管道中的ValueError-FeatureHasher无法正常工作? [英] ValueError in pipeline - featureHasher not working?
本文介绍了管道中的ValueError-FeatureHasher无法正常工作?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我认为我在使矢量化程序在gridsearch管道中工作时遇到问题:
I think I'm having issues getting my vectorizer working within a gridsearch pipeline:
数据作为熊猫df x_train:
data as panda df x_train:
bathrooms bedrooms price building_id manager_id
10 1.5 3 3000 53a5b119ba8f7b61d4e010512e0dfc85 5ba989232d0489da1b5f2c45f6688adc
10000 1.0 2 5465 c5c8a357cba207596b04d1afd1e4f130 7533621a882f71e25173b27e3139d83d
100004 1.0 1 2850 c3ba40552e2120b0acfc3cb5730bb2aa d9039c43983f6e564b1482b273bd7b01
100007 1.0 1 3275 28d9ad350afeaab8027513a3e52ac8d5 1067e078446a7897d2da493d2f741316
100013 1.0 4 3350 0 98e13ad4b495b9613cef886d79a6291f
numeric_predictors = ['bathrooms', 'bedrooms', 'price']
categorical_predictors = ['building_id', 'manager_id']
minMaxScaler fit&转换:
minMaxScaler fit & transform:
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import MinMaxScaler
class MyScaler(BaseEstimator, TransformerMixin):
def __init__(self, cols):
self.cols = cols
def fit(self, X, y=None):
self.scaler = MinMaxScaler()
self.scaler.fit(X[self.cols])
return self
def transform(self, X):
return self.scaler.transform(X[self.cols])
我的分类特征哈希向量化器:
My categorical feature hashing vectorizer:
from sklearn.feature_extraction import FeatureHasher
from sklearn.feature_extraction.text import HashingVectorizer
class MyVectorizer(BaseEstimator, TransformerMixin):
"""
Vectorize a set of categorical variables
"""
def __init__(self, cols, hashing=None):
"""
args:
cols: a list of column names of the categorical variables
hashing:
If None, then vectorization is a simple one-hot-encoding.
If an integer, then hashing is the number of features in the output.
"""
self.cols = cols
self.hashing = hashing
def fit(self, X, y=None):
data = X[self.cols]
# Choose a vectorizer
if self.hashing is None:
self.myvec = HashingVectorizer()
else:
self.myvec = FeatureHasher(n_features = self.hashing)
self.myvec.fit(X[self.cols].to_dict(orient='records'))
return self
def transform(self, X):
# Vectorize Input
if self.hashing is None:
return pd.DataFrame(
self.myvec.transform(X[self.cols].to_dict(orient='records')),
columns = self.myvec.feature_names_
)
else:
return pd.DataFrame(
self.myvec.transform(X[self.cols].to_dict(orient='records')).toarray()
)
GridSearch超参数:
GridSearch hyperparameters:
search_params = {
'preprocess__vectorize__hashing': [20, 40, 80],
'predict__alpha': [.01, .1, 1, 2, 10]
}
管道:
from sklearn.pipeline import Pipeline
from sklearn.pipeline import FeatureUnion
from sklearn.linear_model import LinearRegression
pipeline = Pipeline([
('preprocess', FeatureUnion([
('scale', MyScaler(cols=numeric_predictors)),
('vectorize', MyVectorizer(cols=categorical_predictors, hashing=5))
])),
('predict', MultinomialNB())
])
最后，使用gridsearchCV分类器进行调用:
And last, calling this with the gridsearchCV classifier:
grid_search = GridSearchCV(pipeline, search_params)
grid_search.fit(x_train, y_train)
我收到ValueError:输入X必须为非负数.我检查了一下，然后我的numeric_predictor列的数据全部为非负数，因此我将其范围缩小到分类预测变量的散列问题.
I get a ValueError: Input X must be non-negative. I checked and my numeric_predictor columns' data all non-negative, so I am narrowing it down to an issue with the hashing of the categorical predictors.
ValueError Traceback (most recent call last)
<ipython-input-62-50522376d1e5> in <module>()
1 grid_search = GridSearchCV(pipeline, search_params)
----> 2 grid_search.fit(x_train, y_train)
3 grid_search.best_params_
/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/model_selection/_search.pyc in fit(self, X, y, groups, **fit_params)
636 error_score=self.error_score)
637 for parameters, (train, test) in product(candidate_params,
--> 638 cv.split(X, y, groups)))
639
640 # if one choose to see train score, "out" will contain train score info
/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
777 # was dispatched. In particular this covers the edge
778 # case of Parallel used with an exhausted iterator.
--> 779 while self.dispatch_one_batch(iterator):
780 self._iterating = True
781 else:
/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch_one_batch(self, iterator)
623 return False
624 else:
--> 625 self._dispatch(tasks)
626 return True
627
/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in _dispatch(self, batch)
586 dispatch_timestamp = time.time()
587 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 588 job = self._backend.apply_async(batch, callback=cb)
589 self._jobs.append(job)
590
/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc in apply_async(self, func, callback)
109 def apply_async(self, func, callback=None):
110 """Schedule a func to be run"""
--> 111 result = ImmediateResult(func)
112 if callback:
113 callback(result)
/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc in __init__(self, batch)
330 # Don't delay the application, to avoid keeping the input
331 # arguments in memory
--> 332 self.results = batch()
333
334 def get(self):
/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
435 estimator.fit(X_train, **fit_params)
436 else:
--> 437 estimator.fit(X_train, y_train, **fit_params)
438
439 except Exception as e:
/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/pipeline.pyc in fit(self, X, y, **fit_params)
257 Xt, fit_params = self._fit(X, y, **fit_params)
258 if self._final_estimator is not None:
--> 259 self._final_estimator.fit(Xt, y, **fit_params)
260 return self
261
/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/naive_bayes.pyc in fit(self, X, y, sample_weight)
602 self.feature_count_ = np.zeros((n_effective_classes, n_features),
603 dtype=np.float64)
--> 604 self._count(X, Y)
605 alpha = self._check_alpha()
606 self._update_feature_log_prob(alpha)
/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/naive_bayes.pyc in _count(self, X, Y)
706 """Count and smooth feature occurrences."""
707 if np.any((X.data if issparse(X) else X) < 0):
--> 708 raise ValueError("Input X must be non-negative")
709 self.feature_count_ += safe_sparse_dot(Y.T, X)
710 self.class_count_ += Y.sum(axis=0)
ValueError: Input X must be non-negative
> /home/fred/anaconda2/lib/python2.7/site-packages/sklearn/naive_bayes.py(708)_count()
706 """Count and smooth feature occurrences."""
707 if np.any((X.data if issparse(X) else X) < 0):
--> 708 raise ValueError("Input X must be non-negative")
709 self.feature_count_ += safe_sparse_dot(Y.T, X)
710 self.class_count_ += Y.sum(axis=0)
推荐答案
是的，当哈希不为None时，选择FeatureHasher()，它可以输出负值.
Yes, when hashing is not None, FeatureHasher() is chosen, which can output negative values.
但是您可以按照给定的
But you can remove convert those negative values to positive by using the non_negative parameter of FeatureHashser as given in documentation:
非负数:布尔值，可选，默认为假
non_negative : boolean, optional, default False
为True时，绝对值先应用于要素矩阵 返回它.当与 alter_sign = True，这将显着减少内部乘积 保存属性.
When True, an absolute value is applied to the features matrix prior to returning it. When used in conjunction with alternate_sign=True, this significantly reduces the inner product preservation property.
因此，请在MyVectorizer中更改此行:
So change this line in MyVectorizer:
self.myvec = FeatureHasher(n_features = self.hashing)
对此:
self.myvec = FeatureHasher(n_features = self.hashing, non_negative=True)
注意:
- 此参数从0.19版本开始不推荐使用，并将在0.21中删除.
- 您需要研究此参数如何影响分类问题.
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