ML engineering pipelines
Feature prep, batch inference, model-serving hooks, and production ML workflow glue.
How to Compute a Confusion Matrix in Python
Compute a multi-class confusion matrix from true and predicted labels using pure Python dictionaries and nested lists, then format it for readable output.
from collections import defaultdict
def compute_confusion_matrix(y_true, y_pred, labels):
"""Compute confusion matrix using Python dicts and nested lists."""
label_index = {label: i for i, label in enumerate(labels)}
matrix = [[0] * len(labels) for _ in range(len(labels))]
for true, pred in zip(y…How to Detect Data Drift with PSI in Python
Calculate the Population Stability Index (PSI) in Python to compare expected vs actual distributions and detect data drift in machine learning pipelines.
import numpy as np
def calculate_psi(expected, actual, buckets=10):
"""Calculate Population Stability Index (PSI) between two distributions."""
# Create bucket edges based on expected distribution percentiles
edges = np.percentile(expected, np.linspace(0, 100, buckets + 1))
edges[-1] = np.inf # Ensur…How to Run Batch Predictions with a Mock Model in Python
Build a lightweight mock model class and run predictions across a batch of samples, returning results as a plain Python list.
import numpy as np
class MockModel:
def __init__(self, weights):
self.weights = np.array(weights)
def predict(self, X):
return X @ self.weights
def predict_batch(model, batch):
"""Run predictions for a batch of samples and return results as a list."""
return model.predict(np.array(ba…How to do feature selection with VarianceThreshold in Python
This code demonstrates how to use scikit-learn's VarianceThreshold to remove low-variance features from a NumPy array, keeping only those that vary enough to be useful for modeling.
import numpy as np
from sklearn.feature_selection import VarianceThreshold
def main():
# Mock dataset: 4 samples, 5 features
X = np.array([
[0.1, 0.2, 1.0, 1.0, 0.5],
[0.2, 0.2, 0.0, 1.0, 0.4],
[0.1, 0.2, 1.0, 1.0, 0.6],
[0.3, 0.2, 1.0, 0.0, 0.5]
])
# Select features w…One Hot Encode Categories in Python
Convert a list of categorical strings into one-hot encoded numeric vectors using pure Python and NumPy.
import numpy as np
categories = ["red", "green", "blue", "red", "blue", "green", "red"]
unique = sorted(set(categories))
lookup = {cat: i for i, cat in enumerate(unique)}
one_hot = []
for cat in categories:
row = [0] * len(unique)
row[lookup[cat]] = 1
one_hot.append(row)
print("Categories:", categories…Train Logistic Regression From Scratch in Python
Trains a binary logistic regression model using gradient descent on mock data, printing learned weights and probabilities.
import numpy as np
# Mock data: 2 features, binary classification
X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
y = np.array([0, 0, 1, 1, 1])
# Add bias term (column of ones)
X_b = np.c_[np.ones((X.shape[0], 1)), X]
# Initialize parameters
theta = np.zeros(X_b.shape[1])
# Hyperparameters
learning_rate = 0…Browse by section
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ML engineering pipelines — Python code examples
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