Amazon Machine Learning Engineer Interview Preparation Guide - Entry Level
Amazon's Machine Learning Engineer interview process for entry-level candidates follows a structured progression designed to assess technical depth, problem-solving ability, and alignment with Amazon's leadership principles. The process includes initial recruiter screening, two technical phone screens covering coding fundamentals and ML concepts, followed by four onsite rounds evaluating coding, ML theory, system design, and behavioral alignment. Each round targets different dimensions of the role and the interviewer feedback directly influences hiring decisions.
Interview Rounds
Recruiter Screening
What to Expect
This 30-minute initial call with an Amazon recruiter establishes your background and fit for the Machine Learning Engineer role. The recruiter will discuss your resume, experience with machine learning, motivation for joining Amazon, and clarify the role's scope and team structure. This is your opportunity to ask preliminary questions about compensation, location, team dynamics, and the interview timeline. While technical depth isn't evaluated, you should confidently discuss any ML coursework, projects, or internships from your resume. The recruiter will assess communication skills and genuine interest in the position. A positive recruiter interaction increases the likelihood of moving to the technical phone screens.
Tips & Advice
Research Amazon's machine learning capabilities and recent initiatives before the call. Prepare a 2-minute personal pitch covering your background, ML experience, and career motivation. Have 3-4 specific questions about the role (team size, focus area, technologies used, mentorship structure). Keep answers concise but substantive. When discussing past projects, explain what you built, the tools you used, and what you learned. Demonstrate enthusiasm without appearing desperate. Ask about next steps and timeline. Follow up with a thank you email within 24 hours.
Focus Topics
Technical Competency Indicators
When discussing projects, reference specific ML techniques used (e.g., 'I used logistic regression for classification' or 'I implemented a neural network using PyTorch'). Mention your comfort level with programming languages (Python, Java, C++). Discuss any experience with cloud platforms or deployment.
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Communication and Professionalism
Speak clearly at a moderate pace. Avoid filler words like 'um' and 'uh'. Listen carefully to each question and answer directly before elaborating. Show genuine enthusiasm through tone and word choice without being overly casual. Be respectful and authentic.
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Understanding the Role and Team
Prepare thoughtful questions about the specific role: What problems does the team solve? What's the typical tech stack (TensorFlow, PyTorch, SageMaker)? What's the team size and organizational structure? How is mentorship provided for entry-level engineers? What are key metrics the team optimizes?
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Motivation and Career Trajectory
Articulate why machine learning interests you, why Amazon specifically appeals to you, and how this role fits your career goals. Reference specific Amazon ML products, initiatives, or company values that resonate with you. Explain what you hope to learn and build at Amazon.
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Resume Experience Articulation
Clearly and concisely explain each relevant project, internship, or coursework on your resume. For each, prepare a 30-60 second description covering: problem statement, your role and contributions, tools/technologies used, and measurable outcome or learning.
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Technical Phone Screen - Coding Fundamentals
What to Expect
This 60-minute interview assesses coding ability and algorithmic problem-solving through 1-2 live coding problems of medium difficulty (comparable to LeetCode Medium). You'll code in a shared online editor (CoderPad, LeetCode interview interface, or Google Doc) while an Amazon software engineer or ML engineer observes and asks clarifying questions. The interviewer evaluates your approach, code quality, problem-solving methodology, and communication throughout. They assess whether you can break down unfamiliar problems, write clean code, consider edge cases, and optimize solutions. For entry-level candidates, demonstrating clear thinking and correct logic is more important than perfect optimization. Partial solutions with good problem-solving approach can result in a positive evaluation.
Tips & Advice
Before coding, spend 5 minutes clarifying the problem: What are constraints? What are edge cases? What's the expected input/output format? Verbally outline your approach and discuss time/space complexity before writing code. Write clean, readable code with meaningful variable names and proper structure. Test your solution against provided examples and identify additional edge cases. If stuck, think aloud and ask for hints rather than sitting silently. Write pseudocode first if helpful. For entry-level, a working solution with O(n log n) complexity or even O(n²) is acceptable for most problems if clearly explained. Practice on LeetCode 4-6 weeks before your interview, targeting medium difficulty in your preferred language (Python is most popular for ML roles).
Focus Topics
Problem-Solving Communication and Thinking Aloud
Verbally walk through your approach before implementing. Explain your reasoning as you code. Ask clarifying questions when problem statement is ambiguous. Discuss edge cases aloud. Explain your optimization decisions. If stuck, explain what you've tried and ask for hints.
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Code Quality and Readability
Write clean, readable code with descriptive variable names (not single letters except loop counters). Structure code logically with clear sections. Handle edge cases explicitly. Add minimal comments only for non-obvious logic. Avoid code duplication and unnecessary complexity.
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Algorithm Design Patterns (Two Pointers, Sliding Window, Binary Search, DFS/BFS, Sorting, Dynamic Programming Basics)
Master recognition and application of common algorithmic patterns. Practice 50+ problems across these categories. Learn to identify which pattern applies to a given problem. Understand trade-offs between patterns (iterative vs recursive, BFS vs DFS).
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Big-O Analysis and Complexity Optimization
Analyze time and space complexity of your solution. Identify bottlenecks and optimization opportunities. Compare your approach with alternative solutions. Discuss when to trade space for time (hash maps vs nested loops). Explain why your chosen approach is optimal or if better solutions exist.
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Data Structures (Arrays, Strings, Hash Maps, Linked Lists, Trees, Graphs, Heaps)
Proficiency implementing and using fundamental data structures without external libraries. Understand access/insert/delete complexity for each. Know traversal methods (in-order, pre-order, post-order for trees; BFS, DFS for graphs). Practice problems requiring combination of multiple data structures.
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Technical Phone Screen - Machine Learning Fundamentals
What to Expect
This 60-minute interview assesses understanding of core machine learning concepts through 5-7 conceptual questions and potentially brief pseudocode or pseudomathematical explanations. The interviewer will ask about bias-variance trade-off, overfitting, evaluation metrics, common algorithms, loss functions, and feature engineering. They may ask 'walk me through how Random Forest works' or 'how would you handle an imbalanced dataset?' This round tests breadth of ML knowledge. For entry-level candidates, clarity of explanation and understanding the 'why' behind concepts matters more than mathematical rigor or memorization. The interviewer probes with follow-up questions to gauge depth of understanding.
Tips & Advice
Use diagrams and real-world examples to explain abstract concepts. If asked about an unfamiliar algorithm, reason through it logically rather than admitting complete ignorance. Emphasize trade-offs (accuracy vs interpretability, bias vs variance, training time vs model complexity). For each concept, discuss practical implications: Why does it matter? When would you encounter it? How would you address it? Reference your own project experience when relevant. Keep explanations clear and at appropriate technical depth—avoid unnecessary jargon but demonstrate real understanding. If uncertain, ask clarifying questions or propose solutions: 'I'm not familiar with that specific technique, but here's how I might approach it...'
Focus Topics
Feature Engineering and Data Preprocessing
Techniques for handling missing data (imputation vs deletion strategies). Categorical encoding (one-hot, ordinal, target encoding). Numerical scaling (standardization, min-max normalization). Why normalization matters for distance-based algorithms. Handling outliers and skewed distributions. Feature selection basics.
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Hyperparameter Tuning and Regularization
Understand grid search, random search, and Bayesian optimization conceptually. Know regularization methods (L1, L2, elastic net for regression; max_depth, min_samples_leaf for trees; dropout for neural networks). Explain why regularization prevents overfitting. Discuss learning rate's impact on training and convergence.
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Cross-Validation and Model Evaluation Methodology
Explain k-fold cross-validation: why it's necessary, how it works, advantages over simple train/test split. Understand stratified cross-validation for imbalanced data. Discuss train/validation/test splits and why separate test sets are critical. Explain temporal cross-validation for time-series data.
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Evaluation Metrics Selection and Interpretation
Know when to use accuracy, precision, recall, F1 score, AUC-ROC, RMSE, MAE, MAPE. Understand why accuracy is misleading for imbalanced classification. For different business contexts (fraud detection, recommendation, regression), explain which metrics matter most. Discuss cost of false positives vs false negatives.
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Supervised Learning Algorithms (Regression, Classification, Tree-based Methods)
Understand linear regression, logistic regression, decision trees, random forests, SVM, and k-NN. For each: How does it work conceptually? What are assumptions? Strengths and weaknesses? When to use one over another? Understand ensemble methods and why random forests often outperform single trees.
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Bias-Variance Trade-Off and Overfitting/Underfitting
Conceptual and intuitive understanding of bias-variance decomposition. Bias represents underfitting (model too simple), variance represents overfitting (model too complex). Explain symptoms of each and mitigation strategies: regularization for overfitting, more training data or features for underfitting. Relate to cross-validation and train/test performance gaps.
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Onsite Round 1 - Coding and Algorithms
What to Expect
Similar in scope to the phone screen but conducted in-person in an Amazon office or interview room. You'll solve 1-2 algorithmic problems in 45-60 minutes total. Problems are typically medium difficulty with an optional harder component. You'll code on a whiteboard, whiteboard+laptop, or interview laptop depending on room setup. The in-person setting adds a slight social dynamic—interviewers observe your body language, energy, and collaborative approach. The technical expectations remain identical to the phone screen: clear problem-solving process, clean code, efficient complexity analysis, and thoughtful edge case handling.
Tips & Advice
On whiteboard: Write clearly with adequate spacing; use erasable markers; don't write everything at once. Pseudocode first, then refine. Leave room for corrections without rewriting entire sections. On laptop: Use consistent formatting and indent properly. For both: Make eye contact and verbally walk through your approach before coding. Show enthusiasm for problem-solving. Be collaborative—ask clarifying questions, discuss trade-offs, and iterate based on interviewer hints. If stuck, think aloud and ask for guidance rather than sitting silently. Energy and genuine interest matter in person; display both through your demeanor and word choices. Practice on a physical whiteboard 5-10 times before your onsite to build muscle memory and comfort.
Focus Topics
Edge Case Identification and Defensive Programming
Systematically think through edge cases: empty inputs, single elements, duplicates, negative numbers, very large numbers, null pointers, circular references in graphs. Write code that handles these gracefully without crashing. Discuss edge cases before implementing and test them explicitly.
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Time Management and Pacing Under Pressure
Practice solving problems in strict 45-minute windows. Allocate: ~10 min clarifying and planning, ~25 min coding, ~10 min testing/optimization. Learn to recognize when to ask for help or move on rather than getting stuck. Understand that partial solutions with strong reasoning often score well.
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Whiteboard Coding Proficiency and In-Person Communication
Practice writing code on actual whiteboards or glass boards, not just on laptops. Get comfortable with pen/marker flow, spacing, and legibility. Verbally explain your approach before coding. Make eye contact with the interviewer. Discuss trade-offs and complexity. Ask for feedback and iterate.
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LeetCode Medium Difficulty Problems (Arrays, Strings, Hash Tables, Trees, Graphs)
Consistent, rapid ability to solve medium-difficulty problems. Specifically practice: Two Sum variants, Longest Substring Without Repeating Characters, Number of Islands, Binary Tree Level Order Traversal, Word Ladder, LRU Cache, Merge K Sorted Lists. Aim to solve 5-10 per week for 6-8 weeks before onsite.
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Onsite Round 2 - Machine Learning Fundamentals and Theory
What to Expect
A 60-minute deep-dive into machine learning concepts with 5-7 conceptual questions and potential pseudocode or simplified mathematical explanations. The interviewer will probe your understanding of ML algorithms, feature engineering, data handling, and evaluation approaches. Example questions: 'Walk me through how Random Forest works step-by-step', 'How would you approach building a classifier for an imbalanced dataset?', 'Explain the bias-variance trade-off and its practical implications', 'Compare logistic regression and SVM'. This round evaluates the 'depth' of your theoretical understanding. For entry-level, demonstrating solid fundamentals and ability to reason through problems logically is more important than advanced techniques. Interviewers expect you to ask clarifying questions for unfamiliar concepts.
Tips & Advice
Draw diagrams to visualize concepts (e.g., decision boundaries for classification, bias-variance curve, training/test performance curves). Walk through algorithms step-by-step using small examples. Discuss trade-offs and practical implications: When would you use Random Forest over logistic regression? When would you prioritize interpretability over accuracy? Use real-world examples or reference your own projects. For unfamiliar concepts, reason through them logically and ask clarifying questions. Connect concepts to production concerns: scalability, interpretability, monitoring. Keep explanations clear—avoid unnecessary jargon. Show genuine curiosity about understanding topics deeply, which aligns with Amazon's 'Learn and Be Curious' principle.
Focus Topics
Handling Imbalanced Datasets and Class Weighting
Problem definition: Why imbalanced data causes issues with standard metrics. Solutions: stratified sampling, oversampling (SMOTE), undersampling, class weights, cost-sensitive learning. Appropriate metrics: precision, recall, F1, AUC-ROC instead of accuracy. Discuss trade-offs of each approach.
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Feature Engineering, Selection, and Data Preprocessing
Techniques for handling missing data: imputation strategies (mean, median, model-based), deletion approaches, and rationale for each. Categorical encoding: one-hot encoding, ordinal encoding, target encoding. Numerical scaling and normalization. Feature selection methods: statistical tests, model-based importance. Handling outliers and imbalanced classes.
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Bias-Variance Trade-Off, Overfitting, Underfitting, and Regularization
Conceptual understanding of bias (systematic error), variance (sensitivity to training data), and their decomposition. Causes and symptoms of overfitting vs underfitting. Regularization techniques: L1 (Lasso), L2 (Ridge), elastic net, dropout, early stopping. Relationship to cross-validation and generalization. Practical strategies for diagnosis and correction.
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Supervised Learning Algorithms: Regression and Classification Fundamentals
Deep understanding of linear regression (how it works, assumptions, when to use), logistic regression (decision boundary, probability interpretation), and classification algorithms. Understand the relationship between linear and logistic regression. Discuss when to use regularization and its effects.
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Loss Functions, Evaluation Metrics, and Model Selection
Different loss functions: MSE/RMSE for regression, cross-entropy for classification, hinge loss for SVM. When to use each and their optimization implications. Evaluation metrics: accuracy, precision, recall, F1, AUC-ROC, ROC curves. Why accuracy is insufficient for imbalanced data. Discuss cost-benefit analysis of false positives vs false negatives in business context.
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Tree-Based and Ensemble Methods (Decision Trees, Random Forests, Gradient Boosting)
Understand decision tree construction (recursive splitting, information gain, gini impurity). Explain Random Forest step-by-step: bootstrap sampling, random feature selection, aggregation. Compare with gradient boosting (sequential tree building vs parallel). Discuss why ensemble methods reduce overfitting. Know when to use each method.
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Onsite Round 3 - Machine Learning System Design
What to Expect
A 60-minute interview where you design an end-to-end machine learning system to solve a real-world problem. You'll be given a problem statement such as 'Design a recommendation system for Amazon Prime Video', 'Design a fraud detection system for payment transactions', or 'Design a demand forecasting system for inventory management'. Your task is to outline how you'd approach building a complete ML solution: defining the problem and success metrics, collecting and processing data, engineering features, selecting and training models, deploying to production, and monitoring performance. The interviewer will ask follow-up questions to probe specific areas. For entry-level, the focus is on systems thinking, understanding the full ML lifecycle, and making reasonable design decisions—not on micro-architectural details or deep mathematical optimization.
Tips & Advice
Start by clarifying the problem: What's the business objective? What's the success metric? What are constraints (latency, throughput, cost, data privacy)? Ask these questions rather than making assumptions. Outline your approach at a high level (draw a diagram showing data pipeline → feature store → model training → deployment → monitoring). Then dive into specific areas the interviewer probes. For entry-level, it's perfectly acceptable to reference existing tools: 'I'd use SageMaker for training and deployment', 'I'd use a feature store to manage features', 'I'd implement A/B testing using SageMaker'. Focus on demonstrating understanding of the full pipeline and thoughtful trade-offs rather than reinventing infrastructure. Discuss monitoring for model drift. Be prepared to discuss why you chose a particular approach over alternatives. Draw diagrams on the whiteboard to illustrate your system architecture.
Focus Topics
Monitoring, Model Drift Detection, and Feedback Loops
Monitoring in production: accuracy degradation, model drift (model outputs diverge from expected behavior), data drift (input data distribution changes). Detection methods: comparing current performance to baseline, monitoring prediction distributions. Feedback loops: collecting true labels, retraining triggers, continuous model improvement. Discuss shadow deployments and canary rollouts for safe model updates.
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Problem Scoping, Business Objective Definition, and Success Metrics
Ability to decompose ambiguous problems into clear requirements. Define business objective (increase engagement, reduce fraud, improve personalization, optimize resource allocation). Identify the appropriate success metric (accuracy, precision, recall, AUC, revenue impact, cost savings). Discuss constraints: latency (real-time vs batch), throughput, cost, compliance (GDPR, data privacy). Aligns to job description responsibility of 'solving complex problems and automate decision-making processes'.
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Model Selection, Training Strategy, and Evaluation Framework
How to choose a model: start with simple baseline (e.g., logistic regression for classification), then iterate to more complex if needed. Training strategy: online learning vs batch retraining, hyperparameter tuning approach, cross-validation methodology. Evaluation: metrics chosen, holdout test sets, stratification for imbalanced data. Discuss A/B testing framework for production model comparisons.
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Data Collection, Pipeline Architecture, and Feature Engineering
How to identify data sources (logs, user behavior, transactions, external data). Design data collection infrastructure for scale (event streaming, log aggregation). Data quality checks and versioning. Feature engineering process: identifying useful features, transformations, feature interactions. Discuss feature store concept for consistency between training and serving. Address data privacy and compliance considerations.
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Model Deployment, Serving Infrastructure, and Production Considerations
Deployment strategy options: batch inference (overnight processing), real-time serving (API endpoint), streaming predictions. Infrastructure choices: containerization with Docker, deployment platforms (SageMaker, Lambda), model versioning and rollback. Latency and throughput requirements. Horizontal and vertical scaling. Job description emphasizes 'Deploying ML models to production environments' and 'implementing model serving infrastructure'.
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Onsite Round 4 - Behavioral and Amazon Leadership Principles
What to Expect
A 60-minute interview assessing your fit with Amazon's culture through behavioral questions rooted in Amazon's 14 leadership principles. You'll be asked 5-7 questions typically phrased as 'Tell me about a time when...' Your responses should follow the STAR method (Situation, Task, Action, Result). Example questions: 'Tell me about a time you took ownership of a problem', 'Describe a situation where you had to learn something new quickly', 'Tell me about a time you made a decision with incomplete information', 'Give an example of you going above and beyond', 'Tell me about a time you had to work with someone you disagreed with'. For entry-level, interviewers expect you to draw from coursework, projects, internships, and personal experiences—you're not expected to have led teams or owned large projects. They're assessing learning ability, collaboration, curiosity, and alignment with Amazon values.
Tips & Advice
Prepare 8-10 well-crafted stories using the STAR format. Choose stories from diverse contexts: academic projects, internships, side projects, team collaborations. For each story, practice a 2-3 minute telling. Focus on your individual role and learning rather than team accomplishments. Be specific with details (project name, technologies, quantifiable outcomes). Connect your stories to Amazon's leadership principles during the interview. Listen carefully to each question and answer specifically—don't deliver generic responses. Show genuine enthusiasm for your work. Be honest about challenges and mistakes—what did you learn? For entry-level, stories about curiosity, learning from failure, taking initiative, and collaborating with others are particularly valuable. Avoid stories that make you seem inflexible, unwilling to learn, or unable to work with others. Practice telling your stories aloud multiple times before the interview.
Focus Topics
Amazon Leadership Principle: Dive Deep
Prepare a story showing you go beyond surface-level understanding. Example: A time you investigated a problem thoroughly, asked detailed questions, found unexpected insights, or debugged a complex issue by understanding the root cause rather than treating symptoms.
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Amazon Leadership Principle: Bias for Action
Prepare a story showing you move forward despite uncertainty. Example: A time you made a decision or took action with incomplete information, iterating and learning along the way rather than seeking perfect information before acting.
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Communication, Collaboration, and Teamwork
Prepare stories demonstrating good communication, ability to work with diverse team members, seeking diverse perspectives, and willingness to help colleagues. Example: A project requiring cross-functional collaboration, resolving disagreements with teammates, or mentoring someone junior. These stories don't need to map to a single principle but span multiple principles.
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Amazon Leadership Principle: Learn and Be Curious
Prepare a story about picking up a new technology, diving into a problem you didn't initially understand, seeking feedback, or learning from colleagues. Show ongoing growth mindset. Example: A project where you had to learn a new framework or dataset, and how you approached the learning process.
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Amazon Leadership Principle: Ownership
Prepare a story showing you take responsibility for outcomes. Example: Identifying a problem (even if not explicitly assigned to you), taking initiative to address it, and seeing it through to resolution. Discuss accountability for both successes and failures. Show willingness to go beyond job description.
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Amazon Leadership Principle: Customer Obsession
Prepare a story demonstrating that you think about end users and their needs. Example: A project where you researched user pain points and designed a solution addressing them, or a time when you prioritized user experience over technical convenience. For ML engineers, this could involve recommending improvements to a model based on user feedback.
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Frequently Asked Machine Learning Engineer Interview Questions
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import os, random, numpy as np, torch
def set_seeds(seed=42):
os.environ['PYTHONHASHSEED'] = str(seed)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
# deterministic CUDA ops
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = FalseSample Answer
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Sample Answer
from dataclasses import dataclass, field
from typing import List, Dict, Any
import pandas as pd
import numpy as np
import hashlib
import pickle
@dataclass
class DeterministicTransformer:
numeric_cols: List[str]
categorical_cols: List[str]
onehot_threshold: int = 10
hash_bins: int = 32
means: Dict[str, float] = field(default_factory=dict)
onehot_categories: Dict[str, List[Any]] = field(default_factory=dict)
def _hash_bin(self, value: Any) -> int:
# deterministic hash to bin index; None -> reserved bin 0
if pd.isna(value):
return 0
h = hashlib.sha256(str(value).encode('utf-8')).hexdigest()
return (int(h, 16) % (self.hash_bins - 1)) + 1 # reserve 0 for missing
def fit(self, df: pd.DataFrame):
for col in self.numeric_cols:
self.means[col] = float(df[col].mean(skipna=True)) if col in df else 0.0
for col in self.categorical_cols:
vals = df[col].dropna().unique().tolist() if col in df else []
if len(vals) <= self.onehot_threshold:
self.onehot_categories[col] = sorted(vals)
else:
self.onehot_categories[col] = None # signal hashing
return self
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
out = df.copy().reset_index(drop=True)
# impute numerics
for col in self.numeric_cols:
out[col] = out.get(col)
out[col] = out[col].fillna(self.means.get(col, 0.0))
# handle categoricals
for col in self.categorical_cols:
if self.onehot_categories.get(col) is not None:
# one-hot: create stable columns for each learned category
for cat in self.onehot_categories[col]:
out[f"{col}__is__{cat}"] = (out[col] == cat).astype(int)
out[f"{col}__is__UNK"] = (~out[col].isin(self.onehot_categories[col]) | out[col].isna()).astype(int)
out.drop(columns=[col], inplace=True)
else:
# hashing into bins
bins = out[col].apply(self._hash_bin)
for i in range(self.hash_bins):
out[f"{col}__bin_{i}"] = (bins == i).astype(int)
out.drop(columns=[col], inplace=True)
return out
def save(self, path: str):
with open(path, 'wb') as f:
pickle.dump(self, f)
@staticmethod
def load(path: str):
with open(path, 'rb') as f:
return pickle.load(f)Sample Answer
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