def test_save_evolution(self):
_feature_engineer: FeatureEngineer = FeatureEngineer(
df=DATA_SET, target_feature='AveragePrice', temp_dir=DATA_DIR)
_feature_engineer.set_predictors(exclude_original_data=False)
_si: SwarmIntelligence = SwarmIntelligence(
mode='model',
target=_feature_engineer.get_target(),
input_file_path=None,
train_data_file_path=None,
test_data_file_path=None,
valid_data_file_path=None,
df=None,
data_set=None,
features=_feature_engineer.get_predictors(),
re_split_data=False,
re_sample_cases=False,
re_sample_features=False,
re_populate=True,
max_trials=2,
max_features=-1,
labels=None,
models=['cat'],
model_params=None,
burn_in_adjustments=-1,
warm_start=True,
warm_start_strategy='monotone',
warm_start_constant_hidden_layers=0,
warm_start_constant_category='very_small',
max_adjustments=5,
pop_size=64,
adjustment_rate=0.1,
adjustment_prob=0.85,
early_stopping=0,
convergence=True,
convergence_measure='median',
timer_in_seconds=43200,
force_target_type=None,
plot=False,
output_file_path='data',
deep_learning_type='batch',
deep_learning_output_size=None,
log=False,
feature_engineer=_feature_engineer,
sampling_function=None)
_si.optimize()
_si.save_evolution(si=True,
model=True,
evolution_history=True,
adjustment_history=True,
final_adjustment=True)
_found_pickles: List[bool] = []
for pickle in [
'model', 'GeneticAlgorithm', 'evolution_history',
'adjustment_history', 'final_adjustment'
]:
if os.path.isfile('data/{}.p'.format(pickle)):
_found_pickles.append(True)
else:
_found_pickles.append(False)
self.assertTrue(expr=all(_found_pickles))
def test_merge_engineer(self):
_all_features: List[str] = FEATURE_ENGINEER.get_features()
_all_features.sort(reverse=False)
_engineer: FeatureEngineer = FeatureEngineer(
df=None, file_path=DATA_FILE_PATH, target_feature='AveragePrice')
_categorical_features: List[str] = _engineer.get_feature_types().get(
'categorical')
_engineer.save(file_path='data/feature_learning_cat.p',
cls_obj=True,
overwrite=True,
create_dir=False)
del _engineer
_feature_engineer: FeatureEngineer = FeatureEngineer(
feature_engineer_file_path='data/feature_learning_cat.p')
_feature_engineer.clean(markers=dict(features=_categorical_features))
_feature_engineer.merge_engineer(
feature_engineer_file_path='data/feature_learning_cat.p')
_features: List[str] = _feature_engineer.get_features()
_features.sort(reverse=False)
self.assertListEqual(list1=_all_features, list2=_features)
def test_optimize_modeling(self):
_feature_engineer: FeatureEngineer = FeatureEngineer(
df=DATA_SET,
target_feature='AveragePrice',
temp_dir=DATA_DIR,
auto_typing=False)
_feature_engineer.set_predictors(exclude_original_data=False)
_ga: GeneticAlgorithm = GeneticAlgorithm(
mode='model',
target=_feature_engineer.get_target(),
input_file_path=None,
train_data_file_path=None,
test_data_file_path=None,
valid_data_file_path=None,
df=None,
data_set=None,
features=_feature_engineer.get_predictors(),
re_split_data=False,
re_sample_cases=False,
re_sample_features=False,
re_populate=True,
max_trials=2,
max_features=-1,
labels=None,
models=['cat'],
model_params=None,
burn_in_generations=-1,
warm_start=True,
warm_start_strategy='monotone',
warm_start_constant_hidden_layers=0,
warm_start_constant_category='very_small',
max_generations=2,
pop_size=4,
mutation_rate=0.1,
mutation_prob=0.85,
parents_ratio=0.5,
early_stopping=0,
convergence=True,
convergence_measure='median',
timer_in_seconds=43200,
force_target_type=None,
plot=False,
output_file_path='data',
deep_learning_type='batch',
deep_learning_output_size=None,
log=False,
verbose=False,
checkpoint=True,
feature_engineer=_feature_engineer,
sampling_function=None)
_ga.optimize()
self.assertTrue(expr=_ga.evolution_gradient.get('max')[0] <=
_ga.evolution_gradient.get('max')[-1])
def test_optimize_continue(self):
_feature_engineer: FeatureEngineer = FeatureEngineer(
df=DATA_SET, target_feature='AveragePrice', temp_dir=DATA_DIR)
_feature_engineer.set_predictors(exclude_original_data=False)
_max_adjustments: int = 5
_si: SwarmIntelligence = SwarmIntelligence(
mode='model',
target=_feature_engineer.get_target(),
input_file_path=None,
train_data_file_path=None,
test_data_file_path=None,
valid_data_file_path=None,
df=None,
data_set=None,
features=_feature_engineer.get_predictors(),
re_split_data=False,
re_sample_cases=False,
re_sample_features=False,
re_populate=True,
max_trials=2,
max_features=-1,
labels=None,
models=['cat'],
model_params=None,
burn_in_adjustments=-1,
warm_start=True,
warm_start_strategy='monotone',
warm_start_constant_hidden_layers=0,
warm_start_constant_category='very_small',
max_adjustments=_max_adjustments,
pop_size=64,
adjustment_rate=0.1,
adjustment_prob=0.85,
early_stopping=0,
convergence=True,
convergence_measure='median',
timer_in_seconds=43200,
force_target_type=None,
plot=False,
output_file_path='data',
deep_learning_type='batch',
deep_learning_output_size=None,
log=False,
feature_engineer=_feature_engineer,
sampling_function=None)
_si.optimize()
self.assertTrue(
expr=_si.evolution_gradient.get(
'max')[0] <= _si.evolution_gradient.get('max')[-1]
and len(_si.evolution_gradient.get('max')) > _max_adjustments)
def test_supervised_reg(self):
_feature_engineer: FeatureEngineer = FeatureEngineer(
df=DATA_SET, target_feature='AveragePrice')
_feature_engineer.set_predictors(exclude_original_data=False)
DataMiner(df=_feature_engineer.get_data(dask_df=True),
file_path=None,
target=_feature_engineer.get_target(),
predictors=_feature_engineer.get_predictors(),
feature_engineer=_feature_engineer,
feature_generator=True,
train_critic=True,
plot=True,
output_path='data',
**dict(max_generations=2)).supervised(
models=['cat', 'xgb', 'svm'],
feature_selector='shapley',
top_features=0.5,
optimizer='ga',
force_target_type=None,
train=True,
train_size=0.8,
random=True,
clf_eval_metric='auc',
reg_eval_metric='rmse_norm',
save_train_test_data=True,
save_ga=True,
**dict(engineer_categorical=False))
_found_results: List[bool] = []
for result in [
'feature_learning_data.parquet', 'feature_learning.p',
'feature_importance_shapley.html',
'feature_tournament_game_size.html', 'genetic.p', 'model.p'
]:
if os.path.isfile('data/{}'.format(result)):
_found_results.append(True)
else:
_found_results.append(False)
self.assertTrue(expr=all(_found_results))
def test_visualize_reg(self):
_feature_engineer: FeatureEngineer = FeatureEngineer(
df=DATA_SET, target_feature='AveragePrice', temp_dir=DATA_DIR)
_feature_engineer.set_predictors(exclude_original_data=False)
_si: SwarmIntelligence = SwarmIntelligence(
mode='model',
target=_feature_engineer.get_target(),
input_file_path=None,
train_data_file_path=None,
test_data_file_path=None,
valid_data_file_path=None,
df=None,
data_set=None,
features=_feature_engineer.get_predictors(),
re_split_data=False,
re_sample_cases=False,
re_sample_features=False,
re_populate=True,
max_trials=2,
max_features=-1,
labels=None,
models=None,
model_params=None,
burn_in_adjustments=-1,
warm_start=True,
warm_start_strategy='monotone',
warm_start_constant_hidden_layers=0,
warm_start_constant_category='very_small',
max_adjustments=5,
pop_size=4,
adjustment_rate=0.1,
adjustment_prob=0.85,
early_stopping=0,
convergence=True,
convergence_measure='median',
timer_in_seconds=43200,
force_target_type=None,
plot=False,
output_file_path='data',
deep_learning_type='batch',
deep_learning_output_size=None,
log=False,
feature_engineer=_feature_engineer,
sampling_function=None)
_si.optimize()
_si.visualize(results_table=True,
model_distribution=True,
model_evolution=True,
param_distribution=True,
train_time_distribution=True,
breeding_map=True,
breeding_graph=True,
fitness_distribution=True,
fitness_evolution=True,
fitness_dimensions=True,
per_adjustment=True,
prediction_of_best_model=True,
epoch_stats=True)
_found_plot: List[bool] = []
for plot in [
'ga_metadata_table',
'ga_model_evolution',
'ga_model_distribution',
#'ga_parameter_treemap',
'ga_training_time_distribution',
#'ga_breeding_heatmap',
#'ga_breeding_graph',
'ga_fitness_score_distribution_per_adjustment',
'ga_metadata_evolution_coords_actor_only',
'ga_evolution_fitness_score',
'ga_prediction_evaluation_coords',
'ga_prediction_scatter_contour'
]:
if os.path.isfile('data/{}.html'.format(plot)):
_found_plot.append(True)
else:
_found_plot.append(False)
self.assertTrue(expr=all(_found_plot))
def test_data_import(self):
_feature_engineer = FeatureEngineer(file_path='data/avocado.csv')
self.assertTrue(expr=_feature_engineer.get_n_cases() > 0)
import unittest
from happy_learning.feature_engineer import FeatureEngineer, PROCESSING_ACTION_SPACE, SUPPORTED_TYPES
from typing import Dict, List
DATA_FILE_PATH: str = 'data/avocado.csv'
FEATURE_ENGINEER_FILE_PATH: str = 'data/feature_engineer.p'
FEATURE_ENGINEER: FeatureEngineer = FeatureEngineer(
df=None,
file_path=DATA_FILE_PATH,
target_feature='AveragePrice',
generate_new_feature=True,
keep_original_data=True,
unify_invalid_values=True,
encode_missing_data=False,
max_level_processing=5,
activate_actor=False,
missing_value_analysis=True,
auto_cleaning=False,
auto_typing=True,
auto_engineering=False,
auto_text_mining=False,
seed=1234,
partitions=4)
def _check_feature_orchestra(meth: str, features: List[str]) -> bool:
"""
Check internal FeatureOrchestra decorator for the FeatureEngineer class
:param meth: str
def test_ga_clf(self):
_feature_engineer: FeatureEngineer = FeatureEngineer(
df=DATA_SET, target_feature='type')
_feature_engineer.set_predictors(exclude=None,
exclude_original_data=False)
_ga: GeneticAlgorithm = GeneticAlgorithm(
mode='model',
target=_feature_engineer.get_target(),
input_file_path=None,
train_data_file_path=None,
test_data_file_path=None,
valid_data_file_path=None,
df=None,
data_set=None,
features=_feature_engineer.get_predictors(),
re_split_data=False,
re_sample_cases=False,
re_sample_features=False,
re_populate=True,
max_trials=2,
max_features=-1,
labels=None,
models=['cat'],
model_params=None,
burn_in_generations=-1,
warm_start=True,
warm_start_strategy='monotone',
warm_start_constant_hidden_layers=0,
warm_start_constant_category='very_small',
max_generations=10,
pop_size=64,
mutation_rate=0.1,
mutation_prob=0.15,
parents_ratio=0.5,
early_stopping=0,
convergence=True,
convergence_measure='min',
timer_in_seconds=43200,
force_target_type=None,
plot=False,
output_file_path='data',
deep_learning_type='batch',
deep_learning_output_size=None,
log=False,
feature_engineer=_feature_engineer,
sampling_function=None)
_ga.optimize()
_feature_learning: FeatureLearning = FeatureLearning(
feature_engineer=_feature_engineer,
df=None,
file_path=None,
target=_feature_engineer.get_target(),
force_target_type=None,
max_features=-1,
keep_fittest_only=True,
train_categorical_critic=False,
train_continuous_critic=False,
engineer_time_disparity=True,
engineer_categorical=True,
engineer_text=True,
output_path='data')
_feature_learning_engineer = _feature_learning.ga()
_ga_using_new_features: GeneticAlgorithm = GeneticAlgorithm(
mode='model',
target=_feature_learning_engineer.get_target(),
input_file_path=None,
train_data_file_path=None,
test_data_file_path=None,
valid_data_file_path=None,
df=None,
data_set=None,
features=_feature_learning_engineer.get_predictors(),
re_split_data=False,
re_sample_cases=False,
re_sample_features=False,
re_populate=True,
max_trials=2,
max_features=-1,
labels=None,
models=['cat'],
model_params=None,
burn_in_generations=-1,
warm_start=True,
warm_start_strategy='monotone',
warm_start_constant_hidden_layers=0,
warm_start_constant_category='very_small',
max_generations=10,
pop_size=64,
mutation_rate=0.1,
mutation_prob=0.15,
parents_ratio=0.5,
early_stopping=0,
convergence=True,
convergence_measure='min',
timer_in_seconds=43200,
force_target_type=None,
plot=False,
output_file_path='data',
deep_learning_type='batch',
deep_learning_output_size=None,
log=False,
feature_engineer=_feature_learning_engineer,
sampling_function=None)
_ga_using_new_features.optimize()
self.assertTrue(
expr=_ga_using_new_features.final_generation[
_ga_using_new_features.best_individual_idx]['fitness_score'] >=
_ga.final_generation[_ga.best_individual_idx]['fitness_score'])
