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plotting_nb_gen.py
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import os
import os.path as path
import yaml
import sys
import nbformat as nbf
data_dir = os.path.join(os.path.dirname(
os.path.abspath(__file__)), 'pipelining')
user_repo = 'DingLi23'
branch = 'pipelining'
# user_repo = 'youyinnn'
def gen_for_normal(exp_name, description, sample_list):
for sample_name in sample_list:
p_nb_file = path.join(data_dir, exp_name,
f'{exp_name}_{sample_name}_plotting.ipynb')
if (not path.exists(p_nb_file)):
print(
f'Generating plotting notebook for {exp_name} at {p_nb_file}')
nb = nbf.v4.new_notebook()
nb.metadata.kernelspec = {
"display_name": "Python 3",
"name": "python3"
}
nb.metadata.language_info = {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.0"
}
open_in_colab_href = f'<a href="https://colab.research.google.com/github/{user_repo}/s2search/blob/{branch}/pipelining/{exp_name}/{exp_name}_{sample_name}_plotting.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>'
exp_des = f'### Experiment Description\n\n{description.strip()}\n\n> This notebook is for experiment \\<{exp_name}\\> and data sample \\<{sample_name}\\>.'
init_md = '### Initialization'
init_code = f'''%load_ext autoreload
%autoreload 2
import numpy as np, sys, os
in_colab = 'google.colab' in sys.modules
# fetching code and data(if you are using colab
if in_colab:
!rm -rf s2search
!git clone --branch pipelining https://github.com/youyinnn/s2search.git
sys.path.insert(1, './s2search')
%cd s2search/pipelining/{exp_name}/
pic_dir = os.path.join('.', 'plot')
if not os.path.exists(pic_dir):
os.mkdir(pic_dir)
'''
loading_data_md = '### Loading data'
loading_data_code = f'''sys.path.insert(1, '../../')
import numpy as np, sys, os
from getting_data import get
from feature_masking import masking_options
sample_data_and_config_arr = get('{exp_name}', '{sample_name}')
for sample_data_and_config in sample_data_and_config_arr:
y_values = []
sample_origin_npy = sample_data_and_config['origin']
for array in sample_data_and_config['feature_stack']:
# define your y axis value here
y_value = np.absolute((sample_origin_npy - array) / sample_origin_npy)
# y_value = sample_origin_npy - array
y_values.append(y_value)
y_values = np.array(y_values)
sample_data_and_config['y_values'] = y_values
'''
plot_data_md = "### Plot the data"
plot_data_code = '''import matplotlib.pyplot as plt
def plot_scores_d(sample_name, y_values, sample_origin_npy, query, sample_masking_option_keys, sample_task_number):
plt.figure(figsize=(20, 15), dpi=80)
i = 0
for key in sample_masking_option_keys:
plt.scatter(
sample_origin_npy,
y_values[i],
c=masking_options[key]['color'],
marker=masking_options[key]['marker'],
label=masking_options[key]['plot_legend']
)
i += 1
plt.xlabel('Orginal Ranking Score',fontsize=16)
plt.ylabel('$y = \\\\frac{|Score_0 - Score\_feature_j|}{Score_0}$', fontsize=16)
plt.title(f'Distrubution of Ranking Score and Masking Features Difference\\nfor \\'{sample_name}\\', with query \\'{query}\\'', fontsize=20, pad=20)
x_max = 10
x_min = 0
x_pace = 1
y_max = 10
y_min = 0
y_pace = 1
# plt.xticks(np.arange(x_min, x_max, x_pace), size = 9)
# plt.yticks(np.arange(y_min, y_max, y_pace), size = 9)
# plt.ylim(y_min, y_max)
# plt.xlim(x_min, x_max)
plt.legend(prop={'size': 16})
plt.savefig(os.path.join('.', 'plot', f'{sample_name}-t{sample_task_number}.png'), facecolor='white', transparent=False)
plt.show()
for sample_data_and_config in sample_data_and_config_arr:
sample_and_task_name = sample_data_and_config['sample_and_task_name']
sample_origin_npy = sample_data_and_config['origin']
y_values = sample_data_and_config['y_values']
valid_data_count = 0
total_data_count = 0
for d in sample_origin_npy:
total_data_count += 1
if d > -10:
valid_data_count += 1
print(f'({valid_data_count}/{total_data_count}) ({round((valid_data_count / total_data_count) * 100, 2)}%) valid data for {sample_and_task_name} (original score is greater than -10)')
sample_query = sample_data_and_config['query']
sample_masking_option_keys = sample_data_and_config['masking_option_keys']
sample_task_number = sample_data_and_config['task_number']
plot_scores_d(sample_and_task_name, y_values, sample_origin_npy, sample_query, sample_masking_option_keys, sample_task_number)
'''
nb['cells'] = [
nbf.v4.new_markdown_cell(open_in_colab_href),
nbf.v4.new_markdown_cell(exp_des),
nbf.v4.new_markdown_cell(init_md),
nbf.v4.new_code_cell(init_code),
nbf.v4.new_markdown_cell(loading_data_md),
nbf.v4.new_code_cell(loading_data_code),
nbf.v4.new_markdown_cell(plot_data_md),
nbf.v4.new_code_cell(plot_data_code),
]
nbf.write(nb, str(p_nb_file))
def gen_for_ale(exp_name, description, sample_list):
for sample_name in sample_list:
ale_1w_score_file_name = f'{sample_name}_1w_ale_title.npz'
ale_1w_score_file = path.join(
data_dir, exp_name, 'scores', ale_1w_score_file_name)
if (path.exists(ale_1w_score_file)):
ale_1w_nb_file_name = f'{exp_name}_{sample_name}_1w_ale_plotting.ipynb'
ale_1w_nb_file = path.join(data_dir, exp_name, ale_1w_nb_file_name)
if (not path.exists(ale_1w_nb_file)):
print(
f'Generating plotting notebook for {exp_name} at {ale_1w_nb_file}')
nb = nbf.v4.new_notebook()
nb.metadata.kernelspec = {
"display_name": "Python 3",
"name": "python3"
}
nb.metadata.language_info = {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
}
open_in_colab_href = f'<a href="https://colab.research.google.com/github/{user_repo}/s2search/blob/{branch}/pipelining/{exp_name}/{ale_1w_nb_file_name}" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>'
exp_des = f'### Experiment Description\n\n{description.strip()}\n\n> This notebook is for experiment \\<{exp_name}\\> and data sample \\<{sample_name}\\>.'
init_md = '### Initialization'
init_code = f'''%load_ext autoreload
%autoreload 2
import numpy as np, sys, os
in_colab = 'google.colab' in sys.modules
# fetching code and data(if you are using colab
if in_colab:
!rm -rf s2search
!git clone --branch pipelining https://github.com/youyinnn/s2search.git
sys.path.insert(1, './s2search')
%cd s2search/pipelining/{exp_name}/
pic_dir = os.path.join('.', 'plot')
if not os.path.exists(pic_dir):
os.mkdir(pic_dir)
'''
loading_data_md = '### Loading data'
loading_data_code = f'''
sys.path.insert(1, '../../')
import numpy as np, sys, os, pandas as pd
from getting_data import read_conf
from s2search_score_pdp import pdp_based_importance
sample_name = '{sample_name}'
f_list = [
'title', 'abstract', 'venue', 'authors',
'year',
'n_citations'
]
ale_xy = {{}}
ale_metric = pd.DataFrame(columns=['feature_name', 'ale_range', 'ale_importance', 'absolute mean'])
for f in f_list:
file = os.path.join('.', 'scores', f'{{sample_name}}_1w_ale_{{f}}.npz')
if os.path.exists(file):
nparr = np.load(file)
quantile = nparr['quantile']
ale_result = nparr['ale_result']
values_for_rug = nparr.get('values_for_rug')
ale_xy[f] = {{
'x': quantile,
'y': ale_result,
'rug': values_for_rug,
'weird': ale_result[len(ale_result) - 1] > 20
}}
if f != 'year' and f != 'n_citations':
ale_xy[f]['x'] = list(range(len(quantile)))
ale_xy[f]['numerical'] = False
else:
ale_xy[f]['xticks'] = quantile
ale_xy[f]['numerical'] = True
ale_metric.loc[len(ale_metric.index)] = [f, np.max(ale_result) - np.min(ale_result), pdp_based_importance(ale_result, f), np.mean(np.abs(ale_result))]
# print(len(ale_result))
print(ale_metric.sort_values(by=['ale_importance'], ascending=False))
print()
'''
plot_data_md = "### ALE Plots"
plot_data_code = '''import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import MaxNLocator
categorical_plot_conf = [
{
'xlabel': 'Title',
'ylabel': 'ALE',
'ale_xy': ale_xy['title']
},
{
'xlabel': 'Abstract',
'ale_xy': ale_xy['abstract']
},
{
'xlabel': 'Authors',
'ale_xy': ale_xy['authors'],
# 'zoom': {
# 'inset_axes': [0.3, 0.3, 0.47, 0.47],
# 'x_limit': [89, 93],
# 'y_limit': [-1, 14],
# }
},
{
'xlabel': 'Venue',
'ale_xy': ale_xy['venue'],
# 'zoom': {
# 'inset_axes': [0.3, 0.3, 0.47, 0.47],
# 'x_limit': [89, 93],
# 'y_limit': [-1, 13],
# }
},
]
numerical_plot_conf = [
{
'xlabel': 'Year',
'ylabel': 'ALE',
'ale_xy': ale_xy['year'],
# 'zoom': {
# 'inset_axes': [0.15, 0.4, 0.4, 0.4],
# 'x_limit': [2019, 2023],
# 'y_limit': [1.9, 2.1],
# },
},
{
'xlabel': 'Citations',
'ale_xy': ale_xy['n_citations'],
# 'zoom': {
# 'inset_axes': [0.4, 0.65, 0.47, 0.3],
# 'x_limit': [-1000.0, 12000],
# 'y_limit': [-0.1, 1.2],
# },
},
]
def pdp_plot(confs, title):
fig, axes_list = plt.subplots(nrows=1, ncols=len(confs), figsize=(20, 5), dpi=100)
subplot_idx = 0
plt.suptitle(title, fontsize=20, fontweight='bold')
# plt.autoscale(False)
for conf in confs:
axes = axes if len(confs) == 1 else axes_list[subplot_idx]
sns.rugplot(conf['ale_xy']['rug'], ax=axes, height=0.02)
axes.axhline(y=0, color='k', linestyle='-', lw=0.8)
axes.plot(conf['ale_xy']['x'], conf['ale_xy']['y'])
axes.grid(alpha = 0.4)
# axes.set_ylim([-2, 20])
axes.xaxis.set_major_locator(MaxNLocator(integer=True))
axes.yaxis.set_major_locator(MaxNLocator(integer=True))
if ('ylabel' in conf):
axes.set_ylabel(conf.get('ylabel'), fontsize=20, labelpad=10)
# if ('xticks' not in conf['ale_xy'].keys()):
# xAxis.set_ticklabels([])
axes.set_xlabel(conf['xlabel'], fontsize=16, labelpad=10)
if not (conf['ale_xy']['weird']):
if (conf['ale_xy']['numerical']):
axes.set_ylim([-1.5, 1.5])
pass
else:
axes.set_ylim([-7, 15])
pass
if 'zoom' in conf:
axins = axes.inset_axes(conf['zoom']['inset_axes'])
axins.xaxis.set_major_locator(MaxNLocator(integer=True))
axins.yaxis.set_major_locator(MaxNLocator(integer=True))
axins.plot(conf['ale_xy']['x'], conf['ale_xy']['y'])
axins.set_xlim(conf['zoom']['x_limit'])
axins.set_ylim(conf['zoom']['y_limit'])
axins.grid(alpha=0.3)
rectpatch, connects = axes.indicate_inset_zoom(axins)
connects[0].set_visible(False)
connects[1].set_visible(False)
connects[2].set_visible(True)
connects[3].set_visible(True)
subplot_idx += 1
pdp_plot(categorical_plot_conf, f"ALE for {len(categorical_plot_conf)} categorical features")
# plt.savefig(os.path.join('.', 'plot', f'{sample_name}-1wale-categorical.png'), facecolor='white', transparent=False, bbox_inches='tight')
pdp_plot(numerical_plot_conf, f"ALE for {len(numerical_plot_conf)} numerical features")
# plt.savefig(os.path.join('.', 'plot', f'{sample_name}-1wale-numerical.png'), facecolor='white', transparent=False, bbox_inches='tight')
'''
nb['cells'] = [
nbf.v4.new_markdown_cell(open_in_colab_href),
nbf.v4.new_markdown_cell(exp_des),
nbf.v4.new_markdown_cell(init_md),
nbf.v4.new_code_cell(init_code),
nbf.v4.new_markdown_cell(loading_data_md),
nbf.v4.new_code_cell(loading_data_code),
nbf.v4.new_markdown_cell(plot_data_md),
nbf.v4.new_code_cell(plot_data_code),
]
nbf.write(nb, str(ale_1w_nb_file))
else:
print(f'no 1-way score file for {sample_name}')
ale_2w_score_file_name = f'{sample_name}_2w_ale_title_abstract.npz'
ale_2w_score_file = path.join(
data_dir, exp_name, 'scores', ale_2w_score_file_name)
if (path.exists(ale_2w_score_file)):
ale_2w_nb_file_name = f'{exp_name}_{sample_name}_2w_ale_plotting.ipynb'
ale_2w_nb_file = path.join(data_dir, exp_name, ale_2w_nb_file_name)
if (not path.exists(ale_2w_nb_file)):
print(
f'Generating plotting notebook for {exp_name} at {ale_2w_nb_file}')
nb = nbf.v4.new_notebook()
nb.metadata.kernelspec = {
"display_name": "Python 3",
"name": "python3"
}
nb.metadata.language_info = {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
}
open_in_colab_href = f'<a href="https://colab.research.google.com/github/{user_repo}/s2search/blob/{branch}/pipelining/{exp_name}/{ale_2w_nb_file_name}" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>'
exp_des = f'### Experiment Description\n\n{description.strip()}\n\n> This notebook is for experiment \\<{exp_name}\\> and data sample \\<{sample_name}\\>.'
init_md = '### Initialization'
init_code = f'''%load_ext autoreload
%autoreload 2
import numpy as np, sys, os
in_colab = 'google.colab' in sys.modules
# fetching code and data(if you are using colab
if in_colab:
!rm -rf s2search
!git clone --branch pipelining https://github.com/youyinnn/s2search.git
sys.path.insert(1, './s2search')
%cd s2search/pipelining/{exp_name}/
pic_dir = os.path.join('.', 'plot')
if not os.path.exists(pic_dir):
os.mkdir(pic_dir)
'''
loading_data_md = '### Loading data'
loading_data_code = f'''sys.path.insert(1, '../../')
import numpy as np, sys, os, pandas as pd
from getting_data import read_conf
sample_name = '{sample_name}'
f_list = [
'title',
'abstract',
'venue',
'authors',
'year',
'n_citations'
]
ale_rs = []
ale_metric = pd.DataFrame(columns=['f1_name', 'f2_name', 'mean', 'var', 'std'])
def replace_quantile(feature_name, quantile):
if feature_name == 'year' or feature_name == 'n_citations':
return quantile
else:
return list(range(len(quantile)))
mean_arr = []
var_arr = []
std_arr = []
for i in range(len(f_list)):
f1_name = f_list[i]
for j in range(i + 1, len(f_list)):
f2_name = f_list[j]
file = os.path.join('.', 'scores', f'{{sample_name}}_2w_ale_{{f1_name}}_{{f2_name}}.npz')
if os.path.exists(file):
nparr = np.load(file)
quantile_1 = nparr['quantile_1']
quantile_2 = nparr['quantile_2']
ale_result = nparr['ale_result']
mean_arr.append(np.mean(ale_result))
var_arr.append(np.var(ale_result, ddof=1))
std_arr.append(np.std(ale_result, ddof=1))
t = f'The mean of the 2-way ale - ({{f1_name}} * {{f2_name}})'
ale = {{
'ale': (ale_result),
'f1_quantile': replace_quantile(f1_name, quantile_1),
'f2_quantile': replace_quantile(f2_name, quantile_2),
'f1_name': f1_name,
'f2_name': f2_name,
'title': t
}}
ale_rs.append(ale)
pd.set_option('display.expand_frame_repr', False)
idx = 0
for i in range(len(f_list)):
f1_name = f_list[i]
for j in range(i + 1, len(f_list)):
f2_name = f_list[j]
ale_metric.loc[len(ale_metric.index)] = [f1_name, f2_name, mean_arr[idx], var_arr[idx], std_arr[idx]]
idx += 1
print(ale_metric.sort_values(by=['std'], ascending=False))
'''
plot_data_md = "### ALE Plots"
plot_data_code = '''
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
for ale in ale_rs:
fig, ax = plt.subplots(figsize=(14, 10), constrained_layout=True)
cmap = plt.colormaps['BuGn']
map = ax.pcolormesh(ale['f1_quantile'], ale['f2_quantile'], ale['ale'], cmap='bwr', edgecolors='k', linewidths=0, alpha=0.6)
# map = ax.contourf(ale['f1_quantile'], ale['f2_quantile'], ale['ale'], cmap='bwr', levels=30, alpha=0.7)
ax.set_xlabel(ale['f1_name'], fontsize=16, labelpad=10)
ax.set_ylabel(ale['f2_name'], fontsize=16, labelpad=10)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
ax.set_title(ale['title'], fontsize=20, pad=10)
fig.colorbar(map, ax=ax, shrink=0.6, pad=0.03)
plt.savefig(os.path.join('.', 'plot', f"{sample_name}-2wale-{ale['f1_name']}-{ale['f2_name']}.png"), facecolor='white', transparent=False, bbox_inches='tight')
'''
nb['cells'] = [
nbf.v4.new_markdown_cell(open_in_colab_href),
nbf.v4.new_markdown_cell(exp_des),
nbf.v4.new_markdown_cell(init_md),
nbf.v4.new_code_cell(init_code),
nbf.v4.new_markdown_cell(loading_data_md),
nbf.v4.new_code_cell(loading_data_code),
nbf.v4.new_markdown_cell(plot_data_md),
nbf.v4.new_code_cell(plot_data_code),
]
nbf.write(nb, str(ale_2w_nb_file))
else:
print(f'no 2-way score file for {sample_name}')
def gen_for_pdp(exp_name, description, sample_list):
for sample_name in sample_list:
p_nb_file = path.join(data_dir, exp_name,
f'{exp_name}_{sample_name}_plotting.ipynb')
if (not path.exists(p_nb_file)):
print(
f'Generating plotting notebook for {exp_name} at {p_nb_file}')
nb = nbf.v4.new_notebook()
nb.metadata.kernelspec = {
"display_name": "Python 3",
"name": "python3"
}
nb.metadata.language_info = {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
open_in_colab_href = f'<a href="https://colab.research.google.com/github/{user_repo}/s2search/blob/{branch}/pipelining/{exp_name}/{exp_name}_{sample_name}_plotting.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>'
exp_des = f'### Experiment Description\n\n{description.strip()}\n\n> This notebook is for experiment \\<{exp_name}\\> and data sample \\<{sample_name}\\>.'
init_md = '### Initialization'
init_code = f'''%load_ext autoreload
%autoreload 2
import numpy as np, sys, os
in_colab = 'google.colab' in sys.modules
# fetching code and data(if you are using colab
if in_colab:
!rm -rf s2search
!git clone --branch pipelining https://github.com/youyinnn/s2search.git
sys.path.insert(1, './s2search')
%cd s2search/pipelining/{exp_name}/
pic_dir = os.path.join('.', 'plot')
if not os.path.exists(pic_dir):
os.mkdir(pic_dir)
'''
loading_data_md = '### Loading data'
loading_data_code = f'''sys.path.insert(1, '../../')
import numpy as np, sys, os, pandas as pd
from s2search_score_pdp import pdp_based_importance, apply_order
sample_name = '{sample_name}'
f_list = ['title', 'abstract', 'venue', 'authors', 'year', 'n_citations']
pdp_xy = {{}}
pdp_metric = pd.DataFrame(columns=['feature_name', 'pdp_range', 'pdp_importance'])
for f in f_list:
file = os.path.join('.', 'scores', f'{{sample_name}}_pdp_{{f}}.npz')
if os.path.exists(file):
data = np.load(file)
sorted_pdp_data = apply_order(data)
feature_pdp_data = [np.mean(pdps) for pdps in sorted_pdp_data]
pdp_xy[f] = {{
'y': feature_pdp_data,
'numerical': True
}}
if f == 'year' or f == 'n_citations':
pdp_xy[f]['x'] = np.sort(data['arr_1'])
else:
pdp_xy[f]['y'] = feature_pdp_data
pdp_xy[f]['x'] = list(range(len(feature_pdp_data)))
pdp_xy[f]['numerical'] = False
pdp_metric.loc[len(pdp_metric.index)] = [f, np.max(feature_pdp_data) - np.min(feature_pdp_data), pdp_based_importance(feature_pdp_data, f)]
pdp_xy[f]['weird'] = feature_pdp_data[len(feature_pdp_data) - 1] > 30
print(pdp_metric.sort_values(by=['pdp_importance'], ascending=False))
'''
plot_data_md = "### PDP"
plot_data_code = '''import matplotlib.pyplot as plt
categorical_plot_conf = [
{
'xlabel': 'Title',
'ylabel': 'Scores',
'pdp_xy': pdp_xy['title']
},
{
'xlabel': 'Abstract',
'pdp_xy': pdp_xy['abstract']
},
{
'xlabel': 'Authors',
'pdp_xy': pdp_xy['authors']
},
{
'xlabel': 'Venue',
'pdp_xy': pdp_xy['venue'],
# 'zoom': {
# 'inset_axes': [0.15, 0.45, 0.47, 0.47],
# 'x_limit': [950, 1010],
# 'y_limit': [-9, 7],
# 'connects': [True, True, False, False]
# }
},
]
numerical_plot_conf = [
{
'xlabel': 'Year',
'ylabel': 'Scores',
'pdp_xy': pdp_xy['year']
},
{
'xlabel': 'Citation Count',
'pdp_xy': pdp_xy['n_citations'],
# 'zoom': {
# 'inset_axes': [0.5, 0.2, 0.47, 0.47],
# 'x_limit': [-100, 1000],
# 'y_limit': [-7.3, -6.2],
# 'connects': [False, False, True, True]
# }
}
]
def pdp_plot(confs, title):
fig, axes = plt.subplots(nrows=1, ncols=len(confs), figsize=(20, 5), dpi=100)
subplot_idx = 0
plt.suptitle(title, fontsize=20, fontweight='bold')
# plt.autoscale(False)
for conf in confs:
axess = axes if len(confs) == 1 else axes[subplot_idx]
axess.plot(conf['pdp_xy']['x'], conf['pdp_xy']['y'])
axess.grid(alpha = 0.4)
if ('ylabel' in conf):
axess.set_ylabel(conf.get('ylabel'), fontsize=20, labelpad=10)
axess.set_xlabel(conf['xlabel'], fontsize=16, labelpad=10)
if not (conf['pdp_xy']['weird']):
if (conf['pdp_xy']['numerical']):
# axess.set_ylim([-1, 3])
pass
else:
axess.set_ylim([-15, 10])
pass
if 'zoom' in conf:
axins = axess.inset_axes(conf['zoom']['inset_axes'])
axins.plot(conf['pdp_xy']['x'], conf['pdp_xy']['y'])
axins.set_xlim(conf['zoom']['x_limit'])
axins.set_ylim(conf['zoom']['y_limit'])
axins.grid(alpha=0.3)
rectpatch, connects = axess.indicate_inset_zoom(axins)
connects[0].set_visible(conf['zoom']['connects'][0])
connects[1].set_visible(conf['zoom']['connects'][1])
connects[2].set_visible(conf['zoom']['connects'][2])
connects[3].set_visible(conf['zoom']['connects'][3])
subplot_idx += 1
pdp_plot(categorical_plot_conf, "PDPs for four categorical features")
plt.savefig(os.path.join('.', 'plot', f'{sample_name}-categorical.png'), facecolor='white', transparent=False, bbox_inches='tight')
# second fig
pdp_plot(numerical_plot_conf, "PDPs for two numerical features")
plt.savefig(os.path.join('.', 'plot', f'{sample_name}-numerical.png'), facecolor='white', transparent=False, bbox_inches='tight')
'''
nb['cells'] = [
nbf.v4.new_markdown_cell(open_in_colab_href),
nbf.v4.new_markdown_cell(exp_des),
nbf.v4.new_markdown_cell(init_md),
nbf.v4.new_code_cell(init_code),
nbf.v4.new_markdown_cell(loading_data_md),
nbf.v4.new_code_cell(loading_data_code),
nbf.v4.new_markdown_cell(plot_data_md),
nbf.v4.new_code_cell(plot_data_code),
]
nbf.write(nb, str(p_nb_file))
ice_nb_file = path.join(data_dir, exp_name,
f'{exp_name}_ice_{sample_name}_plotting.ipynb')
if (not path.exists(ice_nb_file)):
print(
f'Generating plotting notebook for {exp_name} at {ice_nb_file}')
nb = nbf.v4.new_notebook()
nb.metadata.kernelspec = {
"display_name": "Python 3",
"name": "python3"
}
nb.metadata.language_info = {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
open_in_colab_href = f'<a href="https://colab.research.google.com/github/{user_repo}/s2search/blob/{branch}/pipelining/{exp_name}/{exp_name}_ice_{sample_name}_plotting.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>'
exp_des = f'### Experiment Description\n\n{description.strip()}\n\n> This notebook is for experiment \\<{exp_name}\\> and data sample \\<{sample_name}\\>.'
init_md = '### Initialization'
init_code = f'''%load_ext autoreload
%autoreload 2
import numpy as np, sys, os
in_colab = 'google.colab' in sys.modules
# fetching code and data(if you are using colab
if in_colab:
!rm -rf s2search
!git clone --branch pipelining https://github.com/youyinnn/s2search.git
sys.path.insert(1, './s2search')
%cd s2search/pipelining/{exp_name}/
pic_dir = os.path.join('.', 'plot')
if not os.path.exists(pic_dir):
os.mkdir(pic_dir)
'''
loading_data_md = '### Loading data'
loading_data_code = f'''sys.path.insert(1, '../../')
import numpy as np, sys, os, pandas as pd
from s2search_score_pdp import pdp_based_importance, apply_order
sample_name = '{sample_name}'
f_list = ['title',
'abstract', 'venue', 'authors', 'year', 'n_citations']
pdp_xy = {{}}
for f in f_list:
file = os.path.join('.', 'scores', f'{{sample_name}}_pdp_{{f}}.npz')
if os.path.exists(file):
data = np.load(file)
sorted_pdp_data = apply_order(data)
average_pdp = [np.mean(pdps) for pdps in sorted_pdp_data]
# tran = sorted_pdp_data
tran = np.flipud(np.rot90(sorted_pdp_data))
average_pdp -= average_pdp[0]
for instance in tran:
anchor = instance[0]
instance -= anchor
pdp_xy[f] = {{
'y': tran,
'numerical': True,
'average': average_pdp
}}
if f == 'year' or f == 'n_citations':
pdp_xy[f]['x'] = np.sort(data['arr_1'])
else:
pdp_xy[f]['x'] = list(range(len(sorted_pdp_data)))
pdp_xy[f]['numerical'] = False
'''
plot_data_md = "### PDP"
plot_data_code = '''import matplotlib.pyplot as plt
categorical_plot_conf = [
{
'xlabel': 'Title',
'ylabel': 'Scores',
'pdp_xy': pdp_xy['title']
},
{
'xlabel': 'Abstract',
'pdp_xy': pdp_xy['abstract']
},
{
'xlabel': 'Authors',
'pdp_xy': pdp_xy['authors']
},
{
'xlabel': 'Venue',
'pdp_xy': pdp_xy['venue'],
# 'zoom': {
# 'inset_axes': [0.15, 0.45, 0.47, 0.47],
# 'x_limit': [950, 1010],
# 'y_limit': [-9, 7],
# 'connects': [True, True, False, False]
# }
},
]
numerical_plot_conf = [
{
'xlabel': 'Year',
'ylabel': 'Scores',
'pdp_xy': pdp_xy['year']
},
{
'xlabel': 'Citation Count',
'pdp_xy': pdp_xy['n_citations'],
# 'zoom': {
# 'inset_axes': [0.5, 0.2, 0.47, 0.47],
# 'x_limit': [-100, 1000],
# 'y_limit': [-7.3, -6.2],
# 'connects': [False, False, True, True]
# }
}
]
def pdp_plot(confs, title):
fig, axes = plt.subplots(nrows=1, ncols=len(confs), figsize=(20, 5), dpi=100)
subplot_idx = 0
plt.suptitle(title, fontsize=20, fontweight='bold')
# plt.autoscale(False)
c = '#AAAAAA'
for conf in confs:
axess = axes if len(confs) == 1 else axes[subplot_idx]
axess.plot(conf['pdp_xy']['x'], conf['pdp_xy']['average'], c='#FFF301', lw=2, zorder=2)
for y in conf['pdp_xy']['y'][:]:
axess.plot(conf['pdp_xy']['x'], y,
c=c,zorder=1
)
axess.grid(alpha = 0.4)
if ('ylabel' in conf):
axess.set_ylabel(conf.get('ylabel'), fontsize=20, labelpad=10)
axess.set_xlabel(conf['xlabel'], fontsize=16, labelpad=10)
if (conf['pdp_xy']['numerical']):
axess.set_ylim([-0.1, 2.5])
pass
else:
axess.set_ylim([-1, 27])
pass
if 'zoom' in conf:
axins = axess.inset_axes(conf['zoom']['inset_axes'])
axins.plot(conf['pdp_xy']['x'], conf['pdp_xy']['average'], c='#FFF301', lw=2, zorder=2)
for y in conf['pdp_xy']['y'][:]:
axins.plot(conf['pdp_xy']['x'], y,
c=c,zorder=1
)
axins.set_xlim(conf['zoom']['x_limit'])
axins.set_ylim(conf['zoom']['y_limit'])
axins.grid(alpha=0.3)
rectpatch, connects = axess.indicate_inset_zoom(axins)
connects[0].set_visible(conf['zoom']['connects'][0])
connects[1].set_visible(conf['zoom']['connects'][1])
connects[2].set_visible(conf['zoom']['connects'][2])
connects[3].set_visible(conf['zoom']['connects'][3])
subplot_idx += 1
pdp_plot(categorical_plot_conf, "ICE Plots for four categorical features")
# second fig
pdp_plot(numerical_plot_conf, "ICE Plots for two numerical features")
'''
nb['cells'] = [
nbf.v4.new_markdown_cell(open_in_colab_href),
nbf.v4.new_markdown_cell(exp_des),
nbf.v4.new_markdown_cell(init_md),
nbf.v4.new_code_cell(init_code),
nbf.v4.new_markdown_cell(loading_data_md),
nbf.v4.new_code_cell(loading_data_code),
nbf.v4.new_markdown_cell(plot_data_md),
nbf.v4.new_code_cell(plot_data_code),
]
nbf.write(nb, str(ice_nb_file))
def gen_for_shapley_value(exp_name, description, sample_list):
for sample_name in sample_list:
p_nb_file = path.join(data_dir, exp_name,
f'{exp_name}_{sample_name}_shapley_value.ipynb')
if (not path.exists(p_nb_file)):
print(
f'Generating plotting notebook for {exp_name} at {p_nb_file}')
nb = nbf.v4.new_notebook()
nb.metadata.kernelspec = {
"display_name": "Python 3",
"name": "python3"
}
nb.metadata.language_info = {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
open_in_colab_href = f'<a href="https://colab.research.google.com/github/{user_repo}/s2search/blob/{branch}/pipelining/{exp_name}/{exp_name}_{sample_name}_shapley_value.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>'
exp_des = f'### Experiment Description\n\n{description.strip()}\n\n> This notebook is for experiment \\<{exp_name}\\> and data sample \\<{sample_name}\\>.'
init_md = '### Initialization'
init_code = f'''%reload_ext autoreload
%autoreload 2
import numpy as np, sys, os
sys.path.insert(1, '../../')
from shapley_value import compute_shapley_value, feature_key_list
sv = compute_shapley_value('{exp_name}', '{sample_name}')
'''
plotting_md = '### Plotting'
plotting_1_code = f'''import matplotlib.pyplot as plt
import numpy as np
from s2search_score_pdp import pdp_based_importance
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5), dpi=200)
# generate some random test data
all_data = []
average_sv = []
sv_global_imp = []
for player_sv in [f'{{player}}_sv' for player in feature_key_list]:
all_data.append(sv[player_sv])
average_sv.append(pdp_based_importance(sv[player_sv]))
sv_global_imp.append(np.mean(np.abs(list(sv[player_sv]))))
# average_sv.append(np.std(sv[player_sv]))
# print(np.max(sv[player_sv]))
# plot violin plot
axs[0].violinplot(all_data,
showmeans=False,
showmedians=True)
axs[0].set_title('Violin plot')
# plot box plot
axs[1].boxplot(all_data,
showfliers=False,
showmeans=True,
)
axs[1].set_title('Box plot')
# adding horizontal grid lines
for ax in axs:
ax.yaxis.grid(True)
ax.set_xticks([y + 1 for y in range(len(all_data))],
labels=['title', 'abstract', 'venue', 'authors', 'year', 'n_citations'])
ax.set_xlabel('Features')
ax.set_ylabel('Shapley Value')
plt.show()
'''