Summary  
Demonstrates how to perform multi-class classification using the k-nearest neighbors algorithm by feeding a target variable with more than two classes into the same `KNeighborsClassifier` and selecting the majority class among neighbors.

General domain of usage  
Movie rating classification

**Mehrklassenklassifikation** mit k-NN ist genauso einfach wie die binäre Klassifikation. Es wird einfach die Klasse gewählt, die in der Nachbarschaft überwiegt.  


Der `KNeighborsClassifier` führt automatisch eine Mehrklassenklassifikation durch, wenn `y` mehr als zwei Merkmale enthält, sodass **keine Änderungen erforderlich sind**. Das Einzige, was sich ändert, ist die `y`-Variable, die an die `.fit()`-Methode übergeben wird.

Nun wird eine **Mehrklassenklassifikation mit k-NN** durchgeführt. Betrachten Sie dazu den folgenden Datensatz:

import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/starwars_multiple.csv')
print(df.head())

Es ist dasselbe wie im Beispiel des vorherigen Kapitels, aber nun kann das Ziel drei Werte annehmen:
- **0**: "Fand ich schrecklich" (Bewertung ist weniger als 3/5);
- **1**: "Geht so" (Bewertung zwischen 3/5 und 4/5);
- **2**: "Fand ich gut" (Bewertung ist 4/5 oder höher).

import unittest
import importlib
import numpy as np


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


def get_first_differing_index(expected_array, actual_array):
    for i, (val_1, val_2) in enumerate(zip(expected_array, actual_array)):
        if not np.array_equal(val_1, val_2):
            return i, expected_array[i], actual_array[i]

    if len(expected_array) > len(actual_array):
        return len(actual_array), expected_array[len(actual_array)], None
    else:
        return len(expected_array), None, actual_array[len(expected_array)]


class TestUserCode(unittest.TestCase):

    def test_scaler_is_declared(self):
        import user_code

        variable = 'scaler'
        _dynamic_test(
            self,
            hasattr(user_code, variable),
            f"The `{variable}` variable is declared.",
            f"Expected `{variable}` to be declared."
        )

    def test_scaler_is_standard_scaler(self):
        import user_code
        from sklearn.preprocessing import StandardScaler

        try:
            condition = isinstance(user_code.scaler, StandardScaler)
            failure_message = f"Expected `scaler` to be a `StandardScaler`, but got `{type(user_code.scaler).__name__}`."
        except AttributeError:
            condition = False
            failure_message = "The `scaler` variable is not declared."

        _dynamic_test(
            self,
            condition,
            "`scaler` is a `StandardScaler`.",
            failure_message
        )

    def test_X_train_is_scaled_correctly(self):
        import user_code
        from sklearn.preprocessing import StandardScaler

        df = user_code.pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/starwars_multiple.csv')
        X_train = df.drop('StarWars6', axis=1)

        scaler = StandardScaler()
        expected_value = scaler.fit_transform(X_train)

        variable = 'X_train'
        actual_value = getattr(user_code, variable, None)
        condition = False
        if actual_value is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_value, np.ndarray):
            failure_message = f"`{variable}` is not a `numpy.ndarray`."
        elif actual_value.size == 0:
            failure_message = f"`{variable}` is empty."
        elif np.array_equal(actual_value, expected_value):
            condition = True
            failure_message = None
        else:
            idx, expected_element, actual_element = get_first_differing_index(expected_value, actual_value)
            failure_message = f"Expected `{variable}` to contain `{expected_element}` at index {idx}, but got `{actual_element}`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )

    def test_X_test_is_scaled_correctly(self):
        import user_code
        from sklearn.preprocessing import StandardScaler

        df = user_code.pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/starwars_multiple.csv')
        X_train = df.drop('StarWars6', axis=1)
        X_test = np.array([[5, 5], [4.5, 4]])

        scaler = StandardScaler()
        scaler.fit(X_train)
        expected_value = scaler.transform(X_test)

        variable = 'X_test'
        actual_value = getattr(user_code, variable, None)
        condition = False
        if actual_value is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_value, np.ndarray):
            failure_message = f"`{variable}` is not a `numpy.ndarray`."
        elif actual_value.size == 0:
            failure_message = f"`{variable}` is empty."
        elif np.array_equal(actual_value, expected_value):
            condition = True
            failure_message = None
        else:
            idx, expected_element, actual_element = get_first_differing_index(expected_value, actual_value)
            failure_message = f"Expected `{variable}` to contain `{expected_element}` at index {idx}, but got `{actual_element}`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )

    def test_knn_is_declared(self):
        import user_code

        variable = 'knn'
        _dynamic_test(
            self,
            hasattr(user_code, variable),
            f"The `{variable}` variable is declared.",
            f"Expected `{variable}` to be declared."
        )

    def test_knn_is_initialized_correctly(self):
        import user_code
        from sklearn.neighbors import KNeighborsClassifier

        variable = 'knn'
        actual_value = getattr(user_code, variable, None)
        if actual_value is None:
            condition = False
            failure_message = f"The `{variable}` variable is not declared."
        elif isinstance(actual_value, KNeighborsClassifier):
            condition = actual_value.n_neighbors == 13
            failure_message = f"Expected `{variable}` to be a `KNeighborsClassifier` with `13` neighbors, but got `n_neighbors={actual_value.n_neighbors}`."
        else:
            condition = False
            failure_message = f"`{variable}` is not a `KNeighborsClassifier`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` is a `KNeighborsClassifier` with `13` neighbors.",
            failure_message
        )

    def test_y_pred_is_declared(self):
        import user_code

        variable = 'y_pred'
        _dynamic_test(
            self,
            hasattr(user_code, variable),
            f"The `{variable}` variable is declared.",
            f"Expected `{variable}` to be declared."
        )

    def test_y_pred_is_correct(self):
        import user_code

        expected_value = np.array([2, 1])

        variable = 'y_pred'
        actual_value = getattr(user_code, variable, None)
        condition = False
        if actual_value is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_value, np.ndarray):
            failure_message = f"`{variable}` is not a `numpy.ndarray`."
        elif actual_value.size == 0:
            failure_message = f"`{variable}` is empty."
        elif np.array_equal(actual_value, expected_value):
            condition = True
            failure_message = None
        else:
            idx, expected_element, actual_element = get_first_differing_index(expected_value, actual_value)
            failure_message = f"Expected `{variable}` to contain `{expected_element}` at index {idx}, but got `{actual_element}`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )


if __name__ == '__main__':
    unittest.main()

test_main.py

Beherrschung der grundlegenden Algorithmen des überwachten Lernens und deren Implementierung mit Scikit-learn. Untersuchung von linearer und polynomialer Regression zur Preisvorhersage sowie Übergang zur Klassifikation mit k-NN, logistischer Regression und Entscheidungsbäumen. Bewertung von Modellen durch Kreuzvalidierung, Steuerung von Overfitting mittels Regularisierung und Optimierung von Hyperparametern. Aufbau robuster prädiktiver Systeme und Definition komplexer Entscheidungsgrenzen für Aufgaben der Mehrklassenklassifikation.

Mehrklassenklassifikation

Lösung