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plot_predictions.py
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plot_predictions.py
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import pandas as pd
import os
from os.path import join
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import rc, cm
import seaborn as sns
from collections import OrderedDict
import numpy as np
from my_palette import (color_palette, atlas_palette,
datasets_palette)
#####
# seaborn version == '0.7.1'
def _pandas_data_frame(path):
"""Load path to pandas data frame
Parameters
----------
path : str
Path to csv file
Returns
-------
data : pd.DataFrame
Pandas data frame per path.
"""
data = pd.read_csv(path)
scores = data['scores'].str.strip('[ ]')
del data['scores']
data = data.join(scores)
data.scores = data.scores.astype(float)
return data
def _get_markers():
marker_dict = {'COBRE': 'o',
'ADNI': 's',
'ADNIDOD': '>',
'ACPI': '^',
'ABIDE': 'v',
'HCP': '<'}
return marker_dict
def _add_axvline(ax):
# draw a default vline at x=0 that spans the yrange
ax.axvline(x=0, ymin=0.715, linewidth=4, zorder=0, color='0.6')
ax.axvline(x=0, ymax=0.665, ymin=0.526, linewidth=4, zorder=0, color='0.6')
ax.axvline(x=0, ymax=0.473, linewidth=4, zorder=0, color='0.6')
return ax
def _add_yticklabels(ax):
ax_yticklabels = []
for y_label in ax.get_yticklabels():
if y_label.get_text() not in ['dummy1', 'dummy2']:
ax_yticklabels.append(y_label.get_text())
else:
ax_yticklabels.append("")
ax.set_yticklabels(ax_yticklabels)
return ax
def _add_xticklabels(ax):
# make the positive labels with "+"
ax_xticklabels = []
for x in ax.get_xticks():
if x > 0:
ax_xticklabels.append('+' + str(np.round(x, decimals=2)))
else:
ax_xticklabels.append(str(np.round(x, decimals=2)))
ax.set_xticklabels(ax_xticklabels)
return ax
def _add_bgcolors(df, ax):
# background
for a, method in enumerate(df['method'].unique()):
if a % 2:
ax.axhspan(a - .5, a + .5, color='0.9', zorder=-1)
if a <= 5:
facecolor = cm.Set2(7)
elif a > 6 and a < 9:
facecolor = cm.Set2(3)
elif a > 10:
facecolor = cm.Set2(4)
if method not in ['dummy1', 'dummy2']:
ax.axhspan(a - .5, a + .5, facecolor=facecolor, alpha=0.15)
return ax
def _scatter_plot(df, ax):
marker_dict = _get_markers()
methods = df.method.unique()
n_datasets = len(df['dataset'].unique())
width_of_boxplot = 0.9
offset = width_of_boxplot / n_datasets
n_offsets = [-offset * 2.5, -offset * 1.5, -offset * 0.9, 0,
offset * 1.2, offset * 2.2]
for i, (off, dataset) in enumerate(zip(n_offsets,
['COBRE', 'ADNI', 'ADNIDOD',
'ACPI', 'ABIDE', 'HCP'])):
# Plot dataset by dataset
data = df[(df['dataset'] == dataset)]
for ii, method in enumerate(methods):
this_method = data[data['method'] == method]
if not this_method.empty or \
this_method.method.unique() not in ['dummy1', 'dummy2']:
rank = len(this_method) * [ii]
y_ = np.add(rank, len(this_method) * [off])
ax.scatter(x=this_method[x], y=y_, data=this_method,
color=datasets_palette[dataset],
marker=marker_dict[dataset], s=28)
return ax
def _add_legend(ax):
marker_dict = _get_markers()
cobre_marker1 = plt.Line2D([], [], color=datasets_palette['COBRE'],
marker=marker_dict['COBRE'], linestyle='')
adni_marker2 = plt.Line2D([], [], color=datasets_palette['ADNI'],
marker=marker_dict['ADNI'], linestyle='')
adnidod_marker3 = plt.Line2D([], [], color=datasets_palette['ADNIDOD'],
marker=marker_dict['ADNIDOD'], linestyle='')
acpi_marker4 = plt.Line2D([], [], color=datasets_palette['ACPI'],
marker=marker_dict['ACPI'], linestyle='')
abide_marker5 = plt.Line2D([], [], color=datasets_palette['ABIDE'],
marker=marker_dict['ABIDE'], linestyle='')
hcp_marker6 = plt.Line2D([], [], color=datasets_palette['HCP'],
marker=marker_dict['HCP'], linestyle='')
ax.legend([cobre_marker1, adni_marker2, adnidod_marker3, acpi_marker4,
abide_marker5, hcp_marker6],
['COBRE', 'ADNI', 'ADNIDOD', 'ACPI', 'ABIDE', 'HCP'],
loc='lower left', handletextpad=-0.5, borderaxespad=0,
fontsize=14, frameon=True, scatterpoints=1,
markerscale=0.9, borderpad=None,
ncol=1, columnspacing=-0.2)
return ax
def boxplot(df, x=None, y=None, hue=None, axx=None):
rc('xtick', labelsize=14)
rc('ytick', labelsize=14)
sns.boxplot(data=df, x=x, y=y, fliersize=0, linewidth=2,
boxprops={'facecolor': 'lightcyan', 'edgecolor': '.0'},
width=0.9, ax=axx)
axx = _scatter_plot(df, axx)
axx = _add_axvline(axx)
axx.set_ylabel('')
axx = _add_yticklabels(axx)
axx = _add_xticklabels(axx)
axx = _add_bgcolors(df, axx)
axx = _add_legend(axx)
plt.tight_layout(rect=[0.1, .01, 1, 0.98], pad=0.1, w_pad=1)
axx.set_xlabel('Relative prediction scores (AUC)',
fontsize=14, fontweight='normal')
axx.text(0.15, 3.55, 'Regions-definition \n pre-computed atlases',
fontsize=14, rotation='vertical', va='bottom')
axx.text(0.15, 7.2, 'Connectivity', fontsize=14,
rotation='vertical')
axx.text(0.15, 12.8, 'Classifiers', fontsize=14, rotation='vertical')
return
###############################################################################
# Gather data
def _get_data():
"""
"""
data = []
dataset_names = ['COBRE', 'ADNI', 'ADNIDOD', 'ACPI', 'ABIDE',
'HCP']
for dataset in dataset_names:
each_atlas_path = join('predictions', dataset, 'scores.csv')
this_data = _pandas_data_frame(each_atlas_path)
data.append(this_data)
data = pd.concat(data)
return data
data = _get_data()
data = data.drop('Unnamed: 0', axis=1)
##############################################################################
# Prepare data for plotting: calculate mean
def demean(group):
return group - group.mean()
# Take the average over iter_shuffle_split
df = data.groupby(['classifier', 'measure', 'atlas', 'dataset']).mean()
df = df.reset_index()
df.pop('iter_shuffle_split')
demeaned_scores_atlas = df.groupby(['classifier', 'measure',
'dataset'])['scores'].transform(demean)
demeaned_scores_measure = df.groupby(['atlas', 'classifier',
'dataset'])['scores'].transform(demean)
demeaned_scores_classifier = df.groupby(['atlas', 'measure',
'dataset'])['scores'].transform(demean)
df['demeaned_scores_atlas'] = demeaned_scores_atlas
df['demeaned_scores_measure'] = demeaned_scores_measure
df['demeaned_scores_classifier'] = demeaned_scores_classifier
from aliases import new_names_atlas, new_names_measure, new_names_classifier
df = df.replace(to_replace={'atlas': new_names_atlas(),
'measure': new_names_measure(),
'classifier': new_names_classifier()})
# change the name of the dataset to upper
df['dataset'] = df['dataset'].str.upper()
df = df[df['classifier'] != 'lasso']
###############################################################################
# Combine into one dataframe
new_df_c = df[['classifier', 'dataset', 'dimensionality',
'demeaned_scores_classifier']]
new_df_c = new_df_c.rename(index=str,
columns={'classifier': 'method',
'demeaned_scores_classifier': 'demeaned_scores'})
new_df_m = df[['measure', 'dataset', 'dimensionality', 'demeaned_scores_measure']]
new_df_m = new_df_m.rename(index=str,
columns={'measure': 'method',
'demeaned_scores_measure': 'demeaned_scores'})
dummy2 = pd.DataFrame({"method": ["dummy2", 'dummy2', 'dummy2',
'dummy2', 'dummy2', 'dummy2'],
"dataset": ['COBRE', 'ADNI', 'ADNIDOD', 'ACPI',
'ABIDE', 'HCP']})
new_df_m = pd.concat([new_df_m, dummy2])
new_df_a = df[['atlas', 'dataset', 'dimensionality', 'demeaned_scores_atlas']]
new_df_a = new_df_a.rename(index=str,
columns={'atlas': 'method',
'demeaned_scores_atlas': 'demeaned_scores'})
dummy1 = pd.DataFrame({"method": ["dummy1", 'dummy1', 'dummy1',
'dummy1', 'dummy1', 'dummy1'],
"dataset": ['COBRE', 'ADNI', 'ADNIDOD',
'ACPI', 'ABIDE', 'HCP']})
new_df_a = pd.concat([new_df_a, dummy1])
df = pd.concat([new_df_a, new_df_m, new_df_c])
dic = {'AAL \n (116 regions)': 1,
'Harvard Oxford \n (118 regions)': 2,
'BASC \n (122 networks)': 4,
'Power \n (264 regions)': 3,
'MODL dict. learning \n (64 networks)': 5,
'MODL dict. learning \n (128 networks)': 6,
'dummy1': 7,
'Partial \n Correlation': 8,
'Correlation': 9,
'Tangent': 10,
'dummy2': 11,
'K-NN': 12,
'Random Forest': 13,
'Gaussian \n Naive Bayes': 14,
'SVC-$\\ell_1$': 15,
'ANOVA + \n SVC-$\\ell_1$': 16,
'Logistic-$\\ell_1$': 17,
'Ridge': 18,
'SVC-$\\ell_2$': 19,
'Logistic-$\\ell_2$': 21,
'ANOVA + \n SVC-$\\ell_2$': 20}
df['rank'] = df['method'].map(dic)
df.sort_values(by=['rank'], inplace=True)
###############################################################################
# Plotting goes here
hue = 'dataset'
x = 'demeaned_scores'
y = 'method'
fig, axes = plt.subplots(figsize=(5.5, 11.4))
sns.set(color_codes=True)
sns.set_style("whitegrid", {'axes.edgecolor': '.6', 'grid.color': '.8'})
sns.set_palette('dark')
boxplot(df, x=x, y=y, hue=hue, axx=axes)
plt.yticks(fontsize=14)
plt.xticks(fontsize=14)
plt.show()