class_docstr: A experiment tracker backed by a SQLite database
  
  :doc:`Click here <../comparison/SQLiteTracker>` to see the user guide.
  
  Parameters
  ----------
  path
      Database location
  
  Examples
  --------
  >>> from sklearn_evaluation import SQLiteTracker
  >>> tracker = SQLiteTracker("experiments.db")
  >>> experiment = tracker.new_experiment() # new experiment
  >>> experiment.log("accuracy", 0.8) # log metric
  0.8
  >>> tracker.get(experiment.uuid) # retrieve it later with the uuid
  Experiment({'accuracy': 0.8})
  >>> experiment.log_confusion_matrix([1, 1, 0, 0], [1, 0, 1, 0]) # doctest: +SKIP
  >>> data = tracker.get(experiment.uuid)
  >>> data['confusion_matrix'] # doctest: +SKIP
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code: def __init__(self, path: str):
      self.conn = sqlite3.connect(path)
  
      cur = self.conn.cursor()
      cur.execute(
          """
          CREATE TABLE IF NOT EXISTS experiments (
              uuid TEXT NOT NULL UNIQUE,
              created TIMESTAMP default current_timestamp,
              parameters TEXT,
              comment TEXT
          )
          """
      )
      cur.close()
  
imports: from collections.abc import Mapping
  from uuid import uuid4
  import sqlite3
  import json
  import importlib
  import pandas
  from jinja2 import Template
  from sklearn_evaluation.table import Table
  from sklearn_evaluation.report.serialize import try_serialize_figures, figure2html
  from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
  from sklearn_evaluation.nb.NotebookCollection import add_compare_tab, tabs_html_from_content, HTMLMapping
  from sklearn_evaluation import plot
label_desc: Use image node_id 1 to create a new SQLiteTracker object  with example: obj = SQLiteTracker(path)