I think I'm having issues getting my vectorizer working within a gridsearch pipeline:

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   98e13ad4b495b9613cef886d79a6291fnumeric_predictors = ['bathrooms', 'bedrooms', 'price']
categorical_predictors = ['building_id', 'manager_id']

minMaxScaler fit & transform:

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import MinMaxScalerclass MyScaler(BaseEstimator, TransformerMixin):def __init__(self, cols):self.cols = colsdef fit(self, X, y=None):self.scaler = MinMaxScaler()self.scaler.fit(X[self.cols])return selfdef transform(self, X):return self.scaler.transform(X[self.cols])

My categorical feature hashing vectorizer:

from sklearn.feature_extraction import FeatureHasher
from sklearn.feature_extraction.text import HashingVectorizerclass 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 variableshashing: 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 = colsself.hashing = hashingdef fit(self, X, y=None):data = X[self.cols]# Choose a vectorizerif 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 selfdef transform(self, X):# Vectorize Inputif 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 hyperparameters:

search_params = {'preprocess__vectorize__hashing': [20, 40, 80],'predict__alpha': [.01, .1, 1, 2, 10]
}

pipeline:

from sklearn.pipeline import Pipeline
from sklearn.pipeline import FeatureUnion
from sklearn.linear_model import LinearRegressionpipeline = Pipeline([('preprocess', FeatureUnion([('scale', MyScaler(cols=numeric_predictors)),('vectorize', MyVectorizer(cols=categorical_predictors, hashing=5))])),('predict', MultinomialNB())
])

And last, calling this with the gridsearchCV classifier:

grid_search = GridSearchCV(pipeline, search_params)
grid_search.fit(x_train, y_train)

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 edge778             # case of Parallel used with an exhausted iterator.
--> 779             while self.dispatch_one_batch(iterator):780                 self._iterating = True781             else:/home/fred/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch_one_batch(self, iterator)623                 return False624             else:
--> 625                 self._dispatch(tasks)626                 return True627 /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 input331         # 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 self261 /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)

Yes, when hashing is not None, FeatureHasher() is chosen, which can output negative values.

But you can remove convert those negative values to positive by using the non_negative parameter of FeatureHashser as given in documentation:

non_negative : boolean, optional, default False

When True, an absolute value is applied to the features matrix prior to returning it. When used in conjunction withalternate_sign=True, this significantly reduces the inner productpreservation property.

So change this line in MyVectorizer:

self.myvec = FeatureHasher(n_features = self.hashing)

to this:

self.myvec = FeatureHasher(n_features = self.hashing, non_negative=True)

Note:

• This parameter has been deprecated since version 0.19 and will be removed in 0.21.
• You need to study how this parameter will affect your classification problem.