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Track ML experiments with proper logging and reproducibility. Use when training models or running experiments.
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World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
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| name | ml-experiment |
| description | Track ML experiments with proper logging and reproducibility. Use when training models or running experiments. |
import random
import numpy as np
def set_seeds(seed: int = 42) -> None:
"""Set all random seeds for reproducibility."""
random.seed(seed)
np.random.seed(seed)
# PyTorch
try:
import torch
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
except ImportError:
pass
# TensorFlow
try:
import tensorflow as tf
tf.random.set_seed(seed)
except ImportError:
pass
from datetime import datetime
from pathlib import Path
import json
class Experiment:
def __init__(self, name: str):
self.name = name
self.timestamp = datetime.now().isoformat()
self.params = {}
self.metrics = {}
self.artifacts = []
def log_params(self, **params):
self.params.update(params)
def log_metrics(self, **metrics):
self.metrics.update(metrics)
def log_artifact(self, path: str):
self.artifacts.append(path)
def save(self, output_dir: str = "experiments"):
Path(output_dir).mkdir(exist_ok=True)
path = f"{output_dir}/{self.name}_{self.timestamp}.json"
with open(path, "w") as f:
json.dump({
"name": self.name,
"timestamp": self.timestamp,
"params": self.params,
"metrics": self.metrics,
"artifacts": self.artifacts,
}, f, indent=2)
return path
# Usage
exp = Experiment("baseline_model")
exp.log_params(
learning_rate=0.001,
batch_size=32,
epochs=100,
model_type="transformer",
)
# ... training ...
exp.log_metrics(
train_loss=0.05,
val_loss=0.08,
accuracy=0.92,
f1_score=0.89,
)
exp.log_artifact("models/baseline_v1.pt")
exp.save()
from dataclasses import dataclass
@dataclass
class TrainingConfig:
# Model
model_type: str = "transformer"
hidden_size: int = 256
num_layers: int = 4
dropout: float = 0.1
# Training
learning_rate: float = 1e-4
batch_size: int = 32
epochs: int = 100
# Data
train_split: float = 0.8
val_split: float = 0.1
# Reproducibility
seed: int = 42
config = TrainingConfig(learning_rate=0.001)
def save_checkpoint(model, optimizer, epoch, loss, path):
"""Save training checkpoint."""
torch.save({
"epoch": epoch,
"model_state_dict": model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"loss": loss,
}, path)
def load_checkpoint(model, optimizer, path):
"""Load training checkpoint."""
checkpoint = torch.load(path)
model.load_state_dict(checkpoint["model_state_dict"])
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
return checkpoint["epoch"], checkpoint["loss"]
def compare_experiments(exp_dir: str = "experiments"):
"""Load and compare all experiments."""
import pandas as pd
from pathlib import Path
experiments = []
for path in Path(exp_dir).glob("*.json"):
with open(path) as f:
experiments.append(json.load(f))
# Flatten for comparison
rows = []
for exp in experiments:
row = {"name": exp["name"], "timestamp": exp["timestamp"]}
row.update(exp["params"])
row.update(exp["metrics"])
rows.append(row)
return pd.DataFrame(rows).sort_values("accuracy", ascending=False)
project/
├── experiments/ # Experiment logs
│ └── baseline_2024-01-15.json
├── models/ # Saved models
│ └── baseline_v1.pt
├── data/ # Data (versioned separately)
│ ├── raw/
│ └── processed/
├── notebooks/ # Exploration
├── src/ # Production code
└── configs/ # Config files
└── training.yaml