def __repr__(self, N_CHAR_MAX=700):
# N_CHAR_MAX is the (approximate) maximum number of non-blank
# characters to render. We pass it as an optional parameter to ease
# the tests.
from sklearn.utils._pprint import _EstimatorPrettyPrinter
N_MAX_ELEMENTS_TO_SHOW = 30 # number of elements to show in sequences
# use ellipsis for sequences with a lot of elements
pp = _EstimatorPrettyPrinter(
compact=True,
indent=1,
indent_at_name=True,
n_max_elements_to_show=N_MAX_ELEMENTS_TO_SHOW)
repr_ = pp.pformat(self)
# Use bruteforce ellipsis when there are a lot of non-blank characters
n_nonblank = len(''.join(repr_.split()))
if n_nonblank > N_CHAR_MAX:
lim = N_CHAR_MAX // 2 # apprx number of chars to keep on both ends
regex = r'^(\s*\S){%d}' % lim
# The regex '^(\s*\S){%d}' % n
# matches from the start of the string until the nth non-blank
# character:
# - ^ matches the start of string
# - (pattern){n} matches n repetitions of pattern
# - \s*\S matches a non-blank char following zero or more blanks
left_lim = re.match(regex, repr_).end()
right_lim = re.match(regex, repr_[::-1]).end()
if '\n' in repr_[left_lim:-right_lim]:
# The left side and right side aren't on the same line.
# To avoid weird cuts, e.g.:
# categoric...ore',
# we need to start the right side with an appropriate newline
# character so that it renders properly as:
# categoric...
# handle_unknown='ignore',
# so we add [^\n]*\n which matches until the next \n
regex += r'[^\n]*\n'
right_lim = re.match(regex, repr_[::-1]).end()
ellipsis = '...'
if left_lim + len(ellipsis) < len(repr_) - right_lim:
# Only add ellipsis if it results in a shorter repr
repr_ = repr_[:left_lim] + '...' + repr_[-right_lim:]
return repr_
def test_n_max_elements_to_show():
n_max_elements_to_show = 30
pp = _EstimatorPrettyPrinter(compact=True,
indent=1,
indent_at_name=True,
n_max_elements_to_show=n_max_elements_to_show)
# No ellipsis
vocabulary = {i: i for i in range(n_max_elements_to_show)}
vectorizer = CountVectorizer(vocabulary=vocabulary)
expected = r"""
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
tokenizer=None,
vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
27: 27, 28: 28, 29: 29})"""
expected = expected[1:] # remove first \n
assert pp.pformat(vectorizer) == expected
# Now with ellipsis
vocabulary = {i: i for i in range(n_max_elements_to_show + 1)}
vectorizer = CountVectorizer(vocabulary=vocabulary)
expected = r"""
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
tokenizer=None,
vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
27: 27, 28: 28, 29: 29, ...})"""
expected = expected[1:] # remove first \n
assert pp.pformat(vectorizer) == expected
# Also test with lists
param_grid = {'C': list(range(n_max_elements_to_show))}
gs = GridSearchCV(SVC(), param_grid)
expected = """
GridSearchCV(cv='warn', error_score='raise-deprecating',
estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='auto_deprecated', kernel='rbf', max_iter=-1,
probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
iid='warn', n_jobs=None,
param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 29]},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)"""
expected = expected[1:] # remove first \n
assert pp.pformat(gs) == expected
# Now with ellipsis
param_grid = {'C': list(range(n_max_elements_to_show + 1))}
gs = GridSearchCV(SVC(), param_grid)
expected = """
GridSearchCV(cv='warn', error_score='raise-deprecating',
estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='auto_deprecated', kernel='rbf', max_iter=-1,
probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
iid='warn', n_jobs=None,
param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 29, ...]},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)"""
expected = expected[1:] # remove first \n
assert pp.pformat(gs) == expected
def test_gridsearch_pipeline():
# render a pipeline inside a gridsearch
pp = _EstimatorPrettyPrinter(compact=True, indent=1, indent_at_name=True)
pipeline = Pipeline([('reduce_dim', PCA()), ('classify', LinearSVC())])
N_FEATURES_OPTIONS = [2, 4, 8]
C_OPTIONS = [1, 10, 100, 1000]
param_grid = [{
'reduce_dim': [PCA(iterated_power=7), NMF()],
'reduce_dim__n_components': N_FEATURES_OPTIONS,
'classify__C': C_OPTIONS
}, {
'reduce_dim': [SelectKBest(chi2)],
'reduce_dim__k': N_FEATURES_OPTIONS,
'classify__C': C_OPTIONS
}]
gspipline = GridSearchCV(pipeline, cv=3, n_jobs=1, param_grid=param_grid)
expected = """
GridSearchCV(cv=3, error_score='raise-deprecating',
estimator=Pipeline(memory=None,
steps=[('reduce_dim',
PCA(copy=True, iterated_power='auto',
n_components=None,
random_state=None,
svd_solver='auto', tol=0.0,
whiten=False)),
('classify',
LinearSVC(C=1.0, class_weight=None,
dual=True, fit_intercept=True,
intercept_scaling=1,
loss='squared_hinge',
max_iter=1000,
multi_class='ovr',
penalty='l2',
random_state=None, tol=0.0001,
verbose=0))]),
iid='warn', n_jobs=1,
param_grid=[{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [PCA(copy=True, iterated_power=7,
n_components=None,
random_state=None,
svd_solver='auto', tol=0.0,
whiten=False),
NMF(alpha=0.0, beta_loss='frobenius',
init=None, l1_ratio=0.0,
max_iter=200, n_components=None,
random_state=None, shuffle=False,
solver='cd', tol=0.0001,
verbose=0)],
'reduce_dim__n_components': [2, 4, 8]},
{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [SelectKBest(k=10,
score_func=<function chi2 at some_address>)],
'reduce_dim__k': [2, 4, 8]}],
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)"""
expected = expected[1:] # remove first \n
repr_ = pp.pformat(gspipline)
# Remove address of '<function chi2 at 0x.....>' for reproducibility
repr_ = re.sub('function chi2 at 0x.*>', 'function chi2 at some_address>',
repr_)
assert repr_ == expected
def test_gridsearch_pipeline(print_changed_only_false):
# render a pipeline inside a gridsearch
pp = _EstimatorPrettyPrinter(compact=True, indent=1, indent_at_name=True)
pipeline = Pipeline([("reduce_dim", PCA()), ("classify", SVC())])
N_FEATURES_OPTIONS = [2, 4, 8]
C_OPTIONS = [1, 10, 100, 1000]
param_grid = [
{
"reduce_dim": [PCA(iterated_power=7), NMF()],
"reduce_dim__n_components": N_FEATURES_OPTIONS,
"classify__C": C_OPTIONS,
},
{
"reduce_dim": [SelectKBest(chi2)],
"reduce_dim__k": N_FEATURES_OPTIONS,
"classify__C": C_OPTIONS,
},
]
gspipline = GridSearchCV(pipeline, cv=3, n_jobs=1, param_grid=param_grid)
expected = """
GridSearchCV(cv=3, error_score='raise-deprecating',
estimator=Pipeline(memory=None,
steps=[('reduce_dim',
PCA(copy=True, iterated_power='auto',
n_components=None,
random_state=None,
svd_solver='auto', tol=0.0,
whiten=False)),
('classify',
SVC(C=1.0, cache_size=200,
class_weight=None, coef0=0.0,
decision_function_shape='ovr',
degree=3, gamma='auto_deprecated',
kernel='rbf', max_iter=-1,
probability=False,
random_state=None, shrinking=True,
tol=0.001, verbose=False))]),
iid='warn', n_jobs=1,
param_grid=[{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [PCA(copy=True, iterated_power=7,
n_components=None,
random_state=None,
svd_solver='auto', tol=0.0,
whiten=False),
NMF(alpha=0.0, beta_loss='frobenius',
init=None, l1_ratio=0.0,
max_iter=200, n_components=None,
random_state=None, shuffle=False,
solver='cd', tol=0.0001,
verbose=0)],
'reduce_dim__n_components': [2, 4, 8]},
{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [SelectKBest(k=10,
score_func=<function chi2 at some_address>)],
'reduce_dim__k': [2, 4, 8]}],
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)"""
expected = expected[1:] # remove first \n
repr_ = pp.pformat(gspipline)
# Remove address of '<function chi2 at 0x.....>' for reproducibility
repr_ = re.sub("function chi2 at 0x.*>", "function chi2 at some_address>", repr_)
assert repr_ == expected
def test_n_max_elements_to_show():
n_max_elements_to_show = 30
pp = _EstimatorPrettyPrinter(
compact=True, indent=1, indent_at_name=True,
n_max_elements_to_show=n_max_elements_to_show
)
# No ellipsis
vocabulary = {i: i for i in range(n_max_elements_to_show)}
vectorizer = CountVectorizer(vocabulary=vocabulary)
expected = r"""
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
tokenizer=None,
vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
27: 27, 28: 28, 29: 29})"""
expected = expected[1:] # remove first \n
assert pp.pformat(vectorizer) == expected
# Now with ellipsis
vocabulary = {i: i for i in range(n_max_elements_to_show + 1)}
vectorizer = CountVectorizer(vocabulary=vocabulary)
expected = r"""
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
tokenizer=None,
vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
27: 27, 28: 28, 29: 29, ...})"""
expected = expected[1:] # remove first \n
assert pp.pformat(vectorizer) == expected
# Also test with lists
param_grid = {'C': list(range(n_max_elements_to_show))}
gs = GridSearchCV(SVC(), param_grid)
expected = """
GridSearchCV(cv='warn', error_score='raise-deprecating',
estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='auto_deprecated', kernel='rbf', max_iter=-1,
probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
iid='warn', n_jobs=None,
param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 29]},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)"""
expected = expected[1:] # remove first \n
assert pp.pformat(gs) == expected
# Now with ellipsis
param_grid = {'C': list(range(n_max_elements_to_show + 1))}
gs = GridSearchCV(SVC(), param_grid)
expected = """
GridSearchCV(cv='warn', error_score='raise-deprecating',
estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='auto_deprecated', kernel='rbf', max_iter=-1,
probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
iid='warn', n_jobs=None,
param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 29, ...]},
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)"""
expected = expected[1:] # remove first \n
assert pp.pformat(gs) == expected
def test_gridsearch_pipeline():
# render a pipeline inside a gridsearch
pp = _EstimatorPrettyPrinter(compact=True, indent=1, indent_at_name=True)
pipeline = Pipeline([
('reduce_dim', PCA()),
('classify', LinearSVC())
])
N_FEATURES_OPTIONS = [2, 4, 8]
C_OPTIONS = [1, 10, 100, 1000]
param_grid = [
{
'reduce_dim': [PCA(iterated_power=7), NMF()],
'reduce_dim__n_components': N_FEATURES_OPTIONS,
'classify__C': C_OPTIONS
},
{
'reduce_dim': [SelectKBest(chi2)],
'reduce_dim__k': N_FEATURES_OPTIONS,
'classify__C': C_OPTIONS
}
]
gspipline = GridSearchCV(pipeline, cv=3, n_jobs=1, param_grid=param_grid)
expected = """
GridSearchCV(cv=3, error_score='raise-deprecating',
estimator=Pipeline(memory=None,
steps=[('reduce_dim',
PCA(copy=True, iterated_power='auto',
n_components=None,
random_state=None,
svd_solver='auto', tol=0.0,
whiten=False)),
('classify',
LinearSVC(C=1.0, class_weight=None,
dual=True, fit_intercept=True,
intercept_scaling=1,
loss='squared_hinge',
max_iter=1000,
multi_class='ovr',
penalty='l2',
random_state=None, tol=0.0001,
verbose=0))]),
iid='warn', n_jobs=1,
param_grid=[{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [PCA(copy=True, iterated_power=7,
n_components=None,
random_state=None,
svd_solver='auto', tol=0.0,
whiten=False),
NMF(alpha=0.0, beta_loss='frobenius',
init=None, l1_ratio=0.0,
max_iter=200, n_components=None,
random_state=None, shuffle=False,
solver='cd', tol=0.0001,
verbose=0)],
'reduce_dim__n_components': [2, 4, 8]},
{'classify__C': [1, 10, 100, 1000],
'reduce_dim': [SelectKBest(k=10,
score_func=<function chi2 at some_address>)],
'reduce_dim__k': [2, 4, 8]}],
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
scoring=None, verbose=0)"""
expected = expected[1:] # remove first \n
repr_ = pp.pformat(gspipline)
# Remove address of '<function chi2 at 0x.....>' for reproducibility
repr_ = re.sub('function chi2 at 0x.*>',
'function chi2 at some_address>', repr_)
assert repr_ == expected
