Specified numpy version, included test script

pyproject.toml

@@ -13,7 +13,7 @@ packages = [
 [tool.poetry.dependencies]
 python = ">=3.9,<3.12"
 tensorflow = "2.11.0"
-numpy = "*"
+numpy = "1.26.4"
 utils = "*"
 scikit-learn = "^1.4.2"
 matplotlib = "^3.1.1"

timegan/test.py

@@ -0,0 +1,67 @@
+# mypy: ignore-errors
+# Necessary packages
+
+import warnings
+
+import numpy as np
+
+# 1. TimeGAN model
+from timegan import timegan
+
+# 2. Data loading
+from timegan.data_loading import real_data_loading, sine_data_generation
+
+# 3. Metrics
+from timegan.metrics.discriminative_metrics import discriminative_score_metrics
+from timegan.metrics.predictive_metrics import predictive_score_metrics
+from timegan.metrics.visualization_metrics import visualization
+
+warnings.filterwarnings("ignore")
+
+## Data loading
+data_name = "stock"
+seq_len = 24
+
+if data_name in ["stock", "energy"]:
+    ori_data = real_data_loading(data_name, seq_len)
+elif data_name == "sine":
+    # Set number of samples and its dimensions
+    no, dim = 10000, 5
+    ori_data = sine_data_generation(no, seq_len, dim)
+
+print(data_name + " dataset is ready.")
+## Newtork parameters
+parameters = dict()
+
+parameters["module"] = "gru"
+parameters["hidden_dim"] = 24
+parameters["num_layer"] = 3
+parameters["iterations"] = 10000
+parameters["batch_size"] = 128
+
+
+# Run TimeGAN
+generated_data = timegan.train_timegan(ori_data, parameters)
+# timed_training = timegan.train_timegan_timed(ori_data, parameters,filename = 'timegan_save', seconds=60, phase=1, current_iter = 0)
+
+
+print("Finish Synthetic Data Generation")
+
+metric_iteration = 5
+
+discriminative_score = list()
+for _ in range(metric_iteration):
+    temp_disc = discriminative_score_metrics(ori_data, generated_data)
+    discriminative_score.append(temp_disc)
+
+print("Discriminative score: " + str(np.round(np.mean(discriminative_score), 4)))
+
+predictive_score = list()
+for tt in range(metric_iteration):
+    temp_pred = predictive_score_metrics(ori_data, generated_data)
+    predictive_score.append(temp_pred)
+
+print("Predictive score: " + str(np.round(np.mean(predictive_score), 4)))
+
+visualization(ori_data, generated_data, "pca")
+visualization(ori_data, generated_data, "tsne")