diabetes_regression_default.ipynb

{
 "cells": [
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   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting azureml-sdk\n",
      "  Downloading https://files.pythonhosted.org/packages/62/5c/2f4d83cf6cd587a046f3db43d0ef1155649fd502d621ded6ff6148c77918/azureml_sdk-1.3.0-py3-none-any.whl\n",
      "Collecting azureml-train-automl-client~=1.3.0 (from azureml-sdk)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/7a/400cef11b06d4698b8fba32cf8ee885ac759efdef0bff4f5e41b27ca6d53/azureml_train_automl_client-1.3.0-py3-none-any.whl (79kB)\n",
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      "Collecting azureml-dataprep[fuse]<1.4.5a,>=1.4.3a (from azureml-sdk)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6f/b7/977b4f5475e69bf2f4a665dfc53fea49df5c1bc7456a1469f267cb9307a3/azureml_dataprep-1.4.3-py3-none-any.whl (26.7MB)\n",
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      "\u001b[?25hCollecting azureml-pipeline~=1.3.0 (from azureml-sdk)\n",
      "  Downloading https://files.pythonhosted.org/packages/87/9c/e0c31e313e87c112f537df156687ce6f0d928d9a16ffc772b3588f22ab56/azureml_pipeline-1.3.0-py3-none-any.whl\n",
      "Requirement already satisfied, skipping upgrade: pytz in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-train-automl-client~=1.3.0->azureml-sdk) (2019.3)\n",
      "Collecting azureml-telemetry~=1.3.0 (from azureml-train-automl-client~=1.3.0->azureml-sdk)\n",
      "  Downloading https://files.pythonhosted.org/packages/50/e7/5cc5805095efaf5825f8c7a6afc4924c752579677c554a33e3371bdcb5a3/azureml_telemetry-1.3.0-py3-none-any.whl\n",
      "Collecting azureml-automl-core~=1.3.0 (from azureml-train-automl-client~=1.3.0->azureml-sdk)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e9/53/e2e4e399696ffec6e85b4d4835d522edea10e0dd2fca470082e128f3dc16/azureml_automl_core-1.3.0-py3-none-any.whl (117kB)\n",
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      "Requirement already satisfied, skipping upgrade: requests>=2.19.1 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (2.22.0)\n",
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      "Requirement already satisfied, skipping upgrade: contextlib2 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (0.6.0)\n",
      "Requirement already satisfied, skipping upgrade: msrest>=0.5.1 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (0.6.10)\n",
      "Requirement already satisfied, skipping upgrade: ruamel.yaml<=0.15.89,>=0.15.35 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (0.15.89)\n",
      "Requirement already satisfied, skipping upgrade: urllib3>=1.23 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (1.24.2)\n",
      "Requirement already satisfied, skipping upgrade: azure-mgmt-authorization>=0.40.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (0.60.0)\n",
      "Requirement already satisfied, skipping upgrade: azure-graphrbac>=0.40.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (0.61.1)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (2.8.0)\n",
      "Requirement already satisfied, skipping upgrade: azure-common>=1.1.12 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (1.1.24)\n",
      "Requirement already satisfied, skipping upgrade: jsonpickle in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (1.2)\n",
      "Requirement already satisfied, skipping upgrade: PyJWT in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (1.7.1)\n",
      "Requirement already satisfied, skipping upgrade: cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.* in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (2.7)\n",
      "Requirement already satisfied, skipping upgrade: SecretStorage in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-core~=1.3.0->azureml-sdk) (3.1.1)\n",
      "Collecting azureml-train-core~=1.3.0 (from azureml-train~=1.3.0->azureml-sdk)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/35/e1/6fc7ebda396d3445900bc2346a590daf7d4bfbedc2308ddce65fca39c187/azureml_train_core-1.3.0.post1-py3-none-any.whl (8.6MB)\n",
      "\u001b[K     |████████████████████████████████| 8.6MB 4.0MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting azureml-dataprep-native<15.0.0,>=14.1.0 (from azureml-dataprep[fuse]<1.4.5a,>=1.4.3a->azureml-sdk)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5e/5f/02258a3d50d112e05a491038c9d463b52820b4ac3edbd6468a9c60703f03/azureml_dataprep_native-14.1.0-cp36-cp36m-manylinux1_x86_64.whl (1.3MB)\n",
      "\u001b[K     |████████████████████████████████| 1.3MB 5.5MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: cloudpickle>=1.1.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-dataprep[fuse]<1.4.5a,>=1.4.3a->azureml-sdk) (1.2.2)\n",
      "Collecting azure-identity<1.3.0,>=1.2.0 (from azureml-dataprep[fuse]<1.4.5a,>=1.4.3a->azureml-sdk)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c4/7a/9372cd51fc3408ede0fab950ef9a6518cad34ef36e199982bb1ddfa18512/azure_identity-1.2.0-py2.py3-none-any.whl (58kB)\n",
      "\u001b[K     |████████████████████████████████| 61kB 7.0MB/s eta 0:00:011\n",
      "\u001b[?25hCollecting dotnetcore2>=2.1.13 (from azureml-dataprep[fuse]<1.4.5a,>=1.4.3a->azureml-sdk)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/4d/3dab8c1a2e830fe9dd112295afeac764faa8fd1f45d6694d908646b4c019/dotnetcore2-2.1.13-py3-none-manylinux1_x86_64.whl (29.3MB)\n",
      "\u001b[K     |████████████████████████████████| 29.3MB 3.4MB/s eta 0:00:01\n",
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      "Collecting azureml-pipeline-steps~=1.3.0 (from azureml-pipeline~=1.3.0->azureml-sdk)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ce/27/7db0134275a5d9aa8d33f6bb21c151254b41342a0c166f49e6b4d592f544/azureml_pipeline_steps-1.3.0-py3-none-any.whl (49kB)\n",
      "\u001b[K     |████████████████████████████████| 51kB 7.7MB/s eta 0:00:011\n",
      "\u001b[?25hCollecting azureml-pipeline-core~=1.3.0 (from azureml-pipeline~=1.3.0->azureml-sdk)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/33/d2/45a0d27cc9a5129e6415c1422bab319e549e4f178240d29d763d4f5356b8/azureml_pipeline_core-1.3.0-py3-none-any.whl (271kB)\n",
      "\u001b[K     |████████████████████████████████| 276kB 5.3MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: applicationinsights in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-telemetry~=1.3.0->azureml-train-automl-client~=1.3.0->azureml-sdk) (0.11.9)\n",
      "Requirement already satisfied, skipping upgrade: backports.weakref in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from backports.tempfile->azureml-core~=1.3.0->azureml-sdk) (1.0.post1)\n",
      "Requirement already satisfied, skipping upgrade: six>=1.5.2 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from pyopenssl->azureml-core~=1.3.0->azureml-sdk) (1.13.0)\n",
      "Requirement already satisfied, skipping upgrade: websocket-client>=0.32.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from docker->azureml-core~=1.3.0->azureml-sdk) (0.57.0)\n",
      "Requirement already satisfied, skipping upgrade: pyasn1>=0.1.1 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from ndg-httpsclient->azureml-core~=1.3.0->azureml-sdk) (0.4.8)\n",
      "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from requests>=2.19.1->azureml-core~=1.3.0->azureml-sdk) (2019.11.28)\n",
      "Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from requests>=2.19.1->azureml-core~=1.3.0->azureml-sdk) (2.8)\n",
      "Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from requests>=2.19.1->azureml-core~=1.3.0->azureml-sdk) (3.0.4)\n",
      "Requirement already satisfied, skipping upgrade: isodate>=0.6.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from msrest>=0.5.1->azureml-core~=1.3.0->azureml-sdk) (0.6.0)\n",
      "Requirement already satisfied, skipping upgrade: requests-oauthlib>=0.5.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from msrest>=0.5.1->azureml-core~=1.3.0->azureml-sdk) (1.3.0)\n",
      "Requirement already satisfied, skipping upgrade: cffi!=1.11.3,>=1.8 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*->azureml-core~=1.3.0->azureml-sdk) (1.12.3)\n",
      "Requirement already satisfied, skipping upgrade: asn1crypto>=0.21.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*->azureml-core~=1.3.0->azureml-sdk) (1.0.1)\n",
      "Requirement already satisfied, skipping upgrade: jeepney in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from SecretStorage->azureml-core~=1.3.0->azureml-sdk) (0.4.1)\n",
      "Collecting azureml-train-restclients-hyperdrive~=1.3.0 (from azureml-train-core~=1.3.0->azureml-train~=1.3.0->azureml-sdk)\n",
      "  Downloading https://files.pythonhosted.org/packages/5d/6c/43f56cc119c55fb1600c0dcc403c22eda0cea68e2f73e5cdf85e78dfdc1b/azureml_train_restclients_hyperdrive-1.3.0-py3-none-any.whl\n",
      "Requirement already satisfied, skipping upgrade: flake8<=3.7.9,>=3.1.0; python_version >= \"3.6\" in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from azureml-train-core~=1.3.0->azureml-train~=1.3.0->azureml-sdk) (3.7.9)\n",
      "Collecting azure-core<2.0.0,>=1.0.0 (from azure-identity<1.3.0,>=1.2.0->azureml-dataprep[fuse]<1.4.5a,>=1.4.3a->azureml-sdk)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7b/11/9c7b177c559124f957d0e56182e2f13119531f7227367b1b649095ebc49a/azure_core-1.4.0-py2.py3-none-any.whl (114kB)\n",
      "\u001b[K     |████████████████████████████████| 122kB 5.8MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting msal-extensions~=0.1.3 (from azure-identity<1.3.0,>=1.2.0->azureml-dataprep[fuse]<1.4.5a,>=1.4.3a->azureml-sdk)\n",
      "  Downloading https://files.pythonhosted.org/packages/21/9b/8bc67822e98573fe0460e30ad0202ab9e0638a42878041c65a6fe857babe/msal_extensions-0.1.3-py2.py3-none-any.whl\n",
      "Collecting msal<2.0.0,>=1.0.0 (from azure-identity<1.3.0,>=1.2.0->azureml-dataprep[fuse]<1.4.5a,>=1.4.3a->azureml-sdk)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/42/90ba15ffce79f131331983cbb9a0d7bd4a582c55f28019dc83fe03dd77c5/msal-1.2.0-py2.py3-none-any.whl (46kB)\n",
      "\u001b[K     |████████████████████████████████| 51kB 7.1MB/s eta 0:00:011\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: distro>=1.2.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from dotnetcore2>=2.1.13->azureml-dataprep[fuse]<1.4.5a,>=1.4.3a->azureml-sdk) (1.4.0)\n",
      "Requirement already satisfied, skipping upgrade: oauthlib>=3.0.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from requests-oauthlib>=0.5.0->msrest>=0.5.1->azureml-core~=1.3.0->azureml-sdk) (3.1.0)\n",
      "Requirement already satisfied, skipping upgrade: pycparser in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from cffi!=1.11.3,>=1.8->cryptography!=1.9,!=2.0.*,!=2.1.*,!=2.2.*->azureml-core~=1.3.0->azureml-sdk) (2.19)\n",
      "Requirement already satisfied, skipping upgrade: pycodestyle<2.6.0,>=2.5.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from flake8<=3.7.9,>=3.1.0; python_version >= \"3.6\"->azureml-train-core~=1.3.0->azureml-train~=1.3.0->azureml-sdk) (2.5.0)\n",
      "Requirement already satisfied, skipping upgrade: mccabe<0.7.0,>=0.6.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from flake8<=3.7.9,>=3.1.0; python_version >= \"3.6\"->azureml-train-core~=1.3.0->azureml-train~=1.3.0->azureml-sdk) (0.6.1)\n",
      "Requirement already satisfied, skipping upgrade: entrypoints<0.4.0,>=0.3.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from flake8<=3.7.9,>=3.1.0; python_version >= \"3.6\"->azureml-train-core~=1.3.0->azureml-train~=1.3.0->azureml-sdk) (0.3)\n",
      "Requirement already satisfied, skipping upgrade: pyflakes<2.2.0,>=2.1.0 in /anaconda/envs/azureml_py36/lib/python3.6/site-packages (from flake8<=3.7.9,>=3.1.0; python_version >= \"3.6\"->azureml-train-core~=1.3.0->azureml-train~=1.3.0->azureml-sdk) (2.1.1)\n",
      "Collecting portalocker~=1.0 (from msal-extensions~=0.1.3->azure-identity<1.3.0,>=1.2.0->azureml-dataprep[fuse]<1.4.5a,>=1.4.3a->azureml-sdk)\n",
      "  Downloading https://files.pythonhosted.org/packages/53/84/7b3146ec6378d28abc73ab484f09f47dfa008ad6f03f33d90a369f880e25/portalocker-1.7.0-py2.py3-none-any.whl\n",
      "\u001b[31mERROR: azureml-widgets 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-widgets 1.0.83 has requirement azureml-telemetry==1.0.83.*, but you'll have azureml-telemetry 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-train-automl 1.0.83 has requirement azureml-automl-core==1.0.83.*, but you'll have azureml-automl-core 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-train-automl 1.0.83 has requirement azureml-dataprep[fuse,pandas]<1.2.0a,>=1.1.35a, but you'll have azureml-dataprep 1.4.3 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-train-automl 1.0.83 has requirement azureml-train-automl-client==1.0.83.*, but you'll have azureml-train-automl-client 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-train-automl-runtime 1.0.83.1 has requirement azureml-automl-core==1.0.83.*, but you'll have azureml-automl-core 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-train-automl-runtime 1.0.83.1 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-train-automl-runtime 1.0.83.1 has requirement azureml-dataprep[fuse,pandas]<1.2.0a,>=1.1.35a, but you'll have azureml-dataprep 1.4.3 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-train-automl-runtime 1.0.83.1 has requirement azureml-pipeline-core==1.0.83.*, but you'll have azureml-pipeline-core 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-train-automl-runtime 1.0.83.1 has requirement azureml-telemetry==1.0.83.*, but you'll have azureml-telemetry 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-train-automl-runtime 1.0.83.1 has requirement azureml-train-automl-client==1.0.83.*, but you'll have azureml-train-automl-client 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-train-automl-runtime 1.0.83.1 has requirement scipy<=1.1.0,>=1.0.0, but you'll have scipy 1.4.1 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-train-automl-runtime 1.0.83.1 has requirement wheel==0.30.0, but you'll have wheel 0.33.6 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-tensorboard 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-opendatasets 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-opendatasets 1.0.83 has requirement azureml-dataprep[fuse,pandas]<1.2.0a,>=1.1.35a, but you'll have azureml-dataprep 1.4.3 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-opendatasets 1.0.83 has requirement azureml-telemetry==1.0.83.*, but you'll have azureml-telemetry 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-opendatasets 1.0.83 has requirement scipy<=1.1.0,>=1.0.0, but you'll have scipy 1.4.1 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-mlflow 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-defaults 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-defaults 1.0.83 has requirement flask==1.0.3, but you'll have flask 1.1.1 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-datadrift 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-datadrift 1.0.83 has requirement azureml-dataprep[fuse,pandas]<1.2.0a,>=1.1.35a, but you'll have azureml-dataprep 1.4.3 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-datadrift 1.0.83 has requirement azureml-pipeline-core==1.0.83.*, but you'll have azureml-pipeline-core 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-datadrift 1.0.83 has requirement azureml-pipeline-steps==1.0.83.*, but you'll have azureml-pipeline-steps 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-datadrift 1.0.83 has requirement azureml-telemetry==1.0.83.*, but you'll have azureml-telemetry 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-datadrift 1.0.83 has requirement matplotlib==3.0.2, but you'll have matplotlib 3.1.2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-services 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-server 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-reinforcementlearning 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-reinforcementlearning 1.0.83 has requirement azureml-train-core==1.0.83.*, but you'll have azureml-train-core 1.3.0.post1 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-pipeline-steps 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-pipeline-steps 1.0.83 has requirement azureml-pipeline-core==1.0.83.*, but you'll have azureml-pipeline-core 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-pipeline-steps 1.0.83 has requirement azureml-pipeline-steps==1.0.83.*, but you'll have azureml-pipeline-steps 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-notebook 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-notebook 1.0.83 has requirement azureml-pipeline-core==1.0.83.*, but you'll have azureml-pipeline-core 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-datadrift 1.0.83 has requirement azureml-core==1.0.83.*, but you'll have azureml-core 1.3.0.post2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-datadrift 1.0.83 has requirement azureml-dataprep[fuse,pandas]<1.2.0a,>=1.1.35a, but you'll have azureml-dataprep 1.4.3 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-datadrift 1.0.83 has requirement azureml-pipeline-core==1.0.83.*, but you'll have azureml-pipeline-core 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-datadrift 1.0.83 has requirement azureml-pipeline-steps==1.0.83.*, but you'll have azureml-pipeline-steps 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-contrib-datadrift 1.0.83 has requirement azureml-telemetry==1.0.83.*, but you'll have azureml-telemetry 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-automl-runtime 1.0.83 has requirement azureml-automl-core==1.0.83, but you'll have azureml-automl-core 1.3.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-automl-runtime 1.0.83 has requirement azureml-dataprep[fuse,pandas]<1.2.0a,>=1.1.35a, but you'll have azureml-dataprep 1.4.3 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-automl-runtime 1.0.83 has requirement scipy<=1.1.0,>=1.0.0, but you'll have scipy 1.4.1 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: azureml-automl-runtime 1.0.83 has requirement wheel==0.30.0, but you'll have wheel 0.33.6 which is incompatible.\u001b[0m\n",
      "Installing collected packages: azureml-core, azureml-dataprep-native, azure-core, portalocker, msal, msal-extensions, azure-identity, dotnetcore2, azureml-dataprep, azureml-telemetry, azureml-automl-core, azureml-train-automl-client, azureml-train-restclients-hyperdrive, azureml-train-core, azureml-train, azureml-pipeline-core, azureml-pipeline-steps, azureml-pipeline, azureml-sdk\n",
      "  Found existing installation: azureml-core 1.0.83\n",
      "    Uninstalling azureml-core-1.0.83:\n",
      "      Successfully uninstalled azureml-core-1.0.83\n",
      "  Found existing installation: azureml-dataprep-native 13.2.0\n",
      "    Uninstalling azureml-dataprep-native-13.2.0:\n",
      "      Successfully uninstalled azureml-dataprep-native-13.2.0\n",
      "  Found existing installation: dotnetcore2 2.1.11\n",
      "    Uninstalling dotnetcore2-2.1.11:\n",
      "      Successfully uninstalled dotnetcore2-2.1.11\n",
      "  Found existing installation: azureml-dataprep 1.1.35\n",
      "    Uninstalling azureml-dataprep-1.1.35:\n",
      "      Successfully uninstalled azureml-dataprep-1.1.35\n",
      "  Found existing installation: azureml-telemetry 1.0.83\n",
      "    Uninstalling azureml-telemetry-1.0.83:\n",
      "      Successfully uninstalled azureml-telemetry-1.0.83\n",
      "  Found existing installation: azureml-automl-core 1.0.83.1\n",
      "    Uninstalling azureml-automl-core-1.0.83.1:\n",
      "      Successfully uninstalled azureml-automl-core-1.0.83.1\n",
      "  Found existing installation: azureml-train-automl-client 1.0.83\n",
      "    Uninstalling azureml-train-automl-client-1.0.83:\n",
      "      Successfully uninstalled azureml-train-automl-client-1.0.83\n",
      "  Found existing installation: azureml-train-restclients-hyperdrive 1.0.83\n",
      "    Uninstalling azureml-train-restclients-hyperdrive-1.0.83:\n",
      "      Successfully uninstalled azureml-train-restclients-hyperdrive-1.0.83\n",
      "  Found existing installation: azureml-train-core 1.0.83\n",
      "    Uninstalling azureml-train-core-1.0.83:\n",
      "      Successfully uninstalled azureml-train-core-1.0.83\n",
      "  Found existing installation: azureml-train 1.0.83\n",
      "    Uninstalling azureml-train-1.0.83:\n",
      "      Successfully uninstalled azureml-train-1.0.83\n",
      "  Found existing installation: azureml-pipeline-core 1.0.83\n",
      "    Uninstalling azureml-pipeline-core-1.0.83:\n",
      "      Successfully uninstalled azureml-pipeline-core-1.0.83\n",
      "  Found existing installation: azureml-pipeline-steps 1.0.83\n",
      "    Uninstalling azureml-pipeline-steps-1.0.83:\n",
      "      Successfully uninstalled azureml-pipeline-steps-1.0.83\n",
      "  Found existing installation: azureml-pipeline 1.0.83\n",
      "    Uninstalling azureml-pipeline-1.0.83:\n",
      "      Successfully uninstalled azureml-pipeline-1.0.83\n",
      "  Found existing installation: azureml-sdk 1.0.83\n",
      "    Uninstalling azureml-sdk-1.0.83:\n",
      "      Successfully uninstalled azureml-sdk-1.0.83\n",
      "Successfully installed azure-core-1.4.0 azure-identity-1.2.0 azureml-automl-core-1.3.0 azureml-core-1.3.0.post2 azureml-dataprep-1.4.3 azureml-dataprep-native-14.1.0 azureml-pipeline-1.3.0 azureml-pipeline-core-1.3.0 azureml-pipeline-steps-1.3.0 azureml-sdk-1.3.0 azureml-telemetry-1.3.0 azureml-train-1.3.0 azureml-train-automl-client-1.3.0 azureml-train-core-1.3.0.post1 azureml-train-restclients-hyperdrive-1.3.0 dotnetcore2-2.1.13 msal-1.2.0 msal-extensions-0.1.3 portalocker-1.7.0\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade azureml-sdk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azure ML SDK Version:  1.5.0\n"
     ]
    }
   ],
   "source": [
    "import azureml.core\n",
    "from azureml.core import Workspace\n",
    "\n",
    "# check core SDK version number\n",
    "print(\"Azure ML SDK Version: \", azureml.core.VERSION)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eacbmlservicews\twestus2\tadlsgen2\twestus2\n"
     ]
    }
   ],
   "source": [
    "# load workspace configuration from the config.json file in the current folder.\n",
    "ws = Workspace.from_config()\n",
    "\n",
    "print(ws.name, ws.location, ws.resource_group, ws.location, sep='\\t')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>diabetes_dxd</td><td>eacbmlservicews</td><td><a href=\"https://ml.azure.com/experiments/diabetes_dxd?wsid=/subscriptions/f80606e5-788f-4dc3-a9ea-2eb9a7836082/resourcegroups/adlsgen2/workspaces/eacbmlservicews\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.Experiment?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
      ],
      "text/plain": [
       "Experiment(Name: diabetes_dxd,\n",
       "Workspace: eacbmlservicews)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_name = 'diabetes_dxd'\n",
    "\n",
    "from azureml.core import Experiment\n",
    "exp = Experiment(workspace=ws, name=experiment_name)\n",
    "\n",
    "exp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Dataset\n",
    "\n",
    "dataset = Dataset.get_by_name(ws, name='diabetes')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = dataset.to_pandas_dataframe().drop(\"Path\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>32.1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>157</td>\n",
       "      <td>93.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.8598</td>\n",
       "      <td>87</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>21.6</td>\n",
       "      <td>87.0</td>\n",
       "      <td>183</td>\n",
       "      <td>103.2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8918</td>\n",
       "      <td>69</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>30.5</td>\n",
       "      <td>93.0</td>\n",
       "      <td>156</td>\n",
       "      <td>93.6</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.6728</td>\n",
       "      <td>85</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>25.3</td>\n",
       "      <td>84.0</td>\n",
       "      <td>198</td>\n",
       "      <td>131.4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8903</td>\n",
       "      <td>89</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>192</td>\n",
       "      <td>125.4</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.2905</td>\n",
       "      <td>80</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  SEX   BMI     BP   S1     S2    S3   S4      S5  S6    Y\n",
       "0   59    2  32.1  101.0  157   93.2  38.0  4.0  4.8598  87  151\n",
       "1   48    1  21.6   87.0  183  103.2  70.0  3.0  3.8918  69   75\n",
       "2   72    2  30.5   93.0  156   93.6  41.0  4.0  4.6728  85  141\n",
       "3   24    1  25.3   84.0  198  131.4  40.0  5.0  4.8903  89  206\n",
       "4   50    1  23.0  101.0  192  125.4  52.0  4.0  4.2905  80  135"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 442 entries, 0 to 441\n",
      "Data columns (total 11 columns):\n",
      "AGE    442 non-null int64\n",
      "SEX    442 non-null int64\n",
      "BMI    442 non-null float64\n",
      "BP     442 non-null float64\n",
      "S1     442 non-null int64\n",
      "S2     442 non-null float64\n",
      "S3     442 non-null float64\n",
      "S4     442 non-null float64\n",
      "S5     442 non-null float64\n",
      "S6     442 non-null int64\n",
      "Y      442 non-null int64\n",
      "dtypes: float64(6), int64(5)\n",
      "memory usage: 38.1 KB\n"
     ]
    }
   ],
   "source": [
    "diabetes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BP</th>\n",
       "      <th>S1</th>\n",
       "      <th>S2</th>\n",
       "      <th>S3</th>\n",
       "      <th>S4</th>\n",
       "      <th>S5</th>\n",
       "      <th>S6</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>442.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.518100</td>\n",
       "      <td>1.468326</td>\n",
       "      <td>26.375792</td>\n",
       "      <td>94.647014</td>\n",
       "      <td>189.140271</td>\n",
       "      <td>115.439140</td>\n",
       "      <td>49.788462</td>\n",
       "      <td>4.070249</td>\n",
       "      <td>4.641411</td>\n",
       "      <td>91.260181</td>\n",
       "      <td>152.133484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.109028</td>\n",
       "      <td>0.499561</td>\n",
       "      <td>4.418122</td>\n",
       "      <td>13.831283</td>\n",
       "      <td>34.608052</td>\n",
       "      <td>30.413081</td>\n",
       "      <td>12.934202</td>\n",
       "      <td>1.290450</td>\n",
       "      <td>0.522391</td>\n",
       "      <td>11.496335</td>\n",
       "      <td>77.093005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.258100</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.200000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>164.250000</td>\n",
       "      <td>96.050000</td>\n",
       "      <td>40.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.276700</td>\n",
       "      <td>83.250000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.620050</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>140.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.275000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>209.750000</td>\n",
       "      <td>134.500000</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.997200</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>211.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>42.200000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>242.400000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>9.090000</td>\n",
       "      <td>6.107000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>346.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AGE         SEX         BMI          BP          S1          S2  \\\n",
       "count  442.000000  442.000000  442.000000  442.000000  442.000000  442.000000   \n",
       "mean    48.518100    1.468326   26.375792   94.647014  189.140271  115.439140   \n",
       "std     13.109028    0.499561    4.418122   13.831283   34.608052   30.413081   \n",
       "min     19.000000    1.000000   18.000000   62.000000   97.000000   41.600000   \n",
       "25%     38.250000    1.000000   23.200000   84.000000  164.250000   96.050000   \n",
       "50%     50.000000    1.000000   25.700000   93.000000  186.000000  113.000000   \n",
       "75%     59.000000    2.000000   29.275000  105.000000  209.750000  134.500000   \n",
       "max     79.000000    2.000000   42.200000  133.000000  301.000000  242.400000   \n",
       "\n",
       "               S3          S4          S5          S6           Y  \n",
       "count  442.000000  442.000000  442.000000  442.000000  442.000000  \n",
       "mean    49.788462    4.070249    4.641411   91.260181  152.133484  \n",
       "std     12.934202    1.290450    0.522391   11.496335   77.093005  \n",
       "min     22.000000    2.000000    3.258100   58.000000   25.000000  \n",
       "25%     40.250000    3.000000    4.276700   83.250000   87.000000  \n",
       "50%     48.000000    4.000000    4.620050   91.000000  140.500000  \n",
       "75%     57.750000    5.000000    4.997200   98.000000  211.500000  \n",
       "max     99.000000    9.090000    6.107000  124.000000  346.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_names = ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
    "target = \"Y\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate train and test data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = diabetes.drop(target, axis=1)\n",
    "y = diabetes[\"Y\"].values.reshape(-1,1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4526027629719189"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are three steps to model something with sklearn\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 1. Set up the model\n",
    "model = LinearRegression()\n",
    "# 2. Use fit\n",
    "model.fit(X_train, y_train)\n",
    "# 3. Check the score\n",
    "model.score(X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "# plot prediction and actual data\n",
    "y_pred = model.predict(X_test) \n",
    "plt.plot(y_test, y_pred, '.')\n",
    "\n",
    "# plot a line, a perfit predict would all fall on this line\n",
    "x = np.linspace(0, 330, 100)\n",
    "y = x\n",
    "plt.plot(x, y)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/diabetes_regression_model.pkl']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "import pickle\n",
    "\n",
    "os.makedirs('models', exist_ok=True)\n",
    "\n",
    "# note file saved in the outputs folder is automatically uploaded into experiment record\n",
    "joblib.dump(value=model, filename='models/diabetes_regression_model.pkl')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20.3\n"
     ]
    }
   ],
   "source": [
    "# sckit learn version\n",
    "import sklearn\n",
    "print(sklearn.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registering model diabetes_regression_model\n",
      "Name: diabetes_regression_model\n",
      "Version: 5\n"
     ]
    }
   ],
   "source": [
    "# Register the model (from the locally train)\n",
    "\n",
    "from azureml.core.model import Model\n",
    "\n",
    "model = Model.register(model_path=\"models/diabetes_regression_model.pkl\",\n",
    "                       model_name=\"diabetes_regression_model\",\n",
    "                       model_framework=Model.Framework.SCIKITLEARN,  # Framework used to create the model.\n",
    "                       model_framework_version='0.20.3',             # Version of scikit-learn used to create the model.\n",
    "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
    "                       description=\"Linear regression model to predict diabetes\",\n",
    "                       workspace=ws)\n",
    "\n",
    "print('Name:', model.name)\n",
    "print('Version:', model.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diabetes_regression_model:1\tdiabetes_regression_model\t1\n"
     ]
    }
   ],
   "source": [
    "# load an existing model\n",
    "\n",
    "from azureml.core.model import Model\n",
    "\n",
    "model = Model(ws, \"diabetes_regression_model\")\n",
    "\n",
    "print(model.id, model.name, model.version, sep='\\t')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define model with input and output datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Dataset\n",
    "\n",
    "input_dataset = Dataset.get_by_name(ws, name='diabetes').drop_columns(\"y\")\n",
    "output_dataset = Dataset.get_by_name(ws, name='diabetes').keep_columns(\"y\", validate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registering model diabetes_reg_model_wsample\n",
      "Name: diabetes_reg_model_wsample\n",
      "Version: 1\n"
     ]
    }
   ],
   "source": [
    "# Register model with sample inpout and output\n",
    "\n",
    "from azureml.core import Model\n",
    "from azureml.core.resource_configuration import ResourceConfiguration\n",
    "\n",
    "model = Model.register(workspace=ws,\n",
    "                       model_name=\"diabetes_reg_model_wsample\",                # Name of the registered model in your workspace.\n",
    "                       model_path=\"models/diabetes_regression_model.pkl\",      # Local file to upload and register as a model.\n",
    "                       model_framework=Model.Framework.SCIKITLEARN,            # Framework used to create the model.\n",
    "                       model_framework_version='0.20.3',                       # Version of scikit-learn used to create the model.\n",
    "                       sample_input_dataset=input_dataset,\n",
    "                       sample_output_dataset=output_dataset,\n",
    "                       resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),\n",
    "                       description='Linear regression model to predict diabetes progression.',\n",
    "                       tags={'area': 'diabetes', 'type': 'regression'})\n",
    "\n",
    "print('Name:', model.name)\n",
    "print('Version:', model.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model(workspace=Workspace.create(name='eacbmlservicews', subscription_id='f80606e5-788f-4dc3-a9ea-2eb9a7836082', resource_group='adlsgen2'), name=diabetes_reg_model_wsample, id=diabetes_reg_model_wsample:1, version=1, tags={'area': 'diabetes', 'type': 'regression'}, properties={})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load an existing model\n",
    "\n",
    "from azureml.core.model import Model\n",
    "\n",
    "model = Model(ws, \"diabetes_reg_model_wsample\")\n",
    "model\n",
    "\n",
    "#VOIR LA FONCTION get_model_path()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy in a default environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running...........................................................................\n",
      "Succeeded\n",
      "ACI service creation operation finished, operation \"Succeeded\"\n"
     ]
    }
   ],
   "source": [
    "# Use a default environment\n",
    "# nécessite d'avoir précisé l'environnement lors de l'enregistrement du modèle\n",
    "# Default environments are only provided for these frameworks: ['Onnx', 'ScikitLearn', 'TensorFlow'].\"\n",
    "\n",
    "from azureml.core import Webservice\n",
    "from azureml.exceptions import WebserviceException\n",
    "\n",
    "service_name = 'diabetes-service-default'\n",
    "\n",
    "# Remove any existing service under the same name.\n",
    "try:\n",
    "    Webservice(ws, service_name).delete()\n",
    "except WebserviceException:\n",
    "    pass\n",
    "\n",
    "service = Model.deploy(ws, service_name, [model])\n",
    "service.wait_for_deployment(show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diabetes-service-default\thttp://1859b69e-d0fb-4aee-a65f-46b0e2452e4a.westus2.azurecontainer.io/score\n"
     ]
    }
   ],
   "source": [
    "# reload the model\n",
    "\n",
    "from azureml.core import Webservice\n",
    "\n",
    "service_name = 'diabetes-service-default'\n",
    "service = Webservice(ws, service_name)\n",
    "\n",
    "print(service.name, service.scoring_uri, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healthy\n"
     ]
    }
   ],
   "source": [
    "print(service.state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://1859b69e-d0fb-4aee-a65f-46b0e2452e4a.westus2.azurecontainer.io/score\n"
     ]
    }
   ],
   "source": [
    "print(service.scoring_uri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://1859b69e-d0fb-4aee-a65f-46b0e2452e4a.westus2.azurecontainer.io/swagger.json\n"
     ]
    }
   ],
   "source": [
    "print(service.swagger_uri)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predict': [[210.74209144739257], [212.1189695974167]]}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "input_payload = json.dumps({\n",
    "    'data': [\n",
    "        [59, 2, 32.1, 101.0, 157, 93.2, 38.0, 4.0, 4.8598, 87],\n",
    "        [69, 2, 32.1, 101.0, 157, 93.2, 38.0, 4.0, 4.8598, 87]\n",
    "    ],\n",
    "    'method': 'predict'  # If you have a classification model, you can get probabilities by changing this to 'predict_proba'.\n",
    "})\n",
    "\n",
    "output = service.run(input_payload)\n",
    "\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#service.delete()"
   ]
  }
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