import unittest
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

def _dynamic_test(test_case, condition, success_msg, failure_msg):
    if condition:
        test_case._testMethodName = success_msg
        test_case.assertTrue(True, success_msg)
    else:
        test_case._testMethodName = failure_msg
        test_case.fail(failure_msg)

class TestPipeline(unittest.TestCase):

    def setUp(self):
        try:
            from user_code import pipeline, accuracy
            self.pipeline = pipeline
            self.accuracy = accuracy
        except ImportError:
            self.fail("Variables 'pipeline' and 'accuracy' must be defined.")

    def test_pipeline_instance(self):
        """Check that 'pipeline' is a scikit-learn Pipeline"""
        _dynamic_test(
            self,
            isinstance(self.pipeline, Pipeline),
            "Pipeline created successfully.",
            "Pipeline must be an instance of sklearn.pipeline.Pipeline."
        )

    def test_scaler_in_pipeline(self):
        """Check that StandardScaler is included"""
        steps = dict(self.pipeline.named_steps)
        _dynamic_test(
            self,
            isinstance(steps.get("scaler"), StandardScaler),
            "StandardScaler found in pipeline.",
            "Pipeline must include StandardScaler with name 'scaler'."
        )

    def test_classifier_in_pipeline(self):
        """Check that RandomForestClassifier is included"""
        steps = dict(self.pipeline.named_steps)
        _dynamic_test(
            self,
            isinstance(steps.get("clf"), RandomForestClassifier),
            "RandomForestClassifier found in pipeline.",
            "Pipeline must include RandomForestClassifier with name 'clf'."
        )

    def test_accuracy_threshold(self):
        """Check that accuracy meets threshold"""
        _dynamic_test(
            self,
            accuracy_score.__call__ and self.accuracy > 0.8,
            "Accuracy meets expectations.",
            "Accuracy should be greater than 0.8."
        )

test_main.py

Master the essentials of automated machine learning (AutoML) by exploring core concepts, optimization strategies, and hands-on frameworks. Learn to automate preprocessing, model selection, and hyperparameter tuning using leading Python tools.

Explore the core ideas behind AutoML, its workflow, and how it builds on traditional ML pipelines.

Dive into automated hyperparameter tuning methods and their impact on model performance.

Get hands-on with leading AutoML frameworks and see how they automate the ML pipeline.

Assess AutoML results, compare frameworks, and discuss deployment and limitations.

Challenge: Build a Baseline Pipeline

Solution