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53ebf66 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | import os
import sys
import pandas as pd
import numpy as np
import PATH
import torch
import argparse
import seaborn as sns
#
from models import reader
from models import train_val
from models.popen import Auto_popen
from models import max_activation_patch as MAP
#
from sklearn.linear_model import Lasso, Ridge, ElasticNet, LassoCV, RidgeCV, ElasticNetCV
import warnings
from scipy.cluster import hierarchy
from matplotlib.backends.backend_pdf import PdfPages
warnings.filterwarnings('ignore')
#
parser = argparse.ArgumentParser('the script to evlauate the effect of ')
parser.add_argument("-c", "--config", type=str, required=True, help='the model config file: xxx.ini')
parser.add_argument("-s", "--set", type=int, default=2, help='train - 0 ,val - 1, test - 2 ')
parser.add_argument("-p", "--n_max_act", type=int, default=500, help='the number of seq')
parser.add_argument("-k", "--kfold_cv", type=int, default=1, help='the repeat')
parser.add_argument("-d", "--device", type=str, default='cpu', help='the device to use to extract featmap, digit or cpu')
args = parser.parse_args()
# args = parser.parse_args(["-c","/ssd/users/wergillius/Project/MTtrans/log/Backbone/RL_hard_share/3M/small_repective_filed_strides1113.ini",
# "-s","2",
# "-d", "1"])
config_path = args.config
save_path = config_path.replace(".ini", "_coef")
config = Auto_popen(config_path)
config.batch_size = 256
config.shuffle = False
config.kfold_cv = 'train_val'
all_task = config.cycle_set
task_channel_effect = {}
task_performance = {}
# path check
if os.path.exists(config_path) and not os.path.exists(save_path):
os.mkdir(save_path)
saved_pdf =os.path.join(save_path, 'changepoint_actmap.pdf')
pp = PdfPages(saved_pdf)
channel_cluster_task = {}
for task in all_task:
# .... format featmap as data ....
print(f"\n\nevaluating for task: {task}")
# re-instance the map for each task
map_task = MAP.Maximum_activation_patch(popen=config, which_layer=4,
n_patch=args.n_max_act,
kfold_index=args.kfold_cv,
device_string=args.device)
# extract feature map and rl decision chain
featmap = map_task.extract_feature_map(task=task, which_set=args.set)
cum_rl_trend = map_task.cumulative_rl_decision(task=task, which_set=args.set)
# truncate the featmap and rl trend according to sequence length
seq_len_ls = map_task.df[config.seq_col].apply(len)
total_stride = np.product(map_task.strides)
to_stay = seq_len_ls//total_stride - 4
trunc_start = featmap.shape[2] - to_stay
# if max_seq_len == 50:
# trunc_start = -12
# find the low rl sequences
rl_pred = cum_rl_trend[:,-1]
threshold = np.quantile(rl_pred, [0.05, 0.95])
low_rl = rl_pred < threshold[0]
high_rl = rl_pred > threshold[1]
# subset find low rl change point feature
lowrl_rl_chain = cum_rl_trend[low_rl]
lowrl_ft = featmap[low_rl]
lowrl_detect_region = [slice(s,None) for s in trunc_start[low_rl]]
changepoint_map = map_task.retrieve_featmap_at_changepoint(lowrl_ft, lowrl_rl_chain,
threshold=-1, direction='less', detect_region=lowrl_detect_region)
print(changepoint_map.shape)
# sample high rl feature
highrl_rl_chain = cum_rl_trend[high_rl]
highrl_ft = featmap[high_rl]
highrl_detect_region = [slice(s,None) for s in trunc_start[high_rl]]
background_map = map_task.retrieve_featmap_at_changepoint(highrl_ft, highrl_rl_chain,
threshold=0.5, direction='greater', detect_region=highrl_detect_region)
# and then subsample
n_background = background_map.shape[0]
n_foreground = changepoint_map.shape[0]
if n_background > n_foreground:
downsample_seed = np.random.choice(np.arange(0,n_background), size=n_foreground)
background_map = background_map[downsample_seed]
n_background = background_map.shape[0]
# concate and order two group
row_colors = ["#E09832"]*n_background + ["#192D48"]*n_foreground
all_act=np.concatenate([background_map, changepoint_map], axis=0)
feat_norm_act = (all_act - all_act.min(axis=0)) / (all_act.max(axis=0) - all_act.min(axis=0))
# Visualization
g=sns.clustermap(feat_norm_act, row_colors=row_colors);
# 'array', 'axis', 'calculate_dendrogram', 'calculated_linkage', 'data',
# 'dendrogram', 'dependent_coord', 'independent_coord', 'label', 'linkage',
# 'method', 'metric', 'plot', 'reordered_ind', 'rotate', 'shape', 'xlabel',
# 'xticklabels', 'xticks', 'ylabel', 'yticklabels', 'yticks'
Z_col=g.dendrogram_col.linkage
thres = 0.8*max(Z_col[:,2])
R = hierarchy.dendrogram(Z_col ,color_threshold=thres, truncate_mode=None,
above_threshold_color='#AAAAAA', p=10, orientation='top',ax=g.ax_col_dendrogram);
# R['leaves']
# R['ivl']
Z_row=g.dendrogram_row.linkage
thres = 0.8*max(Z_row[:,2])
R2 = hierarchy.dendrogram(Z_row ,color_threshold=thres, truncate_mode=None, orientation='left',
above_threshold_color='#AAAAAA', p=10, ax=g.ax_row_dendrogram);
g.ax_row_dendrogram.invert_yaxis()
g.figure.suptitle(task)
pp.savefig(g.figure)
channel_cluster_task[task] = pd.DataFrame(dict(zip(R['leaves'],R['leaves_color_list'])))
print("No..")
pp.close()
channel_cluster_df = pd.DataFrame(channel_cluster_task)
saved_csv = os.path.join(save_path, "changepoint_channel_cluster.csv")
channel_cluster_df.to_csv(saved_csv, index=False)
print("==DONE==")
print(f"result save to {saved_pdf}\n \t\t {saved_csv}")
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