From e9282d26bcbb16a8b841432bfb40a62f706e03b4 Mon Sep 17 00:00:00 2001
From: Wenjie Du <wenjay.du@gmail.com>
Date: Tue, 15 Oct 2024 22:03:13 +0800
Subject: [PATCH] test: add SegRNN testing cases;

---
 tests/imputation/segrnn.py | 117 +++++++++++++++++++++++++++++++++++++
 1 file changed, 117 insertions(+)
 create mode 100644 tests/imputation/segrnn.py

diff --git a/tests/imputation/segrnn.py b/tests/imputation/segrnn.py
new file mode 100644
index 00000000..17b877ad
--- /dev/null
+++ b/tests/imputation/segrnn.py
@@ -0,0 +1,117 @@
+"""
+Test cases for SegRNN imputation model.
+"""
+
+# Created by Wenjie Du <wenjay.du@gmail.com>
+# License: BSD-3-Clause
+
+
+import os.path
+import unittest
+
+import numpy as np
+import pytest
+
+from pypots.imputation import SegRNN
+from pypots.optim import Adam
+from pypots.utils.logging import logger
+from pypots.utils.metrics import calc_mse
+from tests.global_test_config import (
+    DATA,
+    EPOCHS,
+    DEVICE,
+    TRAIN_SET,
+    VAL_SET,
+    TEST_SET,
+    GENERAL_H5_TRAIN_SET_PATH,
+    GENERAL_H5_VAL_SET_PATH,
+    GENERAL_H5_TEST_SET_PATH,
+    RESULT_SAVING_DIR_FOR_IMPUTATION,
+    check_tb_and_model_checkpoints_existence,
+)
+
+
+class TestSegRNN(unittest.TestCase):
+    logger.info("Running tests for an imputation model SegRNN...")
+
+    # set the log and model saving path
+    saving_path = os.path.join(RESULT_SAVING_DIR_FOR_IMPUTATION, "SegRNN")
+    model_save_name = "saved_segrnn_model.pypots"
+
+    # initialize an Adam optimizer
+    optimizer = Adam(lr=0.001, weight_decay=1e-5)
+
+    # initialize a SegRNN model
+    segrnn = SegRNN(
+        DATA["n_steps"],
+        DATA["n_features"],
+        seg_len=4,
+        dropout=0,
+        epochs=EPOCHS,
+        saving_path=saving_path,
+        optimizer=optimizer,
+        device=DEVICE,
+    )
+
+    @pytest.mark.xdist_group(name="imputation-segrnn")
+    def test_0_fit(self):
+        self.segrnn.fit(TRAIN_SET, VAL_SET)
+
+    @pytest.mark.xdist_group(name="imputation-segrnn")
+    def test_1_impute(self):
+        imputation_results = self.segrnn.predict(TEST_SET)
+        assert not np.isnan(
+            imputation_results["imputation"]
+        ).any(), "Output still has missing values after running impute()."
+
+        test_MSE = calc_mse(
+            imputation_results["imputation"],
+            DATA["test_X_ori"],
+            DATA["test_X_indicating_mask"],
+        )
+        logger.info(f"SegRNN test_MSE: {test_MSE}")
+
+    @pytest.mark.xdist_group(name="imputation-segrnn")
+    def test_2_parameters(self):
+        assert hasattr(self.segrnn, "model") and self.segrnn.model is not None
+
+        assert hasattr(self.segrnn, "optimizer") and self.segrnn.optimizer is not None
+
+        assert hasattr(self.segrnn, "best_loss")
+        self.assertNotEqual(self.segrnn.best_loss, float("inf"))
+
+        assert hasattr(self.segrnn, "best_model_dict") and self.segrnn.best_model_dict is not None
+
+    @pytest.mark.xdist_group(name="imputation-segrnn")
+    def test_3_saving_path(self):
+        # whether the root saving dir exists, which should be created by save_log_into_tb_file
+        assert os.path.exists(self.saving_path), f"file {self.saving_path} does not exist"
+
+        # check if the tensorboard file and model checkpoints exist
+        check_tb_and_model_checkpoints_existence(self.segrnn)
+
+        # save the trained model into file, and check if the path exists
+        saved_model_path = os.path.join(self.saving_path, self.model_save_name)
+        self.segrnn.save(saved_model_path)
+
+        # test loading the saved model, not necessary, but need to test
+        self.segrnn.load(saved_model_path)
+
+    @pytest.mark.xdist_group(name="imputation-segrnn")
+    def test_4_lazy_loading(self):
+        self.segrnn.fit(GENERAL_H5_TRAIN_SET_PATH, GENERAL_H5_VAL_SET_PATH)
+        imputation_results = self.segrnn.predict(GENERAL_H5_TEST_SET_PATH)
+        assert not np.isnan(
+            imputation_results["imputation"]
+        ).any(), "Output still has missing values after running impute()."
+
+        test_MSE = calc_mse(
+            imputation_results["imputation"],
+            DATA["test_X_ori"],
+            DATA["test_X_indicating_mask"],
+        )
+        logger.info(f"Lazy-loading SegRNN test_MSE: {test_MSE}")
+
+
+if __name__ == "__main__":
+    unittest.main()