File size: 3,018 Bytes
5032722 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.font_manager
import matplotlib.ticker as mticker
from matplotlib import rc
plt.rcParams['text.usetex'] = True
plt.rcParams["font.family"] = 'Optima'
plt.rcParams["font.size"] = 20
plt.rc('axes', unicode_minus=False)
plt.tight_layout()
def make_plots(log_scale: bool):
aic_fig, aic_ax = plt.subplots(figsize=[8,6])
xax_lab = 'Number of parameters'
def add_to_plots(mixture_type,
label,
color,
linestyle = '-',
marker = 'o'):
# extract
sub_df = df[ df['model_type'] == mixture_type ]
sub_df = sub_df.sort_values(by='parameters')
assert len(sub_df) > 0
# add to aic plot
aic_ax.plot( sub_df['parameters'],
sub_df['aic_gain'],
marker = marker,
linestyle = linestyle,
color = color,
label = label,
linewidth = 3.5,
markersize = 10)
del sub_df, label
add_to_plots(mixture_type = 'pairhmm_domain_mix',
label = 'Mixture of domain classes',
color = 'tab:purple')
add_to_plots(mixture_type = 'pairhmm_fragment_mix',
label = 'Mixture of fragment classes',
color = 'tab:green')
add_to_plots(mixture_type = 'pairhmm_site_mix',
label = 'Mixture of site classes',
color = 'tab:orange')
aic_ax.grid()
aic_ax.legend()
aic_ax.set_xlabel(xax_lab)
aic_ax.set_ylabel('$\Delta$AIC (×$10^7$)')
new_y_tick_labels = [f"{x:.1e}".split("e")[0] for x in aic_ax.get_yticks()]
aic_ax.set_yticklabels( new_y_tick_labels )
del new_y_tick_labels
if log_scale:
aic_ax.set_xscale('log')
aic_fig.savefig(f'AIC_parameters_log_{log_scale}.pdf',
bbox_inches="tight")
def read_file(file):
df = pd.read_csv(file, sep='\t', index_col=0)
sub_df = df[ (df['dataset'] == 'data1') &
(df['sub_model'] == 'f81') &
(df['indel_model'] == 'tkf92') ]
sub_df = sub_df[['sub_model',
'indel_model',
'model_type',
'parameters',
'aic',
'bic']]
sub_df = sub_df.sort_values(by='parameters')
ref = sub_df[ sub_df['model_type']=='pairhmm_reference' ]
sub_df['aic_gain'] = ref['aic'].item() - sub_df['aic']
sub_df['bic_gain'] = ref['bic'].item() - sub_df['bic']
sub_df = sub_df.drop( ['aic','bic'], axis=1 )
sub_df = sub_df[ sub_df['model_type'] !='pairhmm_reference' ]
return sub_df
if __name__ == '__main__':
df = read_file('hierarchical_mixture_models_train_set_loglikes.tsv')
make_plots(log_scale = True)
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