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show_results.py
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show_results.py
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import hpbandster.core.result as hpres
import wget
import zipfile
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import urllib.request, json, pprint
import seaborn as sns
import argparse
class Results:
"""Class for results processing."""
def __init__(self, path, model, grams, tokens, flavor, metric, option):
sns.set(style="ticks", color_codes=True)
self.results = {'DNN_IBM': "https://onedrive.live.com/download?cid="
"888296E6B085BF40&resid=888296E6B085BF40%212451&authkey=ALF_fHrkrtCkk2I",
'ET': "https://onedrive.live.com/download?cid="
"888296E6B085BF40&resid=888296E6B085BF40%212444&authkey=AEBXN0JE1Ru5HmI",
'ET_IBM': "https://onedrive.live.com/download?cid="
"888296E6B085BF40&resid=888296E6B085BF40%212447&authkey=AAPG5on77C6Ou-M",
'RF': "https://onedrive.live.com/download?cid="
"888296E6B085BF40&resid=888296E6B085BF40%212442&authkey=ACZHMcwzA6_C3jA",
'RF_IBM': "https://onedrive.live.com/download?cid="
"888296E6B085BF40&resid=888296E6B085BF40%212448&authkey=AN-IxIQRKiUGNLs",
'SVM_IBM': "https://onedrive.live.com/download?cid="
"888296E6B085BF40&resid=888296E6B085BF40%212452&authkey=ABylpAEmXkIeo4Y",
'XGB': "https://onedrive.live.com/download?cid="
"888296E6B085BF40&resid=888296E6B085BF40%212445&authkey=AGLrkyb-sW7KO0E",
'XGB_IBM': "https://onedrive.live.com/download?cid="
"888296E6B085BF40&resid=888296E6B085BF40%212449&authkey=AFzuxe2GAz4UyQw"}
self.worker = path
self.model = model
self.grams = grams
self.tokens = tokens
self.flavor = flavor
self.metric = metric
self.option = option
self.best_results = None
self.old_res_data_frame = None
# self.download_results()
# self.store_best_result_and_config(metric='test_mean_c_MCC')
self.store_best_result_and_config(metric=metric)
# self.show_best_results('test_mean_c_MCC')
# self.computing_time_vs_runs()
# self.metric_vs_runs('accuracy')
# self.show_comparison_old_new_results(metric='test_mean_c_MCC', best=True)
# self.confusion_matrix(metric='test_mean_c_MCC', normalized=True)
def download_results(self):
"""Use this function only if you want to download results from OneDrive."""
wget.download(self.results[self.worker], out='{}.zip'.format(self.worker))
# Unzip
zip_ref = zipfile.ZipFile('{}.zip'.format(self.worker), 'r')
zip_ref.extractall(self.worker)
zip_ref.close()
# Delete zipped file
self.remove_file('{}.zip'.format(self.worker))
print('{}.zip downloaded and unpacked.'.format(self.worker))
def store_best_result_and_config(self, preprocessed=None, pipeline=None, metric='test_mean_c_MCC'):
pd.DataFrame(columns=['article', 'n_gram', 'tokens', 'model', 'flavor', 'preprocessed', 'pipeline',
'test_mean_accuracy', 'test_std_accuracy', 'test_mean_f-1', 'test_std_f-1',
'train_mean_accuracy', 'train_std_accuracy', 'train_mean_f-1', 'train_std_f-1',
'train_mean_c_MCC', 'train_std_c_MCC', 'test_mean_c_MCC', 'test_std_c_MCC',
'train_mean_TP', 'train_mean_TN', 'train_mean_FP', 'train_mean_FN', 'train_std_TP',
'train_std_TN', 'train_std_FP', 'train_std_FN', 'test_mean_TP', 'test_mean_TN',
'test_mean_FP', 'test_mean_FN', 'test_std_TP', 'test_std_TN', 'test_std_FP',
'test_std_FN', 'config_space']).to_csv('best_results.csv', index=False)
if metric == 'test_mean_c_MCC':
metric = 'test mean c_MCC'
else:
metric = 'test mean accuracy'
for (dirpath, dirnames, filenames) in os.walk(os.path.join('.', self.worker)):
if filenames:
try:
n_gram = dirpath.split('\\')[2].split('_')[0]
tokens = dirpath.split('\\')[3].split('_')[0]
article = dirpath.split('\\')[4].split('_')[0]
flavor = dirpath.split('\\')[5]
model = dirpath.split('\\')[6]
except IndexError as e:
n_gram = dirpath.split('/')[2].split('_')[0]
tokens = dirpath.split('/')[3].split('_')[0]
article = dirpath.split('/')[4].split('_')[0]
flavor = dirpath.split('/')[5]
model = dirpath.split('/')[6]
# load the example run from the log files
result = hpres.logged_results_to_HBS_result(dirpath)
# get all executed runs
all_runs = result.get_all_runs()
# get the 'dict' that translates config ids to the actual configurations
id2conf = result.get_id2config_mapping()
mcc = -1
best_run = None
# Find best scored run
for run in all_runs:
if run.info[metric] > mcc and int(run.budget) == 5000:
mcc = run.info[metric]
best_run = run
# Append best_results.csv with data
with open('best_results.csv', 'a+') as f:
f.write('{article},{n_gram},{tokens},{model},{flavor},{preprocessed},'
'{pipeline},{test_mean_accuracy},'
'{test_std_accuracy},{test_mean_f},{test_std_f},'
'{train_mean_accuracy},{train_std_accuracy},'
'{train_mean_f},{train_std_f},{train_mean_c_MCC},{train_std_c_MCC},'
'{test_mean_c_MCC},{test_std_c_MCC},'
'{train_mean_TP},{train_mean_TN},{train_mean_FP},{train_mean_FN},'
'{train_std_TP},{train_std_TN},'
'{train_std_FP},{train_std_FN},{test_mean_TP},{test_mean_TN},'
'{test_mean_FP},{test_mean_FN},'
'{test_std_TP},{test_std_TN},{test_std_FP},{test_std_FN},'
'{config_space}\n'.format(article=article,
n_gram=n_gram,
tokens=tokens,
model=model,
flavor=flavor,
preprocessed=preprocessed,
pipeline=pipeline,
test_mean_accuracy=best_run.info['test mean accuracy'],
test_std_accuracy=best_run.info['test std accuracy'],
test_mean_f=best_run.info['test mean f-1'],
test_std_f=best_run.info['test std f-1'],
train_mean_accuracy=best_run.info['train mean accuracy'],
train_std_accuracy=best_run.info['train std accuracy'],
train_mean_f=best_run.info['train mean f-1'],
train_std_f=best_run.info['train std f-1'],
train_mean_c_MCC=best_run.info['train mean c_MCC'],
train_std_c_MCC=best_run.info['train std c_MCC'],
test_mean_c_MCC=best_run.info['test mean c_MCC'],
test_std_c_MCC=best_run.info['test std c_MCC'],
train_mean_TP=best_run.info['train mean TP'],
train_mean_TN=best_run.info['train mean TN'],
train_mean_FP=best_run.info['train mean FP'],
train_mean_FN=best_run.info['train mean FN'],
test_mean_TP=best_run.info['test mean TP'],
test_mean_TN=best_run.info['test mean TN'],
test_mean_FP=best_run.info['test mean FP'],
test_mean_FN=best_run.info['test mean FN'],
train_std_TP=best_run.info['train std TP'],
train_std_TN=best_run.info['train std TN'],
train_std_FP=best_run.info['train std FP'],
train_std_FN=best_run.info['train std FN'],
test_std_TP=best_run.info['test std TP'],
test_std_TN=best_run.info['test std TN'],
test_std_FP=best_run.info['test std FP'],
test_std_FN=best_run.info['test std FN'],
config_space='\"{}\"'.format(
str(id2conf[best_run.config_id]['config']))))
def show_best_results(self, metric):
"""Plot metric heatmap per article for particular n_gram and model."""
self.best_results = pd.read_csv('best_results.csv')
# print(self.best_results)
# print(self.best_results.loc[(self.best_results['model'] == model) & (self.best_results['n_gram'] == 1) & (
# self.best_results['article'] == '1'), ['tokens', 'flavor', metric]])
if metric == 'test_mean_accuracy':
vmin, vmax = 0.5, 1.0
elif metric == 'test_mean_c_MCC':
vmin, vmax = 0.0, 1.0
for n_gram in [1, 2, 3, 4, 5, 6]:
fig, axis = plt.subplots(6, 2, sharey=True, sharex=True, figsize=(50, 50))
cbar_ax = fig.add_axes([.91, .3, .03, .4])
for i, art in enumerate([['1', None], ['2', '3'], ['5', '6'], ['8', '10'], ['11', '13'], ['34', 'p1']]):
transformed_table = pd.pivot_table(self.best_results.loc[
(self.best_results['model'] == self.model) & (
self.best_results['n_gram'] == n_gram) & (
self.best_results['article'] == art[0]), ['tokens',
'flavor',
metric]],
index='tokens',
columns='flavor', values=metric)
print(transformed_table)
sns.heatmap(transformed_table.T, annot=True, vmin=vmin, vmax=vmax, ax=axis[i, 0], cbar_ax=cbar_ax)
axis[i, 0].set_title('Article={}'.format(art[0]))
axis[i, 0].set_ylabel('')
axis[i, 0].set_xlabel('')
axis[i, 0].tick_params(axis=u'both', which=u'both', length=0)
for tick in axis[i, 0].get_yticklabels():
tick.set_rotation(0)
if art[1] is None:
axis[i, 1].axis('off')
continue
transformed_table_2 = pd.pivot_table(self.best_results.loc[
(self.best_results['model'] == self.model) & (
self.best_results['n_gram'] == n_gram) & (
self.best_results['article'] == art[1]), ['tokens',
'flavor',
metric]],
index='tokens',
columns='flavor', values=metric)
print(transformed_table_2)
sns.heatmap(transformed_table.T, annot=True, vmin=vmin, vmax=vmax, ax=axis[i, 1], cbar_ax=cbar_ax)
axis[i, 1].set_title('Article={}'.format(art[1]))
axis[i, 1].set_ylabel('')
axis[i, 1].set_xlabel('')
axis[i, 1].tick_params(axis=u'both', which=u'both', length=0)
for tick in axis[i, 1].get_yticklabels():
tick.set_rotation(0)
plt.subplots_adjust(hspace=0.5)
fig.suptitle('N_gram: {}\nModel: {}, Metric: {}, '.format(n_gram, self.model, metric))
fig.text(0.5, 0.04, 'Tokens', ha='center')
fig.text(0.04, 0.5, 'Flavor', va='center', rotation='vertical')
plt.show()
def computing_time_vs_runs(self):
# TODO: check if there will be some better way to display the results
"""Creates optimization times vs optimization runs graphs."""
empty_df = pd.DataFrame(columns=['tokens', 'article', 'flavor', 'run_0', 'run_1',
'run_2', 'run_3', 'run_4', 'run_5', 'run_6', 'run_7'])
# sum_cases = np.sum([951, 1124, 2573, 2292, 6891, 1289, 560, 213, 1090, 136, 1301])
# # Normalized weights
# num_cases = {'1': 951/sum_cases, '2': 1124/sum_cases, '3': 2573/sum_cases, '5': 2292/sum_cases,
# '6': 6891/sum_cases, '8': 1289/sum_cases, '10': 560/sum_cases, '11': 213/sum_cases,
# '13': 1090/sum_cases, '34': 136/sum_cases, 'p1': 1301/sum_cases}
self.time_vs_run = {1: empty_df,
2: empty_df,
3: empty_df,
4: empty_df,
5: empty_df,
6: empty_df}
for (dirpath, dirnames, filenames) in os.walk(os.path.join('.', self.worker)):
if filenames:
try:
n_gram = dirpath.split('\\')[2].split('_')[0]
tokens = dirpath.split('\\')[3].split('_')[0]
article = dirpath.split('\\')[4].split('_')[0]
flavor = dirpath.split('\\')[5]
model = dirpath.split('\\')[6]
except IndexError as e:
n_gram = dirpath.split('/')[2].split('_')[0]
tokens = dirpath.split('/')[3].split('_')[0]
article = dirpath.split('/')[4].split('_')[0]
flavor = dirpath.split('/')[5]
model = dirpath.split('/')[6]
if n_gram == '1' and model == 'random_forest':
# load the example run from the log files
result = hpres.logged_results_to_HBS_result(dirpath)
# get all executed runs
all_runs = result.get_all_runs()
# get the 'dict' that translates config ids to the actual configurations
id2conf = result.get_id2config_mapping()
times = {'run_0': [np.inf, 0], 'run_1': [np.inf, 0], 'run_2': [np.inf, 0],
'run_3': [np.inf, 0], 'run_4': [np.inf, 0],
'run_5': [np.inf, 0], 'run_6': [np.inf, 0], 'run_7': [np.inf, 0],
'run_8': [np.inf, 0], 'run_9': [np.inf, 0]}
for run in all_runs:
if run.time_stamps['started'] < times['run_{}'.format(run.config_id[0])][0]:
times['run_{}'.format(run.config_id[0])][0] = run.time_stamps['started']
if run.time_stamps['finished'] > times['run_{}'.format(run.config_id[0])][1]:
times['run_{}'.format(run.config_id[0])][1] = run.time_stamps['finished']
dict_to_append = {'tokens': int(tokens),
'article': article,
'flavor': flavor,
'run_0': times['run_0'][1] - times['run_0'][0],
'run_1': times['run_1'][1] - times['run_1'][0],
'run_2': times['run_1'][1] - times['run_1'][0],
'run_3': times['run_3'][1] - times['run_3'][0],
'run_4': times['run_4'][1] - times['run_4'][0],
'run_5': times['run_5'][1] - times['run_5'][0],
'run_6': times['run_6'][1] - times['run_6'][0],
'run_7': times['run_7'][1] - times['run_7'][0],
'run_8': times['run_8'][1] - times['run_8'][0],
'run_9': times['run_9'][1] - times['run_9'][0],
}
self.time_vs_run[int(n_gram)] = \
self.time_vs_run[int(n_gram)].append(dict_to_append, ignore_index=True)
# [self.time_vs_run[i].set_index('tokens', inplace=True) for i in range(1, 7)]
# [self.time_vs_run[i].set_index('tokens', inplace=True) for i in range(1, 2)]
for n_gram, value in self.time_vs_run.items():
# sns.lineplot(x='tokens', y='run_8', hue="flavor", data=value.groupby('article').mean)
fig, axs = plt.subplots(5, 2)
for i in range(5):
sns.barplot(x='tokens', y='run_{}'.format(i), hue="flavor", data=value, ax=axs[i, 0])
sns.barplot(x='tokens', y='run_{}'.format(i + 5), hue="flavor", data=value, ax=axs[i, 1])
plt.show()
# desc_mean = value.loc[value['flavor']=='desc'].groupby('tokens').mean()
# bow_mean = value.loc[value['flavor']=='bow'].groupby('tokens').mean()
# desc_bow_mean = value.loc[value['flavor']=='desc+bow'].groupby('tokens').mean()
#
# desc_std = value.loc[value['flavor'] == 'desc'].groupby('tokens').std()
# bow_std = value.loc[value['flavor'] == 'bow'].groupby('tokens').std()
# desc_bow_std = value.loc[value['flavor'] == 'desc+bow'].groupby('tokens').std()
#
# # plt.plot(x=desc_mean.index.values, y=desc_mean['run_0'].values)
# desc_mean.plot()
# plt.show()
# print(value.loc[value['flavor']=='desc'].groupby('tokens').mean())
# exit()
fig, axs = plt.subplots(5, 2)
for i in range(5):
sns.lineplot(x='tokens', y='run_{}'.format(i), hue="flavor", data=value, ax=axs[i, 0])
sns.lineplot(x='tokens', y='run_{}'.format(i + 5), hue="flavor", data=value, ax=axs[i, 1])
plt.show()
value = value[
['tokens', 'article', 'flavor', 'run_9', 'run_8', 'run_7', 'run_6', 'run_5', 'run_4', 'run_3', 'run_2',
'run_1', 'run_0']]
sns.heatmap(value.loc[(value['flavor'] == 'desc') & (value['article'] == '1')].drop(['flavor', 'article'],
axis=1).sort_values(
by=['tokens']).set_index('tokens').T, annot=True)
plt.title('Run computation time[s] vs number of tokens. (flavor=desc, article=1)')
plt.yticks(rotation='horizontal')
plt.show()
def metric_vs_runs(self, metric):
"""Creates optimization times vs optimization runs graphs."""
empty_df = pd.DataFrame(columns=['tokens', 'article', 'flavor', 'run_0', 'run_1',
'run_2', 'run_3', 'run_4', 'run_5', 'run_6', 'run_7'])
# sum_cases = np.sum([951, 1124, 2573, 2292, 6891, 1289, 560, 213, 1090, 136, 1301])
# # Normalized weights
# num_cases = {'1': 951/sum_cases, '2': 1124/sum_cases, '3': 2573/sum_cases, '5': 2292/sum_cases,
# '6': 6891/sum_cases, '8': 1289/sum_cases, '10': 560/sum_cases, '11': 213/sum_cases,
# '13': 1090/sum_cases, '34': 136/sum_cases, 'p1': 1301/sum_cases}
self.time_vs_run = {1: empty_df,
2: empty_df,
3: empty_df,
4: empty_df,
5: empty_df,
6: empty_df}
for (dirpath, dirnames, filenames) in os.walk(os.path.join('.', self.worker)):
if filenames:
try:
n_gram = dirpath.split('\\')[2].split('_')[0]
tokens = dirpath.split('\\')[3].split('_')[0]
article = dirpath.split('\\')[4].split('_')[0]
flavor = dirpath.split('\\')[5]
model = dirpath.split('\\')[6]
except IndexError as e:
n_gram = dirpath.split('/')[2].split('_')[0]
tokens = dirpath.split('/')[3].split('_')[0]
article = dirpath.split('/')[4].split('_')[0]
flavor = dirpath.split('/')[5]
model = dirpath.split('/')[6]
if n_gram == '1' and model == 'random_forest':
# load the example run from the log files
result = hpres.logged_results_to_HBS_result(dirpath)
# get all executed runs
all_runs = result.get_all_runs()
# get the 'dict' that translates config ids to the actual configurations
id2conf = result.get_id2config_mapping()
times = {'run_0': [0, 0], 'run_1': [0, 0], 'run_2': [0, 0],
'run_3': [0, 0], 'run_4': [0, 0],
'run_5': [0, 0], 'run_6': [0, 0], 'run_7': [0, 0],
'run_8': [0, 0], 'run_9': [0, 0]}
for run in all_runs:
if int(run.budget) == 5000:
if run.info['test mean {}'.format(metric)] > times['run_{}'.format(run.config_id[0])][0]:
times['run_{}'.format(run.config_id[0])][0] = run.info['test mean {}'.format(metric)]
times['run_{}'.format(run.config_id[0])][1] = run.info['test std {}'.format(metric)]
dict_to_append = {'tokens': int(tokens),
'article': article,
'flavor': flavor,
'run_0': times['run_0'][0],
'run_1': times['run_1'][0],
'run_2': times['run_1'][0],
'run_3': times['run_3'][0],
'run_4': times['run_4'][0],
'run_5': times['run_5'][0],
'run_6': times['run_6'][0],
'run_7': times['run_7'][0],
'run_8': times['run_8'][0],
'run_9': times['run_9'][0],
}
self.time_vs_run[int(n_gram)] = \
self.time_vs_run[int(n_gram)].append(dict_to_append, ignore_index=True)
for n_gram, value in self.time_vs_run.items():
# fig, axs = plt.subplots(5, 2)
# for i in range(5):
# sns.barplot(x='tokens', y='run_{}'.format(i), hue="flavor", data=value, ax=axs[i, 0])
# sns.barplot(x='tokens', y='run_{}'.format(i+5), hue="flavor", data=value, ax=axs[i, 1])
# plt.show()
# print(value.set_index(['tokens', 'article', 'flavor']))
# g = sns.FacetGrid(value.set_index(['article']), row='tokens', col='flavor')
# g.map(sns.distplot, "run_9")
# # value.set_index(['tokens', 'article', 'flavor']).plot()
# plt.show()
# exit()
# fig, axs = plt.subplots(5, 2)
# for i in range(5):
# sns.lineplot(x='tokens', y='run_{}'.format(i), hue="flavor", data=value, ax=axs[i, 0])
# sns.lineplot(x='tokens', y='run_{}'.format(i + 5), hue="flavor", data=value, ax=axs[i, 1])
# plt.show()
value = value[['tokens', 'article', 'flavor', 'run_9', 'run_8', 'run_7',
'run_6', 'run_5', 'run_4', 'run_3', 'run_2', 'run_1', 'run_0']]
if metric == 'accuracy':
vmin, vmax = 0.6, 1.0
elif metric == 'c_MCC':
vmin, vmax = 0.0, 1.0
for art in ['1', '2', '3', '5', '6', '8', '10', '11', '13', '34', 'p1']:
fig, axs = plt.subplots(1, 3, figsize=(20, 20), sharey=True)
for i, flav in enumerate(['desc', 'bow', 'desc+bow']):
sns.heatmap(value.loc[(value['flavor'] == '{}'.format(flav)) & (value['article'] == art)].drop(
['flavor', 'article'], axis=1).sort_values(by=['tokens']).set_index('tokens').T,
annot=True, ax=axs[i], vmin=vmin, vmax=vmax)
axs[i].set_title(
'Model {}\n{} per run vs number of tokens.\n(flavor={}, article={}, n_grams={})'.format(model,
metric,
flav,
art,
n_gram))
plt.show()
def show_comparison_old_new_results(self, best=True):
"""Show comparison between old results and new one.
metric[str] - name of the metric to compare
best[bool] - indicator to compare with best old results"""
self.best_results = pd.read_csv('best_results.csv')
self.load_standard_results()
self.old_res_data_frame = pd.DataFrame(columns=['article', 'n_gram', 'tokens', 'model', 'flavor',
'test_mean_accuracy', 'test_std_accuracy', 'test_mean_f-1',
'train_mean_accuracy', 'train_std_accuracy', 'train_mean_f-1',
'train_mean_c_MCC', 'test_mean_c_MCC'])
if self.model == 'random_forest' and not best:
old_model = 'Random Forest'
self.transform_old_results([self.model, old_model])
elif self.model == 'extratrees' and not best:
old_model = 'Extra Tree'
self.transform_old_results([self.model, old_model])
elif self.model == 'xgboost' and not best:
old_model = 'Gradient Boosting'
self.transform_old_results([self.model, old_model])
elif self.model == 'dnn' and not best:
old_model = 'Neural Net'
self.transform_old_results([self.model, old_model])
elif self.model == 'svm' and not best:
old_model = 'Linear SVC'
self.transform_old_results([self.model, old_model])
elif best:
self.transform_old_results([self.model, None])
# print(self.old_res_data_frame)
if self.metric == 'test_mean_accuracy':
vmin, vmax = 0.5, 1.0
elif self.metric == 'test_mean_c_MCC':
vmin, vmax = 0.0, 1.0
if best: # Plot the best old result for comparison
fig = plt.figure(constrained_layout=False, figsize=(50, 100))
# Defines widths and heights of all subplots on a figure
widths = [5, 1, 0.8, 5, 1, 0.1]
heights = [3, 3, 3, 3, 3, 3]
spec = fig.add_gridspec(ncols=6, nrows=6, width_ratios=widths, height_ratios=heights)
# Do not plot figures where is None value (these are dump figures for spacing)
articles = [['1', '1', None, None, None, None], ['2', '2', None, '3', '3', None],
['5', '5', None, '6', '6', None], ['8', '8', None, '10', '11', None],
['11', '11', None, '13', '13', None], ['34', '34', None, 'p1', 'p1', None]]
# Define heatbar parameters (horizontal and manual position on a figure)
cbar_ax = fig.add_axes([.55, .85, .35, .03])
cbar_kws = {"orientation": "horizontal"}
for row, row_art in enumerate(articles):
for col, art in enumerate(row_art):
ax = fig.add_subplot(spec[row, col])
if art is None: # Do not plot empty figures (spacing)
ax.axis('off')
continue
if col == 1 or col == 4: # These are old results
# Get results from one article
x = self.old_res_data_frame.loc[(self.old_res_data_frame['article'] == art), ['model',
'tokens',
'flavor',
self.metric]]
# print(x)
x_best = pd.DataFrame(columns=['model', 'tokens', 'flavor', self.metric])
# Leave only the best results per flavor (keep model names)
for f in ['desc', 'bow', 'desc+bow']:
x_part = x.where(x['flavor'] == f)
x_best = x_best.append(
x_part.loc[x_part[self.metric] == x_part[self.metric].max()].drop_duplicates(
subset=['flavor']))
# Merge model names with flavor (we need to display this on a graph)
x_best['flavor'] = x_best['flavor'] + ' ' + x_best['model']
x_best.drop(['model'], axis=1, inplace=True)
# print(x_best)
transformed_old = pd.pivot_table(x_best,
index='tokens',
columns='flavor',
values=self.metric).rename(index={5000: '5000_old'})
sns.heatmap(transformed_old.T, annot=True, vmin=vmin, vmax=vmax, ax=ax,
cbar=False, cbar_kws=cbar_kws)
ax.yaxis.tick_right()
ax.set_ylabel('')
if row != 5: # Get rid of all x axis ticks text leave only for the last row
ax.set_xlabel('')
ax.set_xticklabels([])
ax.tick_params(axis=u'x', which=u'both', length=0)
for tick in ax.get_yticklabels():
tick.set_rotation(0)
elif col == 0 or col == 3: # Plot new results
transformed_table = pd.pivot_table(self.best_results.loc[
(self.best_results['model'] == self.model) & (
self.best_results['n_gram'] == self.grams) & (
self.best_results['article'] == art), [
'tokens',
'flavor',
self.metric]],
index='tokens',
columns='flavor', values=self.metric)
# print(transformed_table)
sns.heatmap(transformed_table.T, annot=True, vmin=vmin, vmax=vmax, ax=ax,
cbar_ax=cbar_ax, cbar_kws=cbar_kws)
ax.set_title('Article={}'.format(art))
ax.set_ylabel('')
ax.yaxis.tick_right()
if row != 5:
ax.set_xlabel('')
ax.set_xticklabels([])
ax.tick_params(axis=u'x', which=u'both', length=0)
for tick in ax.get_xticklabels():
tick.set_rotation(180)
for tick in ax.get_yticklabels():
tick.set_rotation(0)
plt.subplots_adjust(hspace=0.5, wspace=0.5)
fig.suptitle(
'Current N_gram: {} --- Model: {} --- Metric: {}\n'
'Old N_gram: {} --- Model: best --- Metric: {}'.format(self.grams, self.model, self.metric, 4,
self.metric))
fig.text(0.5, 0.04, 'Tokens', ha='center')
fig.text(0.095, 0.5, 'Flavor', va='center', rotation='vertical')
plt.show()
else:
fig, axis = plt.subplots(6, 2, sharey=True, sharex=True, figsize=(50, 50))
cbar_ax = fig.add_axes([.91, .3, .03, .4])
for i, art in enumerate([['1', None], ['2', '3'], ['5', '6'], ['8', '10'], ['11', '13'], ['34', 'p1']]):
transformed_table = pd.pivot_table(self.best_results.loc[
(self.best_results['model'] == self.model) & (
self.best_results['n_gram'] == self.grams) & (
self.best_results['article'] == art[0]), ['tokens',
'flavor',
self.metric]],
index='tokens',
columns='flavor', values=self.metric)
# print(transformed_table)
transformed_old = pd.pivot_table(self.old_res_data_frame.loc[
(self.old_res_data_frame['model'] == self.model) & (
self.old_res_data_frame['article'] == art[0]), ['tokens',
'flavor',
self.metric]],
index='tokens',
columns='flavor', values=self.metric).rename(index={5000: '5000_old'})
# print(transformed_old)
transformed_table = transformed_table.append(transformed_old)
sns.heatmap(transformed_table.T, annot=True, vmin=vmin, vmax=vmax, ax=axis[i, 0], cbar_ax=cbar_ax)
axis[i, 0].set_title('Model: {}, Metric: {}, Article={}'.format(self.model,
self.metric,
art[0]))
axis[i, 0].set_ylabel('')
axis[i, 0].set_xlabel('')
axis[i, 0].tick_params(axis=u'both', which=u'both', length=0)
for tick in axis[i, 0].get_yticklabels():
tick.set_rotation(0)
if art[1] is None:
axis[i, 1].axis('off')
continue
transformed_table_2 = pd.pivot_table(self.best_results.loc[
(self.best_results['model'] == self.model) & (
self.best_results['n_gram'] == self.grams) & (
self.best_results['article'] == art[1]), ['tokens',
'flavor',
self.metric]],
index='tokens',
columns='flavor', values=self.metric)
# print(transformed_table_2)
transformed_old = pd.pivot_table(self.old_res_data_frame.loc[
(self.old_res_data_frame['model'] == self.model) & (
self.old_res_data_frame['article'] == art[1]), ['tokens',
'flavor',
self.metric]],
index='tokens',
columns='flavor', values=self.metric).rename(index={5000: '5000_old'})
# print(transformed_old)
transformed_table_2 = transformed_table_2.append(transformed_old)
print(transformed_table_2)
sns.heatmap(transformed_table.T, annot=True, vmin=vmin, vmax=vmax, ax=axis[i, 1], cbar_ax=cbar_ax)
axis[i, 1].set_title('Model: {}, Metric: {}, Article={}'.format(self.model,
self.metric,
art[1]))
axis[i, 1].set_ylabel('')
axis[i, 1].set_xlabel('')
axis[i, 1].tick_params(axis=u'both', which=u'both', length=0)
for tick in axis[i, 1].get_yticklabels():
tick.set_rotation(0)
plt.subplots_adjust(hspace=0.5)
fig.suptitle('Current N_gram: {}\nOld N_gram: {}'.format(self.grams, 4))
fig.text(0.5, 0.04, 'Tokens', ha='center')
fig.text(0.04, 0.5, 'Flavor', va='center', rotation='vertical')
plt.show()
def load_standard_results(self):
"""Loads results from basic experiments."""
with urllib.request.urlopen(
"https://raw.githubusercontent.com/aquemy/ECHR-OD_predictions/master/data/output/result_binary.json") as url:
self.standard_results = json.loads(url.read().decode())
def transform_old_results(self, models):
"""Takes old result in json format and transforms it into pandas dataframe for further processing."""
for key, val in self.standard_results.items():
key_items = key.split(' ')
article = key_items[1]
if len(key_items) == 5: # bow
flavor = 'bow'
elif len(key_items) == 6: # desc
flavor = 'desc'
else: # desc+bow
flavor = 'desc+bow'
if models[1] is not None:
append_dict = {'article': article, 'n_gram': 4, 'tokens': 5000, 'model': models[0], 'flavor': flavor,
'test_mean_accuracy': val['methods'][models[1]]['test']['test_acc'],
'test_std_accuracy': val['methods'][models[1]]['test']['test_acc_std'],
'test_mean_f-1': val['methods'][models[1]]['test']['test_f1_weighted'],
'train_mean_accuracy': val['methods'][models[1]]['train']['train_acc'],
'train_std_accuracy': val['methods'][models[1]]['train']['train_acc_std'],
'train_mean_f-1': val['methods'][models[1]]['train']['train_f1_weighted'],
'train_mean_c_MCC': val['methods'][models[1]]['train']['train_mcc'],
'test_mean_c_MCC': val['methods'][models[1]]['test']['test_mcc']}
self.old_res_data_frame = self.old_res_data_frame.append(append_dict, ignore_index=True)
else:
for model in val['methods'].keys():
append_dict = {'article': article, 'n_gram': 4, 'tokens': 5000, 'model': model,
'flavor': flavor,
'test_mean_accuracy': val['methods'][model]['test']['test_acc'],
'test_std_accuracy': val['methods'][model]['test']['test_acc_std'],
'test_mean_f-1': val['methods'][model]['test']['test_f1_weighted'],
'train_mean_accuracy': val['methods'][model]['train']['train_acc'],
'train_std_accuracy': val['methods'][model]['train']['train_acc_std'],
'train_mean_f-1': val['methods'][model]['train']['train_f1_weighted'],
'train_mean_c_MCC': val['methods'][model]['train']['train_mcc'],
'test_mean_c_MCC': val['methods'][model]['test']['test_mcc']}
self.old_res_data_frame = self.old_res_data_frame.append(append_dict, ignore_index=True)
def confusion_matrix(self, normalized=False):
"""Plot confusion matrices (results can be sorted by metric passed to self.store_best_result_and_config(metric)
normalized[bool] - normalisation indicator (range [0,1] for all matrices).
Plot per model, n_gram, tokens, flavor."""
self.best_results = pd.read_csv('best_results.csv')
# print(self.best_results.columns)
fig = plt.figure(constrained_layout=False, figsize=(50, 100))
# Defines widths and heights of all subplots on a figure
widths = [4, 4, 0.8, 4, 4]
heights = [3, 3, 3, 3, 3, 3]
spec = fig.add_gridspec(ncols=5, nrows=6, width_ratios=widths, height_ratios=heights)
# Do not plot figures where is None value (these are dump figures for spacing)
articles = [['1', '1', None, 'example', None], ['2', '2', None, '3', '3'],
['5', '5', None, '6', '6'], ['8', '8', None, '10', '11'],
['11', '11', None, '13', '13'], ['34', '34', None, 'p1', 'p1']]
for row, row_art in enumerate(articles):
for col, art in enumerate(row_art):
ax = fig.add_subplot(spec[row, col])
if art is None: # Do not plot empty figures (spacing)
ax.axis('off')
continue
if art == 'example':
annot = np.array([['True\nPositive', 'False\nPositive'], ['False\nNegative', 'True\nNegative']])
if normalized:
example = pd.DataFrame(
data={'Violation': [0.3, 0],
'No Violation': [0.7, 1]}).rename(
index={0: 'Violation', 1: 'No Violation'})
sns.heatmap(example, annot=annot, fmt='', ax=ax, vmin=0, vmax=1)
else:
example = pd.DataFrame(
data={'Violation': [2, 0],
'No Violation': [1, 3]}).rename(
index={0: 'Violation', 1: 'No Violation'})
sns.heatmap(example, annot=annot, fmt='', ax=ax, vmin=0, vmax=3)
ax.xaxis.tick_top()
ax.set_title('Example')
for tick in ax.get_yticklabels():
tick.set_rotation(0)
continue
if col == 0 or col == 3:
train = self.best_results.loc[
(self.best_results['model'] == self.model) & (self.best_results['n_gram'] == self.grams) & (
self.best_results['article'] == art) & (self.best_results['tokens'] == self.tokens) & (
self.best_results['flavor'] == self.flavor), ['train_mean_TP',
'train_mean_TN', 'train_mean_FP',
'train_mean_FN']]
# print(train)
if normalized:
norm_1 = train['train_mean_TN'].values[0] + train['train_mean_FP'].values[0]
norm_2 = train['train_mean_FN'].values[0] + train['train_mean_TP'].values[0]
train_conf_matrix = pd.DataFrame(
data={'Violation': [train['train_mean_TP'].values[0] / norm_2,
train['train_mean_FN'].values[0] / norm_2],
'No Violation': [train['train_mean_FP'].values[0] / norm_1,
train['train_mean_TN'].values[0] / norm_1],
}).rename(
index={0: 'Violation', 1: ' No Violation'})
# print(train_conf_matrix)
sns.heatmap(train_conf_matrix, annot=True, fmt='.3f', ax=ax, vmin=0, vmax=1,
cbar_kws={"ticks": [0, 0.5, 1]})
else:
train_conf_matrix = pd.DataFrame(
data={'Violation': [train['train_mean_TP'].values[0],
train['train_mean_FN'].values[0]],
'No Violation': [train['train_mean_FP'].values[0],
train['train_mean_TN'].values[0]],
}).rename(
index={0: 'Violation', 1: ' No Violation'})
# print(train_conf_matrix)
vmax = train.sum(axis=1).values[0]
sns.heatmap(train_conf_matrix, annot=True, fmt='.1f', ax=ax, vmin=0, vmax=vmax,
cbar_kws={"ticks": [0, vmax // 2, vmax]})
ax.invert_xaxis()
ax.set_title('Train Article={}'.format(art))
if row != 5: # Get rid of all x axis ticks text leave only for the last row
ax.set_xticklabels([])
ax.tick_params(axis=u'x', which=u'both', length=0)
if col != 0:
ax.set_yticklabels([])
ax.tick_params(axis=u'y', which=u'both', length=0)
for tick in ax.get_yticklabels():
tick.set_rotation(0)
elif col == 1 or col == 4:
test = self.best_results.loc[
(self.best_results['model'] == self.model) & (self.best_results['n_gram'] == self.grams) & (
self.best_results['article'] == art) & (self.best_results['tokens'] == self.tokens) & (
self.best_results['flavor'] == self.flavor), ['test_mean_TP', 'test_mean_TN',
'test_mean_FP',
'test_mean_FN']]
# print(test)
if normalized:
norm_1 = test['test_mean_TN'].values[0] + test['test_mean_FP'].values[0]
norm_2 = test['test_mean_FN'].values[0] + test['test_mean_TP'].values[0]
test_conf_matrix = pd.DataFrame(
data={'Violation': [test['test_mean_TP'].values[0] / norm_2,
test['test_mean_FN'].values[0] / norm_2],
'No Violation': [test['test_mean_FP'].values[0] / norm_1,
test['test_mean_TN'].values[0] / norm_1],
}).rename(
index={0: 'Violation', 1: ' No Violation'})
sns.heatmap(test_conf_matrix, annot=True, fmt='.3f', ax=ax, vmin=0, vmax=1,
cbar_kws={"ticks": [0, 0.5, 1]})
else:
test_conf_matrix = pd.DataFrame(
data={'Violation': [test['test_mean_TP'].values[0],
test['test_mean_FN'].values[0]],
'No Violation': [test['test_mean_FP'].values[0],
test['test_mean_TN'].values[0]],
}).rename(
index={0: 'Violation', 1: ' No Violation'})
vmax = test.sum(axis=1).values[0]
sns.heatmap(test_conf_matrix, annot=True, fmt='.1f', ax=ax, vmin=0, vmax=vmax,
cbar_kws={"ticks": [0, vmax // 2, vmax]})
# print(test_conf_matrix)
ax.invert_xaxis()
ax.set_title('Test Article={}'.format(art))
ax.set_yticklabels([])
ax.tick_params(axis=u'y', which=u'both', length=0)
if row != 5: # Get rid of all x axis ticks text leave only for the last row
ax.set_xticklabels([])
ax.tick_params(axis=u'x', which=u'both', length=0)
if normalized:
fig.suptitle(
'Normalized confusion Matrices by best metric: {}\nN_gram: {} Model: {} Tokens: {}'.format(
self.metric, self.grams, self.model, self.tokens))
else:
fig.suptitle(
'Confusion Matrices by best metric: {}\nN_gram: {} Model: {} Tokens: {}'.format(self.metric,
self.grams,
self.model,
self.tokens))
fig.text(0.5, 0.04, 'True', ha='center')
fig.text(0.04, 0.5, 'Predicted', va='center', rotation='vertical')
plt.subplots_adjust(hspace=0.4)
plt.show()
@staticmethod
def remove_file(file_):
try:
os.remove(file_)
print("File Removed! {}".format(file_))
except Exception as e:
print(e)
exit()
def chose_option(self):
if self.option == 'new_best_results':
self.show_best_results(args.metric)
elif self.option == 'computing_times':
self.computing_time_vs_runs()
elif self.option == 'metrics_vs_runs':
self.metric_vs_runs('accuracy')
elif self.option == 'old_vs_new':
self.show_comparison_old_new_results(best=True)
elif self.option == 'confusion_matrix':
self.confusion_matrix(normalized=True)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Main script for result comparison.')
parser.add_argument('--results_path',
type=str,
help='Path to results folder.',
default='results')
parser.add_argument('--model',
type=str,
help='Model name to compare/analysis.',
default='random_forest',
choices=['extratrees', 'random_forest', 'dnn', 'xgboost', 'svm'])
parser.add_argument('--n_grams',
type=int,
help='N-gram to compare.',
default=5,
choices=[1, 2, 3, 4, 5, 6])
parser.add_argument('--tokens',
type=int,
help='Token number to compare.',
default=5000,
choices=[1000, 5000, 7000, 10000, 30000, 60000, 80000, 100000])
parser.add_argument('--flavor',
type=str,
help='Flavor to compare.',
default='desc',
choices=['desc', 'bow', 'desc+bow'])
parser.add_argument('--metric',
type=str,
help='Measurement metric.',
default='test_mean_c_MCC',
choices=['test_mean_c_MCC', 'test_mean_accuracy'])
parser.add_argument('--option',
type=str,
help='What to display.',
default='old_vs_new',
choices=['new_best_results', 'computing_times', 'metrics_vs_runs',
'old_vs_new', 'confusion_matrix'])
parser.add_argument('--onedrive',
type=bool,
help='Use OneDrive storage?',
default=False)
args = parser.parse_args()
if args.model == 'extratrees':
results = Results(path=args.results_path, model='extratrees', grams=args.n_grams, tokens=args.tokens,
flavor=args.flavor, metric=args.metric, option=args.option)
results.chose_option()
elif args.model == 'random_forest':
results = Results(path=args.results_path, model='random_forest', grams=args.n_grams, tokens=args.tokens,
flavor=args.flavor, metric=args.metric, option=args.option)
results.chose_option()
elif args.model == 'dnn':
results = Results(path=args.results_path, model='dnn', grams=args.n_grams, tokens=args.tokens,
flavor=args.flavor, metric=args.metric, option=args.option)
results.chose_option()
elif args.model == 'xgboost':
results = Results(path=args.results_path, model='xgboost', grams=args.n_grams, tokens=args.tokens,
flavor=args.flavor, metric=args.metric, option=args.option)
results.chose_option()
else:
results = Results(path=args.results_path, model='svm', grams=args.n_grams, tokens=args.tokens,
flavor=args.flavor, metric=args.metric, option=args.option)
results.chose_option()