CLEAN Formatted with black

docs/source/conf.py

@@ -189,13 +189,7 @@ latex_documents = [
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    (
-        master_doc,
-        "default_kedro_157",
-        "default_kedro_157 Documentation",
-        [author],
-        1,
-    )
+    (master_doc, "default_kedro_157", "default_kedro_157 Documentation", [author], 1,)
 ]
 
 # -- Options for Texinfo output ----------------------------------------------

iris_pipeline.json

@@ -0,0 +1,158 @@
+{
+    "edges": [
+        {
+            "source": "f7b62b1a",
+            "target": "76ddffce"
+        },
+        {
+            "source": "3f82e2e9",
+            "target": "76ddffce"
+        },
+        {
+            "source": "76ddffce",
+            "target": "61aea938"
+        },
+        {
+            "source": "61aea938",
+            "target": "23e0c9e9"
+        },
+        {
+            "source": "40cc0777",
+            "target": "23e0c9e9"
+        },
+        {
+            "source": "7fe1d536",
+            "target": "27087ee9"
+        },
+        {
+            "source": "d577578a",
+            "target": "27087ee9"
+        },
+        {
+            "source": "27087ee9",
+            "target": "3f82e2e9"
+        },
+        {
+            "source": "27087ee9",
+            "target": "40cc0777"
+        },
+        {
+            "source": "27087ee9",
+            "target": "57103b64"
+        },
+        {
+            "source": "27087ee9",
+            "target": "7a1cb4c1"
+        },
+        {
+            "source": "57103b64",
+            "target": "a158446c"
+        },
+        {
+            "source": "7a1cb4c1",
+            "target": "a158446c"
+        },
+        {
+            "source": "f1f1425b",
+            "target": "a158446c"
+        },
+        {
+            "source": "a158446c",
+            "target": "f7b62b1a"
+        }
+    ],
+    "nodes": [
+        {
+            "full_name": "predict",
+            "id": "76ddffce",
+            "name": "Predict",
+            "tags": [],
+            "type": "task"
+        },
+        {
+            "full_name": "report_accuracy",
+            "id": "23e0c9e9",
+            "name": "Report Accuracy",
+            "tags": [],
+            "type": "task"
+        },
+        {
+            "full_name": "split_data",
+            "id": "27087ee9",
+            "name": "Split Data",
+            "tags": [],
+            "type": "task"
+        },
+        {
+            "full_name": "train_model",
+            "id": "a158446c",
+            "name": "Train Model",
+            "tags": [],
+            "type": "task"
+        },
+        {
+            "full_name": "example_iris_data",
+            "id": "7fe1d536",
+            "name": "Example Iris Data",
+            "tags": [],
+            "type": "data"
+        },
+        {
+            "full_name": "example_model",
+            "id": "f7b62b1a",
+            "name": "Example Model",
+            "tags": [],
+            "type": "data"
+        },
+        {
+            "full_name": "example_predictions",
+            "id": "61aea938",
+            "name": "Example Predictions",
+            "tags": [],
+            "type": "data"
+        },
+        {
+            "full_name": "example_test_x",
+            "id": "3f82e2e9",
+            "name": "Example Test X",
+            "tags": [],
+            "type": "data"
+        },
+        {
+            "full_name": "example_test_y",
+            "id": "40cc0777",
+            "name": "Example Test Y",
+            "tags": [],
+            "type": "data"
+        },
+        {
+            "full_name": "example_train_x",
+            "id": "57103b64",
+            "name": "Example Train X",
+            "tags": [],
+            "type": "data"
+        },
+        {
+            "full_name": "example_train_y",
+            "id": "7a1cb4c1",
+            "name": "Example Train Y",
+            "tags": [],
+            "type": "data"
+        },
+        {
+            "full_name": "parameters",
+            "id": "f1f1425b",
+            "name": "Parameters",
+            "tags": [],
+            "type": "parameters"
+        },
+        {
+            "full_name": "params:example_test_data_ratio",
+            "id": "d577578a",
+            "name": "Params:example Test Data Ratio",
+            "tags": [],
+            "type": "parameters"
+        }
+    ],
+    "tags": []
+}

kedro_cli.py

@@ -361,9 +361,7 @@ def build_docs(open_docs):
     """Build the project documentation."""
     python_call("pip", ["install", "src/[docs]"])
     python_call("pip", ["install", "-r", "src/requirements.txt"])
-    python_call(
-        "ipykernel", ["install", "--user", "--name=default_kedro_157"]
-    )
+    python_call("ipykernel", ["install", "--user", "--name=default_kedro_157"])
     shutil.rmtree("docs/build", ignore_errors=True)
     call(
         [

src/default_kedro_157/pipeline.py

@@ -33,7 +33,6 @@ from typing import Dict
 from kedro.pipeline import Pipeline
 
 
-
 ###########################################################################
 # Here you can find an example pipeline, made of two modular pipelines.
 #
@@ -56,7 +55,6 @@ def create_pipelines(**kwargs) -> Dict[str, Pipeline]:
 
     """
 
-
     data_engineering_pipeline = de.create_pipeline()
     data_science_pipeline = ds.create_pipeline()
 
@@ -65,4 +63,3 @@ def create_pipelines(**kwargs) -> Dict[str, Pipeline]:
         "ds": data_science_pipeline,
         "__default__": data_engineering_pipeline + data_science_pipeline,
     }
-

src/setup.py

@@ -28,9 +28,7 @@
 
 from setuptools import find_packages, setup
 
-entry_point = (
-    "default-kedro-157 = default_kedro_157.run:run_package"
-)
+entry_point = "default-kedro-157 = default_kedro_157.run:run_package"
 
 
 # get the dependencies and installs