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code coverage
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@sdpython
sdpython committed Jun 26, 2020
1 parent c9223e5 commit e32b37a
Showing 1 changed file with 32 additions and 28 deletions.
60 changes: 32 additions & 28 deletions src/papierstat/mltricks/sklearn_base_learner_category.py
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Expand Up @@ -38,10 +38,10 @@ def __init__(self, colnameind=None, model=None, **kwargs):
@param model model à appliquer sur chaque catégorie
"""
if not isinstance(colnameind, (int, str)):
raise TypeError(
raise TypeError( # pragma: no cover
"colnameind must be str or int not {0}".format(type(colnameind)))
if model is None:
raise ValueError("model must not be None")
raise ValueError("model must not be None") # pragma: no cover
kwargs['colnameind'] = colnameind
SkBaseLearner.__init__(self, **kwargs)
self.model = model
Expand All @@ -61,20 +61,19 @@ def Models(self):
"""
if hasattr(self, 'models'):
return self.models
else:
raise RuntimeError('No trained models')
raise RuntimeError('No trained models') # pragma: no cover

def _get_cat(self, X):
"""
Retourne les catégories indiquées par *colnameind*.
"""
if isinstance(self.colnameind, str):
if not hasattr(X, 'columns'):
raise TypeError("colnameind='{0}' and X is not a DataFrame but {1}".format(
self.colnameind, type(X)))
raise TypeError( # pragma: no cover
"colnameind='{0}' and X is not a DataFrame but {1}".format(
self.colnameind, type(X)))
return X[self.colnameind]
else:
return X[:, self.colnameind]
return X[:, self.colnameind]

def _filter_cat(self, c, X, y=None, sample_weight=None):
"""
Expand All @@ -83,8 +82,9 @@ def _filter_cat(self, c, X, y=None, sample_weight=None):
indices = numpy.arange(0, X.shape[0])
if isinstance(self.colnameind, str):
if not hasattr(X, 'columns'):
raise TypeError("colnameind='{0}' and X is not a DataFrame but {1}".format(
self.colnameind, type(X)))
raise TypeError( # pragma: no cover
"colnameind='{0}' and X is not a DataFrame but {1}".format(
self.colnameind, type(X)))
ind = X[self.colnameind] == c
sa = None if sample_weight is None else sample_weight[ind]
y = None if y is None else y[ind]
Expand All @@ -100,11 +100,15 @@ def _filter_cat(self, c, X, y=None, sample_weight=None):
y = None if y is None else y[ind]
ind, x = indices[ind], X[ind, -self.colnameind]
if y is not None and x.shape[0] != y.shape[0]:
raise RuntimeError("Input arrays have different shapes for value='{0}': {1} != {2} (expected: {3}) type(X)={4}".format(
c, X.shape[0], y.shape[0], ind.shape, type(X)))
raise RuntimeError( # pragma: no cover
"Input arrays have different shapes for value='{0}': {1} != {2} "
"(expected: {3}) type(X)={4}".format(
c, X.shape[0], y.shape[0], ind.shape, type(X)))
if sa is not None and x.shape[0] != sa.shape[0]:
raise RuntimeError("Input arrays have different shapes for value='{0}': {1} != {2} (expected: {3}) type(X)={4}".format(
c, X.shape[0], sa.shape[0], ind.shape, type(X)))
raise RuntimeError(
"Input arrays have different shapes for value='{0}': {1} != {2} "
"(expected: {3}) type(X)={4}".format(
c, X.shape[0], sa.shape[0], ind.shape, type(X)))
return ind, x, y, sa

###################
Expand All @@ -124,7 +128,8 @@ def fit(self, X, y=None, **kwargs):
cats = set(self._get_cat(X))
for c in cats:
if not isinstance(c, str) and numpy.isnan(c):
raise ValueError("One of the row has a missing category.")
raise ValueError( # pragma: no cover
"One of the row has a missing category.")

sample_weight = kwargs.get('sample_weight', None)
res = {}
Expand All @@ -150,7 +155,7 @@ def _any_predict(self, X, fct, *args):
cats = set(self._get_cat(X))
for c in cats:
if not isinstance(c, str) and numpy.isnan(c):
raise NotImplementedError(
raise NotImplementedError( # pragma: no cover
"No default value is implemented in case of missing value.")

res = []
Expand All @@ -170,7 +175,7 @@ def _any_predict(self, X, fct, *args):
res.append(pred)
try:
final = numpy.vstack(res)
except ValueError:
except ValueError: # pragma: no cover
# Only one dimension.
final = numpy.hstack(res)
df = pandas.DataFrame(final)
Expand Down Expand Up @@ -202,9 +207,8 @@ def decision_function(self, X):
"""
if hasattr(self.model, 'decision_function'):
return self._any_predict(X, 'decision_function')
else:
raise NotImplementedError(
"No decision_function for {0}".format(self.model))
raise NotImplementedError(
"No decision_function for {0}".format(self.model))

def predict_proba(self, X):
"""
Expand All @@ -216,9 +220,8 @@ def predict_proba(self, X):
"""
if hasattr(self.model, 'predict_proba'):
return self._any_predict(X, 'predict_proba')
else:
raise NotImplementedError(
"No method predict_proba for {0}".format(self.model))
raise NotImplementedError( # pragma: no cover
"No method predict_proba for {0}".format(self.model))

def score(self, X, y=None, sample_weight=None):
"""
Expand All @@ -232,11 +235,11 @@ def score(self, X, y=None, sample_weight=None):
if self._estimator_type == 'classifier':
from sklearn.metrics import accuracy_score
return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
elif self._estimator_type == 'regressor':
if self._estimator_type == 'regressor':
from sklearn.metrics import r2_score
return r2_score(y, self.predict(X), sample_weight=sample_weight)
else:
raise RuntimeError("Unexpected estimator type '{0}', cannot guess default scoring metric.".format(
raise RuntimeError( # pragma: no cover
"Unexpected estimator type '{0}', cannot guess default scoring metric.".format(
self._estimator_type))

##############
Expand Down Expand Up @@ -272,8 +275,9 @@ def set_params(self, **values):
self.model = values['model']
del values['model']
elif not hasattr(self, 'model') or self.model is None:
raise KeyError("Missing key '{0}' in [{1}]".format(
'model', ', '.join(sorted(values))))
raise KeyError( # pragma: no cover
"Missing key '{0}' in [{1}]".format(
'model', ', '.join(sorted(values))))
prefix = 'model__'
ext = {k[len(prefix):]: v for k, v in values.items()
if k.startswith(prefix)}
Expand Down

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