Create test_app.py

tests/test_app.py

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+import pytest
+import pandas as pd
+import numpy as np
+from prophet import Prophet
+from datetime import datetime
+
+# ======================================
+# Test 1: Data Generation
+# ======================================
+def test_sample_data_shape():
+    """Test that sample data has correct structure"""
+    dates = pd.date_range(start="2021-01-01", end="2023-12-31", freq="D")
+    df = pd.DataFrame({
+        "ds": dates,
+        "y": np.random.randint(50, 200, len(dates)).astype(float)
+    })
+    assert "ds" in df.columns, "Missing ds column"
+    assert "y" in df.columns, "Missing y column"
+    assert len(df) > 0, "Empty dataframe"
+    print("✅ Test 1 passed: Data shape correct")
+
+# ======================================
+# Test 2: Data Types
+# ======================================
+def test_data_types():
+    """Test correct data types"""
+    dates = pd.date_range(start="2022-01-01", periods=100, freq="D")
+    df = pd.DataFrame({"ds": dates, "y": np.random.rand(100) * 100})
+    assert pd.api.types.is_datetime64_any_dtype(df['ds']), "ds must be datetime"
+    assert pd.api.types.is_numeric_dtype(df['y']), "y must be numeric"
+    print("✅ Test 2 passed: Data types correct")
+
+# ======================================
+# Test 3: No Null Values
+# ======================================
+def test_no_nulls():
+    """Test no null values in data"""
+    dates = pd.date_range(start="2022-01-01", periods=100, freq="D")
+    df = pd.DataFrame({"ds": dates, "y": np.random.rand(100) * 100})
+    assert df['ds'].isnull().sum() == 0, "Nulls found in ds"
+    assert df['y'].isnull().sum() == 0, "Nulls found in y"
+    print("✅ Test 3 passed: No null values")
+
+# ======================================
+# Test 4: Prophet Model Training
+# ======================================
+def test_prophet_trains():
+    """Test Prophet model trains without error"""
+    dates = pd.date_range(start="2021-01-01", periods=365, freq="D")
+    df = pd.DataFrame({
+        "ds": dates,
+        "y": np.random.rand(365) * 100 + 50
+    })
+    model = Prophet(yearly_seasonality=True)
+    model.fit(df)
+    assert model is not None, "Model failed to train"
+    print("✅ Test 4 passed: Prophet model trained")
+
+# ======================================
+# Test 5: Prophet Forecast Output
+# ======================================
+def test_prophet_forecast():
+    """Test Prophet generates correct forecast"""
+    dates = pd.date_range(start="2021-01-01", periods=365, freq="D")
+    df = pd.DataFrame({
+        "ds": dates,
+        "y": np.random.rand(365) * 100 + 50
+    })
+    model = Prophet()
+    model.fit(df)
+    future = model.make_future_dataframe(periods=30)
+    forecast = model.predict(future)
+    
+    assert "yhat" in forecast.columns, "Missing yhat in forecast"
+    assert "yhat_lower" in forecast.columns, "Missing yhat_lower"
+    assert "yhat_upper" in forecast.columns, "Missing yhat_upper"
+    assert len(forecast) == len(df) + 30, "Forecast length incorrect"
+    print("✅ Test 5 passed: Forecast output correct")
+
+# ======================================
+# Test 6: Positive Demand Values
+# ======================================
+def test_positive_demand():
+    """Test that demand values are positive"""
+    dates = pd.date_range(start="2021-01-01", periods=365, freq="D")
+    y = np.random.rand(365) * 100 + 50
+    assert all(y > 0), "Negative demand values found"
+    print("✅ Test 6 passed: All demand values positive")
+
+if __name__ == "__main__":
+    test_sample_data_shape()
+    test_data_types()
+    test_no_nulls()
+    test_prophet_trains()
+    test_prophet_forecast()
+    test_positive_demand()
+    print("\n🎉 All tests passed!")