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simplify PredictableTSNE
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sdpython committed Feb 10, 2019
1 parent 366efb9 commit ea251ac
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Showing 2 changed files with 4 additions and 56 deletions.
4 changes: 3 additions & 1 deletion _unittests/ut_mlmodel/test_tsne_predictable.py
Expand Up @@ -12,6 +12,7 @@
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.manifold import TSNE
from pyquickhelper.pycode import ExtTestCase

try:
Expand Down Expand Up @@ -86,7 +87,8 @@ def test_predictable_tsne_relevance(self):
Ys.extend([cl for i in range(n)])
X = numpy.vstack(Xs)
Y = numpy.array(Ys)
clk = PredictableTSNE(t_n_components=3, normalizer=StandardScaler(with_mean=False),
clk = PredictableTSNE(transformer=TSNE(n_components=3),
normalizer=StandardScaler(with_mean=False),
keep_tsne_outputs=True)
clk.fit(X, Y)
pred = clk.transform(X)
Expand Down
56 changes: 1 addition & 55 deletions src/mlinsights/mlmodel/predictable_tsne.py
Expand Up @@ -23,7 +23,7 @@ class PredictableTSNE(BaseEstimator, TransformerMixin):
"""

def __init__(self, normalizer=None, transformer=None, estimator=None,
normalize=True, keep_tsne_outputs=False, **kwargs):
normalize=True, keep_tsne_outputs=False):
"""
@param normalizer None by default
@param transformer :epkg:`sklearn:manifold:TSNE`
Expand All @@ -36,9 +36,6 @@ def __init__(self, normalizer=None, transformer=None, estimator=None,
@param keep_tsne_output if True, keep raw outputs of
:epkg:`TSNE` is stored in member
*tsne_outputs_*
@param kwargs sent to :meth:`set_params
<mlinsights.mlmodel.tsne_transformer.PredictableTSNE.set_params>`,
see its documentation to understand how to specify parameters
"""
TransformerMixin.__init__(self)
BaseEstimator.__init__(self)
Expand All @@ -60,8 +57,6 @@ def __init__(self, normalizer=None, transformer=None, estimator=None,
raise AttributeError(
"estimator {} does not have a 'predict' method.".format(type(estimator)))
self.normalize = normalize
if kwargs:
self.set_params(**kwargs)

def fit(self, X, y, sample_weight=None):
"""
Expand Down Expand Up @@ -161,52 +156,3 @@ def transform(self, X):
pred -= self.mean_
pred *= self.inv_std_
return pred

def get_params(self, deep=True):
"""
Returns the parameters for all the embedded objects.
@param deep unused here
@return dict
:meth:`set_params <mlinsights.mlmodel.tsne_transformer.PredictableTSNE.set_params>`
describes the pattern parameters names follow.
"""
res = {}
if self.normalizer is not None:
for k, v in self.normalizer.get_params().items():
res["n_" + k] = v
for k, v in self.transformer.get_params().items():
res["t_" + k] = v
for k, v in self.estimator.get_params().items():
res["e_" + k] = v
return res

def set_params(self, **values):
"""
Sets the parameters before training.
Every parameter prefixed by ``'e_'`` is an estimator
parameter, every parameter prefixed by ``'n_'`` is for
a normalizer parameter, every parameter prefixed by
``t_`` is for a transformer parameter.
@param values valeurs
@return dict
"""
pt, pe, pn = {}, {}, {}
for k, v in values.items():
if k.startswith('e_'):
pe[k[2:]] = v
elif k.startswith('t_'):
pt[k[2:]] = v
elif k.startswith('n_'):
pn[k[2:]] = v
else:
raise ValueError("Unexpected parameter name '{0}'".format(k))
self.transformer.set_params(**pt)
self.estimator.set_params(**pe)
if self.normalizer is not None:
self.normalizer.set_params(**pn)
elif pn and self.normalizer is None:
raise ValueError(
"There is no normalizer, cannot change parameter {}.".format(pn))

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