Advanced queries

pylidc is built on SQLAlchemy, using a SQLite database for storage, and so, if you are familar with a SQL-like query language, you will find familiar capabilities in pylidc. This short tutorial is not a comprehensive guide to everything you can do with SQLAlchemy — if you’d like to explore this aspect further you should visit the SQLAlchemy documentation directly.

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Get a random result

One common objective for data exploration is to grab a random instance from some query. You can accomplish this by import func from sqlalchemy, ordering the results by random, and taking the first result:

from sqlalchemy import func

# Fetch all highly suspicious nodules
anns = pl.query(pl.Annotation).filter(pl.Annotation.malignancy == 5)

ann = anns.order_by(func.random()).first()
print(, ann.Malignancy)
# => 2516, 'Highly Suspicious'

ann = anns.order_by(func.random()).first()
print(, ann.Malignancy)
# => 4749, 'Highly Suspicious'

Query multiple model parameters with a join

Another common objective is to query for an Annotation object that is constrained by the queryable parameters of its corresponding Scan in some way. This can be accomplished with using a join. For example:

# Fetch all annotations with non-indeterminate malignancy value
# and whose respective scan has slice thickness less than 1 mm.
anns = pl.query(pl.Annotation).join(pl.Scan)\
         .filter(pl.Scan.slice_thickness < 1,
                 pl.Annotation.malignancy != 3)
# => 181

Meanwhile, the number of non-indeterminate annotations is:

anns = pl.query(pl.Annotation)\
         .filter(pl.Annotation.malignancy != 3)
# => 4253

And, the number of scans with slice thickness less than 1 mm is:

scans = pl.query(pl.Scan)\
          .filter(pl.Scan.slice_thickness < 1)
# => 39


When querying with a join, be careful what object type you actually would like returned. In the join example above, we joined on Annotation and Scan objects, but returned all Annotation objects with the desired properties.

Filtering with an in clause

Suppose we wanted to count the total number of annotations belonging to the scans in the query above. One way is to loop through all the scans and sum up the number of annotations belonging to each:

scans = pl.query(pl.Scan)\
          .filter(pl.Scan.slice_thickness < 1)
n_anns = sum([len(scan.annotations) for scan in scans])
# => 300

Alternatively, a more SQL-like approach is to query for all Annotations whose scan_id attribute is an id from the ids from scans:

sids = [ for scan in scans]
anns = pl.query(pl.Annotation).filter(pl.Annotation.scan_id.in_( sids ))
# => 300

Grouping nodules with similar attributes

Let’s suppose we’d like to know which Annotation s share identical characteristic values (i.e., lobulation, spiculation, etc.).

This can be accomplished by making use of the SQLite aggregate functions, such as count and group_concat, as well as the group_by and having functions:

from sqlalchemy import func

# Get a list of the attributes. Alternatively,
# we could list them all out in the query, i.e.,
# pl.Annotation.spiculation, pl.Annotation.malignancy, etc...
all_features = [getattr(pl.Annotation, fname)
                    for fname in pl.annotation_feature_names]

groups = pl.query(func.group_concat(\
           .group_by( *all_features )\
           .having(func.count('*') > 1)

The previous query fetches all annotations that share identical numerical feature sets. This is accomplished by using the SQL group by statement and filtering to including groups with size strictly greater than 1 using the SQL having statement.

Note that in the query itself, we query for the Annotation ids, not the Annotation objects themselves as we would normally do. If pl.Annotation is used as the query argument, then only a single member of the group is returned. group_concat returns the entire group in the query, rather than a single member; however, a SQL literal must be used inside of group_concat, so we query for the Annotation id instead (the Annotation object can be obtained from the id, anyway).

So what exactly is the result?:

# => 837

This means there are 837 nodule annotation groups whose group members have identical characteristic feature values. The individual results can be inspected:

print(groups[0], groups[1])
# => (u'6764,6766',), (u'5781,5782,5937',)

Each result is a 1-tuple whose element is a string of Annotation ids, separated by commas. We can assert that the feature values are indeed identical in a particular group:

# Id's for the first group:
ids = groups[0][0].split(',')

for id in ids:
    ann = pl.query(pl.Annotation).get(id)
# => [1 1 3 3 5 1 1 5 1]
# => [1 1 3 3 5 1 1 5 1]

Let’s look at the largest such group:

group_sizes = [len(g[0].split(',')) for g in groups]
# => 296

So, there is a group of annotations of size 296, all having identical feature values, and this is the biggest such group. Let’s look at what the feature values are:

import numpy as np

# Get the location of the maximum sized group.
i = np.argmax(group_sizes)

# Grab the first id in the maximum sized group, for no particular reason.
id = groups[i][0].split(',')[0]

ann = pl.query(pl.Annotation).get(id)
# => Feature              Meaning                    #
# => -                    -                          -
# => Subtlety           | Fairly Subtle            | 3
# => Internalstructure  | Soft Tissue              | 1
# => Calcification      | Absent                   | 6
# => Sphericity         | Round                    | 5
# => Margin             | Sharp                    | 5
# => Lobulation         | No Lobulation            | 1
# => Spiculation        | No Spiculation           | 1
# => Texture            | Solid                    | 5
# => Malignancy         | Moderately Unlikely      | 2

There are 296 annotations with values identical to the above.

We can confirm that the feature values for all these annotations by computing the variance of the each feature value computed over all annotations in the group:

# Fetch the feature values for all annotations in the group
ids = [id for id in groups[i][0].split(',')]

fvals = pl.query(*all_features)\
          .filter( ids )).all()
fvals = np.array(fvals)

# => (296, 9)

# => [0. 0. 0. 0. 0. 0. 0. 0. 0.]