Formatting

Author: htahir1

credit-scorer/src/steps/deployment/post_run_annex.py

@@ -175,7 +175,7 @@ def generate_enhanced_annex_iv_html(
         "name", "Credit Scoring Pipeline"
     )
     pipeline_version = metadata.get("pipeline", {}).get("version", "Unknown")
-    pipeline_run = metadata.get("pipeline_run", {})
+    _ = metadata.get("pipeline_run", {})
     stack_info = metadata.get("stack", {})
     git_info = metadata.get("git_info", {})
 

floracast/__init__.py

@@ -1,3 +1,3 @@
 """FloraCast - ZenML Forecasting Template for DFG."""
 
-__version__ = "0.1.0"
+__version__ = "0.1.0"

floracast/materializers/__init__.py

@@ -2,4 +2,4 @@
 
 from .tft_materializer import TFTModelMaterializer
 
-__all__ = ["TFTModelMaterializer"]
+__all__ = ["TFTModelMaterializer"]

floracast/materializers/tft_materializer.py

@@ -2,21 +2,22 @@
 Custom materializer for Darts TFT model objects using io_utils approach.
 """
 
+import json
 import os
-import tempfile
 import pickle
-import json
-import pandas as pd
+import tempfile
+from typing import Any, Dict, Type
+
 import numpy as np
+import pandas as pd
 import torch
-from typing import Type, Any, Dict
-from darts.models import TFTModel
 from darts import TimeSeries
-from zenml.materializers.base_materializer import BaseMaterializer
+from darts.models import TFTModel
 from zenml.enums import ArtifactType
-from zenml.metadata.metadata_types import MetadataType
 from zenml.io import fileio
 from zenml.logger import get_logger
+from zenml.materializers.base_materializer import BaseMaterializer
+from zenml.metadata.metadata_types import MetadataType
 
 logger = get_logger(__name__)
 

floracast/materializers/timeseries_materializer.py

@@ -7,26 +7,24 @@
 - static_covariates.csv (optional): static covariates if present
 """
 
-import os
 import json
+import os
 import tempfile
 from typing import Any, Dict, Type
 
-import pandas as pd
-import numpy as np
 import matplotlib
+import numpy as np
+import pandas as pd
 
 # Use a non-interactive backend for headless environments
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
-
 from darts import TimeSeries
-from zenml.materializers.base_materializer import BaseMaterializer
 from zenml.enums import ArtifactType, VisualizationType
-from zenml.metadata.metadata_types import MetadataType
 from zenml.io import fileio
 from zenml.logger import get_logger
-
+from zenml.materializers.base_materializer import BaseMaterializer
+from zenml.metadata.metadata_types import MetadataType
 
 logger = get_logger(__name__)
 
@@ -67,7 +65,7 @@ def load(self, data_type: Type[Any]) -> Any:
         time_col = metadata.get("time_col", "time")
         value_cols = metadata.get("value_cols")
         freq = metadata.get("freq")
-        time_index_type = metadata.get("time_index_type")
+        _ = metadata.get("time_index_type")
         time_tz = metadata.get("time_tz")
         dtypes_map = metadata.get("dtypes") or {}
 

floracast/pipelines/__init__.py

@@ -1,9 +1,6 @@
 """ZenML pipelines for FloraCast."""
 
-from .train_forecast_pipeline import train_forecast_pipeline
 from .batch_inference_pipeline import batch_inference_pipeline
+from .train_forecast_pipeline import train_forecast_pipeline
 
-__all__ = [
-    "train_forecast_pipeline",
-    "batch_inference_pipeline"
-]
+__all__ = ["train_forecast_pipeline", "batch_inference_pipeline"]

floracast/pipelines/batch_inference_pipeline.py

@@ -2,12 +2,11 @@
 Batch inference pipeline for FloraCast forecasting models.
 """
 
+from steps.batch_infer import batch_inference_predict
+from steps.ingest import ingest_data
 from zenml import pipeline
 from zenml.logger import get_logger
 
-from steps.ingest import ingest_data
-from steps.batch_infer import batch_inference_predict
-
 logger = get_logger(__name__)
 
 

floracast/pipelines/train_forecast_pipeline.py

@@ -2,14 +2,13 @@
 Training pipeline for FloraCast forecasting models.
 """
 
-from zenml import pipeline
-from zenml.logger import get_logger
-
+from steps.evaluate import evaluate
 from steps.ingest import ingest_data
 from steps.preprocess import preprocess_data
-from steps.train import train_model
-from steps.evaluate import evaluate
 from steps.promote import promote_model
+from steps.train import train_model
+from zenml import pipeline
+from zenml.logger import get_logger
 
 logger = get_logger(__name__)
 

floracast/run.py

@@ -2,8 +2,9 @@
 Entry point for running FloraCast pipelines using ZenML e2e pattern.
 """
 
-import click
 from pathlib import Path
+
+import click
 from pipelines import batch_inference_pipeline, train_forecast_pipeline
 from zenml.logger import get_logger
 

floracast/steps/__init__.py

@@ -1,17 +1,17 @@
 """ZenML pipeline steps for FloraCast."""
 
+from .batch_infer import batch_inference_predict
+from .evaluate import evaluate
 from .ingest import ingest_data
 from .preprocess import preprocess_data
-from .train import train_model
-from .evaluate import evaluate
 from .promote import promote_model
-from .batch_infer import batch_inference_predict
+from .train import train_model
 
 __all__ = [
     "ingest_data",
     "preprocess_data",
     "train_model",
     "evaluate",
     "promote_model",
-    "batch_inference_predict"
-]
+    "batch_inference_predict",
+]