fixed week1

Author: javedhassans

notebooks/week1/00.dataexploration.ipynb

@@ -390,11 +390,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Merge the aggregated actigraphy features with the train data\n",
-    "combined_df = pd.merge(train_df, aggregated_actigraphy_df, on=\"id\", how=\"left\")\n",
+    "# # Merge the aggregated actigraphy features with the train data\n",
+    "# combined_df = pd.merge(train_df, aggregated_actigraphy_df, on=\"id\", how=\"left\")\n",
     "\n",
-    "# Inspect the combined DataFrame\n",
-    "print(combined_df.head())"
+    "# # Inspect the combined DataFrame\n",
+    "# print(combined_df.head())"
    ]
   },
   {

notebooks/week1/01.dataExploraton.py

@@ -14,6 +14,7 @@
 from concurrent.futures import ThreadPoolExecutor
 
 import pandas as pd
+from pyspark.dbutils import DBUtils
 from pyspark.sql import SparkSession
 
 from childHealth.config import ProjectConfig
@@ -22,60 +23,51 @@
 
 # Initialize Spark session
 spark = SparkSession.builder.getOrCreate()
+dbutils = DBUtils(spark)
 
 # Define original paths
 dirname_train_ts = "/Volumes/mlops_students/javedhassi/data/series_train.parquet"
 dirname_test_ts = "/Volumes/mlops_students/javedhassi/data/series_test.parquet"
 
-# COMMAND ----------
-
 # Load project configuration from YAML file
 config = ProjectConfig.from_yaml(config_path="../../project_config.yml")
 num_features = config.num_features
 cat_features = config.cat_features
 
 
-# COMMAND ----------
 def process_file(filename, dirname):
     filepath = os.path.join(dirname, filename, "part-0.parquet")
-    df = spark.read.parquet(filepath)
-    df = df.drop("step")
-    # Ensure 'id' column is included
+    df = spark.read.parquet(filepath).drop("step")
     if "id" not in df.columns:
-        df = df.withColumn("id", df["relative_date_PCIAT"])  # Use an existing column or create a new one
+        df = df.withColumn("id", df["relative_date_PCIAT"])
     return df.toPandas(), filename.split("=")[1]
 
 
 def load_time_series(dirname) -> pd.DataFrame:
-    # List all subdirectories in the specified path
     directories = [file.path for file in dbutils.fs.ls(dirname) if file.path.endswith("/")]
-
     results = []
     with ThreadPoolExecutor() as executor:
         futures = {executor.submit(process_file, path.split("/")[-2], dirname): path for path in directories}
         for i, future in enumerate(futures):
             result = future.result()
             results.append(result)
             print(f"Processed {i + 1}/{len(directories)} files")
-
-    # Separate stats and identifiers
     stats, indexes = zip(*results, strict=False) if results else ([], [])
-
-    # Create DataFrame with statistics and identifiers
     combined_df = pd.concat([df for df in stats], ignore_index=True)
     combined_df["id"] = indexes
-
     return combined_df
 
 
-# COMMAND ----------
+def update(df):
+    for c in cat_features:
+        df[c] = df[c].fillna("Missing").astype("category")
+    return df
+
 
 # Load time series data
 train_ts = load_time_series(dirname_train_ts)
 test_ts = load_time_series(dirname_test_ts)
 
-# COMMAND ----------
-
 # Load train and test CSV files with Spark
 train = spark.read.csv("/Volumes/mlops_students/javedhassi/data/childHealth.csv", header=True, inferSchema=True)
 test = spark.read.csv("/Volumes/mlops_students/javedhassi/data/test.csv", header=True, inferSchema=True)
@@ -85,69 +77,36 @@ def load_time_series(dirname) -> pd.DataFrame:
 test_pd = test.toPandas()
 
 # Ensure 'id' column exists in both DataFrames
-if "id" not in train_pd.columns:
-    train_pd["id"] = train_pd.index
-if "id" not in test_pd.columns:
-    test_pd["id"] = test_pd.index
-
-# COMMAND ----------
+train_pd["id"] = train_pd.get("id", train_pd.index)
+test_pd["id"] = test_pd.get("id", test_pd.index)
 
 # Merge the data
 train_merged = pd.merge(train_pd, train_ts, how="left", on="id")
 test_merged = pd.merge(test_pd, test_ts, how="left", on="id")
 
-# Check the result
-print(train_merged.head())
-print(test_merged.head())
-
-# COMMAND ----------
-
 # Update the list of numerical features to include time series columns
 time_series_cols = train_ts.columns.tolist()
-time_series_cols.remove("id")  # Temporarily remove 'id' column from the list of time series columns
+time_series_cols.remove("id")
 num_features += time_series_cols
 
-# COMMAND ----------
-
-
-def update(df):
-    for c in cat_features:
-        df[c] = df[c].fillna("Missing")
-        df[c] = df[c].astype("category")
-    return df
-
-
-# COMMAND ----------
-
 # Update the train and test DataFrames
 train_merged = update(train_merged)
 test_merged = update(test_merged)
 
-# COMMAND ----------
-
-# # Include 'id' column back in the numerical features if needed
-# num_features.append('id')
-
 # Check the updated DataFrames
 print(train_merged.head())
 print(test_merged.head())
 
-
-# COMMAND ----------
-# Read the Parquet file
+# Read and show the Parquet file
 df = spark.read.parquet(
     "/Volumes/mlops_students/javedhassi/data/series_train.parquet/id=00115b9f/part-0.parquet",
     header=True,
     inferSchema=True,
 )
-
-# Show the DataFrame
 df.show()
 
-# COMMAND ----------
-
+# Convert to Pandas DataFrame
 df_pandas = df.toPandas()
-# COMMAND ----------
-train = spark.read.csv("/Volumes/mlops_students/javedhassi/data/childHealth.csv", header=True, inferSchema=True)
+
+# Filter and show specific train data
 train.filter(train.id == "00115b9f").show()
-# COMMAND ----------

src/childHealth/config.py

@@ -7,8 +7,8 @@
 class ProjectConfig(BaseModel):
     catalog_name: str
     schema_name: str
-    random_forest_parameters: Dict[str, Any]  # Dictionary to hold Random Forest parameters
-    lgb_parameters: Dict[str, Any]  # Dictionary to hold LightGBM parameters
+    random_forest_parameters: Dict[str, Any]
+    lgb_parameters: Dict[str, Any]
     num_features: List[str]
     cat_features: List[str]
     target: str
@@ -20,12 +20,12 @@ def from_yaml(cls, config_path: str) -> "ProjectConfig":
             with open(config_path, "r") as f:
                 config_dict = yaml.safe_load(f)
             return cls(**config_dict)
-        except FileNotFoundError:
-            raise FileNotFoundError(f"Configuration file not found: {config_path}")
+        except FileNotFoundError as e:
+            raise FileNotFoundError(f"Configuration file not found: {config_path}") from e
         except yaml.YAMLError as e:
-            raise ValueError(f"Error parsing YAML file: {e}")
+            raise ValueError(f"Error parsing YAML file: {e}") from e
         except ValidationError as e:
-            raise ValueError(f"Validation error: {e}")
+            raise ValueError(f"Validation error: {e}") from e
 
 
 # Example usage: