Processing multiple texts

In the previous tutorials, we've seen how to apply a spaCy NLP pipeline to a single text. Once the pipeline is tested and ready to be applied on an entire corpus, we'll want to deploy it efficiently.

In this tutorial, we'll cover a few best practices and some caveats to avoid. Then, we'll explore methods that EDS-NLP provides to perform inference on multiple texts.

Consider this simple pipeline:

import edsnlp, edsnlp.pipes as eds

nlp = edsnlp.blank("eds")

nlp.add_pipe(eds.sentences())
nlp.add_pipe(eds.normalizer())

nlp.add_pipe(
    eds.matcher(
        terms=dict(patient=["patient", "malade"]),
        attr="NORM",
    ),
)

# Add qualifiers
nlp.add_pipe(eds.negation())
nlp.add_pipe(eds.hypothesis())
nlp.add_pipe(eds.family())

# Add date detection
nlp.add_pipe(eds.dates())

Let's deploy it on a large number of documents.

What about a `for` loop?

Suppose we have a corpus of text:

text = (
    "Patient admis le 25 septembre 2021 pour suspicion de Covid.\n"
    "Pas de cas de coronavirus dans ce service.\n"
    "Le père du patient est atteint du covid."
)

corpus = [text] * 10000  # (1)

This is admittedly ugly. But you get the idea, we have a corpus of 10 000 documents we want to process...

You could just apply the pipeline document by document.

# ↑ Omitted code above ↑

docs = [nlp(text) for text in corpus]

Next, you might want to convert these documents to a DataFrame for further analysis or storage. You could do this with a loop like this:

import pandas as pd

rows = []
for doc in docs:
    for ent in doc.ents:
        d = dict(
            begin=ent.start_char,
            end=ent.end_char,
            label=ent.label_,
            entity_text=ent.text,
            negation=ent._.negation,
            hypothesis=ent._.hypothesis,
            family=ent._.family,
        )
        rows.append(d)

    for date in doc.spans.get("dates", []):
        d = dict(
            begin=date.start_char,
            end=date.end_char,
            label="date",
            entity_text=date.text,
            datetime=date._.date.datetime,
        )
        rows.append(d)
df = pd.DataFrame(rows)

There are a few issues with this approach:

If our model contains deep learning components (which it does not in this tutorial), we don't benefit from optimized batched matrix operations : ideally, we'd like to process multiple documents at once.
We may have multiple cores available but we don't use them to apply the pipes of our model to multiple documents at the same time.
We would also like to perform the Doc -> Dict conversion step in parallel and avoid transferring full Doc instances back and forth between processes.
And ideally, being able to switch between input/output formats, or sequential/parallel processing, without changing the code too much.

Lazy inference and parallelization

To efficiently perform the same operations on multiple documents at once, EDS-NLP uses lazy collections, which record the operations to perform on the documents without actually executing them directly, similar to the way Spark does, or polars with its LazyFrame.

This allows EDS-NLP to distribute these operations on multiple cores or machines when it is time to execute them. We can configure how the collection operations are run (how many jobs/workers, how many gpus, whether to use the spark engine) via the lazy collection .set_processing(...) method.

For instance,

docs = edsnlp.data.from_iterable(corpus)
print(docs)
# <edsnlp.core.lazy_collection.LazyCollection object at 0x7f3e3c3e3d90>

as well as any edsnlp.data.read_* or edsnlp.data.from_* return a lazy collection, that we can iterate over or complete with more operations. To apply the model on our collection of documents, we can simply do:

docs = docs.map_pipeline(nlp)
# or à la spaCy :
# docs = nlp.pipe(docs)

SpaCy vs EDS-NLP

SpaCy's nlp.pipe method is not the same as EDS-NLP's nlp.pipe method, and will iterate over anything you pass to it, therefore executing the operations scheduled in our lazy collection.

We recommend you instantiate your models using nlp = edsnlp.blank(...) or nlp = edsnlp.load(...).

Otherwise, use the following to apply a spaCy model on a lazy collection docs without triggering its execution:

docs = docs.map_pipeline(nlp)

Finally, we can convert the documents to a DataFrame (or other formats / files) using the edsnlp.data.to_* or edsnlp.data.write_* methods. This triggers the execution of the operations scheduled in the lazy collection and produces the rows of the DataFrame.

We can pass our previous Doc to Dict code as a function to the converter parameter of the to_pandas method. Note that this specific conversion is already implemented in EDS-NLP, so you can just pass the string "ents" to the converter parameter, and customize the conversion by passing more parameters to the to_pandas method, as described here.

def convert_doc_to_rows(doc):
    entities = []

    for ent in doc.ents:
        d = dict(
            start=ent.start_char,
            end=ent.end_char,
            label=ent.label_,
            lexical_variant=ent.text,
            negation=ent._.negation,
            hypothesis=ent._.hypothesis,
            family=ent._.family,
        )
        entities.append(d)

    for date in doc.spans.get("dates", []):
        d = dict(
            begin=date.start_char,
            end=date.end_char,
            label="date",
            lexical_variant=date.text,
            datetime=date._.date.datetime,
        )
        entities.append(d)

    return entities


df = docs.to_pandas(converter=convert_doc_to_rows)
# or equivalently:
df = docs.to_pandas(
    converter="ents",
    span_getter=["ents", "dates"],
    span_attributes={
        # span._.*** name: column name
        "negation": "negation",
        "hypothesis": "hypothesis",
        "family": "family",
        "date.datetime": "datetime",
    },
)

We can also iterate over the documents, which also triggers the execution of the operations scheduled in the lazy collection.

for doc in docs:
    # do something with the doc
    pass

Processing a DataFrame

Processing text within a pandas DataFrame is a very common use case. In many applications, you'll select a corpus of documents over a distributed cluster, load it in memory and process all texts.

The OMOP CDM

In every tutorial that mentions distributing EDS-NLP over a corpus of documents, we will expect the data to be organised using a flavour of the OMOP Common Data Model.

The OMOP CDM defines two tables of interest to us:

the note table contains the clinical notes
the note_nlp table holds the results of a NLP pipeline applied to the note table.

We can use the converter="omop" argument to the edsnlp.data methods to read data in this format. More information about this converter can be found here.

To make sure we can follow along, we propose three recipes for getting the DataFrame: using a dummy dataset like before, loading a CSV or by loading a Spark DataFrame into memory.

Dummy exampleLoading data from a CSVLoading data from a Spark DataFrame

import pandas as pd

text = (
    "Patient admis le 25 septembre 2021 pour suspicion de Covid.\n"
    "Pas de cas de coronavirus dans ce service.\n"
    "Le père du patient est atteint du covid."
)

corpus = [text] * 1000

data = pd.DataFrame(dict(note_text=corpus))
data["note_id"] = range(len(data))

import pandas as pd

data = pd.read_csv("note.csv")

from pyspark.sql.session import SparkSession

spark = SparkSession.builder.getOrCreate()

df = spark.sql("SELECT * FROM note")
df = df.select("note_id", "note_text")

data = df.limit(1000).toPandas()  # (1)

We limit the size of the DataFrame to make sure we do not overwhelm our machine.

We'll see in what follows how we can efficiently deploy our pipeline on the data object.

Locally without parallelization

# Read from a dataframe & use the omop converter
docs = edsnlp.data.from_pandas(data, converter="omop")

# Add the pipeline to operations that will be run
docs = docs.map_pipeline(nlp)

# Convert each doc to a list of dicts (one by entity)
# and store the result in a pandas DataFrame
note_nlp = docs.to_pandas(
    converter="ents",
    # Below are the arguments to the converter
    span_getter=["ents", "dates"],
    span_attributes={  # (1)
        # span._.*** name: column name
        "negation": "negation",
        "hypothesis": "hypothesis",
        "family": "family",
        "date.datetime": "datetime",
        # having individual columns for each date part
        # can be useful for incomplete dates (eg, "in May")
        "date.day": "date_day",
        "date.month": "date_month",
        "date.year": "date_year",
    },
)

You can just pass a list if you don't want to rename the attributes.

The result on the first note:

start	end	label	lexical_variant	negation	hypothesis	family	key
0	7	patient	Patient	0	0	0	ents
114	121	patient	patient	0	0	1	ents
17	34	2021-09-25	25 septembre 2021	nan	nan	nan	dates

Locally, using multiple parallel workers

Caveat

Since workers can produce their results in any order, the order of the rows in the resulting DataFrame may not be the same as the order of the input data.

# Read from a dataframe & use the omop converter
docs = edsnlp.data.from_pandas(data, converter="omop")

# Add the pipeline to operations that will be run
docs = docs.map_pipeline(nlp)

# The operations of our lazy collection will be distributed on multiple workers
docs = docs.set_processing(backend="multiprocessing")

# Convert each doc to a list of dicts (one by entity)
# and store the result in a pandas DataFrame
note_nlp = docs.to_pandas(
    converter="ents",
    span_getter=["ents", "dates"],
    span_attributes={
        "negation": "negation",
        "hypothesis": "hypothesis",
        "family": "family",
        "date.datetime": "datetime",

        # having individual columns for each date part
        # can be useful for incomplete dates (eg, "in May")
        "date.day": "date_day",
        "date.month": "date_month",
        "date.year": "date_year",
    },
)

In a distributed fashion with spark

To use the Spark engine to distribute the computation, we create our lazy collection from the Spark dataframe directly and write the result to a new Spark dataframe. EDS-NLP will automatically distribute the operations on the cluster (setting backend="spark" behind the scenes), but you can change the backend (for instance to multiprocessing to run locally).

# Read from the pyspark dataframe & use the omop converter
docs = edsnlp.data.from_spark(df, converter="omop")

# Add the pipeline to operations that will be run
docs = docs.map_pipeline(nlp

# Convert each doc to a list of dicts (one by entity)
# and store the result in a pyspark DataFrame
note_nlp = docs.to_spark(
    converter="ents",
    span_getter=["ents", "dates"],
    span_attributes={
        "negation": "negation",
        "hypothesis": "hypothesis",
        "family": "family",
        "date.datetime": "datetime",

        # having individual columns for each date part
        # can be useful for incomplete dates (eg, "in May")
        "date.day": "date_day",
        "date.month": "date_month",
        "date.year": "date_year",
    },
    dtypes=None,  # (1)
)

If you don't pass a dtypes argument, EDS-NLP will print the inferred schema it such that you can copy-paste it in your code.