Converted notebook to colab
"In this notebook, we illustrate how optimize the hyper-parameters of the ETM model trough the Bayesian Optimization."
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Colab configuration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!git clone https://github.com/MIND-Lab/OCTIS\n",
+ "!pip install scikit-optimize\n",
+ "from pathlib import Path\n",
+ "Path(\"/content/20NewsGroup\").mkdir(parents=True, exist_ok=True)\n",
+ "!wget -P /content/20NewsGroup/ https://raw.githubusercontent.com/MIND-Lab/OCTIS/master/octis/preprocessed_datasets/20NewsGroup/corpus.txt\n",
+ "!wget -P /content/20NewsGroup/ https://raw.githubusercontent.com/MIND-Lab/OCTIS/master/octis/preprocessed_datasets/20NewsGroup/labels.txt\n",
+ "!wget -P /content/20NewsGroup/ https://raw.githubusercontent.com/MIND-Lab/OCTIS/master/octis/preprocessed_datasets/20NewsGroup/metadata.json\n",
+ "!wget -P /content/20NewsGroup/ https://raw.githubusercontent.com/MIND-Lab/OCTIS/master/octis/preprocessed_datasets/20NewsGroup/vocabulary.txt\n",
+ "import sys\n",
+ "sys.path.insert(0,'/content/OCTIS')"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"dataset = Dataset()\n",
- "dataset.load(\"octis/preprocessed_datasets/20NewsGroup\")"
+ "dataset.load(\"/content/20NewsGroup\")"
]
},
{
