Split examples
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11 changed files with 401 additions and 252 deletions
6
.vscode/tasks.json
vendored
6
.vscode/tasks.json
vendored
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@ -4,9 +4,9 @@
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{
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"label": "Format and lint Python",
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"type": "shell",
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"command": "source .env/bin/activate && scripts/format-python.sh great_ai && scripts/format-python.sh examples",
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"command": "source .env/bin/activate && scripts/format-python.sh great_ai && scripts/format-python.sh examples/simple && scripts/format-python.sh examples/complex",
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"windows": {
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"command": ".env/bin/activate.bat; scripts/format-python.sh great_ai; scripts\\format-python.sh examples"
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"command": ".env\\bin\\activate.bat; scripts\\format-python.sh great_ai; scripts\\format-python.sh examples\\simple; scripts\\format-python.sh examples\\complex"
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},
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"group": "test",
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"presentation": {
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@ -22,7 +22,7 @@
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"type": "shell",
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"command": "source .env/bin/activate && python3 -m pytest great_ai",
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"windows": {
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"command": ".env/bin/activate.bat; python3 -m pytest great_ai"
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"command": ".env\\bin\\activate.bat; python3 -m pytest great_ai"
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},
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"group": "test",
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"presentation": {
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25
examples/complex/Dockerfile
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25
examples/complex/Dockerfile
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@ -0,0 +1,25 @@
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FROM python:3.8.12-slim-bullseye
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ENV ENVIRONMENT production
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WORKDIR /app
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RUN apt-get update &&\
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apt-get install curl -y &&\
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rm -rf /var/lib/apt/lists/*
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RUN pip install --no-cache-dir en-core-web-sm@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
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COPY sus sus
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RUN pip install --no-cache-dir --use-feature=in-tree-build ./sus
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COPY requirements.txt ./
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RUN pip install --no-cache-dir --requirement requirements.txt
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COPY . .
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RUN rm -rf sus secrets
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EXPOSE 5000
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HEALTHCHECK --interval=60s --timeout=60s --start-period=90s --retries=5 CMD [ "curl", "--fail", "http://localhost:5000/health" ]
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CMD ["uvicorn", "main:app", "--proxy-headers", "--backlog", "8196", "--timeout-keep-alive", "900", "--host", "0.0.0.0", "--port", "5000"]
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22
examples/complex/Dockerfile.development
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22
examples/complex/Dockerfile.development
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@ -0,0 +1,22 @@
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FROM python:3.8.12-slim-bullseye
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ENV ENVIRONMENT development
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VOLUME /app
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WORKDIR /app
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RUN apt-get update &&\
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apt-get install build-essential -y &&\
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rm -rf /var/lib/apt/lists/*
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RUN python3 -m pip install --no-cache-dir --upgrade pip &&\
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python3 -m pip install --no-cache-dir en-core-web-sm@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
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COPY requirements.txt ./
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RUN python3 -m pip install --no-cache-dir -r requirements.txt
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EXPOSE 5000
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VOLUME /dependencies
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CMD ["/bin/bash", "-c", "python3 -m pip install -e /dependencies/sus && python3 -m uvicorn --reload --host 0.0.0.0 --port 5000 main:app"]
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@ -1,8 +1,6 @@
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#!/usr/bin/env python3
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from great_ai import configure, create_service
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# configure(development_mode_override=True)
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configure(development_mode_override=True)
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from predict_domain import predict_domain
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@ -1,5 +1,5 @@
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import re
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from typing import Dict, Iterable, List
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from typing import Dict, Iterable, List
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from preprocess import preprocess
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from pydantic import BaseModel
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86
examples/simple/predict_domain.py
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86
examples/simple/predict_domain.py
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from great_ai import configure, create_service, log_argument, log_metric, use_model
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configure(development_mode_override=True)
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import re
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from typing import Dict, Iterable, List
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from preprocess import preprocess
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from pydantic import BaseModel
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from sklearn.pipeline import Pipeline
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from great_ai.utilities.clean import clean
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class DomainPrediction(BaseModel):
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domain: str
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probability: float
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explanation: List[str]
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@create_service
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@use_model("small-domain-prediction-v2", version="latest")
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@log_argument("text", validator=lambda t: len(t) > 0)
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def predict_domain(
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text: str, model: Pipeline, cut_off_probability: float = 0.2
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) -> List[DomainPrediction]:
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assert 0 <= cut_off_probability <= 1
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"""
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Predict the scientific domain of the input text.
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Return labels until their sum likelihood is larger than cut_off_probability.
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"""
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log_metric("text_length", len(text))
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cleaned = clean(text, convert_to_ascii=True)
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text = re.sub(r"[^a-zA-Z0-9]", " ", cleaned)
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feature_names = model.named_steps["vectorizer"].get_feature_names_out()
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token_mapping = {preprocess(original): original for original in text.split(" ")}
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features = model.named_steps["vectorizer"].transform(
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[" ".join(token_mapping.keys())]
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)
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prediction = model.named_steps["classifier"].predict_proba(features)[0]
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best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True)
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results: List[DomainPrediction] = []
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for class_index, probability in best_classes:
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weights = model.named_steps["classifier"].feature_log_prob_[class_index]
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domain = model.named_steps["classifier"].classes_[class_index]
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results.append(
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DomainPrediction(
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domain=domain,
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probability=round(probability * 100),
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explanation=_get_explanation(
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feature_names=feature_names,
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features=features.A[0],
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weights=weights,
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token_mapping=token_mapping,
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),
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)
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)
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if sum(r.probability for r in results) >= cut_off_probability * 100:
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break
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return results
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def _get_explanation(
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feature_names: Iterable[str],
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features: Iterable[float],
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weights: Iterable[float],
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token_mapping: Dict[str, str],
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) -> List[str]:
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influential = [
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(weight, name)
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for weight, value, name in zip(weights, features, feature_names)
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if value
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]
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most_influential = sorted(influential, reverse=True)[:5]
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return [token_mapping[name] for _, name in most_influential]
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8
examples/simple/preprocess.py
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8
examples/simple/preprocess.py
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import re
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from great_ai.utilities.lemmatize_text import lemmatize_text
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def preprocess(text: str) -> str:
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lemmas = [re.sub(r"\d[\d.,]*", "NUM", lemma) for lemma in lemmatize_text(text)]
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return " ".join(lemmas)
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@ -1,248 +1,258 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Train Domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import matplotlib.pyplot as plt\n",
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"from pathlib import Path\n",
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"import pandas as pd\n",
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"from sklearn import metrics, set_config\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.naive_bayes import MultinomialNB\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"\n",
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"from great_ai.utilities.clean import clean\n",
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"from great_ai.utilities.parallel_map import parallel_map\n",
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"from great_ai.utilities.language import is_english, predict_language\n",
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"from great_ai import save_model, configure, LargeFile\n",
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"\n",
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"from preprocess import preprocess"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Configuration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"PREFIX = \"domain-\"\n",
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"DATASET_KEY = \"data\"\n",
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"MAX_FILE_COUNT = 5\n",
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"MODEL_KEY = \"small-domain-prediction-v2\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"configure()\n",
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"corpus_path = LargeFile(DATASET_KEY).get()\n",
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"\n",
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"set_config(display=\"diagram\")\n",
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"plt.rcParams[\"figure.figsize\"] = (30, 15)\n",
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"plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
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"plt.rcParams[\"font.size\"] = 12\n",
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"plt.rcParams[\"axes.xmargin\"] = 0"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Preprocessing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def clean_file(p: Path) -> None:\n",
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" try:\n",
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" processed_path = p.with_name(f\"{PREFIX}{p.stem}{p.suffix}\")\n",
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"\n",
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" if processed_path.exists():\n",
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" return\n",
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"\n",
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" with open(p) as f:\n",
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" content = json.load(f)\n",
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"\n",
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" result = {\n",
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" preprocess(\n",
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" clean(f'{c[\"title\"]} {c[\"abstract\"]}', convert_to_ascii=True)\n",
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" ): c[\"domain\"]\n",
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" for c in content\n",
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" if (\n",
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" c[\"domain\"]\n",
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" and c[\"abstract\"]\n",
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" and is_english(predict_language(c[\"abstract\"]))\n",
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" )\n",
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" }\n",
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"\n",
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" with open(processed_path, \"w\") as f:\n",
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" json.dump(result, f)\n",
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" except Exception as e:\n",
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" print(f\"Error ({e}) processing {p}\")\n",
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"\n",
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"\n",
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"parallel_map(\n",
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" clean_file,\n",
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" list(corpus_path.glob(\"s2-corpus-*.json\"))[:MAX_FILE_COUNT],\n",
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" chunk_size=1,\n",
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")\n",
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"None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"corpora = list(corpus_path.glob(f\"{PREFIX}*.json\"))[:MAX_FILE_COUNT]\n",
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"print(f\"Found {len(corpora)} files\")\n",
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"\n",
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"data = []\n",
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"for p in corpora:\n",
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" with open(p) as f:\n",
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" data.extend(json.load(f).items())\n",
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"\n",
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"print(f\"Found {len(data)} documents\")\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(\n",
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" [d[0] for d in data], [d[1] for d in data], test_size=0.1, random_state=1\n",
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")\n",
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"\n",
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"X_train = [x for x, y in zip(X_train, y_train) for domain in y]\n",
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"y_train = [domain for x, y in zip(X_train, y_train) for domain in y]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Naive Bayes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"classifier = GridSearchCV(\n",
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" Pipeline(steps=[(\"vectorizer\", TfidfVectorizer(token_pattern=r\"[^ ]+\")), (\"classifier\", MultinomialNB())]),\n",
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" {\n",
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" \"vectorizer__max_df\": [0.05, 0.1, 0.3],\n",
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" \"vectorizer__min_df\": [5, 10, 30],\n",
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" \"vectorizer__sublinear_tf\": [True, False],\n",
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" \"classifier__alpha\": [0.1, 0.25, 0.5, 0.75, 1],\n",
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" \"classifier__fit_prior\": [True, False],\n",
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" },\n",
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" scoring=\"f1_macro\",\n",
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" cv=3,\n",
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" n_jobs=4,\n",
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" verbose=1,\n",
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")\n",
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"classifier.fit(X_train, y_train)\n",
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"\n",
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"results = pd.DataFrame(classifier.cv_results_)\n",
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"results.sort_values(\"rank_test_score\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"classifier = Pipeline(\n",
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" steps=[\n",
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" (\"vectorizer\", TfidfVectorizer(min_df=10, max_df=0.05, sublinear_tf=True, token_pattern=r\"[^ ]+\")),\n",
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" (\"classifier\", MultinomialNB(alpha=0.5, fit_prior=False)),\n",
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" ]\n",
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")\n",
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"\n",
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"classifier.fit(X_train, y_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"predicted = classifier.predict(X_test)\n",
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"\n",
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"y_test_aligned = [p if p in y else y[0] for p, y in zip(predicted, y_test)]\n",
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"\n",
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"print(metrics.classification_report(y_test_aligned, predicted))\n",
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"metrics.ConfusionMatrixDisplay.from_predictions(\n",
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" y_true=y_test_aligned,\n",
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" y_pred=predicted,\n",
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" xticks_rotation=\"vertical\",\n",
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" normalize=\"pred\",\n",
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" values_format=\".2f\",\n",
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")\n",
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"None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"save_model(classifier, key=MODEL_KEY, keep_last_n=1)"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "acc70e949538f42041ccc57bc4df2261507e3fd7d6b9ce5dcc28e3bcf9d48274"
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},
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"kernelspec": {
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"display_name": "Python 3.8.5 ('.env': venv)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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},
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"orig_nbformat": 4
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Train Domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus)"
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]
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},
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"nbformat": 4,
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"nbformat_minor": 2
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import matplotlib.pyplot as plt\n",
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"from pathlib import Path\n",
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"import pandas as pd\n",
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"from sklearn import metrics, set_config\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.naive_bayes import MultinomialNB\n",
|
||||
"from sklearn.pipeline import Pipeline\n",
|
||||
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
|
||||
"from sklearn.model_selection import GridSearchCV\n",
|
||||
"\n",
|
||||
"from great_ai.utilities.clean import clean\n",
|
||||
"from great_ai.utilities.parallel_map import parallel_map\n",
|
||||
"from great_ai.utilities.language import is_english, predict_language\n",
|
||||
"from great_ai import save_model, configure, LargeFile\n",
|
||||
"\n",
|
||||
"from preprocess import preprocess"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Configuration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"PREFIX = \"domain-\"\n",
|
||||
"DATASET_KEY = \"data\"\n",
|
||||
"MAX_FILE_COUNT = 5\n",
|
||||
"MODEL_KEY = \"small-domain-prediction-v2\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"configure()\n",
|
||||
"corpus_path = LargeFile(DATASET_KEY).get()\n",
|
||||
"\n",
|
||||
"set_config(display=\"diagram\")\n",
|
||||
"plt.rcParams[\"figure.figsize\"] = (30, 15)\n",
|
||||
"plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
|
||||
"plt.rcParams[\"font.size\"] = 12\n",
|
||||
"plt.rcParams[\"axes.xmargin\"] = 0"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Preprocessing"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def clean_file(p: Path) -> None:\n",
|
||||
" try:\n",
|
||||
" processed_path = p.with_name(f\"{PREFIX}{p.stem}{p.suffix}\")\n",
|
||||
"\n",
|
||||
" if processed_path.exists():\n",
|
||||
" return\n",
|
||||
"\n",
|
||||
" with open(p) as f:\n",
|
||||
" content = json.load(f)\n",
|
||||
"\n",
|
||||
" result = {\n",
|
||||
" preprocess(\n",
|
||||
" clean(f'{c[\"title\"]} {c[\"abstract\"]}', convert_to_ascii=True)\n",
|
||||
" ): c[\"domain\"]\n",
|
||||
" for c in content\n",
|
||||
" if (\n",
|
||||
" c[\"domain\"]\n",
|
||||
" and c[\"abstract\"]\n",
|
||||
" and is_english(predict_language(c[\"abstract\"]))\n",
|
||||
" )\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" with open(processed_path, \"w\") as f:\n",
|
||||
" json.dump(result, f)\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"Error ({e}) processing {p}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"parallel_map(\n",
|
||||
" clean_file,\n",
|
||||
" list(corpus_path.glob(\"s2-corpus-*.json\"))[:MAX_FILE_COUNT],\n",
|
||||
" chunk_size=1,\n",
|
||||
")\n",
|
||||
"None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"corpora = list(corpus_path.glob(f\"{PREFIX}*.json\"))[:MAX_FILE_COUNT]\n",
|
||||
"print(f\"Found {len(corpora)} files\")\n",
|
||||
"\n",
|
||||
"data = []\n",
|
||||
"for p in corpora:\n",
|
||||
" with open(p) as f:\n",
|
||||
" data.extend(json.load(f).items())\n",
|
||||
"\n",
|
||||
"print(f\"Found {len(data)} documents\")\n",
|
||||
"\n",
|
||||
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
||||
" [d[0] for d in data], [d[1] for d in data], test_size=0.1, random_state=1\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"X_train = [x for x, y in zip(X_train, y_train) for domain in y]\n",
|
||||
"y_train = [domain for x, y in zip(X_train, y_train) for domain in y]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Naive Bayes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"classifier = GridSearchCV(\n",
|
||||
" Pipeline(\n",
|
||||
" steps=[\n",
|
||||
" (\"vectorizer\", TfidfVectorizer(token_pattern=r\"[^ ]+\")),\n",
|
||||
" (\"classifier\", MultinomialNB()),\n",
|
||||
" ]\n",
|
||||
" ),\n",
|
||||
" {\n",
|
||||
" \"vectorizer__max_df\": [0.05, 0.1, 0.3],\n",
|
||||
" \"vectorizer__min_df\": [5, 10, 30],\n",
|
||||
" \"vectorizer__sublinear_tf\": [True, False],\n",
|
||||
" \"classifier__alpha\": [0.1, 0.25, 0.5, 0.75, 1],\n",
|
||||
" \"classifier__fit_prior\": [True, False],\n",
|
||||
" },\n",
|
||||
" scoring=\"f1_macro\",\n",
|
||||
" cv=3,\n",
|
||||
" n_jobs=4,\n",
|
||||
" verbose=1,\n",
|
||||
")\n",
|
||||
"classifier.fit(X_train, y_train)\n",
|
||||
"\n",
|
||||
"results = pd.DataFrame(classifier.cv_results_)\n",
|
||||
"results.sort_values(\"rank_test_score\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"classifier = Pipeline(\n",
|
||||
" steps=[\n",
|
||||
" (\n",
|
||||
" \"vectorizer\",\n",
|
||||
" TfidfVectorizer(\n",
|
||||
" min_df=10, max_df=0.05, sublinear_tf=True, token_pattern=r\"[^ ]+\"\n",
|
||||
" ),\n",
|
||||
" ),\n",
|
||||
" (\"classifier\", MultinomialNB(alpha=0.5, fit_prior=False)),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"classifier.fit(X_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predicted = classifier.predict(X_test)\n",
|
||||
"\n",
|
||||
"y_test_aligned = [p if p in y else y[0] for p, y in zip(predicted, y_test)]\n",
|
||||
"\n",
|
||||
"print(metrics.classification_report(y_test_aligned, predicted))\n",
|
||||
"metrics.ConfusionMatrixDisplay.from_predictions(\n",
|
||||
" y_true=y_test_aligned,\n",
|
||||
" y_pred=predicted,\n",
|
||||
" xticks_rotation=\"vertical\",\n",
|
||||
" normalize=\"pred\",\n",
|
||||
" values_format=\".2f\",\n",
|
||||
")\n",
|
||||
"None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"save_model(classifier, key=MODEL_KEY, keep_last_n=1)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "acc70e949538f42041ccc57bc4df2261507e3fd7d6b9ce5dcc28e3bcf9d48274"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8.5 ('.env': venv)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.5"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue