CategoricalNB¶

Naive Bayes classifier for categorical features.

Categorical Naive Bayes learns a separate model for each categorical feature. For each class, a frequency is maintained for every value seen for every feature. Prediction is done by computing the joint log-likelihood of each class given the observed category values.

Parameters¶

alpha

Default → 1.0

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

Attributes¶

class_counts (collections.Counter)

Number of times each class has been seen.
feature_counts (collections.defaultdict)

Total frequencies per feature, value, and class.

Examples¶

from river import naive_bayes

X = [
    {"outlook": "sunny", "temp": "hot"},
    {"outlook": "sunny", "temp": "hot"},
    {"outlook": "sunny", "temp": "mild"},
    {"outlook": "sunny", "temp": "cool"},
    {"outlook": "rainy", "temp": "cool"},
    {"outlook": "rainy", "temp": "cool"},
    {"outlook": "rainy", "temp": "mild"},
    {"outlook": "rainy", "temp": "hot"},
    {"outlook": "overcast", "temp": "cool"},
    {"outlook": "overcast", "temp": "mild"},
    {"outlook": "overcast", "temp": "mild"},
    {"outlook": "overcast", "temp": "hot"},
]

y = ["no", "no", "no", "no", "yes", "yes", "yes", "yes",
     "yes", "yes", "yes", "yes"]

model = naive_bayes.CategoricalNB(alpha=1)

for x, yi in zip(X, y):
    model.learn_one(x, yi)

model.predict_one({"outlook": "sunny", "temp": "mild"})

'no'

model.predict_proba_one({"outlook": "sunny", "temp": "mild"})

{'no': 0.755..., 'yes': 0.244...}

You can also train and predict in mini-batch mode.

import pandas as pd

df = pd.DataFrame(X)
y = pd.Series(y)

batch_model = naive_bayes.CategoricalNB(alpha=1)
batch_model.learn_many(df, y)

unseen = pd.DataFrame([{"outlook": "rainy", "temp": "cool"}])

batch_model.predict_many(unseen)

0    yes
dtype: object

batch_model.predict_proba_many(unseen)

           no       yes
0  0.109900  0.890100

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 — IntoDataFrame

Returns

IntoDataFrame: Input samples joint log likelihood.

learn_many

Learn from a batch of categorical feature vectors.

Parameters

X — IntoDataFrame
y — IntoSeries

learn_one

Updates the model with a single observation.

Parameters

x — dict[base.typing.FeatureName, Any]
y — base.typing.ClfTarget

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[base.typing.FeatureName, Any]
kwargs — Any

Returns

base.typing.ClfTarget | None: The predicted label.

predict_proba_many

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

Parameters

X — IntoDataFrame

predict_proba_one

Return probabilities using the log-likelihoods.

Parameters

x — dict[base.typing.FeatureName, Any]

References¶

CategoricalNB