def run_inference(num_observations: int = 1000): """Run xgboost for specified number of observations""" # Load data train_x_df = common.get_test_data_df(X=common.X_df, size=num_observations) train_y_df = common.get_test_data_df(X=common.y_df, size=num_observations) num_rows = len(train_x_df) ###################### print("_______________________________________") print("Total Number of Rows", num_rows) run_times = [] inference_times = [] for _ in range(NUM_LOOPS): start_time = timer() MODEL = d4p.decision_forest_regression_training(nTrees=100) train_result = MODEL.compute(train_x_df, train_y_df) end_time = timer() total_time = end_time - start_time run_times.append(total_time * 10e3) inference_time = total_time * (10e6) / num_rows inference_times.append(inference_time) return_elem = common.calculate_stats(inference_times) print(num_observations, ", ", return_elem) return return_elem
def run_inference(num_observations:int = 1000): """Run xgboost for specified number of observations""" # Load data test_df = common.get_test_data_df(X=common.X_dfc,size = num_observations) num_rows = len(test_df) ###################### print("_______________________________________") print("Total Number of Rows", num_rows) run_times = [] inference_times = [] for _ in range(NUM_LOOPS): start_time = timer() cluster = DBSCAN(eps=0.3, min_samples=10) cluster.fit(test_df) #predictor.compute(data, MODEL) end_time = timer() total_time = end_time - start_time run_times.append(total_time*10e3) inference_time = total_time*(10e6)/num_rows inference_times.append(inference_time) return_elem = common.calculate_stats(inference_times) print(num_observations, ", ", return_elem) return return_elem
def run_inference(num_observations: int = 1000): """Run xgboost for specified number of observations""" # Load data train_x_df = common.get_test_data_df(X=common.X_dfc, size=num_observations) train_y = common.get_test_data_yc(size=num_observations) num_rows = len(train_x_df) ###################### print("_______________________________________") print("Total Number of Rows", num_rows) run_times = [] inference_times = [] for _ in range(NUM_LOOPS): start_time = timer() reg = LogisticRegression().fit(train_x_df, train_y) end_time = timer() total_time = end_time - start_time run_times.append(total_time * 10e3) inference_time = total_time * (10e6) / num_rows inference_times.append(inference_time) return_elem = common.calculate_stats(inference_times) print(num_observations, ", ", return_elem) return return_elem
def run_inference(num_observations: int = 1000): """Run xgboost for specified number of observations""" # Load data test_df = common.get_test_data_df(X=common.X_dfc, size=num_observations) num_rows = len(test_df) ###################### print("_______________________________________") print("Total Number of Rows", num_rows) run_times = [] inference_times = [] for _ in range(NUM_LOOPS): start_time = timer() predict_algo = d4p.logistic_regression_prediction( nClasses=2, resultsToEvaluate= "computeClassLabels|computeClassProbabilities|computeClassLogProbabilities" ) predict_result = predict_algo.compute(test_df, train_result.model) #predictor.compute(data, MODEL) end_time = timer() total_time = end_time - start_time run_times.append(total_time * 10e3) inference_time = total_time * (10e6) / num_rows inference_times.append(inference_time) return_elem = common.calculate_stats(inference_times) print(num_observations, ", ", return_elem) return return_elem
def run_inference(num_observations:int = 1000): """Run xgboost for specified number of observations""" # Load data test_df = common.get_test_data_df(X=common.X_dfc,size = num_observations) num_rows = len(test_df) ###################### print("_______________________________________") print("Total Number of Rows", num_rows) run_times = [] inference_times = [] for _ in range(NUM_LOOPS): start_time = timer() init_alg = d4p.kmeans_init(nClusters = 5, fptype = "float", method = "randomDense") centroids = init_alg.compute(test_df).centroids alg = d4p.kmeans(nClusters = 5, maxIterations = 100, fptype = "float", accuracyThreshold = 0, assignFlag = False) result = alg.compute((test_df), centroids) end_time = timer() total_time = end_time - start_time run_times.append(total_time*10e3) inference_time = total_time*(10e6)/num_rows inference_times.append(inference_time) return_elem = common.calculate_stats(inference_times) print(num_observations, ", ", return_elem) return return_elem
def run_inference(num_observations: int = 1000): """Run xgboost for specified number of observations""" # Load data test_df = common.get_test_data_df(X=common.X_dfc, size=num_observations) num_rows = len(test_df) ###################### print("_______________________________________") print("Total Number of Rows", num_rows) run_times = [] inference_times = [] for _ in range(NUM_LOOPS): start_time = timer() cluster = KMeans(n_clusters=5, **kmeans_kwargs) cluster.fit(test_df) end_time = timer() total_time = end_time - start_time run_times.append(total_time * 10e3) inference_time = total_time * (10e6) / num_rows inference_times.append(inference_time) return_elem = common.calculate_stats(inference_times) print(num_observations, ", ", return_elem) return return_elem