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How to Mock a Catalyst Logical Plan in Python
Build a small Python class that mimics Spark Catalyst's logical plan tree for teaching or testing query optimizations.
from typing import Any, Dict, List, Optional
class CatalystLogicalPlan:
"""A minimal mock of Catalyst's logical plan for teaching purposes."""
def __init__(self, node_type: str, **kwargs: Any) -> None:
self.node_type = node_type
self.attributes: Dict[str, Any] = kwargs
self.child…How to Mock a Compute-Collect Action Trigger in Python
Mock a compute-collect action trigger using Python's unittest.mock to simulate Spark-style job execution and assert trigger behavior.
Here's a Python code sample for the problem title "Action trigger compute collect mock":How to Mock and Test a Rate-Limited Source Stream in Python
Build a class that rate-limits emitted items using a sliding window and test it with a simulated stream in Python.
import time
from collections import deque
class RateLimitedSource:
def __init__(self, max_rate, window=1.0):
self.max_rate = max_rate
self.window = window
self._timestamps = deque()
def emit(self, item):
now = time.monotonic()
while self._timestamps and self._timestam…How to use foreachBatch with a mock sink in PySpark
Demonstrates using Spark Structured Streaming's foreachBatch sink to capture and verify streaming batches by writing them into a custom mock sink object.
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, lit
class MockSink:
def __init__(self):
self.batches = []
def write_batch(self, batch_df, batch_id):
# Collect batch data as list of dicts for verification
records = batch_df.collect()
self.batches…How to Mock MLflow Model Registration in Python
Build a lightweight in-memory mock of MLflow's MlflowClient to test model registration, versioning, and stage transitions without a tracking server.
from mlflow.tracking import MlflowClient
from mlflow.entities import ModelVersion, Model
class MockMlflowClient:
"""Minimal mock of MlflowClient's model registration methods."""
def __init__(self):
self.registered_models = {}
self.model_versions = {}
def register_model(self, mod…How to Mock ROC AUC in Python
Compute ROC AUC from scratch in Python using pairwise comparisons between positive and negative score distributions, ideal for testing ML models without sklearn.
import random
from math import comb
def mock_roc_auc(scores, labels):
"""Compute mock ROC AUC by simulating a classifier's score distribution."""
random.seed(42)
n = len(labels)
pos_scores = [scores[i] for i in range(n) if labels[i] == 1]
neg_scores = [scores[i] for i in range(n) if labels[i] == …How to mock an artifact store with local paths in Python for ML pipelines
Create a temporary local artifact store with dummy files and metadata to test ML pipeline code without real storage.
import tempfile
from pathlib import Path
import json
def create_artifact_store_mock(base_path: Path = None):
"""Create a local artifact store mock directory structure."""
if base_path is None:
base_path = Path(tempfile.mkdtemp())
store_layout = {
"artifacts": [
{"name": "mode…Bayesian A/B Test Credible Interval in Python
Simulates A/B test data and computes posterior credible intervals and the probability that variant B outperforms A using Bayesian Beta-Binomial inference.
import numpy as np
from scipy import stats
# Simulated A/B test data
n_A = 1000
n_B = 1000
conversions_A = 120
conversions_B = 140
# Prior: Beta(1, 1) uniform
alpha_prior, beta_prior = 1, 1
# Posterior parameters
alpha_A = alpha_prior + conversions_A
beta_A = beta_prior + n_A - conversions_A
alpha_B = alpha_prior +…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…Check Covariate Balance in Python
Compute standardized mean differences and KS tests to check covariate balance between treatment and control groups in Python.
import numpy as np
from scipy import stats
def balance_check(treatment, covariate):
"""Check covariate balance between treatment and control groups."""
treat_vals = covariate[treatment == 1]
control_vals = covariate[treatment == 0]
# Standardized mean difference
pooled_std = np.sqrt((np.var(t…Check Sample Ratio Mismatch in Python
Estimates the probability that a simple random sample's proportion differs from the population proportion by more than 10% using simulation.
import random
def sample_ratio_mismatch(population_size: int, sample_size: int, p: float) -> float:
"""
Estimate the probability that a simple random sample's proportion
differs from the population proportion by more than 10%.
"""
total_counts = [0, 0]
for _ in range(10000):
sample = …Chi-Square Test in Python for Conversion Mock Data
Compute the chi-square statistic and approximate p-value for a mock A/B conversion test using the standard library.
import math
from collections import Counter
def chi_square_statistic(observed):
"""
Compute chi-square statistic for a mock conversion test.
observed: dict mapping outcomes to observed frequencies.
"""
observed = Counter(observed)
n = sum(observed.values())
expected = n / len(observed) if …Delta Method for Ratio Metrics in A/B Testing with Python
Computes the confidence interval for the difference between two ratio metrics using the delta method, with mock A/B test data.
import numpy as np
from scipy.stats import norm
def delta_method_ratio_delta(control: np.ndarray, treatment: np.ndarray, confidence: float = 0.95):
"""Estimate confidence interval for ratio metric using delta method.
Args:
control: numerator/denominator pairs from control group (n x 2 array)
…Epsilon Greedy Bandit Mock in Python
A simple epsilon-greedy multi-armed bandit simulation that balances exploration and exploitation to estimate true means of several Bernoulli-like reward distributions.
import random
class Bandit:
def __init__(self, true_mean):
self.true_mean = true_mean
self.estimated_mean = 0.0
self.n_pulls = 0
def pull(self):
return random.gauss(self.true_mean, 1.0)
def update(self, reward):
self.n_pulls += 1
self.estimated_mean += (r…How to Compute CUPED Variance Reduction in Python
Implement CUPED in Python to reduce variance of A/B test treatment effect estimates using pre-experiment covariates.
import numpy as np
def compute_cuped_reduction(control, variant, covariate):
"""
Compute variance reduction using CUPED (Controlled Experiment with
Pre-Experiment Data). Uses pre-experiment covariate values to
reduce variance of the treatment effect estimate.
"""
control = np.asarray(control, …How to Compute Mann-Whitney U Test in Python
Compute the Mann-Whitney U statistic and p-value manually in Python with tie correction and a normal approximation for independent samples.
import numpy as np
from scipy import stats
def mann_whitney_u_mock(sample_a, sample_b):
"""Compute Mann-Whitney U and p-value manually."""
# Combine and rank
combined = sample_a + sample_b
n_a, n_b = len(sample_a), len(sample_b)
n_total = n_a + n_b
# Rank with ties handling (average ranks…How to Conduct a Two-Sample T-Test in Python
Performs Welch's t-test for two independent samples, computing the t-statistic, degrees of freedom, and p-value using NumPy and SciPy.
import numpy as np
def two_sample_t_test(sample1, sample2):
"""Perform Welch's t-test for two independent samples."""
n1, n2 = len(sample1), len(sample2)
mean1, mean2 = np.mean(sample1), np.mean(sample2)
var1, var2 = np.var(sample1, ddof=1), np.var(sample2, ddof=1)
# Standard error of difference
…How to Create an Interrupted Time Series Mock in Python
Generate simulated interrupted time series data with a pre/post-intervention trend, level shift, and noise to test segmented regression models.
import numpy as np
# Mock interrupted time series data
np.random.seed(42)
n_pre = 50
n_post = 50
time = np.arange(0, n_pre + n_post)
# Pre-intervention: linear trend + noise
pre_trend = 0.05 * time[:n_pre] + np.random.normal(0, 0.5, n_pre)
# Post-intervention: new slope + level shift + noise
post_trend = 0.05 * tim…How to Generate an Orthogonal Array for A/B Testing in Python
Generate a mock orthogonal array for multi-layer experiments with NumPy, ensuring balanced level combinations across experiment groups.
import numpy as np
def orthogonal_mock_layers(n_experiments: int, n_layers: int, n_levels: int) -> np.ndarray:
"""Generate an orthogonal array for multi-layer experiment design using base-level logic."""
ortho = np.indices((n_levels,) * n_layers).reshape(n_layers, -1).T
ortho = ortho % n_levels # Classic…How to Mock Mutual Exclusion for A/B Experiment Groups in Python
Simulate mutual exclusion for experiment groups using a thread-safe lock, ensuring only one member updates the shared counter at a time.
import threading
import time
import random
class CountingGate:
"""A mock mutual exclusion gate using a lock."""
def __init__(self):
self.counter = 0
self.lock = threading.Lock()
def enter(self, group_id, member_id):
with self.lock:
current = self.counter
t…How to Mock Sequential Calls in Python with unittest.mock
Use Mock.side_effect to return a different result for each sequential call and verify the call order with assert_has_calls.
import unittest
from unittest.mock import Mock
class Service:
def fetch(self, item_id):
raise NotImplementedError
def process_items(service, ids):
results = []
for item_id in ids:
result = service.fetch(item_id)
results.append(result)
return results
if __name__ == "__main__":…How to Mock Time for Cache TTL Testing in Python
This code demonstrates how to test a cache's TTL expiration logic by mocking time.time with unittest.mock to control the passage of time.
import time
from unittest.mock import patch
class ConfigCache:
def __init__(self, ttl=60):
self.ttl = ttl
self._store = {}
self._timestamps = {}
def get(self, key):
if key not in self._store:
return None
if time.time() - self._timestamps[key] > self.ttl:
…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…How to Perform Welch's t-Test in Python
Calculate the Welch t-statistic and degrees of freedom for two samples with unequal variances using Python's statistics module.
import math
from statistics import mean, variance
def welch_t_test(sample1, sample2):
n1, n2 = len(sample1), len(sample2)
mean1, mean2 = mean(sample1), mean(sample2)
var1, var2 = variance(sample1), variance(sample2)
# Welch's t statistic
t_stat = (mean1 - mean2) / math.sqrt(var1 / n1 + var2 / n2…Browse by section
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