具有多个功能的Sklearn幼稚贝叶斯

Question

背景

我正在努力在python中以sklearn跨多个功能实现幼稚的贝叶斯分类器。

我拥有的功能是：

1. 标题 - 一些简短的文本
2. 描述 - 一些更长的文本
3. 时间戳 - 代表一天中一个小时的浮点（例如18.0 = 6：00 = 6：00 pm，11.5 = 11：30 AM）

标签/类是分类字符串：例如” Class1“，“ class2”，“ class3”

目标

我的目标是使用3个功能，以构建3个功能的天真贝叶斯分类器，以预测类标签。我特别希望同时使用所有功能，即不仅仅是 Description 功能。

初始方法

我使用sklearn设置了一些预处理管道，如下所示：

from sklearn import preprocessing, naive_bayes, feature_extraction, pipeline, model_selection, compose,

text_columns = ['title', 'description']
time_columns = ['timestamp']

# get an 80-20 test-train split
X_train, X_test, y_train, y_test = model_selection.train_test_split(train[text_columns + time_columns], train['class'], test_size=0.2, random_state=RANDOM_STATE)

# convert the text data into vectors
text_pipeline = pipeline.Pipeline([
    ('vect', feature_extraction.text.CountVectorizer()),
    ('tfidf', feature_extraction.text.TfidfTransformer()),
])

# preprocess by scaling the data, and binning the data
time_pipeline = pipeline.Pipeline([
    ('scaler', preprocessing.StandardScaler()),
    ('bin', preprocessing.KBinsDiscretizer(n_bins=6, encode='ordinal', strategy='quantile')),
])

# combine the pre-processors
preprocessor = compose.ColumnTransformer([
    ('text', text_pipeline, text_columns),
    ('time', time_pipeline, time_columns),
])

clf = pipeline.Pipeline([
    ('preprocessor', preprocessor),
    ('clf', naive_bayes.MultinomialNB()),
])

train是pandas带有功能和标签从.csv这样的文件中：

ID,title,description,timestamp,class
1,First Title String,"A description of the first title",13.0,Class1
2,Second Title String,"A description of the second title",17.5,Class2

还请注意，我不是为变形金刚/分类器设置大多数参数，因为我想使用网格搜索以稍后查找最佳访问量。

我调用clf.fit（x_train，y_train）时的问题

，我会收到以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_7500/3039541201.py in <module>
     33 
     34 # x = pd.DataFrame(text_pipeline.fit_transform(X_train['mean_checkin_time']))
---> 35 x = clf.fit(X_train, y_train)
     36 # # print the number of features
     37 

~/.local/lib/python3.9/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
    388         """
    389         fit_params_steps = self._check_fit_params(**fit_params)
--> 390         Xt = self._fit(X, y, **fit_params_steps)
    391         with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)):
    392             if self._final_estimator != "passthrough":

~/.local/lib/python3.9/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params_steps)
    346                 cloned_transformer = clone(transformer)
    347             # Fit or load from cache the current transformer
--> 348             X, fitted_transformer = fit_transform_one_cached(
    349                 cloned_transformer,
    350                 X,

~/.local/lib/python3.9/site-packages/joblib/memory.py in __call__(self, *args, **kwargs)
    347 
    348     def __call__(self, *args, **kwargs):
--> 349         return self.func(*args, **kwargs)
    350 
    351     def call_and_shelve(self, *args, **kwargs):

~/.local/lib/python3.9/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
    891     with _print_elapsed_time(message_clsname, message):
    892         if hasattr(transformer, "fit_transform"):
--> 893             res = transformer.fit_transform(X, y, **fit_params)
    894         else:
    895             res = transformer.fit(X, y, **fit_params).transform(X)

~/.local/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py in fit_transform(self, X, y)
    697         self._record_output_indices(Xs)
    698 
--> 699         return self._hstack(list(Xs))
    700 
    701     def transform(self, X):

~/.local/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py in _hstack(self, Xs)
    789         else:
    790             Xs = [f.toarray() if sparse.issparse(f) else f for f in Xs]
--> 791             return np.hstack(Xs)
    792 
    793     def _sk_visual_block_(self):

<__array_function__ internals> in hstack(*args, **kwargs)

~/.local/lib/python3.9/site-packages/numpy/core/shape_base.py in hstack(tup)
    344         return _nx.concatenate(arrs, 0)
    345     else:
--> 346         return _nx.concatenate(arrs, 1)
    347 
    348 

<__array_function__ internals> in concatenate(*args, **kwargs)

ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 2 and the array at index 1 has size 3001

我有以下形状的x_train和y_train y_train ：

X_train: (3001, 3)
y_train: (3001,)

步骤操作的

单个功能

我可以使用具有单个功能的相同管道（通过更改text_features和time_features arrays），并获得一个完美的分类器。例如，仅使用“标题”字段，或仅使用“时间戳”字段。不幸的是，这些单个功能不够准确，因此我想使用所有功能来构建更准确的分类器。这个问题似乎是当我尝试将多个功能结合起来时。

我愿意使用多个幼稚的贝叶斯分类器，并试图将概率相连以获得总体概率，但老实说，我不知道该怎么做，而且我敢肯定，我只是在这里错过了一些简单的东西。

的时间功能

删除我尝试仅运行text_features，即“标题”和“描述”

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_7500/1900884535.py in <module>
     33 
     34 # x = pd.DataFrame(text_pipeline.fit_transform(X_train['mean_checkin_time']))
---> 35 x = clf.fit(X_train, y_train)
     36 # # print the number of features
     37 

~/.local/lib/python3.9/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
    392             if self._final_estimator != "passthrough":
    393                 fit_params_last_step = fit_params_steps[self.steps[-1][0]]
--> 394                 self._final_estimator.fit(Xt, y, **fit_params_last_step)
    395 
    396         return self

~/.local/lib/python3.9/site-packages/sklearn/naive_bayes.py in fit(self, X, y, sample_weight)
    661             Returns the instance itself.
    662         """
--> 663         X, y = self._check_X_y(X, y)
    664         _, n_features = X.shape
    665 

~/.local/lib/python3.9/site-packages/sklearn/naive_bayes.py in _check_X_y(self, X, y, reset)
    521     def _check_X_y(self, X, y, reset=True):
    522         """Validate X and y in fit methods."""
--> 523         return self._validate_data(X, y, accept_sparse="csr", reset=reset)
    524 
    525     def _update_class_log_prior(self, class_prior=None):

~/.local/lib/python3.9/site-packages/sklearn/base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
    579                 y = check_array(y, **check_y_params)
    580             else:
--> 581                 X, y = check_X_y(X, y, **check_params)
    582             out = X, y
    583 

~/.local/lib/python3.9/site-packages/sklearn/utils/validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
    979     y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric)
    980 
--> 981     check_consistent_length(X, y)
    982 
    983     return X, y

~/.local/lib/python3.9/site-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
    330     uniques = np.unique(lengths)
    331     if len(uniques) > 1:
--> 332         raise ValueError(
    333             "Found input variables with inconsistent numbers of samples: %r"
    334             % [int(l) for l in lengths]

ValueError: Found input variables with inconsistent numbers of samples: [2, 3001]

，我会收到以下错误：并且我有以下形状：

X_train: (3001, 2)
y_train: (3001,)

的标签

重塑我尝试重新调整 y_train变量通过调用包装在[]之类的样本中，以便：这样

# new
X_train, X_test, y_train, y_test = model_selection.train_test_split(train[text_columns + time_columns], train[['class']], test_size=0.2, random_state=RANDOM_STATE)

# previous
X_train, X_test, y_train, y_test = model_selection.train_test_split(train[text_columns + time_columns], train['class'], test_size=0.2, random_state=RANDOM_STATE)

，因此结果形状为：

X_train: (3001, 3)
y_train: (3001, 1)

但是不幸的是，这似乎并没有解决此问题。

删除天真的贝叶斯分类器

删除管道的最后一步时，请 （naiveBayes.multinomialnb（）），然后删除text_features（“ timestamp”功能），然后i i可以构建一个预处理的预处理器，该处理程序适合文本。即，我可以预处理文本字段（“标题”，“描述”），但是当我添加分类器时，我会在“删除时间功能”下面的错误下得到错误。

Answer 1

矢量化多个文本功能时，您应该为每个功能创建countvectorizer（或tfidfvectorizer）实例：

title_pipeline = pipeline.Pipeline([
    ('vect', feature_extraction.text.CountVectorizer()),
    ('tfidf', feature_extraction.text.TfidfTransformer()),
])
description_pipeline = pipeline.Pipeline([
    ('vect', feature_extraction.text.CountVectorizer()),
    ('tfidf', feature_extraction.text.TfidfTransformer()),
])
preprocessor = compose.ColumnTransformer([
    ('title', title_pipeline, text_columns[0]),
    ('description', description_pipeline, text_columns[1]),
    ('time', time_pipeline, time_columns),
])

ps Count> Countvectorizer和tfidftransformer的组合等效于tfidfvectorizer。此外，您可以跳过TF-IDF加权，并仅使用CountVectorizer 多inimialnb。