233 lines
7.8 KiB
Text
233 lines
7.8 KiB
Text
{
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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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"# Simple example: data engineering\n",
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"\n",
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"Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best-practices. In short, we train a domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus) by taking full advantage of `great-ai`. Subsequently, we create a production-ready deployment.\n",
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"\n",
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"\n",
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"> The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook.\n",
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"\n",
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"Since the true scope of `great-ai` is the phase between proof-of-concept code and production-ready service, it is predominantly used in the [deployment notebook](/examples/simple/deploy). Feel free to skip there, or continue reading if you'd like to see the full picture.\n",
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"\n",
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"### Extract\n",
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"\n",
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"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.\n",
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"\n",
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"In this example, we download the semantic scholar dataset from a public S3 bucket."
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"MAX_CHUNK_COUNT = 4"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Processing 4 out of the 6002 available chunks'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import urllib.request\n",
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"from random import shuffle\n",
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"\n",
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"manifest = (\n",
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" urllib.request.urlopen(\n",
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" \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\"\n",
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" \"open-corpus/2022-02-01/manifest.txt\"\n",
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" )\n",
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" .read()\n",
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" .decode()\n",
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") # a list of available chunks separated by '\\n' characters\n",
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"\n",
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"lines = manifest.split()\n",
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"shuffle(lines)\n",
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"chunks = lines[:MAX_CHUNK_COUNT]\n",
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"\n",
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"f\"\"\"Processing {len(chunks)} out of the {\n",
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" len(manifest.split())\n",
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"} available chunks\"\"\""
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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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"### Transform\n",
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"\n",
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"- Filter out non-English abstracts using `great_ai.utilities.predict_language`\n",
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"- Project it to only keep the necessary components (text and labels), clean the textual content using `great_ai.utilities.clean`\n",
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"- We will speed up processing using `great_ai.utilities.simple_parallel_map`."
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 4/4 [04:22<00:00, 65.51s/it] \n"
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]
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}
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],
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"source": [
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"from typing import List, Tuple\n",
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"import json\n",
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"import gzip\n",
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"from great_ai.utilities import (\n",
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" simple_parallel_map,\n",
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" clean,\n",
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" is_english,\n",
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" predict_language,\n",
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" unchunk,\n",
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")\n",
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"\n",
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"\n",
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"def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]:\n",
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" response = urllib.request.urlopen(\n",
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" f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\"\n",
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" f\"open-corpus/2022-02-01/{chunk_key}\"\n",
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" ) # a gzipped JSON Lines file\n",
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"\n",
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" decompressed = gzip.decompress(response.read())\n",
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" decoded = decompressed.decode()\n",
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" chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line]\n",
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"\n",
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" # Transform\n",
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" return [\n",
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" (\n",
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" clean(\n",
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" f'{c[\"title\"]} {c[\"paperAbstract\"]} '\n",
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" f'{c[\"journalName\"]} {c[\"venue\"]}',\n",
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" convert_to_ascii=True,\n",
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" ), # The text is cleaned to remove common artifacts\n",
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" c[\"fieldsOfStudy\"],\n",
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" ) # Create pairs of `(text, [...domains])`\n",
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" for c in chunk\n",
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" if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"])))\n",
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" ]\n",
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"\n",
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"\n",
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"preprocessed_data = unchunk(\n",
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" simple_parallel_map(preprocess_chunk, chunks, concurrency=4)\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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"X, y = zip(*preprocessed_data) # X is the input, y is the expected output"
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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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"### Load\n",
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"\n",
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"Upload the dataset (or a part of it) to a central repository using `great_ai.add_ground_truth`. This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given.\n",
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"\n",
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"#### Production-ready backend\n",
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"\n",
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"The MongoDB driver is automatically configured if `mongo.ini` exists with the following scheme:\n",
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"\n",
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"```ini\n",
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"mongo_connection_string=mongodb://localhost:27017/\n",
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"mongo_database=my_great_ai_db\n",
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"```\n",
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"> You can install MongoDB from [here](https://www.mongodb.com/docs/manual/installation) or [use it as a service](https://www.mongodb.com/cloud/atlas/register)\n",
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"\n",
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"Otherwise, TinyDB is used which is just a local JSON file."
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[38;5;226mEnvironment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n",
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"\u001b[38;5;226mCannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n",
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"\u001b[38;5;226mThe selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n",
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"\u001b[38;5;226mCannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n",
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"\u001b[38;5;39mGreatAI (v0.1.6): configured ✅\u001b[0m\n",
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"\u001b[38;5;39m 🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n",
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"\u001b[38;5;39m 🔩 large_file_implementation: LargeFileLocal\u001b[0m\n",
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"\u001b[38;5;39m 🔩 is_production: False\u001b[0m\n",
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"\u001b[38;5;39m 🔩 should_log_exception_stack: True\u001b[0m\n",
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"\u001b[38;5;39m 🔩 prediction_cache_size: 512\u001b[0m\n",
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"\u001b[38;5;39m 🔩 dashboard_table_size: 50\u001b[0m\n",
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"\u001b[38;5;226mYou still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n",
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"\u001b[38;5;226m> Find out more at https://se-ml.github.io/practices\u001b[0m\n"
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]
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}
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],
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"source": [
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"from great_ai import add_ground_truth\n",
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"\n",
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"add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2)"
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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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"### Next: [Part 2](/examples/simple/train)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.10.4 ('.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": "3.10.4"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "02dd6d3afbfa9fbbe1037d64ad9014965528a1ccad21929d6e72f466389a68ad"
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}
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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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