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How to Order Partition Key Events in Python (Mock Stream)
Generate a mock event stream grouped by partition key and sort it deterministically by key then sequence in Python.
import itertools
import random
def partition_key_events(keys, events_per_key=3, seed=None):
"""Produce a realistic-looking, but mock, event stream grouped by partition key.
Args:
keys: iterable of partition keys (e.g. strings or ints).
events_per_key: how many events we want per key.
…How to Broadcast a Small Lookup Table in Python
Simulates broadcasting a small lookup table by iterating key-value pairs and emitting packed rows to subscribers with deterministic output.
import random
# Generate a deterministic mock broadcast of a small lookup table
# with 5 keys and random integer values (seeded for reproducibility)
data = {
"sensor_a": 22,
"sensor_b": 87,
"sensor_c": 43,
"sensor_d": 65,
"sensor_e": 31,
}
# Simulate a broadcast to subscribers by iterating and p…How to Mock a UDAF Aggregate Function in Python
This code provides a minimal mock of a User-Defined Aggregate Function (UDAF), simulating the initialize-update-merge-finalize lifecycle with a defaultdict counter.
from collections import defaultdict
class MockUDAF:
"""A minimal mock of a User-Defined Aggregate Function.
Simulates aggregate lifecycle: initialize, update per row,
and finalize the result.
"""
def __init__(self):
self._buffer = defaultdict(int)
def initialize(self):
"""Re…Bayesian Optimization in Python: A Simplified Mock Implementation
A toy Bayesian optimization loop with a Gaussian process prior, expected improvement acquisition, and noisy sampling to find a function's minimum.
import random
import math
class BayesianOptimizer:
def __init__(self, noise=0.1):
self.noise = noise
self.observations = []
def objective(self, x):
return (math.sin(3*x) + 0.5*x) / (1 + x**2)
def gaussian_process_prior(self, x1, x2, length_scale=0.5):
return math.…Create a Minimal Great Expectations Suite Mock in Python
Build a small Python class that mimics a Great Expectations suite, storing and serializing column expectations as JSON.
import json
class GreatExpectationsSuite:
"""A minimal mock of a Great Expectations suite."""
def __init__(self, suite_name, expectations=None):
self.suite_name = suite_name
self.expectations = expectations or []
def add_expectation(self, expectation_type, column=None, kwargs=None):
…How to Build a Mock ML Pipeline with Prefect in Python
Create a lightweight Prefect flow with mock preprocessing, training, and evaluation tasks to prototype an ML pipeline end-to-end.
from prefect import task, flow
from datetime import datetime
@task
def preprocess_data(raw_value: float) -> float:
"""Mock preprocessing: normalize the input value."""
return raw_value / 100.0
@task
def train_model(features: float) -> dict:
"""Mock training: return a fake model artifact."""
return …How to Build a Simple ML Pipeline with ZenML in Python
Build a mock machine learning pipeline with ZenML steps for data loading, training, and evaluation, and run it to print the final accuracy.
from zenml import pipeline, step
@step
def load_data() -> dict:
"""Simulate loading data from a source."""
return {"accuracy": 0.0, "loss": 1.0}
@step
def train_model(data: dict) -> dict:
"""Simulate training a model."""
data["accuracy"] = 0.95
data["loss"] = 0.1
return data
@step
def eva…How to Create a Mock Metaflow Flow in Python
Build a minimal Metaflow flow with two sequential steps that pass data between them using instance attributes.
from metaflow import FlowSpec, step, current
class MockFlow(FlowSpec):
"""A minimal Metaflow flow to demonstrate basic steps and branching."""
@step
def start(self):
self.category = "mock"
print(f"Start step for {self.category} flow")
self.next(self.process)
@step
def pr…How to Create a Mock ONNX Model in Python
Build and export a minimal mock ONNX model with a Reshape and Gemm layer using the onnx helper API.
import onnx
import numpy as np
from onnx import helper, TensorProto
def create_mock_model():
# Define input and output tensors
input_tensor = helper.make_tensor_value_info('input', TensorProto.FLOAT, [1, 3, 224, 224])
output_tensor = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10])
…How to Load CSV Training Data in Python Without Pandas
Load CSV training data using Python's standard library and mock it with io.StringIO for testing, returning headers and rows as dictionaries.
import csv
from pathlib import Path
def load_csv_training_data(file_path: str | Path) -> tuple[list[str], list[dict[str, str]]]:
"""Load CSV training data and return headers plus rows as dictionaries."""
with open(file_path, mode="r", newline="", encoding="utf-8") as csv_file:
reader = csv.DictReader…How to Mock MLflow log_params and log_metrics in Python
Use unittest.mock to patch MLflow's log_param and log_metric, run the training function, and verify logging calls without touching a real tracking server.
from unittest.mock import Mock, patch
import mlflow
def train_model():
mlflow.log_param("learning_rate", 0.01)
mlflow.log_param("epochs", 10)
mlflow.log_metric("accuracy", 0.95)
mlflow.log_metric("loss", 0.05)
return "Training completed"
if __name__ == "__main__":
with patch("mlflow.log_par…How to Mock a Kubeflow Pipeline in Python
Build a minimal in-memory mock of a Kubeflow pipeline DAG using dataclasses and OrderedDict to chain component functions.
from typing import Dict, Any
from dataclasses import dataclass, field
from collections import OrderedDict
@dataclass
class KubeflowPipelineMock:
"""A minimal mock of a Kubeflow pipeline DAG."""
name: str
components: OrderedDict[str, callable] = field(default_factory=OrderedDict)
def add_component(se…How to Mock train_test_split in Python for Unit Testing
Build a lightweight mock of sklearn's train_test_split to unit test ML pipeline code without needing the full library or deterministic random state.
import numpy as np
from sklearn.model_selection import train_test_split
from unittest.mock import patch
def mock_train_test_split(X, y, test_size=0.25, random_state=None, **kwargs):
"""A simple mock implementation of train_test_split."""
n_samples = len(X)
n_test = int(n_samples * test_size)
n_train =…How to Simulate an Airflow ML Pipeline in Python
Mock an Airflow ML pipeline in plain Python by defining steps, simulating their execution with delays, and returning a success summary.
from datetime import datetime, timedelta
import time
class MLPipeline:
def __init__(self, pipeline_name):
self.pipeline_name = pipeline_name
self.steps = []
def add_step(self, step_name, duration_seconds):
self.steps.append({"name": step_name, "duration": duration_seconds})
def …How to Trigger Model Retraining on Drift in Python
Automatically detects accuracy drift in a mock ML model and triggers retraining when performance falls below a threshold.
import random
import time
class MockModel:
def __init__(self, name):
self.name = name
self.accuracy = 0.85
self.version = 1
def train(self, data_size):
# Simulate training time and accuracy improvement
time.sleep(0.1)
drift = random.uniform(-0.02, 0.02)
…How to implement a canary traffic split in Python
Route incoming traffic between stable and canary model or service versions using a weight-based random split with deterministic testing.
import random
def canary_route(service_name: str, canary_weight: float = 0.2) -> str:
"""Route traffic between stable and canary versions based on weight."""
rng = random.Random(42) # deterministic for reproducible demo
if rng.random() < canary_weight:
return f"{service_name}-canary"
return …Load CSV Training Data Without Pandas in Python
This code loads a CSV file into a list of dictionaries using only the standard library, ideal for small ML training data without heavy dependencies.
import csv
from pathlib import Path
def load_csv(path):
"""Load CSV file into list of dicts without pandas."""
rows = []
with open(path, newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
rows.append(dict(row))
return rows
if __name__ == "__m…Training Pipeline Orchestration Mock DAG in Python
Build a mock DAG orchestrator that runs ML pipeline stages in dependency order using topological sorting (Kahn's algorithm).
from collections import deque
from dataclasses import dataclass, field
@dataclass
class DAGNode:
name: str
task: callable
dependencies: list[str] = field(default_factory=list)
class MockDAG:
def __init__(self, nodes: list[DAGNode]):
self.nodes = {n.name: n for n in nodes}
self.execu…Benjamini Hochberg FDR Correction in Python
Implement the Benjamini-HHochberg false discovery rate (FDR) procedure in Python to control the expected proportion of false positives among rejected hypotheses.
import numpy as np
def benjamini_hochberg(p_values, alpha=0.05):
p_values = np.array(p_values)
n = len(p_values)
sorted_idx = np.argsort(p_values)
sorted_p = p_values[sorted_idx]
thresholds = (np.arange(1, n + 1) / n) * alpha
significant = sorted_p <= thresholds
if not significan…How to Build a Simple Binary Protocol Parser Mock in Python
Defines a mock binary protocol with field definitions, encoding, and decoding to simulate network packet parsing for A/B testing and experiment setup.
class SimpleProtocol:
def __init__(self, name, version):
self.name = name
self.version = version
self.fields = []
def add_field(self, field_name, field_size):
self.fields.append((field_name, field_size))
def parse(self, data):
if len(data) != sum(size for _, size i…How to Calculate Minimum Sample Size for a T-Test in Python
Compute the minimum sample size per group for a two-sample t-test using effect size, significance level, and statistical power.
import math
from scipy.stats import norm
def min_sample_size(effect_size, alpha=0.05, power=0.8):
"""
Calculate minimum sample size for a two-sample t-test (equal groups).
Args:
effect_size: Cohen's d (standardized mean difference)
alpha: significance level (Type I error)
power: …How to Hash a User ID to an Experiment Bucket in Python
Deterministically map a user ID to one of N experiment buckets using MD5 hashing and modulo arithmetic.
import hashlib
def hash_to_bucket(user_id: str, num_buckets: int = 10) -> int:
"""Deterministically map a user_id to a bucket (0 to num_buckets-1)."""
digest = hashlib.md5(user_id.encode("utf-8")).hexdigest()
return int(digest[:8], 16) % num_buckets
if __name__ == "__main__":
# Mock experiment: split…How to Mock Stratified Assignment by Segment in Python
Simulate stratified assignment for A/B experiments by sampling a fixed proportion of units from each segment, with deterministic seeds for reproducibility.
import random
def stratified_assignment(segments, seed=None):
"""
Mock stratified assignment: given a dict of segment -> population size,
return a dict of segment -> sampled unit ids (deterministic with seed).
"""
if seed is not None:
random.seed(seed)
rng = random.Random(seed)
res…How to Perform Intent-to-Treat Analysis in Python
Runs an intent-to-treat analysis on mock A/B test data, comparing outcomes by initial group assignment with a t-test for significance.
import pandas as pd
import numpy as np
def intent_to_treat_analysis(data):
"""Perform intent-to-treat (ITT) analysis.
ITT compares outcomes based on initial treatment assignment,
regardless of whether participants actually received the treatment.
"""
# Create a copy to avoid mutating the origina…Browse by section
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