def plot_matrix(dataset_ids): matrix = [] for dataset_id in dataset_ids: dataset_values = [] for technique_id in technique_list: history = Parser.parse_rectangles(technique_id, dataset_id) avg = compute_location_drift(history) dataset_values.append(avg) print(Globals.acronyms[technique_id], dataset_id, avg) matrix.append(dataset_values) matrix = np.array(matrix).transpose() MatrixPlot.plot(matrix, dataset_ids, technique_list, cell_text=True, title='Location Drift')
def plot_matrix(dataset_ids): matrix = [] for dataset_id in dataset_ids: dataset_values = [] for technique_id in technique_list: history = Parser.parse_rectangles(technique_id, dataset_id) all_ratios = np.array([]) for revision in range(len(history) - 1): distances = compute_shneiderman(history[revision], history[revision + 1]) all_ratios = np.append(all_ratios, distances.values) dataset_values.append(all_ratios.mean()) print(Globals.acronyms[technique_id], dataset_id, all_ratios.mean()) matrix.append(dataset_values) matrix = np.array(matrix).transpose() MatrixPlot.plot(matrix, dataset_ids, technique_list, cell_text=True, title='Shneiderman-Wattenberg')
def pearson_matrix(dataset_ids): matrix = [] for dataset_id in dataset_ids: dataset_values = [] for technique_id in technique_list: # print(Globals.acronyms[technique_id], dataset_id) history = Parser.parse_rectangles(technique_id, dataset_id) # Compute all delta_vis and delta_data values for a dataset (1 pair per cell) all_delta_data = np.array([]) all_delta_vis = np.array([]) for revision in range(len(history) - 1): delta_data = DeltaMetrics.compute_delta_data(history[revision], history[revision + 1]) all_delta_data = np.append(all_delta_data, delta_data) delta_vis = DeltaMetrics.compute_delta_vis(history[revision], history[revision + 1]) all_delta_vis = np.append(all_delta_vis, delta_vis) # Compute linear regression statistics slope, intercept, r_value, p_value, std_err = stats.linregress(all_delta_data, all_delta_vis) dataset_values.append(r_value) print(Globals.acronyms[technique_id], dataset_id, r_value) matrix.append(dataset_values) matrix = np.array(matrix).transpose() MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=False, cell_text=True, title='Pearson') MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=True, cell_text=True, title='Pearson') MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=False, cell_text=False, title='Pearson') MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=True, cell_text=False, title='Pearson')
def plot_ar_matrix(dataset_ids): matrix = [] for dataset_id in dataset_ids: dataset_values = [] for technique_id in technique_list: history = Parser.parse_rectangles(technique_id, dataset_id) all_ratios = np.array([]) for revision in range(len(history) - 1): ratios = compute_aspect_ratios(history[revision]) all_ratios = np.append(all_ratios, ratios.values) dataset_values.append(all_ratios.mean()) print(Globals.acronyms[technique_id], dataset_id, all_ratios.mean()) matrix.append(dataset_values) matrix = np.array(matrix).transpose() MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=False, cell_text=True, title='Aspect ratio') MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=True, cell_text=True, title='Aspect ratio') MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=False, cell_text=False, title='Aspect ratio') MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=True, cell_text=False, title='Aspect ratio')
def delta_ratio_matrix(dataset_ids): matrix = [] for dataset_id in dataset_ids: dataset_values = [] for technique_id in technique_list: history = Parser.parse_rectangles(technique_id, dataset_id) all_ratios = np.array([]) for revision in range(len(history) - 1): delta_vis = compute_delta_vis(history[revision], history[revision + 1]) delta_data = compute_delta_data(history[revision], history[revision + 1]) ratio = (1 - delta_vis) / (1 - delta_data) all_ratios = np.append(all_ratios, ratio.values) dataset_values.append(all_ratios.mean()) print(Globals.acronyms[technique_id], dataset_id, all_ratios.mean()) matrix.append(dataset_values) matrix = np.array(matrix).transpose() MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=False, cell_text=True, title='Delta ratio') MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=True, cell_text=True, title='Delta ratio') MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=False, cell_text=False, title='Delta ratio') MatrixPlot.plot(matrix, dataset_ids, technique_list, shared_cm=True, cell_text=False, title='Delta ratio')