wolvr 0.0.7

Utility Belt for Machine Learning

Utility Library Module for Advanced Machine Learning

Install

pip install wolvr

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Example 1: Naive Bayes

from wolvr import naive_bayes as nb
nb.demo(dataset="SMS_spam")

Selected Output:

This demo uses a public dataset of SMS spam, which has a total of 5574 messages = 747 spam and 4827 ham (legitimate).
The goal is to use 'term frequency in message' to predict whether the message is ham (class=0) or spam (class=1).

Using a grid search and a multinomial naive bayes classifier, the best hyperparameters were found as following:
   Step1: Tokenizing text: CountVectorizer(analyzer = 'word', ngram_range = (1, 1));
   Step2: Transforming from occurrences to frequency: TfidfTransformer(use_idf = True).

The top 2 terms with highest probability of a message being a spam (the classification is either spam or ham):
   "claim": 80.73%
   "prize": 80.06%

Application example:
   - Message: "URGENT! We are trying to contact U. Todays draw shows that you have won a 2000 prize GUARANTEED. Call 090 5809 4507 from a landline. Claim 3030. Valid 12hrs only."
   - Probability of class=1 (spam): 98.32%
   - Classification: spam

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Example 2: k-Nearest Neighbors

from wolvr import kNN
kNN.demo("Social_Network_Ads")

Selected Output:

This demo uses a public dataset of Social Network Ads, which is used to determine what audience a car company should target in its ads in order to sell a SUV on a social network website.

Using a grid search and a kNN classifier, the best hyperparameters were found as following:
   Step1: scaler: StandardScaler(with_mean=True, with_std=True);
   Step2: classifier: kNN_classifier(n_neighbors=8, weights='uniform', p=1.189207115002721, metric='minkowski').

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Example 3: Decision Boundary Comparison

from wolvr import kNN
kNN.demo("Social_Network_Ads")

from wolvr import naive_bayes as nb
nb.demo("Social_Network_Ads")

from wolvr import SVM
SVM.demo("Social_Network_Ads")

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module: model_evaluation

function	description
plot_confusion_matrix()	plots the confusion matrix, along with key statistics, and returns accuracy
plot_ROC_curve()	plots the ROC (Receiver Operating Characteristic) curve, along with statistics
plot_PR_curve()	plots the precision-recall curve, along with statistics
plot_ROC_and_PR_curves()	plots both the ROC and the precision-recall curves, along with statistics
demo()	provides a demo of the major functions in this module

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module: naive_bayes

function	description
naive_bayes_Bernoulli()	when X are independent binary variables (e.g., whether a word occurs in a document or not)
naive_bayes_multinomial()	when X are independent discrete variables with 3+ levels (e.g., term frequency in the document)
naive_bayes_Gaussian()	when X are continuous variables
demo()	provides a demo of selected functions in this module

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module: kNN

function	description
demo()	provides a demo of selected functions in this module

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module: neural_network

function	description
rnn()	Recurrent neural network
demo()	provides a demo of selected functions in this module

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module: decision_tree

function	description
boost()	Boosting
demo()	provides a demo of selected functions in this module