def plot_results(data, type=None):
import numpy as np
import matplotlib.pyplot as plt
for context in data['ctx'].keys():
labels = []
baseline_metrics = (data['ctx'][context]['2d']['Precision'], data['ctx'][context]['2d']['Recall'], data['ctx'][context]['2d']['F1-score'])
contextual_metrics = AutoVivification()
labels.extend(['Baseline', 'Baseline', 'Baseline'])
for condition in data['ctx'][context].keys():
if condition == '2d':
continue
contextual_metrics[condition] = (data['ctx'][context][condition]['Precision'],
data['ctx'][context][condition]['Recall'],
data['ctx'][context][condition]['F1-score'])
label_name = ContextConditionMappings[context][int(condition)]
labels.extend([label_name, label_name, label_name])
print baseline_metrics
print contextual_metrics
fig, ax = plt.subplots()
index = np.arange(3)
bar_width = 0.15
opacity = 0.8
rects1 = plt.bar(index, baseline_metrics, bar_width,
alpha=opacity,
color='b',
label='Baseline')
colors = {
'1': 'r',
'2': 'g',
'3': 'y',
'4': 'c',
'5': 'm',
'6': 'k',
'7': '#ee1f3f',
'8': '#123ef2'
}
i = 1
for condition in contextual_metrics.keys():
plt.bar(index + bar_width * i, contextual_metrics[condition], bar_width,
alpha=opacity,
color=colors[condition],
label=ContextConditionMappings[context][int(condition)])
i += 1
rects = ax.patches
# Now make some labels
# labels = ["label%d" % i for i in xrange(len(rects))]
for rect, label in zip(rects, labels):
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2, height, label, ha='center', va='bottom')
# plt.xlabel('Metric')
# plt.ylabel('Value')
plt.title('Evaluation Metrics for ' + ContextMappings[context] + ' context')
plt.xticks(index + bar_width + 0.15, ('Precision', 'Recall', 'F1Score'))
plt.legend()
plt.tight_layout()
plt.show()
def plot(self):
import numpy as np
import matplotlib.pyplot as plt
for context in self.metrics.keys():
labels = []
baseline_metrics = (self.metrics[context][self.metrics[context].keys()[0]]['precision'],
self.metrics[context][self.metrics[context].keys()[0]]['recall'],
self.metrics[context][self.metrics[context].keys()[0]]['f1Score'])
contextual_metrics = AutoVivification()
labels.extend(['Baseline', 'Baseline', 'Baseline'])
for condition in self.metrics[context].keys():
contextual_metrics[condition] = (self.metrics[context][condition]['precision-ctx'],
self.metrics[context][condition]['recall-ctx'],
self.metrics[context][condition]['f1Score-ctx'])
labels.extend([condition, condition, condition])
fig, ax = plt.subplots()
index = np.arange(3)
bar_width = 0.15
opacity = 0.8
rects1 = plt.bar(index, baseline_metrics, bar_width,
alpha=opacity,
color='b',
label='Baseline')
colors = {
'Weekend': 'y',
'Weekday': 'c',
'Home': 'y',
'Cinema': 'c',
'Alone': 'y',
'Partner': 'c',
'Family': '#ee1f3f',
}
i = 1
for condition in contextual_metrics.keys():
plt.bar(index + bar_width * i, contextual_metrics[condition], bar_width,
alpha=opacity,
color=colors[condition],
label=condition)
i += 1
rects = ax.patches
# Now make some labels
# labels = ["label%d" % i for i in xrange(len(rects))]
for rect, label in zip(rects, labels):
height = rect.get_height()
# ax.text(rect.get_x() + rect.get_width() / 2, height, None, ha='center', va='bottom')
# plt.xlabel('Metric')
# plt.ylabel('Value')
plt.title('Evaluation Metrics for ' + context)
plt.xticks(index + bar_width + 0.15, ('Precision', 'Recall', 'F1Score'))
import math
plt.ylim([0, 1])
plt.legend()
plt.tight_layout()
plt.show()
def plot(self):
import numpy as np
import matplotlib.pyplot as plt
for context in self.metrics.keys():
labels = []
baseline_metrics = (self.metrics[context][
self.metrics[context].keys()[0]]['precision'],
self.metrics[context][
self.metrics[context].keys()[0]]['recall'],
self.metrics[context]
[self.metrics[context].keys()[0]]['f1Score'])
contextual_metrics = AutoVivification()
labels.extend(['Baseline', 'Baseline', 'Baseline'])
for condition in self.metrics[context].keys():
contextual_metrics[condition] = (
self.metrics[context][condition]['precision-ctx'],
self.metrics[context][condition]['recall-ctx'],
self.metrics[context][condition]['f1Score-ctx'])
labels.extend([condition, condition, condition])
fig, ax = plt.subplots()
index = np.arange(3)
bar_width = 0.15
opacity = 0.8
rects1 = plt.bar(index,
baseline_metrics,
bar_width,
alpha=opacity,
color='b',
label='Baseline')
colors = {
'Weekend': 'y',
'Weekday': 'c',
'Home': 'y',
'Cinema': 'c',
'Alone': 'y',
'Partner': 'c',
'Family': '#ee1f3f',
}
i = 1
for condition in contextual_metrics.keys():
plt.bar(index + bar_width * i,
contextual_metrics[condition],
bar_width,
alpha=opacity,
color=colors[condition],
label=condition)
i += 1
rects = ax.patches
# Now make some labels
# labels = ["label%d" % i for i in xrange(len(rects))]
for rect, label in zip(rects, labels):
height = rect.get_height()
# ax.text(rect.get_x() + rect.get_width() / 2, height, None, ha='center', va='bottom')
# plt.xlabel('Metric')
# plt.ylabel('Value')
plt.title('Evaluation Metrics for ' + context)
plt.xticks(index + bar_width + 0.15,
('Precision', 'Recall', 'F1Score'))
import math
plt.ylim([0, 1])
plt.legend()
plt.tight_layout()
plt.show()