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96638b2 ee9f8cb 96638b2 ee9f8cb 96638b2 ee9f8cb 96638b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | import pandas as pd
from pathlib import Path
from config.settings import DATASET_1_PATH, DATASET_2_PATH
import functools
import sys
# Ensure UTF-8 encoding for print statements
if sys.stdout.encoding != 'utf-8':
sys.stdout.reconfigure(encoding='utf-8')
class DataLoader:
"""Handles loading and validation of health datasets."""
@staticmethod
@functools.lru_cache(maxsize=1)
def load_datasets():
"""
Loads both health datasets with caching.
Returns: tuple(df1, df2)
"""
try:
print(f"Loading datasets from {DATASET_1_PATH} and {DATASET_2_PATH}...")
if not Path(DATASET_1_PATH).exists() or not Path(DATASET_2_PATH).exists():
raise FileNotFoundError("One or both dataset files are missing.")
df1 = pd.read_csv(DATASET_1_PATH)
df2 = pd.read_csv(DATASET_2_PATH)
# Basic validation
DataLoader._validate_structure(df1, df2)
# Feature Engineering (Mandatory 2a)
df1 = DataLoader._feature_engineering(df1)
print(f"[OK] Datasets loaded successfully: DF1({len(df1)}), DF2({len(df2)})")
return df1, df2
except Exception as e:
print(f"[ERROR] Error loading datasets: {e}")
raise e
@staticmethod
def _feature_engineering(df):
"""Adds derived features to the dataset."""
if 'BMI' in df.columns:
# Categorize BMI according to standard ranges
bins = [0, 18.5, 25, 30, 100]
labels = ['Underweight', 'Normal', 'Overweight', 'Obese']
df['BMI_Category'] = pd.cut(df['BMI'], bins=bins, labels=labels)
return df
@staticmethod
def _validate_structure(df1, df2):
"""Validates that necessary columns exist."""
required_key = "Patient_Number"
if required_key not in df1.columns:
raise ValueError(f"Dataset 1 missing required key: {required_key}")
if required_key not in df2.columns:
raise ValueError(f"Dataset 2 missing required key: {required_key}")
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