def test_calibrate_uncertainty():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {
'MEDV': 'output'
, 'CHAS': 'categorical'
}
df_boston_train, uncertainty_data = train_test_split(df_boston_train, test_size=0.5)
uncertainty_data, uncertainty_calibration_data = train_test_split(uncertainty_data, test_size=0.5)
ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions)
uncertainty_calibration_settings = {
'num_buckets': 3
, 'percentiles': [25, 50, 75]
}
ml_predictor.train(df_boston_train, perform_feature_selection=True, train_uncertainty_model=True, uncertainty_data=uncertainty_data, calibrate_uncertainty=True, uncertainty_calibration_settings=uncertainty_calibration_settings, uncertainty_calibration_data=uncertainty_calibration_data)
uncertainty_score = ml_predictor.predict_uncertainty(df_boston_test)
assert 'percentile_25_delta' in list(uncertainty_score.columns)
assert 'percentile_50_delta' in list(uncertainty_score.columns)
assert 'percentile_75_delta' in list(uncertainty_score.columns)
assert 'bucket_num' in list(uncertainty_score.columns)
def test_perform_feature_scaling_false_classification(model_name=None):
np.random.seed(0)
df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset()
column_descriptions = {
'survived': 'output'
, 'embarked': 'categorical'
, 'pclass': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='classifier', column_descriptions=column_descriptions)
ml_predictor.train(df_titanic_train, perform_feature_scaling=False, model_names=model_name)
test_score = ml_predictor.score(df_titanic_test, df_titanic_test.survived)
print('test_score')
print(test_score)
lower_bound = -0.215
if model_name == 'DeepLearningClassifier':
lower_bound = -0.235
assert lower_bound < test_score < -0.17
def test_include_bad_y_vals_predict_classification(model_name=None):
np.random.seed(0)
df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset()
column_descriptions = {
'survived': 'output'
, 'embarked': 'categorical'
, 'pclass': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='classifier', column_descriptions=column_descriptions)
df_titanic_test.ix[1, 'survived'] = float('nan')
df_titanic_test.ix[8, 'survived'] = float('inf')
df_titanic_test.ix[26, 'survived'] = None
ml_predictor.train(df_titanic_train, model_names=model_name)
test_score = ml_predictor.score(df_titanic_test.to_dict('records'), df_titanic_test.survived)
print('test_score')
print(test_score)
assert -0.215 < test_score < -0.17
def test_perform_feature_selection_true_regression(model_name=None):
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {
'MEDV': 'output'
, 'CHAS': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, perform_feature_selection=True, model_names=model_name)
test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV)
print('test_score')
print(test_score)
# Bumping this up since without these features our score drops
lower_bound = -4.0
if model_name == 'DeepLearningRegressor':
lower_bound = -14.5
if model_name == 'LGBMRegressor':
lower_bound = -4.95
assert lower_bound < test_score < -2.8
def test_select_from_multiple_classification_models_using_X_test_and_y_test():
df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset(
)
column_descriptions = {
'survived': 'output',
'embarked': 'categorical',
'pclass': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='classifier',
column_descriptions=column_descriptions)
ml_predictor.train(df_titanic_train,
model_names=[
'LogisticRegression', 'RandomForestClassifier',
'RidgeClassifier', 'GradientBoostingClassifier',
'ExtraTreesClassifier', 'AdaBoostClassifier',
'SGDClassifier', 'Perceptron',
'PassiveAggressiveClassifier'
],
X_test=df_titanic_test,
y_test=df_titanic_test.survived)
test_score = ml_predictor.score(df_titanic_test, df_titanic_test.survived)
print('test_score')
print(test_score)
assert -0.215 < test_score < -0.17
def test_all_algos_regression():
# a random seed of 42 has ExtraTreesRegressor getting the best CV score, and that model doesn't generalize as well as GradientBoostingRegressor.
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(
df_boston_train,
model_names=[
'LinearRegression', 'RandomForestRegressor', 'Ridge',
'GradientBoostingRegressor', 'ExtraTreesRegressor',
'AdaBoostRegressor', 'SGDRegressor', 'PassiveAggressiveRegressor',
'Lasso', 'LassoLars', 'ElasticNet', 'OrthogonalMatchingPursuit',
'BayesianRidge', 'ARDRegression', 'MiniBatchKMeans',
'DeepLearningRegressor'
])
test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV)
print('test_score')
print(test_score)
assert -3.35 < test_score < -2.8
def test_unmarked_date_column():
np.random.seed(0)
df_twitter_train, df_twitter_test = utils.get_twitter_sentiment_multilabel_classification_dataset(
)
column_descriptions = {
'airline_sentiment': 'output',
'airline': 'categorical',
'text': 'nlp',
'tweet_location': 'categorical',
'user_timezone': 'categorical'
# tweet_created is our date column. We want to test that "forgetting" to mark it as a date column throws a user warning before throwing the error
# , 'tweet_created': 'date'
}
ml_predictor = Predictor(type_of_estimator='classifier',
column_descriptions=column_descriptions)
with warnings.catch_warnings(record=True) as w:
try:
ml_predictor.train(df_twitter_train)
except TypeError as e:
pass
print('Here are the caught warnings:')
print(w)
assert len(w) == 1
def test_linear_model_analytics_classification(model_name=None):
np.random.seed(0)
df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset(
)
column_descriptions = {
'survived': 'output',
'sex': 'categorical',
'embarked': 'categorical',
'pclass': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='classifier',
column_descriptions=column_descriptions)
ml_predictor.train(df_titanic_train, model_names='RidgeClassifier')
test_score = ml_predictor.score(df_titanic_test, df_titanic_test.survived)
print('test_score')
print(test_score)
# Linear models aren't super great on this dataset...
assert -0.21 < test_score < -0.17
def test_pass_in_list_of_dictionaries_predict_classification():
np.random.seed(0)
df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset(
)
column_descriptions = {
'survived': 'output',
'embarked': 'categorical',
'pclass': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='classifier',
column_descriptions=column_descriptions)
list_titanic_train = df_titanic_train.to_dict('records')
ml_predictor.train(df_titanic_train)
test_score = ml_predictor.score(df_titanic_test.to_dict('records'),
df_titanic_test.survived)
print('test_score')
print(test_score)
assert -0.215 < test_score < -0.17
def ensemble_regressor_basic_test():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {
'MEDV': 'output'
, 'CHAS': 'categorical'
}
ensemble_config = [
{
'model_name': 'LGBMRegressor'
}
, {
'model_name': 'RandomForestRegressor'
}
]
ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, ensemble_config=None)
test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV)
print('test_score')
print(test_score)
assert -3.0 < test_score < -2.8
def test_all_algos_classification(model_name=None):
np.random.seed(0)
df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset(
)
column_descriptions = {
'survived': 'output',
'embarked': 'categorical',
'pclass': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='classifier',
column_descriptions=column_descriptions)
ml_predictor.train(
df_titanic_train,
model_names=[
'LogisticRegression', 'RandomForestClassifier', 'RidgeClassifier',
'GradientBoostingClassifier', 'ExtraTreesClassifier',
'AdaBoostClassifier', 'SGDClassifier', 'Perceptron',
'PassiveAggressiveClassifier', 'DeepLearningClassifier',
'XGBClassifier', 'LGBMClassifier'
])
test_score = ml_predictor.score(df_titanic_test, df_titanic_test.survived)
print('test_score')
print(test_score)
assert -0.215 < test_score < -0.17
def test_nlp_multilabel_classification():
np.random.seed(0)
df_twitter_train, df_twitter_test = utils.get_twitter_sentiment_multilabel_classification_dataset(
)
column_descriptions = {
'airline_sentiment': 'output',
'airline': 'categorical',
'text': 'nlp',
'tweet_location': 'categorical',
'user_timezone': 'categorical',
'tweet_created': 'date'
}
ml_predictor = Predictor(type_of_estimator='classifier',
column_descriptions=column_descriptions)
ml_predictor.train(df_twitter_train)
test_score = ml_predictor.score(df_twitter_test,
df_twitter_test.airline_sentiment,
verbose=0)
# Make sure our score is good, but not unreasonably good
print('test_score')
print(test_score)
assert 0.67 < test_score < 0.79
def optimize_final_model_regression(model_name=None):
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train,
optimize_final_model=True,
model_names=model_name)
test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV)
print('test_score')
print(test_score)
# the random seed gets a score of -3.21 on python 3.5
# There's a ton of noise here, due to small sample sizes
lower_bound = -3.4
if model_name == 'DeepLearningRegressor':
lower_bound = -13
if model_name == 'LGBMRegressor':
lower_bound = -5.5
if model_name == 'GradientBoostingRegressor':
lower_bound = -3.5
assert lower_bound < test_score < -2.8
def categorical_ensembling_regression(model_name=None):
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train_categorical_ensemble(df_boston_train,
perform_feature_selection=True,
model_names=model_name,
categorical_column='CHAS')
test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV)
print('test_score')
print(test_score)
# Bumping this up since without these features our score drops
lower_bound = -4.0
if model_name == 'DeepLearningRegressor':
# NOTE: the model fails to learn for one of the classes. might be worth looking into more
lower_bound = -16
if model_name == 'LGBMRegressor':
lower_bound = -4.95
assert lower_bound < test_score < -2.8
def test_throws_warning_when_fl_data_equals_df_train():
df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset(
)
column_descriptions = {
'survived': 'output',
'sex': 'categorical',
'embarked': 'categorical',
'pclass': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='classifier',
column_descriptions=column_descriptions)
with warnings.catch_warnings(record=True) as w:
try:
ml_predictor.train(df_titanic_train,
feature_learning=True,
fl_data=df_titanic_train)
except KeyError as e:
pass
# We should not be getting to this line- we should be throwing an error above
for thing in w:
print(thing)
assert len(w) >= 1
assert True
def test_verify_features_finds_no_missing_features_when_none_are_missing():
np.random.seed(0)
df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset(
)
column_descriptions = {
'survived': 'output',
'sex': 'categorical',
'embarked': 'categorical',
'pclass': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='classifier',
column_descriptions=column_descriptions)
ml_predictor.train(df_titanic_train, verify_features=True)
file_name = ml_predictor.save(str(random.random()))
with open(file_name, 'rb') as read_file:
saved_ml_pipeline = dill.load(read_file)
os.remove(file_name)
missing_features = saved_ml_pipeline.named_steps[
'final_model'].verify_features(df_titanic_test)
print('missing_features')
print(missing_features)
print("len(missing_features['prediction_not_training'])")
print(len(missing_features['prediction_not_training']))
print("len(missing_features['training_not_prediction'])")
print(len(missing_features['training_not_prediction']))
assert len(missing_features['prediction_not_training']) == 0
assert len(missing_features['training_not_prediction']) == 0
def test_categorical_ensemble_basic_classifier():
np.random.seed(0)
df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset(
)
column_descriptions = {
'survived': 'output',
'pclass': 'categorical',
'embarked': 'categorical',
'sex': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='classifier',
column_descriptions=column_descriptions)
ml_predictor.train_categorical_ensemble(df_titanic_train,
categorical_column='pclass',
optimize_final_model=False)
test_score = ml_predictor.score(df_titanic_test, df_titanic_test.survived)
print('test_score')
print(test_score)
# Small sample sizes mean there's a fair bit of noise here
assert -0.155 < test_score < -0.135
def ensemble_classifier_basic_test(model_name=None):
np.random.seed(0)
df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset()
column_descriptions = {
'survived': 'output'
, 'sex': 'categorical'
, 'embarked': 'categorical'
, 'pclass': 'categorical'
}
ensemble_config = [
{
'model_name': 'LGBMClassifier'
}
, {
'model_name': 'RandomForestClassifier'
}
]
ml_predictor = Predictor(type_of_estimator='classifier', column_descriptions=column_descriptions)
ml_predictor.train(df_titanic_train, ensemble_config=ensemble_config)
test_score = ml_predictor.score(df_titanic_test, df_titanic_test.survived)
print('test_score')
print(test_score)
assert -0.15 < test_score < -0.131
def test_prediction_intervals_actually_work():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, predict_intervals=[0.05, 0.95])
df_boston_test = df_boston_test.reset_index(drop=True)
intervals = ml_predictor.predict_intervals(df_boston_test)
actuals = df_boston_test.MEDV
count_under = 0
count_over = 0
# print(intervals)
for idx, row in intervals.iterrows():
actual = actuals.iloc[idx]
if actual < row['interval_0.05']:
count_under += 1
if actual > row['interval_0.95']:
count_over += 1
len_intervals = len(intervals)
pct_under = count_under * 1.0 / len_intervals
pct_over = count_over * 1.0 / len_intervals
# There's a decent bit of noise since this is such a small dataset
assert pct_under < 0.15
assert pct_over < 0.1
def train_basic_regressor(df_boston_train):
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, verbose=False)
return ml_predictor
def train_basic_binary_classifier(df_titanic_train):
column_descriptions = {
'survived': 'output'
, 'embarked': 'categorical'
, 'pclass': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='classifier', column_descriptions=column_descriptions)
ml_predictor.train(df_titanic_train)
return ml_predictor
def train_basic_multilabel_classifier(df_twitter_train):
column_descriptions = {
'airline_sentiment': 'output'
, 'airline': 'categorical'
, 'text': 'ignore'
, 'tweet_location': 'categorical'
, 'user_timezone': 'categorical'
, 'tweet_created': 'date'
}
ml_predictor = Predictor(type_of_estimator='classifier', column_descriptions=column_descriptions)
ml_predictor.train(df_twitter_train)
return ml_predictor
def __init__(self):
# self.trained_ml_pipeline = Sequential()
self.trained_ml_pipeline = Predictor(
type_of_estimator='regressor',
column_descriptions=column_descriptions)
with open(file_name, 'rb') as read_file:
datastruct = dill.load(read_file)
for step in datastruct.named_steps:
pipeline_step = datastruct.named_steps[step]
if pipeline_step.get(
'model_name',
'reallylongnonsensicalstring')[:12] == 'DeepLearning':
keras_file_name = file_name[:-5] + pipeline_step.model + '_keras_deep_learning_model.h5'
print(keras_file_name)
self.trained_ml_pipeline = load_model(keras_file_name)
def test_predict_intervals_should_fail_if_not_trained():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train)
try:
intervals = ml_predictor.predict_intervals(df_boston_test)
assert False
except ValueError:
assert True
def test_compare_all_models_regression():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, compare_all_models=True)
test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV)
print('test_score')
print(test_score)
assert -3.2 < test_score < -2.8
def test_perform_feature_selection_false_regression():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, perform_feature_selection=False)
test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV)
print('test_score')
print(test_score)
assert -3.2 < test_score < -2.8
def test_all_algos_regression():
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {
'MEDV': 'output'
, 'CHAS': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, model_names=['LinearRegression', 'RandomForestRegressor', 'Ridge', 'GradientBoostingRegressor', 'ExtraTreesRegressor', 'AdaBoostRegressor', 'SGDRegressor', 'PassiveAggressiveRegressor'])
test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV)
print('test_score')
print(test_score)
assert -3.25 < test_score < -2.8
def test_compute_power_1_regression():
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {
'MEDV': 'output'
, 'CHAS': 'categorical'
}
ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, compute_power=1)
test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV)
print('test_score')
print(test_score)
assert -3.2 < test_score < -2.8
def test_prediction_intervals_lets_the_user_specify_number_of_intervals():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train,
predict_intervals=True,
prediction_intervals=[.2])
intervals = ml_predictor.predict_intervals(df_boston_test,
return_type='list')
assert len(intervals[0]) == 2
def test_perform_feature_selection_true_regression():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {'MEDV': 'output', 'CHAS': 'categorical'}
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, perform_feature_selection=True)
test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV)
print('test_score')
print(test_score)
# Bumping this up since without these features our score drops
assert -4.0 < test_score < -2.8