multinomial.ipynb

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   "source": [
    "# TF-IDF + Multinomial\n",
    "\n",
    "Transform the text through a TF-IDF vectorizer, and run a multinomial naive Bayes model."
   ]
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      "CPU times: user 55.4 ms, sys: 7.57 ms, total: 63 ms\n",
      "Wall time: 63 ms\n",
      "CPU times: user 20.3 ms, sys: 8.16 ms, total: 28.4 ms\n",
      "Wall time: 28.2 ms\n"
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   "source": [
    "vec = TfidfVectorizer(stop_words='english', ngram_range=(1,3))\n",
    "x_train = vec.fit_transform(X_train_text)\n",
    "x_test = vec.transform(X_test_text)\n",
    "\n",
    "mnb = MultinomialNB()\n",
    "%time mnb.fit(x_train, y_train)\n",
    "%time mnb.score(x_test, y_test)"
   ]
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     "text": [
      "CPU times: user 264 ms, sys: 67.6 ms, total: 332 ms\n",
      "Wall time: 332 ms\n",
      "CPU times: user 31.8 ms, sys: 27.9 ms, total: 59.7 ms\n",
      "Wall time: 59.4 ms\n"
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   "source": [
    "vec = TfidfVectorizer(stop_words='english', ngram_range=(1,3))\n",
    "x_train = vec.fit_transform(X_train_text)\n",
    "x_test = vec.transform(X_test_text)\n",
    "x_train_np, x_test_np = x_train.get(), x_test.get()\n",
    "y_train_np, y_test_np = y_train.to_numpy(), y_test.to_numpy()\n",
    "\n",
    "mnb = MultinomialNB_sk()\n",
    "%time mnb.fit(x_train_np, y_train_np)\n",
    "%time mnb.score(x_test_np, y_test_np)"
   ]
  }
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