def main(api_base):
stages_df = get_df('stages', api_base=api_base).rename(columns={
'id': 'stage_id',
'name': 'stage_name'
}).drop(['parameters', 'script', 'script_md5', 'states'], axis=1)
states_df = get_df('states',
api_base=api_base).rename(columns={'id': 'state_id'})
subjects_df = get_df('subjects',
api_base=api_base).rename(columns={'id': 'state_id'})
subjects_df['state_id'] = subjects_df['state'].map(lambda x: x['id'])
subjects_df.drop(['state'], axis=1, inplace=True)
stages_states_df = pd.merge(stages_df, states_df, on='stage_id')
regimens_df = get_df('regimens',
api_base=api_base).rename(columns={
'id': 'regimen_id',
'name': 'regimen_name'
}).drop(['states', 'active'], axis=1)
stages_states_regimens_df = pd.merge(stages_states_df,
regimens_df,
on='regimen_id')
# print stages_states_regimens_df
# print subjects_df
subjects_df = (pd.merge(
stages_states_regimens_df, subjects_df,
on='state_id').sort_values('LabTracks_ID').set_index('LabTracks_ID'))[[
'regimen_name', 'stage_name', 'regimen_id', 'stage_id', 'state_id'
]]
print(subjects_df)
def fe7():
prefix = '分支机构信息'
df, train_num = utils.get_df(prefix, ['企业名称', '分支机构状态', '分支成立时间', '分支死亡时间'])
df[prefix + '_分支机构状态'] = df[prefix + '_分支机构状态'].apply(lambda x: 1 if 1 == x else 0)
df = pd.get_dummies(df, prefix=['dummy_' + prefix + '_分支机构状态'], columns=[prefix + '_分支机构状态'])
df[prefix + '_分支成立时间'] = df[prefix + '_分支成立时间'].astype('str').apply(
lambda x: utils.get_date(x) if x != 'nan' and x != '1899/12/30' else np.nan)
df[prefix + '_分支死亡时间'] = df[prefix + '_分支死亡时间'].astype('str').apply(
lambda x: utils.get_date(x) if x != 'nan' else np.nan)
df[prefix + '_分支成立时间_分支死亡时间_diff']\
= df[prefix + '_分支成立时间'] - df[prefix + '_分支死亡时间']
raw_features = df.columns.values[1:]
group = df.groupby('企业名称', as_index=False)
for f in raw_features:
if 'dummy' in f:
df = pd.merge(df, utils.get_agg(group, f, ['sum', 'mean']), on='企业名称', how='left')
df = pd.merge(df, group['企业名称'].agg({prefix + '_count': 'count'}), on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_分支成立时间', ['max', 'min', 'mean', 'diff_mean']),
on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_分支死亡时间', ['max', 'min', 'mean', 'diff_mean']),
on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_分支成立时间_分支死亡时间_diff', ['max', 'min', 'mean']),
on='企业名称', how='left')
for f in raw_features:
del df[f]
train_df, test_df = df[:train_num], df[train_num:]
train_df.drop_duplicates('企业名称', inplace=True)
test_df.drop_duplicates('企业名称', inplace=True)
return prefix, train_df, test_df
def fe4():
prefix = '双公示-法人行政许可信息'
df, train_num = utils.get_df(prefix, ['企业名称', '许可决定日期', '许可截止期'])
df[prefix + '_许可决定日期'] = df[prefix + '_许可决定日期'].astype('str').apply(
lambda x: utils.get_date(x) if x != 'nan' else np.nan)
df[prefix + '_许可截止期'] = df[prefix + '_许可截止期'].astype('str').apply(
lambda x: utils.get_date(x) if x != 'nan' else np.nan)
df[prefix +
'_许可决定日期_许可截止期_diff'] = df[prefix + '_许可决定日期'] - df[prefix + '_许可截止期']
group = df.groupby('企业名称', as_index=False)
df = pd.merge(df,
utils.get_agg(group, prefix + '_许可决定日期',
['max', 'min', 'mean', 'diff_mean']),
on='企业名称',
how='left')
df = pd.merge(df,
utils.get_agg(group, prefix + '_许可截止期',
['max', 'min', 'mean']),
on='企业名称',
how='left')
df = pd.merge(df,
utils.get_agg(group, prefix + '_许可决定日期_许可截止期_diff',
['max', 'min', 'mean']),
on='企业名称',
how='left')
for f in ['许可决定日期', '许可截止期', '许可决定日期_许可截止期_diff']:
del df[prefix + '_' + f]
train_df, test_df = df[:train_num], df[train_num:]
train_df.drop_duplicates('企业名称', inplace=True)
test_df.drop_duplicates('企业名称', inplace=True)
return prefix, train_df, test_df
def fe6():
prefix = '许可资质年检信息'
df, train_num = utils.get_df(prefix, ['企业名称', '年检结果', '年检事项名称', '年检日期'])
df[prefix + '_年检结果'] = df[prefix + '_年检结果'].astype('str').apply(
lambda x: '合格' if '合格' in x else '通过')
df[prefix + '_年检事项名称'] = df[prefix + '_年检事项名称'].astype('str').apply(
lambda x: x if '单位年检' == x or '对道路' in x else '其他')
df = pd.get_dummies(df, prefix=['dummy_' + prefix + '_年检事项名称', 'dummy_' + prefix + '_年检结果'],
columns=[prefix + '_年检事项名称', prefix + '_年检结果'])
del df['dummy_' + prefix + '_年检事项名称_其他']
df[prefix + '_年检日期'] = df[prefix + '_年检日期'].astype('str').apply(
lambda x: utils.get_date(x) if x != 'nan' else np.nan)
raw_features = df.columns.values[1:]
group = df.groupby('企业名称', as_index=False)
for f in raw_features:
if 'dummy' in f:
df = pd.merge(df, utils.get_agg(group, f, ['sum', 'mean']), on='企业名称', how='left')
df = pd.merge(df, group['企业名称'].agg({prefix + '_count': 'count'}), on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_年检日期', ['max', 'min', 'mean', 'diff_mean']),
on='企业名称', how='left')
for f in raw_features:
del df[f]
train_df, test_df = df[:train_num], df[train_num:]
train_df.drop_duplicates('企业名称', inplace=True)
test_df.drop_duplicates('企业名称', inplace=True)
return prefix, train_df, test_df
def figs_rf_abc():
df = get_df(path=f"{basepath_dfs}/supplement_rf_abc.csv", )
subfolder = "rf_abc"
df.loc[df["algorithm"] == "RF-ABC", "algorithm"] = " RF-ABC"
for tasks in tqdm(all_tasks):
plot_task_metric(df, tasks, "C2ST", subfolder=subfolder)
plot_task_metric(df, tasks, "C2ST", subfolder=subfolder, labels=False)
def fe2():
def recruiting_numbers(x):
if '若干' == x:
return np.nan
if '人' in x:
return x[:-1]
return x
prefix = '招聘数据'
df, train_num = utils.get_df(prefix, ['企业名称', '招聘人数', '招聘日期'])
df[prefix + '_招聘人数'] = df[prefix + '_招聘人数'].fillna('若干').astype('str').apply(
recruiting_numbers).astype('float32')
df[prefix + '_招聘日期'] = df[prefix + '_招聘日期'].apply(utils.get_date)
group = df.groupby('企业名称', as_index=False)
df = pd.merge(df, group['企业名称'].agg({prefix + '_count': 'count'}), on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_招聘人数', ['mean', 'sum']),
on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_招聘日期', ['max', 'min', 'mean', 'diff_mean']),
on='企业名称', how='left')
df[prefix + '_freq'] = df[prefix + '_count'] / (
df[prefix + '_招聘日期_max'] - df[prefix + '_招聘日期_min'] + 1)
for f in ['招聘人数', '招聘日期']:
del df[prefix + '_' + f]
train_df, test_df = df[:train_num], df[train_num:]
train_df.drop_duplicates('企业名称', inplace=True)
test_df.drop_duplicates('企业名称', inplace=True)
return prefix, train_df, test_df
def train(task, size, data, shards, checkpoint):
name = '{}/{}-{}/'.format(task, size, shards)
model = getattr(models, task)[size]
task = getattr(tasks, task)
df = utils.get_df(data, shards)
df = utils.normalize_df(df)
df = df.sample(frac=1)
dataset = utils.Data(df, task)
callbacks = get_callbacks('logs/{}'.format(name))
model.compile(optimizer='adam',
loss=task['outputs'],
metrics=task.get('metrics'))
model.summary()
model.fit(
dataset,
callbacks=callbacks,
workers=2,
max_queue_size=10,
use_multiprocessing=True,
)
if checkpoint:
model.save('checkpoints/{}'.format(name))
def fe9():
def date_proc(x1, x2):
if 'nan' == x1 and 'nan' == x2:
return np.nan
if x1 != 'nan':
return utils.get_date(x1)
return utils.get_date(x2)
def cert_name(x):
if '高新技术' in x:
return '高新技术'
if '建筑施工' in x:
return '建筑施工'
return '其他'
prefix = '资质登记（变更）信息'
df, train_num = utils.get_df(prefix,
['企业名称', '资质名称', '资质生效期', '资质截止期', '认定日期'])
df[prefix + '_资质名称'] = df[prefix + '_资质名称'].astype('str').apply(cert_name)
df = pd.get_dummies(df,
prefix=['dummy_' + prefix + '_资质名称'],
columns=[prefix + '_资质名称'])
df[prefix + '_资质生效期'] = df[[
prefix + '_资质生效期', prefix + '_认定日期'
]].astype('str').apply(
lambda x: date_proc(x[prefix + '_资质生效期'], x[prefix + '_认定日期']), axis=1)
df[prefix +
'_资质截止期'] = df[prefix +
'_资质截止期'].astype('str').apply(lambda x: utils.get_date(
x) if x != 'nan' and x[:4] != '1950' else np.nan)
df[prefix +
'_资质生效期_资质截止期_diff'] = df[prefix + '_资质生效期'] - df[prefix + '_资质截止期']
raw_features = df.columns.values[1:]
group = df.groupby('企业名称', as_index=False)
for f in raw_features:
if 'dummy' in f:
df = pd.merge(df,
utils.get_agg(group, f, ['sum', 'mean']),
on='企业名称',
how='left')
df = pd.merge(df,
utils.get_agg(group, prefix + '_资质生效期',
['max', 'min', 'mean']),
on='企业名称',
how='left')
df = pd.merge(df,
utils.get_agg(group, prefix + '_资质截止期', ['min']),
on='企业名称',
how='left')
df = pd.merge(df,
utils.get_agg(group, prefix + '_资质生效期_资质截止期_diff', ['min']),
on='企业名称',
how='left')
for f in raw_features:
del df[f]
train_df, test_df = df[:train_num], df[train_num:]
train_df.drop_duplicates('企业名称', inplace=True)
test_df.drop_duplicates('企业名称', inplace=True)
return prefix, train_df, test_df
def update_data(session, dataset):
print(f"Inserting data into table article")
ospaths = get_paths()
session = ospaths["datadir"] + session + "/"
df = get_df(session + dataset, drop_nans=True)
df.to_sql(name="article",
con=db.engine,
index=False,
if_exists="replace")
def fe5():
prefix = '企业非正常户认定'
df, train_num = utils.get_df(prefix, ['企业名称', '认定日期'])
df[prefix] = 1
df[prefix + '_认定日期'] = df[prefix + '_认定日期'].apply(utils.get_date)
train_df, test_df = df[:train_num], df[train_num:]
train_df.drop_duplicates('企业名称', inplace=True)
test_df.drop_duplicates('企业名称', inplace=True)
return prefix, train_df, test_df
def figs_sl():
df = get_df(path=f"{basepath_dfs}/supplement_sl.csv", )
subfolder = "sl"
df.loc[df["algorithm"] == "SL", "algorithm"] = " SL"
df.loc[df["algorithm"] == "NLE-MAF", "algorithm"] = "NLE"
df.loc[df["algorithm"] == "SNLE-MAF", "algorithm"] = "SNLE"
for tasks in tqdm(all_tasks):
plot_task_metric(df, tasks, "C2ST", subfolder=subfolder)
plot_task_metric(df, tasks, "C2ST", subfolder=subfolder, labels=False)
def figs_main_paper():
df = get_df(path=f"{basepath_dfs}/main_paper.csv", )
df.loc[df["algorithm"] == "REJ-ABC", "algorithm"] = " REJ-ABC"
for tasks in tqdm(all_tasks):
for metric in all_metrics:
plot_task_metric(df, tasks, metric, subfolder="main_paper")
plot_task_metric(df,
tasks,
metric,
subfolder="main_paper",
labels=False)
def test_ping_h1_h5_and_h2_h5_balance_of_charges():
utils.get_output("wireshark/s2-eth1-h1-ping-h5.pcap",
"test/s2-ping-h1-h5.txt")
utils.get_output("wireshark/s2-eth1-h2-ping-h5.pcap",
"test/s2-ping-h2-h5.txt")
utils.get_output("wireshark/s3-eth1-h1-ping-h5.pcap",
"test/s3-ping-h1-h5.txt")
utils.get_output("wireshark/s3-eth1-h2-ping-h5.pcap",
"test/s3-ping-h2-h5.txt")
def callback(x):
return x['dst'] == '10.0.0.5' and x['protocol'] == 'ICMP'
df_1 = utils.get_df("test/s2-ping-h1-h5.txt")
df_1.columns = ['src', 'dst', 'protocol']
df_1 = utils.filter_by(df_1, ['dst', 'protocol'], callback)
size_1 = len(df_1.index)
df_2 = utils.get_df("test/s2-ping-h2-h5.txt")
df_2.columns = ['src', 'dst', 'protocol']
df_2 = utils.filter_by(df_2, ['dst', 'protocol'], callback)
size_2 = len(df_2.index)
df_3 = utils.get_df("test/s3-ping-h1-h5.txt")
df_3.columns = ['src', 'dst', 'protocol']
df_3 = utils.filter_by(df_3, ['dst', 'protocol'], callback)
size_3 = len(df_3.index)
df_4 = utils.get_df("test/s3-ping-h2-h5.txt")
df_4.columns = ['src', 'dst', 'protocol']
df_4 = utils.filter_by(df_4, ['dst', 'protocol'], callback)
size_4 = len(df_4.index)
os.system("rm test/s2-ping-h1-h5.txt")
os.system("rm test/s2-ping-h2-h5.txt")
os.system("rm test/s3-ping-h1-h5.txt")
os.system("rm test/s3-ping-h2-h5.txt")
ecmp_h1_h5 = (size_1 == 0 and size_3 != 0) or (size_1 != 0 and size_3 == 0)
ecmp_h2_h5 = (size_2 == 0 and size_4 != 0) or (size_2 != 0 and size_4 == 0)
assert ecmp_h1_h5 and ecmp_h2_h5
def run(session_name, target_size):
print(f"Starting preprocessing of images for session {session_name}")
global SESSION, IMAGE_SESSION
SESSION = session_name
IMAGE_SESSION = SESSION + "resized_images/"
if not os.path.exists(IMAGE_SESSION):
os.mkdir(IMAGE_SESSION)
print(f"New directory: {IMAGE_SESSION}")
f = utils.get_df(SESSION + "id_path.csv")
f = f.path.values.tolist()
copy_and_resize_sample_images(f, target_size)
def compute_distance(df, sp, feature, normalize):
print(f"Computing distance similarity for {feature}")
if not isinstance(df, pd.DataFrame):
measures = utils.get_df(df)
else:
measures = df
func = calculate_distance
if normalize:
func = normalize_and_calculate_distance
results = for_pivot(measures[feature], measures, func)
print()
save_as_pivot(results, sp=sp)
def fe1():
prefix = '企业基本信息&高管信息&投资信息'
df, train_num = utils.get_df(prefix, ['企业名称', '注册资金', '注册资本(金)币种名称', '企业(机构)类型名称',
'行业门类代码', '成立日期', '核准日期', '住所所在地省份', '姓名',
'法定代表人标志', '首席代表标志', '职务', '投资人', '出资比例'])
df[prefix + '_出资比例'] = df[prefix + '_出资比例'].apply(lambda x: x if x <= 1 else x / 100)
df[prefix + '_注册资金'] = df[[prefix + '_注册资金', prefix + '_注册资本(金)币种名称']].apply(
lambda x: x[prefix + '_注册资金'] if x[prefix + '_注册资本(金)币种名称'] not in utils.exch_rate.keys()
else x[prefix + '_注册资金'] * utils.exch_rate[x[prefix + '_注册资本(金)币种名称']], axis=1).fillna(0)
df[prefix + '_注册资金_binning'] = df[prefix + '_注册资金'].apply(
lambda x: utils.binning(x, [300, 500, 1000, 3000, 6000]))
df[prefix + '_成立日期'] = df[prefix + '_成立日期'].astype('str').apply(
lambda x: utils.get_date(x[:10]) if x != 'nan' else np.nan)
df[prefix + '_核准日期'] = df[prefix + '_核准日期'].apply(lambda x: utils.get_date(x[:10]))
df[prefix + '_成立日期_核准日期_diff'] = df[prefix + '_成立日期'] - df[prefix + '_核准日期']
df[prefix + '_法定代表人职务'] = df[[prefix + '_法定代表人标志', prefix + '_职务']].apply(
lambda x: x[prefix + '_职务'] if x[prefix + '_法定代表人标志'] == '是' else np.nan, axis=1)
df[prefix + '_首席代表职务'] = df[[prefix + '_首席代表标志', prefix + '_职务']].apply(
lambda x: x[prefix + '_职务'] if x[prefix + '_首席代表标志'] == '是' else np.nan, axis=1)
df = pd.merge(df, df.dropna(subset=[prefix + '_姓名']).groupby(
prefix + '_姓名', as_index=False)['企业名称'].agg({prefix + '_姓名_企业名称_nunique': 'nunique'}),
on=prefix + '_姓名', how='left')
df = pd.merge(df, df.dropna(subset=[prefix + '_投资人']).groupby(
prefix + '_投资人', as_index=False)['企业名称'].agg({prefix + '_投资人_企业名称_nunique': 'nunique'}),
on=prefix + '_投资人', how='left')
group = df.groupby('企业名称', as_index=False)
df = pd.merge(df, utils.get_agg(group, prefix + '_姓名', ['nunique']), on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_投资人', ['nunique']), on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_姓名_企业名称_nunique', ['max', 'mean', 'sum']),
on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_投资人_企业名称_nunique', ['max', 'mean', 'sum']),
on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_出资比例', ['max', 'min', 'mean']), on='企业名称', how='left')
f_pairs = [
[prefix + '_住所所在地省份', prefix + '_企业(机构)类型名称'],
[prefix + '_住所所在地省份', prefix + '_行业门类代码'],
[prefix + '_注册资金_binning', prefix + '_企业(机构)类型名称'],
[prefix + '_注册资金_binning', prefix + '_行业门类代码'],
[prefix + '_企业(机构)类型名称', prefix + '_行业门类代码']
]
df = utils.get_ratio(df, f_pairs)
for f in ['注册资本(金)币种名称', '姓名', '法定代表人标志', '首席代表标志', '职务', '投资人', '出资比例',
'姓名_企业名称_nunique', '投资人_企业名称_nunique']:
del df[prefix + '_' + f]
train_df, test_df = df[:train_num], df[train_num:]
train_df.drop_duplicates('企业名称', inplace=True)
train_df.dropna(subset=[prefix + '_成立日期'], inplace=True)
test_df.drop_duplicates('企业名称', inplace=True)
for f in ['法定代表人职务', '首席代表职务', '企业(机构)类型名称', '行业门类代码', '住所所在地省份']:
label_dict = dict(zip(train_df[prefix + '_' + f].unique(), range(train_df[prefix + '_' + f].nunique())))
train_df[prefix + '_' + f] = train_df[prefix + '_' + f].map(label_dict).fillna(-1).astype('int16')
test_df[prefix + '_' + f] = test_df[prefix + '_' + f].map(label_dict).fillna(-1).astype('int16')
return prefix, train_df, test_df
def test_tcp_balance_of_charges():
utils.get_output("wireshark/s2-eth1-iperf-h1-h4.pcap", "test/s2-h1-h4.txt")
utils.get_output("wireshark/s3-eth1-iperf-h1-h4.pcap", "test/s3-h1-h4.txt")
def callback(x):
return x['dst'] == '10.0.0.4' and x['protocol'] == 'TCP'
df_1 = utils.get_df("test/s2-h1-h4.txt")
df_1.columns = ['src', 'dst', 'protocol']
df_1 = utils.filter_by(df_1, ['dst', 'protocol'], callback)
size_1 = len(df_1.index)
df_2 = utils.get_df("test/s3-h1-h4.txt")
df_2.columns = ['src', 'dst', 'protocol']
df_2 = utils.filter_by(df_2, ['dst', 'protocol'], callback)
size_2 = len(df_2.index)
os.system("rm test/s2-h1-h4.txt")
os.system("rm test/s3-h1-h4.txt")
assert (size_1 == 0 and size_2 != 0) or (size_1 != 0 and size_2 == 0)
def fe12():
prefix = '法人行政许可注（撤、吊）销信息'
df, train_num = utils.get_df(prefix, ['企业名称', '注（撤、吊）销批准日期'])
df[prefix + '_注（撤、吊）销批准日期'] = df[prefix + '_注（撤、吊）销批准日期'].apply(utils.get_date)
group = df.groupby('企业名称', as_index=False)
df = pd.merge(df, group['企业名称'].agg({prefix + '_count': 'count'}), on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_注（撤、吊）销批准日期', ['max']), on='企业名称', how='left')
for f in ['注（撤、吊）销批准日期']:
del df[prefix + '_' + f]
train_df, test_df = df[:train_num], df[train_num:]
train_df.drop_duplicates('企业名称', inplace=True)
test_df.drop_duplicates('企业名称', inplace=True)
return prefix, train_df, test_df
def figs_hyperparameters_snre():
df = get_df(path=f"{basepath_dfs}/supplement_hyperparameters_snre.csv", )
for tasks in tqdm(all_tasks):
for metric in ["C2ST", "RT"]:
plot_task_metric(df,
tasks,
metric,
subfolder="hyperparameters_snre")
plot_task_metric(df,
tasks,
metric,
subfolder="hyperparameters_snre",
labels=False)
def copy_images(src, dst):
""""
:param src -- path to the current sample CSV
"""
df = utils.get_df(src, drop_nans=False, dt=True)
idpath = utils.get_id_path_pairs(df, from_path="drive")
images = list(idpath.values())
ids = list(idpath.keys())
for i in range(len(images)):
file = images[i]
ext = file[file.rfind("."):]
shutil.copyfile(file, dst + ids[i] + ext)
def fe11():
prefix = '双打办打击侵权假冒处罚案件信息'
df, train_num = utils.get_df(prefix, ['企业名称', '提供日期'])
df[prefix + '_提供日期'] = df[prefix + '_提供日期'].astype('str').apply(
lambda x: utils.get_date(x) if x != 'nan' else np.nan)
group = df.groupby('企业名称', as_index=False)
df = pd.merge(df, group['企业名称'].agg({prefix + '_count': 'count'}), on='企业名称', how='left')
df = pd.merge(df, utils.get_agg(group, prefix + '_提供日期', ['max']), on='企业名称', how='left')
for f in ['提供日期']:
del df[prefix + '_' + f]
train_df, test_df = df[:train_num], df[train_num:]
train_df.drop_duplicates('企业名称', inplace=True)
test_df.drop_duplicates('企业名称', inplace=True)
return prefix, train_df, test_df
def raw(self):
if self._raw is not None:
return self._raw
df = get_df(
self.host,
self.path,
self._refresh,
self._verbose,
errors='raise',
parse_dates=['data'],
)
self._raw = df # cache
return df
def test_udp_denial_of_service():
utils.get_output("wireshark/s5-eth1-iperf-udp-h1-h5.pcap",
"test/s5-udp-h1-h5.txt")
def callback(x):
return x['dst'] == '10.0.0.5' and x['protocol'] == 'UDP'
df_1 = utils.get_df("test/s5-udp-h1-h5.txt")
df_1.columns = ['src', 'dst', 'protocol']
df_1 = utils.filter_by(df_1, ['dst', 'protocol'], callback)
size_1 = len(df_1.index)
assert size_1 < 893
os.system("rm test/s5-udp-h1-h5.txt")
def fe8():
prefix = '企业税务登记信息'
df, train_num = utils.get_df(prefix, ['企业名称', '审核时间', '登记注册类型', '审核结果'])
df[prefix + '_审核时间'] = df[prefix + '_审核时间'].apply(utils.get_date)
df[prefix + '_审核结果'] = df[prefix + '_审核结果'].apply(
lambda x: x if '江苏省苏州地方税务局' == x or '开业' == x or '正常' == x else '其他')
f_pairs = [[prefix + '_登记注册类型', prefix + '_审核结果']]
df = utils.get_ratio(df, f_pairs)
train_df, test_df = df[:train_num], df[train_num:]
train_df.drop_duplicates('企业名称', keep='last', inplace=True)
test_df.drop_duplicates('企业名称', keep='last', inplace=True)
for f in [prefix + '_审核结果', prefix + '_登记注册类型']:
label_dict = dict(zip(train_df[f].unique(), range(train_df[f].nunique())))
train_df[f] = train_df[f].map(label_dict).fillna(-1).astype('int16')
test_df[f] = test_df[f].map(label_dict).fillna(-1).astype('int16')
return prefix, train_df, test_df
def create_image_paths(df_path, cat):
df = utils.get_df(df_path,
category=cat,
drop_nans=True,
drop_duplicates=True)
file_paths = utils.get_id_path_pairs(df,
from_path="drive",
ignore_types="gif")
file_paths = list(file_paths.keys())
df = df[df.id.isin(file_paths)]
df.to_csv(SESSION + "politics.csv", index=False)
utils.save_clean_copy(df, SESSION + "politics.csv")
utils.get_id_path_pairs(df,
save_path=SESSION + "id_path_all.csv",
from_path="drive")
def _get_data(self, refresh, verbose):
"""Download or fetch data from cache.
Downloads 1 file per day, and unions files with the same schema.
Unioning across schemas is taken care of `self._consolidate`.
Returns:
List[DataFrame]: One DataFrame for each schema version.
"""
schemas = {
'20200122': {
'dates': pd.date_range('2020-01-22', '2020-02-29'),
'kwargs': dict(parse_dates=['Last Update']),
},
'20200301': {
'dates': pd.date_range('2020-03-01', '2020-03-21'),
'kwargs': dict(parse_dates=['Last Update']),
},
'20200322': {
'dates': pd.date_range('2020-03-22', dt.datetime.today()),
'kwargs': dict(parse_dates=['Last_Update']),
},
}
schemas_dfs = {}
for schema_name, schema in schemas.items():
files_dfs = []
for date in schema['dates']:
filename = f"{date.strftime('%m-%d-%Y')}.csv"
df = get_df(
self.host,
f"{self.path}/{filename}",
refresh,
verbose,
errors='ignore',
**schema['kwargs']
)
if df is not None:
files_dfs.append(df.assign(_filedate=date, _filename=filename))
# validate schemas
columns = list(map(lambda df: set(df.columns), files_dfs))
assert all(x == columns[0] for x in columns), f"schemas differ for {schema_name}"
schemas_dfs[schema_name] = pd.concat(files_dfs)
return schemas_dfs
def raw(self):
if self._raw is not None:
return self._raw
df = get_df(
self.host,
self.path,
self._refresh,
self._verbose,
errors='raise',
parse_dates=['dateRep'],
dayfirst=True,
keep_default_na=False, # don't treat Namibia as 'NA'
)
self._raw = df # cache
return df
def figs_hyperparameters_rej_abc():
df = get_df(
path=f"{basepath_dfs}/supplement_hyperparameters_rej_abc.csv", )
for tasks in tqdm(all_tasks):
for metric in ["C2ST"]:
plot_task_metric(
df,
tasks,
metric,
subfolder="hyperparameters_rej_abc",
default_color=get_colors(df=df, include_defaults=True)["REJ"],
)
plot_task_metric(
df,
tasks,
metric,
subfolder="hyperparameters_rej_abc",
labels=False,
default_color=get_colors(df=df, include_defaults=True)["REJ"],
)
def figs_abc_lra_sass():
df = get_df(path=f"{basepath_dfs}/supplement_abc_lra_sass.csv", )
for tasks in tqdm(all_tasks):
for metric in ["C2ST"]:
plot_task_metric(
df,
tasks,
metric,
subfolder="abc_lra_sass",
default_color=get_colors(df=df),
)
plot_task_metric(
df,
tasks,
metric,
subfolder="abc_lra_sass",
labels=False,
default_color=get_colors(df=df),
)
def get_data_loaders(bs=8, num_workers=0, shuffle=True, ts=0.2):
train_df, img_2_ohe_vector = get_df()
train_imgs, val_imgs = train_test_split(train_df['Image'].values,
test_size=ts,
stratify=train_df['Class'].map(lambda x: str(sorted(list(x)))),
random_state=42)
print(train_imgs)
print(val_imgs)
print(len(train_imgs))
print(len(val_imgs))
train_dataset = CloudDataset(img_2_ohe_vector, img_ids=train_imgs,
transforms=get_training_augmentation())
train_loader = DataLoader(train_dataset, batch_size=bs,
shuffle=shuffle, num_workers=num_workers)
val_dataset = CloudDataset(img_2_ohe_vector, img_ids=val_imgs,
transforms=get_validation_augmentation())
val_loader = DataLoader(val_dataset, batch_size=bs,
shuffle=shuffle, num_workers=num_workers)
return train_loader, val_loader
def setUp(self):
df = get_df()
self.check = Check(df)
self.jvmCheck = self.check.jvmCheck
#perform the fit
res = optimize.minimize(func_to_optimize, x0, args=(distance_array, time_array, fare_array), method='TNC', bounds=bnds)
grid_dist = utils.grid_distance(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon)
#get the predictions
time_pred = utils.time_prediction(speed_array.mean(), grid_dist, dist_sf_array.mean())
fare_pred = utils.fare_prediction(res.x[0], grid_dist, dist_sf_array.mean(), res.x[1], res.x[2], res.x[3])
if res.success == True:
return [fare_pred, time_pred]
else:
return [0,0]
#read in dataframe and cache it
df1 = utils.get_df(sqlContext, 1, 1)
df1.cache()
#get random sample for prediction
test_sample=df1.sample(False, 0.1, seed=42).limit(500).toPandas()
test_sample.columns = df1.columns
test_fare = test_sample["total_notip"].tolist()
test_time = test_sample["trip_time_in_secs"].tolist()
pred_fare = []
pred_time = []
#get prediction for each event
for index, row in test_sample.iterrows():
print 'Processing event '+str(index)
event = [row['pickup'],utils.dayofweek(row['pickup']),row['pick_lat'],row['pick_lon'],row['drop_lat'],row['drop_lon'],row['pc']]
prediction = make_prediction(event, df1)
