In this section, we introduce the concept of a node, for which the relevant API documentation is kedro.pipeline.node.
Nodes are the building blocks of pipelines, and represent tasks. Pipelines are used to combine nodes to build workflows, which range from simple machine learning workflows to end-to-end (E2E) production workflows.
You must first import libraries from Kedro and other standard tools to run the code snippets demonstrated below.
from kedro.pipeline import *
from kedro.io import *
from kedro.runner import *
import pickle
import osA node is created by specifying a function, input variable names and output variable names. Let's consider a simple function that adds two numbers:
def add(x, y):
return x + yThe function has two inputs (x and y) and a single output (the sum of the inputs).
Here is how a node is created with this function:
adder_node = node(func=add, inputs=["a", "b"], outputs="sum")
adder_nodeHere is the output:
Out[1]: Node(add, ['a', 'b'], 'sum', None)You can also add labels to nodes, which will be used to describe them in logs:
adder_node = node(func=add, inputs=["a", "b"], outputs="sum")
print(str(adder_node))
adder_node = node(func=add, inputs=["a", "b"], outputs="sum", name="adding_a_and_b")
print(str(adder_node))This gives the following output:
add([a,b]) -> [sum]
adding_a_and_b: add([a,b]) -> [sum]Let's break down the node definition:
addis the Python function that will execute when the node runs['a', 'b']specify the input variable namessumspecifies the return variable name. The value returned byaddwill be bound in this variablenameis an optional label for the node, which can be used to provide description of the business logic it provides
A syntax describes function inputs and outputs. This syntax allows different Python functions to be reused in nodes, and supports dependency resolution in pipelines.
| Input syntax | Meaning | Example function parameters | How function is called when node runs |
|---|---|---|---|
None |
No input | def f() |
f() |
'a' |
Single input | def f(arg1) |
f(a) |
['a', 'b'] |
Multiple inputs | def f(arg1, arg2) |
f(a, b) |
dict(arg1='x', arg2='y') |
Keyword inputs | def f(arg1, arg2) |
f(arg1=x, arg2=y) |
| Output syntax | Meaning | Example return statement |
|---|---|---|
None |
No output | Does not return |
'a' |
Single output | return a |
['a', 'b'] |
List output | return [a, b] |
dict(key1='a', key2='b') |
Dictionary output | return dict(key1=a, key2=b) |
Any combinations of the above are possible, except nodes of the form node(f, None, None) (at least a single input or output must be provided).
Sometimes, when creating reporting nodes for instance, you need to know the names of the datasets that your node receives, but you might not have this information in advance. This can be solved by defining a **kwargs-only function:
def reporting(**kwargs):
result = []
for name, data in kwargs.items():
res = example_report(name, data)
result.append(res)
return combined_report(result)Then, when it comes to constructing the Node, simply pass a dictionary to the node inputs:
from kedro.pipeline import node
uk_reporting_node = node(
reporting,
inputs={"uk_input1": "uk_input1", "uk_input2": "uk_input2", ...},
outputs="uk",
)
ge_reporting_node = node(
reporting,
inputs={"ge_input1": "ge_input1", "ge_input2": "ge_input2", ...},
outputs="ge",
)Alternatively, you can also make use of a helper function that creates the mapping for you, so you can reuse it across your codebase.
from kedro.pipeline import node
+mapping = lambda x: {k: k for k in x}
+
uk_reporting_node = node(
reporting,
- inputs={"uk_input1": "uk_input1", "uk_input2": "uk_input2", ...},
+ inputs=mapping(["uk_input1", "uk_input2", ...]),
outputs="uk",
)
ge_reporting_node = node(
reporting,
- inputs={"ge_input1": "ge_input1", "ge_input2": "ge_input2", ...},
+ inputs=mapping(["ge_input1", "ge_input2", ...]),
outputs="ge",
)Tags might be useful to run part of a pipeline without changing the code. For instance, kedro run --tag=ds will only run nodes that have a ds tag attached.
To tag a node, you can simply specify the tags argument:
node(func=add, inputs=["a", "b"], outputs="sum", name="adding_a_and_b", tags="node_tag")Moreover, you can tag all nodes in a Pipeline. If the pipeline definition contains the tags= argument, Kedro will attach the corresponding tag to every node within that pipeline.
To run a pipeline using a tag:
kedro run --tag=pipeline_tagThis will run only the nodes found within the pipeline tagged with pipeline_tag.
To run a node, you must instantiate its inputs. In this case, the node expects two inputs:
adder_node.run(dict(a=2, b=3))The output is as follows:
Out[2]: {'sum': 5}You can also call a node as a regular Python function: `adder_node(dict(a=2, b=3))`. This will call `adder_node.run(dict(a=2, b=3))` behind the scenes.
Generator functions were introduced with PEP 255 and are a special kind of function in Python that returns lazy iterators. They are often used for lazy-loading or lazy-saving of data, which can be particularly useful when dealing with large datasets that do not fit entirely into memory. In the context of Kedro, generator functions can be used in nodes to efficiently process and handle such large datasets.
To demonstrate the use of generator functions in Kedro nodes, first, set up a Kedro project using the pandas-iris starter. If you haven't already created a Kedro project, you can follow the get started guide to create it.
Create the project with this command:
kedro new -s pandas-irisTo use generator functions in Kedro nodes, you need to update the catalog.yml file to include the chunksize argument for the relevant dataset that will be processed using the generator.
You need to add a new dataset in your catalog.yml as follows:
+ X_test:
+ type: pandas.CSVDataset
+ filepath: data/05_model_input/X_test.csv
+ load_args:
+ chunksize: 10With pandas built-in support, you can use the chunksize argument to read data using generator.
To use generators to save data lazily, you need do three things:
- Update the
make_predictionfunction definition to useyieldinstead ofreturn. - Create a custom dataset called
ChunkWiseCSVDataset - Update
catalog.ymlto use a newly createdChunkWiseCSVDataset.
Copy the following code to nodes.py. The main change is to use a new model DecisionTreeClassifier to make prediction by chunks in make_predictions.
Click to open
import logging
from typing import Any, Dict, Tuple, Iterator
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
import numpy as np
import pandas as pd
def split_data(
data: pd.DataFrame, parameters: Dict[str, Any]
) -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:
"""Splits data into features and target training and test sets.
Args:
data: Data containing features and target.
parameters: Parameters defined in parameters.yml.
Returns:
Split data.
"""
data_train = data.sample(
frac=parameters["train_fraction"], random_state=parameters["random_state"]
)
data_test = data.drop(data_train.index)
X_train = data_train.drop(columns=parameters["target_column"])
X_test = data_test.drop(columns=parameters["target_column"])
y_train = data_train[parameters["target_column"]]
y_test = data_test[parameters["target_column"]]
label_encoder = LabelEncoder()
label_encoder.fit(pd.concat([y_train, y_test]))
y_train = label_encoder.transform(y_train)
return X_train, X_test, y_train, y_test
def make_predictions(
X_train: pd.DataFrame, X_test: pd.DataFrame, y_train: pd.Series
) -> pd.Series:
"""Use a DecisionTreeClassifier model to make prediction."""
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
for chunk in X_test:
y_pred = model.predict(chunk)
y_pred = pd.DataFrame(y_pred)
yield y_pred
def report_accuracy(y_pred: pd.Series, y_test: pd.Series):
"""Calculates and logs the accuracy.
Args:
y_pred: Predicted target.
y_test: True target.
"""
accuracy = accuracy_score(y_test, y_pred)
logger = logging.getLogger(__name__)
logger.info("Model has accuracy of %.3f on test data.", accuracy)The ChunkWiseCSVDataset is a variant of the pandas.CSVDataset where the main change is to the _save method that appends data instead of overwriting it. You need to create a file src/<package_name>/chunkwise.py and put this class inside it. Below is an example of the ChunkWiseCSVDataset implementation:
import pandas as pd
from kedro.io.core import (
get_filepath_str,
)
from kedro_datasets.pandas import CSVDataset
class ChunkWiseCSVDataset(CSVDataset):
"""``ChunkWiseCSVDataset`` loads/saves data from/to a CSV file using an underlying
filesystem. It uses pandas to handle the CSV file.
"""
_overwrite = True
def _save(self, data: pd.DataFrame) -> None:
save_path = get_filepath_str(self._get_save_path(), self._protocol)
# Save the header for the first batch
if self._overwrite:
data.to_csv(save_path, index=False, mode="w")
self._overwrite = False
else:
data.to_csv(save_path, index=False, header=False, mode="a")After that, you need to update the catalog.yml to use this new dataset.
+ y_pred:
+ type: <package_name>.chunkwise.ChunkWiseCSVDataset
+ filepath: data/07_model_output/y_pred.csvWith these changes, when you run kedro run in your terminal, you should see y_pred being saved multiple times in the logs as the generator lazily processes and saves the data in smaller chunks.
...
INFO Loading data from 'y_train' (MemoryDataset)... data_catalog.py:475
INFO Running node: make_predictions: make_predictions([X_train,X_test,y_train]) -> [y_pred] node.py:331
INFO Saving data to 'y_pred' (ChunkWiseCSVDataset)... data_catalog.py:514
INFO Saving data to 'y_pred' (ChunkWiseCSVDataset)... data_catalog.py:514
INFO Saving data to 'y_pred' (ChunkWiseCSVDataset)... data_catalog.py:514
INFO Completed 2 out of 3 tasks sequential_runner.py:85
INFO Loading data from 'y_pred' (ChunkWiseCSVDataset)... data_catalog.py:475
... runner.py:105