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4 Logistic Regression

One of the most wide-spread and effective classifiers in statistical learning, logistic regression, can be easily implemented in Alchemy. Here, we only deal with binary predictors and dependent variables, but the extension to these is intuitive as well.

Logistic regression is a regression model of the form:

$\displaystyle \ln \left( \frac{P(Y = 1 \vert X = x)}{P(Y = 0 \vert X = x)} \right) = \alpha + \sum_{i = 1}^{n}{\beta_i x_i}$ (1)

where $ X$ is a vector of binary predictors and $ Y$ is the dependent variable. So how do we describe this model in Markov logic? If we look at the model of Markov networks:

$\displaystyle P(X = x) = \frac{1}{Z}\exp(\sum_{j}{w_j f_i(x)})$ (2)

this implies we need one feature for $ Y$ and one feature for each $ X_i$ ,$ Y$ in order to arrive at the model

$\displaystyle P(Y = y,X = x) = \frac{1}{Z}\exp(\alpha y + \sum_{j}{\beta_j x_j y})$ (3)

resulting in

$\displaystyle \frac{P(Y = 1 \vert X = x)}{P(Y = 0 \vert X = x)}) = \exp(\alpha + \sum_{j}{\beta_j x_j}) / \exp(0) = \exp(\alpha + \sum_{j}^{n}{\beta_j x_j})$ (4)

To represent this as an MLN, each parameter is represented as a weight corresponding to each formula, i.e. $ \alpha \thickspace Y$ and for each $ i$ , $ \beta_i \thickspace X_i \wedge Y$

We demonstrate this on an example from the UCI machine learning data repository, the voting-records dataset, which contains yes/no votes of Congressmen on 16 issues. The class to be determined is ``Republican'' or ``Democrat'' (our $ Y$ ), and each vote is a binary predictor (our $ X_i$ ). So, the resulting clauses in the MLN are:

HandicappedInfants(x) ^ Democrat(x)
WaterProjectCostSharing(x) ^ Democrat(x)
AdoptionOfTheBudgetResolution(x) ^ Democrat(x)

This predicts whether a Congressman is a Democrat (if Democrat(x) is false, x is a Republican). Alternatively, we could have modeled the connection between the predictors and the dependent variable as an implication, i.e.:

HandicappedInfants(x) => Democrat(x)

These two models are equivalent when we condition on the predictors.

We can perform generative or discriminative weight learning on the mln, given the training data voting.db with the following command:

learnwts -g -i voting.mln -o voting-gen.mln -t voting-train.db -ne Democrat


learnwts -d -i voting.mln -o voting-disc.mln -t voting-train.db -ne Democrat

The weights obtained tell us the relative ``goodness'' of each vote as it can predict whether a Congressman is a democrat or not. Given this, we can look at a new Congressman (or several) and his/her voting record and predict whether he/she is a democrat or republican. We run inference with

infer -ms -i voting-disc.mln -r voting.result -e voting-test.db -q Democrat

This produces the file voting.result containing the marginal probabilities of each Congressman being a Democrat.

next up previous
Next: 5 Text Classification and Up: The Alchemy Tutorial Previous: 3 Social Network Analysis
Marc Sumner 2010-01-22