ensemble-learning-technique-1.ipynb

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   "source": [
    "import regex as re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
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   "id": "cb290bf7",
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   "source": [
    "train = pd.read_csv('/kaggle/input/train-v2-drcat-02-csv/train_v2_drcat_02.csv')\n",
    "test = pd.read_csv('/kaggle/input/llm-detect-ai-generated-text/test_essays.csv')"
   ]
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   "cell_type": "code",
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    {
     "data": {
      "text/plain": [
       "label\n",
       "0    14250\n",
       "1    14200\n",
       "Name: count, dtype: int64"
      ]
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     "execution_count": 3,
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   "source": [
    "train_ = train[train.RDizzl3_seven == False].reset_index(drop=True)\n",
    "train_ = train[train[\"label\"]==1].sample(8000, random_state=8)\n",
    "train = train[train.RDizzl3_seven == True].reset_index(drop=True)\n",
    "train = pd.concat([train, train_])\n",
    "train['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c83f213",
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    "tags": []
   },
   "source": [
    "# Data Preprocessing"
   ]
  },
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     "output_type": "stream",
     "text": [
      "CPU times: user 3.88 s, sys: 9.55 ms, total: 3.89 s\n",
      "Wall time: 3.9 s\n"
     ]
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   ],
   "source": [
    "%%time\n",
    "def normalize(text):\n",
    "    # Replace with whitespaces\n",
    "    text = text.replace(r\"\\n\", r\" \")\n",
    "    text = text.replace(r\"\\r\", r\" \")\n",
    "    # Drop puntuation\n",
    "    text = re.sub(r\"\\p{P}\", \" \", text)\n",
    "    # Remove extra spaces \n",
    "    text = re.sub(r\"\\s+\", r\" \", text)\n",
    "    # Remove leading and trailing whitespace\n",
    "    text = text.strip()\n",
    "    return text\n",
    "\n",
    "train['text'] = train['text'].apply(lambda x: normalize(x))\n",
    "test['text'] = test['text'].apply(lambda x: normalize(x))"
   ]
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      "CPU times: user 16.6 s, sys: 5.03 ms, total: 16.6 s\n",
      "Wall time: 16.6 s\n"
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       "<28453x3 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 3 stored elements in Compressed Sparse Row format>"
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   "source": [
    "%%time\n",
    "df = pd.concat([train['text'], test['text']])\n",
    "\n",
    "vectorizer = TfidfVectorizer(ngram_range=(3, 4),\n",
    "                             tokenizer=lambda x: re.findall(r'[^\\W]+', x),\n",
    "                             token_pattern=None,\n",
    "                             strip_accents='unicode',\n",
    "                             )\n",
    "\n",
    "vectorizer = vectorizer.fit(test['text'])\n",
    "X = vectorizer.transform(df)\n",
    "X"
   ]
  },
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   "source": [
    "# Model-Training"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20.3 ms, sys: 1.98 ms, total: 22.3 ms\n",
      "Wall time: 30.3 ms\n"
     ]
    },
    {
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       "                             (&#x27;sgd&#x27;,\n",
       "                              SGDClassifier(loss=&#x27;modified_huber&#x27;,\n",
       "                                            max_iter=5000, random_state=42))],\n",
       "                 voting=&#x27;soft&#x27;, weights=[0.01, 0.99])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression()),\n",
       "                             (&#x27;sgd&#x27;,\n",
       "                              SGDClassifier(loss=&#x27;modified_huber&#x27;,\n",
       "                                            max_iter=5000, random_state=42))],\n",
       "                 voting=&#x27;soft&#x27;, weights=[0.01, 0.99])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>sgd</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(loss=&#x27;modified_huber&#x27;, max_iter=5000, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
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       "VotingClassifier(estimators=[('lr', LogisticRegression()),\n",
       "                             ('sgd',\n",
       "                              SGDClassifier(loss='modified_huber',\n",
       "                                            max_iter=5000, random_state=42))],\n",
       "                 voting='soft', weights=[0.01, 0.99])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "%%time\n",
    "lr_model = LogisticRegression()\n",
    "sgd_model = SGDClassifier(max_iter=5000, loss=\"modified_huber\", random_state=42)\n",
    "\n",
    "ensemble = VotingClassifier(estimators=[('lr', lr_model),\n",
    "                                        ('sgd', sgd_model),\n",
    "                                       ],\n",
    "                            weights=[0.01, 0.99],\n",
    "                            voting='soft'\n",
    "                           )\n",
    "ensemble.fit(X[:train.shape[0]], train.label)"
   ]
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    "preds_test = ensemble.predict_proba(X[train.shape[0]:])[:, 1]\n",
    "pd.DataFrame({'id':test[\"id\"], 'generated':preds_test}).to_csv('submission.csv', index=False)"
   ]
  }
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