add_feature_weights: Add feature weights

In oppr: Optimal Project Prioritization

Add feature weights

Description

Set weights for conserving features in a project prioritization problem().

Usage

add_feature_weights(x, weights)

## S4 method for signature 'ProjectProblem,numeric'
add_feature_weights(x, weights)

## S4 method for signature 'ProjectProblem,character'
add_feature_weights(x, weights)

Arguments

x	ProjectProblem object.
weights	Object that specifies the weights for each feature. See the Details section for more information.

Details

Weights are used to specify the relative importance for maintaining the persistence of specific features. For budget constrained problems (e.g. add_max_richness_objective()), these weights could be used to specify which features are more important than other features according to evolutionary or cultural metrics. Specifically, features with a higher weight value are considered more important. It is generally best to ensure that the feature weights range between 0.0001 and 10,000 to reduce the time required to solve problems using exact algorithm solvers. As a consequence, you might have to rescale the feature weights if the largest or smallest values occur outside this range (excluding zeros). If you want to ensure that certain features conserved in the solutions, it is strongly recommended to lock in the actions for these features instead of setting really high weights for these features. Please note that a warning will be thrown if you attempt to solve problems with weights when an objective has been specified that does not use weights. Currently, all objectives—except for the minimum set objective (i.e. add_min_set_objective())—can use weights.

The weights for a problem can be specified in several different ways:

numeric

vector of weight values for each feature.

character

specifying the name of column in the feature data (i.e. the argument to features in the problem() function) that contains the weights.

See Also

weights.

Examples

# load data
data(sim_projects, sim_features, sim_actions)

# print feature data
print(sim_features)

# build problem with maximum richness objective, $300 budget, and no weights
p1 <- problem(sim_projects, sim_actions, sim_features,
              "name", "success", "name", "cost", "name") %>%
      add_max_richness_objective(budget = 200) %>%
      add_binary_decisions()

# print problem
print(p1)

# build another problem, and specify feature weights using the values in the
# "weight" column of the sim_features table by specifying the column
# name "weight"
p2 <- p1 %>%
      add_feature_weights("weight")

# print problem
print(p2)

# build another problem, and specify feature weights using the
# values in the "weight column of the sim_features table, but
# actually input the values rather than specifying the column name
# "weights" column of the sim_features table
p3 <- p1 %>%
      add_feature_weights(sim_features$weight)

# print problem
print(p3)

## Not run: 
# solve the problems
s1 <- solve(p1)
s2 <- solve(p2)
s3 <- solve(p3)

# print solutions
print(s1)
print(s2)
print(s3)

# plot solutions
plot(p1, s1)
plot(p2, s2)
plot(p3, s3)

## End(Not run)

oppr documentation built on Sept. 8, 2022, 5:07 p.m.