Deploying and saving a model

What is DeploySupervisedModel?

• This class lets one save a model (for recurrent use) and push predictions to a database
• One can do both classification (ie, predict Y/N) as well as regression (ie, predict a numeric field).

Am I ready for model deployment?

Only if you've already completed these steps:

• You've found a model work that works well on your data
• You've created a column called InTestWindowFLG (or something similar), where 'Y' denotes rows that need a prediction and 'N' for rows that train the model.
• You've created the SQL table structure to receive predictions

For classification predictions:

CREATE TABLE [SAM].[dbo].[HCPyDeployClassificationBASE] (
  [BindingID] [int] , 
  [BindingNM] [varchar] (255), 
  [LastLoadDTS] [datetime2] (7), 
  [PatientEncounterID] [decimal] (38, 0), --< change to your grain col
  [PredictedProbNBR] [decimal] (38, 2),
  [Factor1TXT] [varchar] (255), 
  [Factor2TXT] [varchar] (255), 
  [Factor3TXT] [varchar] (255))

For regression predictions:

CREATE TABLE [SAM].[dbo].[HCPyDeployRegressionBASE] (
  [BindingID] [int], 
  [BindingNM] [varchar] (255), 
  [LastLoadDTS] [datetime2] (7), 
  [PatientEncounterID] [decimal] (38, 0), --< change to your grain col
  [PredictedValueNBR] [decimal] (38, 2), 
  [Factor1TXT] [varchar] (255), 
  [Factor2TXT] [varchar] (255), 
  [Factor3TXT] [varchar] (255))

Step 1: Pull in the data

For SQL:

import pyodbc
cnxn = pyodbc.connect("""SERVER=localhost;
                        DRIVER={SQL Server Native Client 11.0};
                        Trusted_Connection=yes;
                        autocommit=True""")

 df = pd.read_sql(
     sql="""SELECT
            *
            FROM [SAM].[dbo].[HCPyDiabetesClinical]""",
     con=cnxn)


 # Handle missing data (if needed)
 df.replace(['None'],[None],inplace=True)

For CSV:

df = pd.read_csv('healthcareai/tests/fixtures/HCPyDiabetesClinical.csv',
                 na_values=['None'])

Step 2: Set your data-prep parameters

The DeploySupervisedModel cleans and prepares the data prior to model creation.

• Return: an object.
• Arguments: : - modeltype: a string. This will either be 'classification' or 'regression'. - df: a data frame. The data your model will be based on. - predictedcol: a string. Name of variable (or column) that you want to predict. - graincol: a string, defaults to None. Name of possible GrainID column in your dataset. If specified, this column will be removed, as it won't help the algorithm. - impute: a boolean. Whether to impute by replacing NULLs with column mean (for numeric columns) or column mode (for categorical columns). - debug: a boolean, defaults to False. If TRUE, console output when comparing models is verbose for easier debugging. - windowcol: a string. Which column in the dataset denotes which rows are test ('Y') or training ('N').

Example code:

p = DeploySupervisedModel(modeltype='regression',
                          df=df,
                          graincol='PatientEncounterID',
                          windowcol='InTestWindowFLG',
                          predictedcol='LDLNBR',
                          impute=True,
                          debug=False)

Step 3: Create and save the model

The deploy creates the model and method makes predictions that are pushed to a database.

• Return: an object.
• Arguments: : - method: a string. If you choose random forest, use 'rf'. If you choose to deploy the linear model, use 'linear'. - cores: an integer. Denotes how many of your processors to use. - server: a string. Which server are you pushing predictions to? - dest_db_schema_table: a string. Which database.schema.table are you pushing predictions to? - trees: an integer, defaults to 200. Use only if working with random forest. This denotes number of trees in the forest. - debug: a boolean, defaults to False. If TRUE, console output when comparing models is verbose for easier debugging.

Example code:

p.deploy(method='rf',
         cores=2,
         server='localhost',
         dest_db_schema_table='[SAM].[dbo].[HCPyDeployRegressionBASE]',
         use_saved_model=False,
         trees=200,
         debug=False)

Full example code

```python from healthcareai import DeploySupervisedModel import pandas as pd import time

def main():

t0 = time.time()

# Load in data
# CSV snippet for reading data into dataframe
df = pd.read_csv('healthcareai/tests/fixtures/HCPyDiabetesClinical.csv',
                na_values=['None'])

# SQL snippet for reading data into dataframe
# import pyodbc
# cnxn = pyodbc.connect("""SERVER=localhost;
#                          DRIVER={SQL Server Native Client 11.0};
#                          Trusted_Connection=yes;
#                          autocommit=True""")
#
# df = pd.read_sql(
#     sql="""SELECT *
#            FROM [SAM].[dbo].[HCPyDiabetesClinical]""",
#     con=cnxn)
#
# # Set None string to be None type
# df.replace(['None'],[None],inplace=True)

# Look at data that's been pulled in
print(df.head())
print(df.dtypes)

# Drop columns that won't help machine learning
df.drop('PatientID', axis=1, inplace=True)

p = DeploySupervisedModel(modeltype='regression',
                          df=df,
                          graincol='PatientEncounterID',
                          windowcol='InTestWindowFLG',
                          predictedcol='LDLNBR',
                          impute=True,
                          debug=False)

p.deploy(method='rf',
         cores=2,
         server='localhost',
         dest_db_schema_table='[SAM].[dbo].[HCPyDeployRegressionBASE]',
         use_saved_model=False,
         trees=200,
         debug=False)

print('\nTime:\n', time.time() - t0)

if __name__ == "__main__":
    main()

``````