Skip to content
Permalink
Browse files

evaluation code

  • Loading branch information...
Sabrina Hoppe
Sabrina Hoppe committed May 5, 2018
1 parent fc7973a commit 3d3cebb9560e0f3973a6dbecda7e2ab4a15f64d4
Showing with 660 additions and 13 deletions.
  1. +234 −0 05_plot_weights.py
  2. +101 −0 06_baselines.py
  3. +155 −0 07_evaluation_across_contexts.py
  4. +99 −0 08_descriptive.py
  5. +37 −0 09_plot_ws_hist.py
  6. +34 −13 README.md
@@ -0,0 +1,234 @@
import numpy as np
import matplotlib.pyplot as plt
from config import conf
import os, sys
import pandas as pns
from config import names as gs
import getopt
import matplotlib.gridspec as gridspec
from sklearn.metrics import f1_score

import seaborn as sns
sns.set(style='whitegrid', color_codes=True)
sns.set_context('poster')

dark_color = sns.xkcd_rgb['charcoal grey']
light_color = sns.xkcd_rgb['cloudy blue']

def plot_weights():
# for each personality trait, compute the list of median feature importances across all cross validation folds and iterations
medianlist = []
for t in xrange(0, conf.n_traits):
medianlist.append(
list(imp_df.loc[imp_df['T'] == t].groupby(by='feat_num')['feature importance'].median()))

# find the 5th to highest feature importance for each trait and write their importances into a .tex table - see Table 2, SI
n = 15
most_important_features = []
most_important_features_lists = []
for ml in medianlist:
locallist = []
for i in xrange(1,(n+1)):
fn = gs.full_long_label_list[int(np.argsort(np.array(ml))[-i])]
locallist.append(fn)
if fn not in most_important_features:
most_important_features.append(fn)
most_important_features_lists.append(locallist)
most_important_features.sort()

# write the full list of feature importances into a .tex table - shown in Table 2, SI
filename = conf.figure_folder + '/table2.tex'
with open(filename, 'w') as f:
f.write('feature&Neur.&Extr.&Open.&Agree.&Consc.&PCS&CEI')
f.write('\\\\\n\hline\n')
for fi in xrange(0, len(most_important_features)):
f.write(most_important_features[fi])
for t in xrange(0, conf.n_traits):
m = imp_df[(imp_df['T'] == t)&(imp_df.feature == most_important_features[fi])]['feature importance'].median()
if most_important_features[fi] in most_important_features_lists[t]:
f.write('& \\textbf{' + '%.3f}' % m)
else:
f.write('&' + '%.3f' % m)
f.write('\\\\\n')
print filename, 'written.'

# create Figure 2
# first collect the set of individual top TOP_N features per trait:
TOP_N = 10
featlabels = []
for trait in xrange(0, conf.n_traits):
basedata = imp_df.loc[imp_df['T'] == trait]
gp = basedata.groupby(by='feature')['feature importance'].median()
order = gp.sort_values(ascending=False)
featlabels.extend(order[:TOP_N].keys())
super_feats = np.unique(np.array(featlabels))

# collect the sum of feature importances for these labels, to sort the features by their median
super_feats_importance_sum = np.zeros((len(super_feats)))
for i in xrange(0, len(super_feats)):
super_feats_importance_sum[i] = imp_df[imp_df.feature==super_feats[i]].groupby(by=['T'])['feature importance'].median().sum()
super_feats_sort_indices = np.argsort(super_feats_importance_sum)[::-1]

# add some interesting features from related work to the list of features whose importance will be shown
must_have_feats = [
'inter quartile range x', 'range x', 'maximum x', 'std x', '1st quartile x', 'range pupil diameter', 'median y',
'mean difference of subsequent x', 'mean fixation duration', '3rd quartile y',
'fixation rate', 'mean saccade amplitude', 'dwelling time'
]
# but only add them if they are not in the list yet
additional_feats = np.array([a for a in must_have_feats if a not in super_feats], dtype=object)

# collect the sum of feature importances for these labels as well, so they can be sorted by their median importance in the plot
additional_feats_importance_sum = np.zeros((len(additional_feats)))
for trait in xrange(0, conf.n_traits):
basedata = imp_df.loc[imp_df['T'] == trait]
for i in xrange(0, len(additional_feats)):
logi = basedata.feature == additional_feats[i]
additional_feats_importance_sum[i] += float(basedata[logi]['feature importance'].median())
additional_feats_sort_indices = np.argsort(additional_feats_importance_sum)[::-1]

# create the figure
plt.figure(figsize=(20, 12))
grs = gridspec.GridSpec(len(super_feats) + len(additional_feats) + 1, conf.n_traits)

for trait in xrange(0, conf.n_traits):
# upper part of the figure, i.e. important features
ax = plt.subplot(grs[:len(super_feats),trait])
basedata = imp_df.loc[imp_df['T'] == trait]
feat_importances = []
for i in xrange(0, len(super_feats)):
logi = basedata.feature == super_feats[super_feats_sort_indices][i]
feat_importances.append(list(basedata[logi]['feature importance']))
bp = plt.boxplot(x=feat_importances, #notch=True, labels=super_feats[super_feats_sort_indices],
patch_artist=True, sym='', vert=False, whis='range', positions=np.arange(0,len(feat_importances)))

# asthetics
for i in xrange(0, len(super_feats)):
bp['boxes'][i].set(color=dark_color)
bp['boxes'][i].set(facecolor=light_color)
bp['whiskers'][2 * i].set(color=dark_color, linestyle='-')
bp['whiskers'][2 * i + 1].set(color=dark_color, linestyle='-')
bp['caps'][2 * i].set(color=dark_color)
bp['caps'][2 * i + 1].set(color=dark_color)
bp['medians'][i].set(color=dark_color)

if not trait == 0:
plt.ylabel('')
plt.setp(ax.get_yticklabels(), visible=False)
else:
ax.set_yticklabels(super_feats[super_feats_sort_indices])

xlimmax = 0.47
xticks = [0.15, 0.35]
plt.xlim((0, xlimmax))
plt.xticks(xticks)
plt.setp(ax.get_xticklabels(), visible=False)

# lower part of the figure, i.e. features from related work
ax = plt.subplot(grs[(-len(additional_feats)):, trait])
basedata = imp_df.loc[imp_df['T'] == trait]
feat_importances = []
for i in xrange(0, len(additional_feats)):
logi = basedata.feature == additional_feats[additional_feats_sort_indices][i]
feat_importances.append(basedata[logi]['feature importance'])
bp = plt.boxplot(x=feat_importances, patch_artist=True, sym='', vert=False, whis='range',
positions=np.arange(0,len(feat_importances)))

# asthetics
for i in xrange(0, len(additional_feats)):
bp['boxes'][i].set(color=dark_color)
bp['boxes'][i].set(facecolor=light_color) #, alpha=0.5)
bp['whiskers'][2 * i].set(color=dark_color, linestyle='-')
bp['whiskers'][2 * i + 1].set(color=dark_color, linestyle='-')
bp['caps'][2 * i].set(color=dark_color)
bp['caps'][2 * i + 1].set(color=dark_color)
bp['medians'][i].set(color=dark_color) #, linewidth=.1)

if not trait == 0:
plt.ylabel('')
plt.setp(ax.get_yticklabels(), visible=False)
else:
ax.set_yticklabels(additional_feats[additional_feats_sort_indices])
plt.xlim((0, xlimmax))
plt.xticks(xticks)
if trait == 3:
plt.xlabel(conf.medium_traitlabels[trait] + '\n\nFeature Importance')
else:
plt.xlabel(conf.medium_traitlabels[trait])

filename = conf.figure_folder + '/figure2.pdf'
plt.savefig(filename, bbox_inches='tight')
print filename.split('/')[-1], 'written.'
plt.close()


if __name__ == "__main__":
# target file names - save table of F1 scores, feature importances and majority predictions there
datapathI = conf.get_result_folder(conf.annotation_all) + '/f1s.csv' # F1 scores from each iteration
datapathII = conf.get_result_folder(conf.annotation_all) + '/feature_importance.csv' # Feature importance from each iteration
datapathIII = conf.get_result_folder(conf.annotation_all) + '/majority_predictions.csv' # Majority voting result for each participant over all iterations

if not os.path.exists(conf.figure_folder):
os.mkdir(conf.figure_folder)

# if target files do not exist yet, create them
if (not os.path.exists(datapathI)) or (not os.path.exists(datapathII)) or (not os.path.exists(datapathIII)):
f1s = []
feature_importances = []
majority_predictions = []
for trait in xrange(0, conf.n_traits):
predictions = np.zeros((conf.n_participants, conf.max_n_iter),dtype=int)-1
ground_truth = np.loadtxt(conf.binned_personality_file, delimiter=',', skiprows=1, usecols=(trait+1,))
for si in xrange(0, conf.max_n_iter):
filename = conf.get_result_filename(conf.annotation_all, trait, False, si, add_suffix=True)
if os.path.exists(filename):
data = np.load(filename)
if (data['predictions'] > 0).all():
assert data['f1'] == f1_score(ground_truth, data['predictions'], average='macro')
f1s.append([data['f1'], conf.medium_traitlabels[trait]])
else:
# if there was no time window for a condition, like if shopping data only is evaluated,
# the F1 score for each person without a single time window will be set to -1
# but should not be used as such to compute the mean F1 score.
# Thus, here the F1 score is re-computed on the relevant participants only.
pr = data['predictions']
pr = pr[pr > 0]

dt = ground_truth[pr > 0]

f1s.append([f1_score(dt, pr, average='macro'), conf.medium_traitlabels[trait]])

for outer_cv_i in xrange(0, 5): # number outer CV, not person anymore
for fi in xrange(0, conf.max_n_feat):
feature_importances.append([data['feature_importances'][outer_cv_i, fi], trait, gs.full_long_label_list[fi], fi])

predictions[:,si] = data['predictions']
else:
print 'did not find', filename

# compute majority voting for each participant over all iterations
for p in xrange(0, conf.n_participants):
(values, counts) = np.unique(predictions[p, predictions[p,:]>0], return_counts=True)
ind = np.argmax(counts)
majority_predictions.append([values[ind], p, conf.medium_traitlabels[trait]])

f1s_df = pns.DataFrame(data=f1s, columns=['F1', 'trait'])
f1s_df.to_csv(datapathI)

imp_df = pns.DataFrame(data=feature_importances, columns=['feature importance', 'T', 'feature', 'feat_num'])
imp_df.to_csv(datapathII)

majority_predictions_df = pns.DataFrame(data=majority_predictions, columns=['prediction','participant','trait'])
majority_predictions_df.to_csv(datapathIII)

else:
print 'No new results are collected as previous results were available. If you want to overwrite them, please delete the following files:'
print datapathI
print datapathII
print datapathIII

f1s_df = pns.read_csv(datapathI)
imp_df = pns.read_csv(datapathII)
majority_predictions_df = pns.read_csv(datapathIII)

plot_weights() # Figure 2
@@ -0,0 +1,101 @@
import numpy as np
import matplotlib.pyplot as plt
from config import conf
import os, sys
import pandas as pns
from config import names as gs
import getopt
import matplotlib.gridspec as gridspec
from sklearn.metrics import f1_score, accuracy_score

import seaborn as sns
sns.set(style='whitegrid', color_codes=True)
sns.set_context('poster')

dark_color = sns.xkcd_rgb['charcoal grey']
light_color = sns.xkcd_rgb['cloudy blue']

max_n_feat = conf.max_n_feat
m_iter = conf.max_n_iter

featurelabels = gs.full_long_label_list
participant_ids = np.arange(0, conf.n_participants)


def plot_overview():
all_baselines.groupby(by=['trait', 'clf_name'])['F1'].mean().to_csv(conf.figure_folder +
'/figure1.csv')
print 'Figure1.csv written'

sns.set(font_scale=2.1)
plt.figure(figsize=(20, 10))
ax = plt.subplot(1,1,1)
sns.barplot(x='trait', y='F1', hue='clf_name', data=all_baselines, capsize=.05, errwidth=3,
linewidth=3, estimator=np.mean, edgecolor=dark_color,
palette={'our classifier': sns.xkcd_rgb['windows blue'],
'most frequent class': sns.xkcd_rgb['faded green'],
'random guess':sns.xkcd_rgb['greyish brown'],
'label permutation':sns.xkcd_rgb['dusky pink']
}
)
plt.plot([-0.5,6.5], [0.33, 0.33], c=dark_color, linestyle='--', linewidth=3, label='theoretical chance level')
handles, labels = ax.get_legend_handles_labels()
ax.legend([handles[1], handles[2], handles[3], handles[4], handles[0]], [labels[1], labels[2], labels[3], labels[4], labels[0]], fontsize=20)
plt.xlabel('')
plt.ylabel('F1 score', fontsize=20)
plt.ylim((0, 0.55))
filename = conf.figure_folder + '/figure1.pdf'
plt.savefig(filename, bbox_inches='tight')
plt.close()
print 'wrote', filename.split('/')[-1]


if __name__ == "__main__":
# collect F1 scores for classifiers on all data from a file that was written by evaluation_single_context.py
datapath = conf.get_result_folder(conf.annotation_all) + '/f1s.csv'
if not os.path.exists(datapath):
print 'could not find', datapath
print 'consider (re-)running evaluation_single_context.py'
sys.exit(1)
our_classifier = pns.read_csv(datapath)
our_classifier['clf_name'] = 'our classifier'

# baseline 1: guess the most frequent class from each training set that was written by train_baseline.py
datapath = conf.result_folder + '/most_frequ_class_baseline.csv'
if not os.path.exists(datapath):
print 'could not find', datapath
print 'consider (re-)running train_baseline.py'
sys.exit(1)
most_frequent_class_df = pns.read_csv(datapath)
most_frequent_class_df['clf_name'] = 'most frequent class'

# compute all other baselines ad hoc
collection = []
for trait in xrange(0, conf.n_traits):
# baseline 2: random guess
truth = np.genfromtxt(conf.binned_personality_file, skip_header=1, usecols=(trait+1,), delimiter=',')
for i in xrange(0, 100):
rand_guess = np.random.randint(1, 4, conf.n_participants)
f1 = f1_score(truth, rand_guess, average='macro')
collection.append([f1, conf.medium_traitlabels[trait], i, 'random guess'])

# baseline 3: label permutation test
# was computed using label_permutation_test.sh and written into results. ie. is just loaded here
for si in xrange(0, m_iter):
filename_rand = conf.get_result_filename(conf.annotation_all, trait, True, si, add_suffix=True)
if os.path.exists(filename_rand):
data = np.load(filename_rand)
pr = data['predictions']
dt = truth[pr > 0]
pr = pr[pr > 0]
f1 = f1_score(dt, pr, average='macro')
collection.append([f1, conf.medium_traitlabels[trait], si, 'label permutation'])
else:
print 'did not find', filename_rand
print 'consider (re-)running label_permutation_test.sh'
sys.exit(1)

collectiondf = pns.DataFrame(data=collection,columns=['F1','trait','iteration','clf_name'])
all_baselines = pns.concat([our_classifier, most_frequent_class_df, collectiondf])

plot_overview() # Figure 1

0 comments on commit 3d3cebb

Please sign in to comment.
You can’t perform that action at this time.