def predict_to_sqlite(self,
prediction_dataframe,
database,
table,
prediction_generator,
predicted_column_name=None):
"""
Given a dataframe you want predictions on, make predictions and save them to an sqlite table.
Args:
prediction_dataframe (pandas.core.frame.DataFrame): Raw prediction dataframe
database (str): database file name
table (str): table name
prediction_generator (method): one of the trained supervised model prediction methods
predicted_column_name (str): optional predicted column name (defaults to PredictedProbNBR or
PredictedValueNBR)
"""
# validate inputs
if type(prediction_generator).__name__ != 'method':
raise HealthcareAIError(
'Use of this method requires a prediction generator from a trained supervised model')
# Get predictions from given generator
sam_df = prediction_generator(prediction_dataframe)
# Rename prediction column to default based on model type or given one
if predicted_column_name is None:
if self.is_classification:
predicted_column_name = 'PredictedProbNBR'
elif self.is_regression:
predicted_column_name = 'PredictedValueNBR'
sam_df.rename(columns={'Prediction': predicted_column_name}, inplace=True)
engine = hcai_db.build_sqlite_engine(database)
healthcareai.common.database_writers.write_to_db_agnostic(engine, table, sam_df)
def predict_to_sqlite(self,
prediction_dataframe,
database,
table,
prediction_generator,
predicted_column_name=None):
"""
Given a dataframe you want predictions on, make predictions and save them to an sqlite table.
Args:
prediction_dataframe (pandas.core.frame.DataFrame): Raw prediction dataframe
database (str): database file name
table (str): table name
prediction_generator (method): one of the trained supervised model prediction methods
predicted_column_name (str): optional predicted column name (defaults to PredictedProbNBR or
PredictedValueNBR)
"""
# validate inputs
if type(prediction_generator).__name__ != 'method':
raise HealthcareAIError(
'Use of this method requires a prediction generator from a trained supervised model')
# Get predictions from given generator
sam_df = prediction_generator(prediction_dataframe)
# Rename prediction column to default based on model type or given one
if predicted_column_name is None:
if self.is_classification:
predicted_column_name = 'PredictedProbNBR'
elif self.is_regression:
predicted_column_name = 'PredictedValueNBR'
sam_df.rename(columns={'Prediction': predicted_column_name}, inplace=True)
engine = hcai_db.build_sqlite_engine(database)
healthcareai.common.database_writers.write_to_db_agnostic(engine, table, sam_df)