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plot_results.py
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952 lines (859 loc) · 49 KB
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import os.path
import re
import pickle
import logging
import warnings
import argparse
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import ujson as json
from joblib import Parallel, delayed
from tqdm import tqdm
from GBDT import GBDT
from train_test_split import load_data
class Record(object):
hedgecut_path = "/data/junhui/HedgeCut"
DART_path = "/data/junhui/DART"
hedgecut_labels_path = "/data/junhui/HedgeCut/labels"
def __init__(self, raw_data, model_type, slice_num, dataset):
self.raw_data = raw_data
self.model_type = model_type
self.slice_num = slice_num
self.dataset = dataset
@classmethod
def load_from_file(cls, dataset, version, model_type, slice_num=None):
# global DART_path
# global hedgecut_path
if model_type == "DART":
path = cls.DART_path
elif model_type == "hedgecut":
path = cls.hedgecut_path
else:
raise ValueError("model_type must be DART or hedgecut")
if model_type == "DART":
with open(path + f'/{dataset}/{version}/data', 'rb') as f:
record = cls(pickle.load(f), model_type, slice_num, dataset)
logging.debug("Done loading DART")
return record
if model_type == "hedgecut":
if dataset == 'susy':
slice_num_ = 100 if slice_num is None else slice_num
ret = {
'vs_origin_test': [],
'vs_origin_forget': [],
'vs_origin_retrain': [],
'vs_retrain_test': [],
'vs_retrain_forget': [],
'vs_retrain_retrain': [],
'vs_forget_test': [],
'vs_forget_forget': [],
'vs_forget_retrain': []
}
for i in range(slice_num_):
with open(path + f'/{dataset}/{version}/{i}.json') as f:
j = json.load(f)
j = json.loads(j)
for k, v in j.items():
ret[k].append(v[0])
record = cls(ret, model_type, slice_num, dataset)
logging.debug("Done loading hedgecut")
return record
else:
with open(path + f'/{dataset}/{version}/data.json') as f:
j = json.load(f)
j = json.loads(j)
if slice_num is not None:
for k, v in j.items():
j[k] = v[:slice_num]
record = cls(j, model_type, slice_num, dataset)
logging.debug("Done loading hedgecut")
return record
def get_real_labels(self, dataset_type):
if self.model_type == "hedgecut":
return pd.read_csv(f'{self.hedgecut_labels_path}/{self.dataset}/test.csv', sep='\t')['label']
if self.model_type == "DART":
return self.raw_data[f'{dataset_type}_data_df'][['real']]
'''
read data as dataframe to calculate the matrix
model_type: from ['origin', 'forget', 'retrain']
dataset_type: from ['test', 'forget', 'retrain']
'''
def read(self, model_type, dataset_type):
if self.model_type == "DART":
return self.raw_data[f'{dataset_type}_data_df'].filter(regex=(f'{model_type}.*'))
if self.model_type == "hedgecut":
return np.array(self.raw_data[f'vs_{model_type}_{dataset_type}'])
def load_2d_array(self, model_type, dataset_type):
logging.debug(f"load_2d_array {model_type} {dataset_type}")
if self.model_type == "DART":
df = self.read(model_type, dataset_type)
df_slice = df.to_numpy()[:, :self.slice_num].T
return df_slice
if self.model_type == "hedgecut":
return self.read(model_type, dataset_type)
def get_accuracy(self, models, dataset):
if self.model_type == "hedgecut":
labels = self.get_real_labels(dataset)
ret = []
for model in models:
arr = self.raw_data[f'vs_{model}_{dataset}']
df = pd.concat([pd.Series(i) for i in arr], axis=1).applymap(lambda x: 1.0 if x > 0.5 else 0.0)
acc = (df.sub(labels, axis=0).abs().sum() / (len(df))).values
ret.append((np.mean(acc), np.std(acc)))
return ret
if self.model_type == "DART":
def get_real_labels(self, dataset):
return self.raw_data[f'{dataset}_data_df'][['real']]
labels = self.get_real_labels(dataset)['real']
ret = []
for model in models:
df = self.read(model, dataset)
x = df.applymap(lambda x: 0.0 if x >= 0.5 else 1.0)
acc = (x.sub(labels, axis=0).abs().sum() / (len(df))).values
ret.append((np.mean(acc), np.std(acc)))
return ret
def get_scores_from_file(file_path, out_fmt='str') -> tuple[str, list]:
with open(file_path, 'r') as f:
f_text = f.read()
metric = re.match(".*INFO (?:deltaboost|gbdt)\.cpp:[0-9]* : Test: (.*) =", f_text)
pattern = re.compile(
".*INFO (?:deltaboost|gbdt)\.cpp:[0-9]* :.*(?:error|RMSE) = ([+-]?[0-9]+(?:[.][0-9]+)?(?:[eE][+-]?[0-9]+)?)")
scores = pattern.findall(f_text)
if out_fmt == 'float':
scores = [float(s) for s in scores]
return metric, scores
def plot_score_before_after_removal(out_dir, datasets, remove_ratios, save_path=None, plot_type='table'):
assert plot_type in ['raw', 'fig', 'table']
summary = []
for dataset in datasets:
df_ratios = []
for raw_ratio in remove_ratios:
ratio = "0.01" if raw_ratio == '1e-02' else "0.001"
out_path = os.path.join(out_dir, f"{dataset}_deltaboost_{ratio}.out")
out_retrain_path = os.path.join(out_dir, f"{dataset}_deltaboost_{ratio}_retrain.out")
metric, out_data = get_scores_from_file(out_path, out_fmt='float')
assert len(out_data) == 6, f"{out_path}, got {len(out_data)}"
scores = [float(x) for x in out_data[:3]]
scores_del = [float(x) for x in out_data[3:]]
_, out_retrain_data = get_scores_from_file(out_retrain_path, out_fmt='float')
scores_retrain = out_retrain_data[:3]
# reorder the result
scores = np.array([scores[2], scores[1], scores[0]])
scores_del = np.array([scores_del[2], scores_del[1], scores_del[0]])
scores_retrain = np.array([scores_retrain[2], scores_retrain[1], scores_retrain[0]])
x_labels = [r'$D_r$', r'$D_f$', r'$D_{test}$']
df = pd.DataFrame(data={'Original': scores, 'Remove (ours)': scores_del,
'Retrain (target)': scores_retrain})
if plot_type == 'fig':
df.set_index(x_labels)
ax = df.plot(kind='bar', rot=0, xlabel="Datset partition", ylabel=metric,
title=f"{dataset} removing {ratio}")
ax.legend()
ax.margins(y=0.3)
plt.show()
elif plot_type in ['table']:
columns = df.columns
df = df.T
df['Dataset'] = [f"\\multirow{{3}}{{*}}{{{dataset}}}", "", ""]
df.rename(columns=dict(zip(range(len(x_labels)), x_labels)), inplace=True)
df.set_index('Dataset', inplace=True)
df_ratios.append(df)
df_combine = pd.concat(df_ratios, axis=1)
df_combine.insert(0, 'Method')
df_combine.style.format(lambda x: f"{x:.6f}")
# df_combine = df_combine[['Method', r'$D$', r'$D_f$', r'$D_r$', r'$D$', r'$D_f$', r'$D_r$']]
summary.append('\n'.join(df_combine.style.to_latex().split('\n')[3:6]))
if plot_type == 'table':
latex_table = '\n\\midrule\n'.join(summary)
print(latex_table)
def plot_deltaboost_vs_gbdt(out_dir, datasets, save_path=None, present='test', n_trees=10, n_rounds=1):
assert present in ['test']
if n_trees == 10:
# 10 trees
xgboost_scores = {
'codrna': 1 - 0.9552,
'covtype': 1 - 0.8016,
'gisette': 1 - 0.9630,
'cadata': 0.1165,
'msd': 0.1143,
}
rf_scores = {
'codrna': 0.1011,
'covtype': 0.2472,
'gisette': 0.0590,
'cadata': 0.1307,
'msd': 0.1170,
}
dt_scores = {
'codrna': 0.0670,
'covtype': 0.2225,
'gisette': 0.0750,
'cadata': 0.1382,
'msd': 0.1185,
}
elif n_trees == 100:
xgboost_scores = {
'codrna': 0.0343,
'covtype': 0.0609,
'gisette': 0.0290,
'cadata': 0.1272,
'msd': 0.1219,
}
rf_scores = {
'codrna': 0.1287,
'covtype': 0.2402,
'gisette': 0.0490,
'cadata': 0.1283,
'msd': 0.1169,
}
dt_scores = {
'codrna': 0.0670,
'covtype': 0.2225,
'gisette': 0.0750,
'cadata': 0.1382,
'msd': 0.1185,
}
else:
assert False
summary = []
for dataset in datasets:
ratio = '1e-03' # either should be the same
deltaboost_scores = []
for i in range(n_rounds):
out_deltaboost_path = os.path.join(out_dir, f"tree{n_trees}/{dataset}_deltaboost_{ratio}_{i}.out")
metric, deltaboost_data = get_scores_from_file(out_deltaboost_path, out_fmt='float')
print(metric, deltaboost_data)
deltaboost_scores.append(deltaboost_data[0])
db_mean = np.mean(deltaboost_scores)
out_gbdt_path = os.path.join(out_dir, f"tree{n_trees}/{dataset}_gbdt_{ratio}.out")
_, gbdt_data = get_scores_from_file(out_gbdt_path, out_fmt='float')
print(gbdt_data)
gbdt_score = gbdt_data[0]
summary.append([db_mean, gbdt_score, xgboost_scores[dataset], rf_scores[dataset], dt_scores[dataset]])
print(f"{dataset} done.")
# # empty datasets
# if len(deltaboost_scores) == 0 or len(gbdt_scores) == 0:
# if present == 'test':
# summary.append([0, 0])
# else:
# assert False
# else:
# if present == 'test':
# summary.append([gbdt_scores[0], deltaboost_scores[0]])
# else:
# assert False
# plot
if present == 'test':
algos = ['DeltaBoost', 'ThunderGBM-CPU', 'XGBoost', 'Random Forest', 'Decision Tree']
summary_df = pd.DataFrame(data=summary, index=datasets, columns=algos)
ax = summary_df.plot(kind='bar', rot=0, xlabel='Datasets', ylabel='Error',
title=f'Error of DeltaBoost and GBDT (tree {n_trees})',
figsize=None)
ax.margins(y=0.1)
ax.legend()
plt.tight_layout()
if save_path is not None:
plt.savefig(save_path)
else:
plt.show()
def summary_model_diff_for_dataset(dataset, n_trees, remove_ratios):
"""
Evaluate the model difference by inference. This function can be very slow.
:param dataset:
:param n_trees:
:param remove_ratios:
:return:
"""
df_ratios = []
for remove_ratio in remove_ratios:
print(f"Remove ratio {remove_ratio}:")
print("Loading models.")
model_path = f"../cache/{dataset}_tree{n_trees}_original_{remove_ratio}_deltaboost.json"
model_remain_path = f"../cache/{dataset}_tree{n_trees}_retrain_{remove_ratio}_deltaboost.json"
model_deleted_path = f"../cache/{dataset}_tree{n_trees}_original_{remove_ratio}_deleted.json"
with open(model_path, 'r') as f1, open(model_remain_path, 'r') as f2, open(model_deleted_path) as f3:
js1 = json.load(f1)
js2 = json.load(f2)
js3 = json.load(f3)
gbdt_original = GBDT.load_from_json(js1, 'deltaboost')
gbdt_retrain = GBDT.load_from_json(js2, 'deltaboost')
gbdt_deleted = GBDT.load_from_json(js3, 'deltaboost')
print("Loading dataset.")
# train_X, train_y = load_data(f"../data/{dataset}.train", data_fmt='libsvm', output_dense=True)
remain_X, remain_y = load_data(f"../data/{dataset}.train.remain_{remove_ratio}", data_fmt='csv',
output_dense=True)
delete_X, delete_y = load_data(f"../data/{dataset}.train.delete_{remove_ratio}", data_fmt='csv',
output_dense=True)
test_X, test_y = load_data(f"../data/{dataset}.test", data_fmt='csv', output_dense=True)
print("Prediction on remain")
original_score_on_remain = gbdt_original.predict_score(remain_X)
retrain_score_on_remain = gbdt_retrain.predict_score(remain_X)
deleted_score_on_remain = gbdt_deleted.predict_score(remain_X)
diff_retrain_original_on_remain = np.average(np.abs(original_score_on_remain - retrain_score_on_remain))
diff_retrain_delete_on_remain = np.average(np.abs(retrain_score_on_remain - deleted_score_on_remain))
print("Prediction on delete")
original_score_on_delete = gbdt_original.predict_score(delete_X)
retrain_score_on_delete = gbdt_retrain.predict_score(delete_X)
deleted_score_on_delete = gbdt_deleted.predict_score(delete_X)
diff_retrain_original_on_delete = np.average(np.abs(original_score_on_delete - retrain_score_on_delete))
diff_retrain_delete_on_delete = np.average(np.abs(retrain_score_on_delete - deleted_score_on_delete))
print("Prediction on test")
original_score_on_test = gbdt_original.predict_score(test_X)
retrain_score_on_test = gbdt_retrain.predict_score(test_X)
deleted_score_on_test = gbdt_deleted.predict_score(test_X)
diff_retrain_original_on_test = np.average(np.abs(original_score_on_test - retrain_score_on_test))
diff_retrain_delete_on_test = np.average(np.abs(retrain_score_on_test - deleted_score_on_test))
x_labels = [r'$D_f$', r'$D_r$', r'$D_{test}$']
df = pd.DataFrame(data={
'original vs. retrain': [diff_retrain_original_on_remain, diff_retrain_original_on_delete,
diff_retrain_original_on_test],
'delete vs. retrain': [diff_retrain_delete_on_remain, diff_retrain_delete_on_delete,
diff_retrain_delete_on_test]})
columns = df.columns
df = df.T
df['Dataset'] = [f"\\multirow{{2}}{{*}}{{{dataset}}}", ""]
df.rename(columns=dict(zip(range(len(x_labels)), x_labels)), inplace=True)
df.set_index('Dataset', inplace=True)
df_ratios.append(df)
df_combine = pd.concat(df_ratios, axis=1)
df_combine.insert(0, 'Method', columns)
return df_combine
def print_model_diff(datasets, n_trees, remove_ratios):
summary = []
for dataset in datasets:
df_combine = summary_model_diff_for_dataset(dataset, n_trees, remove_ratios)
df_combine.style.format(lambda x: f"{x:.6f}")
summary.append('\n'.join(df_combine.style.to_latex().split('\n')[3:5]))
latex_table = '\n\\midrule\n'.join(summary)
print(latex_table)
class ModelDiffSingle:
def __init__(self, dataset, n_trees, remove_ratio, n_rounds, keyword, record: Record = None, n_jobs=1,
deltaboost_path="../cache", deltaboost_predict=False):
"""
:param dataset: dataset name
:param n_trees: number of trees
:param remove_ratio: remove ratio, 1e-02 or 1e-03
:param n_rounds: number of random rounds
:param keyword: ['original', 'retrain', 'delete']
:param record: None means load model and inference on the data, otherwise load results from record
:param n_jobs: number of jobs in parallel
"""
self.dataset = dataset
self.n_trees = n_trees
self.remove_ratio = remove_ratio
self.n_rounds = n_rounds
self.keyword = keyword
self.record = record
self.n_jobs = n_jobs
self.deltaboost_path = deltaboost_path
self.deltaboost_predict = deltaboost_predict
if record is None:
if deltaboost_predict:
# load data from prediction
logging.debug("Loading deltaboost data")
if keyword in ['remain', 'delete']:
self.X, self.y = load_data(f"../data/{dataset}.train.{keyword}_{remove_ratio}",
data_fmt='csv',
output_dense=True)
elif keyword == 'test':
self.X, self.y = load_data(f"../data/{dataset}.test", data_fmt='csv', output_dense=True)
else:
raise ValueError(f"Invalid keyword: {keyword}")
self.original_score = np.zeros((n_rounds, self.X.shape[0]))
self.retrain_score = np.zeros((n_rounds, self.X.shape[0]))
self.deleted_score = np.zeros((n_rounds, self.X.shape[0]))
logging.debug("Done loading deltaboost data")
else:
# get size from example
example_output = np.genfromtxt(
f"{deltaboost_path}/{dataset}_tree{n_trees}_original_{self.remove_ratio}_0_deltaboost_score_{keyword}.csv",
delimiter=',')
n_instance = example_output.shape[0]
# directly load output of deltaboost from csv
logging.debug("Loading deltaboost output.")
self.original_score = np.zeros((n_rounds, n_instance))
self.retrain_score = np.zeros((n_rounds, n_instance))
self.deleted_score = np.zeros((n_rounds, n_instance))
self.X, self.y = None, None
for i in tqdm(range(n_rounds)):
self.original_score[i, :] = np.genfromtxt(
f"{deltaboost_path}/{dataset}_tree{n_trees}_original_{self.remove_ratio}_{i}_deltaboost_score_{keyword}.csv",
delimiter=',')[:, 0]
self.retrain_score[i, :] = np.genfromtxt(
f"{deltaboost_path}/{dataset}_tree{n_trees}_retrain_{self.remove_ratio}_{i}_deltaboost_score_{keyword}.csv",
delimiter=',')[:, 0]
self.deleted_score[i, :] = np.genfromtxt(
f"{deltaboost_path}/{dataset}_tree{n_trees}_original_{self.remove_ratio}_{i}_deleted_score_{keyword}.csv",
delimiter=',')[:, 0]
logging.debug("Done loading deltaboost output.")
else:
# read from Record
logging.debug("Loading from record")
self.X, self.y = None, None
self.original_score = record.load_2d_array('origin', 'test')
self.retrain_score = record.load_2d_array('retrain', 'test')
self.deleted_score = record.load_2d_array('forget', 'test')
logging.debug("Done loading from record")
def predict_i(self, i, n_used_trees=None):
if self.X is None or self.y is None:
raise ValueError("X and y should not be None")
model_original_path = os.path.join(self.deltaboost_path,
f"{self.dataset}_tree{self.n_trees}_original_{self.remove_ratio}_{i}_deltaboost.json")
model_retrain_path = os.path.join(self.deltaboost_path,
f"{self.dataset}_tree{self.n_trees}_retrain_{self.remove_ratio}_{i}_deltaboost.json")
model_deleted_path = os.path.join(self.deltaboost_path,
f"{self.dataset}_tree{self.n_trees}_original_{self.remove_ratio}_{i}_deleted.json")
# load model
with open(model_original_path, 'r') as f1, open(model_retrain_path, 'r') as f2, open(model_deleted_path,
'r') as f3:
js1 = json.load(f1)
js2 = json.load(f2)
js3 = json.load(f3)
gbdt_original = GBDT.load_from_json(js1)
gbdt_retrain = GBDT.load_from_json(js2)
gbdt_deleted = GBDT.load_from_json(js3)
# predict
original_score = gbdt_original.predict_score(self.X, n_used_trees, self.n_jobs)
retrain_score = gbdt_retrain.predict_score(self.X, n_used_trees, self.n_jobs)
deleted_score = gbdt_deleted.predict_score(self.X, n_used_trees, self.n_jobs)
return original_score, retrain_score, deleted_score
def predict_(self, n_used_trees=None):
logging.debug("predicting with deltaboost")
# execute predict_i in parallel with joblib.Parallel
results = Parallel(n_jobs=1)(delayed(self.predict_i)(i, n_used_trees) for i in range(self.n_rounds))
# convert results to numpy array
self.original_score, self.retrain_score, self.deleted_score = np.array(results).transpose((1, 0, 2))
logging.debug("Done")
def get_hellinger_distance(self, n_bins=50, return_std=True):
logging.debug("Calculating Hellinger distance")
min_value = min(np.min(self.original_score), np.min(self.retrain_score), np.min(self.deleted_score))
max_value = max(np.max(self.original_score), np.max(self.retrain_score), np.max(self.deleted_score))
n_rounds = self.original_score.shape[0]
n_instances = self.original_score.shape[1]
if n_rounds > 1000:
warnings.warn("n_rounds is larger than 1000, it may take a long time to calculate Hellinger distance")
def get_hist_i(i):
"""
:param i: instance index
:return: normalized histogram counts
"""
# if i == 81:
# return np.nan, np.nan, np.nan
original_hist_i = np.histogram(self.original_score[:, i], bins=n_bins,
range=(min_value, max_value), density=True)[0]
retrain_hist_i = np.histogram(self.retrain_score[:, i], bins=n_bins,
range=(min_value, max_value), density=True)[0]
deleted_hist_i = np.histogram(self.deleted_score[:, i], bins=n_bins,
range=(min_value, max_value), density=True)[0]
return original_hist_i, retrain_hist_i, deleted_hist_i
# Get the hist of all instances in parallel by joblib.Parallel
results = Parallel(n_jobs=self.n_jobs)(delayed(get_hist_i)(i) for i in range(n_instances))
# convert results to numpy array
original_hist, retrain_hist, deleted_hist = np.array(results).transpose((1, 0, 2))
# Hellinger distance
bin_width = (max_value - min_value) / n_bins
retrain_vs_deleted = 1 - bin_width * np.sum(np.sqrt(retrain_hist * deleted_hist), axis=1)
original_vs_retrain = 1 - bin_width * np.sum(np.sqrt(original_hist * retrain_hist), axis=1)
logging.debug("Done")
if return_std:
return (np.nanmean(original_vs_retrain), np.nanstd(original_vs_retrain)), \
(np.nanmean(retrain_vs_deleted), np.nanstd(retrain_vs_deleted))
else:
return np.nanmean(original_vs_retrain), np.nanmean(retrain_vs_deleted)
def get_accuracy(self):
logging.debug("Loading Accuracy")
ret = self.record.get_accuracy(['origin', 'forget', 'retrain'], 'test')
logging.debug("Done")
return ret
class ModelDiff:
def __init__(self, datasets, remove_ratios, n_trees, n_rounds, n_used_trees=None, keyword='test', n_jobs=1,
hedgecut_path=None, dart_path=None, deltaboost_path="../cache/", deltaboost_out_path=None,
deltaboost_predict=False, forget_table_cache_path=None, accuracy_table_cache_path=None):
"""
Manage model diff of three methods: DeltaBoost, HedgeCut, DaRE
:param datasets: list of dataset names, e.g. cadata
:param remove_ratios: list of remove ratios, e.g. 1e-02
:param n_trees: number of trees
:param n_rounds: number of rounds
:param n_used_trees: number of used trees, if None, use all trees
:param n_jobs: number of jobs for parallel computing
:param hedgecut_path: path of hedgecut model, if None, use default path
:param dart_path: path of dart model, if None, use default path
:param deltaboost_path: path of deltaboost model, if None, use default path
:param deltaboost_predict: whether to predict deltaboost model
:param table_cache_path: path of table cache, if None, load from scratch
:param update_hedgecut: whether to update hedgecut data
:param update_dart: whether to update dart data
:param update_deltaboost: whether to update deltaboost data
"""
self.datasets = datasets
self.remove_ratios = remove_ratios
self.n_trees = n_trees
self.n_rounds = n_rounds
self.n_used_trees = n_used_trees
self.n_jobs = n_jobs
self.keyword = keyword
self.hedgecut_path = hedgecut_path
self.dart_path = dart_path
self.deltaboost_path = deltaboost_path
self.deltaboost_out_path = deltaboost_out_path
self.deltaboost_predict = deltaboost_predict
if forget_table_cache_path is None or os.path.isfile(forget_table_cache_path):
self.forget_table_cache_path = forget_table_cache_path
else:
self.forget_table_cache_path = None
warnings.warn("Forget table cache path not found, calculate from scratch")
if accuracy_table_cache_path is None or os.path.isfile(accuracy_table_cache_path):
self.accuracy_table_cache_path = accuracy_table_cache_path
else:
self.accuracy_table_cache_path = None
warnings.warn("Accuracy table cache path not found, calculate from scratch")
if forget_table_cache_path is None:
self.table_data = np.zeros([len(datasets) * 2, len(remove_ratios) * 3], dtype='U32')
else:
self.table_data = np.genfromtxt(forget_table_cache_path, dtype='U32', delimiter=',')
if accuracy_table_cache_path is None:
self.accuracy_table = np.zeros([len(datasets) * 3, len(remove_ratios) * 3], dtype='U32')
else:
self.accuracy_table = np.genfromtxt(accuracy_table_cache_path, dtype='U32', delimiter=',')
if hedgecut_path is not None:
Record.hedgecut_path = hedgecut_path
if dart_path is not None:
Record.dart_path = dart_path
def get_raw_data_forget_(self, n_bins=50, save_path=None, update_hedgecut=None, update_dart=None,
update_deltaboost=None,
update_datasets=None):
"""
Get raw data of three methods and stored in self.table_data
:param n_bins: number of bins for histogram
:param save_path: path to save forget table
:param update_hedgecut: whether to update hedgecut model
:param update_dart: whether to update dart model
:param update_deltaboost: whether to update deltaboost model
:param update_datasets: the list of datasets to update
:return:
"""
# initialize parameters
if self.forget_table_cache_path is None:
_update_dart = _update_hedgecut = _update_deltaboost = True # default update all
else:
_update_dart = _update_hedgecut = _update_deltaboost = False # default to load from cache
# overwrite parameters if specified
_update_dart = _update_dart if update_dart is None else update_dart
_update_hedgecut = _update_hedgecut if update_hedgecut is None else update_hedgecut
_update_deltaboost = _update_deltaboost if update_deltaboost is None else update_deltaboost
ratio2version = {'1e-03': '0.1%', '1e-02': '1%'}
for i, dataset in enumerate(self.datasets):
for j, remove_ratio in enumerate(self.remove_ratios):
if dataset in ['codrna', 'gisette', 'covtype', 'higgs', 'susy']:
if _update_dart and (update_datasets is None or dataset in update_datasets):
# load dart from record
logging.debug(f"Loading dart record")
record_dart = Record.load_from_file(dataset, ratio2version[remove_ratio], 'DART', self.n_rounds)
logging.debug(f"Done loading, calculating Hellinger distance")
model_diff_dart = ModelDiffSingle(dataset, self.n_trees, remove_ratio, self.n_rounds,
self.keyword, n_jobs=self.n_jobs, record=record_dart)
ovr_dart_data, rvd_dart_data = model_diff_dart.get_hellinger_distance(return_std=True,
n_bins=n_bins)
ovr_dart: str = f"{ovr_dart_data[0]:.4f}\\textpm {ovr_dart_data[1]:.4f}"
rvd_dart: str = f"{rvd_dart_data[0]:.4f}\\textpm {rvd_dart_data[1]:.4f}"
logging.info(f"{dataset} {remove_ratio} dart done.")
else:
logging.debug(f"Loading dart results from cache")
ovr_dart = self.table_data[i * 2, j * 3]
rvd_dart = self.table_data[i * 2 + 1, j * 3]
if _update_hedgecut and (update_datasets is None or dataset in update_datasets):
logging.info(f"{dataset} {remove_ratio} starts getting raw data.")
# load hedgecut from record
logging.debug(f"Loading hedgecut record")
record_hedgecut = Record.load_from_file(dataset, ratio2version[remove_ratio], 'hedgecut',
self.n_rounds)
logging.debug(f"Done loading, calculating Hellinger distance")
model_diff_hedgecut = ModelDiffSingle(dataset, self.n_trees, remove_ratio, self.n_rounds,
self.keyword, n_jobs=self.n_jobs, record=record_hedgecut)
ovr_hedgecut_data, rvd_hedgecut_data = model_diff_hedgecut.get_hellinger_distance(
return_std=True, n_bins=n_bins)
ovr_hedgecut: str = f"{ovr_hedgecut_data[0]:.4f}\\textpm {ovr_hedgecut_data[1]:.4f}"
rvd_hedgecut: str = f"{rvd_hedgecut_data[0]:.4f}\\textpm {rvd_hedgecut_data[1]:.4f}"
logging.info(f"{dataset} {remove_ratio} hedgecut done.")
else:
logging.debug(f"Loading hedgecut results from cache")
ovr_hedgecut = self.table_data[i * 2, j * 3 + 1]
rvd_hedgecut = self.table_data[i * 2 + 1, j * 3 + 1]
else:
ovr_hedgecut = rvd_hedgecut = ovr_dart = rvd_dart = '-'
logging.info(f"{dataset} {remove_ratio} HedgeCut and DART skipped.")
if _update_deltaboost and (update_datasets is None or dataset in update_datasets):
try:
# load deltaboost by inference or outputs
model_diff_deltaboost = ModelDiffSingle(dataset, self.n_trees, remove_ratio, self.n_rounds,
self.keyword, n_jobs=self.n_jobs,
deltaboost_path=self.deltaboost_path,
deltaboost_predict=self.deltaboost_predict)
if self.deltaboost_predict:
model_diff_deltaboost.predict_(self.n_used_trees)
ovr_deltaboost_data, rvd_deltaboost_data = model_diff_deltaboost.get_hellinger_distance(
return_std=True,
n_bins=n_bins)
ovr_deltaboost: str = f"{ovr_deltaboost_data[0]:.4f}\\textpm {ovr_deltaboost_data[1]:.4f}"
rvd_deltaboost: str = f"{rvd_deltaboost_data[0]:.4f}\\textpm {rvd_deltaboost_data[1]:.4f}"
except Exception as e:
logging.error(f"{dataset} {remove_ratio} deltaboost failed: {e}")
ovr_deltaboost = rvd_deltaboost = '-'
else:
logging.debug(f"Loading deltaboost results from cache")
ovr_deltaboost = self.table_data[i * 2, j * 3 + 2]
rvd_deltaboost = self.table_data[i * 2 + 1, j * 3 + 2]
# store data
self.table_data[2 * i, 3 * j] = ovr_dart
self.table_data[2 * i + 1, 3 * j] = rvd_dart
self.table_data[2 * i, 3 * j + 1] = ovr_hedgecut
self.table_data[2 * i + 1, 3 * j + 1] = rvd_hedgecut
self.table_data[2 * i, 3 * j + 2] = ovr_deltaboost
self.table_data[2 * i + 1, 3 * j + 2] = rvd_deltaboost
logging.info(f"{dataset}, {remove_ratio} done.")
if save_path is not None:
np.savetxt(save_path, self.table_data, fmt='%s', delimiter=',')
logging.info(f"Table saved to {save_path}")
def get_raw_data_accuracy_(self, save_path=None, update_hedgecut=None, update_dart=None, update_deltaboost=None,
update_datasets=None):
"""
Get raw accuracy data of three methods and stored in self.table_data
:return:
"""
# initialize parameters
if self.accuracy_table_cache_path is None:
_update_dart = _update_hedgecut = _update_deltaboost = True # default update all
else:
_update_dart = _update_hedgecut = _update_deltaboost = False # default to load from cache
# overwrite parameters if specified
_update_dart = _update_dart if update_dart is None else update_dart
_update_hedgecut = _update_hedgecut if update_hedgecut is None else update_hedgecut
_update_deltaboost = _update_deltaboost if update_deltaboost is None else update_deltaboost
ratio2version = {'1e-03': '0.1%', '1e-02': '1%'}
for i, dataset in enumerate(self.datasets):
for j, remove_ratio in enumerate(self.remove_ratios):
if dataset in ['codrna', 'gisette', 'covtype', 'higgs', 'susy']:
if _update_dart and (update_datasets is None or dataset in update_datasets):
# load dart from record
logging.debug(f"Loading dart record")
record_dart = Record.load_from_file(dataset, ratio2version[remove_ratio], 'DART', self.n_rounds)
logging.debug(f"Done loading, calculating Hellinger distance")
model_diff_dart = ModelDiffSingle(dataset, self.n_trees, remove_ratio, self.n_rounds,
self.keyword, n_jobs=self.n_jobs, record=record_dart)
accs_dart = model_diff_dart.get_accuracy()
acc_dart_origin = f"{accs_dart[0][0]:.4f}\\textpm {accs_dart[0][1]:.4f}"
acc_dart_forget = f"{accs_dart[1][0]:.4f}\\textpm {accs_dart[1][1]:.4f}"
acc_dart_retrain = f"{accs_dart[2][0]:.4f}\\textpm {accs_dart[2][1]:.4f}"
logging.info(f"{dataset} {remove_ratio} dart done.")
else:
logging.debug(f"Loading dart results from cache")
acc_dart_origin = self.accuracy_table[i * 3, j * 3]
acc_dart_forget = self.accuracy_table[i * 3 + 1, j * 3]
acc_dart_retrain = self.accuracy_table[i * 3 + 2, j * 3]
if _update_hedgecut and (update_datasets is None or dataset in update_datasets):
logging.info(f"{dataset} {remove_ratio} starts getting raw data.")
# load hedgecut from record
logging.debug(f"Loading hedgecut record")
record_hedgecut = Record.load_from_file(dataset, ratio2version[remove_ratio], 'hedgecut',
self.n_rounds)
logging.debug(f"Done loading, calculating Hellinger distance")
model_diff_hedgecut = ModelDiffSingle(dataset, self.n_trees, remove_ratio, self.n_rounds,
self.keyword, n_jobs=self.n_jobs, record=record_hedgecut)
accs_hedgecut = model_diff_hedgecut.get_accuracy()
acc_hedgecut_origin = f"{accs_hedgecut[0][0]:.4f}\\textpm {accs_hedgecut[0][1]:.4f}"
acc_hedgecut_forget = f"{accs_hedgecut[1][0]:.4f}\\textpm {accs_hedgecut[1][1]:.4f}"
acc_hedgecut_retrain = f"{accs_hedgecut[2][0]:.4f}\\textpm {accs_hedgecut[2][1]:.4f}"
logging.info(f"{dataset} {remove_ratio} hedgecut done.")
else:
logging.debug(f"Loading hedgecut results from cache")
acc_hedgecut_origin = self.accuracy_table[i * 3, j * 3 + 1]
acc_hedgecut_forget = self.accuracy_table[i * 3 + 1, j * 3 + 1]
acc_hedgecut_retrain = self.accuracy_table[i * 3 + 2, j * 3 + 1]
else:
acc_dart_origin = acc_dart_forget = acc_dart_retrain = '-'
acc_hedgecut_origin = acc_hedgecut_forget = acc_hedgecut_retrain = '-'
logging.info(f"{dataset} {remove_ratio} HedgeCut and DART skipped.")
if _update_deltaboost and (update_datasets is None or dataset in update_datasets):
try:
logging.debug(f"Loading deltaboost data from output")
deltaboost_original_scores = np.zeros(self.n_rounds)
deltaboost_deleted_scores = np.zeros(self.n_rounds)
deltaboost_retrain_scores = np.zeros(self.n_rounds)
keyword2id = {'delete': 0, 'remain': 1, 'test': 2}
for t in range(self.n_rounds):
try:
original_out_path = os.path.join(self.deltaboost_out_path,
f"{dataset}_deltaboost_{remove_ratio}_{t}.out")
retrain_out_path = os.path.join(self.deltaboost_out_path,
f"{dataset}_deltaboost_{remove_ratio}_retrain_{t}.out")
_, raw_original_scores = get_scores_from_file(original_out_path, out_fmt='float')
_, raw_retrain_scores = get_scores_from_file(retrain_out_path, out_fmt='float')
if len(raw_original_scores) != 0:
deltaboost_original_scores[t] = raw_original_scores[keyword2id[self.keyword]]
deltaboost_deleted_scores[t] = raw_original_scores[keyword2id[self.keyword] + 3]
else:
deltaboost_original_scores[t] = np.nan
warnings.warn(f"Empty original scores for {dataset} {remove_ratio} {t}")
if len(raw_retrain_scores) != 0:
deltaboost_retrain_scores[t] = raw_retrain_scores[keyword2id[self.keyword]]
else:
deltaboost_retrain_scores[t] = np.nan
warnings.warn(f"Empty retrain scores for {dataset} {remove_ratio} {t}")
except IndexError:
deltaboost_original_scores[t] = np.nan
deltaboost_retrain_scores[t] = np.nan
warnings.warn(f"Nan Value scores for {dataset} {remove_ratio} {t}")
acc_deltaboost_origin = f"{np.nanmean(deltaboost_original_scores):.4f}\\textpm {np.nanstd(deltaboost_original_scores):.4f}"
acc_deltaboost_forget = f"{np.nanmean(deltaboost_deleted_scores):.4f}\\textpm {np.nanstd(deltaboost_deleted_scores):.4f}"
acc_deltaboost_retrain = f"{np.nanmean(deltaboost_retrain_scores):.4f}\\textpm {np.nanstd(deltaboost_retrain_scores):.4f}"
logging.info(f"Done.")
except Exception as e:
logging.error(f"Failed to load deltaboost data from output, {e}")
acc_deltaboost_origin = acc_deltaboost_forget = acc_deltaboost_retrain = '-'
else:
logging.debug(f"Loading deltaboost results from cache")
acc_deltaboost_origin = self.accuracy_table[i * 3, j * 3 + 2]
acc_deltaboost_forget = self.accuracy_table[i * 3 + 1, j * 3 + 2]
acc_deltaboost_retrain = self.accuracy_table[i * 3 + 2, j * 3 + 2]
self.accuracy_table[3 * i, 3 * j] = acc_dart_origin
self.accuracy_table[3 * i + 1, 3 * j] = acc_dart_forget
self.accuracy_table[3 * i + 2, 3 * j] = acc_dart_retrain
self.accuracy_table[3 * i, 3 * j + 1] = acc_hedgecut_origin
self.accuracy_table[3 * i + 1, 3 * j + 1] = acc_hedgecut_forget
self.accuracy_table[3 * i + 2, 3 * j + 1] = acc_hedgecut_retrain
self.accuracy_table[3 * i, 3 * j + 2] = acc_deltaboost_origin
self.accuracy_table[3 * i + 1, 3 * j + 2] = acc_deltaboost_forget
self.accuracy_table[3 * i + 2, 3 * j + 2] = acc_deltaboost_retrain
logging.info(f"{dataset}, {remove_ratio} done.")
if save_path is not None:
np.savetxt(save_path, self.accuracy_table, fmt='%s', delimiter=',')
logging.info(f"Table saved to {save_path}")
def print_latex_forget(self):
dataset_str = ['\\multirow{2}{*}{%s}' % dataset for dataset in self.datasets]
table_with_title = np.concatenate([
np.array(list(zip(dataset_str, np.array([''] * len(dataset_str))))).reshape(-1, 1),
np.array(
[r"$H^2(M_r,M;\mathbf{D}_{test})$", r"$H^2(M_r,M_d;\mathbf{D}_{test})$"] * len(self.datasets)).reshape(
-1, 1),
self.table_data.astype('U32')], axis=1)
table_df = pd.DataFrame(table_with_title)
table_latex = table_df.to_latex(index=False, header=False, escape=False)
# insert \midrule every two rows
lines = table_latex.splitlines()
i = 2 + 2 # first two lines are header
while i < len(lines) - 2:
lines.insert(i, '\midrule')
i += 3
print('\n'.join(lines))
def print_latex_accuracy(self):
# print accuracy latex table
dataset_str = ['\\multirow{3}{*}{%s}' % dataset for dataset in self.datasets]
table_with_title = np.concatenate([
np.array(
list(zip(dataset_str, np.array([''] * len(dataset_str)), np.array([''] * len(dataset_str))))).reshape(
-1, 1),
np.array([r"$M$", r"$M_{d}$", r"$M_{r}$"] * len(self.datasets)).reshape(-1, 1),
self.accuracy_table], axis=1)
table_df = pd.DataFrame(table_with_title)
table_latex = table_df.to_latex(index=False, header=False, escape=False)
# insert \midrule every three rows
lines = table_latex.splitlines()
i = 2 + 3 # first two lines are header
while i < len(lines) - 2:
lines.insert(i, '\midrule')
i += 4
print('\n'.join(lines))
if __name__ == '__main__':
logging.basicConfig(
format='%(asctime)s,%(msecs)d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',
datefmt='%Y-%m-%d:%H:%M:%S',
level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('-t', '--n-trees', type=int, default=1)
parser.add_argument('-acc', action='store_true', default=False)
parser.add_argument('-p', '--figure-path', type=str, default=None)
args = parser.parse_args()
datasets = ['codrna', 'covtype', 'gisette', 'cadata', 'msd']
# datasets = ['cadata', 'codrna']
remove_ratios = ['1e-03', '1e-02']
# remove_ratios = ['0.001', '0.01']
# plot_score_before_after_removal("../out/remove_test/tree50", datasets, remove_ratios)
# plot_score_before_after_removal("../out/remove_test/tree30", datasets, remove_ratios)
# plot_score_before_after_removal("../out/remove_test/tree10", datasets, remove_ratios)
# plot_score_before_after_removal("../out/remove_test/tree1", datasets, remove_ratios)
# plot_deltaboost_vs_gbdt("../out/remove_test", datasets, n_trees=50)
# plot_deltaboost_vs_gbdt("../out/remove_test", datasets, n_trees=30)
# plot_deltaboost_vs_gbdt("../out/remove_test", datasets, n_trees=10)
# plot_deltaboost_vs_gbdt("../out/remove_test", datasets, n_trees=1)
# print_model_diff(datasets, 1, remove_ratios)
# print_model_diff(datasets, 10, remove_ratios)
# print_model_diff(datasets, 30, remove_ratios)
# print_model_diff(datasets, 50, remove_ratios)
# for dataset in datasets:
# model_diff1 = ModelDiff(f"{dataset}", 1, '1e-03', 100)
# model_diff1.predict_(1)
# df1 = model_diff1.get_helliger_distance_df(1)
# model_diff2 = ModelDiff(f"{dataset}", 1, '1e-02', 100)
# model_diff2.predict_(1)
# df2 = model_diff2.get_helliger_distance_df(1)
# df_combine = pd.concat([df1, df2.drop(columns=df2.columns[0])], axis=1)
# print(df_combine.to_latex(escape=False))
# model_diff = ModelDiff(datasets, remove_ratios, 1, n_rounds=100, n_jobs=1,
# hedgecut_path="/data/junhui/Hedgecut",
# dart_path="/data/junhui/DART",
# deltaboost_out_path="../out/remove_test/tree1/",
# deltaboost_path="../cache_local",
# # forget_table_cache_path="out/forget_table.csv",
# forget_table_cache_path="out/forget_table_susy.csv",
# # accuracy_table_cache_path="out/accuracy_table.csv",
# accuracy_table_cache_path="out/accuracy_table_susy.csv",
# )
# deltaboost_path="/data/zhaomin/DeltaBoost/cache")
# model_diff.get_raw_data_forget_(n_bins=50,
# update_deltaboost=True,
# update_datasets=['susy'],
# # save_path="out/forget_table_susy.csv",
# )
# model_diff.print_latex_forget()
# model_diff.get_raw_data_accuracy_(update_deltaboost=False,
# update_dart=False,
# update_hedgecut=True,
# update_datasets=['susy'],
# # save_path="out/accuracy_table_susy.csv",
# )
# model_diff.print_latex_accuracy()
if args.acc:
if args.figure_path is None:
figure_path = f"../fig/acc-tree{args.n_trees}.jpg"
else:
figure_path = args.figure_path
plot_deltaboost_vs_gbdt("../out/remove_test", datasets, n_trees=args.n_trees, save_path=figure_path)
else:
n_trees = args.n_trees
model_diff = ModelDiff(datasets, remove_ratios, n_trees, n_rounds=100, n_jobs=1,
# hedgecut_path="/data/junhui/Hedgecut",
# dart_path="/data/junhui/DART",
deltaboost_out_path=f"../out/remove_test/tree{n_trees}/",
deltaboost_path="../cache",
# forget_table_cache_path=f"../out/forget_table_tree{n_trees}.csv",
# forget_table_cache_path=None,
# accuracy_table_cache_path=f"../out/accuracy_table_tree{n_trees}.csv",
# accuracy_table_cache_path=None,
)
model_diff.get_raw_data_forget_(n_bins=50,
update_dart=False,
update_hedgecut=False,
update_deltaboost=True,
# update_datasets=['msd'],
save_path=f"../out/forget_table_tree{n_trees}.csv",
)
model_diff.print_latex_forget()
model_diff.get_raw_data_accuracy_(update_dart=False,
update_hedgecut=False,
update_deltaboost=True,
# update_datasets=['msd'],
save_path=f"../out/accuracy_table_tree{n_trees}.csv",
)
model_diff.print_latex_accuracy()