autogluon-gbm-cat-xgb-and-more.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/yaaangzhou/autogluon-gbm-cat-xgb-and-more?scriptVersionId=135619562\" 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","execution_count":1,"id":"9ae5ba96","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2023-07-03T16:27:12.220411Z","iopub.status.busy":"2023-07-03T16:27:12.219183Z","iopub.status.idle":"2023-07-03T16:27:12.235839Z","shell.execute_reply":"2023-07-03T16:27:12.234912Z"},"papermill":{"duration":0.024437,"end_time":"2023-07-03T16:27:12.237931","exception":false,"start_time":"2023-07-03T16:27:12.213494","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/icr-identify-age-related-conditions/sample_submission.csv\n","/kaggle/input/icr-identify-age-related-conditions/greeks.csv\n","/kaggle/input/icr-identify-age-related-conditions/train.csv\n","/kaggle/input/icr-identify-age-related-conditions/test.csv\n"]}],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"code","execution_count":2,"id":"d94f978d","metadata":{"_kg_hide-output":true,"collapsed":true,"execution":{"iopub.execute_input":"2023-07-03T16:27:12.246633Z","iopub.status.busy":"2023-07-03T16:27:12.246334Z","iopub.status.idle":"2023-07-03T16:29:55.418134Z","shell.execute_reply":"2023-07-03T16:29:55.416745Z"},"jupyter":{"outputs_hidden":true},"papermill":{"duration":163.178984,"end_time":"2023-07-03T16:29:55.420716","exception":false,"start_time":"2023-07-03T16:27:12.241732","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting autogluon\r\n","  Downloading autogluon-0.8.2-py3-none-any.whl (9.7 kB)\r\n","Collecting autogluon.core[all]==0.8.2 (from autogluon)\r\n","  Downloading autogluon.core-0.8.2-py3-none-any.whl (224 kB)\r\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.0/224.0 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta 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filename=seqeval-1.2.2-py3-none-any.whl size=16181 sha256=bf2dd3c34df297eec1788fc974302cee06645ad8e3540c0b1c97a14550730314\r\n","  Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\r\n","Successfully built antlr4-python3-runtime seqeval\r\n","Installing collected packages: antlr4-python3-runtime, protobuf, ordered-set, omegaconf, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cublas-cu11, nptyping, nltk, grpcio, window-ops, scikit-image, ray, opendatalab, nvidia-cudnn-cu11, model-index, botocore, transformers, torch, seqeval, openmim, mlforecast, gluonts, gdown, torchvision, statsforecast, pytorch-metric-learning, nlpaug, pytorch-lightning, evaluate, autogluon.common, autogluon.features, autogluon.core, autogluon.tabular, autogluon.multimodal, autogluon.timeseries, autogluon\r\n","  Attempting uninstall: protobuf\r\n","    Found existing installation: protobuf 3.20.3\r\n","    Uninstalling protobuf-3.20.3:\r\n","      Successfully uninstalled protobuf-3.20.3\r\n","  Attempting uninstall: nltk\r\n","    Found existing installation: nltk 3.2.4\r\n","    Uninstalling nltk-3.2.4:\r\n","      Successfully uninstalled nltk-3.2.4\r\n","  Attempting uninstall: grpcio\r\n","    Found existing installation: grpcio 1.51.1\r\n","    Uninstalling grpcio-1.51.1:\r\n","      Successfully uninstalled grpcio-1.51.1\r\n","  Attempting uninstall: scikit-image\r\n","    Found existing installation: scikit-image 0.20.0\r\n","    Uninstalling scikit-image-0.20.0:\r\n","      Successfully uninstalled scikit-image-0.20.0\r\n","  Attempting uninstall: ray\r\n","    Found existing installation: ray 2.4.0\r\n","    Uninstalling ray-2.4.0:\r\n","      Successfully uninstalled ray-2.4.0\r\n","  Attempting uninstall: botocore\r\n","    Found existing installation: botocore 1.29.76\r\n","    Uninstalling botocore-1.29.76:\r\n","      Successfully uninstalled botocore-1.29.76\r\n","  Attempting uninstall: transformers\r\n","    Found existing installation: transformers 4.29.2\r\n","    Uninstalling transformers-4.29.2:\r\n","      Successfully uninstalled transformers-4.29.2\r\n","  Attempting uninstall: torch\r\n","    Found existing installation: torch 2.0.0\r\n","    Uninstalling torch-2.0.0:\r\n","      Successfully uninstalled torch-2.0.0\r\n","  Attempting uninstall: torchvision\r\n","    Found existing installation: torchvision 0.15.1\r\n","    Uninstalling torchvision-0.15.1:\r\n","      Successfully uninstalled torchvision-0.15.1\r\n","  Attempting uninstall: pytorch-lightning\r\n","    Found existing installation: pytorch-lightning 2.0.2\r\n","    Uninstalling pytorch-lightning-2.0.2:\r\n","      Successfully uninstalled pytorch-lightning-2.0.2\r\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n","cudf 23.4.1 requires cupy-cuda11x<12.0.0a0,>=9.5.0, which is not installed.\r\n","cuml 23.4.1 requires cupy-cuda11x<12.0.0a0,>=9.5.0, which is not installed.\r\n","dask-cudf 23.4.1 requires cupy-cuda11x<12.0.0a0,>=9.5.0, which is not installed.\r\n","aiobotocore 2.5.0 requires botocore<1.29.77,>=1.29.76, but you have botocore 1.29.165 which is incompatible.\r\n","apache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.6 which is incompatible.\r\n","apache-beam 2.46.0 requires pyarrow<10.0.0,>=3.0.0, but you have pyarrow 10.0.1 which is incompatible.\r\n","cudf 23.4.1 requires protobuf<4.22,>=4.21.6, but you have protobuf 3.20.2 which is incompatible.\r\n","cuml 23.4.1 requires dask==2023.3.2, but you have dask 2023.5.0 which is incompatible.\r\n","dask-cudf 23.4.1 requires dask==2023.3.2, but you have dask 2023.5.0 which is incompatible.\r\n","google-cloud-artifact-registry 1.8.1 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 1.33.2 which is incompatible.\r\n","google-cloud-datastore 2.15.2 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 1.33.2 which is incompatible.\r\n","google-cloud-dlp 3.12.1 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 1.33.2 which is incompatible.\r\n","google-cloud-pubsub 2.16.1 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 1.33.2 which is incompatible.\r\n","google-cloud-pubsub 2.16.1 requires grpcio<2.0dev,>=1.51.3, but you have grpcio 1.50.0 which is incompatible.\r\n","google-cloud-resource-manager 1.10.0 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 1.33.2 which is incompatible.\r\n","google-cloud-spanner 3.33.0 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0dev,>=1.34.0, but you have google-api-core 1.33.2 which is incompatible.\r\n","kfp 1.8.21 requires google-api-python-client<2,>=1.7.8, but you have google-api-python-client 2.86.0 which is incompatible.\r\n","preprocessing 0.1.13 requires nltk==3.2.4, but you have nltk 3.8.1 which is incompatible.\r\n","tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\r\n","tensorflow-serving-api 2.12.1 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\r\n","torchdata 0.6.0 requires torch==2.0.0, but you have torch 1.13.1 which is incompatible.\u001b[0m\u001b[31m\r\n","\u001b[0mSuccessfully installed antlr4-python3-runtime-4.9.3 autogluon-0.8.2 autogluon.common-0.8.2 autogluon.core-0.8.2 autogluon.features-0.8.2 autogluon.multimodal-0.8.2 autogluon.tabular-0.8.2 autogluon.timeseries-0.8.2 botocore-1.29.165 evaluate-0.3.0 gdown-4.7.1 gluonts-0.13.2 grpcio-1.50.0 mlforecast-0.7.3 model-index-0.1.11 nlpaug-1.1.11 nltk-3.8.1 nptyping-2.4.1 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 omegaconf-2.2.3 opendatalab-0.0.9 openmim-0.3.9 ordered-set-4.1.0 protobuf-3.20.2 pytorch-lightning-1.9.5 pytorch-metric-learning-1.7.3 ray-2.3.1 scikit-image-0.19.3 seqeval-1.2.2 statsforecast-1.4.0 torch-1.13.1 torchvision-0.14.1 transformers-4.26.1 window-ops-0.0.14\r\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n","\u001b[0m"]}],"source":["!pip install autogluon"]},{"cell_type":"code","execution_count":3,"id":"a0be436b","metadata":{"execution":{"iopub.execute_input":"2023-07-03T16:29:55.557223Z","iopub.status.busy":"2023-07-03T16:29:55.556856Z","iopub.status.idle":"2023-07-03T16:29:55.566177Z","shell.execute_reply":"2023-07-03T16:29:55.565328Z"},"papermill":{"duration":0.079894,"end_time":"2023-07-03T16:29:55.568134","exception":false,"start_time":"2023-07-03T16:29:55.48824","status":"completed"},"tags":[]},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import autogluon "]},{"cell_type":"code","execution_count":4,"id":"17792741","metadata":{"execution":{"iopub.execute_input":"2023-07-03T16:29:55.704406Z","iopub.status.busy":"2023-07-03T16:29:55.704059Z","iopub.status.idle":"2023-07-03T16:29:55.758763Z","shell.execute_reply":"2023-07-03T16:29:55.757835Z"},"papermill":{"duration":0.125595,"end_time":"2023-07-03T16:29:55.761197","exception":false,"start_time":"2023-07-03T16:29:55.635602","status":"completed"},"tags":[]},"outputs":[],"source":["train_df = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')\n","test_df = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/test.csv')\n","sample_submission_df = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/sample_submission.csv')"]},{"cell_type":"code","execution_count":5,"id":"12eb7fc8","metadata":{"execution":{"iopub.execute_input":"2023-07-03T16:29:55.897356Z","iopub.status.busy":"2023-07-03T16:29:55.896667Z","iopub.status.idle":"2023-07-03T16:29:55.934394Z","shell.execute_reply":"2023-07-03T16:29:55.933537Z"},"papermill":{"duration":0.107895,"end_time":"2023-07-03T16:29:55.936427","exception":false,"start_time":"2023-07-03T16:29:55.828532","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Id</th>\n","      <th>AB</th>\n","      <th>AF</th>\n","      <th>AH</th>\n","      <th>AM</th>\n","      <th>AR</th>\n","      <th>AX</th>\n","      <th>AY</th>\n","      <th>AZ</th>\n","      <th>BC</th>\n","      <th>...</th>\n","      <th>FL</th>\n","      <th>FR</th>\n","      <th>FS</th>\n","      <th>GB</th>\n","      <th>GE</th>\n","      <th>GF</th>\n","      <th>GH</th>\n","      <th>GI</th>\n","      <th>GL</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>000ff2bfdfe9</td>\n","      <td>0.209377</td>\n","      <td>3109.03329</td>\n","      <td>85.200147</td>\n","      <td>22.394407</td>\n","      <td>8.138688</td>\n","      <td>0.699861</td>\n","      <td>0.025578</td>\n","      <td>9.812214</td>\n","      <td>5.555634</td>\n","      <td>...</td>\n","      <td>7.298162</td>\n","      <td>1.73855</td>\n","      <td>0.094822</td>\n","      <td>11.339138</td>\n","      <td>72.611063</td>\n","      <td>2003.810319</td>\n","      <td>22.136229</td>\n","      <td>69.834944</td>\n","      <td>0.120343</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>007255e47698</td>\n","      <td>0.145282</td>\n","      <td>978.76416</td>\n","      <td>85.200147</td>\n","      <td>36.968889</td>\n","      <td>8.138688</td>\n","      <td>3.632190</td>\n","      <td>0.025578</td>\n","      <td>13.517790</td>\n","      <td>1.229900</td>\n","      <td>...</td>\n","      <td>0.173229</td>\n","      <td>0.49706</td>\n","      <td>0.568932</td>\n","      <td>9.292698</td>\n","      <td>72.611063</td>\n","      <td>27981.562750</td>\n","      <td>29.135430</td>\n","      <td>32.131996</td>\n","      <td>21.978000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>013f2bd269f5</td>\n","      <td>0.470030</td>\n","      <td>2635.10654</td>\n","      <td>85.200147</td>\n","      <td>32.360553</td>\n","      <td>8.138688</td>\n","      <td>6.732840</td>\n","      <td>0.025578</td>\n","      <td>12.824570</td>\n","      <td>1.229900</td>\n","      <td>...</td>\n","      <td>7.709560</td>\n","      <td>0.97556</td>\n","      <td>1.198821</td>\n","      <td>37.077772</td>\n","      <td>88.609437</td>\n","      <td>13676.957810</td>\n","      <td>28.022851</td>\n","      <td>35.192676</td>\n","      <td>0.196941</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>043ac50845d5</td>\n","      <td>0.252107</td>\n","      <td>3819.65177</td>\n","      <td>120.201618</td>\n","      <td>77.112203</td>\n","      <td>8.138688</td>\n","      <td>3.685344</td>\n","      <td>0.025578</td>\n","      <td>11.053708</td>\n","      <td>1.229900</td>\n","      <td>...</td>\n","      <td>6.122162</td>\n","      <td>0.49706</td>\n","      <td>0.284466</td>\n","      <td>18.529584</td>\n","      <td>82.416803</td>\n","      <td>2094.262452</td>\n","      <td>39.948656</td>\n","      <td>90.493248</td>\n","      <td>0.155829</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>044fb8a146ec</td>\n","      <td>0.380297</td>\n","      <td>3733.04844</td>\n","      <td>85.200147</td>\n","      <td>14.103738</td>\n","      <td>8.138688</td>\n","      <td>3.942255</td>\n","      <td>0.054810</td>\n","      <td>3.396778</td>\n","      <td>102.151980</td>\n","      <td>...</td>\n","      <td>8.153058</td>\n","      <td>48.50134</td>\n","      <td>0.121914</td>\n","      <td>16.408728</td>\n","      <td>146.109943</td>\n","      <td>8524.370502</td>\n","      <td>45.381316</td>\n","      <td>36.262628</td>\n","      <td>0.096614</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 58 columns</p>\n","</div>"],"text/plain":["             Id        AB          AF          AH         AM        AR  \\\n","0  000ff2bfdfe9  0.209377  3109.03329   85.200147  22.394407  8.138688   \n","1  007255e47698  0.145282   978.76416   85.200147  36.968889  8.138688   \n","2  013f2bd269f5  0.470030  2635.10654   85.200147  32.360553  8.138688   \n","3  043ac50845d5  0.252107  3819.65177  120.201618  77.112203  8.138688   \n","4  044fb8a146ec  0.380297  3733.04844   85.200147  14.103738  8.138688   \n","\n","         AX        AY         AZ          BC  ...        FL        FR  \\\n","0  0.699861  0.025578   9.812214    5.555634  ...  7.298162   1.73855   \n","1  3.632190  0.025578  13.517790    1.229900  ...  0.173229   0.49706   \n","2  6.732840  0.025578  12.824570    1.229900  ...  7.709560   0.97556   \n","3  3.685344  0.025578  11.053708    1.229900  ...  6.122162   0.49706   \n","4  3.942255  0.054810   3.396778  102.151980  ...  8.153058  48.50134   \n","\n","         FS         GB          GE            GF         GH         GI  \\\n","0  0.094822  11.339138   72.611063   2003.810319  22.136229  69.834944   \n","1  0.568932   9.292698   72.611063  27981.562750  29.135430  32.131996   \n","2  1.198821  37.077772   88.609437  13676.957810  28.022851  35.192676   \n","3  0.284466  18.529584   82.416803   2094.262452  39.948656  90.493248   \n","4  0.121914  16.408728  146.109943   8524.370502  45.381316  36.262628   \n","\n","          GL  Class  \n","0   0.120343      1  \n","1  21.978000      0  \n","2   0.196941      0  \n","3   0.155829      0  \n","4   0.096614      1  \n","\n","[5 rows x 58 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head()"]},{"cell_type":"code","execution_count":6,"id":"3d37c6f6","metadata":{"_kg_hide-output":true,"execution":{"iopub.execute_input":"2023-07-03T16:29:56.074683Z","iopub.status.busy":"2023-07-03T16:29:56.073621Z","iopub.status.idle":"2023-07-03T16:30:22.351444Z","shell.execute_reply":"2023-07-03T16:30:22.350379Z"},"papermill":{"duration":26.348827,"end_time":"2023-07-03T16:30:22.353529","exception":false,"start_time":"2023-07-03T16:29:56.004702","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["No path specified. Models will be saved in: \"AutogluonModels/ag-20230703_162958/\"\n","============ fit kwarg info ============\n","User Specified kwargs:\n","{'verbosity': 3}\n","Full kwargs:\n","{'_feature_generator_kwargs': None,\n"," '_save_bag_folds': None,\n"," 'ag_args': None,\n"," 'ag_args_ensemble': None,\n"," 'ag_args_fit': None,\n"," 'auto_stack': False,\n"," 'calibrate': 'auto',\n"," 'excluded_model_types': None,\n"," 'feature_generator': 'auto',\n"," 'feature_prune_kwargs': None,\n"," 'holdout_frac': None,\n"," 'hyperparameter_tune_kwargs': None,\n"," 'included_model_types': None,\n"," 'keep_only_best': False,\n"," 'name_suffix': None,\n"," 'num_bag_folds': None,\n"," 'num_bag_sets': None,\n"," 'num_stack_levels': None,\n"," 'pseudo_data': None,\n"," 'refit_full': False,\n"," 'save_space': False,\n"," 'set_best_to_refit_full': False,\n"," 'unlabeled_data': None,\n"," 'use_bag_holdout': False,\n"," 'verbosity': 3}\n","========================================\n","Saving AutogluonModels/ag-20230703_162958/learner.pkl\n","Saving AutogluonModels/ag-20230703_162958/predictor.pkl\n","Beginning AutoGluon training ... Time limit = 3600s\n","AutoGluon will save models to \"AutogluonModels/ag-20230703_162958/\"\n","AutoGluon Version:  0.8.2\n","Python Version:     3.10.10\n","Operating System:   Linux\n","Platform Machine:   x86_64\n","Platform Version:   #1 SMP Tue Jun 27 10:54:51 UTC 2023\n","Disk Space Avail:   20.94 GB / 20.96 GB (99.9%)\n","Train Data Rows:    617\n","Train Data Columns: 57\n","Label Column: Class\n","Preprocessing data ...\n","AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n","\t2 unique label values:  [1, 0]\n","\tIf 'binary' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n","Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n","Using Feature Generators to preprocess the data ...\n","Fitting AutoMLPipelineFeatureGenerator...\n","\tAvailable Memory:                    15743.3 MB\n","\tTrain Data (Original)  Memory Usage: 0.35 MB (0.0% of available memory)\n","\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n","\tStage 1 Generators:\n","\t\tFitting AsTypeFeatureGenerator...\n","\t\t\tNote: Converting 1 features to boolean dtype as they only contain 2 unique values.\n","\t\t\tOriginal Features (exact raw dtype, raw dtype):\n","\t\t\t\t('float64', 'float') : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t\t\t('object', 'object') :  2 | ['Id', 'EJ']\n","\t\t\tTypes of features in original data (raw dtype, special dtypes):\n","\t\t\t\t('float', [])  : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t\t\t('object', []) :  2 | ['Id', 'EJ']\n","\t\t\tTypes of features in processed data (raw dtype, special dtypes):\n","\t\t\t\t('float', [])     : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t\t\t('int', ['bool']) :  1 | ['EJ']\n","\t\t\t\t('object', [])    :  1 | ['Id']\n","\t\t\t0.0s = Fit runtime\n","\t\t\t57 features in original data used to generate 57 features in processed data.\n","\tStage 2 Generators:\n","\t\tFitting FillNaFeatureGenerator...\n","\t\t\tTypes of features in original data (raw dtype, special dtypes):\n","\t\t\t\t('float', [])     : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t\t\t('int', ['bool']) :  1 | ['EJ']\n","\t\t\t\t('object', [])    :  1 | ['Id']\n","\t\t\tTypes of features in processed data (raw dtype, special dtypes):\n","\t\t\t\t('float', [])     : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t\t\t('int', ['bool']) :  1 | ['EJ']\n","\t\t\t\t('object', [])    :  1 | ['Id']\n","\t\t\t0.0s = Fit runtime\n","\t\t\t57 features in original data used to generate 57 features in processed data.\n","\tStage 3 Generators:\n","\t\tFitting IdentityFeatureGenerator...\n","\t\t\tTypes of features in original data (raw dtype, special dtypes):\n","\t\t\t\t('float', [])     : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t\t\t('int', ['bool']) :  1 | ['EJ']\n","\t\t\tTypes of features in processed data (raw dtype, special dtypes):\n","\t\t\t\t('float', [])     : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t\t\t('int', ['bool']) :  1 | ['EJ']\n","\t\t\t0.0s = Fit runtime\n","\t\t\t56 features in original data used to generate 56 features in processed data.\n","\t\tFitting CategoryFeatureGenerator...\n","\t\t\tFitting CategoryMemoryMinimizeFeatureGenerator...\n","\t\t\t\tTypes of features in original data (raw dtype, special dtypes):\n","\t\t\t\t\t('category', []) : 1 | ['Id']\n","\t\t\t\tTypes of features in processed data (raw dtype, special dtypes):\n","\t\t\t\t\t('category', []) : 1 | ['Id']\n","\t\t\t\t0.0s = Fit runtime\n","\t\t\t\t1 features in original data used to generate 1 features in processed data.\n","\t\t\tTypes of features in original data (raw dtype, special dtypes):\n","\t\t\t\t('object', []) : 1 | ['Id']\n","\t\t\tTypes of features in processed data (raw dtype, special dtypes):\n","\t\t\t\t('category', []) : 1 | ['Id']\n","\t\t\t0.0s = Fit runtime\n","\t\t\t1 features in original data used to generate 1 features in processed data.\n","\t\tSkipping DatetimeFeatureGenerator: No input feature with required dtypes.\n","\t\tSkipping TextSpecialFeatureGenerator: No input feature with required dtypes.\n","\t\tSkipping TextNgramFeatureGenerator: No input feature with required dtypes.\n","\t\tSkipping IdentityFeatureGenerator: No input feature with required dtypes.\n","\t\tSkipping IsNanFeatureGenerator: No input feature with required dtypes.\n","\tStage 4 Generators:\n","\t\tFitting DropUniqueFeatureGenerator...\n","\t\t\tTypes of features in original data (raw dtype, special dtypes):\n","\t\t\t\t('float', [])     : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t\t\t('int', ['bool']) :  1 | ['EJ']\n","\t\t\tTypes of features in processed data (raw dtype, special dtypes):\n","\t\t\t\t('float', [])     : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t\t\t('int', ['bool']) :  1 | ['EJ']\n","\t\t\t0.0s = Fit runtime\n","\t\t\t56 features in original data used to generate 56 features in processed data.\n","\tStage 5 Generators:\n","\t\tFitting DropDuplicatesFeatureGenerator...\n","\t\t\tTypes of features in original data (raw dtype, special dtypes):\n","\t\t\t\t('float', [])     : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t\t\t('int', ['bool']) :  1 | ['EJ']\n","\t\t\tTypes of features in processed data (raw dtype, special dtypes):\n","\t\t\t\t('float', [])     : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t\t\t('int', ['bool']) :  1 | ['EJ']\n","\t\t\t0.0s = Fit runtime\n","\t\t\t56 features in original data used to generate 56 features in processed data.\n","\tUnused Original Features (Count: 1): ['Id']\n","\t\tThese features were not used to generate any of the output features. Add a feature generator compatible with these features to utilize them.\n","\t\tFeatures can also be unused if they carry very little information, such as being categorical but having almost entirely unique values or being duplicates of other features.\n","\t\tThese features do not need to be present at inference time.\n","\t\t('object', []) : 1 | ['Id']\n","\tTypes of features in original data (exact raw dtype, raw dtype):\n","\t\t('float64', 'float') : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t('object', 'object') :  1 | ['EJ']\n","\tTypes of features in original data (raw dtype, special dtypes):\n","\t\t('float', [])  : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t('object', []) :  1 | ['EJ']\n","\tTypes of features in processed data (exact raw dtype, raw dtype):\n","\t\t('float64', 'float') : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t('int8', 'int')      :  1 | ['EJ']\n","\tTypes of features in processed data (raw dtype, special dtypes):\n","\t\t('float', [])     : 55 | ['AB', 'AF', 'AH', 'AM', 'AR', ...]\n","\t\t('int', ['bool']) :  1 | ['EJ']\n","\t0.3s = Fit runtime\n","\t56 features in original data used to generate 56 features in processed data.\n","\tTrain Data (Processed) Memory Usage: 0.27 MB (0.0% of available memory)\n","Data preprocessing and feature engineering runtime = 0.36s ...\n","AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n","\tTo change this, specify the eval_metric parameter of Predictor()\n","Saving AutogluonModels/ag-20230703_162958/learner.pkl\n","Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 493, Val Rows: 124\n","User-specified model hyperparameters to be fit:\n","{\n","\t'NN_TORCH': {},\n","\t'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],\n","\t'CAT': {},\n","\t'XGB': {},\n","\t'FASTAI': {},\n","\t'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n","\t'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],\n","\t'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],\n","}\n","Saving AutogluonModels/ag-20230703_162958/utils/data/X.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/data/y.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/data/X_val.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/data/y_val.pkl\n","Model configs that will be trained (in order):\n","\tKNeighborsUnif: \t{'weights': 'uniform', 'ag_args': {'valid_stacker': False, 'name_suffix': 'Unif', 'model_type': <class 'autogluon.tabular.models.knn.knn_model.KNNModel'>, 'priority': 100}, 'ag_args_ensemble': {'use_child_oof': True}}\n","\tKNeighborsDist: \t{'weights': 'distance', 'ag_args': {'valid_stacker': False, 'name_suffix': 'Dist', 'model_type': <class 'autogluon.tabular.models.knn.knn_model.KNNModel'>, 'priority': 100}, 'ag_args_ensemble': {'use_child_oof': True}}\n","\tLightGBMXT: \t{'extra_trees': True, 'ag_args': {'name_suffix': 'XT', 'model_type': <class 'autogluon.tabular.models.lgb.lgb_model.LGBModel'>, 'priority': 90}}\n","\tLightGBM: \t{'ag_args': {'model_type': <class 'autogluon.tabular.models.lgb.lgb_model.LGBModel'>, 'priority': 90}}\n","\tRandomForestGini: \t{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass'], 'model_type': <class 'autogluon.tabular.models.rf.rf_model.RFModel'>, 'priority': 80}, 'ag_args_ensemble': {'use_child_oof': True}}\n","\tRandomForestEntr: \t{'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass'], 'model_type': <class 'autogluon.tabular.models.rf.rf_model.RFModel'>, 'priority': 80}, 'ag_args_ensemble': {'use_child_oof': True}}\n","\tCatBoost: \t{'ag_args': {'model_type': <class 'autogluon.tabular.models.catboost.catboost_model.CatBoostModel'>, 'priority': 70}}\n","\tExtraTreesGini: \t{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass'], 'model_type': <class 'autogluon.tabular.models.xt.xt_model.XTModel'>, 'priority': 60}, 'ag_args_ensemble': {'use_child_oof': True}}\n","\tExtraTreesEntr: \t{'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass'], 'model_type': <class 'autogluon.tabular.models.xt.xt_model.XTModel'>, 'priority': 60}, 'ag_args_ensemble': {'use_child_oof': True}}\n","\tNeuralNetFastAI: \t{'ag_args': {'model_type': <class 'autogluon.tabular.models.fastainn.tabular_nn_fastai.NNFastAiTabularModel'>, 'priority': 50}}\n","\tXGBoost: \t{'ag_args': {'model_type': <class 'autogluon.tabular.models.xgboost.xgboost_model.XGBoostModel'>, 'priority': 40}}\n","\tNeuralNetTorch: \t{'ag_args': {'model_type': <class 'autogluon.tabular.models.tabular_nn.torch.tabular_nn_torch.TabularNeuralNetTorchModel'>, 'priority': 25}}\n","\tLightGBMLarge: \t{'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 5, 'ag_args': {'model_type': <class 'autogluon.tabular.models.lgb.lgb_model.LGBModel'>, 'name_suffix': 'Large', 'hyperparameter_tune_kwargs': None, 'priority': 0}}\n","Fitting 13 L1 models ...\n","Fitting model: KNeighborsUnif ... Training model for up to 3599.63s of the 3599.62s of remaining time.\n","\tFitting KNeighborsUnif with 'num_gpus': 0, 'num_cpus': 2\n","\tUsing sklearnex KNN backend...\n","Saving AutogluonModels/ag-20230703_162958/models/KNeighborsUnif/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/KNeighborsUnif/y_pred_proba_val.pkl\n","\t0.8226\t = Validation score   (accuracy)\n","\t0.64s\t = Training   runtime\n","\t0.02s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: KNeighborsDist ... Training model for up to 3598.96s of the 3598.95s of remaining time.\n","\tFitting KNeighborsDist with 'num_gpus': 0, 'num_cpus': 2\n","\tUsing sklearnex KNN backend...\n","Saving AutogluonModels/ag-20230703_162958/models/KNeighborsDist/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/KNeighborsDist/y_pred_proba_val.pkl\n","\t0.8226\t = Validation score   (accuracy)\n","\t0.02s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: LightGBMXT ... Training model for up to 3598.93s of the 3598.92s of remaining time.\n","\tFitting LightGBMXT with 'num_gpus': 0, 'num_cpus': 1\n","\tFitting 10000 rounds... Hyperparameters: {'learning_rate': 0.05, 'extra_trees': True}\n"]},{"name":"stdout","output_type":"stream","text":["[50]\tvalid_set's binary_error: 0.0887097\n","[100]\tvalid_set's binary_error: 0.0967742\n","[150]\tvalid_set's binary_error: 0.0887097\n","[200]\tvalid_set's binary_error: 0.0725806\n","[250]\tvalid_set's binary_error: 0.0725806\n","[300]\tvalid_set's binary_error: 0.0725806\n","[350]\tvalid_set's binary_error: 0.0645161\n","[400]\tvalid_set's binary_error: 0.0645161\n","[450]\tvalid_set's binary_error: 0.0645161\n","[500]\tvalid_set's binary_error: 0.0725806\n","[550]\tvalid_set's binary_error: 0.0645161\n"]},{"name":"stderr","output_type":"stream","text":["Saving AutogluonModels/ag-20230703_162958/models/LightGBMXT/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/LightGBMXT/y_pred_proba_val.pkl\n","\t0.9355\t = Validation score   (accuracy)\n","\t2.77s\t = Training   runtime\n","\t0.01s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: LightGBM ... Training model for up to 3596.13s of the 3596.11s of remaining time.\n","\tFitting LightGBM with 'num_gpus': 0, 'num_cpus': 1\n","\tFitting 10000 rounds... Hyperparameters: {'learning_rate': 0.05}\n"]},{"name":"stdout","output_type":"stream","text":["[600]\tvalid_set's binary_error: 0.0645161\n","[50]\tvalid_set's binary_error: 0.0887097\n","[100]\tvalid_set's binary_error: 0.0806452\n","[150]\tvalid_set's binary_error: 0.0806452\n","[200]\tvalid_set's binary_error: 0.0806452\n","[250]\tvalid_set's binary_error: 0.0887097\n","[300]\tvalid_set's binary_error: 0.0887097\n"]},{"name":"stderr","output_type":"stream","text":["Saving AutogluonModels/ag-20230703_162958/models/LightGBM/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/LightGBM/y_pred_proba_val.pkl\n","\t0.9355\t = Validation score   (accuracy)\n","\t0.84s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: RandomForestGini ... Training model for up to 3595.27s of the 3595.26s of remaining time.\n","\tFitting RandomForestGini with 'num_gpus': 0, 'num_cpus': 2\n"]},{"name":"stdout","output_type":"stream","text":["[350]\tvalid_set's binary_error: 0.0887097\n"]},{"name":"stderr","output_type":"stream","text":["Saving AutogluonModels/ag-20230703_162958/models/RandomForestGini/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/RandomForestGini/y_pred_proba_val.pkl\n","\t0.8952\t = Validation score   (accuracy)\n","\t0.86s\t = Training   runtime\n","\t0.08s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: RandomForestEntr ... Training model for up to 3594.31s of the 3594.29s of remaining time.\n","\tFitting RandomForestEntr with 'num_gpus': 0, 'num_cpus': 2\n","Saving AutogluonModels/ag-20230703_162958/models/RandomForestEntr/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/RandomForestEntr/y_pred_proba_val.pkl\n","\t0.9113\t = Validation score   (accuracy)\n","\t0.86s\t = Training   runtime\n","\t0.08s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: CatBoost ... Training model for up to 3593.35s of the 3593.33s of remaining time.\n","\tFitting CatBoost with 'num_gpus': 0, 'num_cpus': 1\n","\tCatboost model hyperparameters: {'iterations': 10000, 'learning_rate': 0.05, 'random_seed': 0, 'allow_writing_files': False, 'eval_metric': 'Accuracy', 'thread_count': 1}\n"]},{"name":"stdout","output_type":"stream","text":["0:\tlearn: 0.8600406\ttest: 0.8225806\tbest: 0.8225806 (0)\ttotal: 71.3ms\tremaining: 11m 52s\n","20:\tlearn: 0.9594320\ttest: 0.8790323\tbest: 0.8870968 (16)\ttotal: 303ms\tremaining: 2m 23s\n","40:\tlearn: 0.9776876\ttest: 0.8870968\tbest: 0.8870968 (16)\ttotal: 535ms\tremaining: 2m 9s\n","60:\tlearn: 0.9878296\ttest: 0.8870968\tbest: 0.8951613 (46)\ttotal: 764ms\tremaining: 2m 4s\n","80:\tlearn: 0.9979716\ttest: 0.8951613\tbest: 0.9032258 (77)\ttotal: 999ms\tremaining: 2m 2s\n","100:\tlearn: 1.0000000\ttest: 0.9112903\tbest: 0.9112903 (92)\ttotal: 1.23s\tremaining: 2m\n","120:\tlearn: 1.0000000\ttest: 0.9112903\tbest: 0.9112903 (92)\ttotal: 1.46s\tremaining: 1m 58s\n","140:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9193548 (124)\ttotal: 1.69s\tremaining: 1m 58s\n","160:\tlearn: 1.0000000\ttest: 0.9274194\tbest: 0.9274194 (156)\ttotal: 1.92s\tremaining: 1m 57s\n","180:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 2.15s\tremaining: 1m 56s\n","200:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 2.38s\tremaining: 1m 56s\n","220:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 2.62s\tremaining: 1m 55s\n","240:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 2.87s\tremaining: 1m 56s\n","260:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 3.14s\tremaining: 1m 57s\n","280:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 3.38s\tremaining: 1m 56s\n","300:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 3.62s\tremaining: 1m 56s\n","320:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 3.9s\tremaining: 1m 57s\n","340:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 4.14s\tremaining: 1m 57s\n","360:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 4.38s\tremaining: 1m 57s\n","380:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 4.62s\tremaining: 1m 56s\n","400:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 4.85s\tremaining: 1m 56s\n","420:\tlearn: 1.0000000\ttest: 0.9193548\tbest: 0.9274194 (156)\ttotal: 5.08s\tremaining: 1m 55s\n","440:\tlearn: 1.0000000\ttest: 0.9112903\tbest: 0.9274194 (156)\ttotal: 5.32s\tremaining: 1m 55s\n"]},{"name":"stderr","output_type":"stream","text":["Saving AutogluonModels/ag-20230703_162958/models/CatBoost/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/CatBoost/y_pred_proba_val.pkl\n","\t0.9274\t = Validation score   (accuracy)\n","\t6.29s\t = Training   runtime\n","\t0.01s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: ExtraTreesGini ... Training model for up to 3587.04s of the 3587.03s of remaining time.\n","\tFitting ExtraTreesGini with 'num_gpus': 0, 'num_cpus': 2\n"]},{"name":"stdout","output_type":"stream","text":["\n","bestTest = 0.9274193548\n","bestIteration = 156\n","\n","Shrink model to first 157 iterations.\n"]},{"name":"stderr","output_type":"stream","text":["Saving AutogluonModels/ag-20230703_162958/models/ExtraTreesGini/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/ExtraTreesGini/y_pred_proba_val.pkl\n","\t0.879\t = Validation score   (accuracy)\n","\t0.77s\t = Training   runtime\n","\t0.12s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: ExtraTreesEntr ... Training model for up to 3586.13s of the 3586.11s of remaining time.\n","\tFitting ExtraTreesEntr with 'num_gpus': 0, 'num_cpus': 2\n","Saving AutogluonModels/ag-20230703_162958/models/ExtraTreesEntr/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/ExtraTreesEntr/y_pred_proba_val.pkl\n","\t0.879\t = Validation score   (accuracy)\n","\t0.89s\t = Training   runtime\n","\t0.1s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: NeuralNetFastAI ... Training model for up to 3585.1s of the 3585.09s of remaining time.\n","\tFitting NeuralNetFastAI with 'num_gpus': 0, 'num_cpus': 1\n","Fitting Neural Network with parameters {'layers': None, 'emb_drop': 0.1, 'ps': 0.1, 'bs': 'auto', 'lr': 0.01, 'epochs': 'auto', 'early.stopping.min_delta': 0.0001, 'early.stopping.patience': 20, 'smoothing': 0.0}...\n","Using 0/0 categorical features\n","Using 56 cont features\n","Automated batch size selection: 256\n","TabularModel(\n","  (embeds): ModuleList()\n","  (emb_drop): Dropout(p=0.1, inplace=False)\n","  (bn_cont): BatchNorm1d(56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (layers): Sequential(\n","    (0): LinBnDrop(\n","      (0): Linear(in_features=56, out_features=200, bias=False)\n","      (1): ReLU(inplace=True)\n","      (2): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (3): Dropout(p=0.1, inplace=False)\n","    )\n","    (1): LinBnDrop(\n","      (0): Linear(in_features=200, out_features=100, bias=False)\n","      (1): ReLU(inplace=True)\n","      (2): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (3): Dropout(p=0.1, inplace=False)\n","    )\n","    (2): LinBnDrop(\n","      (0): Linear(in_features=100, out_features=2, bias=True)\n","    )\n","  )\n",")\n","Better model found at epoch 0 with accuracy value: 0.6693548560142517.\n","Better model found at epoch 1 with accuracy value: 0.75.\n","Better model found at epoch 2 with accuracy value: 0.8145161271095276.\n","Better model found at epoch 3 with accuracy value: 0.8387096524238586.\n","Better model found at epoch 7 with accuracy value: 0.8548387289047241.\n","Better model found at epoch 10 with accuracy value: 0.8951612710952759.\n","Better model found at epoch 11 with accuracy value: 0.9032257795333862.\n","Model validation metrics: 0.9032257795333862\n","Saving AutogluonModels/ag-20230703_162958/models/NeuralNetFastAI/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/models/NeuralNetFastAI/model-internals.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/NeuralNetFastAI/y_pred_proba_val.pkl\n","\t0.9032\t = Validation score   (accuracy)\n","\t2.22s\t = Training   runtime\n","\t0.01s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: XGBoost ... Training model for up to 3582.85s of the 3582.83s of remaining time.\n","\tFitting XGBoost with 'num_gpus': 0, 'num_cpus': 1\n"]},{"name":"stdout","output_type":"stream","text":["[0]\tvalidation_0-error:0.16129\n","[50]\tvalidation_0-error:0.12097\n","[100]\tvalidation_0-error:0.09677\n","[150]\tvalidation_0-error:0.09677\n","[200]\tvalidation_0-error:0.10484\n","[250]\tvalidation_0-error:0.10484\n","[300]\tvalidation_0-error:0.11290\n","[350]\tvalidation_0-error:0.11290\n","[370]\tvalidation_0-error:0.11290\n"]},{"name":"stderr","output_type":"stream","text":["Saving AutogluonModels/ag-20230703_162958/models/XGBoost/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/XGBoost/y_pred_proba_val.pkl\n","\t0.9113\t = Validation score   (accuracy)\n","\t0.62s\t = Training   runtime\n","\t0.01s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: NeuralNetTorch ... Training model for up to 3582.2s of the 3582.18s of remaining time.\n","\tFitting NeuralNetTorch with 'num_gpus': 0, 'num_cpus': 1\n","Tabular Neural Network treats features as the following types:\n","{\n","    \"continuous\": [\n","        \"AZ\",\n","        \"BN\",\n","        \"CU\",\n","        \"CW \",\n","        \"DH\",\n","        \"DN\",\n","        \"EL\",\n","        \"GH\",\n","        \"GL\"\n","    ],\n","    \"skewed\": [\n","        \"AB\",\n","        \"AF\",\n","        \"AH\",\n","        \"AM\",\n","        \"AR\",\n","        \"AX\",\n","        \"AY\",\n","        \"BC\",\n","        \"BD \",\n","        \"BP\",\n","        \"BQ\",\n","        \"BR\",\n","        \"BZ\",\n","        \"CB\",\n","        \"CC\",\n","        \"CD \",\n","        \"CF\",\n","        \"CH\",\n","        \"CL\",\n","        \"CR\",\n","        \"CS\",\n","        \"DA\",\n","        \"DE\",\n","        \"DF\",\n","        \"DI\",\n","        \"DL\",\n","        \"DU\",\n","        \"DV\",\n","        \"DY\",\n","        \"EB\",\n","        \"EE\",\n","        \"EG\",\n","        \"EH\",\n","        \"EP\",\n","        \"EU\",\n","        \"FC\",\n","        \"FD \",\n","        \"FE\",\n","        \"FI\",\n","        \"FL\",\n","        \"FR\",\n","        \"FS\",\n","        \"GB\",\n","        \"GE\",\n","        \"GF\",\n","        \"GI\"\n","    ],\n","    \"onehot\": [],\n","    \"embed\": [],\n","    \"language\": [],\n","    \"bool\": [\n","        \"EJ\"\n","    ]\n","}\n","\n","\n","Training data for TabularNeuralNetTorchModel has: 493 examples, 56 features (56 vector, 0 embedding)\n","Training on CPU\n","Neural network architecture:\n","EmbedNet(\n","  (main_block): Sequential(\n","    (0): Linear(in_features=56, out_features=128, bias=True)\n","    (1): ReLU()\n","    (2): Dropout(p=0.1, inplace=False)\n","    (3): Linear(in_features=128, out_features=128, bias=True)\n","    (4): ReLU()\n","    (5): Dropout(p=0.1, inplace=False)\n","    (6): Linear(in_features=128, out_features=128, bias=True)\n","    (7): ReLU()\n","    (8): Dropout(p=0.1, inplace=False)\n","    (9): Linear(in_features=128, out_features=128, bias=True)\n","    (10): ReLU()\n","    (11): Linear(in_features=128, out_features=2, bias=True)\n","  )\n","  (softmax): Softmax(dim=1)\n",")\n","Training tabular neural network for up to 500 epochs...\n","Epoch 1 (Update 15).\tTrain loss: 0.5994, Val accuracy: 0.8226, Best Epoch: 1\n","Epoch 2 (Update 30).\tTrain loss: 0.4558, Val accuracy: 0.8226, Best Epoch: 2\n","Epoch 3 (Update 45).\tTrain loss: 0.4285, Val accuracy: 0.8226, Best Epoch: 3\n","Epoch 4 (Update 60).\tTrain loss: 0.3974, Val accuracy: 0.8226, Best Epoch: 4\n","Epoch 5 (Update 75).\tTrain loss: 0.3664, Val accuracy: 0.8226, Best Epoch: 5\n","Epoch 6 (Update 90).\tTrain loss: 0.3422, Val accuracy: 0.8226, Best Epoch: 6\n","Epoch 7 (Update 105).\tTrain loss: 0.3177, Val accuracy: 0.8306, Best Epoch: 7\n","Epoch 8 (Update 120).\tTrain loss: 0.2837, Val accuracy: 0.8468, Best Epoch: 8\n","Epoch 9 (Update 135).\tTrain loss: 0.2467, Val accuracy: 0.8548, Best Epoch: 9\n","Epoch 10 (Update 150).\tTrain loss: 0.2106, Val accuracy: 0.8871, Best Epoch: 10\n","Epoch 11 (Update 165).\tTrain loss: 0.1893, Val accuracy: 0.879, Best Epoch: 10\n","Epoch 12 (Update 180).\tTrain loss: 0.1545, Val accuracy: 0.879, Best Epoch: 10\n","Epoch 13 (Update 195).\tTrain loss: 0.1416, Val accuracy: 0.8629, Best Epoch: 10\n","Epoch 14 (Update 210).\tTrain loss: 0.1236, Val accuracy: 0.879, Best Epoch: 10\n","Epoch 15 (Update 225).\tTrain loss: 0.1035, Val accuracy: 0.8952, Best Epoch: 15\n","Epoch 16 (Update 240).\tTrain loss: 0.0846, Val accuracy: 0.8952, Best Epoch: 16\n","Epoch 17 (Update 255).\tTrain loss: 0.0905, Val accuracy: 0.879, Best Epoch: 16\n","Epoch 18 (Update 270).\tTrain loss: 0.0591, Val accuracy: 0.8871, Best Epoch: 16\n","Epoch 19 (Update 285).\tTrain loss: 0.0633, Val accuracy: 0.8871, Best Epoch: 16\n","Epoch 20 (Update 300).\tTrain loss: 0.0449, Val accuracy: 0.8952, Best Epoch: 20\n","Epoch 21 (Update 315).\tTrain loss: 0.03, Val accuracy: 0.8952, Best Epoch: 21\n","Epoch 22 (Update 330).\tTrain loss: 0.0256, Val accuracy: 0.8952, Best Epoch: 22\n","Epoch 23 (Update 345).\tTrain loss: 0.0298, Val accuracy: 0.8952, Best Epoch: 23\n","Epoch 24 (Update 360).\tTrain loss: 0.023, Val accuracy: 0.8952, Best Epoch: 24\n","Epoch 25 (Update 375).\tTrain loss: 0.0379, Val accuracy: 0.8952, Best Epoch: 25\n","Epoch 26 (Update 390).\tTrain loss: 0.0305, Val accuracy: 0.9113, Best Epoch: 26\n","Epoch 27 (Update 405).\tTrain loss: 0.023, Val accuracy: 0.9032, Best Epoch: 26\n","Epoch 28 (Update 420).\tTrain loss: 0.021, Val accuracy: 0.9113, Best Epoch: 28\n","Epoch 29 (Update 435).\tTrain loss: 0.0133, Val accuracy: 0.9113, Best Epoch: 29\n","Epoch 30 (Update 450).\tTrain loss: 0.0096, Val accuracy: 0.9194, Best Epoch: 30\n","Epoch 31 (Update 465).\tTrain loss: 0.0122, Val accuracy: 0.9113, Best Epoch: 30\n","Epoch 32 (Update 480).\tTrain loss: 0.0098, Val accuracy: 0.9032, Best Epoch: 30\n","Epoch 33 (Update 495).\tTrain loss: 0.0111, Val accuracy: 0.9113, Best Epoch: 30\n","Epoch 34 (Update 510).\tTrain loss: 0.005, Val accuracy: 0.9113, Best Epoch: 30\n","Epoch 35 (Update 525).\tTrain loss: 0.0043, Val accuracy: 0.9113, Best Epoch: 30\n","Epoch 36 (Update 540).\tTrain loss: 0.0128, Val accuracy: 0.9113, Best Epoch: 30\n","Epoch 37 (Update 555).\tTrain loss: 0.0073, Val accuracy: 0.9194, Best Epoch: 37\n","Epoch 38 (Update 570).\tTrain loss: 0.0106, Val accuracy: 0.8952, Best Epoch: 37\n","Epoch 39 (Update 585).\tTrain loss: 0.0129, Val accuracy: 0.9194, Best Epoch: 39\n","Epoch 40 (Update 600).\tTrain loss: 0.0029, Val accuracy: 0.9194, Best Epoch: 40\n","Epoch 41 (Update 615).\tTrain loss: 0.0031, Val accuracy: 0.9274, Best Epoch: 41\n","Epoch 42 (Update 630).\tTrain loss: 0.0013, Val accuracy: 0.9194, Best Epoch: 41\n","Epoch 43 (Update 645).\tTrain loss: 0.0033, Val accuracy: 0.9194, Best Epoch: 41\n","Epoch 44 (Update 660).\tTrain loss: 0.0014, Val accuracy: 0.9113, Best Epoch: 41\n","Epoch 45 (Update 675).\tTrain loss: 0.002, Val accuracy: 0.9113, Best Epoch: 41\n","Epoch 46 (Update 690).\tTrain loss: 0.0019, Val accuracy: 0.9194, Best Epoch: 41\n","Epoch 47 (Update 705).\tTrain loss: 0.0022, Val accuracy: 0.9113, Best Epoch: 41\n","Epoch 48 (Update 720).\tTrain loss: 0.0032, Val accuracy: 0.9113, Best Epoch: 41\n","Epoch 49 (Update 735).\tTrain loss: 0.0022, Val accuracy: 0.9194, Best Epoch: 41\n","Epoch 50 (Update 750).\tTrain loss: 0.0019, Val accuracy: 0.9194, Best Epoch: 41\n","Epoch 51 (Update 765).\tTrain loss: 0.0034, Val accuracy: 0.9032, Best Epoch: 41\n","Epoch 52 (Update 780).\tTrain loss: 0.0214, Val accuracy: 0.9113, Best Epoch: 41\n","Epoch 53 (Update 795).\tTrain loss: 0.003, Val accuracy: 0.8871, Best Epoch: 41\n","Epoch 54 (Update 810).\tTrain loss: 0.0148, Val accuracy: 0.9032, Best Epoch: 41\n","Epoch 55 (Update 825).\tTrain loss: 0.0064, Val accuracy: 0.9032, Best Epoch: 41\n","Epoch 56 (Update 840).\tTrain loss: 0.0029, Val accuracy: 0.9032, Best Epoch: 41\n","Epoch 57 (Update 855).\tTrain loss: 0.0051, Val accuracy: 0.9032, Best Epoch: 41\n","Epoch 58 (Update 870).\tTrain loss: 0.0026, Val accuracy: 0.9113, Best Epoch: 41\n","Epoch 59 (Update 885).\tTrain loss: 0.0011, Val accuracy: 0.9032, Best Epoch: 41\n","Epoch 60 (Update 900).\tTrain loss: 0.0009, Val accuracy: 0.9032, Best Epoch: 41\n","Epoch 61 (Update 915).\tTrain loss: 0.0082, Val accuracy: 0.9032, Best Epoch: 41\n","Best model found on Epoch 41 (Update 615). Val accuracy: 0.9274193548387096\n","Saving AutogluonModels/ag-20230703_162958/models/NeuralNetTorch/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/NeuralNetTorch/y_pred_proba_val.pkl\n","\t0.9274\t = Validation score   (accuracy)\n","\t2.96s\t = Training   runtime\n","\t0.04s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Fitting model: LightGBMLarge ... Training model for up to 3579.19s of the 3579.17s of remaining time.\n","\tFitting LightGBMLarge with 'num_gpus': 0, 'num_cpus': 1\n","\tFitting 10000 rounds... Hyperparameters: {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 5}\n"]},{"name":"stdout","output_type":"stream","text":["[50]\tvalid_set's binary_error: 0.129032\n","[100]\tvalid_set's binary_error: 0.0887097\n","[150]\tvalid_set's binary_error: 0.104839\n","[200]\tvalid_set's binary_error: 0.104839\n","[250]\tvalid_set's binary_error: 0.0967742\n","[300]\tvalid_set's binary_error: 0.0887097\n","[350]\tvalid_set's binary_error: 0.0887097\n","[400]\tvalid_set's binary_error: 0.0887097\n","[450]\tvalid_set's binary_error: 0.0967742\n","[500]\tvalid_set's binary_error: 0.104839\n","[550]\tvalid_set's binary_error: 0.0967742\n"]},{"name":"stderr","output_type":"stream","text":["Saving AutogluonModels/ag-20230703_162958/models/LightGBMLarge/model.pkl\n","Saving AutogluonModels/ag-20230703_162958/utils/attr/LightGBMLarge/y_pred_proba_val.pkl\n","\t0.9194\t = Validation score   (accuracy)\n","\t2.17s\t = Training   runtime\n","\t0.01s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/RandomForestEntr/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/CatBoost/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/KNeighborsDist/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/ExtraTreesEntr/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/XGBoost/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/NeuralNetTorch/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/LightGBMXT/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/ExtraTreesGini/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/LightGBMLarge/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/LightGBM/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/RandomForestGini/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/NeuralNetFastAI/y_pred_proba_val.pkl\n","Loading: AutogluonModels/ag-20230703_162958/utils/attr/KNeighborsUnif/y_pred_proba_val.pkl\n","Model configs that will be trained (in order):\n","\tWeightedEnsemble_L2: \t{'ag_args': {'valid_base': False, 'name_bag_suffix': '', 'model_type': <class 'autogluon.core.models.greedy_ensemble.greedy_weighted_ensemble_model.GreedyWeightedEnsembleModel'>, 'priority': 0}, 'ag_args_ensemble': {'save_bag_folds': True}}\n","Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the 3576.83s of remaining time.\n","\tFitting WeightedEnsemble_L2 with 'num_gpus': 0, 'num_cpus': 2\n","Saving AutogluonModels/ag-20230703_162958/models/WeightedEnsemble_L2/utils/model_template.pkl\n","Loading: AutogluonModels/ag-20230703_162958/models/WeightedEnsemble_L2/utils/model_template.pkl\n","Ensemble size: 2\n","Ensemble weights: \n","[0.  0.  0.5 0.  0.  0.  0.  0.  0.  0.5 0.  0.  0. ]\n","Saving AutogluonModels/ag-20230703_162958/models/WeightedEnsemble_L2/utils/oof.pkl\n","Saving AutogluonModels/ag-20230703_162958/models/WeightedEnsemble_L2/model.pkl\n","\t0.9516\t = Validation score   (accuracy)\n","\t0.82s\t = Training   runtime\n","\t0.0s\t = Validation runtime\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","AutoGluon training complete, total runtime = 24.05s ... Best model: \"WeightedEnsemble_L2\"\n","Loading: AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Saving AutogluonModels/ag-20230703_162958/models/trainer.pkl\n","Saving AutogluonModels/ag-20230703_162958/learner.pkl\n","Saving AutogluonModels/ag-20230703_162958/predictor.pkl\n","Saving AutogluonModels/ag-20230703_162958/__version__ with contents \"0.8.2\"\n","Saving AutogluonModels/ag-20230703_162958/metadata.json\n","TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20230703_162958/\")\n"]},{"name":"stdout","output_type":"stream","text":["                  model  score_val  pred_time_val  fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n","0   WeightedEnsemble_L2   0.951613       0.020734  5.812264                0.001104           0.822899            2       True         14\n","1              LightGBM   0.935484       0.003340  0.838881                0.003340           0.838881            1       True          4\n","2            LightGBMXT   0.935484       0.006163  2.767770                0.006163           2.767770            1       True          3\n","3              CatBoost   0.927419       0.005181  6.286528                0.005181           6.286528            1       True          7\n","4        NeuralNetTorch   0.927419       0.043284  2.957655                0.043284           2.957655            1       True         12\n","5         LightGBMLarge   0.919355       0.007902  2.168899                0.007902           2.168899            1       True         13\n","6               XGBoost   0.911290       0.005716  0.623925                0.005716           0.623925            1       True         11\n","7      RandomForestEntr   0.911290       0.077207  0.864542                0.077207           0.864542            1       True          6\n","8       NeuralNetFastAI   0.903226       0.013467  2.221595                0.013467           2.221595            1       True         10\n","9      RandomForestGini   0.895161       0.077552  0.861944                0.077552           0.861944            1       True          5\n","10       ExtraTreesEntr   0.879032       0.100708  0.889837                0.100708           0.889837            1       True          9\n","11       ExtraTreesGini   0.879032       0.118337  0.770131                0.118337           0.770131            1       True          8\n","12       KNeighborsDist   0.822581       0.003239  0.017517                0.003239           0.017517            1       True          2\n","13       KNeighborsUnif   0.822581       0.024019  0.636763                0.024019           0.636763            1       True          1\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>model</th>\n","      <th>score_val</th>\n","      <th>pred_time_val</th>\n","      <th>fit_time</th>\n","      <th>pred_time_val_marginal</th>\n","      <th>fit_time_marginal</th>\n","      <th>stack_level</th>\n","      <th>can_infer</th>\n","      <th>fit_order</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>WeightedEnsemble_L2</td>\n","      <td>0.951613</td>\n","      <td>0.020734</td>\n","      <td>5.812264</td>\n","      <td>0.001104</td>\n","      <td>0.822899</td>\n","      <td>2</td>\n","      <td>True</td>\n","      <td>14</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>LightGBM</td>\n","      <td>0.935484</td>\n","      <td>0.003340</td>\n","      <td>0.838881</td>\n","      <td>0.003340</td>\n","      <td>0.838881</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>LightGBMXT</td>\n","      <td>0.935484</td>\n","      <td>0.006163</td>\n","      <td>2.767770</td>\n","      <td>0.006163</td>\n","      <td>2.767770</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>CatBoost</td>\n","      <td>0.927419</td>\n","      <td>0.005181</td>\n","      <td>6.286528</td>\n","      <td>0.005181</td>\n","      <td>6.286528</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>NeuralNetTorch</td>\n","      <td>0.927419</td>\n","      <td>0.043284</td>\n","      <td>2.957655</td>\n","      <td>0.043284</td>\n","      <td>2.957655</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>LightGBMLarge</td>\n","      <td>0.919355</td>\n","      <td>0.007902</td>\n","      <td>2.168899</td>\n","      <td>0.007902</td>\n","      <td>2.168899</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>XGBoost</td>\n","      <td>0.911290</td>\n","      <td>0.005716</td>\n","      <td>0.623925</td>\n","      <td>0.005716</td>\n","      <td>0.623925</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>RandomForestEntr</td>\n","      <td>0.911290</td>\n","      <td>0.077207</td>\n","      <td>0.864542</td>\n","      <td>0.077207</td>\n","      <td>0.864542</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>NeuralNetFastAI</td>\n","      <td>0.903226</td>\n","      <td>0.013467</td>\n","      <td>2.221595</td>\n","      <td>0.013467</td>\n","      <td>2.221595</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>RandomForestGini</td>\n","      <td>0.895161</td>\n","      <td>0.077552</td>\n","      <td>0.861944</td>\n","      <td>0.077552</td>\n","      <td>0.861944</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>ExtraTreesEntr</td>\n","      <td>0.879032</td>\n","      <td>0.100708</td>\n","      <td>0.889837</td>\n","      <td>0.100708</td>\n","      <td>0.889837</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>ExtraTreesGini</td>\n","      <td>0.879032</td>\n","      <td>0.118337</td>\n","      <td>0.770131</td>\n","      <td>0.118337</td>\n","      <td>0.770131</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>KNeighborsDist</td>\n","      <td>0.822581</td>\n","      <td>0.003239</td>\n","      <td>0.017517</td>\n","      <td>0.003239</td>\n","      <td>0.017517</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>KNeighborsUnif</td>\n","      <td>0.822581</td>\n","      <td>0.024019</td>\n","      <td>0.636763</td>\n","      <td>0.024019</td>\n","      <td>0.636763</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  model  score_val  pred_time_val  fit_time  \\\n","0   WeightedEnsemble_L2   0.951613       0.020734  5.812264   \n","1              LightGBM   0.935484       0.003340  0.838881   \n","2            LightGBMXT   0.935484       0.006163  2.767770   \n","3              CatBoost   0.927419       0.005181  6.286528   \n","4        NeuralNetTorch   0.927419       0.043284  2.957655   \n","5         LightGBMLarge   0.919355       0.007902  2.168899   \n","6               XGBoost   0.911290       0.005716  0.623925   \n","7      RandomForestEntr   0.911290       0.077207  0.864542   \n","8       NeuralNetFastAI   0.903226       0.013467  2.221595   \n","9      RandomForestGini   0.895161       0.077552  0.861944   \n","10       ExtraTreesEntr   0.879032       0.100708  0.889837   \n","11       ExtraTreesGini   0.879032       0.118337  0.770131   \n","12       KNeighborsDist   0.822581       0.003239  0.017517   \n","13       KNeighborsUnif   0.822581       0.024019  0.636763   \n","\n","    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \\\n","0                 0.001104           0.822899            2       True   \n","1                 0.003340           0.838881            1       True   \n","2                 0.006163           2.767770            1       True   \n","3                 0.005181           6.286528            1       True   \n","4                 0.043284           2.957655            1       True   \n","5                 0.007902           2.168899            1       True   \n","6                 0.005716           0.623925            1       True   \n","7                 0.077207           0.864542            1       True   \n","8                 0.013467           2.221595            1       True   \n","9                 0.077552           0.861944            1       True   \n","10                0.100708           0.889837            1       True   \n","11                0.118337           0.770131            1       True   \n","12                0.003239           0.017517            1       True   \n","13                0.024019           0.636763            1       True   \n","\n","    fit_order  \n","0          14  \n","1           4  \n","2           3  \n","3           7  \n","4          12  \n","5          13  \n","6          11  \n","7           6  \n","8          10  \n","9           5  \n","10          9  \n","11          8  \n","12          2  \n","13          1  "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["from autogluon.tabular import TabularDataset, TabularPredictor\n","\n","train_data = TabularDataset(train_df)\n","\n","label = 'Class'\n","\n","predictor = TabularPredictor(label=label).fit(train_data,\n","                                              time_limit=3600,\n","                                              verbosity=3,)\n","\n","predictor.leaderboard()"]},{"cell_type":"code","execution_count":7,"id":"4d0266de","metadata":{"execution":{"iopub.execute_input":"2023-07-03T16:30:22.559531Z","iopub.status.busy":"2023-07-03T16:30:22.558768Z","iopub.status.idle":"2023-07-03T16:30:22.595785Z","shell.execute_reply":"2023-07-03T16:30:22.594846Z"},"papermill":{"duration":0.141464,"end_time":"2023-07-03T16:30:22.597892","exception":false,"start_time":"2023-07-03T16:30:22.456428","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["Loading: AutogluonModels/ag-20230703_162958/models/LightGBMXT/model.pkl\n","Loading: AutogluonModels/ag-20230703_162958/models/NeuralNetFastAI/model.pkl\n","Loading: AutogluonModels/ag-20230703_162958/models/NeuralNetFastAI/model-internals.pkl\n","Loading: AutogluonModels/ag-20230703_162958/models/WeightedEnsemble_L2/model.pkl\n"]}],"source":["pred = predictor.predict_proba(test_df)"]},{"cell_type":"code","execution_count":8,"id":"3d7f54eb","metadata":{"execution":{"iopub.execute_input":"2023-07-03T16:30:22.810786Z","iopub.status.busy":"2023-07-03T16:30:22.810276Z","iopub.status.idle":"2023-07-03T16:30:22.829327Z","shell.execute_reply":"2023-07-03T16:30:22.826091Z"},"papermill":{"duration":0.129268,"end_time":"2023-07-03T16:30:22.833031","exception":false,"start_time":"2023-07-03T16:30:22.703763","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.808243</td>\n","      <td>0.191757</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.808243</td>\n","      <td>0.191757</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.808243</td>\n","      <td>0.191757</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.808243</td>\n","      <td>0.191757</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.808243</td>\n","      <td>0.191757</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          0         1\n","0  0.808243  0.191757\n","1  0.808243  0.191757\n","2  0.808243  0.191757\n","3  0.808243  0.191757\n","4  0.808243  0.191757"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["pred"]},{"cell_type":"code","execution_count":9,"id":"5be18363","metadata":{"execution":{"iopub.execute_input":"2023-07-03T16:30:23.169638Z","iopub.status.busy":"2023-07-03T16:30:23.169258Z","iopub.status.idle":"2023-07-03T16:30:23.182171Z","shell.execute_reply":"2023-07-03T16:30:23.181039Z"},"papermill":{"duration":0.144989,"end_time":"2023-07-03T16:30:23.184158","exception":false,"start_time":"2023-07-03T16:30:23.039169","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Id</th>\n","      <th>class_0</th>\n","      <th>class_1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>00eed32682bb</td>\n","      <td>0.808243</td>\n","      <td>0.191757</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>010ebe33f668</td>\n","      <td>0.808243</td>\n","      <td>0.191757</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>02fa521e1838</td>\n","      <td>0.808243</td>\n","      <td>0.191757</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>040e15f562a2</td>\n","      <td>0.808243</td>\n","      <td>0.191757</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>046e85c7cc7f</td>\n","      <td>0.808243</td>\n","      <td>0.191757</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             Id   class_0   class_1\n","0  00eed32682bb  0.808243  0.191757\n","1  010ebe33f668  0.808243  0.191757\n","2  02fa521e1838  0.808243  0.191757\n","3  040e15f562a2  0.808243  0.191757\n","4  046e85c7cc7f  0.808243  0.191757"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["sample_submission_df['Id'] = test_df.reset_index()['Id']\n","sample_submission_df['class_0'] = pred[0]\n","sample_submission_df['class_1']  = pred[1]\n","\n","sample_submission_df"]},{"cell_type":"code","execution_count":10,"id":"386a9a96","metadata":{"execution":{"iopub.execute_input":"2023-07-03T16:30:23.396485Z","iopub.status.busy":"2023-07-03T16:30:23.395561Z","iopub.status.idle":"2023-07-03T16:30:23.405923Z","shell.execute_reply":"2023-07-03T16:30:23.405052Z"},"papermill":{"duration":0.119446,"end_time":"2023-07-03T16:30:23.408108","exception":false,"start_time":"2023-07-03T16:30:23.288662","status":"completed"},"tags":[]},"outputs":[],"source":["sample_submission_df.set_index('Id').to_csv('submission.csv')"]},{"cell_type":"code","execution_count":null,"id":"0b5b7954","metadata":{"papermill":{"duration":0.099572,"end_time":"2023-07-03T16:30:23.611446","exception":false,"start_time":"2023-07-03T16:30:23.511874","status":"completed"},"tags":[]},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.10"},"papermill":{"default_parameters":{},"duration":204.876921,"end_time":"2023-07-03T16:30:26.11949","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-07-03T16:27:01.242569","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}