# Queries

In this section, we go over most common probabilistic reasoning tasks, and provide code snippets to compute those queries.

### Setup

First, we load some pretrained PC, and the corresponding data.

```
using CircuitModelZoo: zoo_psdd_file
using DensityEstimationDatasets: twenty_datasets
using ProbabilisticCircuits
using Tables
pc = read(zoo_psdd_file("plants.psdd"), ProbCircuit);
data, _, _ = twenty_datasets("plants");
data = Tables.matrix(data);
println("circuit with $(num_nodes(pc)) nodes and $(num_parameters(pc)) parameters.")
println("dataset with $(size(data, 2)) features and $(size(data, 1)) examples.")
```

circuit with 154867 nodes and 28031 parameters. dataset with 69 features and 17412 examples.

## Full Evidence (EVI)

EVI refers to computing the probability when full evidence is given, i.e. when $x$ is fully observed, the output is $p(x)$. We can use `loglikelihoods`

method to compute $\log{p(x)}$:

```
probs = loglikelihoods(pc, data[1:100, :]; batch_size=64);
probs[1:3]
```

3-element Vector{Float32}: -7.533389 -16.534176 -27.8094

## Partial Evidence (MAR)

In this case we have some missing values. Let $x^o$ denote the observed features, and $x^m$ the missing features. We would like to compute $p(x^o)$ which is defined as $p(x^o) = \sum_{x^m} p(x^o, x^m)$. Of course, computing this directly by summing over all possible ways to fill the missing values is not tractable.

The good news is that given a **smooth** and **decomposable** PC, the marginal can be computed exactly and in linear time to the size of the PC.

First, we randomly make some features go `missing`

.

```
using DataFrames
using Tables
function make_missing(d; keep_prob=0.8)
m = missings(Bool, size(d)...)
flag = rand(size(d)...) .<= keep_prob
m[flag] .= d[flag]
return m
end;
data_miss = make_missing(data[1:1000,:]);
```

Now, we can use `loglikelihoods`

to compute the marginal queries.

```
probs = loglikelihoods(pc, data_miss; batch_size=64);
probs[1:3]
```

3-element Vector{Float32}: -6.186901 -11.569301 -26.675375

Note that `loglikelihoods`

can also be used to compute probabilisties if all data is observed, as we saw in previous section.

## Conditionals (CON)

In this case, given observed features $x^o$, we would like to compute $p(Q \mid x^o)$, where $Q$ is a subset of features disjoint with $x^o$. We can use Bayes rule to compute conditionals as two seperate MAR queries as follows:

Currently, this has to be done manually by the user. We plan to add a simple API for this case in the future.

## Maximum a posteriori (MAP, MPE)

In this case, given the observed features $x^o$ the goal is to fill out the missing features in a way that $p(x^m, x^o)$ is maximized.

We can use the `MAP`

method to compute MAP, which outputs the states that maximize the probability and the log-likelihoods of each state.

```
data_miss = make_missing(data[1:1000,:], keep_prob=0.5);
states = MAP(pc, data_miss; batch_size = 64);
size(states)
```

(1000, 69)

## Sampling

We can also sample from the distrubtion $p(x)$ defined by a Probabilistic Circuit. You can use `sample`

to achieve this task.

```
samples = sample(pc, 100, [Bool]);
size(samples)
```

(100, 1, 69)

Additionally, we can do conditional samples $x \sim p(x \mid x^o)$, where $x^o$ are the observed features ($x^o \subseteq x$), and could be any arbitrary subset of features.

```
#3 random evidences for the examples
evidence = rand( (missing,true,false), (2, num_randvars(pc)));
samples = sample(pc, 3, evidence; batch_size = 2);
size(samples)
```

(3, 2, 69)