Nostr MultinominalNB Spam Detection.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b4a47a8a",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c358ea7",
   "metadata": {},
   "outputs": [
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       "      <th>text</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>gosta mais de \"o anti\" é?\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Yo! What are y’all working on?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ham</td>\n",
       "      <td>We are Damus volunteers. You can choose to fol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>I assume it was regulatory issues. This is the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>proof of #nostrchain\\n\\nhttps://nostr.build/i/...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                               text\n",
       "0   ham                        gosta mais de \"o anti\" é?\\n\n",
       "1   ham                     Yo! What are y’all working on?\n",
       "2   ham  We are Damus volunteers. You can choose to fol...\n",
       "3   ham  I assume it was regulatory issues. This is the...\n",
       "4   ham  proof of #nostrchain\\n\\nhttps://nostr.build/i/..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('labelled_nostr_events_20230225000.csv').dropna();\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a179bb82",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['label'] = np.where(data['label']=='spam',1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9a8bf7bc",
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>gosta mais de \"o anti\" é?\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Yo! What are y’all working on?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>We are Damus volunteers. You can choose to fol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>I assume it was regulatory issues. This is the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>proof of #nostrchain\\n\\nhttps://nostr.build/i/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>ItsATrap.gif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>Sunset\\n\\nhttps://nostr.build/i/3386.jpeg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>Right now you can still get 471,974 sats for $...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>Invoice here...\\n\\nlnbc10u1p3m6m5npp5n5n0ywzw8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>Followed. #Plebchain</td>\n",
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      "text/plain": [
       "   label                                               text\n",
       "0      0                        gosta mais de \"o anti\" é?\\n\n",
       "1      0                     Yo! What are y’all working on?\n",
       "2      0  We are Damus volunteers. You can choose to fol...\n",
       "3      0  I assume it was regulatory issues. This is the...\n",
       "4      0  proof of #nostrchain\\n\\nhttps://nostr.build/i/...\n",
       "5      0                                       ItsATrap.gif\n",
       "6      0          Sunset\\n\\nhttps://nostr.build/i/3386.jpeg\n",
       "7      0  Right now you can still get 471,974 sats for $...\n",
       "8      0  Invoice here...\\n\\nlnbc10u1p3m6m5npp5n5n0ywzw8...\n",
       "9      0                               Followed. #Plebchain"
      ]
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "data.head(10)"
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   "cell_type": "code",
   "execution_count": 5,
   "id": "f903696e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(data['text'], \n",
    "                                                    data['label'], \n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "915fc643",
   "metadata": {},
   "outputs": [
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract features\n",
    "vectorizer = CountVectorizer(ngram_range=(1, 2)).fit(X_train)\n",
    "X_train_vectorized = vectorizer.transform(X_train)\n",
    "X_train_vectorized.toarray().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b179b0cb",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "MultinomialNB(alpha=0.1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MultinomialNB(alpha=0.1)\n",
    "model.fit(X_train_vectorized, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "61c79448",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.2643985838874 %\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(vectorizer.transform(X_test))\n",
    "print(\"Accuracy:\", 100 * sum(predictions == Y_test) / len(predictions), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4cb389f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export model\n",
    "filename = 'models/MultinomialNB.sav'\n",
    "pickle.dump((model, vectorizer), open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f33c4a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.39378688e-22, 1.00000000e+00],\n",
       "       [1.17017618e-12, 1.00000000e+00],\n",
       "       [9.82697167e-01, 1.73028333e-02],\n",
       "       [9.76251055e-01, 2.37489450e-02],\n",
       "       [9.99999999e-01, 1.29857296e-09]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "(loaded_model, vectorizer) = pickle.load(open(filename, 'rb'))\n",
    "loaded_model.predict_proba(vectorizer.transform(\n",
    "    [\n",
    "        \"如果无法私信 可以联系 https://t.me/Jpaibot\",\n",
    "        \"582898.com 无码高清全站免费的极品黄站\",\n",
    "        \"Hello\",\n",
    "        \"PV\",\n",
    "        \"What is spam?\"\n",
    "    ]\n",
    "))"
   ]
  }
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