SnowPark_Basics_Diabetes/Snowpark_For_Python_ML_Diabetes.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5f02f3fc",
   "metadata": {},
   "source": [
    "# Snowpark Introduction\n",
    "This notebook introduces several key features of Snowpark in the process of training a machine learning model for predicting diabetes readmission. \n",
    "\n",
    "* Establish secure connection to Snowflake\n",
    "* Load features and target from Snowflake table into Snowpark DataFrame\n",
    "* Prepare features for model training\n",
    "* Train ML model using Snowpark ML distributed processing\n",
    "* Save the model to the Snowflake Model Registry\n",
    "* Run model predictions inside Snowflake"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6e2e1c4a",
   "metadata": {},
   "source": [
    "## 1. Setup Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "persistent-reasoning",
   "metadata": {
    "papermill": {
     "duration": 0.872363,
     "end_time": "2021-05-15T09:33:41.413139",
     "exception": false,
     "start_time": "2021-05-15T09:33:40.540776",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Snowflake connector\n",
    "from snowflake import connector\n",
    "#from snowflake.ml.utils import connection_params\n",
    "\n",
    "# Snowpark for Python\n",
    "from snowflake.snowpark.session import Session\n",
    "from snowflake.snowpark.types import Variant\n",
    "from snowflake.snowpark.version import VERSION\n",
    "\n",
    "# Snowpark ML\n",
    "from snowflake.ml.modeling.compose import ColumnTransformer\n",
    "from snowflake.ml.modeling.pipeline import Pipeline\n",
    "from snowflake.ml.modeling.preprocessing import PolynomialFeatures, StandardScaler\n",
    "from snowflake.ml.modeling.preprocessing import OrdinalEncoder\n",
    "from snowflake.ml.modeling.impute import SimpleImputer\n",
    "from snowflake.ml.modeling.model_selection import GridSearchCV\n",
    "\n",
    "from snowflake.ml.modeling.ensemble import RandomForestClassifier\n",
    "from snowflake.ml.modeling.model_selection.grid_search_cv import GridSearchCV\n",
    "from snowflake.ml.modeling.xgboost import XGBClassifier\n",
    "\n",
    "#from sklearn import datasets\n",
    "import sklearn \n",
    "\n",
    "# Misc\n",
    "import pandas as pd\n",
    "import json\n",
    "import logging \n",
    "logger = logging.getLogger(\"snowflake.snowpark.session\")\n",
    "logger.setLevel(logging.ERROR)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b855e80e",
   "metadata": {},
   "source": [
    "## Establish Secure Connection to Snowflake\n",
    "\n",
    "Using the Snowpark Python API, it’s quick and easy to establish a secure connection between Snowflake and Notebook.\n",
    " *Note: Other connection options include Username/Password, MFA, OAuth, Okta, SSO*\n",
    "\n",
    "I like to store my credentials in creds.json so they aren't in the notebook.\n",
    "The file should look like this:\n",
    "```\n",
    "{\n",
    "    \"account\": \"awb99999\",\n",
    "    \"user\": \"your_user_name\",\n",
    "    \"password\": \"your_password\",\n",
    "    \"warehouse\": \"your_warehouse\"\n",
    "  }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "953f6906",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../../creds.json') as f:\n",
    "    data = json.load(f)\n",
    "    USERNAME = data['user']\n",
    "    PASSWORD = data['password']\n",
    "    SF_ACCOUNT = data['account']\n",
    "    SF_WH = data['warehouse']\n",
    "\n",
    "CONNECTION_PARAMETERS = {\n",
    "   \"account\": SF_ACCOUNT,\n",
    "   \"user\": USERNAME,\n",
    "   \"password\": PASSWORD,\n",
    "}\n",
    "\n",
    "session = Session.builder.configs(CONNECTION_PARAMETERS).create()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c333eb49",
   "metadata": {},
   "source": [
    "Verify everything is connected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fb1f7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "snowflake_environment = session.sql('select current_user(), current_version()').collect()\n",
    "snowpark_version = VERSION\n",
    "\n",
    "# Current Environment Details\n",
    "print('User                        : {}'.format(snowflake_environment[0][0]))\n",
    "print('Role                        : {}'.format(session.get_current_role()))\n",
    "print('Database                    : {}'.format(session.get_current_database()))\n",
    "print('Schema                      : {}'.format(session.get_current_schema()))\n",
    "print('Warehouse                   : {}'.format(session.get_current_warehouse()))\n",
    "print('Snowflake version           : {}'.format(snowflake_environment[0][1]))\n",
    "print('Snowpark for Python version : {}.{}.{}'.format(snowpark_version[0],snowpark_version[1],snowpark_version[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ac4644",
   "metadata": {},
   "source": [
    "If you need to add some custom packages with specific version, you can run these. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dc73269",
   "metadata": {},
   "outputs": [],
   "source": [
    "#session.custom_package_usage_config = {\"enabled\": True}\n",
    "#session.add_packages(\"numpy\", \"pandas==1.5.3\",\"scikit-learn==1.3.0\",\"xgboost\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2817d70c",
   "metadata": {},
   "source": [
    "## 2. Load Data in Snowflake "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df957705",
   "metadata": {},
   "source": [
    "Let's get the data (100k rows and 32 columns) and also make the column names all upper cases. It's easier to work with columns names that aren't case sensitive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dfb28da",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = pd.read_csv('diabetes_clean.csv')\n",
    "df_clean.columns = df_clean.columns.str.upper()\n",
    "print (df_clean.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c72b14",
   "metadata": {},
   "source": [
    "Let's create a Snowpark dataframe and split the data for test/train. This operation is done inside Snowflake and not in your local environment. We will also save this as a table so we don't ever have to manually upload this dataset again.\n",
    "\n",
    "PRO TIP -- Snowpark will inherit the schema of a pandas dataframe into Snowflake. Either change your schema before importing or after it has landed in snowflake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cbf2e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "input_df = session.create_dataframe(df_clean)\n",
    "train_df, test_df = input_df.random_split(weights=[0.8, 0.2], seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b80af825",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.write.mode('overwrite').save_as_table('DIAB_TRAIN')\n",
    "test_df.write.mode('overwrite').save_as_table('DIAB_TEST')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "128d6339",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df= session.table(\"DIAB_TRAIN\")\n",
    "test_df = session.table(\"DIAB_TEST\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c2b7c9",
   "metadata": {},
   "source": [
    "Snowpark dataframes have lots of operations that can be performed on them. Here I am printing out the column names before we start the feature engineering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47feff87",
   "metadata": {},
   "outputs": [],
   "source": [
    "## To see column names\n",
    "df_clean.select_dtypes(include=['category', 'object']).columns.tolist()\n",
    "train_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "774e0c4f",
   "metadata": {},
   "source": [
    "## 3. Distributed Feature Engineering\n",
    "\n",
    "These operations are done inside the Snowpark warehouse which provides improved performance and scalability with distributed execution for these scikit-learn preprocessing functions. This dataset uses SMALL, but you can always move up to larger ones including Snowpark Optimized warehouses (16x memory per node than a standard warehouse), e.g., `session.sql(\"create or replace warehouse snowpark_opt_wh with warehouse_size = 'MEDIUM' warehouse_type = 'SNOWPARK-OPTIMIZED'\").collect()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3463c44e",
   "metadata": {},
   "outputs": [],
   "source": [
    "session.sql(\"ALTER WAREHOUSE RAJIV SET WAREHOUSE_SIZE = 'SMALL'\").collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5620e966",
   "metadata": {},
   "source": [
    "Feature engineering + random forest from sci kit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad265fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    " ## Distributed Preprocessing - 25X to 50X faster\n",
    "numeric_features = ['TIME_IN_HOSPITAL', 'NUM_LAB_PROCEDURES', 'NUM_PROCEDURES',\n",
    "       'NUM_MEDICATIONS', 'NUMBER_OUTPATIENT', 'NUMBER_EMERGENCY',\n",
    "       'NUMBER_INPATIENT', 'NUMBER_DIAGNOSES', 'CHANGE', 'DIABETESMED']\n",
    "numeric_transformer = Pipeline(steps=[('poly',PolynomialFeatures(degree = 2)),('scaler', StandardScaler())])\n",
    "\n",
    "#categorical_cols = ['RACE', 'GENDER','AGE', 'MEDICAL_SPECIALTY','DIAG_1','DIAG_2','DIAG_3','DISCHARGE_DISPOSITION_ID','ADMISSION_SOURCE_ID','ADMISSION_TYPE_ID']\n",
    "categorical_cols = ['RACE', 'GENDER','AGE', 'MEDICAL_SPECIALTY','DIAG_1','DIAG_2','DIAG_3']\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-99999))\n",
    "])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_cols)\n",
    "        ])\n",
    "\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),('classifier', RandomForestClassifier())])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35dca763",
   "metadata": {},
   "source": [
    "## 4. Distributed Training\n",
    "\n",
    "These operations are done inside the Snowpark warehouse which provides improved performance and scalability with distributed execution for these scikit-learn preprocessing functions and XGBoost training (and many other types of models)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1c973e3",
   "metadata": {},
   "outputs": [],
   "source": [
    " ## Distributed HyperParameter Optimization\n",
    "hyper_param = dict(\n",
    "        classifier__max_depth=[5,10,30],\n",
    "       # min_samples_leaf=[1,3,10],\n",
    "      #  min_samples_split=[1.0, 3,10],\n",
    "        classifier__n_estimators = [20,50,200]\n",
    "    )\n",
    "\n",
    "model = GridSearchCV(\n",
    "    estimator=pipeline,\n",
    "    param_grid=hyper_param,\n",
    "    cv=5,\n",
    "    input_cols=numeric_features + categorical_cols,\n",
    "    label_cols=['READMITTED'],\n",
    "    output_cols=[\"READMITTED_PRED\"],\n",
    "    verbose=2\n",
    ")\n",
    "\n",
    "# Fit and Score - Takes 2 minutes\n",
    "model.fit(train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1788805",
   "metadata": {},
   "source": [
    "## 5. Model Evaluation\n",
    "Look at the results of the mode. cv_results is a dictionary, where each key is a string describing one of the metrics or parameters, and the corresponding value is an array with one entry per combination of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70d680fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results = model.to_sklearn().cv_results_\n",
    "\n",
    "# cv_results is a dictionary, where each key is a string describing one of the metrics or parameters,\n",
    "# and the corresponding value is an array with one entry per combination of parameters\n",
    "\n",
    "# To print the parameter setting and the corresponding mean test score, for example:\n",
    "for i in range(len(cv_results['params'])):\n",
    "    print(f\"Parameters: {cv_results['params'][i]}\")\n",
    "    print(f\"Mean Test Score: {cv_results['mean_test_score'][i]}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c34113",
   "metadata": {},
   "source": [
    "Look at the accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da43cfed",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score = model.score(train_df)\n",
    "test_score = model.score(test_df)\n",
    "\n",
    "# R2 score on train and test datasets\n",
    "print(f\"Accuracy on Train : {train_score}\")\n",
    "print(f\"Accuracy on Test  : {test_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a93fa47b",
   "metadata": {},
   "source": [
    "Dig into results a bit more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9489eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "testproba = model.predict_proba(test_df)\n",
    "testproba.write.save_as_table(table_name='DIABETES_TEST_PROBA', mode='overwrite')\n",
    "\n",
    "testpreds = model.predict(test_df)\n",
    "testpreds.write.save_as_table(table_name='DIABETES_TEST_SCORED', mode='overwrite')\n",
    "testpreds.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39e5be6",
   "metadata": {},
   "source": [
    "Using metrics from snowpark so calculation is done inside snowflake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0071e434",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.ml.modeling.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
    "print('Acccuracy:', accuracy_score(df=testpreds,y_true_col_names= 'READMITTED', y_pred_col_names= 'READMITTED_PRED'))\n",
    "print('Precision:', precision_score(df=testpreds, y_true_col_names='READMITTED', y_pred_col_names='READMITTED_PRED'))\n",
    "print('Recall:', recall_score(df=testpreds, y_true_col_names='READMITTED', y_pred_col_names='READMITTED_PRED'))\n",
    "print('F1:', f1_score(df=testpreds, y_true_col_names='READMITTED', y_pred_col_names='READMITTED_PRED'))\n",
    "##AUC needs probabilities\n",
    "print('AUC:', roc_auc_score(df=testproba, y_true_col_names='READMITTED', y_score_col_names='\"predict_proba_1\"'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59228269",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtaining and plotting a simple confusion matrix\n",
    "import seaborn as sns\n",
    "from snowflake.ml.modeling.metrics import confusion_matrix\n",
    "cf_matrix = confusion_matrix(df=testpreds, y_true_col_name='READMITTED', y_pred_col_name='READMITTED_PRED')\n",
    "sns.heatmap(cf_matrix, annot=True, fmt='.0f', cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad3227f4",
   "metadata": {},
   "source": [
    "## 6. Let's do this all over with XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f9a1430",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.ml.modeling.xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ffaa88c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#session.sql(\"create or replace warehouse snowpark_opt_wh with warehouse_size = 'MEDIUM' warehouse_type = 'SNOWPARK-OPTIMIZED'\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "471e77e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),('classifier', XGBClassifier())])\n",
    " ## Distributed HyperParameter Optimization\n",
    "hyper_param = dict(\n",
    "        classifier__max_depth=[2,4],\n",
    "       # min_samples_leaf=[1,3,10],\n",
    "      #  min_samples_split=[1.0,3,10],\n",
    "        classifier__n_estimators = [20,50,200]\n",
    "    )\n",
    "\n",
    "xg_model = GridSearchCV(\n",
    "    estimator=pipeline,\n",
    "    param_grid=hyper_param,\n",
    "    cv=5,\n",
    "    input_cols=numeric_features + categorical_cols,\n",
    "    label_cols=['READMITTED'],\n",
    "    output_cols=[\"READMITTED_PRED\"],\n",
    "    scoring=\"roc_auc\",\n",
    "    verbose=2\n",
    ")\n",
    "\n",
    "# Fit and Score\n",
    "xg_model.fit(train_df)\n",
    "##Takes 80 seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3661c1f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results = xg_model.to_sklearn().cv_results_\n",
    "\n",
    "for i in range(len(cv_results['params'])):\n",
    "    print(f\"Parameters: {cv_results['params'][i]}\")\n",
    "    print(f\"Mean Test Score: {cv_results['mean_test_score'][i]}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4e1bd0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "testproba = xg_model.predict_proba(test_df)\n",
    "print('AUC:', roc_auc_score(df=testproba, y_true_col_names='READMITTED', y_score_col_names='\"predict_proba_1\"'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a39a775d",
   "metadata": {},
   "source": [
    "## 7. Let's Store the XGBoost Model in the Model Registry and Run Predictions Inside Snowflake (Python/SQL)\n",
    "\n",
    "Connect to the registry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c97f06d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.ml.registry import Registry\n",
    "\n",
    "reg = Registry(session=session, database_name=\"RAJIV\", schema_name=\"PUBLIC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99012d7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ref = reg.log_model(\n",
    "    model_name=\"Diabetes_XGBooster\",\n",
    "    version_name=\"v7\",    \n",
    "    model=xg_model,\n",
    "    conda_dependencies=[\"scikit-learn\",\"xgboost\"],\n",
    "    sample_input_data=train_df,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ac62368",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg.show_models()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46750d4e",
   "metadata": {},
   "source": [
    "Let's retrieve the model from the registry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16f2b93f",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_model = reg.get_model(\"DIABETES_XGBOOSTER\").version(\"v7\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68bc70af",
   "metadata": {},
   "source": [
    "Let's do predictions inside the warehouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf7f3388",
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_prediction = reg_model.run(test_df, function_name='predict_proba')\n",
    "remote_prediction.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c615b1b",
   "metadata": {},
   "source": [
    "If you look in the activity view, you can find the SQL which will run a bit faster.  This SQL command is showing the result in a snowflake dataframe. You could use `collect` to pull the info out into your local session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d60a60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = session.sql(\"\"\"SELECT \"ADMISSION_TYPE_ID\", \"DISCHARGE_DISPOSITION_ID\", \"ADMISSION_SOURCE_ID\", \"MAX_GLU_SERUM\", \"A1CRESULT\", \"ACETOHEXAMIDE\", \"TOLBUTAMIDE\", \"TROGLITAZONE\", \"EXAMIDE\", \"CITOGLIPTON\", \"GLIPIZIDE-METFORMIN\", \"GLIMEPIRIDE-PIOGLITAZONE\", \"METFORMIN-ROSIGLITAZONE\", \"METFORMIN-PIOGLITAZONE\", \"READMITTED\",  CAST (\"TMP_RESULT\"['TIME_IN_HOSPITAL'] AS BYTEINT) AS \"TIME_IN_HOSPITAL\",  CAST (\"TMP_RESULT\"['NUM_LAB_PROCEDURES'] AS SMALLINT) AS \"NUM_LAB_PROCEDURES\",  CAST (\"TMP_RESULT\"['NUM_PROCEDURES'] AS BYTEINT) AS \"NUM_PROCEDURES\",  CAST (\"TMP_RESULT\"['NUM_MEDICATIONS'] AS BYTEINT) AS \"NUM_MEDICATIONS\",  CAST (\"TMP_RESULT\"['NUMBER_OUTPATIENT'] AS BYTEINT) AS \"NUMBER_OUTPATIENT\",  CAST (\"TMP_RESULT\"['NUMBER_EMERGENCY'] AS BYTEINT) AS \"NUMBER_EMERGENCY\",  CAST (\"TMP_RESULT\"['NUMBER_INPATIENT'] AS BYTEINT) AS \"NUMBER_INPATIENT\",  CAST (\"TMP_RESULT\"['NUMBER_DIAGNOSES'] AS BYTEINT) AS \"NUMBER_DIAGNOSES\",  CAST (\"TMP_RESULT\"['CHANGE'] AS BYTEINT) AS \"CHANGE\",  CAST (\"TMP_RESULT\"['DIABETESMED'] AS BYTEINT) AS \"DIABETESMED\",  CAST (\"TMP_RESULT\"['RACE'] AS STRING) AS \"RACE\",  CAST (\"TMP_RESULT\"['GENDER'] AS STRING) AS \"GENDER\",  CAST (\"TMP_RESULT\"['AGE'] AS STRING) AS \"AGE\",  CAST (\"TMP_RESULT\"['MEDICAL_SPECIALTY'] AS STRING) AS \"MEDICAL_SPECIALTY\",  CAST (\"TMP_RESULT\"['DIAG_1'] AS STRING) AS \"DIAG_1\",  CAST (\"TMP_RESULT\"['DIAG_2'] AS STRING) AS \"DIAG_2\",  CAST (\"TMP_RESULT\"['DIAG_3'] AS STRING) AS \"DIAG_3\",  CAST (\"TMP_RESULT\"['predict_proba_0'] AS DOUBLE) AS \"predict_proba_0\",  CAST (\"TMP_RESULT\"['predict_proba_1'] AS DOUBLE) AS \"predict_proba_1\" \n",
    "FROM (\n",
    "    WITH MODEL_VERSION_ALIAS AS MODEL RAJIV.PUBLIC.DIABETES_XGBOOSTER VERSION V7\n",
    "    SELECT *,\n",
    "        MODEL_VERSION_ALIAS!PREDICT_PROBA(TIME_IN_HOSPITAL, NUM_LAB_PROCEDURES, NUM_PROCEDURES, NUM_MEDICATIONS, NUMBER_OUTPATIENT, NUMBER_EMERGENCY, NUMBER_INPATIENT, NUMBER_DIAGNOSES, CHANGE, DIABETESMED, RACE, GENDER, AGE, MEDICAL_SPECIALTY, DIAG_1, DIAG_2, DIAG_3) AS TMP_RESULT\n",
    "    FROM (SELECT * FROM RAJIV.PUBLIC.DIAB_TEST ))\"\"\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d13506f",
   "metadata": {},
   "source": [
    "Optimize inference by randomly moving the data around"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b1aa381",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = session.sql(\"\"\"SELECT \"ADMISSION_TYPE_ID\", \"DISCHARGE_DISPOSITION_ID\", \"ADMISSION_SOURCE_ID\", \"MAX_GLU_SERUM\", \"A1CRESULT\", \"ACETOHEXAMIDE\", \"TOLBUTAMIDE\", \"TROGLITAZONE\", \"EXAMIDE\", \"CITOGLIPTON\", \"GLIPIZIDE-METFORMIN\", \"GLIMEPIRIDE-PIOGLITAZONE\", \"METFORMIN-ROSIGLITAZONE\", \"METFORMIN-PIOGLITAZONE\", \"READMITTED\",  CAST (\"TMP_RESULT\"['TIME_IN_HOSPITAL'] AS BYTEINT) AS \"TIME_IN_HOSPITAL\",  CAST (\"TMP_RESULT\"['NUM_LAB_PROCEDURES'] AS SMALLINT) AS \"NUM_LAB_PROCEDURES\",  CAST (\"TMP_RESULT\"['NUM_PROCEDURES'] AS BYTEINT) AS \"NUM_PROCEDURES\",  CAST (\"TMP_RESULT\"['NUM_MEDICATIONS'] AS BYTEINT) AS \"NUM_MEDICATIONS\",  CAST (\"TMP_RESULT\"['NUMBER_OUTPATIENT'] AS BYTEINT) AS \"NUMBER_OUTPATIENT\",  CAST (\"TMP_RESULT\"['NUMBER_EMERGENCY'] AS BYTEINT) AS \"NUMBER_EMERGENCY\",  CAST (\"TMP_RESULT\"['NUMBER_INPATIENT'] AS BYTEINT) AS \"NUMBER_INPATIENT\",  CAST (\"TMP_RESULT\"['NUMBER_DIAGNOSES'] AS BYTEINT) AS \"NUMBER_DIAGNOSES\",  CAST (\"TMP_RESULT\"['CHANGE'] AS BYTEINT) AS \"CHANGE\",  CAST (\"TMP_RESULT\"['DIABETESMED'] AS BYTEINT) AS \"DIABETESMED\",  CAST (\"TMP_RESULT\"['RACE'] AS STRING) AS \"RACE\",  CAST (\"TMP_RESULT\"['GENDER'] AS STRING) AS \"GENDER\",  CAST (\"TMP_RESULT\"['AGE'] AS STRING) AS \"AGE\",  CAST (\"TMP_RESULT\"['MEDICAL_SPECIALTY'] AS STRING) AS \"MEDICAL_SPECIALTY\",  CAST (\"TMP_RESULT\"['DIAG_1'] AS STRING) AS \"DIAG_1\",  CAST (\"TMP_RESULT\"['DIAG_2'] AS STRING) AS \"DIAG_2\",  CAST (\"TMP_RESULT\"['DIAG_3'] AS STRING) AS \"DIAG_3\",  CAST (\"TMP_RESULT\"['predict_proba_0'] AS DOUBLE) AS \"predict_proba_0\",  CAST (\"TMP_RESULT\"['predict_proba_1'] AS DOUBLE) AS \"predict_proba_1\" \n",
    "FROM (\n",
    "    WITH MODEL_VERSION_ALIAS AS MODEL RAJIV.PUBLIC.DIABETES_XGBOOSTER VERSION V7\n",
    "    SELECT *,\n",
    "        MODEL_VERSION_ALIAS!PREDICT_PROBA(TIME_IN_HOSPITAL, NUM_LAB_PROCEDURES, NUM_PROCEDURES, NUM_MEDICATIONS, NUMBER_OUTPATIENT, NUMBER_EMERGENCY, NUMBER_INPATIENT, NUMBER_DIAGNOSES, CHANGE, DIABETESMED, RACE, GENDER, AGE, MEDICAL_SPECIALTY, DIAG_1, DIAG_2, DIAG_3) AS TMP_RESULT\n",
    "    FROM (SELECT * FROM RAJIV.PUBLIC.DIAB_TEST ORDER BY RANDOM()))\"\"\").show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "26304617",
   "metadata": {},
   "source": [
    "### Save Trained Model to Snowflake Stage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72fe42c3",
   "metadata": {},
   "source": [
    "Let's take the model object save it locally and also save a copy of the model in a snowflake stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62db126d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from joblib import dump\n",
    "\n",
    "## Create a stage to store the model\n",
    "session.sql('CREATE OR REPLACE STAGE raj_models').show()\n",
    "\n",
    "# Extract SKLearn object \n",
    "sk_model = xg_model.to_sklearn()\n",
    "\n",
    "#Using current file path, you can modify with another directory\n",
    "model_filename = 'model.joblib'\n",
    "model_file = os.path.join(os.getcwd(), model_filename)\n",
    "\n",
    "dump(sk_model, model_file)\n",
    "\n",
    "session.file.put(model_file, \"@raj_models\", overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1bfa1bd",
   "metadata": {},
   "source": [
    "Is there a command to see the models in the stage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1030d6d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "session.sql('LIST @raj_models').show()"
   ]
  }
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