def perform1HotEncoding(self):
if not hasattr(self, 'data'):
raise ValueError('perform1HotEncoding can only be called when '
'data is loaded')
if hasattr(self, 'encoder_'):
raise ValueError('perform1HotEncoding can only be called on '
'non-encoded data.')
self._encoder = None
sparse = True if self.info['is_sparse'] == 1 else False
has_missing = True if self.info['has_missing'] else False
to_encode = ['categorical']
if has_missing:
to_encode += ['binary']
encoding_mask = [
feat_type.lower() in to_encode for feat_type in self.feat_type
]
categorical = [
True if feat_type.lower() == 'categorical' else False
for feat_type in self.feat_type
]
predicted_RAM_usage = float(
predict_RAM_usage(self.data['X_train'], categorical)) / 1024 / 1024
if predicted_RAM_usage > 1000:
sparse = True
if any(encoding_mask):
encoder = OneHotEncoder(categorical_features=encoding_mask,
dtype=np.float32,
sparse=sparse)
self.data['X_train'] = encoder.fit_transform(self.data['X_train'])
if 'X_valid' in self.data:
self.data['X_valid'] = encoder.transform(self.data['X_valid'])
if 'X_test' in self.data:
self.data['X_test'] = encoder.transform(self.data['X_test'])
if not sparse and scipy.sparse.issparse(self.data['X_train']):
self.data['X_train'] = self.data['X_train'].todense()
if 'X_valid' in self.data:
self.data['X_valid'] = self.data['X_valid'].todense()
if 'X_test' in self.data:
self.data['X_test'] = self.data['X_test'].todense()
self.encoder = encoder
self.info['is_sparse'] = 1 if sparse else 0
def perform1HotEncoding(self):
if not hasattr(self, 'data'):
raise ValueError('perform1HotEncoding can only be called when '
'data is loaded')
if hasattr(self, 'encoder_'):
raise ValueError('perform1HotEncoding can only be called on '
'non-encoded data.')
self._encoder = None
sparse = True if self.info['is_sparse'] == 1 else False
has_missing = True if self.info['has_missing'] else False
to_encode = ['categorical']
if has_missing:
to_encode += ['binary']
encoding_mask = [feat_type.lower() in to_encode
for feat_type in self.feat_type]
categorical = [True if feat_type.lower() == 'categorical' else False
for feat_type in self.feat_type]
predicted_RAM_usage = float(predict_RAM_usage(
self.data['X_train'], categorical)) / 1024 / 1024
if predicted_RAM_usage > 1000:
sparse = True
if any(encoding_mask):
encoder = OneHotEncoder(categorical_features=encoding_mask,
dtype=np.float32,
sparse=sparse)
self.data['X_train'] = encoder.fit_transform(self.data['X_train'])
if 'X_valid' in self.data:
self.data['X_valid'] = encoder.transform(self.data['X_valid'])
if 'X_test' in self.data:
self.data['X_test'] = encoder.transform(self.data['X_test'])
if not sparse and scipy.sparse.issparse(self.data['X_train']):
self.data['X_train'] = self.data['X_train'].todense()
if 'X_valid' in self.data:
self.data['X_valid'] = self.data['X_valid'].todense()
if 'X_test' in self.data:
self.data['X_test'] = self.data['X_test'].todense()
self.encoder = encoder
self.info['is_sparse'] = 1 if sparse else 0
def perform_hot_encoding(self):
if not hasattr(self, '_data') and self._data is not None:
raise ValueError('perform1HotEncoding can only be called when '
'data is loaded')
if hasattr(self, '_encoder') and self._encoder is not None:
raise ValueError('perform1HotEncoding can only be called on '
'non-encoded data.')
sparse = True if self.info['is_sparse'] == 1 else False
has_missing = True if self.info['has_missing'] else False
to_encode = ['categorical']
if has_missing:
to_encode += ['binary']
encoding_mask = [feat_type.lower() in to_encode
for feat_type in self._feat_type]
categorical = [True if feat_type.lower() == 'categorical' else False
for feat_type in self._feat_type]
predicted_RAM_usage = float(
predict_RAM_usage(self.data['X_train'], categorical)) / pow(1024, 2)
if predicted_RAM_usage > 1000:
sparse = True
if any(encoding_mask):
encoder = OneHotEncoder(categorical_features=encoding_mask,
dtype=np.float32,
sparse=sparse)
to_dence_flg = False
for x in ['X_train', 'X_valid', 'X_test']:
if x in self.data:
self.data[x] = encoder.fit_transform(self.data[x])
if x == 'X_train':
to_dence_flg = not sparse and scipy.sparse.issparse(self.data[x])
if to_dence_flg:
self.data[x] = self.data[x].todense()
self._encoder = encoder
self.info['is_sparse'] = 1 if sparse else 0
def perform_one_hot_encoding(sparse, categorical, data):
predicted_RAM_usage = float(predict_RAM_usage(data[0], categorical)) / 1024 / 1024
if predicted_RAM_usage > 1000:
sparse = True
rvals = []
if any(categorical):
encoder = OneHotEncoder(categorical_features=categorical,
dtype=np.float32,
sparse=sparse)
rvals.append(encoder.fit_transform(data[0]))
for d in data[1:]:
rvals.append(encoder.transform(d))
if not sparse and scipy.sparse.issparse(rvals[0]):
for i in range(len(rvals)):
rvals[i] = rvals[i].todense()
else:
rvals = data
return rvals, sparse
def perform_one_hot_encoding(sparse, categorical, data):
predicted_RAM_usage = float(
predict_RAM_usage(data[0], categorical)) / 1024 / 1024
if predicted_RAM_usage > 1000:
sparse = True
rvals = []
if any(categorical):
encoder = OneHotEncoder(categorical_features=categorical,
dtype=np.float32,
sparse=sparse)
rvals.append(encoder.fit_transform(data[0]))
for d in data[1:]:
rvals.append(encoder.transform(d))
if not sparse and scipy.sparse.issparse(rvals[0]):
for i in range(len(rvals)):
rvals[i] = rvals[i].todense()
else:
rvals = data
return rvals, sparse