python

CREATE TYPE outbox_status AS ENUM ('pending', 'retry', 'processing', 'done', 'dead');

CREATE TABLE outbox_events (
    id           BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
    dedupe_key   TEXT        NOT NULL,
    topic        TEXT        NOT NULL,

import random
from dataclasses import dataclass
from typing import Iterator


@dataclass(frozen=True)

from django.db import transaction
from django.shortcuts import get_object_or_404
from django.http import JsonResponse
from .models import Product

import numpy as np

features = np.array([
    [120.0, 3.0, 10.0],
    [90.0, 5.0, 7.0],
    [150.0, 2.0, 14.0],

import torch

best_val_loss = float('inf')

for epoch in range(1, num_epochs + 1):
    model.train()

import pandera as pa
from pandera.typing import Series

class ChurnTrainingSchema(pa.DataFrameModel):
    customer_id: Series[int] = pa.Field(unique=True)
    age: Series[int] = pa.Field(ge=18, le=100)

from django.db import models
from django.utils import timezone


class TimeStampedModel(models.Model):
    """Abstract base class with created/updated timestamps."""

import joblib
from skl2onnx import to_onnx
from skl2onnx.common.data_types import FloatTensorType

joblib.dump(model, 'artifacts/model.joblib')

from haystack import indexes
from blog.models import Post


class PostIndex(indexes.SearchIndex, indexes.Indexable):
    text = indexes.CharField(document=True, use_template=True)

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler

X_scaled = StandardScaler().fit_transform(X)

import great_expectations as gx

context = gx.get_context()
data_source = context.data_sources.add_pandas(name='training_data')
asset = data_source.add_dataframe_asset(name='churn_asset')
batch_definition = asset.add_batch_definition_whole_dataframe('full_dataframe')

import time
import requests
from bs4 import BeautifulSoup

session = requests.Session()
session.headers.update({'User-Agent': 'research-bot/1.0'})