BernoulliNB

Bernoulli Naive Bayes.

Bernoulli Naive Bayes model learns from occurrences between features such as word counts and discrete classes. The input vector must contain positive values, such as counts or TF-IDF values.

Parameters

• alpha – defaults to 1.0

  Additive (Laplace/Lidstone) smoothing parameter (use 0 for no smoothing).

• true_threshold – defaults to 0.0

  Threshold for binarizing (mapping to booleans) features.

Attributes

• class_counts (collections.Counter)

  Number of times each class has been seen.

• feature_counts (collections.defaultdict)

  Total frequencies per feature and class.

Examples

>>> import pandas as pd
>>> from river import compose
>>> from river import feature_extraction
>>> from river import naive_bayes

>>> docs = [
...     ("Chinese Beijing Chinese", "yes"),
...     ("Chinese Chinese Shanghai", "yes"),
...     ("Chinese Macao", "yes"),
...     ("Tokyo Japan Chinese", "no")
... ]

>>> model = compose.Pipeline(
...     ("tokenize", feature_extraction.BagOfWords(lowercase=False)),
...     ("nb", naive_bayes.BernoulliNB(alpha=1))
... )

>>> for sentence, label in docs:
...     model = model.learn_one(sentence, label)

>>> model["nb"].p_class("yes")
0.75
>>> model["nb"].p_class("no")
0.25

>>> model.predict_proba_one("test")
{'yes': 0.8831539823829913, 'no': 0.11684601761700895}

>>> model.predict_one("test")
'yes'

You can train the model and make predictions in mini-batch mode using the class methods learn_many and predict_many.

>>> df_docs = pd.DataFrame(docs, columns = ["docs", "y"])

>>> X = pd.Series([
...    "Chinese Beijing Chinese",
...    "Chinese Chinese Shanghai",
...    "Chinese Macao",
...    "Tokyo Japan Chinese"
... ])

>>> y = pd.Series(["yes", "yes", "yes", "no"])

>>> model = compose.Pipeline(
...     ("tokenize", feature_extraction.BagOfWords(lowercase=False)),
...     ("nb", naive_bayes.BernoulliNB(alpha=1))
... )

>>> model = model.learn_many(X, y)

>>> unseen = pd.Series(["Taiwanese Taipei", "Chinese Shanghai"])

>>> model.predict_proba_many(unseen)
         no       yes
0  0.116846  0.883154
1  0.047269  0.952731

>>> model.predict_many(unseen)
0    yes
1    yes
dtype: object

Methods

joint_log_likelihood

Computes the joint log likelihood of input features.

Parameters

• x (dict)

Returns

float: Mapping between classes and joint log likelihood.

joint_log_likelihood_many

Computes the joint log likelihood of input features.

Parameters

• X (pandas.core.frame.DataFrame)

Returns

DataFrame: Input samples joint log likelihood.

learn_many

Learn from a batch of count vectors.

Parameters

• X (pandas.core.frame.DataFrame)
• y (pandas.core.series.Series)

Returns

MiniBatchClassifier: self

learn_one

Updates the model with a single observation.

Parameters

• x (dict)
• y (Union[bool, str, int])

Returns

Classifier: self

p_class

p_class_many

p_feature_given_class

predict_many

Predict the outcome for each given sample.

Parameters

• X ('pd.DataFrame')

Returns

pd.Series: The predicted labels.

predict_one

Predict the label of a set of features x.

Parameters

• x (dict)
• kwargs

Returns

typing.Union[bool, str, int, NoneType]: The predicted label.

predict_proba_many

Return probabilities using the log-likelihoods in mini-batchs setting.

Parameters

• X (pandas.core.frame.DataFrame)

predict_proba_one

Return probabilities using the log-likelihoods.

Parameters

• x (dict)

References

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1. The Bernoulli model ↩