53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
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import sys
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import os
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import pandas as pd
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import numpy as np
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import pytest
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# Ensure src is in path
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sys.path.append(os.getcwd())
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from src.data import generate_data, preprocess_data
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def test_generate_data_structure():
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"""Test if generate_data returns a DataFrame with correct shape and columns."""
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df = generate_data(n_samples=50)
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assert isinstance(df, pd.DataFrame)
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assert len(df) == 50
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expected_cols = [
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"study_hours", "sleep_hours", "attendance_rate",
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"study_type", "stress_level", "is_pass"
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]
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for col in expected_cols:
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assert col in df.columns
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def test_generate_data_content_range():
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"""Test if generated data falls within expected value ranges."""
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df = generate_data(n_samples=50)
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assert df["study_hours"].min() >= 0
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assert df["study_hours"].max() <= 20 # Based on generation logic (0-15 actually, but safely below 20)
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assert df["sleep_hours"].min() >= 0
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assert df["stress_level"].between(1, 5).all()
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assert df["is_pass"].isin([0, 1]).all()
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def test_generate_data_missing_values():
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"""Test if generate_data creates missing values as expected (it has random logic)."""
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# Generate enough samples to likely get nans
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df = generate_data(n_samples=500, random_seed=42)
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# Check if we have nans in specific columns that are supposed to have them
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# In source: attendance_rate has 5% chance of nan
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assert df["attendance_rate"].isnull().sum() >= 0
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def test_preprocess_data():
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"""Test basic preprocessing (deduplication)."""
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df = pd.DataFrame({
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"a": [1, 2, 2, 3],
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"b": [1, 2, 2, 3]
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})
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clean_df = preprocess_data(df)
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assert len(clean_df) == 3
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