Experiment tracking

SQLiteTracker provides a powerful and flexible way to track computational (e.g., Machine Learning) experiments using a SQLite database. Allows you to use SQL as the query language, giving you a powerful tool for experiment comparison, and comes with plotting features to compare plots side-by-side and to combine plots for better comparison.

Read more about the motivations in our blog post, check out the HN discussion.

This tutorial will walk you through the features with a Machine Learning use case; however, the tracker is generic enough to be used in any other domains.

from pathlib import Path

from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# delete our example database, if any
db = Path("my_experiments.db")

if db.exists():
    db.unlink()

from sklearn_evaluation import SQLiteTracker

tracker = SQLiteTracker("my_experiments.db")

X, y = datasets.make_classification(200, 10, n_informative=5, class_sep=0.65)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=42
)

models = [RandomForestClassifier(), LogisticRegression(), DecisionTreeClassifier()]

Training and logging models

for m in models:
    model = type(m).__name__
    print(f"Fitting {model}")

    experiment = tracker.new_experiment()
    m.fit(X_train, y_train)
    y_pred = m.predict(X_test)
    acc = accuracy_score(y_test, y_pred)

    # log a dictionary with log_dict
    experiment.log_dict({"accuracy": acc, "model": model, **m.get_params()})

Fitting RandomForestClassifier

Fitting LogisticRegression
Fitting DecisionTreeClassifier

Displaying latest experiments

Display the tracker object to show last experiments:

tracker

SQLiteTracker

uuid	created	parameters	comment
c2d133ad	2023-03-07 18:28:17	{"accuracy": 0.803030303030303, "model": "RandomForestClassifier", "bootstrap": true, "ccp_alpha": 0.0, "class_weight": null, "criterion": "gini", "max_depth": null, "max_features": "sqrt", "max_leaf_nodes": null, "max_samples": null, "min_impurity_decrease": 0.0, "min_samples_leaf": 1, "min_samples_split": 2, "min_weight_fraction_leaf": 0.0, "n_estimators": 100, "n_jobs": null, "oob_score": false, "random_state": null, "verbose": 0, "warm_start": false}
1d77aae9	2023-03-07 18:28:17	{"accuracy": 0.7727272727272727, "model": "LogisticRegression", "C": 1.0, "class_weight": null, "dual": false, "fit_intercept": true, "intercept_scaling": 1, "l1_ratio": null, "max_iter": 100, "multi_class": "auto", "n_jobs": null, "penalty": "l2", "random_state": null, "solver": "lbfgs", "tol": 0.0001, "verbose": 0, "warm_start": false}
7de524ae	2023-03-07 18:28:17	{"accuracy": 0.6515151515151515, "model": "DecisionTreeClassifier", "ccp_alpha": 0.0, "class_weight": null, "criterion": "gini", "max_depth": null, "max_features": null, "max_leaf_nodes": null, "min_impurity_decrease": 0.0, "min_samples_leaf": 1, "min_samples_split": 2, "min_weight_fraction_leaf": 0.0, "random_state": null, "splitter": "best"}

(Most recent experiments)

Tip

Click here to see the detailed user guide.