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8 changed files with 2 additions and 2 deletions
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examples/simple/train.ipynb
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269
examples/simple/train.ipynb
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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.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 helpers import preprocess, lemmatize"
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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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" lemmatize(preprocess(f'{c[\"title\"]} {c[\"abstract\"]}')): 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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"## Optimise and train Multinomial Naive Bayes classifier"
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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 create_pipeline() -> Pipeline:\n",
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" return Pipeline(\n",
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" steps=[\n",
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" (\"vectorizer\", TfidfVectorizer()),\n",
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" (\"classifier\", MultinomialNB()),\n",
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" ]\n",
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" )"
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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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"optimisation_pipeline = GridSearchCV(\n",
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" create_pipeline(),\n",
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" {\n",
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" \"vectorizer__max_df\": [0.05, 0.1],\n",
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" \"vectorizer__min_df\": [5, 20],\n",
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" \"vectorizer__sublinear_tf\": [True, False],\n",
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" \"classifier__alpha\": [0.5, 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=8,\n",
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" verbose=1,\n",
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")\n",
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"optimisation_pipeline.fit(X_train, y_train)\n",
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"\n",
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"results = pd.DataFrame(optimisation_pipeline.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 = create_pipeline()\n",
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"classifier.set_params(**optimisation_pipeline.best_params_)\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": "markdown",
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"metadata": {},
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"source": [
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"## Check accuracy on the test split"
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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": "markdown",
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"metadata": {},
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"source": [
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"## Export the model using GreatAI"
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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=5)"
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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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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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