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| import pytest | |
| import pandas as pd | |
| import numpy as np | |
| import os | |
| import sys | |
| sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../backend'))) | |
| from preprocessing import preprocess_data, resolve_model_dir | |
| def test_resolve_model_dir(): | |
| md = resolve_model_dir() | |
| assert md.endswith("model") | |
| def test_preprocess_data_training(tmp_path): | |
| df = pd.DataFrame({ | |
| 'Heart Rate (bpm)': [80, 90, np.nan], | |
| 'SpO2 Level (%)': [98, 97, 95], | |
| 'Systolic Blood Pressure (mmHg)': [120, 125, 130], | |
| 'Diastolic Blood Pressure (mmHg)': [80, 85, 90], | |
| 'Body Temperature (°C)': [37.0, 37.2, 37.5], | |
| 'Predicted Disease': ['Normal', 'Asthma', 'Normal'] | |
| }) | |
| # Use tmp_path to isolate test artifacts from production models | |
| X, y = preprocess_data(df, is_training=True, model_dir=str(tmp_path)) | |
| assert not X.isnull().values.any() | |
| assert len(y) == 3 | |
| assert 'Risk_Severity' in X.columns | |
| assert os.path.exists(os.path.join(str(tmp_path), 'label_encoder.pkl')) | |
| def test_preprocess_data_inference(tmp_path): | |
| # First fit on dummy data inside tmp_path | |
| train_df = pd.DataFrame({ | |
| 'Heart Rate (bpm)': [80, 90], | |
| 'SpO2 Level (%)': [98, 97], | |
| 'Systolic Blood Pressure (mmHg)': [120, 125], | |
| 'Diastolic Blood Pressure (mmHg)': [80, 85], | |
| 'Body Temperature (°C)': [37.0, 37.2], | |
| 'Predicted Disease': ['Normal', 'Asthma'] | |
| }) | |
| preprocess_data(train_df, is_training=True, model_dir=str(tmp_path)) | |
| df = pd.DataFrame({ | |
| 'Heart Rate (bpm)': [100], | |
| 'SpO2 Level (%)': [99], | |
| 'Systolic Blood Pressure (mmHg)': [120], | |
| 'Diastolic Blood Pressure (mmHg)': [80], | |
| 'Body Temperature (°C)': [37.0], | |
| }) | |
| X, y = preprocess_data(df, is_training=False, model_dir=str(tmp_path)) | |
| assert y is None | |
| assert 'Risk_Severity' in X.columns | |
| def test_preprocess_data_alias_normalization(tmp_path): | |
| """Test that schema field names (e.g. Heart_Rate) are automatically normalized without error.""" | |
| # First fit on dummy data inside tmp_path | |
| train_df = pd.DataFrame({ | |
| 'Heart Rate (bpm)': [80, 90], | |
| 'SpO2 Level (%)': [98, 97], | |
| 'Systolic Blood Pressure (mmHg)': [120, 125], | |
| 'Diastolic Blood Pressure (mmHg)': [80, 85], | |
| 'Body Temperature (°C)': [37.0, 37.2], | |
| 'Predicted Disease': ['Normal', 'Asthma'] | |
| }) | |
| preprocess_data(train_df, is_training=True, model_dir=str(tmp_path)) | |
| df = pd.DataFrame({ | |
| 'Heart_Rate': [115], | |
| 'SpO2_Level': [92], | |
| 'Systolic_BP': [145], | |
| 'Diastolic_BP': [92], | |
| 'Body_Temp': [37.4], | |
| }) | |
| X, y = preprocess_data(df, is_training=False, model_dir=str(tmp_path)) | |
| assert 'Risk_Severity' in X.columns | |
| assert 'Heart Rate (bpm)' in X.columns | |
| assert not X.isnull().values.any() | |