def random_forest_classification(self,
feature_importance_limit=15,
save_plot=False):
"""Train a random forest classification model, print performance metrics
and show a feature importance plot.
Args:
feature_importance_limit (int): The maximum number of features to
show in the feature importance plot
save_plot (bool): For the feature importance plot, True to save
plot (will not display). False by default to display.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
model = self._advanced_trainer.random_forest_classifier(
trees=200, scoring_metric='roc_auc', randomized_search=True)
# Save or show the feature importance graph
hcai_tsm.plot_rf_features_from_tsm(
model,
self._advanced_trainer.x_train,
feature_limit=feature_importance_limit,
save=save_plot)
return model
def random_forest_classification(self, feature_importance_limit=15, save_plot=False):
"""Train a random forest classification model, print performance metrics
and show a feature importance plot.
Args:
feature_importance_limit (int): The maximum number of features to
show in the feature importance plot
save_plot (bool): For the feature importance plot, True to save
plot (will not display). False by default to display.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
model = self._advanced_trainer.random_forest_classifier(
trees=200,
scoring_metric='roc_auc',
randomized_search=True)
# Save or show the feature importance graph
hcai_tsm.plot_rf_features_from_tsm(
model,
self._advanced_trainer.x_train,
feature_limit=feature_importance_limit,
save=save_plot)
return model
def random_forest_classification(self, save_plot=False):
""" Train a random forest classification model, print out performance metrics and show a feature importance plot.
Args:
save_plot (bool): For the feature importance plot, True to save plot (will not display). False by default to
display.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
model_name = 'Random Forest Classification'
print('\nTraining {}'.format(model_name))
# Train the model
trained_model = self._advanced_trainer.random_forest_classifier(
trees=200, scoring_metric='roc_auc', randomized_search=True)
# Display the model metrics
trained_model.print_training_results()
# Save or show the feature importance graph
hcai_tsm.plot_rf_features_from_tsm(trained_model,
self._advanced_trainer.x_train,
save=save_plot)
return trained_model
def main():
"""Template script for ADVANCED USERS using healthcareai."""
# Load the included diabetes sample data
dataframe = healthcareai.load_diabetes()
# ...or load your own data from a .csv file: Uncomment to pull data from your CSV
# dataframe = healthcareai.load_csv('path/to/your.csv')
# ...or load data from a MSSQL server: Uncomment to pull data from MSSQL server
# server = 'localhost'
# database = 'SAM'
# query = """SELECT *
# FROM [SAM].[dbo].[DiabetesClincialSampleData]
# -- In this step, just grab rows that have a target
# WHERE ThirtyDayReadmitFLG is not null"""
#
# engine = hcai_db.build_mssql_engine_using_trusted_connections(server=server, database=database)
# dataframe = pd.read_sql(query, engine)
# Peek at the first 5 rows of data
print(dataframe.head(5))
# Drop columns that won't help machine learning
dataframe.drop(['PatientID'], axis=1, inplace=True)
# Step 1: Prepare the data using optional imputation. There are two options for this:
# ## Option 1: Use built in data prep pipeline that does enocding, imputation, null filtering, dummification
clean_training_dataframe = hcai_pipelines.full_pipeline(
'classification',
'ThirtyDayReadmitFLG',
'PatientEncounterID',
impute=True).fit_transform(dataframe)
# ## Option 2: Build your own pipeline using healthcare.ai methods, your own, or a combination of either.
# - Please note this is intentionally spartan, so we don't hinder your creativity. :)
# - Also note that many of the healthcare.ai transformers intentionally return dataframes, compared to scikit that
# return numpy arrays
# custom_pipeline = Pipeline([
# ('remove_grain_column', hcai_filters.DataframeColumnRemover(columns_to_remove=['PatientEncounterID', 'PatientID'])),
# ('imputation', hcai_transformers.DataFrameImputer(impute=True)),
# ('convert_target_to_binary', hcai_transformers.DataFrameConvertTargetToBinary('classification', 'ThirtyDayReadmitFLG')),
# # ('prediction_to_numeric', hcai_transformers.DataFrameConvertColumnToNumeric('ThirtyDayReadmitFLG')),
# # ('create_dummy_variables', hcai_transformers.DataFrameCreateDummyVariables(excluded_columns=['ThirtyDayReadmitFLG'])),
# ])
#
# clean_training_dataframe = custom_pipeline.fit_transform(dataframe)
# Step 2: Instantiate an Advanced Trainer class with your clean and prepared training data
classification_trainer = healthcareai.AdvancedSupervisedModelTrainer(
dataframe=clean_training_dataframe,
model_type='classification',
predicted_column='ThirtyDayReadmitFLG',
grain_column='PatientEncounterID',
verbose=False)
# Step 3: split the data into train and test
classification_trainer.train_test_split()
# Step 4: Train some models
# ## Train a KNN classifier with a randomized search over custom hyperparameters
knn_hyperparameters = {
'algorithm': ['ball_tree', 'kd_tree'],
'n_neighbors': [1, 4, 6, 8, 10, 15, 20, 30, 50, 100, 200],
'weights': ['uniform', 'distance']
}
trained_knn = classification_trainer.knn(
scoring_metric='accuracy',
hyperparameter_grid=knn_hyperparameters,
randomized_search=True,
# Set this relative to the size of your hyperparameter space. Higher will train more models and be slower
# Lower will be faster and possibly less performant
number_iteration_samples=10)
# ## Train a random forest classifier with a randomized search over custom hyperparameters
# TODO these are bogus hyperparams for random forest
random_forest_hyperparameters = {
'n_estimators': [50, 100, 200, 300],
'max_features': [1, 2, 3, 4],
'max_leaf_nodes': [None, 30, 400]
}
trained_random_forest = classification_trainer.random_forest_classifier(
scoring_metric='accuracy',
hyperparameter_grid=random_forest_hyperparameters,
randomized_search=True,
# Set this relative to the size of your hyperparameter space. Higher will train more models and be slower
# Lower will be faster and possibly less performant
number_iteration_samples=10)
# Show the random forest feature importance graph
hcai_tsm.plot_rf_features_from_tsm(trained_random_forest,
classification_trainer.x_train,
feature_limit=20,
save=False)
# ## Train a custom ensemble of models
# The ensemble methods take a dictionary of TrainedSupervisedModels by a name of your choice
custom_ensemble = {
'KNN':
classification_trainer.knn(hyperparameter_grid=knn_hyperparameters,
randomized_search=False,
scoring_metric='roc_auc'),
'Logistic Regression':
classification_trainer.logistic_regression(),
'Random Forest Classifier':
classification_trainer.random_forest_classifier(
randomized_search=False, scoring_metric='roc_auc')
}
trained_ensemble = classification_trainer.ensemble_classification(
scoring_metric='roc_auc', trained_model_by_name=custom_ensemble)
# Step 5: Evaluate and compare the models
# Create a list of all the models you just trained that you want to compare
models_to_compare = [trained_knn, trained_random_forest, trained_ensemble]
# Create a ROC plot that compares all the them.
hcai_tsm.tsm_classification_comparison_plots(
trained_supervised_models=models_to_compare,
plot_type='ROC',
save=False)
# Create a PR plot that compares all the them.
hcai_tsm.tsm_classification_comparison_plots(
trained_supervised_models=models_to_compare,
plot_type='PR',
save=False)
# Inspect the raw ROC or PR cutoffs
print(trained_random_forest.roc(print_output=False))
print(trained_random_forest.pr(print_output=False))
def main():
"""Template script for ADVANCED USERS using healthcareai."""
# Load the included diabetes sample data
dataframe = healthcareai.load_diabetes()
# ...or load your own data from a .csv file: Uncomment to pull data from your CSV
# dataframe = healthcareai.load_csv('path/to/your.csv')
# ...or load data from a MSSQL server: Uncomment to pull data from MSSQL server
# server = 'localhost'
# database = 'SAM'
# query = """SELECT *
# FROM [SAM].[dbo].[DiabetesClincialSampleData]
# -- In this step, just grab rows that have a target
# WHERE ThirtyDayReadmitFLG is not null"""
#
# engine = hcai_db.build_mssql_engine_using_trusted_connections(server=server, database=database)
# dataframe = pd.read_sql(query, engine)
# Peek at the first 5 rows of data
print(dataframe.head(5))
# Drop columns that won't help machine learning
dataframe.drop(['PatientID'], axis=1, inplace=True)
# Step 1: Prepare the data using optional imputation. There are two options for this:
# ## Option 1: Use built in data prep pipeline that does enocding, imputation, null filtering, dummification
clean_training_dataframe = hcai_pipelines.full_pipeline(
'classification',
'ThirtyDayReadmitFLG',
'PatientEncounterID',
impute=True).fit_transform(dataframe)
# ## Option 2: Build your own pipeline using healthcare.ai methods, your own, or a combination of either.
# - Please note this is intentionally spartan, so we don't hinder your creativity. :)
# - Also note that many of the healthcare.ai transformers intentionally return dataframes, compared to scikit that
# return numpy arrays
# custom_pipeline = Pipeline([
# ('remove_grain_column', hcai_filters.DataframeColumnRemover(columns_to_remove=['PatientEncounterID', 'PatientID'])),
# ('imputation', hcai_transformers.DataFrameImputer(impute=True)),
# ('convert_target_to_binary', hcai_transformers.DataFrameConvertTargetToBinary('classification', 'ThirtyDayReadmitFLG')),
# # ('prediction_to_numeric', hcai_transformers.DataFrameConvertColumnToNumeric('ThirtyDayReadmitFLG')),
# # ('create_dummy_variables', hcai_transformers.DataFrameCreateDummyVariables(excluded_columns=['ThirtyDayReadmitFLG'])),
# ])
#
# clean_training_dataframe = custom_pipeline.fit_transform(dataframe)
# Step 2: Instantiate an Advanced Trainer class with your clean and prepared training data
classification_trainer = healthcareai.AdvancedSupervisedModelTrainer(
dataframe=clean_training_dataframe,
model_type='classification',
predicted_column='ThirtyDayReadmitFLG',
grain_column='PatientEncounterID',
verbose=False)
# Step 3: split the data into train and test
classification_trainer.train_test_split()
# Step 4: Train some models
# ## Train a KNN classifier with a randomized search over custom hyperparameters
knn_hyperparameters = {
'algorithm': ['ball_tree', 'kd_tree'],
'n_neighbors': [1, 4, 6, 8, 10, 15, 20, 30, 50, 100, 200],
'weights': ['uniform', 'distance']}
trained_knn = classification_trainer.knn(
scoring_metric='accuracy',
hyperparameter_grid=knn_hyperparameters,
randomized_search=True,
# Set this relative to the size of your hyperparameter space. Higher will train more models and be slower
# Lower will be faster and possibly less performant
number_iteration_samples=10
)
# ## Train a random forest classifier with a randomized search over custom hyperparameters
# TODO these are bogus hyperparams for random forest
random_forest_hyperparameters = {
'n_estimators': [50, 100, 200, 300],
'max_features': [1, 2, 3, 4],
'max_leaf_nodes': [None, 30, 400]}
trained_random_forest = classification_trainer.random_forest_classifier(
scoring_metric='accuracy',
hyperparameter_grid=random_forest_hyperparameters,
randomized_search=True,
# Set this relative to the size of your hyperparameter space. Higher will train more models and be slower
# Lower will be faster and possibly less performant
number_iteration_samples=10
)
# Show the random forest feature importance graph
hcai_tsm.plot_rf_features_from_tsm(
trained_random_forest,
classification_trainer.x_train,
feature_limit=20,
save=False)
# ## Train a custom ensemble of models
# The ensemble methods take a dictionary of TrainedSupervisedModels by a name of your choice
custom_ensemble = {
'KNN': classification_trainer.knn(
hyperparameter_grid=knn_hyperparameters,
randomized_search=False,
scoring_metric='roc_auc'),
'Logistic Regression': classification_trainer.logistic_regression(),
'Random Forest Classifier': classification_trainer.random_forest_classifier(
randomized_search=False,
scoring_metric='roc_auc')}
trained_ensemble = classification_trainer.ensemble_classification(
scoring_metric='roc_auc',
trained_model_by_name=custom_ensemble)
# Step 5: Evaluate and compare the models
# Create a list of all the models you just trained that you want to compare
models_to_compare = [trained_knn, trained_random_forest, trained_ensemble]
# Create a ROC plot that compares all the them.
hcai_tsm.tsm_classification_comparison_plots(
trained_supervised_models=models_to_compare,
plot_type='ROC',
save=False)
# Create a PR plot that compares all the them.
hcai_tsm.tsm_classification_comparison_plots(
trained_supervised_models=models_to_compare,
plot_type='PR',
save=False)
# Inspect the raw ROC or PR cutoffs
print(trained_random_forest.roc(print_output=False))
print(trained_random_forest.pr(print_output=False))