Pliers is meant to provide a unified interface to a wide range of feature extraction tools and services, so it’s imperative that the results we get back from different extractors can also be represented and accessed in a standardized way. In this section, we provide a detailed description of the pliers ExtractorResult class and the various options available for exporting results to pandas DataFrames. In many typical workflows, the export process will be done implicitly, so that all a user has to worry about is making sure to specify appropriate formatting arguments. In particular, if you only ever work with the Graph API, you may want to skip down to the Graph results section.

The ExtractorResult class

Calling transform() on an instantiated Extractor returns an object of class ExtractorResult. This is a lightweight container that contains all of the extracted feature information returned by the Extractor, references to the Stim and Extractor objects used to generate the result, and both “raw” and processed forms of the results returned by the Extractor (though note that many Extractors don’t set a .raw property). For example:

>>> from pliers.extractors import FaceRecognitionFaceLocationsExtractor
>>> ext = FaceRecognitionFaceLocationsExtractor()
>>> result = ext.transform(image)



>>> result.raw
[(142, 349, 409, 82)]

Exporting results to pandas DataFrames

Typically, we’ll want to work with the data in a more convenient form. Fortunately, every ExtractorResult instance provides a .to_df() method that returns a pandas DataFrame:

>>> result.to_df()
onset   duration   object_id   face_locations
NaN     NaN        0           (142, 349, 409, 82)

Here, the 'face_locations' column is properly labeled with the name of the feature returned by the Extractor. Not surprisingly, you’ll still need to know something about the feature extraction tool you’re using in order to understand what you’re getting back. In this case, consulting the documentation for the face_recognition package’s face_locations function reveals that the values (142, 349, 409, 82) give us the bounding box coordinates of the detected face in CSS order (i.e., top, right, bottom, left).

Timing columns

You’re probably wondering what the other columns are. The 'onset' and 'duration' columns providing timing information for the event in question, if applicable. In this case, because our source Stim was a static image, there’s no meaningful timing information to be had. But to_df() still returns these columns by default. This becomes important in cases where we want to preserve some temporal context as we pass Stim objects through a feature extraction pipeline:

>>> ext = FaceRecognitionFaceLocationsExtractor()
>>> image = Stim('obama.jpg', onset=14, duration=1)
>>> result = ext.transform(image)
>>> result.to_df()
onset   duration    object_id   face_locations
14      1           0           (142, 349, 409, 82)

Of course, if we really don’t want the timing columns, we can easily suppress them:

>>> result.to_df(timing=False)
object_id   face_locations
0           (142, 349, 409, 82)

We could also pass timing='auto', which would drop the 'onset' and 'duration' columns if and only if all values are NaN.

Understanding object_ids

What about the 'object_id' column? This one’s not so intuitive, but can in some cases be very important. Consider a situation where there are multiple valid results objects in a single Stim. For example, suppose we feed an image to our FaceRecognitionFaceLocationsExtractor that contains multiple faces. How are we supposed to keep track of these different faces in the results? They come from the same Stim and share the same timing parameters (e.g., in the last example, where we explicitly specified the onset and duration, all detected faces will have onset=14 and duration=1). But we obviously need to have some way of distinguishing distinct records.

The solution is to serially assign each distinct result object a different object_id. Let’s modify the last example to feed in an image that contains 4 separate faces:

>>> ext = FaceRecognitionFaceLocationsExtractor()
>>> image = Stim('obama.jpg', onset=14, duration=1)

>>> result = ext.transform(image)
onset   duration    object_id   face_locations
14      1           0           (236, 862, 325, 772)
14      1           1           (104, 581, 211, 474)
14      1           2           (365, 782, 454, 693)
14      1           3           (265, 444, 355, 354)

As with the timing columns, if we don’t want to see the object_id column, we can suppress it by calling .to_df(object_id=False) or .to_df(object_id='auto'). In the latter case, the object_id column will be included if and only if the values are non-constant (i.e., there is some value other than 0 somewhere in the DataFrame).

Displaying metadata

Although not displayed by default, it’s also possible to include additional metadata about the Stim and Extractor in the DataFrame returned by to_df:

>>> result = ext.transform('obama.jpg')
>>> result.to_df(timing=False, object_id=False, metadata=True)
face_locations      stim_name   class       filename    history     source_file
(142, 349, 409, 82) obama.jpg   ImageStim   obama.jpg               obama.jpg

Here we get columns for the Stim name (typically just the filename, unless we explicitly specified a different name), the current filename, the Stim history, and the source filename. In the above example, stim_name, filename and source_file are identical, but this won’t always be the case. For example, if the images we’re running through the FaceRecognitionFaceLocationsExtractor had been extracted from frames of video, the source_file would point to the original video, while the filename would point to (temporary) image files corresponding to the extracted frames.

The history column contains a text summary of the Stim transformation history; for more details, see the Transformation history section.

Display mode

By default, DataFrames are in ‘wide’ format. That is, each row represents a single event, and all features are contained in columns. To get a better sense of what this means, it’s helpful to look at an extractor that returns more than one feature:

>>> from pliers.extractors import GoogleVisionAPILabelExtractor
>>> ext = GoogleVisionAPILabelExtractor()
>>> result = ext.transform('apple.jpg')

>>> result.to_df()
onset   duration    object_id   fruit   apple   produce food    natural foods   mcintosh    diet food
NaN     NaN         0           0.968   0.966   0.959   0.824   0.801           0.629   0.607

Here we fed in an image of an apple, and the GoogleVisionAPILabelExtractor automatically returned 7 different high-probability labels (‘fruit’, ‘apple’, etc.)–each one represented as a separate feature in our results.

While there’s nothing at all wrong with this format (indeed, it’s the default!), sometimes we prefer to get back our data in ‘long’ format, where each row represents the intersection of a single event and a single feature:

>>> result.to_df(format='long', timing=False, object_id=False)
feature         value
fruit           0.968
apple           0.966
produce         0.959
food            0.824
natural foods   0.801
mcintosh        0.629
diet food       0.607

Now, the feature names are specified in the 'feature' column, and the extracted values are provided in the 'value' column.

Displaying Extractor names

If we only ever worked with results generated by a single Extractor for a single Stim, we’d rarely have any problems figuring out where our results are coming from. But as we’ll see momentarily, a more common use case is that we want to combine results from multiple Extractors and/or Stims into a single, possibly very large, DataFrame. In this case, figuring out the source of particular features can quickly get confusing–especially because different Extractors can potentially have similar or even identical feature names.

We can ensure that the name of the current Extractor is explicitly added to our results via the extractor_name argument. The precise behavior of extractor_name=True will depend on the format argument. When format='wide', the name will be added as the first level in a pandas MultiIndex; when format='long', a new column will be added. Examples:

>>> results.to_df(format='long', timing=False, object_id=False, extractor_name=True)
feature         value   extractor
fruit           0.968   GoogleVisionAPILabelExtractor
apple           0.966   GoogleVisionAPILabelExtractor
produce         0.959   GoogleVisionAPILabelExtractor
food            0.824   GoogleVisionAPILabelExtractor
natural foods   0.801   GoogleVisionAPILabelExtractor
mcintosh        0.629   GoogleVisionAPILabelExtractor
diet food       0.607   GoogleVisionAPILabelExtractor

>>> results.to_df(timing=False, object_id=False, extractor_name=True)
fruit   apple   produce food    natural foods   mcintosh    diet food
0.968   0.966   0.959   0.824   0.801           0.629       0.607

Merging Extractor results

In most cases, we’ll want to do more than just apply a single Extractor to a single Stim. We might want to apply one Extractor to a set of stims, several different Extractors to a single Stim, or many Extractors to many Stims. As described (in the section on Graphs, pliers makes it easy to build such workflows–and to automatically merge the extracted feature data into one big pandas DataFrame. But in cases where we’re working with multiple results manually, or wish to exercise a little more control over the output format, we can still merge the results ourselves, using the appropriately named merge_results function.

Suppose we have a list of images, and we want to run both face recognition and object labeling on each image. Then we can do the following:

from pliers.extractors import (GoogleVisionAPIFaceExtractor, GoogleVisionAPILabelExtractor)
my_images = ['file1.jpg', 'file2.jpg', ...]

face_ext = GoogleVisionAPIFaceExtractor()
face_feats = face_ext.transform(my_images)

lab_ext = GoogleVisionAPILabelExtractor()
lab_feats = lab_ext.transform(my_images)

Now each of face_feats and lab_feats is a list of ExtractorResult objects. We could explicitly convert each element in each list to a pandas DataFrame (by calling .to_df()), but then we’d still need to figure out how to merge all those DataFrames in a sensible way. Fortunately, merge_results() can do the heavy lifting for us:

from pliers.extractors import merge_results
# merge_results expects a single list, so we concatenate our two lists
df = merge_results(face_feats + lab_feats, timing=False, metadata=True,
                   object_id='auto', format='long',

Nearly all of the arguments to merge_results match the ones we saw above when calling to_df on individual ExtractorResult instances. We can control the output shape (‘wide’ vs. ‘long’) with the format argument, and indicate whether which optional columns to include via the metadata, timing, and object_id flags.

The only notable exception in terms of argument behavior is that the extractor_names argument has different semantics from extractor_name in to_df(). Specifically, rather than just specifying whether or not to include the names of Extractors, we can now also control exactly how they’re displayed (e.g., whether they’re prepended to the feature names, added as a level in a pandas MultiIndex, etc.). Full details can be found in the merge_results() function reference.

In all other respects, the outputs of merge_results should look just like those generated by to_df calls–except of course that the results for different Extractors and Stims are now concatenated together along either the row or the column axes (depending on the format argument). As a general rule of thumb, we recommend using the default format (‘wide’) in cases where one is working with a small number of different Extractors and/or features, and switching to format='long' when the number of Extractors and/or features gets large. (The main reason for this recommendation is that the merged DataFrames are typically sparse, so in ‘wide’ format, one can end up with a very large number of NaN values when working with many Extractors at once. In ‘long’ format, there are virtually no missing values.)

Graph results

In practice, many users will primarily rely on the Graph API for feature extraction. Since standard Graph execution merges results by default, using a Graph means that you probably won’t need to worry about calling to_df or merge_results explicitly. You’ll just get a single, already-merged pandas DataFrame as the result of a Graph.transform call.

The main thing to be aware of in this case is that the .transform call takes any of the keyword arguments supported by merge_results, and simply passes them through. This means you can control the output format and inclusion of various columns exactly as documented above for merge_results (and to_df). Here’s a minimalistic example to illustrate:

from pliers.graph import Graph
from pliers.filters import FrameSamplingFilter
from pliers.extractors import FaceRecognitionFaceLandmarksExtractor
from pliers.tests.utils import get_test_data_path
from os.path import join

# Use a short video from the pliers test suite
video = join(get_test_data_path(), 'video', 'obama_speech.mp4')

# Sample image frames from video, then apply face recognition
nodes = [
    (FrameSamplingFilter(hertz=2), ['FaceRecognitionFaceLocationsExtractor'])

# Construct and run Graph
g = Graph(nodes)
df = g.transform(video, metadata=False, format='long', extractor_names='column')

''' Outputs:

object_id   duration    onset   feature         value               extractor
0           0.50        0.0     face_locations  (36, 223, 79, 180)  FaceRecognitionFaceLocationsExtractor
1           0.50        0.0     face_locations  (58, 101, 94, 65)   FaceRecognitionFaceLocationsExtractor
0           0.50        0.5     face_locations  (26, 213, 70, 170)  FaceRecognitionFaceLocationsExtractor
1           0.50        0.5     face_locations  (58, 101, 94, 65)   FaceRecognitionFaceLocationsExtractor
...         ...         ...     ...             ...                 ...

If you don’t explicitly specify any formatting arguments, you’ll get the same sane defaults used in merge_results(), which should work well in most cases.