cookbook/nested-cv/README.ipynb

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   "source": [
    "To run this locally, [install Ploomber](https://docs.ploomber.io/en/latest/get-started/quick-start.html) and execute: `ploomber examples -n cookbook/nested-cv`\n",
    "\n",
    "Found an issue? [Let us know.](https://github.com/ploomber/projects/issues/new?title=cookbook/nested-cv%20issue)\n",
    "\n",
    "Questions? [Ask us on Slack.](https://ploomber.io/community/)\n"
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   "source": [
    "# Nested cross-validation\n",
    "\n",
    "<!-- start description -->\n",
    "Nested cross-validation for model selection and hyperparameter tuning.\n",
    "<!-- end description -->\n",
    "\n",
    "More details in our [blog.](https://ploomber.io/blog/nested-cv/)"
   ]
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   "source": [
    "```yaml\n",
    "# Content of pipeline.yaml\n",
    "executor: parallel\n",
    "\n",
    "tasks:\n",
    "  - source: tasks/load.py\n",
    "    product:\n",
    "      nb: products/load.html\n",
    "      data: products/data.csv\n",
    "\n",
    "  - source: tasks/fit.py\n",
    "    name: fit-\n",
    "    product:\n",
    "      nb: products/fit.html\n",
    "      model: products/model.pkl\n",
    "    grid:\n",
    "      - model: sklearn.ensemble.RandomForestClassifier\n",
    "        model_params:\n",
    "          # optimize over these parameters\n",
    "          - n_estimators: [2, 5]\n",
    "            criterion: [gini, entropy]\n",
    "    \n",
    "      - model: sklearn.svm.SVC\n",
    "        model_params:\n",
    "          # optimize over these parameters\n",
    "          - kernel: [linear, poly]\n",
    "            C: [0.1, 1.0]\n",
    "      \n",
    "      \n",
    "\n",
    "```"
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   "source": [
    "Plot:"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading pipeline...\n",
      "Plot saved at: pipeline.html\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 3416.48it/s]\n"
     ]
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   "source": [
    "%%sh\n",
    "ploomber plot"
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   "source": [
    "Display the pipeline:"
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('pipeline.png')\n",
    "# NOTE: ploomber plot will generate a pipeline.html (not .png) file if\n",
    "# pygraphviz is missing. In such case, open the file to view the pipeline plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54b1e83",
   "metadata": {
    "papermill": {
     "duration": 0.018657,
     "end_time": "2022-05-13T23:47:54.723885",
     "exception": false,
     "start_time": "2022-05-13T23:47:54.705228",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Run the pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1545ec0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-13T23:47:54.766883Z",
     "iopub.status.busy": "2022-05-13T23:47:54.763263Z",
     "iopub.status.idle": "2022-05-13T23:48:16.301256Z",
     "shell.execute_reply": "2022-05-13T23:48:16.301944Z"
    },
    "papermill": {
     "duration": 21.560716,
     "end_time": "2022-05-13T23:48:16.302211",
     "exception": false,
     "start_time": "2022-05-13T23:47:54.741495",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading pipeline...\n",
      "Finished tasks so far: \n",
      "Remaining tasks: {'load', 'fit-1', 'fit-0'}\n",
      "Finished 0 out of 3 tasks\n",
      "Finished tasks so far: \n",
      "Remaining tasks: {'load', 'fit-1', 'fit-0'}\n",
      "Finished 0 out of 3 tasks\n",
      "Finished tasks so far: \n",
      "Remaining tasks: {'load', 'fit-1', 'fit-0'}\n",
      "Finished 0 out of 3 tasks\n",
      "Finished tasks so far: \n",
      "Remaining tasks: {'load', 'fit-1', 'fit-0'}\n",
      "Finished 0 out of 3 tasks\n",
      "Finished tasks so far: {'load'}\n",
      "Remaining tasks: {'fit-1', 'fit-0'}\n",
      "Finished 1 out of 3 tasks\n",
      "Finished tasks so far: {'load'}\n",
      "Remaining tasks: {'fit-1', 'fit-0'}\n",
      "Finished 1 out of 3 tasks\n",
      "Finished tasks so far: {'load'}\n",
      "Remaining tasks: {'fit-1', 'fit-0'}\n",
      "Finished 1 out of 3 tasks\n",
      "Finished tasks so far: {'load'}\n",
      "Remaining tasks: {'fit-1', 'fit-0'}\n",
      "Finished 1 out of 3 tasks\n",
      "Finished tasks so far: {'load', 'fit-0'}\n",
      "Remaining tasks: {'fit-1'}\n",
      "Finished 2 out of 3 tasks\n",
      "name    Ran?      Elapsed (s)    Percentage\n",
      "------  ------  -------------  ------------\n",
      "load    True          3.48913       23.8055\n",
      "fit-0   True          6.2288        42.4976\n",
      "fit-1   True          4.93891       33.6969\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Executing: 100%|██████████| 6/6 [00:03<00:00,  1.95cell/s]\n",
      "Executing: 100%|██████████| 14/14 [00:05<00:00,  2.53cell/s]\n",
      "Executing: 100%|██████████| 14/14 [00:04<00:00,  3.33cell/s]\n"
     ]
    }
   ],
   "source": [
    "%%sh\n",
    "# run this in a terminal\n",
    "ploomber build"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.1"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 28.328008,
   "end_time": "2022-05-13T23:48:16.542447",
   "environment_variables": {},
   "exception": null,
   "input_path": "cookbook/nested-cv/_build/readme_preprocessed.ipynb",
   "output_path": "cookbook/nested-cv/README.ipynb",
   "parameters": {},
   "start_time": "2022-05-13T23:47:48.214439",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}