def test_label_softclass():
# Given
problem_type = SOFTCLASS
input_labels = pd.Series([2, 4, 2, 2, 4, 1])
# Raise exception
with pytest.raises(NotImplementedError):
LabelCleaner.construct(problem_type=problem_type, y=input_labels, y_uncleaned=None)
def test_label_cleaner_regression():
# Given
problem_type = REGRESSION
input_labels_numpy = np.array([2, 4, 2, 2, 4, 1])
input_labels = pd.Series(input_labels_numpy)
input_labels_new = pd.Series([3, 5, 2])
expected_output_labels = input_labels.copy()
expected_output_labels_new = input_labels_new.copy()
expected_output_labels_new_inverse = input_labels_new.copy()
# When
label_cleaner = LabelCleaner.construct(problem_type=problem_type, y=input_labels, y_uncleaned=None)
# Then
assert isinstance(label_cleaner, LabelCleanerDummy)
assert label_cleaner.problem_type_transform == REGRESSION
output_labels = label_cleaner.transform(input_labels)
output_labels_with_numpy = label_cleaner.transform(input_labels_numpy)
output_labels_new = label_cleaner.transform(input_labels_new)
output_labels_inverse = label_cleaner.inverse_transform(output_labels)
output_labels_new_inverse = label_cleaner.inverse_transform(output_labels_new)
assert expected_output_labels.equals(output_labels)
assert expected_output_labels.equals(output_labels_with_numpy)
assert expected_output_labels_new.equals(output_labels_new)
assert input_labels.equals(output_labels_inverse)
assert expected_output_labels_new_inverse.equals(output_labels_new_inverse)
def test_label_cleaner_multiclass_to_binary():
# Given
problem_type = MULTICLASS
input_labels_numpy = np.array(['l1', 'l2', 'l2', 'l1', 'l1', 'l2'])
input_labels = pd.Series(input_labels_numpy)
input_labels_uncleaned = pd.Series(['l0', 'l1', 'l2', 'l2', 'l1', 'l1', 'l2', 'l3', 'l4'])
input_labels_category = input_labels.astype('category')
input_labels_with_shifted_index = input_labels.copy()
input_labels_with_shifted_index.index += 5
input_labels_new = np.array(['l0', 'l1', 'l2'])
input_labels_proba_transformed = pd.Series([0.7, 0.2, 0.5], index=[5, 2, 8])
expected_output_labels = pd.Series([0, 1, 1, 0, 0, 1])
expected_output_labels_new = pd.Series([np.nan, 0, 1])
expected_output_labels_new_inverse = pd.Series([np.nan, 'l1', 'l2'])
expected_output_labels_proba_transformed_inverse = pd.DataFrame(
data=[
[0, 0.3, 0.7, 0, 0],
[0, 0.8, 0.2, 0, 0],
[0, 0.5, 0.5, 0, 0]
], index=[5, 2, 8], columns=['l0', 'l1', 'l2', 'l3', 'l4'], dtype=np.float32
)
# When
label_cleaner = LabelCleaner.construct(problem_type=problem_type, y=input_labels, y_uncleaned=input_labels_uncleaned)
# Then
assert isinstance(label_cleaner, LabelCleanerMulticlassToBinary)
assert label_cleaner.problem_type_transform == BINARY
assert label_cleaner.cat_mappings_dependent_var == {0: 'l1', 1: 'l2'}
output_labels = label_cleaner.transform(input_labels)
output_labels_with_numpy = label_cleaner.transform(input_labels_numpy)
output_labels_category = label_cleaner.transform(input_labels_category)
output_labels_with_shifted_index = label_cleaner.transform(input_labels_with_shifted_index)
output_labels_new = label_cleaner.transform(input_labels_new)
output_labels_inverse = label_cleaner.inverse_transform(output_labels)
output_labels_with_shifted_index_inverse = label_cleaner.inverse_transform(output_labels_with_shifted_index)
output_labels_new_inverse = label_cleaner.inverse_transform(output_labels_new)
assert expected_output_labels.equals(output_labels)
assert expected_output_labels.equals(output_labels_with_numpy)
assert expected_output_labels.equals(output_labels_category)
assert not expected_output_labels.equals(output_labels_with_shifted_index)
output_labels_with_shifted_index.index -= 5
assert expected_output_labels.equals(output_labels_with_shifted_index)
assert expected_output_labels_new.equals(output_labels_new)
assert input_labels.equals(output_labels_inverse)
assert input_labels_with_shifted_index.equals(output_labels_with_shifted_index_inverse)
assert expected_output_labels_new_inverse.equals(output_labels_new_inverse)
output_labels_proba_transformed_inverse = label_cleaner.inverse_transform_proba(input_labels_proba_transformed, as_pandas=True)
pd.testing.assert_frame_equal(expected_output_labels_proba_transformed_inverse, output_labels_proba_transformed_inverse)
def test_label_cleaner_multiclass():
# Given
problem_type = MULTICLASS
input_labels_numpy = np.array([2, 4, 2, 2, 4, 1])
input_labels = pd.Series(input_labels_numpy)
input_labels_category = input_labels.astype('category')
input_labels_with_shifted_index = input_labels.copy()
input_labels_with_shifted_index.index += 5
input_labels_new = np.array([3, 5, 2])
expected_output_labels = pd.Series([1, 2, 1, 1, 2, 0])
expected_output_labels_new = pd.Series([np.nan, np.nan, 1])
expected_output_labels_new_inverse = pd.Series([np.nan, np.nan, 2])
# When
label_cleaner = LabelCleaner.construct(problem_type=problem_type,
y=input_labels,
y_uncleaned=input_labels)
# Then
assert isinstance(label_cleaner, LabelCleanerMulticlass)
assert label_cleaner.problem_type_transform == MULTICLASS
assert label_cleaner.cat_mappings_dependent_var == {0: 1, 1: 2, 2: 4}
output_labels = label_cleaner.transform(input_labels)
output_labels_with_numpy = label_cleaner.transform(input_labels_numpy)
output_labels_category = label_cleaner.transform(input_labels_category)
output_labels_with_shifted_index = label_cleaner.transform(
input_labels_with_shifted_index)
output_labels_new = label_cleaner.transform(input_labels_new)
output_labels_inverse = label_cleaner.inverse_transform(output_labels)
output_labels_with_shifted_index_inverse = label_cleaner.inverse_transform(
output_labels_with_shifted_index)
output_labels_new_inverse = label_cleaner.inverse_transform(
output_labels_new)
assert expected_output_labels.equals(output_labels)
assert expected_output_labels.equals(output_labels_with_numpy)
assert expected_output_labels.equals(output_labels_category)
assert not expected_output_labels.equals(output_labels_with_shifted_index)
output_labels_with_shifted_index.index -= 5
assert expected_output_labels.equals(output_labels_with_shifted_index)
assert expected_output_labels_new.equals(output_labels_new)
assert input_labels.equals(output_labels_inverse)
assert input_labels_with_shifted_index.equals(
output_labels_with_shifted_index_inverse)
assert expected_output_labels_new_inverse.equals(output_labels_new_inverse)
def test_label_softclass():
# Given
problem_type = SOFTCLASS
input_labels = pd.DataFrame([
[0, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
[0, 0, 0.3, 0.6, 0.1, 0],
])
# When
label_cleaner = LabelCleaner.construct(problem_type=problem_type, y=input_labels, y_uncleaned=None)
# Then
assert input_labels.equals(label_cleaner.transform(input_labels))
assert input_labels.equals(label_cleaner.inverse_transform(input_labels))
assert label_cleaner.num_classes == 6
def fit_dataset(train_data, model, label, fit_args, sample_size=None):
if sample_size is not None and sample_size < len(train_data):
train_data = train_data.sample(n=sample_size, random_state=0)
X = train_data.drop(columns=[label])
y = train_data[label]
problem_type = infer_problem_type(y)
label_cleaner = LabelCleaner.construct(problem_type=problem_type, y=y)
y = label_cleaner.transform(y)
feature_generator = AutoMLPipelineFeatureGenerator()
X = feature_generator.fit_transform(X, y)
X, X_val, y, y_val = generate_train_test_split(
X, y, problem_type=problem_type, test_size=0.2, random_state=0)
model.fit(X=X, y=y, X_val=X_val, y_val=y_val, **fit_args)
return model, label_cleaner, feature_generator
def fit(self,
train_data,
tuning_data=None,
time_limit='auto',
presets=None,
hyperparameters=None,
**kwargs):
"""Automatic fit process for image prediction.
Parameters
----------
train_data : pd.DataFrame
Training data, can be a dataframe like image dataset.
For dataframe like datasets, `image` and `label` columns are required.
`image`: raw image paths. `label`: categorical integer id, starting from 0.
tuning_data : pd.DataFrame, default = None
Another dataset containing validation data reserved for model selection and hyperparameter-tuning,
can be a dataframe like image dataset.
If `None`, the validation dataset will be randomly split from `train_data` according to `holdout_frac`.
time_limit : int, default = 'auto' (defaults to 2 hours if no presets detected)
Time limit in seconds, if `None`, will run until all tuning and training finished.
If `time_limit` is hit during `fit`, the HPO process will interrupt and return the current best configuration.
presets : list or str or dict, default = ['medium_quality_faster_train']
List of preset configurations for various arguments in `fit()`. Can significantly impact predictive accuracy, memory-footprint, and inference latency of trained models,
and various other properties of the returned `predictor`.
It is recommended to specify presets and avoid specifying most other `fit()` arguments or model hyperparameters prior to becoming familiar with AutoGluon.
As an example, to get the most accurate overall predictor (regardless of its efficiency), set `presets='best_quality'`.
To get good quality with faster inference speed, set `presets='good_quality_faster_inference'`
Any user-specified arguments in `fit()` will override the values used by presets.
If specifying a list of presets, later presets will override earlier presets if they alter the same argument.
For precise definitions of the provided presets, see file: `autogluon/vision/configs/presets_configs.py`.
Users can specify custom presets by passing in a dictionary of argument values as an element to the list.
Available Presets: ['best_quality', 'high_quality_fast_inference', 'good_quality_faster_inference', 'medium_quality_faster_train']
It is recommended to only use one `quality` based preset in a given call to `fit()` as they alter many of the same arguments and are not compatible with each-other.
Note that depending on your specific hardware limitation(# gpu, size of gpu memory...) your mileage may vary a lot, you may choose lower quality presets if necessary, and
try to reduce `batch_size` if OOM("RuntimeError: CUDA error: out of memory") happens frequently during the `fit`.
In-depth Preset Info:
# Best predictive accuracy with little consideration to inference time or model size. Achieve even better results by specifying a large time_limit value.
# Recommended for applications that benefit from the best possible model accuracy.
best_quality={
'hyperparameters': {
'model': Categorical('coat_lite_small', 'twins_pcpvt_base', 'swin_base_patch4_window7_224'),
'lr': Real(1e-5, 1e-2, log=True),
'batch_size': Categorical(8, 16, 32, 64, 128),
'epochs': 200,
'early_stop_patience': 50
},
'hyperparameter_tune_kwargs': {
'num_trials': 1024,
'searcher': 'random',
},
'time_limit': 12*3600,
},
# Good predictive accuracy with fast inference.
# Recommended for applications that require reasonable inference speed and/or model size.
good_quality_fast_inference={
'hyperparameters': {
'model': Categorical('resnet50d', 'efficientnet_b1', 'mobilenetv3_large_100'),
'lr': Real(1e-4, 1e-2, log=True),
'batch_size': Categorical(8, 16, 32, 64, 128),
'epochs': 150,
'early_stop_patience': 20
},
'hyperparameter_tune_kwargs': {
'num_trials': 512,
'searcher': 'random',
},
'time_limit': 8*3600,
},
# Medium predictive accuracy with very fast inference and very fast training time.
medium_quality_faster_train={
'hyperparameters': {
'model': 'resnet50d',
'lr': 0.01,
'batch_size': 64,
'epochs': 50,
'early_stop_patience': 5
},
'time_limit': 1*3600,
},
# Medium predictive accuracy with very fast inference.
# Comparing with `medium_quality_faster_train` it uses faster model but explores more hyperparameters.
medium_quality_faster_inference={
'hyperparameters': {
'model': Categorical('resnet18', 'mobilenetv3_small_100', 'resnet18_v1b'),
'lr': Categorical(0.01, 0.005, 0.001),
'batch_size': Categorical(64, 128),
'epochs': Categorical(50, 100),
'early_stop_patience': 10
},
'hyperparameter_tune_kwargs': {
'num_trials': 32,
'searcher': 'random',
},
'time_limit': 2*3600,
},
hyperparameters : dict, default = None
Extra hyperparameters for specific models.
Accepted args includes(not limited to):
epochs : int, default value based on network
The `epochs` for model training.
net : mx.gluon.Block
The custom network. If defined, the model name in config will be ignored so your
custom network will be used for training rather than pulling it from model zoo.
optimizer : mx.Optimizer
The custom optimizer object. If defined, the optimizer will be ignored in config but this
object will be used in training instead.
batch_size : int
Mini batch size
lr : float
Trainer learning rate for optimization process.
early_stop_patience : int, default=10
Number of epochs with no improvement after which train is early stopped. Use `None` to disable.
early_stop_min_delta : float, default=1e-4
The small delta value to ignore when evaluating the metric. A large delta helps stablize the early
stopping strategy against tiny fluctuation, e.g. 0.5->0.49->0.48->0.499->0.500001 is still considered as
a good timing for early stopping.
early_stop_baseline : float, default=None
The minimum(baseline) value to trigger early stopping. For example, with `early_stop_baseline=0.5`,
early stopping won't be triggered if the metric is less than 0.5 even if plateau is detected.
Use `None` to disable.
early_stop_max_value : float, default=None
The max value for metric, early stop training instantly once the max value is achieved. Use `None` to disable.
You can get the list of accepted hyperparameters in `config.yaml` saved by this predictor.
**kwargs :
holdout_frac : float, default = 0.1
The random split ratio for `tuning_data` if `tuning_data==None`.
random_state : int, default = None
The random_state(seed) for shuffling data, only used if `tuning_data==None`.
Note that the `random_state` only affect the splitting process, not model training.
If not specified(None), will leave the original random sampling intact.
nthreads_per_trial : int, default = (# cpu cores)
Number of CPU threads for each trial, if `None`, will detect the # cores on current instance.
ngpus_per_trial : int, default = (# gpus)
Number of GPUs to use for each trial, if `None`, will detect the # gpus on current instance.
hyperparameter_tune_kwargs: dict, default = None
num_trials : int, default = 1
The limit of HPO trials that can be performed within `time_limit`. The HPO process will be terminated
when `num_trials` trials have finished or wall clock `time_limit` is reached, whichever comes first.
searcher : str, default = 'random'
Searcher strategy for HPO, 'random' by default.
Options include: ‘random’ (random search), ‘grid’ (grid search).
max_reward : float, default = None
The reward threashold for stopping criteria. If `max_reward` is reached during HPO, the scheduler
will terminate earlier to reduce time cost.
scheduler_options : dict, default = None
Extra options for HPO scheduler, please refer to :class:`autogluon.core.Searcher` for details.
"""
if self._problem_type is None:
# options: multiclass, binary, regression
self._problem_type = MULTICLASS
assert self._problem_type in (
MULTICLASS, BINARY,
REGRESSION), f"Invalid problem_type: {self._problem_type}"
if self._eval_metric is None:
if self._problem_type == REGRESSION:
# options: rmse
self._eval_metric = 'rmse'
logger.log(
20,
'ImagePredictor sets rmse as default eval_metric for regression problems.'
)
else:
# options: accuracy
self._eval_metric = 'accuracy'
logger.log(
20,
'ImagePredictor sets accuracy as default eval_metric for classification problems.'
)
# init/validate kwargs
kwargs = self._validate_kwargs(kwargs)
# unpack
num_trials = kwargs['hyperparameter_tune_kwargs']['num_trials']
nthreads_per_trial = kwargs['nthreads_per_trial']
ngpus_per_trial = kwargs['ngpus_per_trial']
holdout_frac = kwargs['holdout_frac']
random_state = kwargs['random_state']
scheduler = kwargs['hyperparameter_tune_kwargs']['scheduler']
searcher = kwargs['hyperparameter_tune_kwargs']['searcher']
max_reward = kwargs['hyperparameter_tune_kwargs']['max_reward']
scheduler_options = kwargs['hyperparameter_tune_kwargs'][
'scheduler_options']
# deep copy to avoid inplace overwrite
train_data = copy.deepcopy(train_data)
tuning_data = copy.deepcopy(tuning_data)
log_level = verbosity2loglevel(self._verbosity)
set_logger_verbosity(self._verbosity)
if presets:
if not isinstance(presets, list):
presets = [presets]
logger.log(20, f'Presets specified: {presets}')
if time_limit == 'auto':
# no presets, no user specified time_limit
time_limit = 7200
logger.log(20,
f'`time_limit=auto` set to `time_limit={time_limit}`.')
use_rec = False
if isinstance(train_data, str) and train_data == 'imagenet':
# FIXME: imagenet does not work, crashes in validating data due to empty DataFrames.
logger.warning(
'ImageNet is a huge dataset which cannot be downloaded directly, '
+ 'please follow the data preparation tutorial in GluonCV.' +
'The following record files(symlinks) will be used: \n' +
'rec_train : ~/.mxnet/datasets/imagenet/rec/train.rec\n' +
'rec_train_idx : ~/.mxnet/datasets/imagenet/rec/train.idx\n' +
'rec_val : ~/.mxnet/datasets/imagenet/rec/val.rec\n' +
'rec_val_idx : ~/.mxnet/datasets/imagenet/rec/val.idx\n')
train_data = pd.DataFrame({'image': [], self._label_inner: []})
tuning_data = pd.DataFrame({'image': [], self._label_inner: []})
use_rec = True
if isinstance(train_data, str):
try_import_d8()
from d8.image_classification import Dataset as D8D
names = D8D.list()
if train_data.lower() in names:
train_data = D8D.get(train_data)
else:
valid_names = '\n'.join(names)
raise ValueError(
f'`train_data` {train_data} is not among valid list {valid_names}'
)
if tuning_data is None:
train_data, tuning_data = train_data.split(1 - holdout_frac)
if isinstance(tuning_data, str):
try_import_d8()
from d8.image_classification import Dataset as D8D
names = D8D.list()
if tuning_data.lower() in names:
tuning_data = D8D.get(tuning_data)
else:
valid_names = '\n'.join(names)
raise ValueError(
f'`tuning_data` {tuning_data} is not among valid list {valid_names}'
)
# data sanity check
train_data = self._validate_data(train_data)
train_labels = _get_valid_labels(train_data)
self._label_cleaner = LabelCleaner.construct(
problem_type=self._problem_type,
y=train_labels,
y_uncleaned=train_labels)
train_labels_cleaned = self._label_cleaner.transform(train_labels)
if train_labels_cleaned.dtype.kind in ('i', 'u'):
train_labels_cleaned = train_labels_cleaned.astype('int64')
# converting to internal label set
_set_valid_labels(train_data, train_labels_cleaned)
tuning_data_validated = False
if tuning_data is None:
train_data, tuning_data, _, _ = generate_train_test_split(
X=train_data,
y=train_data[self._label_inner],
problem_type=self._problem_type,
test_size=holdout_frac)
logger.info(
'Randomly split train_data into train[%d]/validation[%d] splits.',
len(train_data), len(tuning_data))
train_data = train_data.reset_index(drop=True)
tuning_data = tuning_data.reset_index(drop=True)
tuning_data_validated = True
train_data = self._validate_data(train_data)
if isinstance(train_data, self.Dataset):
train_data = self.Dataset(train_data, classes=train_data.classes)
if tuning_data is not None and not tuning_data_validated:
tuning_data = self._validate_data(tuning_data)
# converting to internal label set
tuning_labels_cleaned = self._label_cleaner.transform(
_get_valid_labels(tuning_data))
if tuning_labels_cleaned.dtype.kind in ('i', 'u'):
tuning_labels_cleaned = tuning_labels_cleaned.astype('int64')
_set_valid_labels(tuning_data, tuning_labels_cleaned)
if isinstance(tuning_data, self.Dataset):
tuning_data = self.Dataset(tuning_data,
classes=tuning_data.classes)
if self._classifier is not None:
logging.getLogger("ImageClassificationEstimator").propagate = True
self._classifier._logger.setLevel(log_level)
self._fit_summary = self._classifier.fit(train_data,
tuning_data,
1 - holdout_frac,
random_state,
resume=False)
if hasattr(self._classifier, 'fit_history'):
self._fit_summary[
'fit_history'] = self._classifier.fit_history()
return self
# new HPO task
if time_limit is not None and num_trials is None:
num_trials = 99999
if time_limit is None and num_trials is None:
raise ValueError(
'`time_limit` and `num_trials` can not be `None` at the same time, '
'otherwise the training will not be terminated gracefully.')
config = {
'log_dir': self._log_dir,
'num_trials': 99999 if num_trials is None else max(1, num_trials),
'time_limits':
2147483647 if time_limit is None else max(1, time_limit),
'searcher': searcher,
# needed for gluon-cv TODO: remove after gluon-cv is updated https://github.com/dmlc/gluon-cv/issues/1633
'search_strategy': searcher,
'scheduler': scheduler,
}
if max_reward is not None:
config['max_reward'] = max_reward
if nthreads_per_trial is not None:
config['nthreads_per_trial'] = nthreads_per_trial
if ngpus_per_trial is not None:
config['ngpus_per_trial'] = ngpus_per_trial
if isinstance(hyperparameters, dict):
if 'batch_size' in hyperparameters:
bs = hyperparameters['batch_size']
_check_gpu_memory_presets(bs, ngpus_per_trial, 4,
256) # 256MB per sample
net = hyperparameters.pop('net', None)
if net is not None:
config['custom_net'] = net
optimizer = hyperparameters.pop('optimizer', None)
if optimizer is not None:
config['custom_optimizer'] = optimizer
# check if hyperparameters overwriting existing config
for k, v in hyperparameters.items():
if k in config:
raise ValueError(
f'Overwriting {k} = {config[k]} to {v} by hyperparameters is ambiguous.'
)
config.update(hyperparameters)
if scheduler_options is not None:
config.update(scheduler_options)
if use_rec == True:
config['use_rec'] = True
if 'early_stop_patience' not in config:
config['early_stop_patience'] = 10
if config['early_stop_patience'] == None:
config['early_stop_patience'] = -1
# TODO(zhreshold): expose the transform function(or sign function) for converting custom metrics
if 'early_stop_baseline' not in config or config[
'early_stop_baseline'] == None:
config['early_stop_baseline'] = -np.Inf
if 'early_stop_max_value' not in config or config[
'early_stop_max_value'] == None:
config['early_stop_max_value'] = np.Inf
# batch size cannot be larger than dataset size
if ngpus_per_trial is not None and ngpus_per_trial > 1:
min_value = ngpus_per_trial
else:
min_value = 1
bs = sanitize_batch_size(config.get('batch_size', 16),
min_value=min_value,
max_value=len(train_data))
config['batch_size'] = bs
# TODO: remove this once mxnet is deprecated
if timm is None and config.get('model', None) is None:
config['model'] = 'resnet50_v1b'
# verbosity
if log_level > logging.INFO:
logging.getLogger("ImageClassificationEstimator").propagate = False
logging.getLogger("ImageClassificationEstimator").setLevel(
log_level)
task = ImageClassification(config=config,
problem_type=self._problem_type)
# GluonCV can't handle these separately - patching created config
task.search_strategy = scheduler
task.scheduler_options['searcher'] = searcher
task._logger.setLevel(log_level)
task._logger.propagate = True
self._train_classes = train_data.classes
with warnings.catch_warnings(record=True) as w:
# TODO: MXNetErrorCatcher was removed because it didn't return traceback
# Re-add once it returns full traceback regardless of which exception was caught
self._classifier = task.fit(train_data, tuning_data,
1 - holdout_frac, random_state)
self._classifier._logger.setLevel(log_level)
self._classifier._logger.propagate = True
self._fit_summary = task.fit_summary()
if hasattr(task, 'fit_history'):
self._fit_summary['fit_history'] = task.fit_history()
return self
def test_label_cleaner_binary():
# Given
problem_type = BINARY
input_labels_numpy = np.array(['l1', 'l2', 'l2', 'l1', 'l1', 'l2'])
input_labels = pd.Series(input_labels_numpy)
input_labels_category = input_labels.astype('category')
input_labels_with_shifted_index = input_labels.copy()
input_labels_with_shifted_index.index += 5
input_labels_new = np.array(['new', 'l1', 'l2'])
expected_output_labels = pd.Series([0, 1, 1, 0, 0, 1])
expected_output_labels_pos_class_l1 = pd.Series([1, 0, 0, 1, 1, 0])
expected_output_labels_new = pd.Series([np.nan, 0, 1])
expected_output_labels_new_pos_class_l1 = pd.Series([np.nan, 1, 0])
expected_output_labels_new_inverse = pd.Series([np.nan, 'l1', 'l2'])
# When
label_cleaner = LabelCleaner.construct(problem_type=problem_type, y=input_labels) # positive_class='l2'
label_cleaner_pos_class_l1 = LabelCleaner.construct(problem_type=problem_type, y=input_labels, positive_class='l1')
# Raise exception
with pytest.raises(ValueError):
LabelCleaner.construct(problem_type=problem_type, y=input_labels, positive_class='unknown_class')
# Raise exception
with pytest.raises(AssertionError):
LabelCleaner.construct(problem_type=problem_type, y=input_labels_new)
# Then
assert isinstance(label_cleaner, LabelCleanerBinary)
assert label_cleaner.problem_type_transform == BINARY
assert label_cleaner.cat_mappings_dependent_var == {0: 'l1', 1: 'l2'}
assert label_cleaner_pos_class_l1.cat_mappings_dependent_var == {0: 'l2', 1: 'l1'}
output_labels = label_cleaner.transform(input_labels)
output_labels_pos_class_l1 = label_cleaner_pos_class_l1.transform(input_labels)
output_labels_with_numpy = label_cleaner.transform(input_labels_numpy)
output_labels_category = label_cleaner.transform(input_labels_category)
output_labels_with_shifted_index = label_cleaner.transform(input_labels_with_shifted_index)
output_labels_new = label_cleaner.transform(input_labels_new)
output_labels_new_pos_class_l1 = label_cleaner_pos_class_l1.transform(input_labels_new)
output_labels_inverse = label_cleaner.inverse_transform(output_labels)
output_labels_inverse_pos_class_l1 = label_cleaner_pos_class_l1.inverse_transform(output_labels_pos_class_l1)
output_labels_with_shifted_index_inverse = label_cleaner.inverse_transform(output_labels_with_shifted_index)
output_labels_new_inverse = label_cleaner.inverse_transform(output_labels_new)
output_labels_new_inverse_pos_class_l1 = label_cleaner_pos_class_l1.inverse_transform(output_labels_new_pos_class_l1)
assert expected_output_labels.equals(output_labels)
assert expected_output_labels_pos_class_l1.equals(output_labels_pos_class_l1)
assert expected_output_labels.equals(output_labels_with_numpy)
assert expected_output_labels.equals(output_labels_category)
assert not expected_output_labels.equals(output_labels_with_shifted_index)
output_labels_with_shifted_index.index -= 5
assert expected_output_labels.equals(output_labels_with_shifted_index)
assert expected_output_labels_new.equals(output_labels_new)
assert expected_output_labels_new_pos_class_l1.equals(output_labels_new_pos_class_l1)
assert input_labels.equals(output_labels_inverse)
assert input_labels.equals(output_labels_inverse_pos_class_l1)
assert input_labels_with_shifted_index.equals(output_labels_with_shifted_index_inverse)
assert expected_output_labels_new_inverse.equals(output_labels_new_inverse)
assert expected_output_labels_new_inverse.equals(output_labels_new_inverse_pos_class_l1)
