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# Train a domain classifier on the semantic scholar dataset¶

> Part 1: obtain and clean data

> The blue boxes show the steps implemented in this notebook.

### Extract¶

This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data.

In this case, we download the semantic scholar dataset from a public S3 bucket.

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    MAX_CHUNK_COUNT = 1

MAX_CHUNK_COUNT = 1
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    import urllib.request
    from random import shuffle

    manifest = (
        urllib.request.urlopen(
            "https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt"
        )
        .read()
        .decode()
    )  # a list of available chunks separated by '\n' characters

    lines = manifest.split()
    shuffle(lines)
    chunks = lines[:MAX_CHUNK_COUNT]

    f"Processing {len(chunks)} out of the {len(manifest.split())} available chunks"

import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( "https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/manifest.txt" ) .read() .decode() ) # a list of available chunks separated by '\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f"Processing {len(chunks)} out of the {len(manifest.split())} available chunks"
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    'Processing 1 out of the 6002 available chunks'

### Transform¶

- Filter out non-English abstracts using `great_ai.utilities.predict_language`
- Project it to only keep the necessary components (text and labels), clean the textual content using `great_ai.utilities.clean`
- We will speed up processing using `great_ai.utilities.parallel_map`.

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    from typing import List, Tuple
    import json
    import gzip
    from great_ai import parallel_map, clean, is_english, predict_language

    def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]:
        # Extract
        response = urllib.request.urlopen(
            f"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/{chunk_key}"
        )  # a gzipped JSON Lines file

        decompressed = gzip.decompress(response.read())
        decoded = decompressed.decode()
        chunk = [json.loads(line) for line in decoded.split("\n") if line]

        # Transform
        return [
            (
                clean(
                    f'{c["title"]} {c["paperAbstract"]} {c["journalName"]} {c["venue"]}',
                    convert_to_ascii=True,
                ),  # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts
                c["fieldsOfStudy"],
            )  # Create pairs of `(text, [...domains])`
            for c in chunk
            if c["fieldsOfStudy"] and is_english(predict_language(c["paperAbstract"]))
        ]

    preprocessed_chunks = parallel_map(preprocess_chunk, chunks)

from typing import List, Tuple import json import gzip from great_ai import parallel_map, clean, is_english, predict_language def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: # Extract response = urllib.request.urlopen( f"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/open-corpus/2022-02-01/{chunk_key}" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split("\n") if line] # Transform return [ ( clean( f'{c["title"]} {c["paperAbstract"]} {c["journalName"]} {c["venue"]}', convert_to_ascii=True, ), # The text is cleaned to remove PDF extraction, web scraping, and other common artifacts c["fieldsOfStudy"], ) # Create pairs of `(text, [...domains])` for c in chunk if c["fieldsOfStudy"] and is_english(predict_language(c["paperAbstract"])) ] preprocessed_chunks = parallel_map(preprocess_chunk, chunks)

    Spacy model en_core_web_sm not found locally, downloading...
    Collecting en-core-web-sm==3.3.0
      Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.3.0/en_core_web_sm-3.3.0-py3-none-any.whl (12.8 MB)
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 12.8/12.8 MB 3.6 MB/s eta 0:00:00
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    Installing collected packages: en-core-web-sm
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    2022-06-25 14:21:57,983 |  WARNING | Limiting concurrency to 1 because there are only 1 chunks
    2022-06-25 14:21:57,984 |     INFO | Starting parallel map (concurrency: 1, chunk size: 1)
    2022-06-25 14:21:57,984 |  WARNING | Running in series, there is no reason for parallelism
    100%|██████████| 1/1 [03:26<00:00, 206.86s/it]

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    from itertools import chain

    preprocessed_data = list(chain(*preprocessed_chunks))
    X, y = zip(
        *preprocessed_data
    )  # X is the input, y is the expected (ground truth) output

from itertools import chain preprocessed_data = list(chain(*preprocessed_chunks)) X, y = zip( *preprocessed_data ) # X is the input, y is the expected (ground truth) output

### Load¶

Upload the dataset (or a part of it) to a central repository using `great_ai.add_ground_truth`. This step automatically tags each datapoint with a split label according to the ratios we set. Additional tags can be also given.

#### Production-ready backend¶

The MongoDB driver is automatically configured if `mongo.ini` exists with the following scheme:

    mongo_connection_string=mongodb://localhost:27017/
    mongo_database=my_great_ai_db

> You can install MongoDB from here or use it as a service

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    from great_ai import add_ground_truth

    add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2)

from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2)

    2022-06-25 14:25:24,989 |  WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️
    2022-06-25 14:25:24,990 |     INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising MongodbDriver
    2022-06-25 14:25:24,991 |     INFO | Found credentials file (/data/projects/great_ai_example/mongo.ini), initialising LargeFileMongo
    2022-06-25 14:25:24,992 |     INFO | Settings: configured ✅
    2022-06-25 14:25:24,993 |     INFO | 🔩 tracing_database: MongodbDriver
    2022-06-25 14:25:24,994 |     INFO | 🔩 large_file_implementation: LargeFileMongo
    2022-06-25 14:25:24,994 |     INFO | 🔩 is_production: False
    2022-06-25 14:25:24,995 |     INFO | 🔩 should_log_exception_stack: True
    2022-06-25 14:25:24,996 |     INFO | 🔩 prediction_cache_size: 512
    2022-06-25 14:25:24,997 |     INFO | 🔩 dashboard_table_size: 50
    2022-06-25 14:25:24,998 |  WARNING | You still need to check whether you follow all best practices before trusting your deployment.
    2022-06-25 14:25:24,998 |  WARNING | > Find out more at https://se-ml.github.io/practices/

### Next: Part 2¶

Last update: July 8, 2022