In this tutorial, we will apply CrowdTruth metrics to a binary choice crowdsourcing task for Person Identification in video fragments. The workers were asked to watch a short video fragment of about 3-5 seconds and then decide whether there is any person that appears in the video fragment. The task was executed on FigureEight. For more crowdsourcing annotation task examples, click here.
To replicate this experiment, the code used to design and implement this crowdsourcing annotation template is available here: template, css, javascript.
This is a screenshot of the task as it appeared to workers:
A sample dataset for this task is available in this file, containing raw output from the crowd on FigureEight. Download the file and place it in a folder named data that has the same root as this notebook. Now you can check your data:
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import pandas as pd
test_data = pd.read_csv("../data/person-video-binary-choice.csv")
test_data.head()
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In [2]:
import crowdtruth
from crowdtruth.configuration import DefaultConfig
Our test class inherits the default configuration DefaultConfig, while also declaring some additional attributes that are specific to the Person Identification task:
inputColumns: list of input columns from the .csv file with the input dataoutputColumns: list of output columns from the .csv file with the answers from the workersopen_ended_task: boolean variable defining whether the task is open-ended (i.e. the possible crowd annotations are not known beforehand, like in the case of free text input); in the task that we are processing, workers pick the answers from a pre-defined list, therefore the task is not open ended, and this variable is set to Falseannotation_vector: list of possible crowd answers, mandatory to declare when open_ended_task is False; for our task, this is a list containing true and false valuesprocessJudgments: method that defines processing of the raw crowd data; for this task, we process the crowd answers to correspond to the values in annotation_vectorThe complete configuration class is declared below:
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class TestConfig(DefaultConfig):
inputColumns = ["videolocation", "subtitles", "imagetags", "subtitletags"]
outputColumns = ["selected_answer"]
# processing of a closed task
open_ended_task = False
annotation_vector = ["yes", "no"]
def processJudgments(self, judgments):
# pre-process output to match the values in annotation_vector
for col in self.outputColumns:
# transform to lowercase
judgments[col] = judgments[col].apply(lambda x: str(x).lower())
return judgments
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data, config = crowdtruth.load(
file = "../data/person-video-binary-choice.csv",
config = TestConfig()
)
data['judgments'].head()
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In [6]:
results = crowdtruth.run(data, config)
results is a dict object that contains the quality metrics for the video fragments, annotations and crowd workers.
The video fragment metrics are stored in results["units"]:
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results["units"].head()
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The uqs column in results["units"] contains the video fragment quality scores, capturing the overall workers agreement over each video fragment. Here we plot its histogram:
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import matplotlib.pyplot as plt
%matplotlib inline
plt.hist(results["units"]["uqs"])
plt.xlabel("Video Fragment Quality Score")
plt.ylabel("Video Fragment")
The unit_annotation_score column in results["units"] contains the video fragment-annotation scores, capturing the likelihood that an annotation is expressed in a video fragment. For each video fragment, we store a dictionary mapping each annotation to its video fragment-relation score.
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results["units"]["unit_annotation_score"].head()
The worker metrics are stored in results["workers"]:
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results["workers"].head()
The wqs columns in results["workers"] contains the worker quality scores, capturing the overall agreement between one worker and all the other workers.
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plt.hist(results["workers"]["wqs"])
plt.xlabel("Worker Quality Score")
plt.ylabel("Workers")
The annotation metrics are stored in results["annotations"]. The aqs column contains the annotation quality scores, capturing the overall worker agreement over one relation.
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results["annotations"]
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