def plot_classification_evaluations_experiment():
import data
labels = ['Freqency', 'Co-occurrence', 'Dependency']
legend = ['local', 'global']
d = [
[0.5678, 0.5455], # .., -2
[0.5694, 0.5333],
[0.5889, 0.5056]
]
ys = {
.3: '0.0',
.35: '...',
.4: '0.4',
.5: '0.5',
.6: '0.6',
.7: '0.7',
.8: '0.8'
}
fig = plotter.tikz_barchart(d,
labels,
scale=3.5,
yscale=3,
color='black',
legend=legend,
legend_sep=0.6,
low_cut=0.3,
y_tics=ys,
tick=False)
data.write_to_file(
fig,
'../../masteroppgave/report/imgs/tikz/eval_classification.tex',
mode='w')
def plot_retrieval_evaluations_experiment():
import data
labels = ['Freqency','Co-occurrence','Dependency']
legend = ['local','global']
d = [[0.2240,0.2459],
[0.2227,0.2559],
[0.2020,0.2048]]
ys = {0:'0.0', .1:'0.1', .2:'0.2', .3:'0.3', .4:'0.4'}
fig = plotter.tikz_barchart(d, labels, scale = 3.5, yscale=3, color='black', legend=None, y_tics=ys, tick=False)
data.write_to_file(fig,'../../masteroppgave/report/imgs/tikz/eval_retrieval.tex',mode='w')
def plot_classification_evaluations_experiment():
import data
labels = ['Freqency','Co-occurrence','Dependency']
legend = ['local','global']
d = [[0.5678,0.5455], # .., -2
[0.5694,0.5333],
[0.5889,0.5056]]
ys = {.3:'0.0',.35:'...',.4:'0.4',.5:'0.5',.6:'0.6',.7:'0.7',.8:'0.8'}
fig = plotter.tikz_barchart(d, labels, scale = 3.5, yscale=3, color='black', legend=legend, legend_sep=0.6, low_cut=0.3, y_tics=ys, tick=False)
data.write_to_file(fig,'../../masteroppgave/report/imgs/tikz/eval_classification.tex',mode='w')
def plot_classification_comparison_experiment():
import data
labels = ['Freqency','Co-occurrence','Dependency']
legend = ['local','global']
d = [[0.6693,0.6375],
[0.6880,0.6875],
[0.6827,0.6763]]
ys = {.4:'0.0',.45:'...',.5:'0.5',.6:'0.6',.7:'0.7',.8:'0.8'}
fig = plotter.tikz_barchart(d, labels, scale = 3.5, yscale=3, color='black', legend=legend, legend_sep=0.6, low_cut=0.4, y_tics=ys, tick=False)
data.write_to_file(fig,'../../masteroppgave/report/imgs/tikz/comp_classification.tex',mode='w')
def plot_centrality_evaluations():
import data
labels = ['~~~~~Degree','Closeness','Current-flow closeness','Betweenness','Current-flow betweenness','Load','Eigenvector','PageRank','HITS Authorities','HITS Hubs']
d = [
[0.5694444444444444,0.5333333333333333],#[0.5555555555555556,0.5333333333333333],
[0.525,0.5166666666666667],
[0.5194444444444445,0.5111111111111111],
[0.4361111111111111,0.43333333333333335],
[0.42777777777777776,0.4187],#[0.42777777777777776,0.05],
[0.4361111111111111,0.4222222222222222],
[0.5183333333333333,0.5055555555555555],
[0.5573333333333333,0.5433333333333333],
[0.5083333333333333,0.5083333333333333],
[0.5083333333333333,0.5083333333333333]]
ys = {.3:'0.0',.35:'...',.4:'0.4',.5:'0.5',.6:'0.6',.7:'0.7',.8:'0.8'}
fig = plotter.tikz_barchart(d, None, scale = 3.5, yscale=3, color='black', legend = ['TC','TC-ICC'], legend_sep=0.6, low_cut=0.3, y_tics=ys, tick=False)
data.write_to_file(fig,'../../masteroppgave/paper/parts/tikz_bar_co-occurrence.tex',mode='w')
d = [
[0.52500000000000002,0.5028],
[0.58894242452424244,0.5056],#[0.57499999999999996,0.5056],
[0.56944444444444442,0.5028],
[0.36388888888888887,0.3806],
[0.23333333333333334,0.2263],#[0.23333333333333334,0.05],
[0.35555555555555557,0.3778],
[0.49722222222222223,0.4667],
[0.52777777777777779,0.4833],
[0.49722222222222223,0.4611],
[0.49722222222222223,0.4611]]
ys = {.0:'0.0',.1:'',.2:'0.2',.3:'',.4:'0.4',.5:'',.6:'0.6',.7:'',.8:'0.8'}
fig = plotter.tikz_barchart(d, None, scale = 3.5, yscale=1.6, color='black', y_tics=ys, tick=False)
data.write_to_file(fig,'../../masteroppgave/paper/parts/tikz_bar_dependency.tex',mode='w')
fig = plotter.tikz_barchart(d, labels, scale = 3.5, color='black', labels_only=True)
data.write_to_file(fig,'../../masteroppgave/paper/parts/tikz_bar_labels.tex',mode='w')
def plot_exp1():
"""
Plotting the results of the weight evaluation experiment.
"""
legend = ['unweighted', 'weighted']
labels = ['Degree','Closeness','Current-flow closeness','Betweenness','Current-flow betweenness','Load','Eigenvector','PageRank','HITS authorities','HITS hubs']
# classification
d = [[0.52500000000000002,0.49444444444444446], # Degree
[0.57499999999999996,0.57499999999999996], # Closeness
[0.56944444444444442,0.58333333333333337], # Current-flow closeness
[0.36388888888888887,0.36944444444444446], # Betweenness
[0.23333333333333334,0.20833333333333334], # Current-flow betweenness
[0.35555555555555557,0.36666666666666664], # Load
[0.49722222222222223,0.45555555555555555], # Eigenvector
[0.52777777777777779,0.51111111111111107], # PageRank
[0.49722222222222223,0.45555555555555555], # HITS authorities
[0.49722222222222223,0.45555555555555555]] # HITS hubs
ys = {0:'0.0',.1:'0.1',.2:'0.2', .3:'0.3',.4:'0.4',.5:'0.5',.6:'0.6'}
fig = plotter.tikz_barchart(d, labels, scale = 3.5, yscale=2.8, color='black', legend=legend, legend_sep=1.0, tick=False, y_tics=ys)
data.write_to_file(fig,'../../masteroppgave/report/imgs/tikz/dependency_eval_class.tex',mode='w')
# retrieval
d = [[0.18149811054435275,0.18821229318222113], # Degree
[0.17184314735361236,0.18216618328598347], # Closeness
[0.14606637651984622,0.13586098100141117], # Betweenness
[0.17399729543537901,0.17613717518129621], # Current-flow closeness
[0.042019078720146409,0.042019078720146409], # Current-flow betweenness
[0.14700372822743263,0.15104493506838745], # Load
[0.19854658693196564,0.17540014008712554], # Eigenvector
[0.17725358882165362,0.17252331100724849], # PageRank
[0.19854658693196564,0.17540014008712554], # HITS authorities
[0.19854658693196564,0.17540014008712554]] # HITS hubs
ys = {0:'0.0',.05:'0.05', .1:'0.1',.15:'0.15', .2:'0.2'}
fig = plotter.tikz_barchart(d, labels, scale = 3.5, yscale=8, color='black', legend=legend, legend_sep=1.0, tick=False, grid_step=0.05, y_tics=ys)
data.write_to_file(fig,'../../masteroppgave/report/imgs/tikz/dependency_eval_retr.tex',mode='w')
def plot_retrieval_evaluations_experiment():
import data
labels = ['Freqency', 'Co-occurrence', 'Dependency']
legend = ['local', 'global']
d = [[0.2240, 0.2459], [0.2227, 0.2559], [0.2020, 0.2048]]
ys = {0: '0.0', .1: '0.1', .2: '0.2', .3: '0.3', .4: '0.4'}
fig = plotter.tikz_barchart(d,
labels,
scale=3.5,
yscale=3,
color='black',
legend=None,
y_tics=ys,
tick=False)
data.write_to_file(
fig,
'../../masteroppgave/report/imgs/tikz/eval_retrieval.tex',
mode='w')
def plot_classification_comparison_experiment():
import data
labels = ['Freqency', 'Co-occurrence', 'Dependency']
legend = ['local', 'global']
d = [[0.6693, 0.6375], [0.6880, 0.6875], [0.6827, 0.6763]]
ys = {.4: '0.0', .45: '...', .5: '0.5', .6: '0.6', .7: '0.7', .8: '0.8'}
fig = plotter.tikz_barchart(d,
labels,
scale=3.5,
yscale=3,
color='black',
legend=legend,
legend_sep=0.6,
low_cut=0.4,
y_tics=ys,
tick=False)
data.write_to_file(
fig,
'../../masteroppgave/report/imgs/tikz/comp_classification.tex',
mode='w')
def plot_centrality_evaluations():
import data
labels = [
'~~~~~Degree', 'Closeness', 'Current-flow closeness', 'Betweenness',
'Current-flow betweenness', 'Load', 'Eigenvector', 'PageRank',
'HITS Authorities', 'HITS Hubs'
]
d = [
[0.5694444444444444,
0.5333333333333333], #[0.5555555555555556,0.5333333333333333],
[0.525, 0.5166666666666667],
[0.5194444444444445, 0.5111111111111111],
[0.4361111111111111, 0.43333333333333335],
[0.42777777777777776, 0.4187], #[0.42777777777777776,0.05],
[0.4361111111111111, 0.4222222222222222],
[0.5183333333333333, 0.5055555555555555],
[0.5573333333333333, 0.5433333333333333],
[0.5083333333333333, 0.5083333333333333],
[0.5083333333333333, 0.5083333333333333]
]
ys = {
.3: '0.0',
.35: '...',
.4: '0.4',
.5: '0.5',
.6: '0.6',
.7: '0.7',
.8: '0.8'
}
fig = plotter.tikz_barchart(d,
None,
scale=3.5,
yscale=3,
color='black',
legend=['TC', 'TC-ICC'],
legend_sep=0.6,
low_cut=0.3,
y_tics=ys,
tick=False)
data.write_to_file(
fig,
'../../masteroppgave/paper/parts/tikz_bar_co-occurrence.tex',
mode='w')
d = [
[0.52500000000000002, 0.5028],
[0.58894242452424244, 0.5056], #[0.57499999999999996,0.5056],
[0.56944444444444442, 0.5028],
[0.36388888888888887, 0.3806],
[0.23333333333333334, 0.2263], #[0.23333333333333334,0.05],
[0.35555555555555557, 0.3778],
[0.49722222222222223, 0.4667],
[0.52777777777777779, 0.4833],
[0.49722222222222223, 0.4611],
[0.49722222222222223, 0.4611]
]
ys = {
.0: '0.0',
.1: '',
.2: '0.2',
.3: '',
.4: '0.4',
.5: '',
.6: '0.6',
.7: '',
.8: '0.8'
}
fig = plotter.tikz_barchart(d,
None,
scale=3.5,
yscale=1.6,
color='black',
y_tics=ys,
tick=False)
data.write_to_file(
fig,
'../../masteroppgave/paper/parts/tikz_bar_dependency.tex',
mode='w')
fig = plotter.tikz_barchart(d,
labels,
scale=3.5,
color='black',
labels_only=True)
data.write_to_file(fig,
'../../masteroppgave/paper/parts/tikz_bar_labels.tex',
mode='w')
def plot_exp1():
"""
Plotting the results of the weight evaluation experiment.
"""
legend = ['unweighted', 'weighted']
labels = [
'Degree', 'Closeness', 'Current-flow closeness', 'Betweenness',
'Current-flow betweenness', 'Load', 'Eigenvector', 'PageRank',
'HITS authorities', 'HITS hubs'
]
# classification
d = [
[0.52500000000000002, 0.49444444444444446], # Degree
[0.57499999999999996, 0.57499999999999996], # Closeness
[0.56944444444444442, 0.58333333333333337], # Current-flow closeness
[0.36388888888888887, 0.36944444444444446], # Betweenness
[0.23333333333333334, 0.20833333333333334], # Current-flow betweenness
[0.35555555555555557, 0.36666666666666664], # Load
[0.49722222222222223, 0.45555555555555555], # Eigenvector
[0.52777777777777779, 0.51111111111111107], # PageRank
[0.49722222222222223, 0.45555555555555555], # HITS authorities
[0.49722222222222223, 0.45555555555555555]
] # HITS hubs
ys = {
0: '0.0',
.1: '0.1',
.2: '0.2',
.3: '0.3',
.4: '0.4',
.5: '0.5',
.6: '0.6'
}
fig = plotter.tikz_barchart(d,
labels,
scale=3.5,
yscale=2.8,
color='black',
legend=legend,
legend_sep=1.0,
tick=False,
y_tics=ys)
data.write_to_file(
fig,
'../../masteroppgave/report/imgs/tikz/dependency_eval_class.tex',
mode='w')
# retrieval
d = [
[0.18149811054435275, 0.18821229318222113], # Degree
[0.17184314735361236, 0.18216618328598347], # Closeness
[0.14606637651984622, 0.13586098100141117], # Betweenness
[0.17399729543537901, 0.17613717518129621], # Current-flow closeness
[0.042019078720146409,
0.042019078720146409], # Current-flow betweenness
[0.14700372822743263, 0.15104493506838745], # Load
[0.19854658693196564, 0.17540014008712554], # Eigenvector
[0.17725358882165362, 0.17252331100724849], # PageRank
[0.19854658693196564, 0.17540014008712554], # HITS authorities
[0.19854658693196564, 0.17540014008712554]
] # HITS hubs
ys = {0: '0.0', .05: '0.05', .1: '0.1', .15: '0.15', .2: '0.2'}
fig = plotter.tikz_barchart(d,
labels,
scale=3.5,
yscale=8,
color='black',
legend=legend,
legend_sep=1.0,
tick=False,
grid_step=0.05,
y_tics=ys)
data.write_to_file(
fig,
'../../masteroppgave/report/imgs/tikz/dependency_eval_retr.tex',
mode='w')
