class TestLinearDeploy(unittest.TestCase):
def setUp(self):
df = pd.read_csv(fixture('HCPyDiabetesClinical.csv'),
na_values=['None'])
df.drop('PatientID', axis=1, inplace=True) # drop uninformative column
np.random.seed(42)
self.o = DeploySupervisedModel(modeltype='classification',
df=df,
graincol='PatientEncounterID',
windowcol='InTestWindowFLG',
predictedcol='ThirtyDayReadmitFLG',
impute=True)
self.o.deploy(
method='linear',
cores=1,
server='localhost',
dest_db_schema_table='[SAM].[dbo].[HCPyDeployClassificationBASE]',
use_saved_model=False)
def runTest(self):
self.assertAlmostEqual(np.round(self.o.y_pred[5], 5), 0.18087)
def tearDown(self):
del self.o
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)
def setUp(self):
df = pd.read_csv(fixture('DiabetesClinicalSampleData.csv'),
na_values=['None'])
df.drop('PatientID', axis=1, inplace=True) # drop uninformative column
np.random.seed(42)
self.o = DeploySupervisedModel(modeltype='classification',
df=df,
graincol='PatientEncounterID',
windowcol='InTestWindowFLG',
predictedcol='ThirtyDayReadmitFLG',
impute=True)
self.o.deploy(
method='linear',
cores=1,
server='localhost',
dest_db_schema_table='[SAM].[dbo].[HCPyDeployClassificationBASE]',
use_saved_model=False)