def deserialize_gaussian_nb(model_dict):
    model = GaussianNB(model_dict['params'])

    model.classes_ = np.array(model_dict['classes_'])
    model.class_count_ = np.array(model_dict['class_count_'])
    model.class_prior_ = np.array(model_dict['class_prior_'])
    model.theta_ = np.array(model_dict['theta_'])
    model.sigma_ = np.array(model_dict['sigma_'])
    model.epsilon_ = model_dict['epsilon_']

    return model
Example #2
0
def NB_predict():
    X = json.loads(request.form['X'])
    params = json.loads(request.form['params'])
    clf = GaussianNB()
    clf.class_prior_ = np.array(params['class_prior'])
    clf.class_count_ = np.array(params['class_count'])
    clf.theta_ = np.array(params['theta'])
    clf.sigma_ = np.array(params['sigma'])
    clf.classes_ = np.array(params['classes'])
    y = clf.predict(X)
    return jsonify(pred=y.tolist())
Example #3
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 def naiveBayesClassifier(self,X_train,y_train):
     """Method to train a regularized logistic regression classifier
     
     Parameters
     ----------
     X_train: Array shape [n_samples,n_features] for training the model with features
     y_train: Array shape[n_samples] for training the model with features of that target
     
     Returns
     ---------
     model: The trained Logistic Regression model."""
     
     meansArray, varianceArray = self.NBMeanAndVariance(X_train,y_train)
     
     model = GaussianNB()
     #fit the model with training data
     model.fit(X_train,y_train)
     model.theta_ = meansArray
     model.sigma_ = varianceArray
     
     return model
Example #4
0
# In[554]:

#def P9():

### STUDENT END ###
model = GaussianNB()
model.fit(mini_train_data, mini_train_labels)
expected = dev_labels
prediction = model.predict(dev_data)
print "Initial Model"
print classification_report(expected, prediction)

for i in range(1, 100, 5):
    model = GaussianNB()
    model.fit(mini_train_data, mini_train_labels)
    model.sigma_ = model.sigma_ / (i)
    expected = dev_labels
    prediction = model.predict(dev_data)
    acc = accuracy_score(expected, prediction)
    plt.scatter(i, acc, color="blue")
plt.title("Accuracy as Sigma Increases")
plt.ylabel("Accuracy"), plt.xlabel("Sigma")
plt.show()

### STUDENT END ###

#gnb = P9()

# ANSWER: ### As shown in the plot above, modifying Sigma can have a dramatic impact on the accuracy outcome.

# (10) Because Naive Bayes is a generative model, we can use the trained model to generate digits. Train a BernoulliNB model and then generate a 10x20 grid with 20 examples of each digit. Because you're using a Bernoulli model, each pixel output will be either 0 or 1. How do the generated digits compare to the training digits?