# Graphs¶

## Graph specification¶

To this point, we’ve been initializing and running our Transformers one at a time, and explicitly passing stimuli to each one. While this works fine, it can get rather verbose in cases where we want to extract a large number of features. It can also be a bit of a pain to appropriately connect Converters to one another when the routing is complicated.

For example, suppose we have a series of videos (perhaps segments of a full-length movie), and we want to extract both visual and speech transcript-based features. Specifically, let’s say we want to detect faces in the frames of the video, and also run a sentiment analysis model on a speech transcription extracted from the audio track of the videos. This requires us to do all of the following:

• Convert the video to a series of video frames (i.e., static images), probably with some periodic sampling (there’s no point in running face detection on every single frame, since subtitles won’t change nearly that fast—we can probably get away with sampling frames as little as twice per second);
• Run face detection on each extracted frame (we’ll use Google’s Cloud Vision face detection API);
• Extract the audio track from the video;
• Transcribe the audio track to text (we’ll use Google’s Cloud Speech API for this);
• Run a sentiment analysis model (in this case, using the Indico.io API) on the transcribed text.

The code to do this, with transformations made explicit:

Listing 1
from pliers.stimuli import VideoStim
from pliers.filters import FrameSamplingFilter
from pliers.extractors import (IndicoAPITextExtractor, merge_results,

segments = ['segment1.mp4', 'segment2.mp4', 'segment3.mp4']
segments = [VideoStim(s) for s in segments]

# Sample 2 video frames / second
frame_filter = FrameSamplingFilter(hertz=2)
frames = frame_filter.transform(segments)

# Face extraction
face_results = face_ext.transform(frames)

# Run each image through Google's text detection API
transcripts = transcriber.transform(segments)

# # Apply Indico sentiment analysis extractor
indico = IndicoAPITextExtractor(models=['sentiment'])
sentiment_results = indico.transform(transcripts)

# Combine visual and text-based feature data
results = face_results + sentiment_results

# # Merge into a single pandas DF
df = merge_results(results)


## The Graph API¶

The above code really isn’t that bad–it already features a high level of abstraction (each Transformer is initialized and applied in just two lines of code!), and has the advantage of being explicit about every step. Nonetheless, if we want to save ourselves a few dozen keystrokes, we can use pliers’ Graph API to abbreviate the listing down to just this:

Listing 2
from pliers.graph import Graph
from pliers.filters import FrameSamplingFilter
from pliers.extractors import IndicoAPITextExtractor

# Define nodes
nodes = [
]

# Construct and run Graph
g = Graph(nodes)
df = g.transform(segments)


### Node specification¶

Listing 2 produces exactly the same result as in Listing 1. But instead of explicitly initializing and applying each Transformer in sequence, all of the important work is done in the compact node specification. A detailed explanation of the node specification format can be found in the add_nodes() docstring. For present purposes, the key thing to recognize is that each node in the above graph is represented as a tuple with 2 elements. In this case, there are two root nodes–one for image processing (face extraction), the other for speech processing (sentiment analysis on the transcribed text). The first element in each Node definition indicates which Transformer to apply at that node. Here, the first node applies a FrameSamplingFilter, and the second applies a GoogleSpeechAPIConverter.

The second element in each tuple contains any children nodes-—i.e., nodes to which the output of the Transformer specified in the first node are passed. In our case, each node has only one child: the FrameSamplingFilter passes its output to the GoogleVisionAPIFaceExtractor, and the GoogleSpeechAPIConverter passes its output to the IndicoAPITextExtractor. However, in principle, the list of children nodes can be arbitrarily large. For example, we could have written:

nodes = [
(FrameSamplingFilter(hertz=2),
'ClarifaiAPIImageExtractor',
]


In this case, we would have a single root node, and the output of the FrameSamplingFilter would be independently passed to three separate extractors–the Google Vision face extractor, the Clarifai object labelling extractor, and the Google Vision label extractor.

#### Nesting¶

We can also nest additional levels by replacing each string value in the child list with another tuple. For example:

nodes = [
(FrameSamplingFilter(hertz=2),
[
[
IndicoAPITextExtractor(models=['sentiment'])
])
]
)
]


Here, we’re passing video frames to the GoogleVisionAPITextConverter, which detects text labels within images. Any resulting TextStim objects are then passed onto the IndicoAPITextExtractor for sentiment analysis. Using this simple syntax, we can quickly construct Graphs with arbitrarily deep nestings.

#### Other stuff¶

There are two other points worth noting about the Graph specification. First, note that we don’t need to explicitly specify most Stim conversion steps, as these will generally be detected and injected automatically (though, laziness aside, it’s generally a good idea to be explicit). In Listing 2, we never had to specify the VideoToAudioConverter that strips the audio track from the input videos; pliers did the work for us implicitly.

Second, observe that we can define nodes using string values rather than initialized Transformer objects. For example, in Listing 2, instead of passing an initialized GoogleVisionAPIFaceExtractor() object, we pass the string 'GoogleVisionAPIFaceExtractor' (we could also have passed 'googlevisionapifaceextractor', as the specification is case-insensitive). This only works in cases where we don’t need to pass non-default arguments, however.

### JSON specification¶

A convenient feature of the graph API illustrated in Listing 2 is that the list of nodes can be easily serialized as plain text. This allows us to conveniently encode the above graph specification as the following JSON:

Listing 3
nodes = {
'roots': [
{
'transformer': 'FrameSamplingConverter',
'parameters': {
'hertz': 2
},
'children': [
{
}
]
},
{
'children': [
{
'transformer': 'IndicoAPITextExtractor',
'parameters': {
'models': ['sentiment']
}
}
]
}
]
}


We can then construct a Graph simply by passing in the path to the JSON file:

graph = Graph(spec='graph_spec.json')


Note one important difference between the Python specification and the JSON specification: we obviously can’t serialize the already-initialized FrameSamplingFilter in the Python listing as plain text. This means that for Transformers that we want to initialize with non-default arguments, we need to separate the parameters separately, as in the JSON example above.

### Plotting¶

To facilitate understanding and verification of Graphs–which can get complicated in a hurry if a lot of implicit conversion is happening–the Graph class provides rudimentary plotting capabilities. Provided graphiz and PyGraphviz are available, calling graph.draw(filename) on any initialized Graph will generate a simple visual representation of the Graph. For our running example (Listing 2), the result looks like this: