gaussian.ipynb

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   "source": [
    "# Gaussian NB\n",
    "\n",
    "Transform the text through a TF-IDF vectorizer and iterate through the dataset to do multiple partial fits of Gaussian naive Bayes."
   ]
  },
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   "cell_type": "code",
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   "id": "1079a683",
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   "outputs": [
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     "text": [
      "CPU times: user 12.3 s, sys: 2.23 s, total: 14.5 s\n",
      "Wall time: 22 s\n",
      "0.8769999742507935\n",
      "0.8840000033378601\n",
      "0.878083348274231\n",
      "0.8805833458900452\n",
      "0.8756666779518127\n",
      "0.8796666860580444\n",
      "0.8786666393280029\n",
      "0.8777499794960022\n",
      "0.8823529481887817\n",
      "CPU times: user 4.36 s, sys: 2.74 s, total: 7.1 s\n",
      "Wall time: 22.8 s\n"
     ]
    }
   ],
   "source": [
    "vec = TfidfVectorizer(stop_words='english', ngram_range=(1,3), min_df=5)\n",
    "x_train = vec.fit_transform(X_train_text)\n",
    "x_test = vec.transform(X_test_text)\n",
    "\n",
    "def dataset_traversal(X, Y, partial_function):\n",
    "    chunk_size = 12000\n",
    "    classes = cp.unique(Y)\n",
    "    lower = 0\n",
    "    for upper in iter(range(chunk_size, X.shape[0], chunk_size)):\n",
    "        partial_function(X[lower:upper], Y[lower:upper], classes)\n",
    "        lower = upper\n",
    "    partial_function(X[upper:], Y[upper:], classes)\n",
    "\n",
    "mnb = GaussianNB()\n",
    "%time dataset_traversal(x_train,\\\n",
    "                        y_train,\\\n",
    "                        lambda x,y, c: mnb.partial_fit(x, y, c))\n",
    "\n",
    "%time dataset_traversal(x_test,\\\n",
    "                        y_test,\\\n",
    "                        lambda x, y, c: print(mnb.score(x, y)))\n"
   ]
  },
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     "name": "stdout",
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     "text": [
      "CPU times: user 2min 47s, sys: 1min 29s, total: 4min 17s\n",
      "Wall time: 4min 17s\n",
      "0.885\n",
      "0.8736\n",
      "0.8802\n",
      "0.8828\n",
      "0.8836\n",
      "0.8738\n",
      "0.8806\n",
      "0.881\n",
      "0.8832\n",
      "0.8784\n",
      "0.8714\n",
      "0.879\n",
      "0.8754\n",
      "0.8782\n",
      "0.8816\n",
      "0.8844\n",
      "0.875\n",
      "0.8764\n",
      "0.877\n",
      "0.8864\n",
      "0.8796\n",
      "0.8842975206611571\n",
      "CPU times: user 3min 8s, sys: 2min 7s, total: 5min 16s\n",
      "Wall time: 5min 16s\n"
     ]
    }
   ],
   "source": [
    "vec = TfidfVectorizer(stop_words='english', ngram_range=(1,3), min_df=5)\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",
    "def dataset_traversal(X, Y, partial_function):\n",
    "    chunk_size = 5000\n",
    "    classes = np.unique(Y)\n",
    "    lower = 0\n",
    "    for upper in iter(range(chunk_size, X.shape[0], chunk_size)):\n",
    "        partial_function(X[lower:upper], Y[lower:upper], classes)\n",
    "        lower = upper\n",
    "    partial_function(X[upper:], Y[upper:], classes)\n",
    "\n",
    "mnb = GaussianNB_sk()\n",
    "%time dataset_traversal(x_train_np,\\\n",
    "                        y_train_np,\\\n",
    "                        lambda x, y, c: mnb.partial_fit(x.toarray(), y, c))\n",
    "\n",
    "%time dataset_traversal(x_test_np,\\\n",
    "                        y_test_np,\\\n",
    "                        lambda x, y, c: print(mnb.score(x.toarray(), y)))\n"
   ]
  }
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