本文整理汇总了Python中sklearn.externals.six.iteritems方法的典型用法代码示例。如果您正苦于以下问题：Python six.iteritems方法的具体用法？Python six.iteritems怎么用？Python six.iteritems使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块sklearn.externals.six的用法示例。

在下文中一共展示了six.iteritems方法的21个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: fit

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def fit(self, Z, **fit_params):

"""TODO: rewrite docstring

Fit all transformers using X.

Parameters

----------

X : array-like or sparse matrix, shape (n_samples, n_features)

Input data, used to fit transformers.

"""

fit_params_steps = dict((step, {})

for step, _ in self.transformer_list)

for pname, pval in six.iteritems(fit_params):

step, param = pname.split('__', 1)

fit_params_steps[step][param] = pval

transformers = Parallel(n_jobs=self.n_jobs, backend="threading")(

delayed(_fit_one_transformer)(trans, Z, **fit_params_steps[name])

for name, trans in self.transformer_list)

self._update_transformer_list(transformers)

return self

开发者ID:lensacom，项目名称:sparkit-learn，代码行数:22，

示例2: test_type_of_target

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def test_type_of_target():

for group, group_examples in iteritems(EXAMPLES):

for example in group_examples:

assert_equal(type_of_target(example), group,

msg=('type_of_target(%r) should be %r, got %r'

% (example, group, type_of_target(example))))

for example in NON_ARRAY_LIKE_EXAMPLES:

msg_regex = 'Expected array-like \(array or non-string sequence\).*'

assert_raises_regex(ValueError, msg_regex, type_of_target, example)

for example in MULTILABEL_SEQUENCES:

msg = ('You appear to be using a legacy multi-label data '

'representation. Sequence of sequences are no longer supported;'

' use a binary array or sparse matrix instead.')

assert_raises_regex(ValueError, msg, type_of_target, example)

try:

from pandas import SparseSeries

except ImportError:

raise SkipTest("Pandas not found")

y = SparseSeries([1, 0, 0, 1, 0])

msg = "y cannot be class 'SparseSeries'."

assert_raises_regex(ValueError, msg, type_of_target, y)

开发者ID:alvarobartt，项目名称:twitter-stock-recommendation，代码行数:27，

示例3: get_params

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def get_params(self, deep=True):

""" Get classifier parameter names for GridSearch"""

if not deep:

return super(MajorityVoteClassifier, self).get_params(deep=False)

else:

out = self.named_classifiers.copy()

for name, step in six.iteritems(self.named_classifiers):

for key, value in six.iteritems(step.get_params(deep=True)):

out['%s__%s' % (name, key)] = value

return out

开发者ID:rrlyman，项目名称:PythonMachineLearningExamples，代码行数:12，

示例4: _clone_h2o_obj

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def _clone_h2o_obj(estimator, ignore=False, **kwargs):

# do initial clone

est = clone(estimator)

# set kwargs:

if kwargs:

for k, v in six.iteritems(kwargs):

setattr(est, k, v)

# check on h2o estimator

if isinstance(estimator, H2OPipeline):

# the last step from the original estimator

e = estimator.steps[-1][1]

if isinstance(e, H2OEstimator):

last_step = est.steps[-1][1]

# so it's the last step

for k, v in six.iteritems(e._parms):

k, v = _kv_str(k, v)

# if (not k in PARM_IGNORE) and (not v is None):

# e._parms[k] = v

last_step._parms[k] = v

# otherwise it's an BaseH2OFunctionWrapper

return est

开发者ID:tgsmith61591，项目名称:skutil，代码行数:28，

示例5: _new_base_estimator

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def _new_base_estimator(est, clonable_kwargs):

"""When the grid searches are pickled, the estimator

has to be dropped out. When we load it back in, we have

to reinstate a new one, since the fit is predicated on

being able to clone a base estimator, we've got to have

an estimator to clone and fit.

Parameters

----------

est : str

The type of model to build

Returns

-------

estimator : H2OEstimator

The cloned base estimator

"""

est_map = {

'dl': H2ODeepLearningEstimator,

'gbm': H2OGradientBoostingEstimator,

'glm': H2OGeneralizedLinearEstimator,

# 'glrm': H2OGeneralizedLowRankEstimator,

# 'km' : H2OKMeansEstimator,

'nb': H2ONaiveBayesEstimator,

'rf': H2ORandomForestEstimator

}

estimator = est_map[est]() # initialize the new ones

for k, v in six.iteritems(clonable_kwargs):

k, v = _kv_str(k, v)

estimator._parms[k] = v

return estimator

开发者ID:tgsmith61591，项目名称:skutil，代码行数:37，

示例6: transform

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def transform(self, X):

"""Transform a test matrix given the already-fit transformer.

Parameters

----------

X : Pandas ``DataFrame``

The Pandas frame to transform. The operation will

be applied to a copy of the input data, and the result

will be returned.

Returns

-------

X : Pandas ``DataFrame``

The operation is applied to a copy of ``X``,

and the result set is returned.

"""

check_is_fitted(self, 'sq_nms_')

# check on state of X and cols

X, _ = validate_is_pd(X, self.cols)

sq_nms_ = self.sq_nms_

# scale by norms

for nm, the_norm in six.iteritems(sq_nms_):

X[nm] /= the_norm

return X if self.as_df else X.as_matrix()

开发者ID:tgsmith61591，项目名称:skutil，代码行数:32，

示例7: _sort_features

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def _sort_features(self, X, vocabulary):

"""Sort features by name

Returns a reordered matrix and modifies the vocabulary in place

"""

sorted_features = sorted(six.iteritems(vocabulary))

map_index = np.empty(len(sorted_features), dtype=np.int32)

for new_val, (term, old_val) in enumerate(sorted_features):

vocabulary[term] = new_val

map_index[old_val] = new_val

X.indices = map_index.take(X.indices, mode='clip')

return X

开发者ID:prozhuchen，项目名称:2016CCF-sougou，代码行数:15，

示例8: _limit_features

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def _limit_features(self, X, vocabulary, high=None, low=None,

limit=None):

"""Remove too rare or too common features.

Prune features that are non zero in more samples than high or less

documents than low, modifying the vocabulary, and restricting it to

at most the limit most frequent.

This does not prune samples with zero features.

"""

if high is None and low is None and limit is None:

return X, set()

# Calculate a mask based on document frequencies

dfs = _document_frequency(X)

tfs = np.asarray(X.sum(axis=0)).ravel()

mask = np.ones(len(dfs), dtype=bool)

if high is not None:

mask &= dfs <= high

if low is not None:

mask &= dfs >= low

if limit is not None and mask.sum() > limit:

mask_inds = (-tfs[mask]).argsort()[:limit]

new_mask = np.zeros(len(dfs), dtype=bool)

new_mask[np.where(mask)[0][mask_inds]] = True

mask = new_mask

new_indices = np.cumsum(mask) - 1 # maps old indices to new

removed_terms = set()

for term, old_index in list(six.iteritems(vocabulary)):

if mask[old_index]:

vocabulary[term] = new_indices[old_index]

else:

del vocabulary[term]

removed_terms.add(term)

kept_indices = np.where(mask)[0]

if len(kept_indices) == 0:

raise ValueError("After pruning, no terms remain. Try a lower"

" min_df or a higher max_df.")

return X[:, kept_indices], removed_terms

开发者ID:prozhuchen，项目名称:2016CCF-sougou，代码行数:42，

示例9: get_feature_names

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def get_feature_names(self):

"""Array mapping from feature integer indices to feature name"""

self._check_vocabulary()

return [t for t, i in sorted(six.iteritems(self.vocabulary_),

key=itemgetter(1))]

开发者ID:prozhuchen，项目名称:2016CCF-sougou，代码行数:8，

示例10: topological_sort

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def topological_sort(deps):

'''

Topologically sort a DAG, represented by a dict of child => set of parents.

The dependency dict is destroyed during operation.

Uses the Kahn algorithm: http://en.wikipedia.org/wiki/Topological_sorting

Not a particularly good implementation, but we're just running it on tiny

graphs.

'''

order = []

available = set()

def _move_available():

to_delete = []

for n, parents in iteritems(deps):

if not parents:

available.add(n)

to_delete.append(n)

for n in to_delete:

del deps[n]

_move_available()

while available:

n = available.pop()

order.append(n)

for parents in itervalues(deps):

parents.discard(n)

_move_available()

if available:

raise ValueError("dependency cycle found")

return order

开发者ID:djsutherland，项目名称:skl-groups，代码行数:34，

示例11: _set_up_funcs

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def _set_up_funcs(funcs, metas_ordered, Ks, dim, X_ns=None, Y_ns=None):

# replace functions with partials of args

def replace_func(func, info):

needs_alpha = getattr(func, 'needs_alpha', False)

new = None

args = (Ks, dim)

if needs_alpha:

args = (info.alphas,) + args

if hasattr(func, 'chooser_fn'):

args += (X_ns, Y_ns)

if (getattr(func, 'needs_all_ks', False) and

getattr(func.chooser_fn, 'returns_ks', False)):

new, K = func.chooser_fn(*args)

new.K_needed = K

else:

new = func.chooser_fn(*args)

else:

new = partial(func, *args)

for attr in dir(func):

if not (attr.startswith('__') or attr.startswith('func_')):

setattr(new, attr, getattr(func, attr))

return new

rep_funcs = dict(

(replace_func(f, info), info) for f, info in iteritems(funcs))

rep_metas_ordered = OrderedDict(

(replace_func(f, info), info) for f, info in iteritems(metas_ordered))

return rep_funcs, rep_metas_ordered

开发者ID:djsutherland，项目名称:skl-groups，代码行数:34，

示例12: __getitem__

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def __getitem__(self, key):

if (isinstance(key, string_types) or

(isinstance(key, (tuple, list)) and

any(isinstance(x, string_types) for x in key))):

msg = "Features indexing only subsets rows, but got {!r}"

raise TypeError(msg.format(key))

if np.isscalar(key):

return self.features[key]

else:

return type(self)(self.features[key], copy=False, stack=False,

**{k: v[key] for k, v in iteritems(self.meta)})

开发者ID:djsutherland，项目名称:skl-groups，代码行数:14，

示例13: test_type_utils

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def test_type_utils():

tests = {

'bool': (np.array([False, True]), False, True),

'int32': (np.arange(10, dtype=np.int32), True, True),

'int64': (np.arange(10, dtype=np.int64), True, True),

'float32': (np.arange(10, dtype=np.float32), False, False),

'float64': (np.arange(10, dtype=np.float64), False, False),

}

for name, (a, is_int, is_cat) in iteritems(tests):

assert utils.is_integer_type(a) == is_int, name

assert utils.is_categorical_type(a) == is_cat, name

assert utils.is_integer(a[0]) == is_int, name

assert utils.is_categorical(a[0]) == is_cat, name

assert utils.is_integer_type(utils.as_integer_type(tests['float32'][0]))

assert utils.is_integer_type(utils.as_integer_type(tests['float64'][0]))

assert_raises(

ValueError, lambda: utils.as_integer_type(tests['float32'][0] + .2))

assert utils.is_integer(5)

assert utils.is_categorical(False)

assert utils.is_categorical(True)

################################################################################

开发者ID:djsutherland，项目名称:skl-groups，代码行数:28，

示例14: _pre_transform

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def _pre_transform(self, Z, **fit_params):

fit_params_steps = dict((step, {}) for step, _ in self.steps)

for pname, pval in six.iteritems(fit_params):

step, param = pname.split('__', 1)

fit_params_steps[step][param] = pval

Zp = Z.persist()

for name, transform in self.steps[:-1]:

if hasattr(transform, "fit_transform"):

Zt = transform.fit_transform(Zp, **fit_params_steps[name])

else:

Zt = transform.fit(Zp, **fit_params_steps[name]) \

.transform(Zp)

Zp.unpersist()

Zp = Zt.persist()

return Zp, fit_params_steps[self.steps[-1][0]]

开发者ID:lensacom，项目名称:sparkit-learn，代码行数:17，

示例15: get_params

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def get_params(self, deep=True):

if not deep:

return super(SparkPipeline, self).get_params(deep=False)

else:

out = self.named_steps.copy()

for name, step in six.iteritems(self.named_steps):

for key, value in six.iteritems(step.get_params(deep=True)):

out['%s__%s' % (name, key)] = value

out.update(super(SparkPipeline, self).get_params(deep=False))

return out

开发者ID:lensacom，项目名称:sparkit-learn，代码行数:13，

示例16: test_paired_distances

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def test_paired_distances():

# Test the pairwise_distance helper function.

rng = np.random.RandomState(0)

# Euclidean distance should be equivalent to calling the function.

X = rng.random_sample((5, 4))

# Euclidean distance, with Y != X.

Y = rng.random_sample((5, 4))

for metric, func in iteritems(PAIRED_DISTANCES):

S = paired_distances(X, Y, metric=metric)

S2 = func(X, Y)

assert_array_almost_equal(S, S2)

S3 = func(csr_matrix(X), csr_matrix(Y))

assert_array_almost_equal(S, S3)

if metric in PAIRWISE_DISTANCE_FUNCTIONS:

# Check the pairwise_distances implementation

# gives the same value

distances = PAIRWISE_DISTANCE_FUNCTIONS[metric](X, Y)

distances = np.diag(distances)

assert_array_almost_equal(distances, S)

# Check the callable implementation

S = paired_distances(X, Y, metric='manhattan')

S2 = paired_distances(X, Y, metric=lambda x, y: np.abs(x - y).sum(axis=0))

assert_array_almost_equal(S, S2)

# Test that a value error is raised when the lengths of X and Y should not

# differ

Y = rng.random_sample((3, 4))

assert_raises(ValueError, paired_distances, X, Y)

开发者ID:alvarobartt，项目名称:twitter-stock-recommendation，代码行数:31，

示例17: test_is_multilabel

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def test_is_multilabel():

for group, group_examples in iteritems(EXAMPLES):

if group in ['multilabel-indicator']:

dense_assert_, dense_exp = assert_true, 'True'

else:

dense_assert_, dense_exp = assert_false, 'False'

for example in group_examples:

# Only mark explicitly defined sparse examples as valid sparse

# multilabel-indicators

if group == 'multilabel-indicator' and issparse(example):

sparse_assert_, sparse_exp = assert_true, 'True'

else:

sparse_assert_, sparse_exp = assert_false, 'False'

if (issparse(example) or

(hasattr(example, '__array__') and

np.asarray(example).ndim == 2 and

np.asarray(example).dtype.kind in 'biuf' and

np.asarray(example).shape[1] > 0)):

examples_sparse = [sparse_matrix(example)

for sparse_matrix in [coo_matrix,

csc_matrix,

csr_matrix,

dok_matrix,

lil_matrix]]

for exmpl_sparse in examples_sparse:

sparse_assert_(is_multilabel(exmpl_sparse),

msg=('is_multilabel(%r)'

' should be %s')

% (exmpl_sparse, sparse_exp))

# Densify sparse examples before testing

if issparse(example):

example = example.toarray()

dense_assert_(is_multilabel(example),

msg='is_multilabel(%r) should be %s'

% (example, dense_exp))

开发者ID:alvarobartt，项目名称:twitter-stock-recommendation，代码行数:41，

示例18: fit_transform

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def fit_transform(self, X, y=None):

"""Fit the transformer and return the transformed

training array.

Parameters

----------

X : Pandas ``DataFrame``, shape=(n_samples, n_features)

The Pandas frame to fit. The frame will only

be fit on the prescribed ``cols`` (see ``__init__``) or

all of them if ``cols`` is None. Furthermore, ``X`` will

not be altered in the process of the fit.

y : None

Passthrough for ``sklearn.pipeline.Pipeline``. Even

if explicitly set, will not change behavior of ``fit``.

Returns

-------

self

"""

# check on state of X and cols

X, self.cols = validate_is_pd(X, self.cols, assert_all_finite=True) # must all be finite for fortran

_validate_cols(self.cols)

# init drops list

drops = []

# Generate sub matrix for qr decomposition

cols = _cols_if_none(X, self.cols) # get a copy of the cols

x = X[cols].as_matrix()

cols = np.array(cols) # so we can do boolean indexing

# do subroutines

lc_list = _enum_lc(QRDecomposition(x))

if lc_list is not None:

while lc_list is not None:

# we want the first index in each of the keys in the dict

bad = np.array([p for p in set([v[0] for _, v in six.iteritems(lc_list)])])

# get the corresponding bad names

bad_nms = cols[bad]

drops.extend(bad_nms)

# update our X, and then our cols

x = np.delete(x, bad, axis=1)

cols = np.delete(cols, bad)

# keep removing linear dependencies until it resolves

lc_list = _enum_lc(QRDecomposition(x))

# will break when lc_list returns None

# Assign attributes, return

self.drop_ = [p for p in set(drops)] # a list from the a set of the drops

dropped = X.drop(self.drop_, axis=1)

return dropped if self.as_df else dropped.as_matrix()

开发者ID:tgsmith61591，项目名称:skutil，代码行数:63，

示例19: transform

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def transform(self, X):

"""Impute the test data after fit.

Parameters

----------

X : Pandas ``DataFrame``, shape=(n_samples, n_features)

The Pandas frame to transform.

Returns

-------

dropped : Pandas DataFrame or NumPy ndarray

The test frame sans "bad" columns

"""

check_is_fitted(self, 'models_')

# check on state of X and cols

X, _ = validate_is_pd(X, self.cols)

# perform the transformations for missing vals

models = self.models_

for col, kv in six.iteritems(models):

features, model = kv['feature_names'], kv['model']

y = X[col] # the y we're predicting

# this will throw a key error if one of the features isn't there

X_test = X[features] # we need another copy

# if col is in the features, there's something wrong internally

assert col not in features, 'predictive column should not be in fit features (%s)' % col

# since this is a copy, we can add the missing vals where needed

X_test = X_test.fillna(self.fill)

# generate predictions, subset where y was null

y_null = pd.isnull(y)

pred_y = model.predict(X_test.loc[y_null])

# fill where necessary:

if y_null.sum() > 0:

y[y_null] = pred_y # fill where null

X[col] = y # set back to X

return X if self.as_df else X.as_matrix()

开发者ID:tgsmith61591，项目名称:skutil，代码行数:46，

示例20: fit

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def fit(self, Z):

"""Learn a list of feature name -> indices mappings.

Parameters

----------

Z : DictRDD with column 'X'

Dict(s) or Mapping(s) from feature names (arbitrary Python

objects) to feature values (strings or convertible to dtype).

Returns

-------

self

"""

X = Z[:, 'X'] if isinstance(Z, DictRDD) else Z

"""Create vocabulary

"""

class SetAccum(AccumulatorParam):

def zero(self, initialValue):

return set(initialValue)

def addInPlace(self, v1, v2):

v1 |= v2

return v1

accum = X.context.accumulator(set(), SetAccum())

def mapper(X, separator=self.separator):

feature_names = []

for x in X:

for f, v in six.iteritems(x):

if isinstance(v, six.string_types):

f = "%s%s%s" % (f, self.separator, v)

feature_names.append(f)

accum.add(set(feature_names))

X.foreach(mapper) # init vocabulary

feature_names = list(accum.value)

if self.sort:

feature_names.sort()

vocab = dict((f, i) for i, f in enumerate(feature_names))

self.feature_names_ = feature_names

self.vocabulary_ = vocab

return self

开发者ID:lensacom，项目名称:sparkit-learn，代码行数:51，

示例21: setup_module

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# 需要导入模块: from sklearn.externals import six [as 别名]

# 或者: from sklearn.externals.six import iteritems [as 别名]

def setup_module():

"""Test fixture run once and common to all tests of this module"""

if imsave is None:

raise SkipTest("PIL not installed.")

if not os.path.exists(LFW_HOME):

os.makedirs(LFW_HOME)

random_state = random.Random(42)

np_rng = np.random.RandomState(42)

# generate some random jpeg files for each person

counts = {}

for name in FAKE_NAMES:

folder_name = os.path.join(LFW_HOME, 'lfw_funneled', name)

if not os.path.exists(folder_name):

os.makedirs(folder_name)

n_faces = np_rng.randint(1, 5)

counts[name] = n_faces

for i in range(n_faces):

file_path = os.path.join(folder_name, name + '_%04d.jpg' % i)

uniface = np_rng.randint(0, 255, size=(250, 250, 3))

try:

imsave(file_path, uniface)

except ImportError:

raise SkipTest("PIL not installed")

# add some random file pollution to test robustness

with open(os.path.join(LFW_HOME, 'lfw_funneled', '.test.swp'), 'wb') as f:

f.write(six.b('Text file to be ignored by the dataset loader.'))

# generate some pairing metadata files using the same format as LFW

with open(os.path.join(LFW_HOME, 'pairsDevTrain.txt'), 'wb') as f:

f.write(six.b("10\n"))

more_than_two = [name for name, count in six.iteritems(counts)

if count >= 2]

for i in range(5):

name = random_state.choice(more_than_two)

first, second = random_state.sample(range(counts[name]), 2)

f.write(six.b('%s\t%d\t%d\n' % (name, first, second)))

for i in range(5):

first_name, second_name = random_state.sample(FAKE_NAMES, 2)

first_index = random_state.choice(np.arange(counts[first_name]))

second_index = random_state.choice(np.arange(counts[second_name]))

f.write(six.b('%s\t%d\t%s\t%d\n' % (first_name, first_index,

second_name, second_index)))

with open(os.path.join(LFW_HOME, 'pairsDevTest.txt'), 'wb') as f:

f.write(six.b("Fake place holder that won't be tested"))

with open(os.path.join(LFW_HOME, 'pairs.txt'), 'wb') as f:

f.write(six.b("Fake place holder that won't be tested"))

开发者ID:alvarobartt，项目名称:twitter-stock-recommendation，代码行数:56，

注：本文中的sklearn.externals.six.iteritems方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。