681 lines
27 KiB
Python
681 lines
27 KiB
Python
import re
|
|
from pprint import PrettyPrinter
|
|
|
|
import numpy as np
|
|
|
|
from sklearn.utils._pprint import _EstimatorPrettyPrinter
|
|
from sklearn.linear_model import LogisticRegressionCV
|
|
from sklearn.pipeline import make_pipeline
|
|
from sklearn.base import BaseEstimator, TransformerMixin
|
|
from sklearn.feature_selection import SelectKBest, chi2
|
|
from sklearn import config_context
|
|
|
|
|
|
# Ignore flake8 (lots of line too long issues)
|
|
# flake8: noqa
|
|
|
|
# Constructors excerpted to test pprinting
|
|
class LogisticRegression(BaseEstimator):
|
|
def __init__(
|
|
self,
|
|
penalty="l2",
|
|
dual=False,
|
|
tol=1e-4,
|
|
C=1.0,
|
|
fit_intercept=True,
|
|
intercept_scaling=1,
|
|
class_weight=None,
|
|
random_state=None,
|
|
solver="warn",
|
|
max_iter=100,
|
|
multi_class="warn",
|
|
verbose=0,
|
|
warm_start=False,
|
|
n_jobs=None,
|
|
l1_ratio=None,
|
|
):
|
|
self.penalty = penalty
|
|
self.dual = dual
|
|
self.tol = tol
|
|
self.C = C
|
|
self.fit_intercept = fit_intercept
|
|
self.intercept_scaling = intercept_scaling
|
|
self.class_weight = class_weight
|
|
self.random_state = random_state
|
|
self.solver = solver
|
|
self.max_iter = max_iter
|
|
self.multi_class = multi_class
|
|
self.verbose = verbose
|
|
self.warm_start = warm_start
|
|
self.n_jobs = n_jobs
|
|
self.l1_ratio = l1_ratio
|
|
|
|
def fit(self, X, y):
|
|
return self
|
|
|
|
|
|
class StandardScaler(TransformerMixin, BaseEstimator):
|
|
def __init__(self, copy=True, with_mean=True, with_std=True):
|
|
self.with_mean = with_mean
|
|
self.with_std = with_std
|
|
self.copy = copy
|
|
|
|
def transform(self, X, copy=None):
|
|
return self
|
|
|
|
|
|
class RFE(BaseEstimator):
|
|
def __init__(self, estimator, n_features_to_select=None, step=1, verbose=0):
|
|
self.estimator = estimator
|
|
self.n_features_to_select = n_features_to_select
|
|
self.step = step
|
|
self.verbose = verbose
|
|
|
|
|
|
class GridSearchCV(BaseEstimator):
|
|
def __init__(
|
|
self,
|
|
estimator,
|
|
param_grid,
|
|
scoring=None,
|
|
n_jobs=None,
|
|
iid="warn",
|
|
refit=True,
|
|
cv="warn",
|
|
verbose=0,
|
|
pre_dispatch="2*n_jobs",
|
|
error_score="raise-deprecating",
|
|
return_train_score=False,
|
|
):
|
|
self.estimator = estimator
|
|
self.param_grid = param_grid
|
|
self.scoring = scoring
|
|
self.n_jobs = n_jobs
|
|
self.iid = iid
|
|
self.refit = refit
|
|
self.cv = cv
|
|
self.verbose = verbose
|
|
self.pre_dispatch = pre_dispatch
|
|
self.error_score = error_score
|
|
self.return_train_score = return_train_score
|
|
|
|
|
|
class CountVectorizer(BaseEstimator):
|
|
def __init__(
|
|
self,
|
|
input="content",
|
|
encoding="utf-8",
|
|
decode_error="strict",
|
|
strip_accents=None,
|
|
lowercase=True,
|
|
preprocessor=None,
|
|
tokenizer=None,
|
|
stop_words=None,
|
|
token_pattern=r"(?u)\b\w\w+\b",
|
|
ngram_range=(1, 1),
|
|
analyzer="word",
|
|
max_df=1.0,
|
|
min_df=1,
|
|
max_features=None,
|
|
vocabulary=None,
|
|
binary=False,
|
|
dtype=np.int64,
|
|
):
|
|
self.input = input
|
|
self.encoding = encoding
|
|
self.decode_error = decode_error
|
|
self.strip_accents = strip_accents
|
|
self.preprocessor = preprocessor
|
|
self.tokenizer = tokenizer
|
|
self.analyzer = analyzer
|
|
self.lowercase = lowercase
|
|
self.token_pattern = token_pattern
|
|
self.stop_words = stop_words
|
|
self.max_df = max_df
|
|
self.min_df = min_df
|
|
self.max_features = max_features
|
|
self.ngram_range = ngram_range
|
|
self.vocabulary = vocabulary
|
|
self.binary = binary
|
|
self.dtype = dtype
|
|
|
|
|
|
class Pipeline(BaseEstimator):
|
|
def __init__(self, steps, memory=None):
|
|
self.steps = steps
|
|
self.memory = memory
|
|
|
|
|
|
class SVC(BaseEstimator):
|
|
def __init__(
|
|
self,
|
|
C=1.0,
|
|
kernel="rbf",
|
|
degree=3,
|
|
gamma="auto_deprecated",
|
|
coef0=0.0,
|
|
shrinking=True,
|
|
probability=False,
|
|
tol=1e-3,
|
|
cache_size=200,
|
|
class_weight=None,
|
|
verbose=False,
|
|
max_iter=-1,
|
|
decision_function_shape="ovr",
|
|
random_state=None,
|
|
):
|
|
self.kernel = kernel
|
|
self.degree = degree
|
|
self.gamma = gamma
|
|
self.coef0 = coef0
|
|
self.tol = tol
|
|
self.C = C
|
|
self.shrinking = shrinking
|
|
self.probability = probability
|
|
self.cache_size = cache_size
|
|
self.class_weight = class_weight
|
|
self.verbose = verbose
|
|
self.max_iter = max_iter
|
|
self.decision_function_shape = decision_function_shape
|
|
self.random_state = random_state
|
|
|
|
|
|
class PCA(BaseEstimator):
|
|
def __init__(
|
|
self,
|
|
n_components=None,
|
|
copy=True,
|
|
whiten=False,
|
|
svd_solver="auto",
|
|
tol=0.0,
|
|
iterated_power="auto",
|
|
random_state=None,
|
|
):
|
|
self.n_components = n_components
|
|
self.copy = copy
|
|
self.whiten = whiten
|
|
self.svd_solver = svd_solver
|
|
self.tol = tol
|
|
self.iterated_power = iterated_power
|
|
self.random_state = random_state
|
|
|
|
|
|
class NMF(BaseEstimator):
|
|
def __init__(
|
|
self,
|
|
n_components=None,
|
|
init=None,
|
|
solver="cd",
|
|
beta_loss="frobenius",
|
|
tol=1e-4,
|
|
max_iter=200,
|
|
random_state=None,
|
|
alpha=0.0,
|
|
l1_ratio=0.0,
|
|
verbose=0,
|
|
shuffle=False,
|
|
):
|
|
self.n_components = n_components
|
|
self.init = init
|
|
self.solver = solver
|
|
self.beta_loss = beta_loss
|
|
self.tol = tol
|
|
self.max_iter = max_iter
|
|
self.random_state = random_state
|
|
self.alpha = alpha
|
|
self.l1_ratio = l1_ratio
|
|
self.verbose = verbose
|
|
self.shuffle = shuffle
|
|
|
|
|
|
class SimpleImputer(BaseEstimator):
|
|
def __init__(
|
|
self,
|
|
missing_values=np.nan,
|
|
strategy="mean",
|
|
fill_value=None,
|
|
verbose=0,
|
|
copy=True,
|
|
):
|
|
self.missing_values = missing_values
|
|
self.strategy = strategy
|
|
self.fill_value = fill_value
|
|
self.verbose = verbose
|
|
self.copy = copy
|
|
|
|
|
|
def test_basic(print_changed_only_false):
|
|
# Basic pprint test
|
|
lr = LogisticRegression()
|
|
expected = """
|
|
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
|
|
intercept_scaling=1, l1_ratio=None, max_iter=100,
|
|
multi_class='warn', n_jobs=None, penalty='l2',
|
|
random_state=None, solver='warn', tol=0.0001, verbose=0,
|
|
warm_start=False)"""
|
|
|
|
expected = expected[1:] # remove first \n
|
|
assert lr.__repr__() == expected
|
|
|
|
|
|
def test_changed_only():
|
|
# Make sure the changed_only param is correctly used when True (default)
|
|
lr = LogisticRegression(C=99)
|
|
expected = """LogisticRegression(C=99)"""
|
|
assert lr.__repr__() == expected
|
|
|
|
# Check with a repr that doesn't fit on a single line
|
|
lr = LogisticRegression(
|
|
C=99, class_weight=0.4, fit_intercept=False, tol=1234, verbose=True
|
|
)
|
|
expected = """
|
|
LogisticRegression(C=99, class_weight=0.4, fit_intercept=False, tol=1234,
|
|
verbose=True)"""
|
|
expected = expected[1:] # remove first \n
|
|
assert lr.__repr__() == expected
|
|
|
|
imputer = SimpleImputer(missing_values=0)
|
|
expected = """SimpleImputer(missing_values=0)"""
|
|
assert imputer.__repr__() == expected
|
|
|
|
# Defaults to np.NaN, trying with float('NaN')
|
|
imputer = SimpleImputer(missing_values=float("NaN"))
|
|
expected = """SimpleImputer()"""
|
|
assert imputer.__repr__() == expected
|
|
|
|
# make sure array parameters don't throw error (see #13583)
|
|
repr(LogisticRegressionCV(Cs=np.array([0.1, 1])))
|
|
|
|
|
|
def test_pipeline(print_changed_only_false):
|
|
# Render a pipeline object
|
|
pipeline = make_pipeline(StandardScaler(), LogisticRegression(C=999))
|
|
expected = """
|
|
Pipeline(memory=None,
|
|
steps=[('standardscaler',
|
|
StandardScaler(copy=True, with_mean=True, with_std=True)),
|
|
('logisticregression',
|
|
LogisticRegression(C=999, class_weight=None, dual=False,
|
|
fit_intercept=True, intercept_scaling=1,
|
|
l1_ratio=None, max_iter=100,
|
|
multi_class='warn', n_jobs=None,
|
|
penalty='l2', random_state=None,
|
|
solver='warn', tol=0.0001, verbose=0,
|
|
warm_start=False))],
|
|
verbose=False)"""
|
|
|
|
expected = expected[1:] # remove first \n
|
|
assert pipeline.__repr__() == expected
|
|
|
|
|
|
def test_deeply_nested(print_changed_only_false):
|
|
# Render a deeply nested estimator
|
|
rfe = RFE(RFE(RFE(RFE(RFE(RFE(RFE(LogisticRegression())))))))
|
|
expected = """
|
|
RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=LogisticRegression(C=1.0,
|
|
class_weight=None,
|
|
dual=False,
|
|
fit_intercept=True,
|
|
intercept_scaling=1,
|
|
l1_ratio=None,
|
|
max_iter=100,
|
|
multi_class='warn',
|
|
n_jobs=None,
|
|
penalty='l2',
|
|
random_state=None,
|
|
solver='warn',
|
|
tol=0.0001,
|
|
verbose=0,
|
|
warm_start=False),
|
|
n_features_to_select=None,
|
|
step=1,
|
|
verbose=0),
|
|
n_features_to_select=None,
|
|
step=1,
|
|
verbose=0),
|
|
n_features_to_select=None,
|
|
step=1, verbose=0),
|
|
n_features_to_select=None, step=1,
|
|
verbose=0),
|
|
n_features_to_select=None, step=1, verbose=0),
|
|
n_features_to_select=None, step=1, verbose=0),
|
|
n_features_to_select=None, step=1, verbose=0)"""
|
|
|
|
expected = expected[1:] # remove first \n
|
|
assert rfe.__repr__() == expected
|
|
|
|
|
|
def test_gridsearch(print_changed_only_false):
|
|
# render a gridsearch
|
|
param_grid = [
|
|
{"kernel": ["rbf"], "gamma": [1e-3, 1e-4], "C": [1, 10, 100, 1000]},
|
|
{"kernel": ["linear"], "C": [1, 10, 100, 1000]},
|
|
]
|
|
gs = GridSearchCV(SVC(), param_grid, cv=5)
|
|
|
|
expected = """
|
|
GridSearchCV(cv=5, 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': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],
|
|
'kernel': ['rbf']},
|
|
{'C': [1, 10, 100, 1000], 'kernel': ['linear']}],
|
|
pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
|
|
scoring=None, verbose=0)"""
|
|
|
|
expected = expected[1:] # remove first \n
|
|
assert gs.__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(print_changed_only_false):
|
|
|
|
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_bruteforce_ellipsis(print_changed_only_false):
|
|
# Check that the bruteforce ellipsis (used when the number of non-blank
|
|
# characters exceeds N_CHAR_MAX) renders correctly.
|
|
|
|
lr = LogisticRegression()
|
|
|
|
# test when the left and right side of the ellipsis aren't on the same
|
|
# line.
|
|
expected = """
|
|
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
|
|
in...
|
|
multi_class='warn', n_jobs=None, penalty='l2',
|
|
random_state=None, solver='warn', tol=0.0001, verbose=0,
|
|
warm_start=False)"""
|
|
|
|
expected = expected[1:] # remove first \n
|
|
assert expected == lr.__repr__(N_CHAR_MAX=150)
|
|
|
|
# test with very small N_CHAR_MAX
|
|
# Note that N_CHAR_MAX is not strictly enforced, but it's normal: to avoid
|
|
# weird reprs we still keep the whole line of the right part (after the
|
|
# ellipsis).
|
|
expected = """
|
|
Lo...
|
|
warm_start=False)"""
|
|
|
|
expected = expected[1:] # remove first \n
|
|
assert expected == lr.__repr__(N_CHAR_MAX=4)
|
|
|
|
# test with N_CHAR_MAX == number of non-blank characters: In this case we
|
|
# don't want ellipsis
|
|
full_repr = lr.__repr__(N_CHAR_MAX=float("inf"))
|
|
n_nonblank = len("".join(full_repr.split()))
|
|
assert lr.__repr__(N_CHAR_MAX=n_nonblank) == full_repr
|
|
assert "..." not in full_repr
|
|
|
|
# test with N_CHAR_MAX == number of non-blank characters - 10: the left and
|
|
# right side of the ellispsis are on different lines. In this case we
|
|
# want to expend the whole line of the right side
|
|
expected = """
|
|
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
|
|
intercept_scaling=1, l1_ratio=None, max_i...
|
|
multi_class='warn', n_jobs=None, penalty='l2',
|
|
random_state=None, solver='warn', tol=0.0001, verbose=0,
|
|
warm_start=False)"""
|
|
expected = expected[1:] # remove first \n
|
|
assert expected == lr.__repr__(N_CHAR_MAX=n_nonblank - 10)
|
|
|
|
# test with N_CHAR_MAX == number of non-blank characters - 10: the left and
|
|
# right side of the ellispsis are on the same line. In this case we don't
|
|
# want to expend the whole line of the right side, just add the ellispsis
|
|
# between the 2 sides.
|
|
expected = """
|
|
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
|
|
intercept_scaling=1, l1_ratio=None, max_iter...,
|
|
multi_class='warn', n_jobs=None, penalty='l2',
|
|
random_state=None, solver='warn', tol=0.0001, verbose=0,
|
|
warm_start=False)"""
|
|
expected = expected[1:] # remove first \n
|
|
assert expected == lr.__repr__(N_CHAR_MAX=n_nonblank - 4)
|
|
|
|
# test with N_CHAR_MAX == number of non-blank characters - 2: the left and
|
|
# right side of the ellispsis are on the same line, but adding the ellipsis
|
|
# would actually make the repr longer. So we don't add the ellipsis.
|
|
expected = """
|
|
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
|
|
intercept_scaling=1, l1_ratio=None, max_iter=100,
|
|
multi_class='warn', n_jobs=None, penalty='l2',
|
|
random_state=None, solver='warn', tol=0.0001, verbose=0,
|
|
warm_start=False)"""
|
|
expected = expected[1:] # remove first \n
|
|
assert expected == lr.__repr__(N_CHAR_MAX=n_nonblank - 2)
|
|
|
|
|
|
def test_builtin_prettyprinter():
|
|
# non regression test than ensures we can still use the builtin
|
|
# PrettyPrinter class for estimators (as done e.g. by joblib).
|
|
# Used to be a bug
|
|
|
|
PrettyPrinter().pprint(LogisticRegression())
|
|
|
|
|
|
def test_kwargs_in_init():
|
|
# Make sure the changed_only=True mode is OK when an argument is passed as
|
|
# kwargs.
|
|
# Non-regression test for
|
|
# https://github.com/scikit-learn/scikit-learn/issues/17206
|
|
|
|
class WithKWargs(BaseEstimator):
|
|
# Estimator with a kwargs argument. These need to hack around
|
|
# set_params and get_params. Here we mimic what LightGBM does.
|
|
def __init__(self, a="willchange", b="unchanged", **kwargs):
|
|
self.a = a
|
|
self.b = b
|
|
self._other_params = {}
|
|
self.set_params(**kwargs)
|
|
|
|
def get_params(self, deep=True):
|
|
params = super().get_params(deep=deep)
|
|
params.update(self._other_params)
|
|
return params
|
|
|
|
def set_params(self, **params):
|
|
for key, value in params.items():
|
|
setattr(self, key, value)
|
|
self._other_params[key] = value
|
|
return self
|
|
|
|
est = WithKWargs(a="something", c="abcd", d=None)
|
|
|
|
expected = "WithKWargs(a='something', c='abcd', d=None)"
|
|
assert expected == est.__repr__()
|
|
|
|
with config_context(print_changed_only=False):
|
|
expected = "WithKWargs(a='something', b='unchanged', c='abcd', d=None)"
|
|
assert expected == est.__repr__()
|
|
|
|
|
|
def test_complexity_print_changed_only():
|
|
# Make sure `__repr__` is called the same amount of times
|
|
# whether `print_changed_only` is True or False
|
|
# Non-regression test for
|
|
# https://github.com/scikit-learn/scikit-learn/issues/18490
|
|
|
|
class DummyEstimator(TransformerMixin, BaseEstimator):
|
|
nb_times_repr_called = 0
|
|
|
|
def __init__(self, estimator=None):
|
|
self.estimator = estimator
|
|
|
|
def __repr__(self):
|
|
DummyEstimator.nb_times_repr_called += 1
|
|
return super().__repr__()
|
|
|
|
def transform(self, X, copy=None): # pragma: no cover
|
|
return X
|
|
|
|
estimator = DummyEstimator(
|
|
make_pipeline(DummyEstimator(DummyEstimator()), DummyEstimator(), "passthrough")
|
|
)
|
|
with config_context(print_changed_only=False):
|
|
repr(estimator)
|
|
nb_repr_print_changed_only_false = DummyEstimator.nb_times_repr_called
|
|
|
|
DummyEstimator.nb_times_repr_called = 0
|
|
with config_context(print_changed_only=True):
|
|
repr(estimator)
|
|
nb_repr_print_changed_only_true = DummyEstimator.nb_times_repr_called
|
|
|
|
assert nb_repr_print_changed_only_false == nb_repr_print_changed_only_true
|