def __init__(self,
dataframe,
predicted_column,
model_type,
impute=True,
grain_column=None,
verbose=False):
"""
Set up a SupervisedModelTrainer
Args:
dataframe (pandas.core.frame.DataFrame): The training data in a pandas dataframe
predicted_column (str): The name of the prediction column
model_type (str): the trainer type - 'classification' or 'regression'
impute (bool): True to impute data (mean of numeric columns and mode of categorical ones). False to drop rows
that contain any null values.
grain_column (str): The name of the grain column
verbose (bool): Set to true for verbose output. Defaults to False.
"""
self.predicted_column = predicted_column
self.grain_column = grain_column
# Build the pipeline
# Note: Missing numeric values are imputed in prediction. If we don't impute, then some rows on the prediction
# data frame will be removed, which results in missing predictions.
pipeline = hcai_pipelines.full_pipeline(model_type,
predicted_column,
grain_column,
impute=impute)
prediction_pipeline = hcai_pipelines.full_pipeline(model_type,
predicted_column,
grain_column,
impute=True)
# Run the raw data through the data preparation pipeline
clean_dataframe = pipeline.fit_transform(dataframe)
_ = prediction_pipeline.fit_transform(dataframe)
# Instantiate the advanced class
self._advanced_trainer = AdvancedSupervisedModelTrainer(
dataframe=clean_dataframe,
model_type=model_type,
predicted_column=predicted_column,
grain_column=grain_column,
original_column_names=dataframe.columns.values,
verbose=verbose)
# Save the pipeline to the parent class
self._advanced_trainer.pipeline = prediction_pipeline
# Split the data into train and test
self._advanced_trainer.train_test_split()
self._advanced_trainer.categorical_column_info = get_categorical_levels(
dataframe=dataframe,
columns_to_ignore=[grain_column, predicted_column])
def __init__(self,
dataframe,
predicted_column,
model_type,
impute=True,
grain_column=None,
verbose=True,
imputeStrategy='MeanMedian'):
"""
Set up a SupervisedModelTrainer.
Helps the user by checking for high cardinality features (such as IDs or
other unique identifiers) and low cardinality features (a column where
all values are equal.
Args:
dataframe (pandas.core.frame.DataFrame): The training data in a
pandas dataframe
predicted_column (str): The name of the prediction column
model_type (str): the trainer type - 'classification' or 'regression'
impute (bool): True to impute data (mean of numeric columns and
mode of categorical ones). False to drop
rows that contain any null values.
grain_column (str): The name of the grain column
verbose (bool): Set to true for verbose output. Defaults to True.
"""
self.predicted_column = predicted_column
self.grain_column = grain_column
# Build the pipeline
# Note: Missing numeric values are imputed in prediction. If we don't
# impute, then some rows on the prediction
# data frame will be removed, which results in missing predictions.
pipeline = hcai_pipelines.full_pipeline(model_type,
predicted_column,
grain_column,
impute=impute,
verbose=True,
imputeStrategy=imputeStrategy)
prediction_pipeline = hcai_pipelines.full_pipeline(
model_type,
predicted_column,
grain_column,
impute=True,
verbose=False,
imputeStrategy=imputeStrategy)
# Run a low and high cardinality check. Warn the user, and allow
# them to proceed.
hcai_ordinality.check_high_cardinality(dataframe, self.grain_column)
hcai_ordinality.check_one_cardinality(dataframe)
# Run the raw data through the data preparation pipeline
clean_dataframe = pipeline.fit_transform(dataframe)
_ = prediction_pipeline.fit_transform(dataframe)
# Instantiate the advanced class
self._advanced_trainer = AdvancedSupervisedModelTrainer(
pipeline=pipeline,
dataframe=clean_dataframe,
model_type=model_type,
predicted_column=predicted_column,
grain_column=grain_column,
original_column_names=dataframe.columns.values,
verbose=verbose)
# Save the pipeline to the parent class
self._advanced_trainer.pipeline = prediction_pipeline
# Split the data into train and test
self._advanced_trainer.train_test_split()
self._advanced_trainer.categorical_column_info = get_categorical_levels(
dataframe=dataframe,
columns_to_ignore=[grain_column, predicted_column])
class SupervisedModelTrainer(object):
"""Train supervised models.
This class trains models using several common classifiers and regressors and
reports appropriate metrics.
"""
def __init__(self,
dataframe,
predicted_column,
model_type,
impute=True,
grain_column=None,
verbose=True,
imputeStrategy='MeanMedian'):
"""
Set up a SupervisedModelTrainer.
Helps the user by checking for high cardinality features (such as IDs or
other unique identifiers) and low cardinality features (a column where
all values are equal.
Args:
dataframe (pandas.core.frame.DataFrame): The training data in a
pandas dataframe
predicted_column (str): The name of the prediction column
model_type (str): the trainer type - 'classification' or 'regression'
impute (bool): True to impute data (mean of numeric columns and
mode of categorical ones). False to drop
rows that contain any null values.
grain_column (str): The name of the grain column
verbose (bool): Set to true for verbose output. Defaults to True.
"""
self.predicted_column = predicted_column
self.grain_column = grain_column
# Build the pipeline
# Note: Missing numeric values are imputed in prediction. If we don't
# impute, then some rows on the prediction
# data frame will be removed, which results in missing predictions.
pipeline = hcai_pipelines.full_pipeline(model_type,
predicted_column,
grain_column,
impute=impute,
verbose=True,
imputeStrategy=imputeStrategy)
prediction_pipeline = hcai_pipelines.full_pipeline(
model_type,
predicted_column,
grain_column,
impute=True,
verbose=False,
imputeStrategy=imputeStrategy)
# Run a low and high cardinality check. Warn the user, and allow
# them to proceed.
hcai_ordinality.check_high_cardinality(dataframe, self.grain_column)
hcai_ordinality.check_one_cardinality(dataframe)
# Run the raw data through the data preparation pipeline
clean_dataframe = pipeline.fit_transform(dataframe)
_ = prediction_pipeline.fit_transform(dataframe)
# Instantiate the advanced class
self._advanced_trainer = AdvancedSupervisedModelTrainer(
pipeline=pipeline,
dataframe=clean_dataframe,
model_type=model_type,
predicted_column=predicted_column,
grain_column=grain_column,
original_column_names=dataframe.columns.values,
verbose=verbose)
# Save the pipeline to the parent class
self._advanced_trainer.pipeline = prediction_pipeline
# Split the data into train and test
self._advanced_trainer.train_test_split()
self._advanced_trainer.categorical_column_info = get_categorical_levels(
dataframe=dataframe,
columns_to_ignore=[grain_column, predicted_column])
@property
def clean_dataframe(self):
"""Return the dataframe after the preparation pipeline (imputation and such)."""
return self._advanced_trainer.dataframe
def random_forest(self, feature_importance_limit=15, save_plot=False):
"""Train a random forest model and print out the model performance metrics.
Args:
feature_importance_limit (int): The maximum number of features to show
in the feature importance plotl
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.
"""
if self._advanced_trainer.model_type is 'classification':
return self.random_forest_classification(
feature_importance_limit=feature_importance_limit,
save_plot=save_plot)
elif self._advanced_trainer.model_type is 'regression':
return self.random_forest_regression()
@trainer_output
def knn(self):
"""Train a knn model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.knn(scoring_metric='roc_auc',
hyperparameter_grid=None,
randomized_search=True)
@trainer_output
def random_forest_regression(self):
"""Train a random forest regression model and print out the
model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.random_forest_regressor(
trees=200,
scoring_metric='neg_mean_squared_error',
randomized_search=True)
@trainer_output
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
@trainer_output
def logistic_regression(self):
"""Train a logistic regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.logistic_regression(
randomized_search=False)
@trainer_output
def linear_regression(self):
"""Train a linear regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.linear_regression(
randomized_search=False)
@trainer_output
def lasso_regression(self):
"""Train a lasso regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.lasso_regression(randomized_search=False)
@trainer_output
def ensemble(self):
"""Train a ensemble model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
# TODO consider making a scoring parameter (which will necessitate some more logic
if self._advanced_trainer.model_type is 'classification':
metric = 'roc_auc'
model = self._advanced_trainer.ensemble_classification(
scoring_metric=metric)
elif self._advanced_trainer.model_type is 'regression':
# TODO stub
metric = 'neg_mean_squared_error'
model = self._advanced_trainer.ensemble_regression(
scoring_metric=metric)
print('Based on the scoring metric {}, '
'the best algorithm found is: {}'.format(metric,
model.algorithm_name))
return model
@property
def advanced_features(self):
"""Return the underlying AdvancedSupervisedModelTrainer instance. For
advanced users only."""
return self._advanced_trainer
def __init__(self, dataframe, predicted_column, model_type, impute=True, grain_column=None, verbose=True):
"""
Set up a SupervisedModelTrainer.
Helps the user by checking for high cardinality features (such as IDs or
other unique identifiers) and low cardinality features (a column where
all values are equal.
Args:
dataframe (pandas.core.frame.DataFrame): The training data in a
pandas dataframe
predicted_column (str): The name of the prediction column
model_type (str): the trainer type - 'classification' or 'regression'
impute (bool): True to impute data (mean of numeric columns and
mode of categorical ones). False to drop
rows that contain any null values.
grain_column (str): The name of the grain column
verbose (bool): Set to true for verbose output. Defaults to True.
"""
self.predicted_column = predicted_column
self.grain_column = grain_column
# Build the pipeline
# Note: Missing numeric values are imputed in prediction. If we don't
# impute, then some rows on the prediction
# data frame will be removed, which results in missing predictions.
pipeline = hcai_pipelines.full_pipeline(model_type, predicted_column, grain_column, impute=impute,
verbose=True)
prediction_pipeline = hcai_pipelines.full_pipeline(model_type, predicted_column, grain_column, impute=True,
verbose=False)
# Run a low and high cardinality check. Warn the user, and allow
# them to proceed.
hcai_ordinality.check_high_cardinality(dataframe, self.grain_column)
hcai_ordinality.check_one_cardinality(dataframe)
# Run the raw data through the data preparation pipeline
clean_dataframe = pipeline.fit_transform(dataframe)
_ = prediction_pipeline.fit_transform(dataframe)
# Instantiate the advanced class
self._advanced_trainer = AdvancedSupervisedModelTrainer(pipeline=pipeline,
dataframe=clean_dataframe,
model_type=model_type,
predicted_column=predicted_column,
grain_column=grain_column,
original_column_names=dataframe.columns.values,
verbose=verbose)
# Save the pipeline to the parent class
self._advanced_trainer.pipeline = prediction_pipeline
# Split the data into train and test
self._advanced_trainer.train_test_split()
self._advanced_trainer.categorical_column_info = get_categorical_levels(
dataframe=dataframe,
columns_to_ignore=[grain_column, predicted_column])
class SupervisedModelTrainer(object):
"""Train supervised models.
This class trains models using several common classifiers and regressors and
reports appropriate metrics.
"""
def __init__(self, dataframe, predicted_column, model_type, impute=True, grain_column=None, verbose=True):
"""
Set up a SupervisedModelTrainer.
Helps the user by checking for high cardinality features (such as IDs or
other unique identifiers) and low cardinality features (a column where
all values are equal.
Args:
dataframe (pandas.core.frame.DataFrame): The training data in a
pandas dataframe
predicted_column (str): The name of the prediction column
model_type (str): the trainer type - 'classification' or 'regression'
impute (bool): True to impute data (mean of numeric columns and
mode of categorical ones). False to drop
rows that contain any null values.
grain_column (str): The name of the grain column
verbose (bool): Set to true for verbose output. Defaults to True.
"""
self.predicted_column = predicted_column
self.grain_column = grain_column
# Build the pipeline
# Note: Missing numeric values are imputed in prediction. If we don't
# impute, then some rows on the prediction
# data frame will be removed, which results in missing predictions.
pipeline = hcai_pipelines.full_pipeline(model_type, predicted_column, grain_column, impute=impute,
verbose=True)
prediction_pipeline = hcai_pipelines.full_pipeline(model_type, predicted_column, grain_column, impute=True,
verbose=False)
# Run a low and high cardinality check. Warn the user, and allow
# them to proceed.
hcai_ordinality.check_high_cardinality(dataframe, self.grain_column)
hcai_ordinality.check_one_cardinality(dataframe)
# Run the raw data through the data preparation pipeline
clean_dataframe = pipeline.fit_transform(dataframe)
_ = prediction_pipeline.fit_transform(dataframe)
# Instantiate the advanced class
self._advanced_trainer = AdvancedSupervisedModelTrainer(pipeline=pipeline,
dataframe=clean_dataframe,
model_type=model_type,
predicted_column=predicted_column,
grain_column=grain_column,
original_column_names=dataframe.columns.values,
verbose=verbose)
# Save the pipeline to the parent class
self._advanced_trainer.pipeline = prediction_pipeline
# Split the data into train and test
self._advanced_trainer.train_test_split()
self._advanced_trainer.categorical_column_info = get_categorical_levels(
dataframe=dataframe,
columns_to_ignore=[grain_column, predicted_column])
@property
def clean_dataframe(self):
"""Return the dataframe after the preparation pipeline (imputation and such)."""
return self._advanced_trainer.dataframe
def random_forest(self, feature_importance_limit=15, save_plot=False):
"""Train a random forest model and print out the model performance metrics.
Args:
feature_importance_limit (int): The maximum number of features to show
in the feature importance plotl
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.
"""
if self._advanced_trainer.model_type is 'classification':
return self.random_forest_classification(feature_importance_limit=feature_importance_limit,
save_plot=save_plot)
elif self._advanced_trainer.model_type is 'regression':
return self.random_forest_regression()
@trainer_output
def knn(self):
"""Train a knn model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.knn(
scoring_metric='roc_auc',
hyperparameter_grid=None,
randomized_search=True)
@trainer_output
def random_forest_regression(self):
"""Train a random forest regression model and print out the
model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.random_forest_regressor(
trees=200,
scoring_metric='neg_mean_squared_error',
randomized_search=True)
@trainer_output
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
@trainer_output
def logistic_regression(self):
"""Train a logistic regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.logistic_regression(
randomized_search=False)
@trainer_output
def linear_regression(self):
"""Train a linear regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.linear_regression(randomized_search=False)
@trainer_output
def lasso_regression(self):
"""Train a lasso regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.lasso_regression(randomized_search=False)
@trainer_output
def ensemble(self):
"""Train a ensemble model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
# TODO consider making a scoring parameter (which will necessitate some more logic
if self._advanced_trainer.model_type is 'classification':
metric = 'roc_auc'
model = self._advanced_trainer.ensemble_classification(scoring_metric=metric)
elif self._advanced_trainer.model_type is 'regression':
# TODO stub
metric = 'neg_mean_squared_error'
model = self._advanced_trainer.ensemble_regression(scoring_metric=metric)
print('Based on the scoring metric {}, '
'the best algorithm found is: {}'.format(metric, model.algorithm_name))
return model
@property
def advanced_features(self):
"""Return the underlying AdvancedSupervisedModelTrainer instance. For
advanced users only."""
return self._advanced_trainer
class SupervisedModelTrainer(object):
"""
This class helps create a model using several common classifiers and regressors, both of which report appropiate
metrics.
"""
def __init__(self,
dataframe,
predicted_column,
model_type,
impute=True,
grain_column=None,
verbose=False):
"""
Set up a SupervisedModelTrainer
Args:
dataframe (pandas.core.frame.DataFrame): The training data in a pandas dataframe
predicted_column (str): The name of the prediction column
model_type (str): the trainer type - 'classification' or 'regression'
impute (bool): True to impute data (mean of numeric columns and mode of categorical ones). False to drop rows
that contain any null values.
grain_column (str): The name of the grain column
verbose (bool): Set to true for verbose output. Defaults to False.
"""
self.predicted_column = predicted_column
self.grain_column = grain_column
# Build the pipeline
# Note: Missing numeric values are imputed in prediction. If we don't impute, then some rows on the prediction
# data frame will be removed, which results in missing predictions.
pipeline = hcai_pipelines.full_pipeline(model_type,
predicted_column,
grain_column,
impute=impute)
prediction_pipeline = hcai_pipelines.full_pipeline(model_type,
predicted_column,
grain_column,
impute=True)
# Run the raw data through the data preparation pipeline
clean_dataframe = pipeline.fit_transform(dataframe)
_ = prediction_pipeline.fit_transform(dataframe)
# Instantiate the advanced class
self._advanced_trainer = AdvancedSupervisedModelTrainer(
dataframe=clean_dataframe,
model_type=model_type,
predicted_column=predicted_column,
grain_column=grain_column,
original_column_names=dataframe.columns.values,
verbose=verbose)
# Save the pipeline to the parent class
self._advanced_trainer.pipeline = prediction_pipeline
# Split the data into train and test
self._advanced_trainer.train_test_split()
self._advanced_trainer.categorical_column_info = get_categorical_levels(
dataframe=dataframe,
columns_to_ignore=[grain_column, predicted_column])
@property
def clean_dataframe(self):
""" Returns the dataframe after the preparation pipeline (imputation and such) """
return self._advanced_trainer.dataframe
def random_forest(self, save_plot=False):
# TODO Convenience method. Probably not needed?
""" Train a random forest model and print out the model performance metrics.
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.
"""
if self._advanced_trainer.model_type is 'classification':
return self.random_forest_classification(save_plot=save_plot)
elif self._advanced_trainer.model_type is 'regression':
return self.random_forest_regression()
def knn(self):
""" Train a knn model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
model_name = 'KNN'
print('\nTraining {}'.format(model_name))
# Train the model
trained_model = self._advanced_trainer.knn(scoring_metric='roc_auc',
hyperparameter_grid=None,
randomized_search=True)
# Display the model metrics
trained_model.print_training_results()
return trained_model
def random_forest_regression(self):
""" Train a random forest regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
model_name = 'Random Forest Regression'
print('\nTraining {}'.format(model_name))
# Train the model
trained_model = self._advanced_trainer.random_forest_regressor(
trees=200,
scoring_metric='neg_mean_squared_error',
randomized_search=True)
# Display the model metrics
trained_model.print_training_results()
return trained_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 logistic_regression(self):
""" Train a logistic regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
model_name = 'Logistic Regression'
print('\nTraining {}'.format(model_name))
# Train the model
trained_model = self._advanced_trainer.logistic_regression(
randomized_search=False)
# Display the model metrics
trained_model.print_training_results()
return trained_model
def linear_regression(self):
""" Train a linear regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
model_name = 'Linear Regression'
print('\nTraining {}'.format(model_name))
# Train the model
trained_model = self._advanced_trainer.linear_regression(
randomized_search=False)
# Display the model metrics
trained_model.print_training_results()
return trained_model
def lasso_regression(self):
""" Train a lasso regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
model_name = 'Lasso Regression'
print('\nTraining {}'.format(model_name))
# Train the model
trained_model = self._advanced_trainer.lasso_regression(
randomized_search=False)
# Display the model metrics
trained_model.print_training_results()
return trained_model
def ensemble(self):
""" Train a ensemble model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
# TODO consider making a scoring parameter (which will necessitate some more logic
model_name = 'ensemble {}'.format(self._advanced_trainer.model_type)
print('\nTraining {}'.format(model_name))
# Train the appropriate ensemble of models
if self._advanced_trainer.model_type is 'classification':
metric = 'roc_auc'
trained_model = self._advanced_trainer.ensemble_classification(
scoring_metric=metric)
elif self._advanced_trainer.model_type is 'regression':
# TODO stub
metric = 'neg_mean_squared_error'
trained_model = self._advanced_trainer.ensemble_regression(
scoring_metric=metric)
print(
'Based on the scoring metric {}, the best algorithm found is: {}'.
format(metric, trained_model.algorithm_name))
# Display the model metrics
trained_model.print_training_results()
return trained_model
@property
def advanced_features(self):
""" Returns the underlying AdvancedSupervisedModelTrainer instance. For advanced users only. """
return self._advanced_trainer
def __init__(self, dataframe, predicted_column, model_type, impute=True, grain_column=None, verbose=True, imputeStrategy='MeanMode',
tunedRandomForest=False, numeric_columns_as_categorical=None ):
"""
Set up a SupervisedModelTrainer.
Helps the user by checking for high cardinality features (such as IDs or
other unique identifiers) and low cardinality features (a column where
all values are equal.
Args:
-----
dataframe (pandas.core.frame.DataFrame): The training data in a
pandas dataframe
predicted_column (str): The name of the prediction column
model_type (str): the trainer type - 'classification' or 'regression'
impute (bool): True to impute data (mean of numeric columns and
mode of categorical ones). False to drop
rows that contain any null values.
grain_column (str): The name of the grain column
verbose (bool): Set to true for verbose output. Defaults to True.
impute : boolean, default=True
If True, imputation of missing value takes place.
If False, drop rows that contain any null values.
imputeStrategy : string, default='MeanMode'
It decides the technique to be used for imputation of missing values.
If imputeStrategy = 'MeanMode', Columns of dtype object or category
(assumed categorical) and imputed by the mode value of that column.
Columns of other types (assumed continuous) : by mean of column.
If imputeStrategy = 'RandomForest', Columns of dtype object or category
(assumed categorical) : imputed using RandomForestClassifier.
Columns of other types (assumed continuous) : imputed using RandomForestRegressor
tunedRandomForest : boolean, default=False
If set to True, RandomForestClassifier/RandomForestRegressor to be used for
imputation of missing values are tuned using grid search and K-fold cross
validation.
Note:
If set to True, imputation process may take longer time depending upon size of
dataframe and number of columns having missing values.
numeric_columns_as_categorical : List of type String, default=None
List of column names which are numeric(int/float) in dataframe, but by nature
they are to be considered as categorical.
For example:
There is a column JobCode( Levels : 1,2,3,4,5,6)
If there are missing values in JobCode column, panadas will by default convert
this column into type float.
If numeric_columns_as_categorical=None
Missing values of this column will be imputed by Mean value of JobCode column.
type of 'JobCode' column will remain float.
If numeric_columns_as_categorical=['JobCode']
Missing values of this column will be imputed by mode value of JobCode column.
Also final type of 'JobCode' column will be numpy.object
"""
self.predicted_column = predicted_column
self.grain_column = grain_column
# Build the pipeline
# Note: Missing numeric values are imputed in prediction. If we don't
# impute, then some rows on the prediction
# data frame will be removed, which results in missing predictions.
pipeline = hcai_pipelines.full_pipeline(model_type, predicted_column, grain_column, impute=impute,
verbose=True, imputeStrategy=imputeStrategy, tunedRandomForest=tunedRandomForest,
numeric_columns_as_categorical=numeric_columns_as_categorical )
prediction_pipeline = hcai_pipelines.full_pipeline(model_type, predicted_column, grain_column, impute=True,
verbose=False, imputeStrategy=imputeStrategy, tunedRandomForest=tunedRandomForest,
numeric_columns_as_categorical=numeric_columns_as_categorical )
# Run a low and high cardinality check. Warn the user, and allow
# them to proceed.
hcai_ordinality.check_high_cardinality(dataframe, self.grain_column)
hcai_ordinality.check_one_cardinality(dataframe)
# Run the raw data through the data preparation pipeline
clean_dataframe = pipeline.fit_transform(dataframe)
_ = prediction_pipeline.fit_transform(dataframe)
# Instantiate the advanced class
self._advanced_trainer = AdvancedSupervisedModelTrainer(pipeline=pipeline,
dataframe=clean_dataframe,
model_type=model_type,
predicted_column=predicted_column,
grain_column=grain_column,
original_column_names=dataframe.columns.values,
verbose=verbose)
# Save the pipeline to the parent class
self._advanced_trainer.pipeline = prediction_pipeline
# Split the data into train and test
self._advanced_trainer.train_test_split()
self._advanced_trainer.categorical_column_info = get_categorical_levels(
dataframe=dataframe,
columns_to_ignore=[grain_column, predicted_column])
class SupervisedModelTrainer(object):
"""Train supervised models.
This class trains models using several common classifiers and regressors and
reports appropriate metrics.
"""
def __init__(self, dataframe, predicted_column, model_type, impute=True, grain_column=None, verbose=True, imputeStrategy='MeanMode',
tunedRandomForest=False, numeric_columns_as_categorical=None ):
"""
Set up a SupervisedModelTrainer.
Helps the user by checking for high cardinality features (such as IDs or
other unique identifiers) and low cardinality features (a column where
all values are equal.
Args:
-----
dataframe (pandas.core.frame.DataFrame): The training data in a
pandas dataframe
predicted_column (str): The name of the prediction column
model_type (str): the trainer type - 'classification' or 'regression'
impute (bool): True to impute data (mean of numeric columns and
mode of categorical ones). False to drop
rows that contain any null values.
grain_column (str): The name of the grain column
verbose (bool): Set to true for verbose output. Defaults to True.
impute : boolean, default=True
If True, imputation of missing value takes place.
If False, drop rows that contain any null values.
imputeStrategy : string, default='MeanMode'
It decides the technique to be used for imputation of missing values.
If imputeStrategy = 'MeanMode', Columns of dtype object or category
(assumed categorical) and imputed by the mode value of that column.
Columns of other types (assumed continuous) : by mean of column.
If imputeStrategy = 'RandomForest', Columns of dtype object or category
(assumed categorical) : imputed using RandomForestClassifier.
Columns of other types (assumed continuous) : imputed using RandomForestRegressor
tunedRandomForest : boolean, default=False
If set to True, RandomForestClassifier/RandomForestRegressor to be used for
imputation of missing values are tuned using grid search and K-fold cross
validation.
Note:
If set to True, imputation process may take longer time depending upon size of
dataframe and number of columns having missing values.
numeric_columns_as_categorical : List of type String, default=None
List of column names which are numeric(int/float) in dataframe, but by nature
they are to be considered as categorical.
For example:
There is a column JobCode( Levels : 1,2,3,4,5,6)
If there are missing values in JobCode column, panadas will by default convert
this column into type float.
If numeric_columns_as_categorical=None
Missing values of this column will be imputed by Mean value of JobCode column.
type of 'JobCode' column will remain float.
If numeric_columns_as_categorical=['JobCode']
Missing values of this column will be imputed by mode value of JobCode column.
Also final type of 'JobCode' column will be numpy.object
"""
self.predicted_column = predicted_column
self.grain_column = grain_column
# Build the pipeline
# Note: Missing numeric values are imputed in prediction. If we don't
# impute, then some rows on the prediction
# data frame will be removed, which results in missing predictions.
pipeline = hcai_pipelines.full_pipeline(model_type, predicted_column, grain_column, impute=impute,
verbose=True, imputeStrategy=imputeStrategy, tunedRandomForest=tunedRandomForest,
numeric_columns_as_categorical=numeric_columns_as_categorical )
prediction_pipeline = hcai_pipelines.full_pipeline(model_type, predicted_column, grain_column, impute=True,
verbose=False, imputeStrategy=imputeStrategy, tunedRandomForest=tunedRandomForest,
numeric_columns_as_categorical=numeric_columns_as_categorical )
# Run a low and high cardinality check. Warn the user, and allow
# them to proceed.
hcai_ordinality.check_high_cardinality(dataframe, self.grain_column)
hcai_ordinality.check_one_cardinality(dataframe)
# Run the raw data through the data preparation pipeline
clean_dataframe = pipeline.fit_transform(dataframe)
_ = prediction_pipeline.fit_transform(dataframe)
# Instantiate the advanced class
self._advanced_trainer = AdvancedSupervisedModelTrainer(pipeline=pipeline,
dataframe=clean_dataframe,
model_type=model_type,
predicted_column=predicted_column,
grain_column=grain_column,
original_column_names=dataframe.columns.values,
verbose=verbose)
# Save the pipeline to the parent class
self._advanced_trainer.pipeline = prediction_pipeline
# Split the data into train and test
self._advanced_trainer.train_test_split()
self._advanced_trainer.categorical_column_info = get_categorical_levels(
dataframe=dataframe,
columns_to_ignore=[grain_column, predicted_column])
@property
def clean_dataframe(self):
"""Return the dataframe after the preparation pipeline (imputation and such)."""
return self._advanced_trainer.dataframe
def random_forest(self, feature_importance_limit=15, save_plot=False):
"""Train a random forest model and print out the model performance metrics.
Args:
feature_importance_limit (int): The maximum number of features to show
in the feature importance plotl
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.
"""
if self._advanced_trainer.model_type is 'classification':
return self.random_forest_classification(feature_importance_limit=feature_importance_limit,
save_plot=save_plot)
elif self._advanced_trainer.model_type is 'regression':
return self.random_forest_regression()
@trainer_output
def knn(self):
"""Train a knn model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.knn(
scoring_metric='roc_auc',
hyperparameter_grid=None,
randomized_search=True)
@trainer_output
def random_forest_regression(self):
"""Train a random forest regression model and print out the
model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.random_forest_regressor(
trees=200,
scoring_metric='neg_mean_squared_error',
randomized_search=True)
@trainer_output
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
@trainer_output
def logistic_regression(self):
"""Train a logistic regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.logistic_regression(
randomized_search=False)
@trainer_output
def linear_regression(self):
"""Train a linear regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.linear_regression(randomized_search=False)
@trainer_output
def lasso_regression(self):
"""Train a lasso regression model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
return self._advanced_trainer.lasso_regression(randomized_search=False)
@trainer_output
def ensemble(self):
"""Train a ensemble model and print out the model performance metrics.
Returns:
TrainedSupervisedModel: A trained supervised model.
"""
# TODO consider making a scoring parameter (which will necessitate some more logic
if self._advanced_trainer.model_type is 'classification':
metric = 'roc_auc'
model = self._advanced_trainer.ensemble_classification(scoring_metric=metric)
elif self._advanced_trainer.model_type is 'regression':
# TODO stub
metric = 'neg_mean_squared_error'
model = self._advanced_trainer.ensemble_regression(scoring_metric=metric)
print('Based on the scoring metric {}, '
'the best algorithm found is: {}'.format(metric, model.algorithm_name))
return model
@property
def advanced_features(self):
"""Return the underlying AdvancedSupervisedModelTrainer instance. For
advanced users only."""
return self._advanced_trainer