In [1]:

    

import plot_utils as pu


    

In [2]:

    

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline


    

In [3]:

    

df = pd.read_csv("./data/simulations_kraken_centrifuge.txt", delimiter=",", index_col=0)


    

In [4]:

    

df.dtypes


    

    
Out[4]:





aligner         object
depth            int64
tp               int64
fp               int64
fn               int64
precision      float64
recall         float64
f1             float64
level           object
site            object
spearman       float64
pearson        float64
jaccard        float64
depth_log10    float64
dtype: object

In [5]:

    

df.head()


    

    
Out[5]:







  

    

      

      
aligner
      
depth
      
tp
      
fp
      
fn
      
precision
      
recall
      
f1
      
level
      
site
      
spearman
      
pearson
      
jaccard
      
depth_log10
    
  
  

    

      
0
      
BURST 95
      
1012
      
901
      
15
      
96
      
0.983624
      
0.903711
      
0.943668
      
species
      
stool
      
0.971300
      
0.920157
      
0.25
      
3.0
    
    

      
1
      
BURST 95
      
1012
      
919
      
2
      
91
      
0.997828
      
0.909901
      
0.953865
      
species
      
oral
      
0.946423
      
0.799821
      
0.35
      
3.0
    
    

      
2
      
BURST 95
      
1012
      
1002
      
1
      
9
      
0.999003
      
0.991098
      
0.995050
      
species
      
skin
      
0.641937
      
0.681264
      
0.07
      
3.0
    
    

      
3
      
BURST 95
      
1012
      
894
      
9
      
109
      
0.990033
      
0.891326
      
0.940680
      
species
      
stool
      
0.933395
      
0.866673
      
0.22
      
3.0
    
    

      
4
      
BURST 95
      
1012
      
929
      
5
      
78
      
0.994647
      
0.922542
      
0.958594
      
species
      
oral
      
0.938274
      
0.825210
      
0.38
      
3.0
    
  

In [6]:

    

# df = df.sort_values("tool")

df_means = df.groupby(["aligner", "site"]).mean().reset_index()

# tools = ["burst", "bowtie2", "centrifuge", "kraken", "utree"]
# index_tools = dict(zip(tools, range(len(tools))))

# df_means["index_tools"] = df_means.tool.map(index_tools)


# display_names = dict(zip(tools, ["BURST", "Bowtie2", "Centrifuge", "Kraken", "UTree"]))

# df_means["display_names"] = df_means.tool.map(display_names)

# colors = dict(zip(tools, ["#1a1895", "#000000", "#0c5050", "#148d4a", "#bd86e8"]))

# df_means["colors"] = df_means.tool.map(colors)

# markers = dict(zip(tools, ["s", "v", "o", "^", "d"]))

# df_means["markers"] = df_means.tool.map(markers)
# df_means.sort_values('index_tools', inplace=True)


    

In [7]:

    

df_means.columns


    

    
Out[7]:





Index(['aligner', 'site', 'depth', 'tp', 'fp', 'fn', 'precision', 'recall',
       'f1', 'spearman', 'pearson', 'jaccard', 'depth_log10'],
      dtype='object')

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In [8]:

    

# We are interested in the speed up and efficiency of each of the aligners
# y-axis: RAM Usage
# x-axis: N (workers)
# sns.set_style("whitegrid")

sns.set(context="paper", style="ticks", palette="colorblind", font='serif', font_scale=1.5, color_codes=True, rc=pu.figure_setup())
fig_size = pu.get_fig_size(13, 10)
fig, ax = plt.subplots(figsize=fig_size)

plot = sns.barplot(x="aligner", y="f1", hue="site", data=df_means, ax=ax)
plot._remove_legend(plot.get_legend())

ax.set_ylim(.7, 1.0)

pu.stylize_axes(ax)
pu.stylize_fig(fig)

for item in ax.get_xticklabels():
    item.set_rotation(90)
    
ax.set_ylabel('f1-score')
ax.set_xlabel('')


artists = fig.legend(loc="center left", bbox_to_anchor=(1, .9))
artists.set_frame_on(False)
pu.save_plot(fig, "simulations", artists=(artists,))


    

    




findfont: Font family ['serif'] not found. Falling back to DejaVu Sans.






    

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In [9]:

    

# artists.