def __init__(self,
oracle=None,
splitter='best',
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features=None,
seed=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
class_weight="balanced",
presort=False,
impurity_threshold=0.01):
if not isinstance(oracle, ModelType):
raise exceptions.ModelError(
"Incorrect estimator used, create one with skater.model.local.InMemoryModel"
)
self.oracle = oracle
self.logger = build_logger(oracle.logger.level, __name__)
self.__model_type = None
self.feature_names = oracle.feature_names
self.class_names = oracle.target_names
self.impurity_threshold = impurity_threshold
self.criterion_types = {
'classifier': {
'criterion': ['gini', 'entropy']
},
'regressor': {
'criterion': ['mse', 'friedman_mse', 'mae']
}
}
self.splitter_types = ['best', 'random']
self.splitter = splitter if any(
splitter in item for item in self.splitter_types) else 'best'
self.seed = seed
self.__model_type = oracle.model_type
self.__scorer_name = None
self.__best_score = None
# TODO validate the parameters based on estimator type
if self.__model_type == 'classifier':
est = DecisionTreeClassifier(
splitter=self.splitter,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
random_state=seed,
max_leaf_nodes=max_leaf_nodes,
min_impurity_decrease=min_impurity_decrease,
class_weight=class_weight,
presort=presort)
elif self.__model_type == 'regressor':
est = DecisionTreeRegressor(
splitter=self.splitter,
max_depth=None,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
random_state=seed,
max_leaf_nodes=max_leaf_nodes,
min_impurity_split=min_impurity_split,
presort=presort)
else:
raise exceptions.ModelError(
"Model type not supported. Supported options types{'classifier', 'regressor'}"
)
self.__model = est
self.__pred_func = lambda X, prob: self.__model.predict(
X) if prob is False else self.__model.predict_proba(X)
def __init__(self,
estimator_type='classifier',
splitter='best',
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features=None,
seed=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
class_weight="balanced",
class_names=None,
presort=False,
feature_names=None,
impurity_threshold=0.01,
log_level=_WARNING):
self.logger = build_logger(log_level, __name__)
self.__model = None
self.__model_type = None
self.feature_names = feature_names
self.class_names = class_names
self.impurity_threshold = impurity_threshold
self.criterion_types = {
'classifier': {
'criterion': ['gini', 'entropy']
},
'regressor': {
'criterion': ['mse', 'friedman_mse', 'mae']
}
}
self.splitter_types = ['best', 'random']
self.splitter = splitter if any(
splitter in item for item in self.splitter_types) else 'best'
self.seed = seed
# TODO validate the parameters based on estimator type
if estimator_type == 'classifier':
self.__model_type = estimator_type
self.__model = DecisionTreeClassifier(
splitter=self.splitter,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
random_state=seed,
max_leaf_nodes=max_leaf_nodes,
min_impurity_decrease=min_impurity_decrease,
min_impurity_split=min_impurity_split,
class_weight=class_weight,
presort=presort)
elif estimator_type == 'regressor':
self.__model_type = estimator_type
self.__model = DecisionTreeRegressor(
splitter=self.splitter,
max_depth=None,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
random_state=seed,
max_leaf_nodes=max_leaf_nodes,
min_impurity_decrease=min_impurity_decrease,
min_impurity_split=min_impurity_split,
presort=presort)
else:
raise exceptions.ModelError(
"Model type not supported. Supported options types{'classifier', 'regressor'}"
)
def partial_dependence(self,
feature_ids,
modelinstance,
filter_classes=None,
grid=None,
grid_resolution=30,
n_jobs=-1,
grid_range=None,
sample=True,
sampling_strategy='random-choice',
n_samples=1000,
bin_count=50,
return_metadata=False,
progressbar=True,
variance_type='estimate'):
"""
Approximates the partial dependence of the predict_fn with respect to the
variables passed.
Parameters:
-----------
feature_ids: list
the names/ids of the features for which partial dependence is to be computed.
Note that the algorithm's complexity scales exponentially with additional
features, so generally one should only look at one or two features at a
time. These feature ids must be available in the class's associated DataSet.
As of now, we only support looking at 1 or 2 features at a time.
modelinstance: skater.model.model.Model subtype
an estimator function of a fitted model used to derive prediction. Supports
classification and regression. Supports classification(binary, multi-class) and regression.
predictions = predict_fn(data)
Can either by a skater.model.remote.DeployedModel or a
skater.model.local.InMemoryModel
filter_classes: array type
The classes to run partial dependence on. Default None invokes all classes.
Only used in classification models.
grid: numpy.ndarray
2 dimensional array on which we fix values of features. Note this is
determined automatically if not given based on the percentiles of the
dataset.
grid_resolution: int
how many unique values to include in the grid. If the percentile range
is 5% to 95%, then that range will be cut into <grid_resolution>
equally size bins. Defaults to 30.
n_jobs: int
The number of CPUs to use to compute the PDs. -1 means 'all CPUs'.
Defaults to using all cores(-1).
grid_range: tuple
the percentile extrama to consider. 2 element tuple, increasing, bounded
between 0 and 1.
sample: boolean
Whether to sample from the original dataset.
sampling_strategy: string
If sampling, which approach to take. See DataSet.generate_sample for
details.
n_samples: int
The number of samples to use from the original dataset. Note this is
only active if sample = True and sampling strategy = 'uniform'. If
using 'uniform-over-similarity-ranks', use samples per bin
bin_count: int
The number of bins to use when using the similarity based sampler. Note
this is only active if sample = True and
sampling_strategy = 'uniform-over-similarity-ranks'.
total samples = bin_count * samples per bin.
samples_per_bin: int
The number of samples to collect for each bin within the sampler. Note
this is only active if sample = True and
sampling_strategy = 'uniform-over-similarity-ranks'. If using
sampling_strategy = 'uniform', use n_samples.
total samples = bin_count * samples per bin.
variance_type: string
return_metadata: boolean
:Example:
>>> from skater.model import InMemoryModel
>>> from skater.core.explanations import Interpretation
>>> from sklearn.ensemble import RandomForestClassier
>>> from sklearn.datasets import load_boston
>>> boston = load_boston()
>>> X = boston.data
>>> y = boston.target
>>> features = boston.feature_names
>>> rf = RandomForestClassier()
>>> rf.fit(X,y)
>>> model = InMemoryModel(rf, examples = X)
>>> interpreter = Interpretation()
>>> interpreter.load_data(X)
>>> feature_ids = ['ZN','CRIM']
>>> interpreter.partial_dependence.partial_dependence(features,model)
"""
if self.data_set is None:
load_data_not_called_err_msg = "self.interpreter.data_set not found. \n" \
"Please call Interpretation.load_data \n" \
"before running this method."
raise (
exceptions.DataSetNotLoadedError(load_data_not_called_err_msg))
feature_ids = self._check_features(feature_ids)
if filter_classes:
err_msg = "members of filter classes must be \n" \
"members of modelinstance.classes. \n" \
"Expected members of: \n" \
"{0}\n" \
"got: \n" \
"{1}".format(modelinstance.target_names,
filter_classes)
filter_classes = list(filter_classes)
assert all([
i in modelinstance.target_names for i in filter_classes
]), err_msg
# TODO: There might be a better place to do this check
if not isinstance(modelinstance, ModelType):
raise (exceptions.ModelError(
"Incorrect estimator function used for computing partial dependence, try one \n"
"creating one with skater.model.local.InMemoryModel or \n"
"skater.model.remote.DeployedModel"))
if modelinstance.model_type == 'classifier' and modelinstance.probability is False:
if modelinstance.unique_values is None:
raise (exceptions.ModelError(
'If using classifier without probability scores, unique_values cannot \n'
'be None'))
self.interpreter.logger.warn(
"Classifiers with probability scores can be explained \n"
"more granularly than those without scores. If a prediction method with \n"
"scores is available, use that instead.")
# TODO: This we can change easily to functional style
missing_feature_ids = []
for feature_id in feature_ids:
if feature_id not in self.data_set.feature_ids:
missing_feature_ids.append(feature_id)
if missing_feature_ids:
missing_feature_id_err_msg = "Features {0} not found in \n" \
"Interpretation.data_set.feature_ids \n" \
"{1}".format(missing_feature_ids, self.data_set.feature_ids)
raise (KeyError(missing_feature_id_err_msg))
if grid_range is None:
grid_range = (.05, 0.95)
else:
if not hasattr(grid_range, "__iter__"):
err_msg = "Grid range {} needs to be an iterable".format(
grid_range)
raise (exceptions.MalformedGridRangeError(err_msg))
self._check_grid_range(grid_range)
if not modelinstance.has_metadata:
examples = self.data_set.generate_sample(strategy='random-choice',
sample=True,
n_samples=10)
examples = DataManager(examples,
feature_names=self.data_set.feature_ids)
modelinstance._build_model_metadata(examples)
# if you dont pass a grid, build one.
grid = np.array(grid)
if not grid.any():
# Currently, if a given feature has fewer unique values than the value
# of grid resolution, then the grid will be set to those unique values.
# Otherwise it will take the percentile
# range according with grid_resolution bins.
grid = self.data_set.generate_grid(feature_ids,
grid_resolution=grid_resolution,
grid_range=grid_range)
else:
# want to ensure all grids have 2 axes
if len(grid.shape) == 1 and \
(StaticTypes.data_types.is_string(grid[0]) or StaticTypes.data_types.is_numeric(grid[0])):
grid = grid[:, np.newaxis].T
grid_resolution = grid.shape[1]
self.interpreter.logger.debug("Grid shape used for pdp: {}".format(
grid.shape))
self.interpreter.logger.debug(
"Grid resolution for pdp: {}".format(grid_resolution))
# make sure data_set module is giving us correct data structure
self._check_grid(grid, feature_ids)
# generate data
data_sample = self.data_set.generate_sample(strategy=sampling_strategy,
sample=sample,
n_samples=n_samples,
bin_count=bin_count)
assert type(data_sample) == self.data_set.data_type, "Something went wrong\n" \
"Theres a type mismatch between\n" \
"the sampled data and the origina\nl" \
"training set. Check Skater.models\n"
_pdp_metadata = self._build_metadata_dict(modelinstance, feature_ids,
self.data_set.feature_ids,
filter_classes,
variance_type)
self.interpreter.logger.debug("Shape of sampled data: {}".format(
data_sample.shape))
self.interpreter.logger.debug("Feature Ids: {}".format(feature_ids))
self.interpreter.logger.debug("PD metadata: {}".format(_pdp_metadata))
# cartesian product of grid
grid_expanded = pd.DataFrame(list(product(*grid))).values
if grid_expanded.shape[0] <= 0:
empty_grid_expanded_err_msg = "Must have at least 1 pdp value" \
"grid shape: {}".format(grid_expanded.shape)
raise (exceptions.MalformedGridError(empty_grid_expanded_err_msg))
predict_fn = modelinstance._get_static_predictor()
n_jobs = None if n_jobs < 0 else n_jobs
pd_func = functools.partial(_compute_pd,
estimator_fn=predict_fn,
grid_expanded=grid_expanded,
pd_metadata=_pdp_metadata,
input_data=data_sample,
filter_classes=filter_classes)
arg_list = [i for i in range(grid_expanded.shape[0])]
executor_instance = Pool(n_jobs)
if progressbar:
self.interpreter.logger.warn(
"Progress bars slow down runs by 10-20%. For slightly "
"faster runs, do progressbar=False")
mapper = executor_instance.imap
p = ProgressBar(len(arg_list), units='grid cells')
else:
mapper = executor_instance.map
pd_list = []
try:
if n_jobs == 1:
raise ValueError("Skipping to single processing")
for pd_row in mapper(pd_func, arg_list):
if progressbar:
p.animate()
pd_list.append(pd_row)
except:
self.interpreter.logger.info(
"Multiprocessing failed, going single process")
for pd_row in map(pd_func, arg_list):
if progressbar:
p.animate()
pd_list.append(pd_row)
finally:
executor_instance.close()
executor_instance.join()
executor_instance.terminate()
if return_metadata:
return pd.DataFrame(list(pd_list)), _pdp_metadata
else:
return pd.DataFrame(list(pd_list))