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
1.0Additive (Laplace/Lidstone) smoothing parameter (use 0 for no smoothing).
-
true_threshold – defaults to
0.0Threshold 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¶
clone
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
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
Updates the model with a term-frequency or TF-IDF pandas dataframe.
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 A dataframe of features.
Parameters
- X (pandas.core.frame.DataFrame)
Returns
Series: The predicted labels.
predict_one
Predict the label of a set of features x.
Parameters
- x (dict)
Returns
typing.Union[bool, str, int]: 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)