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src/default_kedro_157/pipelines/data_science/pipeline.py

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+# Copyright 2020 QuantumBlack Visual Analytics Limited
+#
+# 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
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND
+# NONINFRINGEMENT. IN NO EVENT WILL THE LICENSOR OR OTHER CONTRIBUTORS
+# BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN
+# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF, OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+# The QuantumBlack Visual Analytics Limited ("QuantumBlack") name and logo
+# (either separately or in combination, "QuantumBlack Trademarks") are
+# trademarks of QuantumBlack. The License does not grant you any right or
+# license to the QuantumBlack Trademarks. You may not use the QuantumBlack
+# Trademarks or any confusingly similar mark as a trademark for your product,
+#     or use the QuantumBlack Trademarks in any other manner that might cause
+# confusion in the marketplace, including but not limited to in advertising,
+# on websites, or on software.
+#
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Example code for the nodes in the example pipeline. This code is meant
+just for illustrating basic Kedro features.
+
+Delete this when you start working on your own Kedro project.
+"""
+
+from kedro.pipeline import Pipeline, node
+
+from .nodes import predict, report_accuracy, train_model
+
+
+def create_pipeline(**kwargs):
+    return Pipeline(
+        [
+            node(
+                train_model,
+                ["example_train_x", "example_train_y", "parameters"],
+                "example_model",
+            ),
+            node(
+                predict,
+                dict(model="example_model", test_x="example_test_x"),
+                "example_predictions",
+            ),
+            node(report_accuracy, ["example_predictions", "example_test_y"], None),
+        ]
+    )