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Set a consistent file name in testing docs (#4513)
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* chore(docs): set a consistent file name

Signed-off-by: Lucas Vittor <[email protected]>

* Improve prose

---------

Signed-off-by: Lucas Vittor <[email protected]>
Co-authored-by: Juan Luis Cano Rodríguez <[email protected]>
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@lvvittor @astrojuanlu
lvvittor and astrojuanlu authored Feb 25, 2025
1 parent 4134f81 commit e975711
Showing 1 changed file with 89 additions and 89 deletions.
178 changes: 89 additions & 89 deletions docs/source/tutorial/test_a_project.md
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Original file line number Diff line number Diff line change
Expand Up @@ -66,33 +66,33 @@ When we put these steps together, we have the following test:
import pandas as pd
from spaceflights.pipelines.data_science.nodes import split_data

def test_split_data():
# Arrange
dummy_data = pd.DataFrame(
{
"engines": [1, 2, 3],
"crew": [4, 5, 6],
"passenger_capacity": [5, 6, 7],
"price": [120, 290, 30],
}
)

dummy_parameters = {
"model_options": {
"test_size": 0.2,
"random_state": 3,
"features": ["engines", "passenger_capacity", "crew"],
}
def test_split_data():
# Arrange
dummy_data = pd.DataFrame(
{
"engines": [1, 2, 3],
"crew": [4, 5, 6],
"passenger_capacity": [5, 6, 7],
"price": [120, 290, 30],
}
)

# Act
X_train, X_test, y_train, y_test = split_data(dummy_data, dummy_parameters["model_options"])
dummy_parameters = {
"model_options": {
"test_size": 0.2,
"random_state": 3,
"features": ["engines", "passenger_capacity", "crew"],
}
}

# Assert
assert len(X_train) == 2
assert len(y_train) == 2
assert len(X_test) == 1
assert len(y_test) == 1
# Act
X_train, X_test, y_train, y_test = split_data(dummy_data, dummy_parameters["model_options"])

# Assert
assert len(X_train) == 2
assert len(y_train) == 2
assert len(X_test) == 1
assert len(y_test) == 1
```

</details>
Expand All @@ -112,31 +112,31 @@ Using the same steps as above, we can write the following test to validate an er
import pandas as pd
from spaceflights.pipelines.data_science.nodes import split_data

def test_split_data_missing_price():
# Arrange
dummy_data = pd.DataFrame(
{
"engines": [1, 2, 3],
"crew": [4, 5, 6],
"passenger_capacity": [5, 6, 7],
# Note the missing price data
}
)

dummy_parameters = {
"model_options": {
"test_size": 0.2,
"random_state": 3,
"features": ["engines", "passenger_capacity", "crew"],
}
def test_split_data_missing_price():
# Arrange
dummy_data = pd.DataFrame(
{
"engines": [1, 2, 3],
"crew": [4, 5, 6],
"passenger_capacity": [5, 6, 7],
# Note the missing price data
}
)

with pytest.raises(KeyError) as e_info:
# Act
X_train, X_test, y_train, y_test = split_data(dummy_data, dummy_parameters["model_options"])
dummy_parameters = {
"model_options": {
"test_size": 0.2,
"random_state": 3,
"features": ["engines", "passenger_capacity", "crew"],
}
}

# Assert
assert "price" in str(e_info.value) # checks that the error is about the missing price data
with pytest.raises(KeyError) as e_info:
# Act
X_train, X_test, y_train, y_test = split_data(dummy_data, dummy_parameters["model_options"])

# Assert
assert "price" in str(e_info.value) # checks that the error is about the missing price data
```
</details>

Expand Down Expand Up @@ -205,46 +205,46 @@ from kedro.io import DataCatalog
from kedro.runner import SequentialRunner
from spaceflights.pipelines.data_science import create_pipeline as create_ds_pipeline

def test_data_science_pipeline(caplog): # Note: caplog is passed as an argument
# Arrange pipeline
pipeline = create_ds_pipeline()

# Arrange data catalog
catalog = DataCatalog()

dummy_data = pd.DataFrame(
{
"engines": [1, 2, 3],
"crew": [4, 5, 6],
"passenger_capacity": [5, 6, 7],
"price": [120, 290, 30],
}
)

duummy_parameters = {
"model_options": {
"test_size": 0.2,
"random_state": 3,
"features": ["engines", "passenger_capacity", "crew"],
}
def test_data_science_pipeline(caplog): # Note: caplog is passed as an argument
# Arrange pipeline
pipeline = create_ds_pipeline()

# Arrange data catalog
catalog = DataCatalog()

dummy_data = pd.DataFrame(
{
"engines": [1, 2, 3],
"crew": [4, 5, 6],
"passenger_capacity": [5, 6, 7],
"price": [120, 290, 30],
}
)

duummy_parameters = {
"model_options": {
"test_size": 0.2,
"random_state": 3,
"features": ["engines", "passenger_capacity", "crew"],
}
}

catalog.add_feed_dict(
{
"model_input_table" : dummy_data,
"params:model_options": dummy_parameters["model_options"],
}
)
catalog.add_feed_dict(
{
"model_input_table" : dummy_data,
"params:model_options": dummy_parameters["model_options"],
}
)

# Arrange the log testing setup
caplog.set_level(logging.DEBUG, logger="kedro") # Ensure all logs produced by Kedro are captured
successful_run_msg = "Pipeline execution completed successfully."
# Arrange the log testing setup
caplog.set_level(logging.DEBUG, logger="kedro") # Ensure all logs produced by Kedro are captured
successful_run_msg = "Pipeline execution completed successfully."

# Act
SequentialRunner().run(pipeline, catalog)
# Act
SequentialRunner().run(pipeline, catalog)

# Assert
assert successful_run_msg in caplog.text
# Assert
assert successful_run_msg in caplog.text

```

Expand Down Expand Up @@ -321,16 +321,16 @@ def test_split_data(dummy_data, dummy_parameters):
In the test `test_data_science_pipeline` we test the data science pipeline, as currently defined, can be run successfully. However, as pipelines are not static, this test is not robust. Instead we should be specific with how we define the pipeline to be tested; we do this by using [pipeline slicing](../nodes_and_pipelines/slice_a_pipeline.md#slice-a-pipeline-by-running-specified-nodes) to specify the pipeline's start and end:

```python
def test_data_science_pipeline(self):
# Arrange pipeline
pipeline = create_pipeline().from_nodes("split_data_node").to_nodes("evaluate_model_node")
...
def test_data_science_pipeline(self):
# Arrange pipeline
pipeline = create_pipeline().from_nodes("split_data_node").to_nodes("evaluate_model_node")
...
```

This ensures that the test will still perform as designed, even with the addition of more nodes to the pipeline.


After incorporating these testing practices, our test file `test_data_science.py` becomes:
After incorporating these testing practices, our test file `test_data_science_pipeline.py` becomes:

<details>

Expand Down Expand Up @@ -428,7 +428,7 @@ To run your tests, run `pytest` from within your project's root directory.

```bash
cd <project_root>
pytest tests/pipelines/test_data_science.py
pytest tests/pipelines/test_data_science_pipeline.py
```

You should see the following output in your shell.
Expand All @@ -438,9 +438,9 @@ You should see the following output in your shell.
...
collected 2 items
tests/pipelines/test_data_science.py .. [100%]
tests/pipelines/test_data_science_pipeline.py .. [100%]
============================== 2 passed in 4.38s ===============================
```

This output indicates that all tests ran successfully in the file `tests/pipelines/test_data_science.py`.
This output indicates that all tests ran without errors in the file `tests/pipelines/test_data_science_pipeline.py`.

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