def test_ids_to_floats():
path = os.path.join(_my_dir, 'train', 'test_input_2examples_1.jsonlines')
examples = load_examples(path, ids_to_floats=True, quiet=True)
assert isinstance(examples.ids[0], float)
examples = load_examples(path, quiet=True)
assert not isinstance(examples.ids[0], float)
assert isinstance(examples.ids[0], str)
def test_backward_compatibility():
'''
Verify that a model from v0.9.17 can still be loaded and generate the same predictions.
'''
predict_path = os.path.join(_my_dir, 'backward_compatibility',
'v0.9.17_test_summary_test_summary_LogisticRegression.predictions')
model_path = os.path.join(_my_dir, 'backward_compatibility',
'v0.9.17_test_summary_test_summary_LogisticRegression.{}.model'.format(sys.version_info[0]))
test_path = os.path.join(_my_dir, 'backward_compatibility', 'v0.9.17_test_summary.jsonlines')
learner = Learner.from_file(model_path)
examples = load_examples(test_path, quiet=True)
new_predictions = learner.predict(examples)[:, 1]
with open(predict_path) as predict_file:
for line, new_val in zip(predict_file, new_predictions):
assert_almost_equal(float(line.strip()), new_val)
def compute_eval_from_predictions(examples_file, predictions_file,
metric_names):
'''
Compute evaluation metrics from prediction files after you have run an
experiment.
:param examples_file: a SKLL examples file (in .jsonlines or other format)
:param predictions_file: a SKLL predictions output TSV file with id
and prediction column names
:param metric_names: a list of SKLL metric names
(e.g., [pearson, unweighted_kappa])
:returns: a dictionary from metrics names to values
'''
# read gold standard labels
data = load_examples(examples_file)
gold = dict(zip(data.ids, data.classes))
# read predictions
pred = {}
with open(predictions_file) as pred_file:
reader = csv.reader(pred_file, dialect=csv.excel_tab)
next(reader) # skip header
for row in reader:
pred[row[0]] = float(row[1])
# make a sorted list of example ids in order to match up
# labels and predictions
if set(gold.keys()) != set(pred.keys()):
raise ValueError('The example and prediction IDs do not match.')
example_ids = sorted(gold.keys())
res = {}
for metric_name in metric_names:
score = use_score_func(metric_name,
[gold[ex_id] for ex_id in example_ids],
[pred[ex_id] for ex_id in example_ids])
res[metric_name] = score
return res
def compute_eval_from_predictions(examples_file, predictions_file,
metric_names):
'''
Compute evaluation metrics from prediction files after you have run an
experiment.
:param examples_file: a SKLL examples file (in .jsonlines or other format)
:param predictions_file: a SKLL predictions output TSV file with id
and prediction column names
:param metric_names: a list of SKLL metric names
(e.g., [pearson, unweighted_kappa])
:returns: a dictionary from metrics names to values
'''
# read gold standard labels
data = load_examples(examples_file)
gold = dict(zip(data.ids, data.classes))
# read predictions
pred = {}
with open(predictions_file) as pred_file:
reader = csv.reader(pred_file, dialect=csv.excel_tab)
next(reader) # skip header
for row in reader:
pred[row[0]] = float(row[1])
# make a sorted list of example ids in order to match up
# labels and predictions
if set(gold.keys()) != set(pred.keys()):
raise ValueError('The example and prediction IDs do not match.')
example_ids = sorted(gold.keys())
res = {}
for metric_name in metric_names:
score = use_score_func(metric_name,
[gold[ex_id] for ex_id in example_ids],
[pred[ex_id] for ex_id in example_ids])
res[metric_name] = score
return res
def _load_featureset(dir_path, feat_files, suffix, label_col='y',
ids_to_floats=False, quiet=False, class_map=None,
feature_hasher=False, num_features=None):
'''
Load a list of feature files and merge them.
:param dir_path: Path to the directory that contains the feature files.
:type dir_path: str
:param feat_files: List of feature file prefixes
:type feat_files: str
:param suffix: Suffix to add to feature file prefixes to get full
filenames.
:type suffix: str
:param label_col: Name of the column which contains the class labels.
If no column with that name exists, or `None` is
specified, the data is considered to be unlabelled.
:type label_col: str
:param ids_to_floats: Convert IDs to float to save memory. Will raise error
if we encounter an a non-numeric ID.
:type ids_to_floats: bool
:param quiet: Do not print "Loading..." status message to stderr.
:type quiet: bool
:param class_map: Mapping from original class labels to new ones. This is
mainly used for collapsing multiple classes into a single
class. Anything not in the mapping will be kept the same.
:type class_map: dict from str to str
:returns: The classes, IDs, features, and feature vectorizer representing
the given featureset.
:rtype: FeatureSet
'''
merged_set = FeatureSet('')
for file_name in sorted(os.path.join(dir_path, featfile + suffix) for
featfile in feat_files):
merged_set += load_examples(file_name, label_col=label_col,
ids_to_floats=ids_to_floats, quiet=quiet,
class_map=class_map,
feature_hasher=feature_hasher,
num_features=num_features)
return merged_set
def _load_featureset(dirpath,
featureset,
suffix,
label_col='y',
ids_to_floats=False,
quiet=False,
class_map=None,
unlabelled=False):
'''
Load a list of feature files and merge them.
:param dirpath: Path to the directory that contains the feature files.
:type dirpath: str
:param featureset: List of feature file prefixes
:type featureset: str
:param suffix: Suffix to add to feature file prefixes to get full filenames.
:type suffix: str
:param label_col: Name of the column which contains the class labels.
If no column with that name exists, or `None` is
specified, the data is considered to be unlabelled.
:type label_col: str
:param ids_to_floats: Convert IDs to float to save memory. Will raise error
if we encounter an a non-numeric ID.
:type ids_to_floats: bool
:param quiet: Do not print "Loading..." status message to stderr.
:type quiet: bool
:param class_map: Mapping from original class labels to new ones. This is
mainly used for collapsing multiple classes into a single
class. Anything not in the mapping will be kept the same.
:type class_map: dict from str to str
:param unlabelled: Is this test we're loading? If so, don't raise an error
if there are no labels.
:type unlabelled: bool
:returns: The classes, IDs, features, and feature vectorizer representing
the given featureset.
:rtype: ExamplesTuple
'''
# Load a list of lists of examples, one list of examples per featureset.
file_names = sorted(
os.path.join(dirpath, featfile + suffix) for featfile in featureset)
example_tuples = [
load_examples(file_name,
label_col=label_col,
ids_to_floats=ids_to_floats,
quiet=quiet,
class_map=class_map) for file_name in file_names
]
# Check that the IDs are unique within each file.
for file_name, examples in zip(file_names, example_tuples):
ex_ids = examples.ids
if len(ex_ids) != len(set(ex_ids)):
raise ValueError(('The example IDs are not unique in ' +
'{}.').format(file_name))
# Check that the different feature files have the same IDs.
# To do this, make a sorted tuple of unique IDs for each feature file,
# and then make sure they are all the same by making sure the set has one
# item in it.
mismatch_num = len(
{tuple(sorted(examples.ids))
for examples in example_tuples})
if mismatch_num != 1:
raise ValueError(('The sets of example IDs in {} feature files do ' +
'not match').format(mismatch_num))
# Make sure there is a unique label for every example (or no label, for
# "unseen" examples).
# To do this, find the unique (id, y) tuples, and then make sure that all
# those ids are unique.
unique_tuples = set(
chain(
*[[(curr_id, curr_label)
for curr_id, curr_label in zip(examples.ids, examples.classes)]
for examples in example_tuples
if any(x is not None for x in examples.classes)]))
if len({tup[0] for tup in unique_tuples}) != len(unique_tuples):
raise ValueError('At least two feature files have different labels ' +
'(i.e., y values) for the same ID.')
# Now, create the final ExamplesTuple of examples with merged features
merged_vectorizer = None
merged_features = None
merged_ids = None
merged_classes = None
for ids, classes, features, feat_vectorizer in example_tuples:
# Combine feature matrices and vectorizers
if merged_features is not None:
# Check for duplicate feature names
if (set(merged_vectorizer.get_feature_names())
& set(feat_vectorizer.get_feature_names())):
raise ValueError('Two feature files have the same feature!')
num_merged = merged_features.shape[1]
merged_features = sp.hstack([merged_features, features], 'csr')
# dictvectorizer sorts the vocabularies within each file
for feat_name, index in sorted(feat_vectorizer.vocabulary_.items(),
key=lambda x: x[1]):
merged_vectorizer.vocabulary_[feat_name] = index + num_merged
merged_vectorizer.feature_names_.append(feat_name)
else:
merged_features = features
merged_vectorizer = feat_vectorizer
# IDs should be the same for each ExamplesTuple, so only store once
if merged_ids is None:
merged_ids = ids
# Check that IDs are in the same order
elif not np.all(merged_ids == ids):
raise ValueError('IDs are not in the same order in each feature ' +
'file!')
# If current ExamplesTuple has labels, check that they don't conflict
if any(x is not None for x in classes):
# Classes should be the same for each ExamplesTuple, so store once
if merged_classes is None:
merged_classes = classes
# Check that classes don't conflict, when specified
elif not np.all(merged_classes == classes):
raise ValueError('Feature files have conflicting labels for ' +
'examples with the same ID!')
# Ensure that at least one file had classes if we're expecting them
if merged_classes is None and not unlabelled:
raise ValueError('No feature files in feature set contain class' +
'labels!')
return ExamplesTuple(merged_ids, merged_classes, merged_features,
merged_vectorizer)
def _load_featureset(dirpath, featureset, suffix, label_col='y',
ids_to_floats=False, quiet=False, class_map=None,
unlabelled=False):
'''
Load a list of feature files and merge them.
:param dirpath: Path to the directory that contains the feature files.
:type dirpath: str
:param featureset: List of feature file prefixes
:type featureset: str
:param suffix: Suffix to add to feature file prefixes to get full filenames.
:type suffix: str
:param label_col: Name of the column which contains the class labels.
If no column with that name exists, or `None` is
specified, the data is considered to be unlabelled.
:type label_col: str
:param ids_to_floats: Convert IDs to float to save memory. Will raise error
if we encounter an a non-numeric ID.
:type ids_to_floats: bool
:param quiet: Do not print "Loading..." status message to stderr.
:type quiet: bool
:param class_map: Mapping from original class labels to new ones. This is
mainly used for collapsing multiple classes into a single
class. Anything not in the mapping will be kept the same.
:type class_map: dict from str to str
:param unlabelled: Is this test we're loading? If so, don't raise an error
if there are no labels.
:type unlabelled: bool
:returns: The classes, IDs, features, and feature vectorizer representing
the given featureset.
:rtype: ExamplesTuple
'''
# Load a list of lists of examples, one list of examples per featureset.
file_names = sorted(os.path.join(dirpath, featfile + suffix) for featfile
in featureset)
example_tuples = [load_examples(file_name, label_col=label_col,
ids_to_floats=ids_to_floats, quiet=quiet,
class_map=class_map)
for file_name in file_names]
# Check that the IDs are unique within each file.
for file_name, examples in zip(file_names, example_tuples):
ex_ids = examples.ids
if len(ex_ids) != len(set(ex_ids)):
raise ValueError(('The example IDs are not unique in ' +
'{}.').format(file_name))
# Check that the different feature files have the same IDs.
# To do this, make a sorted tuple of unique IDs for each feature file,
# and then make sure they are all the same by making sure the set has one
# item in it.
mismatch_num = len({tuple(sorted(examples.ids)) for examples in
example_tuples})
if mismatch_num != 1:
raise ValueError(('The sets of example IDs in {} feature files do ' +
'not match').format(mismatch_num))
# Make sure there is a unique label for every example (or no label, for
# "unseen" examples).
# To do this, find the unique (id, y) tuples, and then make sure that all
# those ids are unique.
unique_tuples = set(chain(*[[(curr_id, curr_label) for curr_id, curr_label
in zip(examples.ids, examples.classes)]
for examples in example_tuples if
any(x is not None for x in examples.classes)]))
if len({tup[0] for tup in unique_tuples}) != len(unique_tuples):
raise ValueError('At least two feature files have different labels ' +
'(i.e., y values) for the same ID.')
# Now, create the final ExamplesTuple of examples with merged features
merged_vectorizer = None
merged_features = None
merged_ids = None
merged_classes = None
for ids, classes, features, feat_vectorizer in example_tuples:
# Combine feature matrices and vectorizers
if merged_features is not None:
# Check for duplicate feature names
if (set(merged_vectorizer.get_feature_names()) &
set(feat_vectorizer.get_feature_names())):
raise ValueError('Two feature files have the same feature!')
num_merged = merged_features.shape[1]
merged_features = sp.hstack([merged_features, features], 'csr')
# dictvectorizer sorts the vocabularies within each file
for feat_name, index in sorted(feat_vectorizer.vocabulary_.items(),
key=lambda x: x[1]):
merged_vectorizer.vocabulary_[feat_name] = index + num_merged
merged_vectorizer.feature_names_.append(feat_name)
else:
merged_features = features
merged_vectorizer = feat_vectorizer
# IDs should be the same for each ExamplesTuple, so only store once
if merged_ids is None:
merged_ids = ids
# Check that IDs are in the same order
elif not np.all(merged_ids == ids):
raise ValueError('IDs are not in the same order in each feature ' +
'file!')
# If current ExamplesTuple has labels, check that they don't conflict
if any(x is not None for x in classes):
# Classes should be the same for each ExamplesTuple, so store once
if merged_classes is None:
merged_classes = classes
# Check that classes don't conflict, when specified
elif not np.all(merged_classes == classes):
raise ValueError('Feature files have conflicting labels for ' +
'examples with the same ID!')
# Ensure that at least one file had classes if we're expecting them
if merged_classes is None and not unlabelled:
raise ValueError('No feature files in feature set contain class' +
'labels!')
return ExamplesTuple(merged_ids, merged_classes, merged_features,
merged_vectorizer)
