metrics_visualization_v2.py

# Copyright 2021 The Kubeflow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from kfp.v2 import dsl
from kfp.v2.dsl import (
    component,
    Output,
    ClassificationMetrics,
    Metrics,
)

@component(
    packages_to_install=['sklearn'],
    base_image='python:3.9',
)

def digit_classification(metrics: Output[Metrics]):
    from sklearn import model_selection
    from sklearn.linear_model import LogisticRegression
    from sklearn import datasets
    from sklearn.metrics import accuracy_score

    # Load digits dataset
    iris = datasets.load_iris()

    # # Create feature matrix
    X = iris.data

    # Create target vector
    y = iris.target

    #test size
    test_size = 0.33

    seed = 7
    #cross-validation settings
    kfold = model_selection.KFold(n_splits=10, random_state=seed, shuffle=True)

    #Model instance
    model = LogisticRegression()
    scoring = 'accuracy'
    results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring)

    #split data
    X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed)
    #fit model
    model.fit(X_train, y_train)

    #accuracy on test set
    result = model.score(X_test, y_test)
    metrics.log_metric('accuracy', (result*100.0))

@component(
    packages_to_install=['sklearn'],
    base_image='python:3.9',
)
def wine_classification(metrics: Output[ClassificationMetrics]):
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import roc_curve
    from sklearn.datasets import load_wine
    from sklearn.model_selection import train_test_split, cross_val_predict

    X, y = load_wine(return_X_y=True)
    # Binary classification problem for label 1.
    y = y == 1

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
    rfc = RandomForestClassifier(n_estimators=10, random_state=42)
    rfc.fit(X_train, y_train)
    y_scores = cross_val_predict(rfc, X_train, y_train, cv=3, method='predict_proba')
    y_predict = cross_val_predict(rfc, X_train, y_train, cv=3, method='predict')
    fpr, tpr, thresholds = roc_curve(y_true=y_train, y_score=y_scores[:,1], pos_label=True)
    metrics.log_roc_curve(fpr, tpr, thresholds)

@component(
    packages_to_install=['sklearn'],
    base_image='python:3.9'
)
def iris_sgdclassifier(test_samples_fraction: float, metrics: Output[ClassificationMetrics]):
    from sklearn import datasets, model_selection
    from sklearn.linear_model import SGDClassifier
    from sklearn.metrics import confusion_matrix

    iris_dataset = datasets.load_iris()
    train_x, test_x, train_y, test_y = model_selection.train_test_split(
        iris_dataset['data'], iris_dataset['target'], test_size=test_samples_fraction)


    classifier = SGDClassifier()
    classifier.fit(train_x, train_y)
    predictions = model_selection.cross_val_predict(classifier, train_x, train_y, cv=3)
    metrics.log_confusion_matrix(
        ['Setosa', 'Versicolour', 'Virginica'],
        confusion_matrix(train_y, predictions).tolist() # .tolist() to convert np array to list.
    )

@dsl.pipeline(
    name='metrics-visualization-pipeline')
def metrics_visualization_pipeline():
    wine_classification_op = wine_classification()
    iris_sgdclassifier_op = iris_sgdclassifier(test_samples_fraction=0.3)
    digit_classification_op = digit_classification()