKdrMx0Lwau28KoTph05Qtlw+/RxrHvD6dOAOofUnj0U4k1kQ2acTH608P7777+oU6cOXFxccOLECRw5csT0O3r0qIOryDAMwzyW6C28VtLV5iscLHgBYPFicll45hnt/GHDaNi2reUMvfqgJeXLA2kPDUgJkOehdWdfREdTh7sRI2S11YK3Vy8SrmvW0PRHH1Fnuu7dtS4NgqFDKUStozuv3b5Nw2LFyD9ZUSiCBZNHyI+d1raqnXkYhmEYxhJqwVu4MOCW7UiYjw8iMYg+K0UOqF7dsgV33DjqyPbii5bXq1iROqF5e1M2WtEYe8c7BN6gOGcGfz+NWBanTO3S8N9/1KlN/NewofxP7dIg6NQJ2LGDXCjefdf6fl27RiL6zTdl3dQcP04W3NdfJ7F//z7NDw+nPnf791NHOhGmjXEy4gLJTy4NgvPnz+PChQto0aIFvL29oSgKDHkxfzLDMAzz6FEL3oLgzgBQ4Nzixcksmsv4+gLvvGP9/6JFSawGBmoF5T23UJj0qc4MrNcsaWky+oM68xtAmdmSkmhcLXg7dyZr8ZYt2jjBenr0IFeNixcBvSfknj1Uzv37VIZa8AYG0uW0fz/FRWbyCHncwpstl4a7d++ibdu2qFSpEjp16oRbt24BAF577TW89957Dq0gwzAM85iiFryPeYQGuylUiEySOQnA60DKlpU+t4JoxYJJNwOhf4XgvXABePiQ5guj9ZUrlEl97lya9vDQ+gtXrw6UKgUkJ2ujO+gRaYqnT9fOP3iQYgoLgRsTQ0ORvyQoSIrvM2esl59XOXGCzovaJztfkMctvNkSvMOGDYO7uzuuXr0KH5W3+PPPP48NGzY4rHIMwzDMY4xaBRUUC28e5fhxckVo1QrwD1cJ3kwsvEJQVq4sQ5YdPgy89hpZcQGy7qobdw0GcmsAZEg1PUK8AuSeIIyCRiNlsEtKkqJW5LNq3BgYMwbo2ZOy3gGZW3hTU6WFOi/www8k8C9fpg+HfEUet/Bmy6Xh77//xl9//YWSwlcpg4oVK+JKvjuDDMMwTLYoiC4NeRRPT7Kc+vkBNT4IBbZl/KGz8IrJ5GSKziAEb5UqcpnwcBK0Dx6QH27x4ubb69yZLMB//AHMmKEVxAAJcMGHH1LSC39/6qB36BB1Slu4EGjaVCt4Gzem8evXSTjWrGl7v19/HViyhNwrWre2vWxuk5RE8Y5F57u4OOp4l288QfNjWLKEhASNZVcQExMDT5GmhWEYhinYqN8TBcWlIY8i7FPx8UCSbwhMZ8aKhRegjmsiA3F4uJxfty4JWVu0aUPxgS9fpmXV8YQB4NgxGnbuDHz1lZzfvj1ls6tTR1p4ExPJUuvhod2fwYNt1+HGDRK7gFawOws3N2DiREoWsncvid34ePPofQBlrIuJIYv8Y0N+DEvWvHlz/Pjjj6Zpg8EAo9GISZMmobWzP6EYhmGYvIGLixRUbOF1Kj4+FCgDAM7eU8UQ01l4PT1lAon4eGld1fsBZ4avrxSk779Pnd/UCMGr92MtUYIyxg0bpo3sdv8+WZtPnbLfgFiiBLBvH/DFFzL7nDNxdwf69QN275ZxjePiLC9buzZZpC9fflS1cwB53MKbLcE7adIkfP/99+jYsSNSU1Px4YcfokaNGtixYwe+Un+qMQzDMAUbIajYwut0hJV36ATrPrwGA/Dzz5SkolAhKXgt9cFLTSWfW2t88gl955w5A8yerf1PLXhjYykT3NWr2nq4ukrRe+8eRaSoXl3G9z1zBli2jHyKrdGoETByJI2np1tf7lFiMJDLBqD1ZX7syY8W3ho1auDs2bNo1qwZunXrhoSEBPTs2RNHjhxB+fLlHV1HhmEY5nFFdO0vW9a59WBQqhQN78B6lAaAIqp17kzhxKwJ3mLFyBocFkai0xIBARQrGKDOZkLcpadLH97atSnk2Lp15Mqgp1Ah0uTx8TQMDpbW5pkzKZXyihXm66mFeHw8ieVy5cg9wlmcO0dZ6GJipJC3ZuG9epWW13WVytvkx05rABAYGIhPPvnEkXVhGIZh8htLllA7NGcHcDpC8PYeFArMyJipT8emw5rgFXF9796V4cMs8dprFGP3zBng11+BV1+l7GgiPm+FCsDHH1Mnuc8+M1//3DmZr2TtWu1/9esDLVoAZcqYr9e2Lfntjh1LUST++otE5K+/WhbWj4IvvgAWLSI/3swsvOJcPVZUrUpq3sJHVF4gWxbehQsXYuXKlWbzV65cicWLF+e4UgzDMEw+oVo185y4jFMQIurCPZVDrgVxsnkzRUu4csW64BX+wG+/DXToYH2brq5Anz40fvQoDYU7Q40a9H/r1sDOnRR7V4+t5Hz9+gHbt5sn33j4kHx358whQeniAnTrRv8dOWK9vNxGfSwzs/D++CNFdFBHs8jzuLnRzllKm5cHyJbgnThxIkIsJO4OCwvDF198keNKMQzDMAzjWITgvXzDnUIgeHpaTIE8diyJyQMHZNIHveAVbgXNmpGrgC0GDCCr7rff0vQTT1BIMVtph3PC+fNkMfbxAYSXZcWKNDx3Lne2aQ/iWAYH27bwxsYCffsCEyY8ZoI3j5Mtl4arV6+irAV/rDJlyuCq2uucYRiGYZg8gRC8W7cC53dtR4XQWIu90Ro1Ik+HwoWB5cvJMqkPsiEsvELE2SI0lH6CcuUyDykmmD8f+O03Cmu2YAFlWfvjD9LqgvR06ggmoksIC3LNmnJehQo0PH/evu1a4rXXgKgocq3ITuxcey28YjmAQsMxjiFbgjcsLAz//vsvwtWB+QAcO3YMhcVdwDAMwzBMnkHtF5ocWASoZDlyxtdfZ15WUBANZ84E3nhDGyPXGikpwJ49WUsAcfYssH49WUQPHSIXCPW2Klcmq+3x4xTBAaAYtoDWbVxYeC9eJIEswoLZy927JLgBik1cujT5IUdGamMU20IteG1ZeFNS5HgejfD1WJItl4bevXtj8ODB2Lp1K9LT05Geno4tW7ZgyJAheOGFFxxdR4ZhGIZhcojae6F06ZyVJSycZ87YZ+1MT6eEFW3aAD/9ZP92evWidLydO9N0UJD59hQFuHNHTlsSvKVKkVBOTdWGP7OXwoWBVavIFUNYq1u0oOAjwqKcGWqXBlsWXha8uUO2LLzjxo3D5cuX0bZtW7hleJQbjUb06dOHfXgZhmEYJg/i6Qn88w8lgRAWRlvcvAns2EFi8ckntf+pw37Z00fJ1ZVCkF27lrWQzI0a0W/PHppWJ6MApGvF3btynhC86qQWrq7kSnH6NLk1ZCdKXvfu2ul//qHh4sUUicIWyclkEQbIwvv005SSuX59y8sK2KXBcWRZ8CqKgtu3b2PRokUYP348jh49Cm9vb9SsWRNlLMUGYRiGYRgmT2BJYOkZP57S/RYtSuKwUSOKeqDGVsIJayxbRtZLL6+srytCnwlXCoFe8N6/Ly24NWtql61YkQTvuXPAU0/Zv21FoaE1S7Y9ySOEO4OLC31sPPmk+UeEgC28uUO2BG+FChVw8uRJVKxYERWFYwzDMAzDMPmC+HgSei1bSt9YNS1aSJ9WezEYsi52798nsb18OU1nJnhFVIPSpc2XFXIlqx3Xzp6lkGkGAzBqFIlltX0vK4I3KEh2pLOGWvCyhddxZFnwuri4oGLFirh79y6LXYZhGIbJZ4hcFA0bWs+i9sorFO+2WbPcrcvZs0DHjnI6M5cG4U9rKc+JiNSQ1dBkW7cC16/T+IABFDVC7QOdFcErgmLExpI4NxjMLb1qlwa28DqObHVa+/LLL/HBBx/gxIkTjq4PwzAMwzBOROSisCW2XFwoTFflyrlbF33UNL3VVqQEEILXkv+uILuxeLds0U4nJmpFqT2CV91hDaAkHM2bA6+/br4sW3hzh2x1WuvTpw8SExNRu3ZteHh4wNvbW/N/jD2B+RiGYRiGyXMIwZsXxJZe8Fqz8IooDZYiNAiyE5rMaCQLr9jW3bt0XNSiVB031xp6C2/hwmRxthTSjC28uUO2BO+0adMcXA2GYRiGYfICQvBu2UIJJ778Enj1VefURW/RzcyHV1hvq1QxL6tkScpeVr48RUywkFXZjBs3SEy7uVH84F9+IQtvUBDw339A1ar2deDTC94aNaxbmtnCmztkS/D27dvX0fVgGIZhGCYPIHx4ASA6OntZxRyFmxvg7w88eEDTtgSvogBffEGhzyyFHXN1BT7+mFIcN2sG/O9/JFhtceUKDUuVktsWIrRSJbLGqrO+WePNNymmcGYd1gCO0pBbZMuHFwAuXLiATz/9FL1790ZUVBQAYP369Th58qTDKscwDMMwzKNFb/m0kH34kaLevq1OawYDCcvx40kkWyI5mTLJHTsGDB9OotcWIsRZmTKAjw+NJybS0MXFPrELUHSKkiUp9m5mqF0aODaA48iW4N2+fTtq1qyJ/fv347fffkN8xifIsWPHMHr0aIdWkGEYhmGYR0deFrzWLLwxMfa5Fnh5AQcOAC++CGzYQALZFsLCW7q0tHwnJFDSicaNc+bq0bQpJcMQ2xCEhVGEjM8+A9ats76+SGTB2Ee2BO+IESMwfvx4bNy4ER6qpNZt2rTBPn10aoZhGIZhHhvULg1A3hG8YWHmUSGE4DUagSNHgO3bKUOcLYoVA+bOpfEHDyyn9xWoLbxqwRsZCezfDyxcSHF5//zT9janTSOL8tGjct7Fi8ClSzKphuCll6jszz+3Xt6335LFef1629tlJNkSvMePH0ePHj3M5oeFheGOOqE1wzAMwzCPFXnVwjt6tLnPracn8OOPwNq1wOrVQKtWwKefZl6mn5+0Ft+4YX05tYVX7dLwxBO0vbAwYNMm4NQp29v7+Wdg6lQSuAKR3tmesGZ6Vq2i4enT9q8jMsYVVLLVaS0oKAi3bt1CWZ1X+JEjR1CiRAmHVIxhGIZhmEdPXhW81sJ/vfIKDc+fJ5/X8uXtK7dkSbKuXr9uvfOa2sIrxGpCAlCkCNCtG4nW69fJBcEWr75KcXerVZPzhD+yJQuz0UiRHBISyHIt4vcKxDHRRYW1SnIyifS6dYElS+xbJ7+RLcH7wgsv4KOPPsLKlSthMBhgNBqxe/duvP/+++jTp4+j68gwDMMwzCNC5akIwNzF4VEjLKEnT5KV0lrUiGHD6GcvJUoAJ07ILGp6FEVr4c3on2/qtAZQqDJ7sJRgwpqFd+RIynAnxHZ8vLngFZEc7O00t3MnHb+TJ4HFi+2LFpHfyNYuf/HFF6hSpQpKlSqF+Ph4VKtWDc2bN0fTpk3xqT1tCQzDMAzD5EkMBmDoUBr38XFuWDJANsX/73+WO6YdPAj89BPFxc0KJUvS0JrgvXdPhgVTuzQkJJAv7k8/AYcOZW2baqxZeKOjSex26EAd7IoUMV9XJLq15eerpk4dbfkFkWwJXg8PD8ybNw8XL17EunXrsHTpUpw9exZLliyBqz2pSxiGYRiGybN0705DIQqdidribEliTJ1Kbg1Z7cCVmeD18gJWrKAOYt7e5Orh7U31+e032uaMGbTdFSuonBkztHF0ASAtDTh8GLh8WetHa83CO3o0ifj584EGDSxbcUXHvMuX7dvX0FDqrGdrfzPjcfcBzrZR+4cffkDHjh3Ro0cPvPzyy+jevTvmz5/vyLoxDMMwDOMEhL+svindGYwbRyLSWtixOnWAJk2A994jgWhvuK7MBK+PD/Dcc8CgQTT91FPkzrB1q0w+ER8PdOoE9OsHdOkCDB5MLgNqbt8G6tenRBVqrFl4S5Uif1tbXaJE3Tt2tLmLFtex1UnPEoMGkZV/zJisrZfXyJbgHTVqFIYMGYKnn34aK1euxMqVK/H0009j2LBhGDVqlKPryDAMwzDMI+Svv2iYF2K9enqSVdWaa8WHHwKLFtH46dNkmbUHISiF4D1/3v5UvmI5kRgiOZmSWQBkzVUTE0PD4GDtPmQWpeHXX4FJk4AzZ6izWdOmsq5iXeF6khkXLpDVGMjcwvv338Brr0l3DpGcQ79fjxvZ6rQ2e/ZszJs3D7179zbN69q1K2rVqoVBgwbhc3udShiGYRiGyXPMmUPDzMJt5RWuXaNhqVL2+xyrLZ579lAUhZ49gZUraf6uXcCdO2SdLVVKu64QvIULU8QEdQQJfcJZ8Z8+2oU1C+/SpVSnqVPJOhwWJuP3Hj5M9c5qpzXxAQNkLngjImgYGgp8+SVlsQOkYH5cyZaFNy0tDU888YTZ/Pr16+Phw4c5rhTDMAzDMM6jVSsaDhni1GrYjRBxWfE5FsvevQuMGEEuE7/8IqMwfPst0KMHWVoBstR27gy0bSsFr6+vTBcsBKyIJiGw5h5izcI7bx7w0UckdgGyHot1RaSI1FQaHjlCojwzQkLkuL0uDRcv0lCIar1LxuNGtgTvK6+8gtmzZ5vN//777/HSSy/luFIMwzAMwziPrVtJtH39tbNrkjm7dpEPLWBuibVFUJCMvKCOPbxrFw0rVCCf4CpVaNpgoIxqW7bI7Gi+vpRi2MUF+OEHGqakSDcGQI7ba+FNTqahiLGbkgK0aSPH1cNhw+hcZcZzz8n4u/Z2WktPp+HgwTTMLNZwXidbLg0AdVr7+++/0bhxYwDA/v37cfXqVfTp0wfDhw83LTdlypSc15JhGIZhGMYC6uQLWbHwGgwUbiwujjrGBQeTO8HGjUD79sAXX9BP4O8PLFhAInnqVJrn6wt89x1FVihViqImlCihjXMrokeUKaPdvrDw6gWvELOFC5M4jY+XVla94AWkJTgzstppTXQSFJExhAAWfPYZMH48jT8OERyyJXhPnDiBevXqAQAuXLgAAAgJCUFISAhOiOBwAAzODt7HMAzDMEy+Rt1cn9UwaidPSnHauTMJ3k2bLC/r5gb070/jX35JQ19f6lAnLMt6C/OJE9Il4p13tP9VrUpxdEuX1s7XC94tW+gHSOuvWvBGRma+n4C2k56tBB716pGvcOXKNC0s1MKNQnDggH3bzStkS/Butcd+zjAMwzAMk8sULizHsxpGzcWFsqmVKUO+uQB1EFu5EqhZU7oz6BF+vplloZswgYbPPANUr679LzycrKR6hKgV+yXELpB9C+/rr0vhnZBAVmXhUqGncWMSvMKq/P33NNy3T7tcz54U0eFxoQAml2MYhmEYJr+gFp1hYVlb99YtEp5VqpDLQu3aNP/55yk6w5492uW3biXhKKJC6AXvyZO0bp8+FE5sxQqan5UktGoLrx4hhm/fllnW1BbemzfJUr1hg3a9yEjpdxwcbDvbmgjrJrYl0Kec7tVLjj8O8Qqy7cPLMAzDMAzjbAwG6lx34QLQrFnW1hXW0zNnyBe4XTuKp6soFGO3fn3t8q+/LqMXALLTm8BoBH7+mayn9+9TOV27SiGt5+FDYPt2cg8YOZLm2RK84r/gYJkuWG3h3bsX2L+f/G47dJDzRUzdxYtJjNti3jzzcgHA3V07LXyQAeDBA/NOeXkNtvAyDMMwDPNY8/77wOzZ9sfgFXTvTqHXDh2iaRGD1tubEi7o49zqLbr66UqVSHy/8ALw++8kEidOtL796GjK4Pbxx8DVqzRP79KgRm11LVKEhmoLr9FIYdb0oc6E4LVUpp4HD2h465Z2vl7wqoW/teQZeQm28DIMwzAMUyDx9QWmTZPT7doBM2cCtWpRpzI9aouui4u54PX0lCmOAeCDD4Bq1axvv1gx8oUNDpZRETKz8MbGUrkiJm9kpOyE9uSTwNq1VK4aIXjV4des4e1NGfYaN9ZGX9AL3mefleP6SBN5ERa8DMMwDMMwINH47rvW/xcCd+lSQJVs1qyMv/+mtMCffJL5Nn/5RY6np0t/WGsW3rg46XYAUPSE+/fJpWDlShKsTZpo1xOCd8sWsjg/+aTlDnOA7JAnyhboBa9a5LKFl2EYhmEYJp8gBG9iom33ieBgGbosK6ijL1iz8Pr7U9xgo5HiAd+/T/62QUEkYh88oDTJ6nBtQvAmJlKaYTc71Z/aheL997X/qUUuW3gZhmEYhmHyCcKlQaQWdgSKQm4JsbFakSt8dNUkJ5OwFVEfli0jwRsZScsL/9u//pIuGYoi53fpQuHRrIVbUxTahqcnuWyoBW+LFnLcaHz8LLzcaY1hGIZhGMYOhIV36FDgjTccV27JkiRCb96kjm9lylCECJGlDQA6dZK+wYKiRWl4+zZw6ZKcP2yYzIyWkiLH69ShNMwZSXLNiIoiUe/qCrRqJQWvl5fWoh0fr/XvfRwEL1t4GYZhGIZh7EDdac1RObgMBrLa3r1LrgZnzsj/RGiwiAjgjz9oPD6eQrD5+0sr8O3b5j628fEUHk24MwCZJ8pQu1TExUnBm5wM7N5Nvr+AucB9HFwa2MLLMAzDMAxjB0IwFi4ss6g5ApH1TC8kRWgwddSFf/4hS23nzsD06WSVHTwYuHxZu65wYxCC19ub3BRWrSJXCH2qYEA7788/KVqD4Ouv5bi+no+DhdfpgnfWrFkIDw+Hl5cXGjVqhAOZJGdeuXIlqlSpAi8vL9SsWRN//vmn2TL//fcfunbtisDAQPj6+qJBgwa4KgLcMQzDMAzDZAMheHv1Ap57znHlCsErsqEB5Ibw8cc07u0t5wsrrKcnuTSEhpKQVbs0AOaCV4Qk69kTeOkl7bb0ZYeEUNlqH151eDW1wP3hBwpRtncvZa1z5HFxJE4VvCtWrMDw4cMxevRoHD58GLVr10ZERASiRHA5HXv27EHv3r3x2muv4ciRI+jevTu6d++OEydOmJa5cOECmjVrhipVqmDbtm34999/8dlnn8FL5MpjGIZhGIbJBrnRaQ2QgnfXLsrK1q0b+dEKzp4lsV25slbwqsnMwuvnR8JYyCG19VYgLLwijbAQvFWqAF98IZcTgrdePeDVV4G6dYHr14ErV8wTVuQVnOrDO2XKFAwYMAD9+/cHAMyZMwd//PEHFixYgBEjRpgtP336dHTo0AEffPABAGDcuHHYuHEjZs6ciTlz5gAAPvnkE3Tq1AmTJk0yrVe+fPlHsDcMwzAMw+RnhIX355+Bjz4CatZ0TLlC8F69Cvz7rxSja9eSQPXyoigJiYnmgnfuXHJR2LFDW6YQvGXKUBY6IXS9vUnIWhK8ouybN8m6/OGHlPpYH8ZM+Oyq0wsLf2PRkS6v4TQLb2pqKg4dOoR27drJyri4oF27dti7d6/Fdfbu3atZHgAiIiJMyxuNRvzxxx+oVKkSIiIiEBYWhkaNGmH16tU265KSkoK4uDjNj2EYhmEYRo2w8KalAV995bhyheAtVYqSVnz/PU0//TSlHm7YkFwW/vnHXPAeO6YVu8HBNBSCt1gx4K23KDoDIN0j1AkmBGof3ilTqDNdixZA06ba5YSF9+FDYMMG6sAnLLsseHXcuXMH6enpKKILNFekSBHcFp8JOm7fvm1z+aioKMTHx+PLL79Ehw4d8Pfff6NHjx7o2bMntm/fbrUuEydORGBgoOlXqlSpHO4dwzAMwzD5jd69ZQzczCIeZAUheA0GEritWmn/9/Qk/9giRcwFb58+wPjxctnq1Wmojs6gRgheWxZeMW40AmvW0Ha7d5f/CcF77hzQsSMwfLi08OrTGucVnN5pzZEYMxJRd+vWDcOGDUOdOnUwYsQIdOnSxeTyYImRI0ciNjbW9Lt27dqjqjLDMAzDMI8J7u7SCpobgteeaAd6wdu4MdC+PY0XLy4zrAkL7+XLlFL43DmaFlbqzAQvABw6RJbeqCggJkbOF/WsWJF8jqtUyfsuDU7z4Q0JCYGrqysiIyM18yMjI1HUytEqWrSozeVDQkLg5uaGauquhACqVq2KXbt2Wa2Lp6cnPPXe3wzDMAzDMDpEh7XcELwHD5I7Q9myZOlVb3PsWPK9DQ+neWrZIiI0lC1L8XkBKXh/+QX44AOyBC9ebNvCqw9VtnGjdJcQySsAKu+VV6gjXMmSNK9uXRqyhVeHh4cH6tevj82bN5vmGY1GbN78//buPDyq+t7j+CchJGwmASIJQfZVZFMgIWABa67BB1GqUooICAgXBUWCkaUCtffeRnsfKFxKpdZWbK2iKKJSL5aC6FUDYZVNU6BIEAlBkSQQCGFy7h/HM0syM4SE5MxM3q/nmefMWebkN5wm/fTb37JJKSkpXj+TkpLicb0kbdy40Xl9ZGSk+vfvrxz3WZsl/fOf/1Tbtm2v8TcAAAB1SW6utGKF+f5aBt7YWHObnS39+79LS5d6nnc4zHlwly93BdnydboWLcywWT7wxsSY3RysGOSvD2/5Cm/Llq6f88P/ie5sb7durrArUeH1Kz09XRMmTFC/fv2UlJSkpUuX6vz5885ZG8aPH69WrVopMzNTkjRz5kwNGTJEixcv1vDhw7V69Wrt2LFDL1i9uyVlZGRo9OjRGjx4sG677TZt2LBB7733nrZs2WLHVwQAACHCPSTWRIXXUn4mVfdwa3UncD82ZIg5wG3WLOmVV8xjVuCdMsV8Wa6mwjt4sFkhHjHCs8JbnsNhdnuQCLxejR49WqdPn9bChQuVl5enPn36aMOGDc6Babm5uQoPdxWhBw4cqFdffVVPP/205s+fr86dO2vdunXq0aOH85qf/OQnWrlypTIzM/X444+ra9eueuutt3TrrbfW+vcDAAChw/3/rq/JwFu+emvNiyu5pgRzvyY+XnrxRfN9bKx5v/JTiVmupg/vhQvm3L2SZ+Bdvlw6fdpcwOKee1zLIYeHm5XmQGRr4JWkGTNmaMaMGV7PeavKjho1SqNGjfJ7z0mTJmnSpEnXonkAAACSzCA5ZIg5N+21DLzJyWZ3hhdfNPvwlg+8YWHmsZIS7xVed3PmmC9f/FV409LMWRnuucfcP3fOe+D94x/N6dAGDXJ1ZZDMVd/cF8wIJCE1SwMAAEBNqqlBa/37m7MsSBW7NLgf81bh9WfKFLMPr7Ukgb/A27atdPfd5uwLkvToo+a0Y5JnH95x46Tp06WOHT2r04E6YE0KgAovAABAsNixw9zWRCXT17LB7sfmzDEHtVkLTFzJv/4lHTzoCrgNG5oV49JS35+xQvH337uOuVd4Z892vXcPvNYcwIGIwAsAAHCVKjNnbmU5HOZ8tz+M0fcbeKOjpRtv9H2vPXvMJYETEqQ//9m1AEWTJuZ2yRJp2TIz9Ja3e7e0f78ZkCXp7FnPNnpjLS/85pvSfff5bpfd6NIAAABwldzGy1dbeLg0f75r31vgtbo0XLzo/17Fxeb8uZ9+au6XD7wREd7DriStWWPO13v5srnvLfBevmwuYpGfLxmGq8JrdbUIVAReAACASjpwQPr7369t4A0Lkx5+2LXvrQ+vFYJffFFauFDatcv7vbp2lf7yF9d8weUDrz8dO5qrtoWHmwPp3GeHsALvqVNSly6u/sZXs0qcnejSAAAAUEndu5uva+35581w+sor/iu8L79sbjt0kG65peJ1zZtLDz7o2rcCr7UgxcaN0u9/L/XrJ82d6/nZyZPNlyUtzQz3kmvQmhVso6PNoG51aZg1y1xm+LbbKvd9axuBFwAAIABUZtBaaqpZxa1s6C5f4T12THrrrYpz7npjDXQbPFiyZnu1Aq9V2XUftOZr7t9AEMBNAwAAqBtKSsyliyX/Fd6JE6UHHvB9H8OQ3nvPXGnt7rtdq6dZgXfQILO7Q8eOV26T1V84I0O66y7zvdVX1wq6VoW3UyezwhuoCLwAAAA2+9nPpG3bzPfWamjuevQwK65Nm/q/T1iYNGqUGXQ//9x13Jo3+MYbfc/y8NBD0tq1ZjiOiJCOHzePu/cp9lXhvflmV/gNRAReAAAAm1nB8dlnzSV7y1u61Nzm5ZkDx5o1k+rX936v666TvvtO+uYbcz8qyve17s6dMyvDly97LkyRnW3OzTtokO/AG+iD1pilAQAAwGaVDY5Dhphz7G7d6vsaa4DayZPm1n2GhsJC6cMPzeWRy7P69T75pLR9u2txjZ//XBoxwnxvrTRn3dPq4mANbgtUBF4AAACbVTbw+hvYZvEXeHNypB//2Jxvtzyrv2/nzuYsDu6ruVnLDVtz9FoV4/vuM6coe+op/+22G4EXAADAZlbg/d3vpH37Kp5fsMAMlseOmfuVCbw7d5rbVq1c56xlg927LFjKh2lr0NqhQ67+xVbgtaq/CQnS119Lzz3nuz2BgD68AAAANnOf3svqNuDu3DlXxVaqXOC96Saz321cnOucFXiLiyt+zqrw5uSYAXbkSGn4cKllS9c11gIU7lOQ+Vq5LZBQ4QUAALCZe+Dt1Kni+VmzPFdXq0zgjY+X0tM9uy+4V3gNw/NzVoV3715zUYozZ6TRo10zPEgVK7zBgsALAABgMyvw9uzpWZG1tGljTv1lBU33ZX/Ls/rsFhVVPGdNeVZWJpWWep6zAq/VlgsXpNOnzSnRrKWUrcAbyItMeEPgBQAAsFllBq05HK4uBZWp8D7/vPSvf3mesyq8UsV+vFaXBqstmzdLb7whHThgvgzDe5eGYBBkzQUAAAg9sbHmNjfXHCzmvtiDZA5Ae/tt135lAm9urjl7wptvus5FRpp9bg3D7Mfr3pWifIVXkmbMcL03DOnRR82+vS1aVPabBQYqvAAAADazQqrkvSvCtm3Sf/2Xa78ygVcy5+11Fxbme6aG8hVeSerf3/Xe4ZBuuEFKSpLatfP98wMRgRcAAMBmVoVX8ux2YClf8fW3cpoVeO+/X3rssYrnrX685QNv+Qpvu3bSxo2u81Z3hmBElwYAAACbNWpkhkuHw3OhCIt7RTcqyv9UYFbgLT8ozVLZCu+FC56zMTgc5opqn39uTnc2cKDvNgQaAi8AAEAASE31fc69wuuvO4MkjR0rPfigFO7j/8f3NRfv88+b/YfbtjX3L170DLxlZdLatdLvfy898wyBFwAAANdQ+QqvP1eaI9dXhXfcOHN7/Li5LSjwDOEOhzRggPm53r2v3OZAQuAFAAAIcO4h9513qncvX314Le7VZPcqsMMhPfSQ+Qo2BF4AAIAAZ4XQLl2klJTq3etvfzO7O7j3FS4tlT74wJy2bMAA1/HyfXiDFYEXAAAgwFkVXmsmhepo2rTisYICacQI7z/DmrfX4TCrwmVlZnuCafEJpiUDAAAIcFaF99gxc/WzmtC/v3TLLZ7LFtev76rylpVJEyaYleGVK2umDTWFwAsAABDg3Pvw/uY31bvXK69IDz8srV/vOhYXJ2Vnmyu6SdLy5ea2TRtX4HVf2jiYqrsSXRoAAAACnvtAsjvuqN69PvlE+uMfzTB7113er7l40fVzf/5zM+hGR0uXL5vHrzQTRKAh8AIAAAQ4q8IbFib94hfVu9c995hhd/Bg39dYMzg0aCAtWOA6ToUXAAAANaJhQ6lDBzP4OhzVC5x33mm+3H3+uXT33VL79tKWLdLChebxffs8r6PCCwAAgBoREyN9+aU5cKwmwua5c1JurueANcms9B46ZC473LGjK/AGW4WXQWsAAABBYPlys4vBgw9W7z5nzki7d5tB1nLpkrm1uk706mVub71VGjRI6tFDOnzY1aUh2Cq8BF4AAIAgYM2Pe6Wlha9k9Wpz+rG5cyve26rw9utnblu1MmdwiIsz+w8Ha4U3yJoLAABQN82fb27z8qp3n4YNza370sLlK7zug9YOHnRdF6wVXgIvAABAECktrd7nGzUyt+6Bt3yFd+pUaejQissYU+EFAABAjenTR9qzR+ratXr38VbhLd9dYuhQ81VesM7SQB9eAACAIDBwoLlt1qx697ECb3Gx65jVpaH8LA2SNG6cNGSIOUUZ8/ACAACgxlyrQWv+ujR4u3d2tvTPf0pnzwZvhZfACwAAEATeeMPcnjxZvfv4G7TmrcIb/kN/AIdDWrlSKix0TVsWLAi8AAAAQaCoyNx+8UX17lOZPrzurGquw+G9X28woA8vAABAHXK1fXjdA2+wosILAAAQBBo1MkPqv/1b9e8jSRcvSoZhLihRmQpvWZm5aEVJiXTXXVLz5tVrR20i8AIAAASBgwelTZuqv7SwVeGVzNDbsKGUliZdd510880Vr3ev8M6aZS58sWcPgRcAAADXWNu20qRJ1b+Pe+AtLjb3b73VfHnjHnhTU6XvvpNiYqrfjtpE4AUAAKhDIiKk2FhzmrG8vCtXat0D71/+UtOtqxkEXgAAgDrmo4+kli2luDhz//Bh6fvvpTZtpPh4z2vdpyULVszSAAAAUMf06iVdf705YE2SnnlGSkqSXnml4rXug9aCFYEXAACgjmvWzKzuNm1a8Zx7l4bYWKlxY+nrr2u1edVGlwYAAIA65ssvpRdekJo0kX75S2nZMvPljXvgPXfO3IYHWcmUwAsAAFDHnD4t/eY3Uvv2ZuD1Z8QIqVMnqXNnVz/eiCBLkEHWXAAAAFTXjTdK6elS165Xvvaxx8yt+6A1q+obLIKsIA0AAIDqiouTFi+Wpk419ydNMgetbdni+zOXL7veU+EFAABAUDlwQNq+XSoqqniuqMhckc2a0UGiwgsAAIAgcOaM9Nln5gC2khLzWFRUxesmTpRatJD++EfXsWCr8BJ4AQAA6qBnn5UGDTK7Nhw7Zh677rqK11nV3EuXXMcIvAAAAAh4HTua25dfNpcZ7thR6t+/4nWvvWYuOjFtmutYsHVpCLJ8DgAAgGuhUydzW1pqbjMyvFduyy8tHB7u2Z83GAREhXfFihVq166dGjRooOTkZGVnZ/u9fs2aNerWrZsaNGignj176v333/d57bRp0xQWFqalS5de41YDAAAELyvwSlJ8vDRhgv/rrVkagq26KwVA4H399deVnp6uRYsWadeuXerdu7fS0tKUn5/v9frPPvtMY8aM0eTJk7V7926NHDlSI0eO1P79+ytc+/bbb2vr1q1KTEys6a8BAAAQVG64QYqMNN8/8YTUoIH361aulEaNktasMfeDrf+uFACBd8mSJZoyZYomTpyo7t27a+XKlWrUqJH+9Kc/eb1+2bJlGjZsmDIyMnTjjTfqP/7jP3TLLbfot7/9rcd1J06c0GOPPaa//vWvql+/fm18FQAAgKBRr540fbo0eLD0yCO+r9uxQ3rzTcmqLQZjhdfWjH7p0iXt3LlT8+bNcx4LDw9XamqqsrKyvH4mKytL6enpHsfS0tK0bt06535ZWZnGjRunjIwM3XTTTVdsR0lJiUqs+TgkFRYWXuU3AQAACD5Lllz5GivgNmtmDnALt71cevVsbfK3334rh8Oh+Ph4j+Px8fHKy8vz+pm8vLwrXv/cc88pIiJCjz/+eKXakZmZqZiYGOerdevWV/lNAAAAQpMVeBs1ksaPlx580N72VEUQZnT/du7cqWXLlmnVqlUKq+QQwnnz5qmgoMD5On78eA23EgAAIDhYgbeszN52VIetgTcuLk716tXTqVOnPI6fOnVKCQkJXj+TkJDg9/r/+7//U35+vtq0aaOIiAhFRETo2LFjmj17ttq1a+f1nlFRUYqOjvZ4AQAAwBV4z5yR3ntP2rzZ3vZUha2BNzIyUn379tWmTZucx8rKyrRp0yalpKR4/UxKSorH9ZK0ceNG5/Xjxo3T3r17tWfPHucrMTFRGRkZ+uCDD2ruywAAAIQgK/B+8YV0993mUsPBxvaJJdLT0zVhwgT169dPSUlJWrp0qc6fP6+JP/xrjh8/Xq1atVJmZqYkaebMmRoyZIgWL16s4cOHa/Xq1dqxY4deeOEFSVLz5s3VvHlzj59Rv359JSQkqGvXrrX75QAAAIKcNUgtKkpKTjbn7A02tgfe0aNH6/Tp01q4cKHy8vLUp08fbdiwwTkwLTc3V+FuwwEHDhyoV199VU8//bTmz5+vzp07a926derRo4ddXwEAACBkWRXebt2k3/zG3rZUVZhhGIbdjQg0hYWFiomJUUFBAf15AQBAnfbzn0u/+pX0+OPSsmV2t8blavJayM3SAAAAgGvHqvA6HPa2ozoIvAAAAPDJCrybN0utW0vDh9vbnqqwvQ8vAAAAApcVeAsLpRMnGLQGAACAENO9u3T//dL582bgjQjC9EiXBgAAAPh0773SmjXSww+b+1bFN5gQeAEAAHBF1qA1KrwAAAAIOYYhlZaa7wm8AAAACCnLl5urrY0da+7TpQEAAAAhJSzMc58KLwAAAELKpEnSqVPSkiXmfjBWeIMwowMAAKC2NGrkeklUeAEAABCignmWhiBsMgAAAGrLtm3Sn/8sbdxo7tOlAQAAACElJ0f63e9c+1R4AQAAEFKsim5srPT001K3brY2p0oIvAAAAPDJCrw33yzNnm1vW6qKQWsAAADwyQq81qC1YETgBQAAgE9W4P3qK3MA27FjtjanSgi8AAAA8Cn8h7SYmysNGCAtXmxve6qCwAsAAACf3Kcha99eiouzry1VxaA1AAAA+GQF3r59pR077G1LVVHhBQAAgE8MWgMAAEBII/ACAAAgpFmBd98+qV8/6Q9/sLc9VUHgBQAAgE/hbmlx504pL8++tlQVgRcAAAA+NW0qDR7s2neftSFYEHgBAADgU69e0kcfSRMmmPsRQTjHF4EXAAAAV2QNWqPCCwAAgJB0+bK5DcYKbxA2GQAAALVl/37pxz+WTp8296nwAgAAIKSUlbnCrhScFV4CLwAAAHzq0sWcg7dbN3OfCi8AAABCSoMGUo8eUocO5j4VXgAAAIQkBq0BAAAgJH3/vbRsmfT3v5v7dGkAAABASCkokJ55xrUfjBVeAi8AAAB8cq/oPvWUa/BaMAnCjA4AAIDaYgXeevWk556zty1VRYUXAAAAPoX/kBatpYWDEYEXAAAAPrl3aThyRLpwwb62VBWBFwAAAD65B95OnaRPP7WvLVVF4AUAAIBP7oE3IkKqX9++tlQVg9YAAADgk3vgLSiQGjWyry1VRYUXAAAAPrkH3mAduEbgBQAAgE8EXgAAAIS0cLe0mJZmztQQbAi8AAAA8Mm9wpudLZ0/b19bqorACwAAAJ/CwqTu3V377gE4WBB4AQAA4NeBA1LTpub7iCCc44vACwAAgCu6fNncUuEFAABASLJmaKDCCwAAgJAzYIBUXGy+p8ILAACAkPPll673VHgBAAAQct57z/WeCi8AAABCzsCBrvdUeAEAABByrBkaJCq8AAAACEEvveR6T4UXAAAAIWfePNd7KrxVtGLFCrVr104NGjRQcnKysrOz/V6/Zs0adevWTQ0aNFDPnj31/vvvO8+VlpZqzpw56tmzpxo3bqzExESNHz9e33zzTU1/DQAAgJAUGWluu3enwlslr7/+utLT07Vo0SLt2rVLvXv3VlpamvLz871e/9lnn2nMmDGaPHmydu/erZEjR2rkyJHav3+/JKm4uFi7du3SggULtGvXLq1du1Y5OTm6++67a/NrAQAAhIwmTcztH/4QnIE3zDAMw84GJCcnq3///vrtb38rSSorK1Pr1q312GOPae7cuRWuHz16tM6fP6/169c7jw0YMEB9+vTRypUrvf6M7du3KykpSceOHVObNm2u2KbCwkLFxMSooKBA0dHRVfxmAAAAoaFLF+nQIenjj6Uf/cju1piuJq/ZWuG9dOmSdu7cqdTUVOex8PBwpaamKisry+tnsrKyPK6XpLS0NJ/XS1JBQYHCwsIUGxvr9XxJSYkKCws9XgAAADCFhZnbYI1Itgbeb7/9Vg6HQ/Hx8R7H4+PjlZeX5/UzeXl5V3X9xYsXNWfOHI0ZM8Zn+s/MzFRMTIzz1bp16yp8GwAAgNB04oS5vesue9tRVbb34a1JpaWl+ulPfyrDMPT888/7vG7evHkqKChwvo4fP16LrQQAAEBNsrXbcVxcnOrVq6dTp055HD916pQSEhK8fiYhIaFS11th99ixY9q8ebPfvh1RUVGKioqq4rcAAAAIbZ07S3v2SGvX2t2SqrG1whsZGam+fftq06ZNzmNlZWXatGmTUlJSvH4mJSXF43pJ2rhxo8f1Vtg9dOiQ/vGPf6h58+Y18wUAAADqAGvuXWt6smBj+8QS6enpmjBhgvr166ekpCQtXbpU58+f18SJEyVJ48ePV6tWrZSZmSlJmjlzpoYMGaLFixdr+PDhWr16tXbs2KEXXnhBkhl277//fu3atUvr16+Xw+Fw9u9t1qyZIoP1SQEAANjECrxlZfa2o6psD7yjR4/W6dOntXDhQuXl5alPnz7asGGDc2Babm6uwsNdheiBAwfq1Vdf1dNPP6358+erc+fOWrdunXr06CFJOnHihN59911JUp8+fTx+1ocffqihQ4fWyvcCAAAIFbm55vbxx6URI+xtS1XYPg9vIGIeXgAAAJcGDaSSEvN9oCTHoJmHFwAAAIEv2Ot/BF4AAAD49dZb5rZLF3vbUVUEXgAAAPjlcJhba/BasCHwAgAAwK/Ll81thO3THVQNgRcAAAB+zZljbvfts7cdVUXgBQAAgF+ff253C6qHwAsAAAC/0tPN7Q/LJAQdAi8AAAD8GjDA3HbqZG87qorACwAAAL8YtAYAAICQ9tln5vbwYXvbUVUEXgAAAPi1bJm5PXHC3nZUFYEXAAAAft1wg7lt1MjedlQVgRcAAAB+paaa2wUL7G1HVRF4AQAA4Je1pHBZmb3tqCoCLwAAAPyyAq/DYW87qirMMAzD7kYEmsLCQsXExKigoEDR0dF2NwcAAMBWYWGu94GSHK8mr1HhBQAAQEgj8AIAAMCvyZPN7dSp9rajqgi8AAAA8MvqMdC0qb3tqCoCLwAAAPwK9kFrBF4AAAD49fe/m9s337S3HVVF4AUAAIBfhYXmNj/f3nZUFYEXAAAAfm3YII0aJWVl2d2SqomwuwEAAAAIbF27Sm+8YXcrqo4KLwAAAEIagRcAAAAhjcALAACAkEbgBQAAQEgj8AIAACCkEXgBAAAQ0gi8AAAACGkEXgAAAIQ0Ai8AAABCGoEXAAAAIY3ACwAAgJBG4AUAAEBII/ACAAAgpBF4AQAAENIIvAAAAAhpBF4AAACENAIvAAAAQhqBFwAAACEtwu4GBCLDMCRJhYWFNrcEAAAA3lg5zcpt/hB4vSgqKpIktW7d2uaWAAAAwJ+ioiLFxMT4vSbMqEwsrmPKysr0zTff6LrrrlNYWFiN/7zCwkK1bt1ax48fV3R0dI3/PFwdnk9g4/kENp5PYOP5BDaej3+GYaioqEiJiYkKD/ffS5cKrxfh4eG64YYbav3nRkdH8x/oAMbzCWw8n8DG8wlsPJ/AxvPx7UqVXQuD1gAAABDSCLwAAAAIaQTeABAVFaVFixYpKirK7qbAC55PYOP5BDaeT2Dj+QQ2ns+1w6A1AAAAhDQqvAAAAAhpBF4AAACENAIvAAAAQhqBFwAAACGNwBsAVqxYoXbt2qlBgwZKTk5Wdna23U2qk37xi18oLCzM49WtWzfn+YsXL2r69Olq3ry5mjRpovvuu0+nTp2yscWh7eOPP9aIESOUmJiosLAwrVu3zuO8YRhauHChWrZsqYYNGyo1NVWHDh3yuObMmTMaO3asoqOjFRsbq8mTJ+vcuXO1+C1C15Wez0MPPVTh92nYsGEe1/B8akZmZqb69++v6667Ti1atNDIkSOVk5PjcU1l/p7l5uZq+PDhatSokVq0aKGMjAxdvny5Nr9KSKrM8xk6dGiF359p06Z5XMPzuToEXpu9/vrrSk9P16JFi7Rr1y717t1baWlpys/Pt7tpddJNN92kkydPOl+ffPKJ89ysWbP03nvvac2aNfroo4/0zTff6N5777WxtaHt/Pnz6t27t1asWOH1/K9//Wv9z//8j1auXKlt27apcePGSktL08WLF53XjB07VgcOHNDGjRu1fv16ffzxx5o6dWptfYWQdqXnI0nDhg3z+H167bXXPM7zfGrGRx99pOnTp2vr1q3auHGjSktLdccdd+j8+fPOa67098zhcGj48OG6dOmSPvvsM7388statWqVFi5caMdXCimVeT6SNGXKFI/fn1//+tfOczyfKjBgq6SkJGP69OnOfYfDYSQmJhqZmZk2tqpuWrRokdG7d2+v586ePWvUr1/fWLNmjfPYF198YUgysrKyaqmFdZck4+2333bul5WVGQkJCcZ///d/O4+dPXvWiIqKMl577TXDMAzj4MGDhiRj+/btzmv+93//1wgLCzNOnDhRa22vC8o/H8MwjAkTJhj33HOPz8/wfGpPfn6+Icn46KOPDMOo3N+z999/3wgPDzfy8vKc1zz//PNGdHS0UVJSUrtfIMSVfz6GYRhDhgwxZs6c6fMzPJ+rR4XXRpcuXdLOnTuVmprqPBYeHq7U1FRlZWXZ2LK669ChQ0pMTFSHDh00duxY5ebmSpJ27typ0tJSj2fVrVs3tWnThmdlg6NHjyovL8/jecTExCg5Odn5PLKyshQbG6t+/fo5r0lNTVV4eLi2bdtW622ui7Zs2aIWLVqoa9eueuSRR/Tdd985z/F8ak9BQYEkqVmzZpIq9/csKytLPXv2VHx8vPOatLQ0FRYW6sCBA7XY+tBX/vlY/vrXvyouLk49evTQvHnzVFxc7DzH87l6EXY3oC779ttv5XA4PP4DK0nx8fH68ssvbWpV3ZWcnKxVq1apa9euOnnypJ555hn96Ec/0v79+5WXl6fIyEjFxsZ6fCY+Pl55eXn2NLgOs/7Nvf3uWOfy8vLUokULj/MRERFq1qwZz6wWDBs2TPfee6/at2+vI0eOaP78+brzzjuVlZWlevXq8XxqSVlZmZ544gkNGjRIPXr0kKRK/T3Ly8vz+vtlncO14e35SNIDDzygtm3bKjExUXv37tWcOXOUk5OjtWvXSuL5VAWBF/jBnXfe6Xzfq1cvJScnq23btnrjjTfUsGFDG1sGBJ+f/exnzvc9e/ZUr1691LFjR23ZskW33367jS2rW6ZPn679+/d7jEdA4PD1fNz7svfs2VMtW7bU7bffriNHjqhjx4613cyQQJcGG8XFxalevXoVRsaeOnVKCQkJNrUKltjYWHXp0kWHDx9WQkKCLl26pLNnz3pcw7Oyh/Vv7u93JyEhocLgz8uXL+vMmTM8Mxt06NBBcXFxOnz4sCSeT22YMWOG1q9frw8//FA33HCD83hl/p4lJCR4/f2yzqH6fD0fb5KTkyXJ4/eH53N1CLw2ioyMVN++fbVp0ybnsbKyMm3atEkpKSk2tgySdO7cOR05ckQtW7ZU3759Vb9+fY9nlZOTo9zcXJ6VDdq3b6+EhASP51FYWKht27Y5n0dKSorOnj2rnTt3Oq/ZvHmzysrKnP/lgdrz9ddf67vvvlPLli0l8XxqkmEYmjFjht5++21t3rxZ7du39zhfmb9nKSkp2rdvn8f/KNm4caOio6PVvXv32vkiIepKz8ebPXv2SJLH7w/P5yrZPWqurlu9erURFRVlrFq1yjh48KAxdepUIzY21mPkJWrH7NmzjS1bthhHjx41Pv30UyM1NdWIi4sz8vPzDcMwjGnTphlt2rQxNm/ebOzYscNISUkxUlJSbG516CoqKjJ2795t7N6925BkLFmyxNi9e7dx7NgxwzAM49lnnzViY2ONd955x9i7d69xzz33GO3btzcuXLjgvMewYcOMm2++2di2bZvxySefGJ07dzbGjBlj11cKKf6eT1FRkfHkk08aWVlZxtGjR41//OMfxi233GJ07tzZuHjxovMePJ+a8cgjjxgxMTHGli1bjJMnTzpfxcXFzmuu9Pfs8uXLRo8ePYw77rjD2LNnj7Fhwwbj+uuvN+bNm2fHVwopV3o+hw8fNn75y18aO3bsMI4ePWq88847RocOHYzBgwc778HzuXoE3gCwfPlyo02bNkZkZKSRlJRkbN261e4m1UmjR482WrZsaURGRhqtWrUyRo8ebRw+fNh5/sKFC8ajjz5qNG3a1GjUqJHxk5/8xDh58qSNLQ5tH374oSGpwmvChAmGYZhTky1YsMCIj483oqKijNtvv93IycnxuMd3331njBkzxmjSpIkRHR1tTJw40SgqKrLh24Qef8+nuLjYuOOOO4zrr7/eqF+/vtG2bVtjypQpFf6HPM+nZnh7LpKMl156yXlNZf6effXVV8add95pNGzY0IiLizNmz55tlJaW1vK3CT1Xej65ubnG4MGDjWbNmhlRUVFGp06djIyMDKOgoMDjPjyfqxNmGIZRe/VkAAAAoHbRhxcAAAAhjcALAACAkEbgBQAAQEgj8AIAACCkEXgBAAAQ0gi8AAAACGkEXgAAAIQ0Ai8AAABCGoEXAOqQhx56SCNHjqzWPbZs2aKwsDCdPXv2mrQJAGpahN0NAADUnmXLlokFNgHUNQReAKgDHA6HwsLCFBMTY3dTAKDW0aUBAALQ0KFDNWPGDM2YMUMxMTGKi4vTggULnNXZkpISPfnkk2rVqpUaN26s5ORkbdmyxfn5VatWKTY2Vu+++666d++uqKgo5ebmVujSUFJSoscff1wtWrRQgwYNdOutt2r79u0ebXn//ffVpUsXNWzYULfddpu++uqrWvgXAIBrh8ALAAHq5ZdfVkREhLKzs7Vs2TItWbJEL774oiRpxowZysrK0urVq7V3716NGjVKw4YN06FDh5yfLy4u1nPPPacXX3xRBw4cUIsWLSr8jKeeekpvvfWWXn75Ze3atUudOnVSWlqazpw5I0k6fvy47r33Xo0YMUJ79uzRww8/rLlz59bOPwAAXCNhBp25ACDgDB06VPn5+Tpw4IDCwsIkSXPnztW7776rDRs2qEOHDsrNzVViYqLzM6mpqUpKStKvfvUrrVq1ShMnTtSePXvUu3dv5zUPPfSQzp49q3Xr1un8+fNq2rSpVq1apQceeECSVFpaqnbt2umJJ55QRkaG5s+fr3feeUcHDhxw3mPu3Ll67rnn9P333ys2NrZ2/kEAoBrowwsAAWrAgAHOsCtJKSkpWrx4sfbt2yeHw6EuXbp4XF9SUqLmzZs79yMjI9WrVy+f9z9y5IhKS0s1aNAg57H69esrKSlJX3zxhSTpiy++UHJyssfnUlJSqvW9AKC2EXgBIMicO3dO9erV086dO1WvXj2Pc02aNHG+b9iwoUdgBoC6ij68ABCgtm3b5rG/detWde7cWTfffLMcDofy8/PVqVMnj1dCQkKl79+xY0dFRkbq008/dR4rLS3V9u3b1b17d0nSjTfeqOzs7ArtAIBgQuAFgACVm5ur9PR05eTk6LXXXtPy5cs1c+ZMdenSRWPHjtX48eO1du1aHT16VNnZ2crMzNTf/va3St+/cePGeuSRR5SRkaENGzbo4MGDmjJlioqLizV58mRJ0rRp03To0CFlZGQoJydHr776qlatWlVD3xgAagZdGgAgQI0fP14XLlxQUlKS6tWrp5kzZ2rq1KmSpJdeekn/+Z//qdmzZ+vEiROKi4vTgAEDdNddd13Vz3j22WdVVlamcePGqaioSP369dMHH3ygpk2bSpLatGmjt956S7NmzdLy5cudg+ImTZp0zb8vANQUZmkAgAA0dOhQ9enTR0uXLrW7KQAQ9OjSAAAAgJBG4AUAAEBIo0sDAAAAQhoVXgAAAIQ0Ai8AAABCGoEXAAAAIY3ACwAAgJBG4AUAAEBII/ACAAAgpBF4AQAAENIIvAAAAAhp/w9vckQGs1de8AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "m = X_train.shape[1]\n",
     "n = X_train.shape[0]\n",
@@ -1368,7 +1331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-02-18T14:04:46.849588Z",
@@ -1386,7 +1349,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-6c322959b3dd>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mSST\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0myMean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# SST: 总平方和\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mSSR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myFit\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0myMean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# SSR: 回归平方和\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mSSE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0myFit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# SSE: 残差平方和\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mFstats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSSR\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSSE\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# F 统计量\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mprobFstats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFstats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# F检验的 P值\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-8-6c322959b3dd>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mSST\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0myMean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# SST: 总平方和\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mSSR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myFit\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0myMean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# SSR: 回归平方和\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mSSE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0myFit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# SSE: 残差平方和\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mFstats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSSR\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSSE\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# F 统计量\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mprobFstats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFstats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# F检验的 P值\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (68,) (272,) "
      ]
     }

benchmark/NBspecific_4/NBspecific_4_fixed.ipynb

@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-19T20:23:40.477106Z",
@@ -48,15 +48,7 @@
      "shell.execute_reply.started": "2023-01-19T20:23:40.477010Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "data/german_credit_data.csv\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Checking the files available in this notebook \n",
     "import os\n",
@@ -79,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-19T20:23:40.515413Z",
@@ -91,6 +83,7 @@
    },
    "outputs": [],
    "source": [
+    "# fix --- execute this cell\n",
     "### Importation des bibliothèques générales que nous utiliserons dans ce notebook\n",
     "import numpy as np # algèbre linéaire\n",
     "import pandas as pd # traitement des données, I/O de fichier CSV (par ex. pd.read_csv)\n",
@@ -103,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
     "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
@@ -115,144 +108,14 @@
      "shell.execute_reply.started": "2023-01-19T20:23:51.792638Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Sex</th>\n",
-       "      <th>Job</th>\n",
-       "      <th>Housing</th>\n",
-       "      <th>Saving accounts</th>\n",
-       "      <th>Checking account</th>\n",
-       "      <th>Credit amount</th>\n",
-       "      <th>Duration</th>\n",
-       "      <th>Purpose</th>\n",
-       "      <th>Risk</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>67</td>\n",
-       "      <td>male</td>\n",
-       "      <td>2</td>\n",
-       "      <td>own</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>little</td>\n",
-       "      <td>1169</td>\n",
-       "      <td>6</td>\n",
-       "      <td>radio/TV</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>22</td>\n",
-       "      <td>female</td>\n",
-       "      <td>2</td>\n",
-       "      <td>own</td>\n",
-       "      <td>little</td>\n",
-       "      <td>moderate</td>\n",
-       "      <td>5951</td>\n",
-       "      <td>48</td>\n",
-       "      <td>radio/TV</td>\n",
-       "      <td>bad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>49</td>\n",
-       "      <td>male</td>\n",
-       "      <td>1</td>\n",
-       "      <td>own</td>\n",
-       "      <td>little</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2096</td>\n",
-       "      <td>12</td>\n",
-       "      <td>education</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>45</td>\n",
-       "      <td>male</td>\n",
-       "      <td>2</td>\n",
-       "      <td>free</td>\n",
-       "      <td>little</td>\n",
-       "      <td>little</td>\n",
-       "      <td>7882</td>\n",
-       "      <td>42</td>\n",
-       "      <td>furniture/equipment</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>53</td>\n",
-       "      <td>male</td>\n",
-       "      <td>2</td>\n",
-       "      <td>free</td>\n",
-       "      <td>little</td>\n",
-       "      <td>little</td>\n",
-       "      <td>4870</td>\n",
-       "      <td>24</td>\n",
-       "      <td>car</td>\n",
-       "      <td>bad</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0  Age     Sex  Job Housing Saving accounts Checking account  \\\n",
-       "0           0   67    male    2     own             NaN           little   \n",
-       "1           1   22  female    2     own          little         moderate   \n",
-       "2           2   49    male    1     own          little              NaN   \n",
-       "3           3   45    male    2    free          little           little   \n",
-       "4           4   53    male    2    free          little           little   \n",
-       "\n",
-       "   Credit amount  Duration              Purpose  Risk  \n",
-       "0           1169         6             radio/TV  good  \n",
-       "1           5951        48             radio/TV   bad  \n",
-       "2           2096        12            education  good  \n",
-       "3           7882        42  furniture/equipment  good  \n",
-       "4           4870        24                  car   bad  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dataset.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-19T20:24:31.194121Z",
@@ -262,356 +125,7 @@
      "shell.execute_reply.started": "2023-01-19T20:24:31.194089Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Sex</th>\n",
-       "      <th>Job</th>\n",
-       "      <th>Housing</th>\n",
-       "      <th>Saving accounts</th>\n",
-       "      <th>Checking account</th>\n",
-       "      <th>Credit amount</th>\n",
-       "      <th>Duration</th>\n",
-       "      <th>Purpose</th>\n",
-       "      <th>Risk</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>67</td>\n",
-       "      <td>male</td>\n",
-       "      <td>2</td>\n",
-       "      <td>own</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>little</td>\n",
-       "      <td>1169</td>\n",
-       "      <td>6</td>\n",
-       "      <td>radio/TV</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>22</td>\n",
-       "      <td>female</td>\n",
-       "      <td>2</td>\n",
-       "      <td>own</td>\n",
-       "      <td>little</td>\n",
-       "      <td>moderate</td>\n",
-       "      <td>5951</td>\n",
-       "      <td>48</td>\n",
-       "      <td>radio/TV</td>\n",
-       "      <td>bad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>49</td>\n",
-       "      <td>male</td>\n",
-       "      <td>1</td>\n",
-       "      <td>own</td>\n",
-       "      <td>little</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2096</td>\n",
-       "      <td>12</td>\n",
-       "      <td>education</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>45</td>\n",
-       "      <td>male</td>\n",
-       "      <td>2</td>\n",
-       "      <td>free</td>\n",
-       "      <td>little</td>\n",
-       "      <td>little</td>\n",
-       "      <td>7882</td>\n",
-       "      <td>42</td>\n",
-       "      <td>furniture/equipment</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>53</td>\n",
-       "      <td>male</td>\n",
-       "      <td>2</td>\n",
-       "      <td>free</td>\n",
-       "      <td>little</td>\n",
-       "      <td>little</td>\n",
-       "      <td>4870</td>\n",
-       "      <td>24</td>\n",
-       "      <td>car</td>\n",
-       "      <td>bad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>35</td>\n",
-       "      <td>male</td>\n",
-       "      <td>1</td>\n",
-       "      <td>free</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9055</td>\n",
-       "      <td>36</td>\n",
-       "      <td>education</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>53</td>\n",
-       "      <td>male</td>\n",
-       "      <td>2</td>\n",
-       "      <td>own</td>\n",
-       "      <td>quite rich</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2835</td>\n",
-       "      <td>24</td>\n",
-       "      <td>furniture/equipment</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>35</td>\n",
-       "      <td>male</td>\n",
-       "      <td>3</td>\n",
-       "      <td>rent</td>\n",
-       "      <td>little</td>\n",
-       "      <td>moderate</td>\n",
-       "      <td>6948</td>\n",
-       "      <td>36</td>\n",
-       "      <td>car</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>61</td>\n",
-       "      <td>male</td>\n",
-       "      <td>1</td>\n",
-       "      <td>own</td>\n",
-       "      <td>rich</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3059</td>\n",
-       "      <td>12</td>\n",
-       "      <td>radio/TV</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>28</td>\n",
-       "      <td>male</td>\n",
-       "      <td>3</td>\n",
-       "      <td>own</td>\n",
-       "      <td>little</td>\n",
-       "      <td>moderate</td>\n",
-       "      <td>5234</td>\n",
-       "      <td>30</td>\n",
-       "      <td>car</td>\n",
-       "      <td>bad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>25</td>\n",
-       "      <td>female</td>\n",
-       "      <td>2</td>\n",
-       "      <td>rent</td>\n",
-       "      <td>little</td>\n",
-       "      <td>moderate</td>\n",
-       "      <td>1295</td>\n",
-       "      <td>12</td>\n",
-       "      <td>car</td>\n",
-       "      <td>bad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>24</td>\n",
-       "      <td>female</td>\n",
-       "      <td>2</td>\n",
-       "      <td>rent</td>\n",
-       "      <td>little</td>\n",
-       "      <td>little</td>\n",
-       "      <td>4308</td>\n",
-       "      <td>48</td>\n",
-       "      <td>business</td>\n",
-       "      <td>bad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>22</td>\n",
-       "      <td>female</td>\n",
-       "      <td>2</td>\n",
-       "      <td>own</td>\n",
-       "      <td>little</td>\n",
-       "      <td>moderate</td>\n",
-       "      <td>1567</td>\n",
-       "      <td>12</td>\n",
-       "      <td>radio/TV</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>60</td>\n",
-       "      <td>male</td>\n",
-       "      <td>1</td>\n",
-       "      <td>own</td>\n",
-       "      <td>little</td>\n",
-       "      <td>little</td>\n",
-       "      <td>1199</td>\n",
-       "      <td>24</td>\n",
-       "      <td>car</td>\n",
-       "      <td>bad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>28</td>\n",
-       "      <td>female</td>\n",
-       "      <td>2</td>\n",
-       "      <td>rent</td>\n",
-       "      <td>little</td>\n",
-       "      <td>little</td>\n",
-       "      <td>1403</td>\n",
-       "      <td>15</td>\n",
-       "      <td>car</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>32</td>\n",
-       "      <td>female</td>\n",
-       "      <td>1</td>\n",
-       "      <td>own</td>\n",
-       "      <td>moderate</td>\n",
-       "      <td>little</td>\n",
-       "      <td>1282</td>\n",
-       "      <td>24</td>\n",
-       "      <td>radio/TV</td>\n",
-       "      <td>bad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>53</td>\n",
-       "      <td>male</td>\n",
-       "      <td>2</td>\n",
-       "      <td>own</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2424</td>\n",
-       "      <td>24</td>\n",
-       "      <td>radio/TV</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>25</td>\n",
-       "      <td>male</td>\n",
-       "      <td>2</td>\n",
-       "      <td>own</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>little</td>\n",
-       "      <td>8072</td>\n",
-       "      <td>30</td>\n",
-       "      <td>business</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>44</td>\n",
-       "      <td>female</td>\n",
-       "      <td>3</td>\n",
-       "      <td>free</td>\n",
-       "      <td>little</td>\n",
-       "      <td>moderate</td>\n",
-       "      <td>12579</td>\n",
-       "      <td>24</td>\n",
-       "      <td>car</td>\n",
-       "      <td>bad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>31</td>\n",
-       "      <td>male</td>\n",
-       "      <td>2</td>\n",
-       "      <td>own</td>\n",
-       "      <td>quite rich</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3430</td>\n",
-       "      <td>24</td>\n",
-       "      <td>radio/TV</td>\n",
-       "      <td>good</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Age     Sex  Job Housing Saving accounts Checking account  Credit amount  \\\n",
-       "0    67    male    2     own             NaN           little           1169   \n",
-       "1    22  female    2     own          little         moderate           5951   \n",
-       "2    49    male    1     own          little              NaN           2096   \n",
-       "3    45    male    2    free          little           little           7882   \n",
-       "4    53    male    2    free          little           little           4870   \n",
-       "5    35    male    1    free             NaN              NaN           9055   \n",
-       "6    53    male    2     own      quite rich              NaN           2835   \n",
-       "7    35    male    3    rent          little         moderate           6948   \n",
-       "8    61    male    1     own            rich              NaN           3059   \n",
-       "9    28    male    3     own          little         moderate           5234   \n",
-       "10   25  female    2    rent          little         moderate           1295   \n",
-       "11   24  female    2    rent          little           little           4308   \n",
-       "12   22  female    2     own          little         moderate           1567   \n",
-       "13   60    male    1     own          little           little           1199   \n",
-       "14   28  female    2    rent          little           little           1403   \n",
-       "15   32  female    1     own        moderate           little           1282   \n",
-       "16   53    male    2     own             NaN              NaN           2424   \n",
-       "17   25    male    2     own             NaN           little           8072   \n",
-       "18   44  female    3    free          little         moderate          12579   \n",
-       "19   31    male    2     own      quite rich              NaN           3430   \n",
-       "\n",
-       "    Duration              Purpose  Risk  \n",
-       "0          6             radio/TV  good  \n",
-       "1         48             radio/TV   bad  \n",
-       "2         12            education  good  \n",
-       "3         42  furniture/equipment  good  \n",
-       "4         24                  car   bad  \n",
-       "5         36            education  good  \n",
-       "6         24  furniture/equipment  good  \n",
-       "7         36                  car  good  \n",
-       "8         12             radio/TV  good  \n",
-       "9         30                  car   bad  \n",
-       "10        12                  car   bad  \n",
-       "11        48             business   bad  \n",
-       "12        12             radio/TV  good  \n",
-       "13        24                  car   bad  \n",
-       "14        15                  car  good  \n",
-       "15        24             radio/TV   bad  \n",
-       "16        24             radio/TV  good  \n",
-       "17        30             business  good  \n",
-       "18        24                  car   bad  \n",
-       "19        24             radio/TV  good  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Nettoyer le jeu de données\n",
     "dataset = dataset.drop('Unnamed: 0', axis='columns')\n",
@@ -681,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-19T22:27:27.062414Z",
@@ -777,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-19T20:24:53.142204Z",
@@ -787,20 +301,7 @@
      "shell.execute_reply.started": "2023-01-19T20:24:53.142166Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Ecrivez votre code ici (vous pouvez réutiliser/copier la plupart du code utilisé au dessus \n",
     "\n",

benchmark/NBspecific_4/NBspecific_4_reproduced.ipynb

@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-19T20:23:40.477106Z",
@@ -48,15 +48,7 @@
      "shell.execute_reply.started": "2023-01-19T20:23:40.477010Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "data/german_credit_data.csv\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Checking the files available in this notebook \n",
     "import os\n",
@@ -202,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-01-19T22:27:27.062414Z",
@@ -221,7 +213,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-ad62a79f7cf8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'talk'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfont_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.9\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      2\u001b[0m \u001b[0;31m# Exemple des types d'analyse qui peuvent être effectués\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Count plot nous aide à visualiser le nombre d'éléments par catégorie\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Sex'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Risk'\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-1-ad62a79f7cf8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'talk'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfont_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.9\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      2\u001b[0m \u001b[0;31m# Exemple des types d'analyse qui peuvent être effectués\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Count plot nous aide à visualiser le nombre d'éléments par catégorie\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Sex'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Risk'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'sns' is not defined"
      ]
     }

benchmark/NBspecific_5/NBspecific_5_fixed.ipynb

@@ -314,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-22T10:20:41.384744Z",
@@ -324,69 +324,7 @@
      "shell.execute_reply.started": "2023-05-22T10:20:41.384638Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "number of classes: 23\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "labels\n",
-       "normal             67343\n",
-       "neptune            41214\n",
-       "satan               3633\n",
-       "ipsweep             3599\n",
-       "portsweep           2931\n",
-       "smurf               2646\n",
-       "nmap                1493\n",
-       "back                 956\n",
-       "teardrop             892\n",
-       "warezclient          890\n",
-       "pod                  201\n",
-       "guess_passwd          53\n",
-       "buffer_overflow       30\n",
-       "warezmaster           20\n",
-       "land                  18\n",
-       "imap                  11\n",
-       "rootkit               10\n",
-       "loadmodule             9\n",
-       "ftp_write              8\n",
-       "multihop               7\n",
-       "phf                    4\n",
-       "perl                   3\n",
-       "spy                    2\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "print('number of classes:', df['labels'].nunique())\n",
     "print('')\n",
@@ -516,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-22T10:21:20.505401Z",
@@ -609,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-22T10:24:08.098032Z",

benchmark/NBspecific_5/NBspecific_5_reproduced.ipynb

@@ -314,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-22T10:20:41.384744Z",
@@ -324,69 +324,7 @@
      "shell.execute_reply.started": "2023-05-22T10:20:41.384638Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "number of classes: 23\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "labels\n",
-       "normal             67343\n",
-       "neptune            41214\n",
-       "satan               3633\n",
-       "ipsweep             3599\n",
-       "portsweep           2931\n",
-       "smurf               2646\n",
-       "nmap                1493\n",
-       "back                 956\n",
-       "teardrop             892\n",
-       "warezclient          890\n",
-       "pod                  201\n",
-       "guess_passwd          53\n",
-       "buffer_overflow       30\n",
-       "warezmaster           20\n",
-       "land                  18\n",
-       "imap                  11\n",
-       "rootkit               10\n",
-       "loadmodule             9\n",
-       "ftp_write              8\n",
-       "multihop               7\n",
-       "phf                    4\n",
-       "perl                   3\n",
-       "spy                    2\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "print('number of classes:', df['labels'].nunique())\n",
     "print('')\n",
@@ -516,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-22T10:21:20.505401Z",
@@ -609,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-05-22T10:24:08.098032Z",
@@ -627,7 +565,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-6-4356dc8164e0>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#column 'num_outbound_cmds' is zero everywhere, we will delete it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'num_outbound_cmds'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\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[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#remove from list of numeric columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mnumeric_columns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'num_outbound_cmds'\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-5-91a4d8dd6ff7>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# [reexecute] - this crash is because this cell was executed more than once\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#column 'num_outbound_cmds' is zero everywhere, we will delete it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'num_outbound_cmds'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\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[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#remove from list of numeric columns\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/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5342\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5343\u001b[0m         \"\"\"\n\u001b[0;32m-> 5344\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5345\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5346\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4709\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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[1;32m   4710\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[0;32m-> 4711\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4713\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\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/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4751\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4752\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-> 4753\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4754\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",

benchmark/NBspecific_6/NBspecific_6_reproduced.ipynb

@@ -128,123 +128,123 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1673789190429</td>\n",
-       "      <td>2023-01-15 13:26:30</td>\n",
-       "      <td>20743.7</td>\n",
-       "      <td>22.673</td>\n",
-       "      <td>20743.6</td>\n",
-       "      <td>0.176</td>\n",
-       "      <td>20743.5</td>\n",
-       "      <td>3.514</td>\n",
-       "      <td>20743.1</td>\n",
-       "      <td>0.022</td>\n",
+       "      <td>1673610944981</td>\n",
+       "      <td>2023-01-13 11:55:44</td>\n",
+       "      <td>18924.2</td>\n",
+       "      <td>26.472</td>\n",
+       "      <td>18924.1</td>\n",
+       "      <td>0.482</td>\n",
+       "      <td>18924.0</td>\n",
+       "      <td>0.126</td>\n",
+       "      <td>18923.9</td>\n",
+       "      <td>3.706</td>\n",
        "      <td>...</td>\n",
-       "      <td>20744.6</td>\n",
-       "      <td>0.743</td>\n",
-       "      <td>20744.7</td>\n",
-       "      <td>0.043</td>\n",
-       "      <td>20744.8</td>\n",
-       "      <td>0.412</td>\n",
-       "      <td>20744.9</td>\n",
-       "      <td>0.138</td>\n",
-       "      <td>20745.0</td>\n",
-       "      <td>1.180</td>\n",
+       "      <td>18925.0</td>\n",
+       "      <td>0.528</td>\n",
+       "      <td>18925.2</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>18925.3</td>\n",
+       "      <td>4.107</td>\n",
+       "      <td>18925.5</td>\n",
+       "      <td>0.137</td>\n",
+       "      <td>18925.6</td>\n",
+       "      <td>0.001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1673594303865</td>\n",
-       "      <td>2023-01-13 07:18:23</td>\n",
-       "      <td>18815.0</td>\n",
-       "      <td>20.859</td>\n",
-       "      <td>18814.8</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>18814.7</td>\n",
-       "      <td>2.228</td>\n",
-       "      <td>18814.5</td>\n",
-       "      <td>8.037</td>\n",
+       "      <td>1673599829908</td>\n",
+       "      <td>2023-01-13 08:50:29</td>\n",
+       "      <td>18833.9</td>\n",
+       "      <td>2.253</td>\n",
+       "      <td>18833.8</td>\n",
+       "      <td>4.126</td>\n",
+       "      <td>18833.3</td>\n",
+       "      <td>0.014</td>\n",
+       "      <td>18833.0</td>\n",
+       "      <td>0.005</td>\n",
        "      <td>...</td>\n",
-       "      <td>18816.0</td>\n",
-       "      <td>1.502</td>\n",
-       "      <td>18816.1</td>\n",
-       "      <td>4.033</td>\n",
-       "      <td>18816.2</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>18816.3</td>\n",
-       "      <td>0.997</td>\n",
-       "      <td>18816.4</td>\n",
-       "      <td>1.135</td>\n",
+       "      <td>18834.5</td>\n",
+       "      <td>0.283</td>\n",
+       "      <td>18834.6</td>\n",
+       "      <td>0.380</td>\n",
+       "      <td>18834.7</td>\n",
+       "      <td>2.505</td>\n",
+       "      <td>18834.8</td>\n",
+       "      <td>0.476</td>\n",
+       "      <td>18834.9</td>\n",
+       "      <td>0.638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1674072140715</td>\n",
-       "      <td>2023-01-18 20:02:20</td>\n",
-       "      <td>20864.2</td>\n",
-       "      <td>15.643</td>\n",
-       "      <td>20864.1</td>\n",
-       "      <td>1.001</td>\n",
-       "      <td>20863.7</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>20863.6</td>\n",
-       "      <td>0.479</td>\n",
+       "      <td>1673821068275</td>\n",
+       "      <td>2023-01-15 22:17:48</td>\n",
+       "      <td>20841.4</td>\n",
+       "      <td>26.090</td>\n",
+       "      <td>20841.3</td>\n",
+       "      <td>0.131</td>\n",
+       "      <td>20841.2</td>\n",
+       "      <td>1.236</td>\n",
+       "      <td>20841.1</td>\n",
+       "      <td>0.013</td>\n",
        "      <td>...</td>\n",
-       "      <td>20865.0</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>20865.2</td>\n",
-       "      <td>1.877</td>\n",
-       "      <td>20865.3</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>20865.4</td>\n",
-       "      <td>6.300</td>\n",
-       "      <td>20865.6</td>\n",
+       "      <td>20842.3</td>\n",
        "      <td>0.001</td>\n",
+       "      <td>20842.6</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>20842.7</td>\n",
+       "      <td>0.085</td>\n",
+       "      <td>20842.8</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>20842.9</td>\n",
+       "      <td>0.021</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1673582254964</td>\n",
-       "      <td>2023-01-13 03:57:34</td>\n",
-       "      <td>18863.5</td>\n",
-       "      <td>64.301</td>\n",
-       "      <td>18863.4</td>\n",
-       "      <td>21.509</td>\n",
-       "      <td>18863.3</td>\n",
-       "      <td>4.832</td>\n",
-       "      <td>18863.2</td>\n",
-       "      <td>2.838</td>\n",
+       "      <td>1673721207006</td>\n",
+       "      <td>2023-01-14 18:33:27</td>\n",
+       "      <td>20708.0</td>\n",
+       "      <td>16.755</td>\n",
+       "      <td>20707.2</td>\n",
+       "      <td>0.270</td>\n",
+       "      <td>20707.1</td>\n",
+       "      <td>0.376</td>\n",
+       "      <td>20707.0</td>\n",
+       "      <td>0.417</td>\n",
        "      <td>...</td>\n",
-       "      <td>18864.6</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>18864.7</td>\n",
-       "      <td>0.021</td>\n",
-       "      <td>18864.8</td>\n",
-       "      <td>0.013</td>\n",
-       "      <td>18865.0</td>\n",
-       "      <td>0.378</td>\n",
-       "      <td>18865.4</td>\n",
-       "      <td>0.904</td>\n",
+       "      <td>20708.7</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>20708.8</td>\n",
+       "      <td>1.316</td>\n",
+       "      <td>20709.0</td>\n",
+       "      <td>0.751</td>\n",
+       "      <td>20709.3</td>\n",
+       "      <td>0.303</td>\n",
+       "      <td>20709.4</td>\n",
+       "      <td>0.404</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1673961482776</td>\n",
-       "      <td>2023-01-17 13:18:02</td>\n",
-       "      <td>21229.2</td>\n",
-       "      <td>30.123</td>\n",
-       "      <td>21229.1</td>\n",
-       "      <td>5.268</td>\n",
-       "      <td>21229.0</td>\n",
-       "      <td>2.208</td>\n",
-       "      <td>21228.8</td>\n",
-       "      <td>2.040</td>\n",
+       "      <td>1673910614914</td>\n",
+       "      <td>2023-01-16 23:10:14</td>\n",
+       "      <td>21135.2</td>\n",
+       "      <td>3.634</td>\n",
+       "      <td>21135.1</td>\n",
+       "      <td>0.229</td>\n",
+       "      <td>21135.0</td>\n",
+       "      <td>1.672</td>\n",
+       "      <td>21134.7</td>\n",
+       "      <td>0.002</td>\n",
        "      <td>...</td>\n",
-       "      <td>21230.7</td>\n",
-       "      <td>0.235</td>\n",
-       "      <td>21230.8</td>\n",
-       "      <td>0.836</td>\n",
-       "      <td>21231.5</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>21231.6</td>\n",
-       "      <td>0.380</td>\n",
-       "      <td>21231.7</td>\n",
-       "      <td>0.017</td>\n",
+       "      <td>21136.0</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>21136.3</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>21136.4</td>\n",
+       "      <td>0.425</td>\n",
+       "      <td>21136.5</td>\n",
+       "      <td>1.554</td>\n",
+       "      <td>21136.6</td>\n",
+       "      <td>0.045</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -272,123 +272,123 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99995</th>\n",
-       "      <td>1673845021673</td>\n",
-       "      <td>2023-01-16 04:57:01</td>\n",
-       "      <td>21145.1</td>\n",
-       "      <td>0.078</td>\n",
-       "      <td>21145.0</td>\n",
-       "      <td>0.365</td>\n",
-       "      <td>21144.7</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>21144.6</td>\n",
-       "      <td>0.008</td>\n",
+       "      <td>1673837297061</td>\n",
+       "      <td>2023-01-16 02:48:17</td>\n",
+       "      <td>21233.8</td>\n",
+       "      <td>5.008</td>\n",
+       "      <td>21233.7</td>\n",
+       "      <td>0.033</td>\n",
+       "      <td>21233.6</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>21233.3</td>\n",
+       "      <td>0.235</td>\n",
        "      <td>...</td>\n",
-       "      <td>21145.8</td>\n",
-       "      <td>0.208</td>\n",
-       "      <td>21145.9</td>\n",
-       "      <td>2.325</td>\n",
-       "      <td>21146.0</td>\n",
-       "      <td>0.165</td>\n",
-       "      <td>21146.1</td>\n",
-       "      <td>0.292</td>\n",
-       "      <td>21146.4</td>\n",
-       "      <td>0.081</td>\n",
+       "      <td>21236.0</td>\n",
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+       "      <td>21236.1</td>\n",
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+       "      <td>21236.2</td>\n",
+       "      <td>1.679</td>\n",
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+       "      <td>0.001</td>\n",
+       "      <td>21236.6</td>\n",
+       "      <td>0.405</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99996</th>\n",
-       "      <td>1673352913221</td>\n",
-       "      <td>2023-01-10 12:15:13</td>\n",
-       "      <td>17244.9</td>\n",
-       "      <td>9.442</td>\n",
-       "      <td>17244.8</td>\n",
+       "      <td>1673848823936</td>\n",
+       "      <td>2023-01-16 06:00:23</td>\n",
+       "      <td>21196.7</td>\n",
+       "      <td>33.906</td>\n",
+       "      <td>21196.6</td>\n",
+       "      <td>1.028</td>\n",
+       "      <td>21196.5</td>\n",
+       "      <td>2.057</td>\n",
+       "      <td>21196.3</td>\n",
        "      <td>0.013</td>\n",
-       "      <td>17244.7</td>\n",
-       "      <td>0.032</td>\n",
-       "      <td>17244.6</td>\n",
-       "      <td>0.025</td>\n",
        "      <td>...</td>\n",
-       "      <td>17245.5</td>\n",
-       "      <td>0.106</td>\n",
-       "      <td>17245.6</td>\n",
-       "      <td>0.394</td>\n",
-       "      <td>17245.7</td>\n",
-       "      <td>2.735</td>\n",
-       "      <td>17245.8</td>\n",
-       "      <td>2.281</td>\n",
-       "      <td>17245.9</td>\n",
-       "      <td>18.193</td>\n",
+       "      <td>21197.5</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>21197.6</td>\n",
+       "      <td>0.186</td>\n",
+       "      <td>21197.7</td>\n",
+       "      <td>0.387</td>\n",
+       "      <td>21197.8</td>\n",
+       "      <td>0.096</td>\n",
+       "      <td>21198.0</td>\n",
+       "      <td>0.249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99997</th>\n",
-       "      <td>1673630358323</td>\n",
-       "      <td>2023-01-13 17:19:18</td>\n",
-       "      <td>19239.9</td>\n",
-       "      <td>3.884</td>\n",
-       "      <td>19239.8</td>\n",
-       "      <td>2.802</td>\n",
-       "      <td>19239.7</td>\n",
-       "      <td>4.690</td>\n",
-       "      <td>19239.5</td>\n",
-       "      <td>0.005</td>\n",
+       "      <td>1673611110905</td>\n",
+       "      <td>2023-01-13 11:58:30</td>\n",
+       "      <td>18904.8</td>\n",
+       "      <td>22.603</td>\n",
+       "      <td>18904.7</td>\n",
+       "      <td>0.095</td>\n",
+       "      <td>18904.6</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>18904.5</td>\n",
+       "      <td>0.553</td>\n",
        "      <td>...</td>\n",
-       "      <td>19240.5</td>\n",
-       "      <td>4.575</td>\n",
-       "      <td>19240.6</td>\n",
-       "      <td>1.533</td>\n",
-       "      <td>19240.7</td>\n",
-       "      <td>4.550</td>\n",
-       "      <td>19240.8</td>\n",
-       "      <td>1.774</td>\n",
-       "      <td>19240.9</td>\n",
-       "      <td>1.610</td>\n",
+       "      <td>18905.4</td>\n",
+       "      <td>0.974</td>\n",
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+       "      <td>4.041</td>\n",
+       "      <td>18905.9</td>\n",
+       "      <td>0.020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99998</th>\n",
-       "      <td>1673794625739</td>\n",
-       "      <td>2023-01-15 14:57:05</td>\n",
-       "      <td>20800.2</td>\n",
-       "      <td>8.381</td>\n",
-       "      <td>20800.1</td>\n",
-       "      <td>3.240</td>\n",
-       "      <td>20800.0</td>\n",
-       "      <td>0.885</td>\n",
-       "      <td>20799.4</td>\n",
-       "      <td>0.026</td>\n",
+       "      <td>1673695517927</td>\n",
+       "      <td>2023-01-14 11:25:17</td>\n",
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+       "      <td>20694.4</td>\n",
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        "      <td>...</td>\n",
-       "      <td>20800.8</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>20800.9</td>\n",
-       "      <td>0.028</td>\n",
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-       "      <td>0.092</td>\n",
-       "      <td>20801.1</td>\n",
-       "      <td>0.347</td>\n",
-       "      <td>20801.2</td>\n",
-       "      <td>0.070</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>99999</th>\n",
-       "      <td>1674193298659</td>\n",
-       "      <td>2023-01-20 05:41:38</td>\n",
-       "      <td>20952.1</td>\n",
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        "      <td>0.001</td>\n",
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-       "      <td>0.994</td>\n",
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-       "      <td>0.070</td>\n",
-       "      <td>20951.8</td>\n",
-       "      <td>0.100</td>\n",
        "      <td>...</td>\n",
-       "      <td>20952.9</td>\n",
-       "      <td>4.707</td>\n",
-       "      <td>20953.0</td>\n",
-       "      <td>9.031</td>\n",
-       "      <td>20953.1</td>\n",
-       "      <td>5.548</td>\n",
-       "      <td>20953.2</td>\n",
-       "      <td>2.938</td>\n",
-       "      <td>20953.3</td>\n",
-       "      <td>0.064</td>\n",
+       "      <td>20785.8</td>\n",
+       "      <td>2.815</td>\n",
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+       "      <td>1.456</td>\n",
+       "      <td>20786.2</td>\n",
+       "      <td>0.021</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -396,44 +396,44 @@
        "</div>"
       ],
       "text/plain": [
-       "                  0                    1        2       3        4       5   \\\n",
-       "0      1673789190429  2023-01-15 13:26:30  20743.7  22.673  20743.6   0.176   \n",
-       "1      1673594303865  2023-01-13 07:18:23  18815.0  20.859  18814.8   0.001   \n",
-       "2      1674072140715  2023-01-18 20:02:20  20864.2  15.643  20864.1   1.001   \n",
-       "3      1673582254964  2023-01-13 03:57:34  18863.5  64.301  18863.4  21.509   \n",
-       "4      1673961482776  2023-01-17 13:18:02  21229.2  30.123  21229.1   5.268   \n",
-       "...              ...                  ...      ...     ...      ...     ...   \n",
-       "99995  1673845021673  2023-01-16 04:57:01  21145.1   0.078  21145.0   0.365   \n",
-       "99996  1673352913221  2023-01-10 12:15:13  17244.9   9.442  17244.8   0.013   \n",
-       "99997  1673630358323  2023-01-13 17:19:18  19239.9   3.884  19239.8   2.802   \n",
-       "99998  1673794625739  2023-01-15 14:57:05  20800.2   8.381  20800.1   3.240   \n",
-       "99999  1674193298659  2023-01-20 05:41:38  20952.1   0.001  20952.0   0.994   \n",
+       "                  0                    1        2       3        4      5   \\\n",
+       "0      1673610944981  2023-01-13 11:55:44  18924.2  26.472  18924.1  0.482   \n",
+       "1      1673599829908  2023-01-13 08:50:29  18833.9   2.253  18833.8  4.126   \n",
+       "2      1673821068275  2023-01-15 22:17:48  20841.4  26.090  20841.3  0.131   \n",
+       "3      1673721207006  2023-01-14 18:33:27  20708.0  16.755  20707.2  0.270   \n",
+       "4      1673910614914  2023-01-16 23:10:14  21135.2   3.634  21135.1  0.229   \n",
+       "...              ...                  ...      ...     ...      ...    ...   \n",
+       "99995  1673837297061  2023-01-16 02:48:17  21233.8   5.008  21233.7  0.033   \n",
+       "99996  1673848823936  2023-01-16 06:00:23  21196.7  33.906  21196.6  1.028   \n",
+       "99997  1673611110905  2023-01-13 11:58:30  18904.8  22.603  18904.7  0.095   \n",
+       "99998  1673695517927  2023-01-14 11:25:17  20694.6   4.137  20694.5  2.055   \n",
+       "99999  1673750073798  2023-01-15 02:34:33  20784.9   0.055  20784.8  0.007   \n",
        "\n",
        "            6      7        8      9   ...       32     33       34     35  \\\n",
-       "0      20743.5  3.514  20743.1  0.022  ...  20744.6  0.743  20744.7  0.043   \n",
-       "1      18814.7  2.228  18814.5  8.037  ...  18816.0  1.502  18816.1  4.033   \n",
-       "2      20863.7  0.001  20863.6  0.479  ...  20865.0  0.003  20865.2  1.877   \n",
-       "3      18863.3  4.832  18863.2  2.838  ...  18864.6  0.010  18864.7  0.021   \n",
-       "4      21229.0  2.208  21228.8  2.040  ...  21230.7  0.235  21230.8  0.836   \n",
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+       "4      21135.0  1.672  21134.7  0.002  ...  21136.0  0.002  21136.3  0.015   \n",
        "...        ...    ...      ...    ...  ...      ...    ...      ...    ...   \n",
-       "99995  21144.7  0.001  21144.6  0.008  ...  21145.8  0.208  21145.9  2.325   \n",
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-       "99999  20951.9  0.070  20951.8  0.100  ...  20952.9  4.707  20953.0  9.031   \n",
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        "\n",
-       "            36     37       38     39       40      41  \n",
-       "0      20744.8  0.412  20744.9  0.138  20745.0   1.180  \n",
-       "1      18816.2  0.001  18816.3  0.997  18816.4   1.135  \n",
-       "2      20865.3  0.001  20865.4  6.300  20865.6   0.001  \n",
-       "3      18864.8  0.013  18865.0  0.378  18865.4   0.904  \n",
-       "4      21231.5  0.001  21231.6  0.380  21231.7   0.017  \n",
-       "...        ...    ...      ...    ...      ...     ...  \n",
-       "99995  21146.0  0.165  21146.1  0.292  21146.4   0.081  \n",
-       "99996  17245.7  2.735  17245.8  2.281  17245.9  18.193  \n",
-       "99997  19240.7  4.550  19240.8  1.774  19240.9   1.610  \n",
-       "99998  20801.0  0.092  20801.1  0.347  20801.2   0.070  \n",
-       "99999  20953.1  5.548  20953.2  2.938  20953.3   0.064  \n",
+       "            36     37       38     39       40     41  \n",
+       "0      18925.3  4.107  18925.5  0.137  18925.6  0.001  \n",
+       "1      18834.7  2.505  18834.8  0.476  18834.9  0.638  \n",
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+       "4      21136.4  0.425  21136.5  1.554  21136.6  0.045  \n",
+       "...        ...    ...      ...    ...      ...    ...  \n",
+       "99995  21236.2  1.679  21236.4  0.001  21236.6  0.405  \n",
+       "99996  21197.7  0.387  21197.8  0.096  21198.0  0.249  \n",
+       "99997  18905.6  1.053  18905.7  4.041  18905.9  0.020  \n",
+       "99998  20695.9  0.503  20696.0  0.120  20696.1  0.267  \n",
+       "99999  20786.0  0.409  20786.1  1.456  20786.2  0.021  \n",
        "\n",
        "[100000 rows x 42 columns]"
       ]
@@ -474,7 +474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-09-05T09:39:29.463834Z",
@@ -486,417 +486,14 @@
     "id": "Cuoj1Qlmi3KE",
     "outputId": "5b6214b3-5747-428d-d523-5303bbb74f36"
    },
-   "outputs": [
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-       "      <th>2023-01-15 13:26:30</th>\n",
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-       "      <th>2023-01-13 07:18:23</th>\n",
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-       "      <td>...</td>\n",
-       "      <td>18816.2</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>18814.0</td>\n",
-       "      <td>0.126</td>\n",
-       "      <td>18816.3</td>\n",
-       "      <td>0.997</td>\n",
-       "      <td>18813.9</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>18816.4</td>\n",
-       "      <td>1.135</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-18 20:02:20</th>\n",
-       "      <td>20864.2</td>\n",
-       "      <td>15.643</td>\n",
-       "      <td>20864.3</td>\n",
-       "      <td>2.263</td>\n",
-       "      <td>20864.1</td>\n",
-       "      <td>1.001</td>\n",
-       "      <td>20864.4</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>20863.7</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>...</td>\n",
-       "      <td>20865.3</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>20862.9</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>20865.4</td>\n",
-       "      <td>6.300</td>\n",
-       "      <td>20862.8</td>\n",
-       "      <td>0.480</td>\n",
-       "      <td>20865.6</td>\n",
-       "      <td>0.001</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-13 03:57:34</th>\n",
-       "      <td>18863.5</td>\n",
-       "      <td>64.301</td>\n",
-       "      <td>18863.6</td>\n",
-       "      <td>0.440</td>\n",
-       "      <td>18863.4</td>\n",
-       "      <td>21.509</td>\n",
-       "      <td>18863.8</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>18863.3</td>\n",
-       "      <td>4.832</td>\n",
-       "      <td>...</td>\n",
-       "      <td>18864.8</td>\n",
-       "      <td>0.013</td>\n",
-       "      <td>18862.7</td>\n",
-       "      <td>7.274</td>\n",
-       "      <td>18865.0</td>\n",
-       "      <td>0.378</td>\n",
-       "      <td>18862.6</td>\n",
-       "      <td>4.579</td>\n",
-       "      <td>18865.4</td>\n",
-       "      <td>0.904</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-17 13:18:02</th>\n",
-       "      <td>21229.2</td>\n",
-       "      <td>30.123</td>\n",
-       "      <td>21229.3</td>\n",
-       "      <td>0.137</td>\n",
-       "      <td>21229.1</td>\n",
-       "      <td>5.268</td>\n",
-       "      <td>21229.5</td>\n",
-       "      <td>0.013</td>\n",
-       "      <td>21229.0</td>\n",
-       "      <td>2.208</td>\n",
-       "      <td>...</td>\n",
-       "      <td>21231.5</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>21228.0</td>\n",
-       "      <td>1.410</td>\n",
-       "      <td>21231.6</td>\n",
-       "      <td>0.380</td>\n",
-       "      <td>21227.9</td>\n",
-       "      <td>0.059</td>\n",
-       "      <td>21231.7</td>\n",
-       "      <td>0.017</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-16 04:57:01</th>\n",
-       "      <td>21145.1</td>\n",
-       "      <td>0.078</td>\n",
-       "      <td>21145.2</td>\n",
-       "      <td>26.444</td>\n",
-       "      <td>21145.0</td>\n",
-       "      <td>0.365</td>\n",
-       "      <td>21145.4</td>\n",
-       "      <td>0.048</td>\n",
-       "      <td>21144.7</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>...</td>\n",
-       "      <td>21146.0</td>\n",
-       "      <td>0.165</td>\n",
-       "      <td>21144.1</td>\n",
-       "      <td>0.486</td>\n",
-       "      <td>21146.1</td>\n",
-       "      <td>0.292</td>\n",
-       "      <td>21144.0</td>\n",
-       "      <td>0.117</td>\n",
-       "      <td>21146.4</td>\n",
-       "      <td>0.081</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-10 12:15:13</th>\n",
-       "      <td>17244.9</td>\n",
-       "      <td>9.442</td>\n",
-       "      <td>17245.0</td>\n",
-       "      <td>52.056</td>\n",
-       "      <td>17244.8</td>\n",
-       "      <td>0.013</td>\n",
-       "      <td>17245.1</td>\n",
-       "      <td>3.704</td>\n",
-       "      <td>17244.7</td>\n",
-       "      <td>0.032</td>\n",
-       "      <td>...</td>\n",
-       "      <td>17245.7</td>\n",
-       "      <td>2.735</td>\n",
-       "      <td>17244.1</td>\n",
-       "      <td>1.249</td>\n",
-       "      <td>17245.8</td>\n",
-       "      <td>2.281</td>\n",
-       "      <td>17244.0</td>\n",
-       "      <td>0.112</td>\n",
-       "      <td>17245.9</td>\n",
-       "      <td>18.193</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-13 17:19:18</th>\n",
-       "      <td>19239.9</td>\n",
-       "      <td>3.884</td>\n",
-       "      <td>19240.0</td>\n",
-       "      <td>1.702</td>\n",
-       "      <td>19239.8</td>\n",
-       "      <td>2.802</td>\n",
-       "      <td>19240.1</td>\n",
-       "      <td>1.335</td>\n",
-       "      <td>19239.7</td>\n",
-       "      <td>4.690</td>\n",
-       "      <td>...</td>\n",
-       "      <td>19240.7</td>\n",
-       "      <td>4.550</td>\n",
-       "      <td>19238.4</td>\n",
-       "      <td>0.228</td>\n",
-       "      <td>19240.8</td>\n",
-       "      <td>1.774</td>\n",
-       "      <td>19238.3</td>\n",
-       "      <td>0.257</td>\n",
-       "      <td>19240.9</td>\n",
-       "      <td>1.610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-15 14:57:05</th>\n",
-       "      <td>20800.2</td>\n",
-       "      <td>8.381</td>\n",
-       "      <td>20800.3</td>\n",
-       "      <td>12.047</td>\n",
-       "      <td>20800.1</td>\n",
-       "      <td>3.240</td>\n",
-       "      <td>20800.4</td>\n",
-       "      <td>0.022</td>\n",
-       "      <td>20800.0</td>\n",
-       "      <td>0.885</td>\n",
-       "      <td>...</td>\n",
-       "      <td>20801.0</td>\n",
-       "      <td>0.092</td>\n",
-       "      <td>20798.8</td>\n",
-       "      <td>0.013</td>\n",
-       "      <td>20801.1</td>\n",
-       "      <td>0.347</td>\n",
-       "      <td>20798.6</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>20801.2</td>\n",
-       "      <td>0.070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-20 05:41:38</th>\n",
-       "      <td>20952.1</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>20952.2</td>\n",
-       "      <td>37.148</td>\n",
-       "      <td>20952.0</td>\n",
-       "      <td>0.994</td>\n",
-       "      <td>20952.3</td>\n",
-       "      <td>6.122</td>\n",
-       "      <td>20951.9</td>\n",
-       "      <td>0.070</td>\n",
-       "      <td>...</td>\n",
-       "      <td>20953.1</td>\n",
-       "      <td>5.548</td>\n",
-       "      <td>20950.8</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>20953.2</td>\n",
-       "      <td>2.938</td>\n",
-       "      <td>20950.6</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>20953.3</td>\n",
-       "      <td>0.064</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>96139 rows × 40 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                          2       3        22      23       4       5   \\\n",
-       "1                                                                        \n",
-       "2023-01-15 13:26:30  20743.7  22.673  20743.8   8.897  20743.6   0.176   \n",
-       "2023-01-13 07:18:23  18815.0  20.859  18815.1   5.267  18814.8   0.001   \n",
-       "2023-01-18 20:02:20  20864.2  15.643  20864.3   2.263  20864.1   1.001   \n",
-       "2023-01-13 03:57:34  18863.5  64.301  18863.6   0.440  18863.4  21.509   \n",
-       "2023-01-17 13:18:02  21229.2  30.123  21229.3   0.137  21229.1   5.268   \n",
-       "...                      ...     ...      ...     ...      ...     ...   \n",
-       "2023-01-16 04:57:01  21145.1   0.078  21145.2  26.444  21145.0   0.365   \n",
-       "2023-01-10 12:15:13  17244.9   9.442  17245.0  52.056  17244.8   0.013   \n",
-       "2023-01-13 17:19:18  19239.9   3.884  19240.0   1.702  19239.8   2.802   \n",
-       "2023-01-15 14:57:05  20800.2   8.381  20800.3  12.047  20800.1   3.240   \n",
-       "2023-01-20 05:41:38  20952.1   0.001  20952.2  37.148  20952.0   0.994   \n",
-       "\n",
-       "                          24     25       6      7   ...       36     37  \\\n",
-       "1                                                    ...                   \n",
-       "2023-01-15 13:26:30  20743.9  0.085  20743.5  3.514  ...  20744.8  0.412   \n",
-       "2023-01-13 07:18:23  18815.4  0.002  18814.7  2.228  ...  18816.2  0.001   \n",
-       "2023-01-18 20:02:20  20864.4  0.008  20863.7  0.001  ...  20865.3  0.001   \n",
-       "2023-01-13 03:57:34  18863.8  0.001  18863.3  4.832  ...  18864.8  0.013   \n",
-       "2023-01-17 13:18:02  21229.5  0.013  21229.0  2.208  ...  21231.5  0.001   \n",
-       "...                      ...    ...      ...    ...  ...      ...    ...   \n",
-       "2023-01-16 04:57:01  21145.4  0.048  21144.7  0.001  ...  21146.0  0.165   \n",
-       "2023-01-10 12:15:13  17245.1  3.704  17244.7  0.032  ...  17245.7  2.735   \n",
-       "2023-01-13 17:19:18  19240.1  1.335  19239.7  4.690  ...  19240.7  4.550   \n",
-       "2023-01-15 14:57:05  20800.4  0.022  20800.0  0.885  ...  20801.0  0.092   \n",
-       "2023-01-20 05:41:38  20952.3  6.122  20951.9  0.070  ...  20953.1  5.548   \n",
-       "\n",
-       "                          18     19       38     39       20     21       40  \\\n",
-       "1                                                                              \n",
-       "2023-01-15 13:26:30  20742.6  0.001  20744.9  0.138  20742.5  0.014  20745.0   \n",
-       "2023-01-13 07:18:23  18814.0  0.126  18816.3  0.997  18813.9  0.007  18816.4   \n",
-       "2023-01-18 20:02:20  20862.9  0.001  20865.4  6.300  20862.8  0.480  20865.6   \n",
-       "2023-01-13 03:57:34  18862.7  7.274  18865.0  0.378  18862.6  4.579  18865.4   \n",
-       "2023-01-17 13:18:02  21228.0  1.410  21231.6  0.380  21227.9  0.059  21231.7   \n",
-       "...                      ...    ...      ...    ...      ...    ...      ...   \n",
-       "2023-01-16 04:57:01  21144.1  0.486  21146.1  0.292  21144.0  0.117  21146.4   \n",
-       "2023-01-10 12:15:13  17244.1  1.249  17245.8  2.281  17244.0  0.112  17245.9   \n",
-       "2023-01-13 17:19:18  19238.4  0.228  19240.8  1.774  19238.3  0.257  19240.9   \n",
-       "2023-01-15 14:57:05  20798.8  0.013  20801.1  0.347  20798.6  0.001  20801.2   \n",
-       "2023-01-20 05:41:38  20950.8  0.001  20953.2  2.938  20950.6  0.005  20953.3   \n",
-       "\n",
-       "                         41  \n",
-       "1                            \n",
-       "2023-01-15 13:26:30   1.180  \n",
-       "2023-01-13 07:18:23   1.135  \n",
-       "2023-01-18 20:02:20   0.001  \n",
-       "2023-01-13 03:57:34   0.904  \n",
-       "2023-01-17 13:18:02   0.017  \n",
-       "...                     ...  \n",
-       "2023-01-16 04:57:01   0.081  \n",
-       "2023-01-10 12:15:13  18.193  \n",
-       "2023-01-13 17:19:18   1.610  \n",
-       "2023-01-15 14:57:05   0.070  \n",
-       "2023-01-20 05:41:38   0.064  \n",
-       "\n",
-       "[96139 rows x 40 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-09-05T09:39:29.600307Z",
@@ -917,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-09-05T09:39:30.064297Z",
@@ -929,115 +526,14 @@
     "id": "uGIijxh4nyaE",
     "outputId": "0361db7f-7048-40e4-f657-e4b190abce04"
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Price</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Time</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2023-01-15 13:26:30</th>\n",
-       "      <td>20743.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-13 07:18:23</th>\n",
-       "      <td>18815.05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-18 20:02:20</th>\n",
-       "      <td>20864.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-13 03:57:34</th>\n",
-       "      <td>18863.55</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-17 13:18:02</th>\n",
-       "      <td>21229.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-16 04:57:01</th>\n",
-       "      <td>21145.15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-10 12:15:13</th>\n",
-       "      <td>17244.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-13 17:19:18</th>\n",
-       "      <td>19239.95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-15 14:57:05</th>\n",
-       "      <td>20800.25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-01-20 05:41:38</th>\n",
-       "      <td>20952.15</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>96139 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                        Price\n",
-       "Time                         \n",
-       "2023-01-15 13:26:30  20743.75\n",
-       "2023-01-13 07:18:23  18815.05\n",
-       "2023-01-18 20:02:20  20864.25\n",
-       "2023-01-13 03:57:34  18863.55\n",
-       "2023-01-17 13:18:02  21229.25\n",
-       "...                       ...\n",
-       "2023-01-16 04:57:01  21145.15\n",
-       "2023-01-10 12:15:13  17244.95\n",
-       "2023-01-13 17:19:18  19239.95\n",
-       "2023-01-15 14:57:05  20800.25\n",
-       "2023-01-20 05:41:38  20952.15\n",
-       "\n",
-       "[96139 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "midprice"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-09-05T09:39:30.088816Z",
@@ -1068,7 +564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-09-05T09:39:30.101491Z",
@@ -1089,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-09-05T09:39:31.142370Z",
@@ -1113,7 +609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-09-05T09:39:34.504304Z",
@@ -1133,7 +629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-09-05T09:39:34.697074Z",
@@ -1173,7 +669,7 @@
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@@ -64,7 +64,7 @@
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-       "0  Average based on United Nations Framework Conv...  \n",
-       "1  Estimates from end-uses are for 2020 or 2021 (...  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.765226Z",
@@ -556,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.774881Z",
@@ -566,33 +298,14 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.774840Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "region       0\n",
-       "country      0\n",
-       "emissions    0\n",
-       "type         0\n",
-       "segment      0\n",
-       "reason       0\n",
-       "baseYear     0\n",
-       "notes        0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.isnull().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.789464Z",
@@ -602,25 +315,14 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.789403Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.duplicated().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.808188Z",
@@ -630,74 +332,14 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.808144Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>emissions</th>\n",
-       "      <td>1548.0</td>\n",
-       "      <td>643.255885</td>\n",
-       "      <td>5566.23818</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.6575</td>\n",
-       "      <td>24.065</td>\n",
-       "      <td>128.42</td>\n",
-       "      <td>141953.77</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            count        mean         std  min     25%     50%     75%  \\\n",
-       "emissions  1548.0  643.255885  5566.23818  0.0  2.6575  24.065  128.42   \n",
-       "\n",
-       "                 max  \n",
-       "emissions  141953.77  "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.describe().T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.830033Z",
@@ -707,21 +349,7 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.829998Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "baseYear\n",
-       "2022         1233\n",
-       "2019-2021     315\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.baseYear.value_counts()"
    ]
@@ -745,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.863938Z",
@@ -762,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.879267Z",
@@ -772,21 +400,7 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.879223Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "baseYear\n",
-       "2022    1233\n",
-       "2020     315\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.baseYear.value_counts()"
    ]
@@ -933,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:15.882728Z",
@@ -945,7 +559,7 @@
    },
    "outputs": [],
    "source": [
-    "# fix -- a future warning (because groupby() is handling categorical variables differently in future versions, observed=True removes unused categories)\n",
+    "# fix 2-- a future warning (because groupby() is handling categorical variables differently in future versions, observed=True removes unused categories)\n",
     "\n",
     "# df6=df.groupby('region').get_group('Asia Pacific').sort_values(by='%percent emission')\n",
     "df6=df.groupby('region', observed=True).get_group('Asia Pacific').sort_values(by='%percent emission') "
@@ -1008,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-04-12T16:58:16.054775Z",
@@ -1019,12 +633,13 @@
    },
    "outputs": [],
    "source": [
+    "# fix 1--- execute this cell\n",
     "df[\"%percent emission\"]= ((df[\"emissions\"]/df[\"emissions\"].sum())*100)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.status.busy": "2023-04-12T16:58:16.057017Z",
@@ -1033,132 +648,7 @@
      "shell.execute_reply.started": "2023-04-12T16:58:16.057268Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>country</th>\n",
-       "      <th>emissions</th>\n",
-       "      <th>type</th>\n",
-       "      <th>segment</th>\n",
-       "      <th>reason</th>\n",
-       "      <th>baseYear</th>\n",
-       "      <th>notes</th>\n",
-       "      <th>%percent emission</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>257.61</td>\n",
-       "      <td>Agriculture</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>All</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>Average based on United Nations Framework Conv...</td>\n",
-       "      <td>0.025871</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>Energy</td>\n",
-       "      <td>Bioenergy</td>\n",
-       "      <td>All</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>Estimates from end-uses are for 2020 or 2021 (...</td>\n",
-       "      <td>0.000005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>130.80</td>\n",
-       "      <td>Energy</td>\n",
-       "      <td>Gas pipelines and LNG facilities</td>\n",
-       "      <td>Fugitive</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>Not available</td>\n",
-       "      <td>0.013136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>69.74</td>\n",
-       "      <td>Energy</td>\n",
-       "      <td>Gas pipelines and LNG facilities</td>\n",
-       "      <td>Vented</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>Not available</td>\n",
-       "      <td>0.007004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>213.99</td>\n",
-       "      <td>Energy</td>\n",
-       "      <td>Onshore gas</td>\n",
-       "      <td>Fugitive</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>Not available</td>\n",
-       "      <td>0.021490</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   region  country  emissions         type                           segment  \\\n",
-       "0  Africa  Algeria     257.61  Agriculture                             Total   \n",
-       "1  Africa  Algeria       0.05       Energy                         Bioenergy   \n",
-       "2  Africa  Algeria     130.80       Energy  Gas pipelines and LNG facilities   \n",
-       "3  Africa  Algeria      69.74       Energy  Gas pipelines and LNG facilities   \n",
-       "4  Africa  Algeria     213.99       Energy                       Onshore gas   \n",
-       "\n",
-       "     reason baseYear                                              notes  \\\n",
-       "0       All     2020  Average based on United Nations Framework Conv...   \n",
-       "1       All     2022  Estimates from end-uses are for 2020 or 2021 (...   \n",
-       "2  Fugitive     2022                                      Not available   \n",
-       "3    Vented     2022                                      Not available   \n",
-       "4  Fugitive     2022                                      Not available   \n",
-       "\n",
-       "   %percent emission  \n",
-       "0           0.025871  \n",
-       "1           0.000005  \n",
-       "2           0.013136  \n",
-       "3           0.007004  \n",
-       "4           0.021490  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]

benchmark/NBspecific_7/NBspecific_7_reproduced.ipynb

@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.529325Z",
@@ -74,139 +74,14 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.529284Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>region</th>\n",
-       "      <th>country</th>\n",
-       "      <th>emissions</th>\n",
-       "      <th>type</th>\n",
-       "      <th>segment</th>\n",
-       "      <th>reason</th>\n",
-       "      <th>baseYear</th>\n",
-       "      <th>notes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>257.611206</td>\n",
-       "      <td>Agriculture</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>All</td>\n",
-       "      <td>2019-2021</td>\n",
-       "      <td>Average based on United Nations Framework Conv...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>0.052000</td>\n",
-       "      <td>Energy</td>\n",
-       "      <td>Bioenergy</td>\n",
-       "      <td>All</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>Estimates from end-uses are for 2020 or 2021 (...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>130.798996</td>\n",
-       "      <td>Energy</td>\n",
-       "      <td>Gas pipelines and LNG facilities</td>\n",
-       "      <td>Fugitive</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>Not available</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>69.741898</td>\n",
-       "      <td>Energy</td>\n",
-       "      <td>Gas pipelines and LNG facilities</td>\n",
-       "      <td>Vented</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>Not available</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>213.987000</td>\n",
-       "      <td>Energy</td>\n",
-       "      <td>Onshore gas</td>\n",
-       "      <td>Fugitive</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>Not available</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0  region  country   emissions         type  \\\n",
-       "0           0  Africa  Algeria  257.611206  Agriculture   \n",
-       "1           1  Africa  Algeria    0.052000       Energy   \n",
-       "2           2  Africa  Algeria  130.798996       Energy   \n",
-       "3           3  Africa  Algeria   69.741898       Energy   \n",
-       "4           4  Africa  Algeria  213.987000       Energy   \n",
-       "\n",
-       "                            segment    reason   baseYear  \\\n",
-       "0                             Total       All  2019-2021   \n",
-       "1                         Bioenergy       All       2022   \n",
-       "2  Gas pipelines and LNG facilities  Fugitive       2022   \n",
-       "3  Gas pipelines and LNG facilities    Vented       2022   \n",
-       "4                       Onshore gas  Fugitive       2022   \n",
-       "\n",
-       "                                               notes  \n",
-       "0  Average based on United Nations Framework Conv...  \n",
-       "1  Estimates from end-uses are for 2020 or 2021 (...  \n",
-       "2                                      Not available  \n",
-       "3                                      Not available  \n",
-       "4                                      Not available  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.563328Z",
@@ -216,27 +91,14 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.563289Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Unnamed: 0', 'region', 'country', 'emissions', 'type', 'segment',\n",
-       "       'reason', 'baseYear', 'notes'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.columns\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.573802Z",
@@ -246,25 +108,14 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.573767Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1548, 9)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.591898Z",
@@ -274,30 +125,7 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.591857Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 1548 entries, 0 to 1547\n",
-      "Data columns (total 9 columns):\n",
-      " #   Column      Non-Null Count  Dtype  \n",
-      "---  ------      --------------  -----  \n",
-      " 0   Unnamed: 0  1548 non-null   int64  \n",
-      " 1   region      1548 non-null   object \n",
-      " 2   country     1548 non-null   object \n",
-      " 3   emissions   1548 non-null   float64\n",
-      " 4   type        1548 non-null   object \n",
-      " 5   segment     1548 non-null   object \n",
-      " 6   reason      1548 non-null   object \n",
-      " 7   baseYear    1548 non-null   object \n",
-      " 8   notes       1548 non-null   object \n",
-      "dtypes: float64(1), int64(1), object(7)\n",
-      "memory usage: 109.0+ KB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.info()"
    ]
@@ -373,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.690937Z",
@@ -392,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.705427Z",
@@ -402,37 +230,14 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.705385Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 1548 entries, 0 to 1547\n",
-      "Data columns (total 9 columns):\n",
-      " #   Column      Non-Null Count  Dtype   \n",
-      "---  ------      --------------  -----   \n",
-      " 0   Unnamed: 0  1548 non-null   int64   \n",
-      " 1   region      1548 non-null   category\n",
-      " 2   country     1548 non-null   object  \n",
-      " 3   emissions   1548 non-null   float64 \n",
-      " 4   type        1548 non-null   category\n",
-      " 5   segment     1548 non-null   object  \n",
-      " 6   reason      1548 non-null   category\n",
-      " 7   baseYear    1548 non-null   object  \n",
-      " 8   notes       1548 non-null   object  \n",
-      "dtypes: category(3), float64(1), int64(1), object(4)\n",
-      "memory usage: 78.0+ KB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.733193Z",
@@ -449,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.743153Z",
@@ -459,87 +264,14 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.743111Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>region</th>\n",
-       "      <th>country</th>\n",
-       "      <th>emissions</th>\n",
-       "      <th>type</th>\n",
-       "      <th>segment</th>\n",
-       "      <th>reason</th>\n",
-       "      <th>baseYear</th>\n",
-       "      <th>notes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>257.611206</td>\n",
-       "      <td>Agriculture</td>\n",
-       "      <td>Total</td>\n",
-       "      <td>All</td>\n",
-       "      <td>2019-2021</td>\n",
-       "      <td>Average based on United Nations Framework Conv...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Africa</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>0.052000</td>\n",
-       "      <td>Energy</td>\n",
-       "      <td>Bioenergy</td>\n",
-       "      <td>All</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>Estimates from end-uses are for 2020 or 2021 (...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   region  country   emissions         type    segment reason   baseYear  \\\n",
-       "0  Africa  Algeria  257.611206  Agriculture      Total    All  2019-2021   \n",
-       "1  Africa  Algeria    0.052000       Energy  Bioenergy    All       2022   \n",
-       "\n",
-       "                                               notes  \n",
-       "0  Average based on United Nations Framework Conv...  \n",
-       "1  Estimates from end-uses are for 2020 or 2021 (...  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.765226Z",
@@ -556,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.774881Z",
@@ -566,33 +298,14 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.774840Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "region       0\n",
-       "country      0\n",
-       "emissions    0\n",
-       "type         0\n",
-       "segment      0\n",
-       "reason       0\n",
-       "baseYear     0\n",
-       "notes        0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.isnull().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.789464Z",
@@ -602,25 +315,14 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.789403Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.duplicated().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.808188Z",
@@ -630,74 +332,14 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.808144Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>emissions</th>\n",
-       "      <td>1548.0</td>\n",
-       "      <td>643.255885</td>\n",
-       "      <td>5566.23818</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.6575</td>\n",
-       "      <td>24.065</td>\n",
-       "      <td>128.42</td>\n",
-       "      <td>141953.77</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            count        mean         std  min     25%     50%     75%  \\\n",
-       "emissions  1548.0  643.255885  5566.23818  0.0  2.6575  24.065  128.42   \n",
-       "\n",
-       "                 max  \n",
-       "emissions  141953.77  "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.describe().T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.830033Z",
@@ -707,21 +349,7 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.829998Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "baseYear\n",
-       "2022         1233\n",
-       "2019-2021     315\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.baseYear.value_counts()"
    ]
@@ -745,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.863938Z",
@@ -762,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:13.879267Z",
@@ -772,21 +400,7 @@
      "shell.execute_reply.started": "2023-04-12T16:58:13.879223Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "baseYear\n",
-       "2022    1233\n",
-       "2020     315\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.baseYear.value_counts()"
    ]
@@ -933,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-04-12T16:58:15.882728Z",
@@ -948,7 +562,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-18-71a6516fd873>:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "<ipython-input-7-71a6516fd873>:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
       "  df6=df.groupby('region').get_group('Asia Pacific').sort_values(by='%percent emission')\n"
      ]
     },
@@ -959,7 +573,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-71a6516fd873>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf6\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'region'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Asia Pacific'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%percent emission'\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-7-71a6516fd873>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf6\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'region'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Asia Pacific'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%percent emission'\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/pandas/core/frame.py\u001b[0m in \u001b[0;36msort_values\u001b[0;34m(self, by, axis, ascending, inplace, kind, na_position, ignore_index, key)\u001b[0m\n\u001b[1;32m   6942\u001b[0m             \u001b[0;31m# len(by) == 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6943\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6944\u001b[0;31m             \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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   6945\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6946\u001b[0m             \u001b[0;31m# need to rewrap column in Series to apply key function\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/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_label_or_level_values\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1842\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1843\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-> 1844\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   1845\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1846\u001b[0m         \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mKeyError\u001b[0m: '%percent emission'"

benchmark/NBspecific_8/NBspecific_8_fixed.ipynb

@@ -322,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "_cell_guid": "500a7db4-22b9-43e1-a2df-0d84927700f6",
     "_uuid": "6476fe5078d5e3335cc5b10b754d164c7872c0f9",
@@ -335,28 +335,7 @@
     },
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, '# transactions')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 600x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#let us check another feature Amount\n",
     "fig, (ax3,ax4) = plt.subplots(2,1, figsize = (6,3), sharex = True)\n",
@@ -386,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "_cell_guid": "532903ce-1f12-4301-804c-c0e351e59079",
     "_uuid": "f726254bf9cdc512c96c3995df10b74ea18f94e5",

benchmark/NBspecific_8/NBspecific_8_reproduced.ipynb

@@ -322,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "_cell_guid": "500a7db4-22b9-43e1-a2df-0d84927700f6",
     "_uuid": "6476fe5078d5e3335cc5b10b754d164c7872c0f9",
@@ -335,28 +335,7 @@
     },
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, '# transactions')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 600x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#let us check another feature Amount\n",
     "fig, (ax3,ax4) = plt.subplots(2,1, figsize = (6,3), sharex = True)\n",
@@ -386,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "_cell_guid": "532903ce-1f12-4301-804c-c0e351e59079",
     "_uuid": "f726254bf9cdc512c96c3995df10b74ea18f94e5",
@@ -414,7 +393,7 @@
       "\u001b[0;31mKeyError\u001b[0m: 'Amount'",
       "\nThe above exception was the direct cause of the following exception:\n",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-5-202ae9b48e70>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# [reexecute] to reproduce the error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'scaled_Amount'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Amount'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Amount'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\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<ipython-input-4-9ed5de5cec7d>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# [reexecute] --- execute twice to reproduce the error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'scaled_Amount'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Amount'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Amount'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\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/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3891\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\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[1;32m   3892\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3893\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3894\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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   3895\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\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/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3796\u001b[0m             ):\n\u001b[1;32m   3797\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3798\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3799\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3800\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mKeyError\u001b[0m: 'Amount'"
@@ -422,7 +401,7 @@
     }
    ],
    "source": [
-    "# [reexecute] to reproduce the error\n",
+    "# [reexecute] --- execute twice to reproduce the error\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "df['scaled_Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1,1))\n",
     "df = df.drop(['Amount'],axis=1)"

benchmark/NBspecific_9/NBspecific_9_reproduced.ipynb

@@ -262,7 +262,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-4-e250a4939452>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# [re-execute] this cell needs to be execute twice to reproduce the crash\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10472\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-4-be81b89470fc>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# [reexecute] --- this cell needs to be execute twice to reproduce the crash\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10472\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5342\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5343\u001b[0m         \"\"\"\n\u001b[0;32m-> 5344\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5345\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5346\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4709\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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[1;32m   4710\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[0;32m-> 4711\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4713\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\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/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4751\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4752\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-> 4753\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4754\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
@@ -272,7 +272,7 @@
     }
    ],
    "source": [
-    "# [re-execute] this cell needs to be execute twice to reproduce the crash\n",
+    "# [reexecute] --- this cell needs to be execute twice to reproduce the crash\n",
     "df.drop([10472],inplace = True);"
    ]
   },