Merge branch 'vaqxai-pylint' into suml-docker

README.md

@@ -38,7 +38,7 @@ conda activate penguins-env
 3. Run the project
 
 ```
-kedro run
+kedro run 
 ```
 
 ## PyCharm Setup

src/penguins/__main__.py

@@ -6,7 +6,7 @@
 
 from kedro.framework.cli.utils import KedroCliError, load_entry_points
 from kedro.framework.project import configure_project
-
+from kedro.framework.cli.project import run as kedro_framework_cli_project_run
 
 def _find_run_command(package_name):
     try:
@@ -21,9 +21,7 @@ def _find_run_command(package_name):
             # use run command from installed plugin if it exists
             return run
         # use run command from `kedro.framework.cli.project`
-        from kedro.framework.cli.project import run
-
-        return run
+        return kedro_framework_cli_project_run
     # fail badly if cli.py exists, but has no `cli` in it
     if not hasattr(project_cli, "cli"):
         raise KedroCliError(f"Cannot load commands from {package_name}.cli")
@@ -34,9 +32,10 @@ def _find_run_command_in_plugins(plugins):
     for group in plugins:
         if "run" in group.commands:
             return group.commands["run"]
-
+    return None
 
 def main(*args, **kwargs):
+    """Program entry point"""
     package_name = Path(__file__).parent.name
     configure_project(package_name)
     run = _find_run_command(package_name)

src/penguins/pipelines/modeling/nodes.py

@@ -1,22 +1,25 @@
-from typing import Dict, Tuple
+"""Nodes for the modeling pipeline."""
+from typing import Tuple
 
 import mlflow
 import pandas as pd
 from autogluon.tabular import TabularPredictor
 from sklearn.model_selection import train_test_split
 
 
-def split_data(data: pd.DataFrame, parameters: Dict) -> Tuple:
+def split_data(data: pd.DataFrame) -> Tuple:
+    """Split data into train and test sets."""
     train, test = train_test_split(data, test_size=0.2)
-    return test, test
+    return train, test
 
 
 def train_model(train: pd.DataFrame, test: pd.DataFrame) -> TabularPredictor:
+    """Train a model on the given data."""
     mlflow.set_experiment("penguins")
     classificator = TabularPredictor(label="species", log_to_file=False, problem_type="multiclass",
                                      eval_metric="accuracy")
     classificator.fit(train, time_limit=120)
-    y_pred = classificator.evaluate(test)
+    classificator.evaluate(test)
     for key, value in classificator.fit_summary()["model_performance"].items():
         mlflow.log_metric(f"{key}_accuracy", value)
     return classificator

src/penguins/pipelines/modeling/pipeline.py

@@ -5,14 +5,15 @@
 
 from kedro.pipeline import Pipeline, node, pipeline
 
-from .nodes import split_data, train_model
+from penguins.pipelines.modeling.nodes import split_data, train_model
 
 
-def create_pipeline(**kwargs) -> Pipeline:
+def create_pipeline() -> Pipeline:
+    """Create the kedro modeling pipeline."""
     return pipeline([
         node(
             func=split_data,
-            inputs=["model_input_table", "params:model_options"],
+            inputs=["model_input_table"],
             outputs=["train", "test"],
             name="split_data_node",
         ),

src/penguins/pipelines/preprocessing/nodes.py

@@ -1,10 +1,12 @@
+"""Nodes for the preprocessing pipeline."""
 import pandas as pd
 from sklearn.preprocessing import LabelEncoder
 
 
 def preprocess_penguins(
         penguins: pd.DataFrame
 ) -> pd.DataFrame:
+    """Preprocess the penguins data by encoding categorical columns."""
     # to encode
     island_encoder = LabelEncoder()
     penguins["island"] = island_encoder.fit_transform(penguins["island"])
@@ -24,7 +26,8 @@ def preprocess_penguins(
 
 
 def create_model_input_table(
-        preprocess_penguins: pd.DataFrame
+        preprocessed_penguins: pd.DataFrame
 ) -> pd.DataFrame:
-    model_input_table = preprocess_penguins.dropna()
+    """Create a model input table by dropping rows with missing values."""
+    model_input_table = preprocessed_penguins.dropna()
     return model_input_table

src/penguins/pipelines/preprocessing/pipeline.py

@@ -5,10 +5,11 @@
 
 from kedro.pipeline import Pipeline, node, pipeline
 
-from .nodes import preprocess_penguins, create_model_input_table
+from penguins.pipelines.preprocessing.nodes import preprocess_penguins, create_model_input_table
 
 
-def create_pipeline(**kwargs) -> Pipeline:
+def create_pipeline() -> Pipeline:
+    """Create the kedro preprocessing pipeline."""
     return pipeline([
         node(
             func=preprocess_penguins,

src/penguins/pipelines/serving/nodes.py

@@ -1,11 +1,17 @@
+"""Serving pipeline nodes"""
 import io
 import pickle
 
 import pandas as pd
 from autogluon.tabular import TabularPredictor
 
 
-def save_data(data: pd.DataFrame, classificator: TabularPredictor, encoders: pickle.OBJ) -> pd.DataFrame:
+def save_data(
+        data: pd.DataFrame,
+        classificator: TabularPredictor,
+        encoders: pickle.OBJ
+    ) -> pd.DataFrame:
+    """Saves model data to a file"""
     df = pd.read_csv(io.StringIO(data), sep=",")
 
     df["island"] = encoders["island"].transform(df["island"])

src/penguins/pipelines/serving/pipeline.py

@@ -5,10 +5,11 @@
 
 from kedro.pipeline import Pipeline, node, pipeline
 
-from .nodes import save_data
+from penguins.pipelines.serving.nodes import save_data
 
 
-def create_pipeline(**kwargs) -> Pipeline:
+def create_pipeline() -> Pipeline:
+    """Create the kedro serving pipeline."""
     return pipeline([
         node(
             func=save_data,

src/penguins/settings.py

@@ -24,7 +24,8 @@
 # Class that manages how configuration is loaded.
 from kedro.config import OmegaConfigLoader  # noqa: import-outside-toplevel
 
-CONFIG_LOADER_CLASS = OmegaConfigLoader
+# CONFIG_LOADER_CLASS = OmegaConfigLoader
+ConfigLoaderClass = OmegaConfigLoader
 # Keyword arguments to pass to the `CONFIG_LOADER_CLASS` constructor.
 # CONFIG_LOADER_ARGS = {
 #       "config_patterns": {

src/tests/test_run.py

@@ -11,23 +11,25 @@
 from pathlib import Path
 
 import pytest
-from kedro.config import ConfigLoader
+from kedro.config import OmegaConfigLoader
 from kedro.framework.context import KedroContext
 from kedro.framework.hooks import _create_hook_manager
 from kedro.framework.project import settings
 
 
 @pytest.fixture
 def config_loader():
-    return ConfigLoader(conf_source=str(Path.cwd() / settings.CONF_SOURCE))
+    """Load the config file for tests"""
+    return OmegaConfigLoader(conf_source=str(Path.cwd() / settings.CONF_SOURCE))
 
 
 @pytest.fixture
-def project_context(config_loader):
+def project_context(cfg_loader):
+    """Introduce project context so tests behave like in a real project."""
     return KedroContext(
         package_name="penguins",
         project_path=Path.cwd(),
-        config_loader=config_loader,
+        config_loader=cfg_loader,
         hook_manager=_create_hook_manager(),
     )
 
@@ -36,5 +38,10 @@ def project_context(config_loader):
 # and should be replaced with the ones testing the project
 # functionality
 class TestProjectContext:
-    def test_project_path(self, project_context):
-        assert project_context.project_path == Path.cwd()
+    """Example test for project context"""
+    def test_project_path(self, proj_context):
+        """Example test for project path"""
+        assert proj_context.project_path == Path.cwd()
+    def test_project_name(self, proj_context):
+        """Example test for project name"""
+        assert proj_context.project_name == "penguins"