def test_parse_log(self): json_data = log_analyzer.parse_log(log_file) # import pprint # pprint.pprint(json_data[0]) self.assertIsNotNone(json_data[0]["best_score"])
def test_plot_matrix(self): json_data = log_analyzer.parse_log(log_file) log_analyzer.plot_matrix( json_data, show_plot=True, show_block=False, save_plot=True, save_path="test.png" ) time.sleep(2) self.assertTrue(os.path.isfile("test.png")) os.remove("test.png")
def test_parse_general_stats(self): json_data = log_analyzer.parse_log(log_file) json_data = log_analyzer.sort_records_by("best_score", json_data) stats = log_analyzer.parse_general_stats(json_data) # import matplotlib.pyplot as plt # from pylab import figure # figure(facecolor='white') # plt.scatter(stats["best_scores"], stats["runtimes"]) # plt.xlabel("best_scores") # plt.ylabel("runtimes") # plt.show() # assert self.assertIsNotNone(stats["best_individuals"]) self.assertIsNotNone(stats["best_scores"]) self.assertIsNotNone(stats["runtimes"])
def test_sort_records_by(self): json_data = log_analyzer.parse_log(log_file) json_data = log_analyzer.sort_records_by("best_score", json_data) # runtimes = [] # best_scores = [] # for record in json_data: # runtimes.append(record["runtime"]) # best_scores.append(record["best_score"]) # import matplotlib.pyplot as plt # plt.scatter(best_scores, runtimes) # plt.show() # assert prev = json_data.pop(0)["best_score"] for record in json_data: self.assertTrue(record["best_score"] >= prev) prev = record["best_score"]