{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# Importing necessary libraries\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport re","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:15.509425Z","iopub.execute_input":"2022-12-29T07:14:15.511396Z","iopub.status.idle":"2022-12-29T07:14:15.516381Z","shell.execute_reply.started":"2022-12-29T07:14:15.511352Z","shell.execute_reply":"2022-12-29T07:14:15.515199Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"# Reading dataset using pandas\ndata = pd.read_csv(\"/kaggle/input/jigsaw-toxic-comment-classification-challenge/train.csv.zip\", compression='zip')\nlabels = ['toxic', 'severe_toxic', 'obscene', 'threat','insult', 'identity_hate']\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:15.517978Z","iopub.execute_input":"2022-12-29T07:14:15.518315Z","iopub.status.idle":"2022-12-29T07:14:16.744332Z","shell.execute_reply.started":"2022-12-29T07:14:15.518280Z","shell.execute_reply":"2022-12-29T07:14:16.743220Z"},"_kg_hide-output":true,"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"                 id                                       comment_text  toxic  \\\n0  0000997932d777bf  Explanation\\nWhy the edits made under my usern...      0   \n1  000103f0d9cfb60f  D'aww! He matches this background colour I'm s...      0   \n2  000113f07ec002fd  Hey man, I'm really not trying to edit war. It...      0   \n3  0001b41b1c6bb37e  \"\\nMore\\nI can't make any real suggestions on ...      0   \n4  0001d958c54c6e35  You, sir, are my hero. Any chance you remember...      0   \n\n   severe_toxic  obscene  threat  insult  identity_hate  \n0             0        0       0       0              0  \n1             0        0       0       0              0  \n2             0        0       0       0              0  \n3             0        0       0       0              0  \n4             0        0       0       0              0  ","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>id</th>\n      <th>comment_text</th>\n      <th>toxic</th>\n      <th>severe_toxic</th>\n      <th>obscene</th>\n      <th>threat</th>\n      <th>insult</th>\n      <th>identity_hate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0000997932d777bf</td>\n      <td>Explanation\\nWhy the edits made under my usern...</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>000103f0d9cfb60f</td>\n      <td>D'aww! He matches this background colour I'm s...</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>000113f07ec002fd</td>\n      <td>Hey man, I'm really not trying to edit war. It...</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>3</th>\n      <td>0001b41b1c6bb37e</td>\n      <td>\"\\nMore\\nI can't make any real suggestions on ...</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>4</th>\n      <td>0001d958c54c6e35</td>\n      <td>You, sir, are my hero. Any chance you remember...</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</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Creating new column named as \"non_toxic\" : if comment doesn't belong in any of the class then \"non_toxic\" will be 1 else 0\n\ndata[\"non_toxic\"] = data.iloc[:,2:8].apply(lambda x: 1 if (sum(x)==0) else 0, axis=1)\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:16.746025Z","iopub.execute_input":"2022-12-29T07:14:16.746440Z","iopub.status.idle":"2022-12-29T07:14:18.095698Z","shell.execute_reply.started":"2022-12-29T07:14:16.746401Z","shell.execute_reply":"2022-12-29T07:14:18.094538Z"},"_kg_hide-output":true,"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"                 id                                       comment_text  toxic  \\\n0  0000997932d777bf  Explanation\\nWhy the edits made under my usern...      0   \n1  000103f0d9cfb60f  D'aww! He matches this background colour I'm s...      0   \n2  000113f07ec002fd  Hey man, I'm really not trying to edit war. It...      0   \n3  0001b41b1c6bb37e  \"\\nMore\\nI can't make any real suggestions on ...      0   \n4  0001d958c54c6e35  You, sir, are my hero. Any chance you remember...      0   \n\n   severe_toxic  obscene  threat  insult  identity_hate  non_toxic  \n0             0        0       0       0              0          1  \n1             0        0       0       0              0          1  \n2             0        0       0       0              0          1  \n3             0        0       0       0              0          1  \n4             0        0       0       0              0          1  ","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>id</th>\n      <th>comment_text</th>\n      <th>toxic</th>\n      <th>severe_toxic</th>\n      <th>obscene</th>\n      <th>threat</th>\n      <th>insult</th>\n      <th>identity_hate</th>\n      <th>non_toxic</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0000997932d777bf</td>\n      <td>Explanation\\nWhy the edits made under my usern...</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>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000103f0d9cfb60f</td>\n      <td>D'aww! He matches this background colour I'm s...</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>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>000113f07ec002fd</td>\n      <td>Hey man, I'm really not trying to edit war. It...</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>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0001b41b1c6bb37e</td>\n      <td>\"\\nMore\\nI can't make any real suggestions on ...</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>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0001d958c54c6e35</td>\n      <td>You, sir, are my hero. Any chance you remember...</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>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"Total_of_Class = {}                   # Total labeled comments from each class\nfor col in data.iloc[:,2:].columns:\n    Total_of_Class[col] = data[col].value_counts()[1]\n    print(f\"{col} : {Total_of_Class[col]}\\n\")","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:18.097316Z","iopub.execute_input":"2022-12-29T07:14:18.097714Z","iopub.status.idle":"2022-12-29T07:14:18.120079Z","shell.execute_reply.started":"2022-12-29T07:14:18.097677Z","shell.execute_reply":"2022-12-29T07:14:18.119054Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"toxic : 15294\n\nsevere_toxic : 1595\n\nobscene : 8449\n\nthreat : 478\n\ninsult : 7877\n\nidentity_hate : 1405\n\nnon_toxic : 143346\n\n","output_type":"stream"}]},{"cell_type":"code","source":"# creating new column named \"total_classes\" which will show total no. of classes comment belongs to\n\ndata[\"total_classes\"] = data.iloc[:,2:8].apply(lambda x: sum(x), axis=1)    # if we take sum of all labels we can tell how many classes comment belong to\ndata                                                                        # for ex. if comment belongs to \"toxic\" and \"threat\" then the sum will be 2","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:18.122630Z","iopub.execute_input":"2022-12-29T07:14:18.123318Z","iopub.status.idle":"2022-12-29T07:14:19.492854Z","shell.execute_reply.started":"2022-12-29T07:14:18.123279Z","shell.execute_reply":"2022-12-29T07:14:19.491704Z"},"_kg_hide-output":true,"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"                      id                                       comment_text  \\\n0       0000997932d777bf  Explanation\\nWhy the edits made under my usern...   \n1       000103f0d9cfb60f  D'aww! He matches this background colour I'm s...   \n2       000113f07ec002fd  Hey man, I'm really not trying to edit war. It...   \n3       0001b41b1c6bb37e  \"\\nMore\\nI can't make any real suggestions on ...   \n4       0001d958c54c6e35  You, sir, are my hero. Any chance you remember...   \n...                  ...                                                ...   \n159566  ffe987279560d7ff  \":::::And for the second time of asking, when ...   \n159567  ffea4adeee384e90  You should be ashamed of yourself \\n\\nThat is ...   \n159568  ffee36eab5c267c9  Spitzer \\n\\nUmm, theres no actual article for ...   \n159569  fff125370e4aaaf3  And it looks like it was actually you who put ...   \n159570  fff46fc426af1f9a  \"\\nAnd ... I really don't think you understand...   \n\n        toxic  severe_toxic  obscene  threat  insult  identity_hate  \\\n0           0             0        0       0       0              0   \n1           0             0        0       0       0              0   \n2           0             0        0       0       0              0   \n3           0             0        0       0       0              0   \n4           0             0        0       0       0              0   \n...       ...           ...      ...     ...     ...            ...   \n159566      0             0        0       0       0              0   \n159567      0             0        0       0       0              0   \n159568      0             0        0       0       0              0   \n159569      0             0        0       0       0              0   \n159570      0             0        0       0       0              0   \n\n        non_toxic  total_classes  \n0               1              0  \n1               1              0  \n2               1              0  \n3               1              0  \n4               1              0  \n...           ...            ...  \n159566          1              0  \n159567          1              0  \n159568          1              0  \n159569          1              0  \n159570          1              0  \n\n[159571 rows x 10 columns]","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>id</th>\n      <th>comment_text</th>\n      <th>toxic</th>\n      <th>severe_toxic</th>\n      <th>obscene</th>\n      <th>threat</th>\n      <th>insult</th>\n      <th>identity_hate</th>\n      <th>non_toxic</th>\n      <th>total_classes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0000997932d777bf</td>\n      <td>Explanation\\nWhy the edits made under my usern...</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>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>000103f0d9cfb60f</td>\n      <td>D'aww! He matches this background colour I'm s...</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>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>000113f07ec002fd</td>\n      <td>Hey man, I'm really not trying to edit war. It...</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>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0001b41b1c6bb37e</td>\n      <td>\"\\nMore\\nI can't make any real suggestions on ...</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>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0001d958c54c6e35</td>\n      <td>You, sir, are my hero. Any chance you remember...</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>1</td>\n      <td>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    </tr>\n    <tr>\n      <th>159566</th>\n      <td>ffe987279560d7ff</td>\n      <td>\":::::And for the second time of asking, when ...</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>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>159567</th>\n      <td>ffea4adeee384e90</td>\n      <td>You should be ashamed of yourself \\n\\nThat is ...</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>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>159568</th>\n      <td>ffee36eab5c267c9</td>\n      <td>Spitzer \\n\\nUmm, theres no actual article for ...</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>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>159569</th>\n      <td>fff125370e4aaaf3</td>\n      <td>And it looks like it was actually you who put ...</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>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>159570</th>\n      <td>fff46fc426af1f9a</td>\n      <td>\"\\nAnd ... I really don't think you understand...</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>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>159571 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"print(\"Total no of \\\"Other Classes\\\" comments belong to when class is\\n\")\nfor col in data.iloc[:,2:8].columns:\n    print(f\"\\\"{col}\\\"\")\n    print(np.sort(data[data[col]==1][\"total_classes\"].unique()-1),\"\\n\")\nprint(\"0 means it only belongs to one class only, 1 means it belongs to 1 other class ie 2 classes in total, etc.\")","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:19.494556Z","iopub.execute_input":"2022-12-29T07:14:19.495026Z","iopub.status.idle":"2022-12-29T07:14:19.524224Z","shell.execute_reply.started":"2022-12-29T07:14:19.494984Z","shell.execute_reply":"2022-12-29T07:14:19.523196Z"},"_kg_hide-output":true,"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"Total no of \"Other Classes\" comments belong to when class is\n\n\"toxic\"\n[0 1 2 3 4 5] \n\n\"severe_toxic\"\n[1 2 3 4 5] \n\n\"obscene\"\n[0 1 2 3 4 5] \n\n\"threat\"\n[0 1 2 3 4 5] \n\n\"insult\"\n[0 1 2 3 4 5] \n\n\"identity_hate\"\n[0 1 2 3 4 5] \n\n0 means it only belongs to one class only, 1 means it belongs to 1 other class ie 2 classes in total, etc.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**From above we can say that this is \"Multilabel Classification Problem\" and target is on nominal scale**","metadata":{}},{"cell_type":"code","source":"multi_label_total = {}\nfor value in data[\"total_classes\"].unique():\n    multi_label_total[value] = data[\"total_classes\"].value_counts()[value]\nmulti_label_total","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:19.525738Z","iopub.execute_input":"2022-12-29T07:14:19.526112Z","iopub.status.idle":"2022-12-29T07:14:19.547314Z","shell.execute_reply.started":"2022-12-29T07:14:19.526076Z","shell.execute_reply":"2022-12-29T07:14:19.546235Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"{0: 143346, 4: 1760, 1: 6360, 3: 4209, 2: 3480, 5: 385, 6: 31}"},"metadata":{}}]},{"cell_type":"code","source":"list(multi_label_total.values()).pop(0)","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:19.548998Z","iopub.execute_input":"2022-12-29T07:14:19.549360Z","iopub.status.idle":"2022-12-29T07:14:19.556195Z","shell.execute_reply.started":"2022-12-29T07:14:19.549323Z","shell.execute_reply":"2022-12-29T07:14:19.555082Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"143346"},"metadata":{}}]},{"cell_type":"code","source":"# creating lists of data for visualization\nklst= list(multi_label_total.keys())\nvlst = list(multi_label_total.values())\n# print(klst,\"\\n\",vlst)\n\nvlst_0 = vlst.copy()\nklst_0 = klst.copy()\nvlst_0.pop(0)\nklst_0.pop(0)","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:19.557750Z","iopub.execute_input":"2022-12-29T07:14:19.558136Z","iopub.status.idle":"2022-12-29T07:14:19.569698Z","shell.execute_reply.started":"2022-12-29T07:14:19.558103Z","shell.execute_reply":"2022-12-29T07:14:19.568695Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"markdown","source":"# Data Visualization","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(15,10))\n\nfr = plt.subplot(2,1,1)\nplt.barh(width=vlst,y=klst)\nfr.set_xlabel(\"Total Number of Comments\", fontsize=10)\nfr.set_ylabel(\"Number of Classes comments belong to\", fontsize=10)\nfr.set_title(\"Plot including unlabeled(0) comments \", fontsize=10)\nfor key in klst:\n    fr.annotate(text=multi_label_total[key], xy=(multi_label_total[key],key), xycoords=\"data\",size=10, va=\"center\")\n\nse = plt.subplot(2,1,2)\nplt.barh(width=vlst_0,y=klst_0)\nse.set_xlabel(\"Total Number of Comments\", fontsize=10)\nse.set_ylabel(\"Number of Classes comments belong to\", fontsize=10)\nse.set_title(\"Plot excluding unlabeled(0) comments\", fontsize=10)\nfor key in klst_0:\n    se.annotate(text=multi_label_total[key], xy=(multi_label_total[key],key), xycoords=\"data\",size=10, va=\"center\")","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:19.571256Z","iopub.execute_input":"2022-12-29T07:14:19.572100Z","iopub.status.idle":"2022-12-29T07:14:20.032119Z","shell.execute_reply.started":"2022-12-29T07:14:19.572046Z","shell.execute_reply":"2022-12-29T07:14:20.031144Z"},"trusted":true},"execution_count":32,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1080x720 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# plotting distribution of classes \n\nfont1 = {\"size\":12}\nall_tox = list(Total_of_Class.values())\nplt.figure(figsize=(20,10))\n#plt.rcParams.update({'font.size': 12})\nplt.rc(\"font\",**font1)\n\nleft = plt.subplot(1,2,1)\nplt.bar(x = ['toxic', 'severe_toxic', 'obscene', 'threat','insult', 'identity_hate','non_toxic'], height = Total_of_Class.values(), color=\"#00DFFF\")\nleft.set_facecolor(\"black\")\nleft.set_title(\"distribution of classes with non toxic values\", fontsize=22)\nfor key in ['toxic', 'severe_toxic', 'obscene', 'threat','insult', 'identity_hate','non_toxic']:\n    left.annotate(text=Total_of_Class[key], xy=(key,Total_of_Class[key]+1000), xycoords=\"data\", color=\"white\", size=14, ha=\"center\")\n\nright = plt.subplot(1,2,2)\nplt.bar(x = ['toxic', 'severe_toxic', 'obscene', 'threat','insult', 'identity_hate'], height = all_tox[:-1], color=\"orange\")\nright.set_facecolor(\"black\")\nright.set_title(\"distribution of classes without non toxic values\",fontsize=22)\nfor key in ['toxic', 'severe_toxic', 'obscene', 'threat','insult', 'identity_hate']:\n    right.annotate(text=Total_of_Class[key], xy=(key,Total_of_Class[key]+100), xycoords=\"data\", color=\"white\", size=14, ha=\"center\")","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:20.033583Z","iopub.execute_input":"2022-12-29T07:14:20.033953Z","iopub.status.idle":"2022-12-29T07:14:20.560604Z","shell.execute_reply.started":"2022-12-29T07:14:20.033918Z","shell.execute_reply":"2022-12-29T07:14:20.559636Z"},"trusted":true},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x720 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# plot of total of all toxic classes and non toxic\n\ntemp = {\"Total Non Toxic comments\":int(all_tox[-1]), \"Total of all toxic classes comments\":int(sum(all_tox[:-1]))}\n# font = {\"size\":25}\nplt.figure(figsize=(20,5))\n# plt.axes().set_facecolor(\"black\")\n\nsubp = plt.subplot(1,1,1)\nsubp.set_facecolor(\"black\")\n# plt.rcParams.update({'font.size': 30})\n# plt.rc(\"font\",**font)\nplt.barh(width=[all_tox[-1],sum(all_tox[:-1])], y=[\"Total Non Toxic comments\", \"Total of all toxic classes comments\"], height=0.4, color=\"white\")\nfor key in temp.keys():\n    subp.annotate(text=temp[key], xy=(temp[key],key), xycoords=\"data\", color=\"white\", size=14)","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:20.562031Z","iopub.execute_input":"2022-12-29T07:14:20.563071Z","iopub.status.idle":"2022-12-29T07:14:20.794450Z","shell.execute_reply.started":"2022-12-29T07:14:20.563030Z","shell.execute_reply":"2022-12-29T07:14:20.793527Z"},"trusted":true},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x360 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# copying our dataset in new variable for further text preprocessing\nnew_data = data.copy()","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:20.802266Z","iopub.execute_input":"2022-12-29T07:14:20.802578Z","iopub.status.idle":"2022-12-29T07:14:20.817632Z","shell.execute_reply.started":"2022-12-29T07:14:20.802551Z","shell.execute_reply":"2022-12-29T07:14:20.816508Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"markdown","source":"# Data Cleaning and Preprocessing","metadata":{}},{"cell_type":"code","source":"# creating copy of data for data cleaning and preprocessing\ncleaned_data = new_data.copy()\n\n# Removing Hyperlinks from text\n# The below code is a re pattern that matches URLs beginning with http or https, followed by :// and one or more \n# non-whitespace characters, or URLs beginning with www., followed by one or more non-whitespace characters. \n\ncleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"https?://\\S+|www\\.\\S+\",\"\",x) )\n\nprint(data.loc[629,\"comment_text\"],\"\\n\\n\")\nprint(\"++++ After Removing URLs ++++\\n\\n \")\nprint(cleaned_data.loc[629,\"comment_text\"])","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:20.819030Z","iopub.execute_input":"2022-12-29T07:14:20.819581Z","iopub.status.idle":"2022-12-29T07:14:21.479788Z","shell.execute_reply.started":"2022-12-29T07:14:20.819545Z","shell.execute_reply":"2022-12-29T07:14:21.478696Z"},"_kg_hide-output":true,"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"Edit request on 29 October 2013 \n\nThe Arctic Monkeys played Walk on the Wild Side in honour of Lou Reed at Echo Arena, Liverpool, a day after the musician's death.\nhttp://www.rollingstone.com/music/videos/arctic-monkeys-cover-lou-reeds-walk-on-the-wild-side-20131029 \n\n\n++++ After Removing URLs ++++\n\n \nEdit request on 29 October 2013 \n\nThe Arctic Monkeys played Walk on the Wild Side in honour of Lou Reed at Echo Arena, Liverpool, a day after the musician's death.\n\n","output_type":"stream"}]},{"cell_type":"code","source":"# Removing emojis from text\ncleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(\"[\"\n                                                                                   u\"\\U0001F600-\\U0001F64F\"\n                                                                                   u\"\\U0001F300-\\U0001F5FF\"  \n                                                                                   u\"\\U0001F680-\\U0001F6FF\" \n                                                                                   u\"\\U0001F1E0-\\U0001F1FF\"  \n                                                                                   u\"\\U00002702-\\U000027B0\"\n                                                                                   u\"\\U000024C2-\\U0001F251\"\n                                                                                   \"]+\",\"\", x, flags=re.UNICODE))\n\nprint(data.loc[728,\"comment_text\"],\"\\n\\n\")\nprint(\"++++ After Removing Emojis ++++\\n\\n \")\nprint(cleaned_data.loc[728,\"comment_text\"])","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:21.481707Z","iopub.execute_input":"2022-12-29T07:14:21.482090Z","iopub.status.idle":"2022-12-29T07:14:23.205081Z","shell.execute_reply.started":"2022-12-29T07:14:21.482052Z","shell.execute_reply":"2022-12-29T07:14:23.203864Z"},"_kg_hide-output":true,"trusted":true},"execution_count":37,"outputs":[{"name":"stdout","text":"AMBER+TAYLOR FOREVER \n\nAMBER + TAYLOR FOREVER♥♥♥♥♥♥♥♥♥ \n\n\n++++ After Removing Emojis ++++\n\n \nAMBER+TAYLOR FOREVER \n\nAMBER + TAYLOR FOREVER\n","output_type":"stream"}]},{"cell_type":"code","source":"# Removing IP addresses from text \n# The below code is a re pattern that matches a string of four groups of digits separated by dots, \n# where each group consists of one to three digits.\ncleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\",\"\",x))\n\nprint(data.loc[744,\"comment_text\"],\"\\n\\n\")\nprint(\"++++ After Removing IP addresses ++++\\n\\n \")\nprint(cleaned_data.loc[744,\"comment_text\"])","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:23.206637Z","iopub.execute_input":"2022-12-29T07:14:23.207019Z","iopub.status.idle":"2022-12-29T07:14:24.857612Z","shell.execute_reply.started":"2022-12-29T07:14:23.206982Z","shell.execute_reply":"2022-12-29T07:14:24.856603Z"},"_kg_hide-output":true,"trusted":true},"execution_count":38,"outputs":[{"name":"stdout","text":"\"\n\nNo not really. We may ask that the mention of fat being the fire source of the cremation of millions be reconsidered though - along with a few other items. The fat cremation \"\"wiki fact\"\" is citable ( www.hdot - Emory U no less, Lipstadt) but doubtful. If the same science was applied to the holocaust as say the tinfoilers or flat earthers the deniers would be overjoyed. Be careful as to who gets the nutty fringe tinfoil label in the end. You get the permits and we'll bring the shovels. 159.105.80.141  \" \n\n\n++++ After Removing IP addresses ++++\n\n \n\"\n\nNo not really. We may ask that the mention of fat being the fire source of the cremation of millions be reconsidered though - along with a few other items. The fat cremation \"\"wiki fact\"\" is citable (  - Emory U no less, Lipstadt) but doubtful. If the same science was applied to the holocaust as say the tinfoilers or flat earthers the deniers would be overjoyed. Be careful as to who gets the nutty fringe tinfoil label in the end. You get the permits and we'll bring the shovels.   \"\n","output_type":"stream"}]},{"cell_type":"code","source":"# matches of css style,class elements and attributes\nidxs_css = []\nfor i in  range(len(cleaned_data)):\n    if re.findall(r\"[{][|].+\\n\",cleaned_data.loc[i,\"comment_text\"]):\n        idxs_css.append(i)\nprint(f\"length of css style elements containing comments : {len(idxs_css)}\")\n\n# Comments Containing Css style, class and attributes\nct=0\nfor i in idxs_css:\n    ct += 1\n    if ct>5:\n        break\n    st = cleaned_data.loc[i,\"comment_text\"]\n    print(f\"Comment No.{i} :\\n\\n{st}\")\n    print(\"---------------------------------------------------------------------------------------------------------------\\n\\n\")","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:24.859472Z","iopub.execute_input":"2022-12-29T07:14:24.860544Z","iopub.status.idle":"2022-12-29T07:14:26.591889Z","shell.execute_reply.started":"2022-12-29T07:14:24.860506Z","shell.execute_reply":"2022-12-29T07:14:26.590721Z"},"_kg_hide-output":true,"trusted":true},"execution_count":39,"outputs":[{"name":"stdout","text":"length of css style elements containing comments : 362\nComment No.162 :\n\n\"\nKatelyn Faber\nCould you weigh in at the bottom of the Talk Page for Katelyn Faber regarding the inclusion of an image of her?   \n\n Template:User Totalbox \n\nThanks for reverting.  \n\n Esperanza Newsletter, Issue #1 \n{| style=\"\"border-spacing:8px;margin:0px -8px\"\" width=\"\"100%\"\"\n|class=\"\"MainPageBG\"\" style=\"\"width: 55%; border:1px solid #cef2e0; background-color:#f5fffa; vertical-align:top;color:#000\"\"|\n Reach outReach out is a program aimed at allowing users to bring issues that they have had in Wikipedia to a listening, sympathetic and caring audience:\n \"\"No one can know how we feel if we do not say. We cannot expect to get understanding if we do not ask for it. No one will dispute that sometimes life's issues are too much for one person. It is fair to say that sometimes Wikipedia's problems fall under the same heading. This is a place where you can bring the bruises that can sometimes be got on this project for attention.\"\" Stress AlertsThe Stress alerts program aims at identifying users who are stressed, alerting the community of thier stress and works in tandem with the Stressbusters at trying to identify causes of stress and eliminating them. Note from the editorWelcome to this new format of the Esperanza Newsletter, which came about during the last Advisory Council meeting - we hope you like it! The major changes are that each month, right after the Council meeting, this will be sent out and will include two featured programs and a sum up of the meeting. Also, it will be signed by all of the Advisory Council members, not just . Have an Esperanzial end of March, everyone!\n|class=\"\"MainPageBG\"\" style=\"\"width: 45%; border:1px solid #cedff2; background-color:#f5faff; vertical-align:top\"\"|\n{| width=\"\"100%\"\" cellpadding=\"\"2\"\" cellspacing=\"\"5\"\" style=\"\"vertical-align:top; background-color:#f5faff\"\"\n! The last AC meeting (full log)\n|-\n|style=\"\"color:#000\"\"|\n Future meetings are to be held monthly, not fortnightly as before.\n Bans and Access level changes (apart from autovoice) in the IRC channel are to be reported at the new log.\n In the IRC channel, there is going to be only one bot at a time.\n The charter requires members to have 150 edits and 2 weeks editing. Why this is the case will be clarified.\n A new Code of Conduct will be drafted by  and proposed to the Esperanza community.\n The NPA reform idea is to be dropped officially.\n Charter ammendments are to be discussed in future, not voted on.\n The Advisory Council is not going to be proposed to be expanded by the Advisory Council themselves, if others want to propose it, they will listen.\n|-\n!\n---------------------------------------------------------------------------------------------------------------\n\n\nComment No.261 :\n\n\"#if:|==  ==|}}\n{| width=\"\"100%\"\" style=\"\"background:transparent\"\"\n{| width=\"\"100%\"\" style=\"\"background-color:#\"\"\n|style=\"\"width: 50%; border:1px solid #; background-color:#; vertical-align:top\"\"|\n Hello, Lceliku, and Welcome to Wikipedia!\nPlease remember to sign your name on talk pages by clicking  or using four tildes (~~~~); this will automatically produce your username and the date. Also, please do your best to always fill in the edit summary field. Below are some useful links to facilitate your involvement.\nHappy editing!   \n{| width=\"\"100%\"\" style=\"\"background-color:#\"\"\n|style=\"\"width: 50%; border:0; background-color:#; vertical-align:top\"\"|\n Getting started Introduction\n The five pillars of Wikipedia\n How to edit a page\n Intuitive guide to Wikipedia Finding your way around Table of contents\n Department directory Editing articles How to develop an article\n Manual of Style\n|style=\"\"width: 50%; border:0; background-color:#; vertical-align:top\"\"|\n{| width=\"\"100%\"\" cellpadding=\"\"2\"\" style=\"\"vertical-align:top; background-color:#\"\"\n! Getting help\n|-\n|style=\"\"color:#000\"\"|\n Frequently asked questions\n Cheatsheet\n Where to ask a question\n Help pages\n New contributors' help page\n Article Wizard - a Wizard to help you create articles\n|-\n!\n---------------------------------------------------------------------------------------------------------------\n\n\nComment No.661 :\n\n\"\n\n Wikipedia:Articles for deletion/Cheongye Kwan \n\nYour post at the above AFD on a new martial art bothers me. This was the version at the time, that you based your post on. You posted:\n\n \"\"I just read the article, and to be quite honest, feel that it does satisfy Wikipedia's guidelines... it is reliably sourced... Since I believe the notability of the article is satisfied... It now has 11 independent sources. And to refute the argument posed by Wayne, it is determined by the Phillip Rhee source that the martial arts is not localised\"\".\n\nPlease consider the concerns this post raises. Simple matters like references, notability and noobie basics of AFDs should be routine for any moderately experienced user, and bread-and-butter for any admin. In fact a cursory check shows that almost everything you stated was.. I don't have a word for it. \"\"Unreal\"\" is close.\n\nThe \"\"eleven independent sources\"\" that you stated established notability and non-localization at the time, were:\n Three references from 2004 [coates, robinson, shaw], certainly before the topic even existed (!) and all related to a passing piece of news about its founder being on the team of a completely different sport in any event (!)\n Two references from 1st half 2005 [davies, white], almost certainly identical and identically irrelevant to the topic's notability.\n The promoter's personal page on his local town website, almost certainly self-published [official village website].\n A 2008 casual interview in a non-notable and possibly non-reliable university magazine (apparently) of the promoter's travels to see experts in a different sport, with tangential tiny (< 1 sentence) mention that he'd discussed his new sport with them too. [ the Lau Philip \"\"LA\"\" interview].\n 3 entries marked \"\"Reference and support of , Written Reference\"\" [chris, cook, rhee], obviously a request by the founder to a friend/colleague write a letter of support, that was never published anyway and isn't cited so you can't have read it (not that it was even possibly valid evidence).\n A short writeup of a \"\"local information\"\" kind, on the award of the first 4 black belts by the school, in its local paper.\n\nIn brief of the 11:\n 5 were before the sport existed and were not even remotely to do with the topic itself much less \"\"sourced\"\" material on it or \"\"substantial\"\" coverage (they had precisely zero coverage); \n 3 were claims of personal \"\"I think  is a great guy\"\" letters of support, evidently solicited and/or written on request, with zero evidence of verifiable existence, and certainly in no way reliably sourced or independent; \n 1 was self-written\n 1 was the briefest of non-mentions in a non-reliable source (pretty much \"\"I discussed the topic with them\"\" in a college magazine interview)\n 1 is local press coverage of his courses that's not even \"\"news for a day\"\"  or evidence of anything beyond \"\"a course by this name is taught twice weekly in our local village hall\"\".\n\nYour assessment of these, at AFD, was:\n It is reliably sourced\n{| style=\"\"border:#c0c0c0 solid 1px\"\" width=\"\"90%\"\"\n| \n There is barely one reliable source in all of those 11 that the sport even exists in the first place (!) - a local newspaper mention that it meets in a village hall on tuesdays and fridays and just awarded its first 4 black belts. Probably via self-promotional input. \n There is zero reliable sourcing for any of the rest.\n|}\n That the cited pages are evidence of notability\n{| style=\"\"border:#c0c0c0 solid 1px\"\" width=\"\"90%\"\"\n| \n They don't mention it, or even date from a time the topic existed, nor are online versions provided! How on earth could they \"\"reliably evidence\"\" it?\n|}\n That the \"\"11 sources\"\" are \"\"independent\"\"\n{| style=\"\"border:#c0c0c0 solid 1px\"\" width=\"\"90%\"\"\n| \n 3 are claims that \"\"please support me\"\" letters exist; they are even stated to be \"\"personal references\"\"; unpublished and uncited\n 1 is his own clearly self-written web page on a local village web host\n|}\n That the cites show it is \"\"non-localized\"\"\n{| style=\"\"border:#c0c0c0 solid 1px\"\" width=\"\"90%\"\"\n| \n The page doesn't list a link so you can't have checked the cite, yet you say the citation \"\"proves\"\" it. \n The reference openly stated \"\"Cambridge University and Bristol Taekwando\"\" (!) a big indication of its actual source\n When a link was provided on request  it turns out it'\n---------------------------------------------------------------------------------------------------------------\n\n\nComment No.666 :\n\n\"{| class=\"\"wikitable\"\" style=\"\"border:1px solid #8888aa; background:#f8f8f8; padding:0; font-size:90%;\"\"\n|-\n!Parliamentary Bloc\n!Alliances\n!Leader\n!Seats\n!Percentage\n!Source\n|-\n|National Alliance||State of Law Coalition,National Iraqi Alliance||Ibrahim al-Jaafari||159||48.92%|| \n|-\n|al-Iraqiya||al-Iraqiyya||Hassan al-Jibburi||91||28%||\n|-\n|Kurdistan Blocs Coalition||Kurdistani List,Kurdistan Islamic Union,Islamic Group of Kurdistan||Fuad Masum||49||15.08%||\n|-\n|Center Current||al-Tawafuq,Unity Alliance of Iraq||unknown||10||3.08%||\n|-\n|Gorran Bloc||Gorran Movement||Shorsh HajiAl-Iraqiya brengt oppositie en Talabani dichterbij elkaar | Azady.nl||8||2.46%||\n|-\n|National Rafidain List||Assyrian Democratic Movement||Yonadam Kanna||3||0.92%||\n|-\n|CSAPC||Chaldean Syriac AssyrianPopular Council||Sarkis Aghajan||2||0.65%||\n|-\n|Independents||—||—||3||—||\n|}\n\n\"\n---------------------------------------------------------------------------------------------------------------\n\n\nComment No.733 :\n\n\"\n{| width=\"\"100%\"\" style=\"\"background:transparent\"\"\n{| width=\"\"100%\"\" style=\"\"background-color:#\"\"\n|style=\"\"width: 50%; border:1px solid #; background-color:#; vertical-align:top\"\"|\n Hello Madstar890, and Welcome to Wikipedia!\nPlease remember to sign your name on talk pages by clicking  or using four tildes (~~~~); this will automatically produce your username and the date. Also, please do your best to always fill in the edit summary field. Below are some useful links to facilitate your involvement.\nHappy editing! —αἰτίας •discussion• \n{| width=\"\"100%\"\" style=\"\"background-color:#\"\"\n|style=\"\"width: 50%; border:0; background-color:#; vertical-align:top\"\"|\n Getting started Introduction\n The five pillars of Wikipedia\n How to edit a page Finding your way around Table of contents\n Department directory Editing articles How to develop an article\n Manual of Style\n|style=\"\"width: 50%; border:0; background-color:#; vertical-align:top\"\"|\n{| width=\"\"100%\"\" cellpadding=\"\"2\"\" style=\"\"vertical-align:top; background-color:#\"\"\n! Getting help\n|-\n|style=\"\"color:#000\"\"|\n Frequently asked questions\n Cheatsheet\n Where to ask a question\n Help pages\n New contributors' help page\n|-\n!\n---------------------------------------------------------------------------------------------------------------\n\n\n","output_type":"stream"}]},{"cell_type":"code","source":"cleaned_data.loc[idxs_css,\"total_classes\"].value_counts()    # total css style labeled comments","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:26.593708Z","iopub.execute_input":"2022-12-29T07:14:26.594465Z","iopub.status.idle":"2022-12-29T07:14:26.606042Z","shell.execute_reply.started":"2022-12-29T07:14:26.594420Z","shell.execute_reply":"2022-12-29T07:14:26.604506Z"},"_kg_hide-output":true,"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"0    359\n1      1\n3      1\n4      1\nName: total_classes, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# As we can see above almost all comments containing css style elements are unlabeled and are useless so we can drop them\ncleaned_data.drop(idxs_css,inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:26.608199Z","iopub.execute_input":"2022-12-29T07:14:26.608636Z","iopub.status.idle":"2022-12-29T07:14:26.635916Z","shell.execute_reply.started":"2022-12-29T07:14:26.608591Z","shell.execute_reply":"2022-12-29T07:14:26.634769Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"# Removing html tags from text \ncleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"<.*?>\",\"\",x))","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:26.637385Z","iopub.execute_input":"2022-12-29T07:14:26.637779Z","iopub.status.idle":"2022-12-29T07:14:26.861543Z","shell.execute_reply.started":"2022-12-29T07:14:26.637742Z","shell.execute_reply":"2022-12-29T07:14:26.860439Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"# There are some comments which contain double quoted words like --> \"\"words\"\"  we will convert these to --> \"words\" \n\ncleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"\\\"\\\"\", \"\\\"\",x))   # replacing \"\" with \"\ncleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"^\\\"\", \"\",x))      # removing quotation from start and the end of the string\ncleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"\\\"$\", \"\",x))\n\nprint(data.loc[15,\"comment_text\"],\"\\n\\n\")\nprint(\"++++ After Removing double quotation ++++\\n\\n \")\nprint(cleaned_data.loc[15,\"comment_text\"])","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:26.863087Z","iopub.execute_input":"2022-12-29T07:14:26.863669Z","iopub.status.idle":"2022-12-29T07:14:28.187210Z","shell.execute_reply.started":"2022-12-29T07:14:26.863612Z","shell.execute_reply":"2022-12-29T07:14:28.186138Z"},"_kg_hide-output":true,"trusted":true},"execution_count":43,"outputs":[{"name":"stdout","text":"\"\n\nJuelz Santanas Age\n\nIn 2002, Juelz Santana was 18 years old, then came February 18th, which makes Juelz turn 19 making songs with The Diplomats. The third neff to be signed to Cam's label under Roc A Fella. In 2003, he was 20 years old coming out with his own singles \"\"Santana's Town\"\" and \"\"Down\"\". So yes, he is born in 1983. He really is, how could he be older then Lloyd Banks? And how could he be 22 when his birthday passed? The homie neff is 23 years old. 1983 - 2006 (Juelz death, god forbid if your thinking about that) equals 23. Go to your caculator and stop changing his year of birth. My god.\" \n\n\n++++ After Removing double quotation ++++\n\n \n\n\nJuelz Santanas Age\n\nIn 2002, Juelz Santana was 18 years old, then came February 18th, which makes Juelz turn 19 making songs with The Diplomats. The third neff to be signed to Cam's label under Roc A Fella. In 2003, he was 20 years old coming out with his own singles \"Santana's Town\" and \"Down\". So yes, he is born in 1983. He really is, how could he be older then Lloyd Banks? And how could he be 22 when his birthday passed? The homie neff is 23 years old. 1983 - 2006 (Juelz death, god forbid if your thinking about that) equals 23. Go to your caculator and stop changing his year of birth. My god.\n","output_type":"stream"}]},{"cell_type":"code","source":"# Removing Punctuation / Special characters (;:'\".?@!%&*+) which appears more than twice in the text \n\ncleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"[^a-zA-Z0-9\\s][^a-zA-Z0-9\\s]+\", \" \",x))\n\nprint(data.loc[1871,\"comment_text\"],\"\\n\\n\")\nprint(\"++++ After Removing consequent special chars ++++\\n\\n \")\nprint(cleaned_data.loc[1871,\"comment_text\"])","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:28.189793Z","iopub.execute_input":"2022-12-29T07:14:28.190610Z","iopub.status.idle":"2022-12-29T07:14:29.044767Z","shell.execute_reply.started":"2022-12-29T07:14:28.190564Z","shell.execute_reply":"2022-12-29T07:14:29.043781Z"},"_kg_hide-output":true,"trusted":true},"execution_count":44,"outputs":[{"name":"stdout","text":"\"==Apocalypse Soon: The Making of 'Citizen Toxie'==\nA {{prod}} template has been added to the article Apocalypse Soon: The Making of 'Citizen Toxie', suggesting that it be deleted according to the proposed deletion process. All contributions are appreciated, but this article may not satisfy Wikipedia's criteria for inclusion, and the deletion notice explains why (see also \"\"What Wikipedia is not\"\" and Wikipedia's deletion policy).  You may contest the proposed deletion by removing the {{dated prod}} notice, but please explain why you disagree with the proposed deletion in your edit summary or on its talk page. Also, please consider improving the article to address the issues raised. Even though removing the deletion notice will prevent deletion through the proposed deletion process, the article may still be deleted if it matches any of the speedy deletion criteria or it can be sent to Articles for Deletion, where it may be deleted if consensus to delete is reached. If you endorse deletion of the article, and you are the only person who has made substantial edits to the page, please tag it with .  \" \n\n\n++++ After Removing consequent special chars ++++\n\n \n Apocalypse Soon: The Making of 'Citizen Toxie \nA  prod  template has been added to the article Apocalypse Soon: The Making of 'Citizen Toxie  suggesting that it be deleted according to the proposed deletion process. All contributions are appreciated, but this article may not satisfy Wikipedia's criteria for inclusion, and the deletion notice explains why (see also \"What Wikipedia is not\" and Wikipedia's deletion policy   You may contest the proposed deletion by removing the  dated prod  notice, but please explain why you disagree with the proposed deletion in your edit summary or on its talk page. Also, please consider improving the article to address the issues raised. Even though removing the deletion notice will prevent deletion through the proposed deletion process, the article may still be deleted if it matches any of the speedy deletion criteria or it can be sent to Articles for Deletion, where it may be deleted if consensus to delete is reached. If you endorse deletion of the article, and you are the only person who has made substantial edits to the page, please tag it with .  \n","output_type":"stream"}]},{"cell_type":"code","source":"# Removing Special characters \n\ncleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"[^a-zA-Z0-9\\s\\\"\\',:;?!.()]\", \" \",x))\n\nprint(data.loc[126,\"comment_text\"],\"\\n\\n\")\nprint(\"++++ After Removing special chars ++++\\n\\n \")\nprint(cleaned_data.loc[126,\"comment_text\"])","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:29.046534Z","iopub.execute_input":"2022-12-29T07:14:29.047416Z","iopub.status.idle":"2022-12-29T07:14:29.819228Z","shell.execute_reply.started":"2022-12-29T07:14:29.047377Z","shell.execute_reply":"2022-12-29T07:14:29.817067Z"},"_kg_hide-output":true,"trusted":true},"execution_count":45,"outputs":[{"name":"stdout","text":"Ahh, Hello Witzeman \n\n203.92.84.161  \nSymbols: ~ | ¡ ¿ † ‡ ↔ ↑ ↓ • ¶   # ½ ⅓ ⅔ ¼ ¾ ⅛ ⅜ ⅝ ⅞ ∞   ‘ “ ’ ” «»   ¤ ₳ ฿ ₵ ¢ ₡ ₢ $ ₫ ₯ € ₠ ₣ ƒ ₴ ₭ ₤ ℳ ₥ ₦ № ₧ ₰ £ ៛ ₨ ₪ ৳ ₮ ₩ ¥   ♠ ♣ ♥ ♦   m² m³ \nCharacters: Á á Ć ć É é Í í Ĺ ĺ Ń ń Ó ó Ŕ ŕ Ś ś Ú ú Ý ý Ź ź   À à È è Ì ì Ò ò Ù ù    â Ĉ ĉ Ê ê Ĝ ĝ Ĥ ĥ Î î Ĵ ĵ Ô ô Ŝ ŝ Û û Ŵ ŵ Ŷ ŷ   Ä ä Ë ë Ï ï Ö ö Ü ü Ÿ ÿ   ß   à ã Ẽ ẽ Ĩ ĩ Ñ ñ Õ õ Ũ ũ Ỹ ỹ   Ç ç Ģ ģ Ķ ķ Ļ ļ Ņ ņ Ŗ ŗ Ş ş Ţ ţ   Đ đ   Ů ů   Ǎ ǎ Č č Ď ď Ě ě Ǐ ǐ Ľ ľ Ň ň Ǒ ǒ Ř ř Š š Ť ť Ǔ ǔ Ž ž   Ā ā Ē ē Ī ī Ō ō Ū ū Ȳ ȳ Ǣ ǣ   ǖ ǘ ǚ ǜ Ă ă Ĕ ĕ Ğ ğ Ĭ ĭ Ŏ ŏ Ŭ ŭ   Ċ ċ Ė ė Ġ ġ İ ı Ż ż   Ą ą Ę ę Į į Ǫ ǫ Ų ų   Ḍ ḍ Ḥ ḥ Ḷ ḷ Ḹ ḹ Ṃ ṃ Ṇ ṇ Ṛ ṛ Ṝ ṝ Ṣ ṣ Ṭ ṭ   Ł ł   Ő ő Ű ű   Ŀ ŀ   Ħ ħ   Ð ð Þ þ   Œ œ   Æ æ Ø ø Å å   Ə ə    \nGreek: Ά ά Έ έ Ή ή Ί ί Ό ό Ύ ύ Ώ ώ   Α α Β β Γ γ Δ δ   Ε ε Ζ ζ Η η Θ θ   Ι ι Κ κ Λ λ Μ μ   Ν ν Ξ ξ Ο ο Π π   Ρ ρ Σ σ ς Τ τ Υ υ   Φ φ Χ χ Ψ ψ Ω ω    \nCyrillic: А а Б б В в Г г   Ґ ґ Ѓ ѓ Д д Ђ ђ   Е е Ё ё Є є Ж ж   З з Ѕ ѕ И и І і   Ї ї Й й Ј ј К к   Ќ ќ Л л Љ љ М м   Н н Њ њ О о П п   Р р С с Т т Ћ ћ   У у Ў ў Ф ф Х х   Ц ц Ч ч Џ џ Ш ш   Щ щ Ъ ъ Ы ы Ь ь   Э э Ю ю Я я \nIPA: t̪ d̪ ʈ ɖ ɟ ɡ ɢ ʡ ʔ   ɸ ʃ ʒ ɕ ʑ ʂ ʐ ʝ ɣ ʁ ʕ ʜ ʢ ɦ   ɱ ɳ ɲ ŋ ɴ   ʋ ɹ ɻ ɰ   ʙ ʀ ɾ ɽ   ɫ ɬ ɮ ɺ ɭ ʎ ʟ   ɥ ʍ ɧ   ɓ ɗ ʄ ɠ ʛ   ʘ ǀ ǃ ǂ ǁ   ɨ ʉ ɯ   ɪ ʏ ʊ   ɘ ɵ ɤ   ə ɚ   ɛ ɜ ɝ ɞ ʌ ɔ   ɐ ɶ ɑ ɒ   ʰ ʷ ʲ ˠ ˤ ⁿ ˡ   ˈ ˌ ː ˑ ̪   \n= My Famous Article ==witze  happiness − wikipedia The Witzeman is a great honour that has been passed down through the generations of many families, regardless of race, age, character or knowledge. The outside world knows little about these elusive characters, and the honour of the Witzeman. For those who have ever been a Witzeman, it has been said to have been a great honour, although the qualities needed for the job have never been disclosed. A person may not now they were a Witzeman for many years, until they are called by former Witzemans via dreams. Associates of the Witzeman are known to be a certain Babe Cool or the much lesser known Witzewoman.\n\nThe Present Witzeman is an 11-year-old boy namely Benjamin Woods, who is said to have become a Witzeman after he felt this 'awesome radiance in his top-right bumcheek'. He has not told a great number of people his testimony, but has promised to do so in years to come.\n\n                           History\nThe present world has only come enlightned with the knowledge of the Witzeman in present years, because former Witzemen have not been so public about the honour, to abide with the formality and conservativism in their time. The term 'Witzeman' is a compound word of the adjective Witze and 'man', witze being an 11th century term for 'a person of extreme humour and radiant intelligence'. Unfortuneatly, Witze is also sometimes associated with bad wind, for unspeakable reasons.\n\n                       Proper History\nAs early as the 14th century, Witzemen were considered outcasts, and young children were encouraged to poke them with sticks. This led to the 1st Witzeman Rebellion of 1555 when the current Witzeman and his followers attempted a coup to take over the local Council chess team. This rebellion further disgraced the Witzeman, as he and his followers were embarassingly pronounced 'gaga' at a Government meeting that year. The period from here to the early 1900s was a dark time in the Witzemans history. However good times were to come for the Witzeman. Many normal people took part in mass demonstrations and protests for the Witzeman in the 1980s, building up to a great moment in the history...the Great Rebellion of 1988. This was when several Witzeman sprinted round the Visitors Gallery of the House of Commons, wearing clown masks, but otherwise completely naked. This was thought to be an act of circambulation. However many people who witnessed this shocking behaviour, were 'mentally, spiritually, and emotionallly scarred for life'. This group of people included many young children who were forced to go to asylums in a state of mental instability, suffering from trauma. It was at this time that the Government finally took positive action for the Witzeman. In 1990, a bill of rights for the Witzeman was signed, stating all Witzemen were allowed to do as they pleased. Although this was a formal agreement, many journalists viewed it as a letter of submission from the Government to the Witzeman, as the behaviour of the Witzeman and his followers became more and more twisted. \n\n\n++++ After Removing special chars ++++\n\n \nAhh, Hello Witzeman \n\n  \nSymbols:                                                                                                                                   m  m  \nCharacters:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \nGreek:                                                                                                                                              \nCyrillic:                                                                                                                                                                                                                   \nIPA: t  d                                                                                                                                                                                                                  \n  My Famous Article  witze  happiness   wikipedia The Witzeman is a great honour that has been passed down through the generations of many families, regardless of race, age, character or knowledge. The outside world knows little about these elusive characters, and the honour of the Witzeman. For those who have ever been a Witzeman, it has been said to have been a great honour, although the qualities needed for the job have never been disclosed. A person may not now they were a Witzeman for many years, until they are called by former Witzemans via dreams. Associates of the Witzeman are known to be a certain Babe Cool or the much lesser known Witzewoman.\n\nThe Present Witzeman is an 11 year old boy namely Benjamin Woods, who is said to have become a Witzeman after he felt this 'awesome radiance in his top right bumcheek  He has not told a great number of people his testimony, but has promised to do so in years to come.\n\n                           History\nThe present world has only come enlightned with the knowledge of the Witzeman in present years, because former Witzemen have not been so public about the honour, to abide with the formality and conservativism in their time. The term 'Witzeman' is a compound word of the adjective Witze and 'man  witze being an 11th century term for 'a person of extreme humour and radiant intelligence  Unfortuneatly, Witze is also sometimes associated with bad wind, for unspeakable reasons.\n\n                       Proper History\nAs early as the 14th century, Witzemen were considered outcasts, and young children were encouraged to poke them with sticks. This led to the 1st Witzeman Rebellion of 1555 when the current Witzeman and his followers attempted a coup to take over the local Council chess team. This rebellion further disgraced the Witzeman, as he and his followers were embarassingly pronounced 'gaga' at a Government meeting that year. The period from here to the early 1900s was a dark time in the Witzemans history. However good times were to come for the Witzeman. Many normal people took part in mass demonstrations and protests for the Witzeman in the 1980s, building up to a great moment in the history the Great Rebellion of 1988. This was when several Witzeman sprinted round the Visitors Gallery of the House of Commons, wearing clown masks, but otherwise completely naked. This was thought to be an act of circambulation. However many people who witnessed this shocking behaviour, were 'mentally, spiritually, and emotionallly scarred for life  This group of people included many young children who were forced to go to asylums in a state of mental instability, suffering from trauma. It was at this time that the Government finally took positive action for the Witzeman. In 1990, a bill of rights for the Witzeman was signed, stating all Witzemen were allowed to do as they pleased. Although this was a formal agreement, many journalists viewed it as a letter of submission from the Government to the Witzeman, as the behaviour of the Witzeman and his followers became more and more twisted.\n","output_type":"stream"}]},{"cell_type":"code","source":"# Removing extra spaces in text\n\ncleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"\\s\\s+\", \" \",x))\nprint(cleaned_data.loc[126,\"comment_text\"])","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:29.820425Z","iopub.execute_input":"2022-12-29T07:14:29.820828Z","iopub.status.idle":"2022-12-29T07:14:30.831416Z","shell.execute_reply.started":"2022-12-29T07:14:29.820788Z","shell.execute_reply":"2022-12-29T07:14:30.829156Z"},"_kg_hide-output":true,"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"Ahh, Hello Witzeman Symbols: m m Characters: Greek: Cyrillic: IPA: t d My Famous Article witze happiness wikipedia The Witzeman is a great honour that has been passed down through the generations of many families, regardless of race, age, character or knowledge. The outside world knows little about these elusive characters, and the honour of the Witzeman. For those who have ever been a Witzeman, it has been said to have been a great honour, although the qualities needed for the job have never been disclosed. A person may not now they were a Witzeman for many years, until they are called by former Witzemans via dreams. Associates of the Witzeman are known to be a certain Babe Cool or the much lesser known Witzewoman. The Present Witzeman is an 11 year old boy namely Benjamin Woods, who is said to have become a Witzeman after he felt this 'awesome radiance in his top right bumcheek He has not told a great number of people his testimony, but has promised to do so in years to come. History\nThe present world has only come enlightned with the knowledge of the Witzeman in present years, because former Witzemen have not been so public about the honour, to abide with the formality and conservativism in their time. The term 'Witzeman' is a compound word of the adjective Witze and 'man witze being an 11th century term for 'a person of extreme humour and radiant intelligence Unfortuneatly, Witze is also sometimes associated with bad wind, for unspeakable reasons. Proper History\nAs early as the 14th century, Witzemen were considered outcasts, and young children were encouraged to poke them with sticks. This led to the 1st Witzeman Rebellion of 1555 when the current Witzeman and his followers attempted a coup to take over the local Council chess team. This rebellion further disgraced the Witzeman, as he and his followers were embarassingly pronounced 'gaga' at a Government meeting that year. The period from here to the early 1900s was a dark time in the Witzemans history. However good times were to come for the Witzeman. Many normal people took part in mass demonstrations and protests for the Witzeman in the 1980s, building up to a great moment in the history the Great Rebellion of 1988. This was when several Witzeman sprinted round the Visitors Gallery of the House of Commons, wearing clown masks, but otherwise completely naked. This was thought to be an act of circambulation. However many people who witnessed this shocking behaviour, were 'mentally, spiritually, and emotionallly scarred for life This group of people included many young children who were forced to go to asylums in a state of mental instability, suffering from trauma. It was at this time that the Government finally took positive action for the Witzeman. In 1990, a bill of rights for the Witzeman was signed, stating all Witzemen were allowed to do as they pleased. Although this was a formal agreement, many journalists viewed it as a letter of submission from the Government to the Witzeman, as the behaviour of the Witzeman and his followers became more and more twisted.\n","output_type":"stream"}]},{"cell_type":"code","source":"cleaned_data.reset_index(inplace=True)\ncleaned_data.drop(columns=[\"index\"], inplace=True)\nFinal_data = cleaned_data.copy()","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:30.833623Z","iopub.execute_input":"2022-12-29T07:14:30.834012Z","iopub.status.idle":"2022-12-29T07:14:30.872561Z","shell.execute_reply.started":"2022-12-29T07:14:30.833975Z","shell.execute_reply":"2022-12-29T07:14:30.871468Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"markdown","source":"> **Since we are going to use BERT for classifying comments we will not remove stopwords and punctuations from the comments.**\n\n\n\n> **Because of imbalanced distribution, model will certainly overfit the most occured class, and that shouldn't happen.\n  For this problem we will keep 5000 comments which are labeled (non_toxic) and drop the rest.**","metadata":{}},{"cell_type":"code","source":"# Keeping 15291 comments from non_toxic comments and dropping the rest\n\nC_indexs = cleaned_data[cleaned_data[\"total_classes\"]==0].index\ndrop_indxs = np.random.choice(C_indexs, size=127696, replace=False)\n\n# Randomly selecting and dropping non_toxic comments from datasets and leaving 5000 behind\nFinal_data.drop(drop_indxs, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:30.873979Z","iopub.execute_input":"2022-12-29T07:14:30.874975Z","iopub.status.idle":"2022-12-29T07:14:30.915392Z","shell.execute_reply.started":"2022-12-29T07:14:30.874938Z","shell.execute_reply":"2022-12-29T07:14:30.914416Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"code","source":"def checkDist(B_data, A_data):\n    \n    Total_of_Class = {}                   # Total labeled comments from each class\n    for col in B_data.iloc[:,2:9].columns:\n        Total_of_Class[col] = B_data[col].value_counts()[1] # number of comments labelled as 1 in each category\n   \n    Total_of_Class_A = {}                   # Total labeled comments from each class\n    for col in A_data.iloc[:,2:9].columns:\n        Total_of_Class_A[col] = A_data[col].value_counts()[1] # #comments labelled 1 in each category\n        \n    multi_label_total_B = {}\n    for value in B_data[\"total_classes\"].unique():\n        multi_label_total_B[value] = B_data[\"total_classes\"].value_counts()[value] # counts of multilabelled comments for each category based on number of classes messages belong to\n    klstB= [key for key in multi_label_total_B.keys()]      # messages belong to [0,1,3,4,5] classes at the same time, so how many belong to each of these\n    vlstB = [key for key in multi_label_total_B.values()]\n    \n    multi_label_total_A = {}\n    for value in A_data[\"total_classes\"].unique():\n        multi_label_total_A[value] = A_data[\"total_classes\"].value_counts()[value]\n    klstA = [key for key in multi_label_total_A.keys()]\n    vlstA = [key for key in multi_label_total_A.values()]\n        \n    # plotting distribution of classes \n\n    font1 = {\"size\":12}\n    all_tox = list(Total_of_Class.values())\n    plt.figure(figsize=(20,20))\n    #plt.rcParams.update({'font.size': 12})\n    plt.rc(\"font\",**font1)\n\n    left = plt.subplot(2,2,1)\n    plt.bar(x = ['toxic', 'severe_toxic', 'obscene', 'threat','insult', 'identity_hate','non_toxic'], height = Total_of_Class.values(), color=\"violet\")\n    left.set_facecolor(\"black\")\n    left.set_title(\"Distribution of classes before \\ndeleting non toxic samples\", fontsize=22)\n    left.set_xlabel(\"Classes\", fontsize=15)\n    left.set_ylabel(\"Frequency\", fontsize=15)\n    for key in ['toxic', 'severe_toxic', 'obscene', 'threat','insult', 'identity_hate','non_toxic']:\n        left.annotate(text=Total_of_Class[key], xy=(key,Total_of_Class[key]+1000), xycoords=\"data\", color=\"white\", size=14, ha=\"center\")\n\n    right = plt.subplot(2,2,2)\n    plt.bar(x = ['toxic', 'severe_toxic', 'obscene', 'threat','insult', 'identity_hate','non_toxic'], height = Total_of_Class_A.values(), color=\"violet\")\n    right.set_facecolor(\"black\")\n    right.set_title(\"Distribution of classes after \\ndeleting non toxic samples\",fontsize=22)\n    right.set_xlabel(\"Classes\", fontsize=15)\n    right.set_ylabel(\"Frequency\", fontsize=15)\n    for key in ['toxic', 'severe_toxic', 'obscene', 'threat','insult', 'identity_hate','non_toxic']:\n        right.annotate(text=Total_of_Class_A[key], xy=(key,Total_of_Class_A[key]+100), xycoords=\"data\", color=\"white\", size=14, ha=\"center\")\n    \n#     bleft = plt.subplot(2,2,3)\n#     plt.bar(x = klstB, height = vlstB, color=\"Yellow\")\n#     bleft.set_facecolor(\"black\")\n#     bleft.set_title(\"Distribution of total labeled comments \\nbefore deleting non toxic samples\",fontsize=22)\n#     bleft.set_xlabel(\"Total no. of classes comments belong\", fontsize=15)\n#     bleft.set_ylabel(\"Frequency\", fontsize=15)\n#     for key in klstB:\n#         bleft.annotate(text=multi_label_total_B[key], xy=(key,multi_label_total_B[key]+100), xycoords=\"data\", color=\"white\", size=14, ha=\"center\")\n\n#     bright = plt.subplot(2,2,4)\n#     plt.bar(x = klstA, height = vlstA, color=\"Yellow\")\n#     bright.set_facecolor(\"black\")\n#     bright.set_title(\"Distribution of total labeled comments \\nafter deleting non toxic samples\",fontsize=22)\n#     bright.set_xlabel(\"Total no. of classes comments belong\", fontsize=15)\n#     bright.set_ylabel(\"Frequency\", fontsize=15)\n#     for key in klstA:\n#         bright.annotate(text=multi_label_total_A[key], xy=(key,multi_label_total_A[key]+100), xycoords=\"data\", color=\"white\", size=14, ha=\"center\")\n\ncheckDist(cleaned_data, Final_data)","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:30.916990Z","iopub.execute_input":"2022-12-29T07:14:30.917360Z","iopub.status.idle":"2022-12-29T07:14:31.504515Z","shell.execute_reply.started":"2022-12-29T07:14:30.917325Z","shell.execute_reply":"2022-12-29T07:14:31.503565Z"},"trusted":true},"execution_count":49,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x1440 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"**Above is the visualization of before and after deleting rest of non_toxic comments and keeping only 5000 of them**","metadata":{}},{"cell_type":"code","source":"# Separating data into training , validation and testing sets\nfrom sklearn.model_selection import train_test_split\n\n\n# we will pass \"total_classes\" column of the dataset to \"stratify\" parameter for the even distribution of data\nX_rest, X_test, Y_rest, Y_test = train_test_split(Final_data.iloc[:,1],Final_data.iloc[:,2:], test_size=0.1, stratify=Final_data.iloc[:,9])\nX_train, X_val, Y_train, Y_val = train_test_split(X_rest,Y_rest, test_size=0.1, stratify=Y_rest.iloc[:,-1])\n\nY_test.drop(columns=[\"total_classes\"], inplace=True)\nY_train.drop(columns=[\"total_classes\"], inplace=True)\nY_val.drop(columns=[\"total_classes\"], inplace=True)\n\nX_train = X_train.reset_index()\nX_train.drop(columns=[\"index\"], inplace=True)\nY_train = Y_train.reset_index()\nY_train.drop(columns=[\"index\"], inplace=True)\nX_test = X_test.reset_index()\nX_test.drop(columns=[\"index\"], inplace=True)\nY_test = Y_test.reset_index()\nY_test.drop(columns=[\"index\"], inplace=True)\nX_val = X_val.reset_index()\nX_val.drop(columns=[\"index\"], inplace=True)\nY_val = Y_val.reset_index()\nY_val.drop(columns=[\"index\"], inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:31.506001Z","iopub.execute_input":"2022-12-29T07:14:31.506442Z","iopub.status.idle":"2022-12-29T07:14:32.037314Z","shell.execute_reply.started":"2022-12-29T07:14:31.506403Z","shell.execute_reply":"2022-12-29T07:14:32.036323Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"code","source":"def check_distribution_V(ytrain, yval, ytest):\n    # creating new column named \"sum\" to show total num of classes comment belongs to\n    y_train = ytrain.copy()\n    y_val = yval.copy()\n    y_test = ytest.copy()\n    y_train[\"sum\"] = y_train.iloc[:, :6].apply(lambda x: sum(x), axis=1) # here axis=1 means apply function is applied to each row\n    y_val[\"sum\"] = y_val.iloc[:, :6].apply(lambda x: sum(x), axis=1)\n    y_test[\"sum\"] = y_test.iloc[:, :6].apply(lambda x: sum(x), axis=1)\n    \n    # creating dict of ytrain and ytest to get total no of values in sum column\n    dtrain = {}\n    dval = {}\n    dtest = {}\n    for i,value in enumerate(y_train[\"sum\"].value_counts()):\n        dtrain[i] = value\n    for i,value in enumerate(y_val[\"sum\"].value_counts()):\n        dval[i] = value\n    for i,value in enumerate(y_test[\"sum\"].value_counts()):\n        dtest[i] = value\n    \n    # creating lists of keys and values for plotting distribution\n    kTrlst= [key for key in dtrain.keys()]\n    vTrlst = [key for key in dtrain.values()]\n    kValst= [key for key in dval.keys()]\n    vValst = [key for key in dval.values()]\n    kTelst= [key for key in dtest.keys()]\n    vTelst = [key for key in dtest.values()]\n    \n    # plotting distribution of dataset \n    plt.figure(figsize=(22,10))\n    \n    # Traning\n    fr = plt.subplot(1,3,1)\n    plt.bar(height=vTrlst,x=kTrlst, color=\"blue\")\n    fr.set_xlabel(\"Number of Classes comments belong to\", fontsize=15)\n    fr.set_ylabel(\"Total Number of Comments\", fontsize=15)\n    fr.set_title(\"Training Data\", fontsize=17)\n    for key in kTrlst:\n        fr.annotate(text=dtrain[key], xy=(key,dtrain[key]), xycoords=\"data\",size=13, ha=\"center\")\n    \n    # Testing\n    se = plt.subplot(1,3,2)\n    plt.bar(height=vTelst,x=kTelst, color=\"red\")\n    se.set_xlabel(\"Number of Classes comments belong to\", fontsize=15)\n    se.set_ylabel(\"Total Number of Comments\", fontsize=15)\n    se.set_title(\"Testing Data\", fontsize=17)\n    for key in kTelst:\n        se.annotate(text=dtest[key], xy=(key,dtest[key]), xycoords=\"data\",size=13, ha=\"center\")\n     \n    # Validation\n    th = plt.subplot(1,3,3)\n    plt.bar(height=vValst,x=kValst,  color=\"purple\")\n    th.set_xlabel(\"Number of Classes comments belong to\", fontsize=15)\n    th.set_ylabel(\"Total Number of Comments\", fontsize=15)\n    th.set_title(\"Validation Data\", fontsize=17)\n    for key in kValst:\n        th.annotate(text=dval[key], xy=(key,dval[key]), xycoords=\"data\",size=13, ha=\"center\")\n\ncheck_distribution_V(Y_train, Y_val, Y_test)","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:32.038961Z","iopub.execute_input":"2022-12-29T07:14:32.039325Z","iopub.status.idle":"2022-12-29T07:14:33.080571Z","shell.execute_reply.started":"2022-12-29T07:14:32.039290Z","shell.execute_reply":"2022-12-29T07:14:33.079592Z"},"trusted":true},"execution_count":51,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1584x720 with 3 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Model building","metadata":{}},{"cell_type":"markdown","source":"**In this problem we will use DistilBERT model for classification which is lightweight version of BERT transformer**","metadata":{}},{"cell_type":"code","source":"from transformers import DistilBertTokenizer\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader, Dataset\n\n# Using Pretrained DistilBertTokenizer\ntokenizer = DistilBertTokenizer.from_pretrained(\"distilbert-base-uncased\")\n\n# Creating Dataset class for Toxic comments and Labels \nclass Toxic_Dataset(Dataset):\n    def __init__(self, Comments_, Labels_):\n        self.comments = Comments_.copy()\n        self.labels = Labels_.copy()\n        \n        self.comments[\"comment_text\"] = self.comments[\"comment_text\"].map(lambda x: tokenizer(x, padding=\"max_length\", truncation=True, return_tensors=\"pt\"))\n    \n    def __len__(self):\n        return len(self.labels)\n    \n    def __getitem__(self, idx):\n        comment = self.comments.loc[idx,\"comment_text\"]\n        label = np.array(self.labels.loc[idx,:])\n        \n        return comment, label","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:33.082349Z","iopub.execute_input":"2022-12-29T07:14:33.083081Z","iopub.status.idle":"2022-12-29T07:14:39.057840Z","shell.execute_reply.started":"2022-12-29T07:14:33.083042Z","shell.execute_reply":"2022-12-29T07:14:39.056924Z"},"trusted":true},"execution_count":52,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cfbe1acd58824d4a82034a2e0676adae"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d3a85ecf8e3d4978885503021664ba3b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/483 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"97e19d24bc8c4685ad7a8acb443c11c4"}},"metadata":{}}]},{"cell_type":"code","source":"# Making Training, Testing and Validation of data using Dataset class\nTrain_data = Toxic_Dataset(X_train, Y_train)\nTest_data = Toxic_Dataset(X_test, Y_test)\nVal_data = Toxic_Dataset(X_val, Y_val)\n\n# Making datasets into batches \nTrain_Loader = DataLoader(Train_data, batch_size=32, shuffle=True)\nTest_Loader = DataLoader(Test_data, shuffle=True)        #batch_size=16, \nVal_Loader = DataLoader(Val_data, shuffle=True)          #batch_size=16, ","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:14:39.059219Z","iopub.execute_input":"2022-12-29T07:14:39.060087Z","iopub.status.idle":"2022-12-29T07:15:42.289552Z","shell.execute_reply.started":"2022-12-29T07:14:39.060046Z","shell.execute_reply":"2022-12-29T07:15:42.288521Z"},"trusted":true},"execution_count":53,"outputs":[]},{"cell_type":"markdown","source":"**Loading pre-trained weights of DistilBert model for sequence classification**\n**and changing classifiers output to 6 because we have 6 labels to classify.**","metadata":{}},{"cell_type":"code","source":"# DistilBERT\n\nfrom transformers import DistilBertForSequenceClassification\n\nDistil_bert = DistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\")\n\nDistil_bert.classifier = nn.Sequential(\n                    nn.Linear(768,7),\n                    nn.Sigmoid()\n                  )\n# print(Distil_bert)","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:15:42.290856Z","iopub.execute_input":"2022-12-29T07:15:42.291215Z","iopub.status.idle":"2022-12-29T07:15:51.070609Z","shell.execute_reply.started":"2022-12-29T07:15:42.291177Z","shell.execute_reply":"2022-12-29T07:15:51.069247Z"},"_kg_hide-output":true,"trusted":true},"execution_count":54,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/256M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bc0f3c253f684be2a4291fe42f1516c8"}},"metadata":{}},{"name":"stderr","text":"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.bias', 'vocab_projector.weight']\n- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\nSome weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'pre_classifier.weight', 'classifier.bias']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**Training Model**","metadata":{}},{"cell_type":"markdown","source":"* `output` is the result of running the model on a batch of input data represented by `input_ids` and `masks`. \\\nIt is important to note that outputs is typically not the final prediction or decision made by the model. Instead, it is an intermediate representation of the model's output that will be used to compute loss and to generate a final prediction using additional processing.","metadata":{}},{"cell_type":"code","source":"from torch.optim import Adam\nfrom tqdm import tqdm\nfrom torch.nn import BCELoss\nfrom torch.optim.lr_scheduler import StepLR\n\ndef train_Model(model,Train_DL, Val_DL, learning_rate, epochs):\n    \n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    \n    Loss = BCELoss()\n    Optimizer = Adam(params=model.parameters(), lr=learning_rate)\n    scheduler = StepLR(Optimizer, step_size=212, gamma=0.1)\n    \n    model.to(device)\n    model.train()\n    \n    train_acc_epochs = []\n    train_loss_epochs = []\n    val_acc_epochs = []\n    val_loss_epochs = []\n    \n    for epoch in range(epochs):\n        training_loss = {}\n        training_accuracy = {}\n        validation_loss = {}\n        validation_accuracy = {}\n        batch = 0\n        \n        for comments, labels in tqdm(Train_DL):\n            \n            labels = labels.to(device)\n            labels = labels.float()\n            masks = comments[\"attention_mask\"].squeeze(1).to(device) # the model used these masks to attend only to the non-padded tokens in the sequence\n            input_ids = comments[\"input_ids\"].squeeze(1).to(device) # contains the tokenized and indexed representation for a batch of comments\n            # squeeze is used to remove the second dimension which has size 1.\n            output = model(input_ids, masks) # vector of logits for each class\n            loss = Loss(output.logits, labels) # compute the loss\n            \n            Optimizer.zero_grad()\n            loss.backward()\n            Optimizer.step()\n            scheduler.step()\n\n            \n            batch += 1 \n            if batch%53 == 0:\n                with torch.no_grad():\n                    acc = []\n                    op = output.logits\n                    for lb in range(len(labels)): # note: labels is of shape (batch_size, num_classes(=7))\n                        correct = 0\n                        for i in range(len(labels[lb])):  # therefore len(labels[lb]) is 7\n                            res = 1 if op[lb,i]>0.5 else 0\n                            if res == labels[lb,i]:\n                                correct += 1\n                        acc.append(correct/len(labels[lb]))\n\n                    training_loss[batch] = loss.item()\n                    training_accuracy[batch] = sum(acc)/len(acc)\n                    print(f\"Epoch:{epoch+1} | batch no:{batch}/{len(Train_DL)} | Loss:{loss.item():.4f} | Accuracy:{sum(acc)/len(acc):.4f}\")\n        \n                    # Testing model on validation Data\n                    accVal = []\n                    val_loss = 0\n                    for comments, labels in Val_DL:\n                        labels = labels.to(device)\n                        labels = labels.float()\n                        masks = comments[\"attention_mask\"].squeeze(1).to(device)\n                        input_ids = comments[\"input_ids\"].squeeze(1).to(device)\n    \n                        output = model(input_ids, masks)\n                        loss = Loss(output.logits, labels)\n                        val_loss += loss.item()\n            \n                        op = output.logits\n                        correct_val = 0\n                        for i in range(7):\n                            res = 1 if op[0,i]>0.5 else 0\n                            if res == labels[0,i]:\n                                correct_val += 1\n                        accVal.append(correct_val/7)\n                    \n                    validation_loss[batch] = val_loss/len(Val_DL)\n                    validation_accuracy[batch] = sum(accVal)/len(accVal)\n                    print(f\" Validation Loss:{val_loss/len(Val_DL):.4f} | Validation Accuracy:{sum(accVal)/len(accVal):.4f}\")\n            \n        train_acc_epochs.append(training_accuracy)\n        train_loss_epochs.append(training_loss)\n        val_acc_epochs.append(validation_accuracy)\n        val_loss_epochs.append(validation_loss)\n    \n    return train_acc_epochs, train_loss_epochs, val_acc_epochs, val_loss_epochs","metadata":{"execution":{"iopub.status.busy":"2022-12-29T07:15:51.072993Z","iopub.execute_input":"2022-12-29T07:15:51.073733Z","iopub.status.idle":"2022-12-29T07:15:51.098165Z","shell.execute_reply.started":"2022-12-29T07:15:51.073685Z","shell.execute_reply":"2022-12-29T07:15:51.097181Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"# Training Model\nTA, TL, VA, VL = train_Model(Distil_bert, Train_Loader, Val_Loader, learning_rate=0.0003, epochs=2)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Evaluating model on Test data\n\ndef Evaluate_Model(model,Test_DL):\n    \n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    Loss = BCELoss()\n    model.to(device)\n    model.eval()\n    \n    accTest = []\n    Test_loss = 0\n    for comments, labels in Test_DL:\n        labels = labels.to(device)\n        labels = labels.float()\n        masks = comments[\"attention_mask\"].squeeze(1).to(device)\n        input_ids = comments[\"input_ids\"].squeeze(1).to(device)\n    \n        output = model(input_ids, masks)\n        loss = Loss(output.logits, labels)\n        Test_loss += loss.item()\n            \n        op = output.logits\n        correct_val = 0\n        for i in range(7):\n            res = 1 if op[0,i]>0.5 else 0\n            if res == labels[0,i]:\n                correct_val += 1\n        accTest.append(correct_val/7)\n    \n    print(\"Testing Dataset:\\n\")\n    print(f\" Test Loss:{Test_loss/len(Test_DL):.4f} | Test Accuracy:{sum(accTest)/len(accTest):.4f}\")\n\nEvaluate_Model(Distil_bert, Test_Loader)","metadata":{"execution":{"iopub.status.busy":"2022-12-29T08:22:13.786617Z","iopub.execute_input":"2022-12-29T08:22:13.787083Z","iopub.status.idle":"2022-12-29T08:23:11.197276Z","shell.execute_reply.started":"2022-12-29T08:22:13.787045Z","shell.execute_reply":"2022-12-29T08:23:11.196199Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"Testing Dataset:\n\n Test Loss:0.1840 | Test Accuracy:0.9257\n","output_type":"stream"}]},{"cell_type":"code","source":"def Acc_Loss_Plot(TA1, TA2, TL1, TL2, VA1, VA2, VL1, VL2):\n    plt.figure(figsize=(20,14))\n    Ac = plt.subplot(2,2,1)\n    plt.plot(TA1.keys(), TA1.values())\n    plt.plot(TA2.keys(), TA2.values())\n    Ac.set_xlabel(\"Step Number\", fontsize=15)\n    Ac.set_ylabel(\"Accuracy\", fontsize=15)\n    Ac.set_title(\"Training Accuracy\", fontsize=20)\n    Ac.legend([\"Epoch 1\",\"Epoch 2\"])\n\n    Ls = plt.subplot(2,2,2)\n    plt.plot(TL1.keys(), TL1.values())\n    plt.plot(TL2.keys(), TL2.values())\n    Ls.set_xlabel(\"Step Number\", fontsize=15)\n    Ls.set_ylabel(\"Loss\", fontsize=15)\n    Ls.set_title(\"Training Loss\", fontsize=20)\n    Ls.legend([\"Epoch 1\",\"Epoch 2\"])\n    \n    VAc = plt.subplot(2,2,3)\n    plt.plot(VA1.keys(), VA1.values())\n    plt.plot(VA2.keys(), VA2.values())\n    VAc.set_xlabel(\"Step Number\", fontsize=15)\n    VAc.set_ylabel(\"Accuracy\", fontsize=15)\n    VAc.set_title(\"Validation Accuracy\", fontsize=20)\n    VAc.legend([\"Epoch 1\",\"Epoch 2\"])\n\n    VLs = plt.subplot(2,2,4)\n    plt.plot(VL1.keys(), VL1.values())\n    plt.plot(VL2.keys(), VL2.values())\n    VLs.set_xlabel(\"Step Number\", fontsize=15)\n    VLs.set_ylabel(\"Loss\", fontsize=15)\n    VLs.set_title(\"Validation Loss\", fontsize=20)\n    VLs.legend([\"Epoch 1\",\"Epoch 2\"])\n\nAcc_Loss_Plot(TA[0], TA[1], TL[0], TL[1], VA[0], VA[1], VL[0], VL[1])","metadata":{"execution":{"iopub.status.busy":"2022-12-29T08:23:15.730729Z","iopub.execute_input":"2022-12-29T08:23:15.731144Z","iopub.status.idle":"2022-12-29T08:23:16.768187Z","shell.execute_reply.started":"2022-12-29T08:23:15.731113Z","shell.execute_reply":"2022-12-29T08:23:16.767232Z"},"trusted":true},"execution_count":59,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x1008 with 4 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Saving model\ntorch.save(Distil_bert,\"dsbert_toxic_balanced.pt\")","metadata":{"execution":{"iopub.status.busy":"2022-12-29T08:23:43.631472Z","iopub.execute_input":"2022-12-29T08:23:43.631907Z","iopub.status.idle":"2022-12-29T08:23:44.246673Z","shell.execute_reply.started":"2022-12-29T08:23:43.631869Z","shell.execute_reply":"2022-12-29T08:23:44.245573Z"},"trusted":true},"execution_count":60,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}