pytorch-auto-pytorch.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/averma111/pytorch-auto-pytorch?scriptVersionId=128587167\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"!pip install autoPyTorch\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-05-07T01:24:52.16707Z","iopub.execute_input":"2023-05-07T01:24:52.168032Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stdout","text":"Collecting autoPyTorch\n  Downloading autoPyTorch-0.2.1-py3-none-any.whl (711 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m711.9/711.9 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: scipy>=1.7 in /opt/conda/lib/python3.10/site-packages (from autoPyTorch) (1.9.3)\nRequirement already satisfied: torch>=1.10.1 in /opt/conda/lib/python3.10/site-packages (from autoPyTorch) (2.0.0+cpu)\nRequirement already satisfied: torchvision in /opt/conda/lib/python3.10/site-packages (from autoPyTorch) (0.15.1+cpu)\nCollecting flaky\n  Downloading flaky-3.7.0-py2.py3-none-any.whl (22 kB)\nRequirement already satisfied: tensorboard in /opt/conda/lib/python3.10/site-packages (from autoPyTorch) (2.11.2)\nRequirement already satisfied: lightgbm in /opt/conda/lib/python3.10/site-packages (from autoPyTorch) (3.3.2)\nRequirement already satisfied: distributed>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from autoPyTorch) (2023.4.0)\nRequirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from autoPyTorch) (1.5.3)\nCollecting smac>=1.2\n  Downloading smac-2.0.0.tar.gz (144 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.9/144.9 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25h  Installing build dependencies ... \u001b[?25ldone\n\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: tabulate in /opt/conda/lib/python3.10/site-packages (from autoPyTorch) (0.9.0)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from autoPyTorch) (1.23.5)\nCollecting pynisher>=0.6.3\n  Downloading pynisher-1.0.5.tar.gz (28 kB)\n  Installing build dependencies ... \u001b[?25ldone\n\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n\u001b[?25h  Installing backend dependencies ... \u001b[?25ldone\n\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: imgaug>=0.4.0 in /opt/conda/lib/python3.10/site-packages (from autoPyTorch) (0.4.0)\nCollecting pyrfr<0.9,>=0.7\n  Downloading pyrfr-0.8.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m57.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hCollecting lockfile\n  Downloading lockfile-0.12.2-py2.py3-none-any.whl (13 kB)\nCollecting ConfigSpace>=0.5.0\n  Downloading ConfigSpace-0.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m81.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: catboost in /opt/conda/lib/python3.10/site-packages (from autoPyTorch) (1.1.1)\nCollecting scikit-learn<0.25.0,>=0.24.0\n  Downloading scikit-learn-0.24.2.tar.gz (7.5 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m78.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25h  Installing build dependencies ... \u001b[?25ldone\n\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25l\\","output_type":"stream"}]},{"cell_type":"code","source":"from autoPyTorch.api.tabular_classification import TabularClassificationTask\n\n# data and metric imports\nimport sklearn.model_selection\nimport sklearn.datasets\nimport sklearn.metrics\nX, y = sklearn.datasets.load_digits(return_X_y=True)\nX_train, X_test, y_train, y_test = \\\n        sklearn.model_selection.train_test_split(X, y, random_state=1)\n\n# initialise Auto-PyTorch api\napi = TabularClassificationTask()\n\n# Search for an ensemble of machine learning algorithms\napi.search(\n    X_train=X_train,\n    y_train=y_train,\n    X_test=X_test,\n    y_test=y_test,\n    optimize_metric='accuracy',\n    total_walltime_limit=300,\n    func_eval_time_limit_secs=50\n)\n\n# Calculate test accuracy\ny_pred = api.predict(X_test)\nscore = api.score(y_pred, y_test)\nprint(\"Accuracy score\", score)","metadata":{},"execution_count":null,"outputs":[]}]}