def save_best_results(network_name, checkpoint_names, set_of_thresholds, set_of_mrs, set_of_homogeneity_scores,
set_of_completeness_scores, speaker_numbers):
if len(set_of_mrs) == 1:
write_result_pickle(network_name + "_best", checkpoint_names, set_of_thresholds, set_of_mrs,
set_of_homogeneity_scores, set_of_completeness_scores, speaker_numbers)
else:
# Find best result (min MR)
min_mrs = []
for mrs in set_of_mrs:
min_mrs.append(np.min(mrs))
min_mr_over_all = min(min_mrs)
best_checkpoint_name = []
set_of_best_mrs = []
set_of_best_homogeneity_scores = []
set_of_best_completeness_scores = []
set_of_best_thresholds = []
best_speaker_numbers = []
for index, min_mr in enumerate(min_mrs):
if min_mr == min_mr_over_all:
best_checkpoint_name.append(checkpoint_names[index])
set_of_best_mrs.append(set_of_mrs[index])
set_of_best_homogeneity_scores.append(set_of_homogeneity_scores[index])
set_of_best_completeness_scores.append(set_of_completeness_scores[index])
set_of_best_thresholds.append(set_of_thresholds[index])
best_speaker_numbers.append(speaker_numbers[index])
write_result_pickle(network_name + "_best", best_checkpoint_name, set_of_best_thresholds, set_of_best_mrs,
set_of_best_homogeneity_scores, set_of_best_completeness_scores, best_speaker_numbers)
def plot_curves(plot_file_name, curve_names, mrs, homogeneity_scores, completeness_scores, number_of_embeddings):
"""
Plots all specified curves and saves the plot into a file.
:param plot_file_name: String value of save file name
:param curve_names: Set of names used in legend to describe this curve
:param mrs: 2D Matrix, each row describes one dataset of misclassification rates for a curve
:param homogeneity_scores: 2D Matrix, each row describes one dataset of homogeneity scores for a curve
:param completeness_scores: 2D Matrix, each row describes one dataset of completeness scores for a curve
:param number_of_embeddings: set of integers, each integer describes how many embeddings is in this curve
"""
logger = get_logger('analysis', logging.INFO)
logger.info('Plot results')
logger.info(plot_file_name)
min_mrs = []
for mr in mrs:
min_mrs.append(np.min(mr))
min_mrs, curve_names, mrs, homogeneity_scores, completeness_scores, number_of_embeddings = \
(list(t) for t in
zip(*sorted(zip(min_mrs, curve_names, mrs, homogeneity_scores, completeness_scores, number_of_embeddings))))
# How many lines to plot
number_of_lines = len(curve_names)
# Get various colors needed to plot
color_map = plt.get_cmap('gist_rainbow')
colors = [color_map(i) for i in np.linspace(0, 1, number_of_lines)]
# Define number of figures
fig1 = plt.figure(1)
fig1.set_size_inches(16, 8)
# Define Plots
mr_plot = plt.subplot2grid((2, 3), (0, 0), colspan=2)
mr_plot.set_ylabel('MR')
mr_plot.set_xlabel('number of clusters')
plt.ylim([-0.02, 1.02])
completeness_scores_plot = add_cluster_subplot(fig1, 234, 'completeness_scores')
homogeneity_scores_plot = add_cluster_subplot(fig1, 235, 'homogeneity_scores')
# Define curves and their values
curves = [[mr_plot, mrs],
[homogeneity_scores_plot, homogeneity_scores],
[completeness_scores_plot, completeness_scores]]
# Plot all curves
for index in range(number_of_lines):
label = curve_names[index] + '\n min MR: ' + str(min_mrs[index])
color = colors[index]
number_of_clusters = np.arange(number_of_embeddings[index], 0, -1)
for plot, value in curves:
plot.plot(number_of_clusters, value[index], color=color, label=label)
# Add legend and save the plot
fig1.legend()
# fig1.show()
fig1.savefig(get_result_png(plot_file_name))
fig1.savefig(get_result_png(plot_file_name + '.svg'), format='svg')
def _save_best_results(network_name, checkpoint_names, metric_sets,
speaker_numbers):
if len(metric_sets[0]) == 1:
_write_result_pickle(network_name + "_best", checkpoint_names,
metric_sets, speaker_numbers)
else:
# Find best result (according to the first metric in metrics)
if (metric_min_values[0] == 1):
best_results = []
for results in metric_sets[0]:
best_results.append(np.min(results))
best_result_over_all = min(best_results)
else:
best_results = []
for results in metric_sets[0]:
best_results.append(np.max(results))
best_result_over_all = max(best_results)
best_checkpoint_name = []
set_of_best_metrics = [[] for _ in metric_sets]
best_speaker_numbers = []
for index, best_result in enumerate(best_results):
if best_result == best_result_over_all:
best_checkpoint_name.append(checkpoint_names[index])
for m, metric_set in enumerate(metric_sets):
set_of_best_metrics[m].append(metric_set[index])
best_speaker_numbers.append(speaker_numbers[index])
_write_result_pickle(network_name + "_best", best_checkpoint_name,
set_of_best_metrics, best_speaker_numbers)
def plot_curves(plot_file_name, curve_names, mrs, homogeneity_scores, completeness_scores, number_of_embeddings):
"""
Plots all specified curves and saves the plot into a file.
"""
logger = get_logger('analysis', logging.INFO)
logger.info('Plot results')
# How many lines to plot
number_of_lines = len(curve_names)
# Get various colors needed to plot
color_map = plt.get_cmap('gist_rainbow')
colors = [color_map(i) for i in np.linspace(0, 1, number_of_lines)]
# Define number of figures
fig1 = plt.figure(1)
fig1.set_size_inches(32, 24)
# Define Plots
mr_plot = plt.subplot2grid((2, 2), (0, 0), colspan=2)
mr_plot.set_title('MR')
mr_plot.set_xlabel('number of clusters')
mr_plot.axis([0, 80, 0, 1])
completeness_scores_plot = add_cluster_subplot(fig1, 223, 'completeness_scores')
homogeneity_scores_plot = add_cluster_subplot(fig1, 224, 'homogeneity_scores')
# Define curves and their values
curves = [[mr_plot, mrs],
[homogeneity_scores_plot, homogeneity_scores],
[completeness_scores_plot, completeness_scores]]
# Plot all curves
for index in range(number_of_lines):
label = curve_names[index]
color = colors[index]
number_of_clusters = np.arange(number_of_embeddings[index], 1, -1)
for plot, value in curves:
plot.plot(number_of_clusters, value[index], color=color, label=label)
min_mr = np.min(mrs[index])
mr_plot.annotate(str(min_mr), xy=(0, min_mr))
# Add legend and save the plot
fig1.legend()
# fig1.show()
fig1.savefig(get_result_png(plot_file_name))
def plot_curves(plot_file_name, curve_names, mrs, homogeneity_scores, completeness_scores, number_of_embeddings,loaded_dict,loaded_dict2,loaded_dict3):
"""
Plots all specified curves and saves the plot into a file.
:param plot_file_name: String value of save file name
:param curve_names: Set of names used in legend to describe this curve
:param mrs: 2D Matrix, each row describes one dataset of misclassification rates for a curve
:param homogeneity_scores: 2D Matrix, each row describes one dataset of homogeneity scores for a curve
:param completeness_scores: 2D Matrix, each row describes one dataset of completeness scores for a curve
:param number_of_embeddings: set of integers, each integer describes how many embeddings is in this curve
"""
logger = get_logger('analysis', logging.INFO)
logger.info('Plot results')
logger.info(plot_file_name)
min_mrs = []
for mr in mrs:
min_mrs.append(np.min(mr))
num_clusters=[]
hierach_MR = []
kmeans_mr = []
ds_mr = []
for x in loaded_dict:
num_clusters.append(x)
hierach_MR.append(loaded_dict[x])
# print(str(loaded_dict[x]) + " appended to " + str(x))
print("\n")
print("Hierachial MR")
print(hierach_MR)
for x in loaded_dict2:
kmeans_mr.append(loaded_dict2[x])
# print(str(loaded_dict[x]) + " appended to " + str(x))
print("\n")
print("Kmeans MR")
print(kmeans_mr)
for x in loaded_dict3:
ds_mr.append(loaded_dict3[x])
# print(str(loaded_dict[x]) + " appended to " + str(x))
print("\n")
print("DS MR")
print(ds_mr)
ks = list(loaded_dict)
ks = list(map(int, ks))
print("\n")
print("Cluster Count")
print(ks)
print("Minimum Cluster : " + str(min(ks)))
print("Maximum CLuster : " + str(max(ks))+ "\n")
maxc = max(ks)
minc = min(ks)
# x = zip(*sorted(zip(min_mrs, curve_names, mrs, homogeneity_scores, completeness_scores, number_of_embeddings)))
#
#
# print("\n")
# print(tuple(x))
# print("\n")
#
# min_mrs, curve_names, mrs, homogeneity_scores, completeness_scores, number_of_embeddings = \
# (list(t) for t in x)
# How many lines to plot
number_of_lines = len(curve_names)
# Get various colors needed to plot
color_map = plt.get_cmap('gist_rainbow')
colors = [color_map(i) for i in np.linspace(0, 1, number_of_lines)]
# Define number of figures
fig1 = plt.figure(1)
fig1.set_size_inches(16, 8)
# Define Plots
mr_plot = plt.subplot2grid((2, 3), (0, 0), colspan=2)
mr_plot.set_ylabel('MR')
mr_plot.set_xlabel('Number of clusters')
plt.grid(True)
plt.axis([minc,maxc, -0.02,1.2])
# print(mrs)
#
#
# for i in range(1,len(mrs),2):
# print(mrs[i])
# print(mrs[i][0])
# kmeans_mr.append(mrs[i][0])
# print(str(mrs[i][0]) + " appended "
completeness_scores_plot = add_cluster_subplot(fig1, 234, 'completeness_scores')
homogeneity_scores_plot = add_cluster_subplot(fig1, 235, 'homogeneity_scores')
value = [hierach_MR , kmeans_mr, ds_mr]
# Define curves and their values
curves = [[mr_plot, value]]
algorithm = ["Agglomerative_Hierachial_Clustering",
"K_Means_Clustering",
"DominantSets_Clustering"]
# Plot all curves
for index in range(3):
label = algorithm[index]
color = colors[index]
# number_of_clusters = np.arange(number_of_embeddings[index], 0, -1)
for plot, value in curves:
print(value[index])
plot.plot(ks,value[index], color=color, label=label)
# Add legend and save the plot
fig1.legend()
# fig1.show()
fig1.savefig(get_result_png(plot_file_name))
print("Plot File saved in " + get_result_png(plot_file_name) )
fig1.savefig(get_result_png(plot_file_name + '.svg'), format='svg')
def _plot_curves(plot_file_name, curve_names, metric_sets,
number_of_embeddings):
"""
Plots all specified curves and saves the plot into a file.
:param plot_file_name: String value of save file name
:param curve_names: Set of names used in legend to describe this curve
:param metric_sets: A list of 2D matrices, each row of a metrics 2D matrix describes one dataset for a curve
:param number_of_embeddings: set of integers, each integer describes how many embeddings is in this curve
"""
logger = get_logger('analysis', logging.INFO)
logger.info('Plot results')
config = load_config(None, join(get_common(), 'config.cfg'))
plot_width = config.getint('common', 'plot_width')
fig_width = config.getint('common', 'fig_width')
fig_height = config.getint('common', 'fig_height')
#Slice results to only 1-80 clusters
for i in range(0, len(metric_sets)):
for j in range(0, len(metric_sets[i])):
metric_sets[i][j] = metric_sets[i][j][-plot_width:]
print(len(metric_sets[i][j]))
best_results = [[] for _ in metric_names]
for m, min_value in enumerate(metric_min_values):
for results in metric_sets[m]:
if (metric_min_values[m] == 0):
best_results[m].append(np.max(results))
else:
best_results[m].append(np.min(results))
'''
This code is used to sort the lines by min mr. Because we now use mutliple metrics and dont sort by a single
metric, this code is not used anymore, but we keep it for now.
min_mrs, curve_names, mrs, acps, aris, homogeneity_scores, completeness_scores, number_of_embeddings = \
(list(t) for t in
zip(*sorted(zip(min_mrs, curve_names, mrs, acps, aris, homogeneity_scores, completeness_scores, number_of_embeddings))))
'''
# How many lines to plot
number_of_lines = len(curve_names)
# Get various colors needed to plot
color_map = plt.get_cmap('gist_rainbow')
colors = [color_map(i) for i in np.linspace(0, 1, number_of_lines)]
#Set fontsize for all plots
plt.rcParams.update({'font.size': 12})
# Define number of figures
fig1 = plt.figure(figsize=(fig_width, fig_height))
# Define Plots
plot_grid = (3, 2)
plots = [None] * len(metric_names)
plots[0] = _add_cluster_subplot(plot_grid, (0, 0), metric_names[0], 1)
plots[1] = _add_cluster_subplot(plot_grid, (0, 1), metric_names[1], 1)
plots[2] = _add_cluster_subplot(plot_grid, (1, 0), metric_names[2], 1)
plots[3] = _add_cluster_subplot(plot_grid, (1, 1), metric_names[3], 1)
#Set the horizontal space between subplots
plt.subplots_adjust(hspace=0.3)
# Define curves and their values
curves = [[] for _ in metric_names]
for m, metric_set in enumerate(metric_sets):
curves[m] = [plots[m], metric_set]
# Plot all curves
for index in range(number_of_lines):
label = curve_names[index]
for m, metric_name in enumerate(metric_names):
label = label + '\n {} {}: {}'.format(
'Max' if metric_min_values[m] == 0 else 'Min', metric_name,
str(best_results[m][index]))
color = colors[index]
number_of_clusters = np.arange(plot_width, 0, -1)
for plot, value in curves:
plot.plot(number_of_clusters,
value[index],
color=color,
label=label)
# Add legend and save the plot
fig1.legend(loc='upper center', bbox_to_anchor=(0.5, 0.33), ncol=4)
#fig1.show()
fig1.savefig(get_result_png(plot_file_name))
fig1.savefig(get_result_png(plot_file_name + '.svg'), format='svg')