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fusai.py
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fusai.py
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import pandas as pd
pd.options.display.max_columns=None
pd.options.display.max_rows=1000
import numpy as np
import os
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import GroupKFold
from sklearn.metrics import f1_score
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import StandardScaler,LabelEncoder
from sklearn.cluster import DBSCAN
import lightgbm as lgb
import shap
import math
from stdbscan import STDBSCAN
import itertools
from scipy import stats
from scipy.spatial.distance import cdist, euclidean
from datetime import datetime
import shapely.geometry as geom
from multiprocessing import Pool,cpu_count
from datetime import timedelta
from gensim.models import Word2Vec,Doc2Vec
import xgboost as xgb
# test_path='./hy_round1_testA_20200102/'
# test_b_path='./hy_round1_testB_20200221/'
train_path='./hy_round2_train_20200225/'
type_map={'拖网':0,'刺网':1,'围网':2}
type_map_rev={0:'拖网',1:'刺网',2:'围网'}
splits=10
base=[25.173215180565872, 119.27677013681826]
def cut_data(data,num,col):
data_part_list=[]
col_list=list(set(data[col]))
if col=='ship':
col_list.sort()
size=int(math.ceil(len(col_list)/num))
for i in range(num):
start=i*size
end=(i+1)*size if (i+1)*size<len(col_list) else len(col_list)
col_part=col_list[start:end]
data_part=data[data[col].isin(col_part)]
data_part_list.append(data_part)
return data_part_list
def multi_func(num,data,col,func):
data_part_list=cut_data(data,num,col)
p=Pool(num)
rlist=p.map(func,data_part_list)
p.close()
p.join()
if func==prepare_data_3:
for i in range(num-1):
rlist[i+1]['group']=rlist[i+1]['group']+rlist[i]['group'].max()
data=pd.concat(rlist,ignore_index=True)
return data
def prepare_data(path,data_name):
if os.path.exists('./data/{}.pkl'.format(data_name)):
data=pd.read_pickle('./data/{}.pkl'.format(data_name))
else:
filenames=os.listdir(path)
data=pd.DataFrame()
for filename in filenames:
print(filename)
df=pd.read_csv(path+filename)
data=pd.concat([df,data],axis=0)
data=data.rename(columns={'渔船ID':'ship','速度':'speed','方向':'direction'})
data.to_pickle('./data/{}.pkl'.format(data_name))
print(data.shape)
print(data.head())
return data
def get_r(lat):
'''
https://planetcalc.com/7721/
'''
a=6378137
b=6356752
lat=np.radians(lat)
up=(a**2*np.cos(lat))**2+(b**2*np.sin(lat))**2
down=(a*np.cos(lat))**2+(b*np.sin(lat))**2
r=np.sqrt(up/down)
return r
def get_haversine_dis(lat1,lon1,lat2,lon2):
r=get_r(lat2)
# for item in [lat1,lon1,lat2,lon2]:
# item=np.radians(item)
lat1,lon1,lat2,lon2=map(np.radians,[lat1,lon1,lat2,lon2])
lat_gap=lat2-lat1
lon_gap=lon2-lon1
a=np.sin(lat_gap/2)**2+np.cos(lat1)*np.cos(lat2)*np.sin(lon_gap/2)**2
c=2*np.arcsin(np.sqrt(a))
dis=r*c
return dis
def get_xy(lat1,lon1,lat2,lon2):
x=get_haversine_dis(lat1,lon1,lat1,lon2)
y=get_haversine_dis(lat1,lon1,lat2,lon1)
if lon2<lon1:
x=-1*x
if lat2<lat1:
y=-1*y
return [x,y]
def get_label(data,name):
if os.path.exists('./data/label_{}.pkl'.format(name)):
label=pd.read_pickle('./data/label_{}.pkl'.format(name))
else:
temp=data.copy()
temp['time']=pd.to_datetime(temp['time'],format='%m%d %H:%M:%S')
temp.sort_values(['ship','group','time'],inplace=True)
label=temp[['ship','group','type','stop']].drop_duplicates()
label.to_pickle('./data/label_{}.pkl'.format(name))
print('label done_{}'.format(name))
return label
def diff_abs_mean(x):
return np.mean(np.abs(np.diff(x)))
def cid_ce(x):#时序数据复杂度
x=x.values
x=(x-np.mean(x))/np.std(x)
x=np.diff(x)
return np.sqrt(np.dot(x,x))
def longest_above_median(x):
x=x.values
x=(x>np.median(x))
x=x.astype(int)
res=[len(list(group)) for value,group in itertools.groupby(x) if value==1]
if len(res)>0:
return np.max(res)
else:
return 0
def angle(x_gap_1,x_gap_2,y_gap_1,y_gap_2):
angle1=math.atan2(y_gap_1,x_gap_1)*180/math.pi
angle2=math.atan2(y_gap_2,x_gap_2)*180/math.pi
if angle1*angle2>=0:
angle=abs(angle1-angle2)
else:
angle=abs(angle1)+abs(angle2)
if angle>180:
angle=360-angle
return angle
def corr(x,y):
return stats.pearsonr(x,y)[0]
def p(x,y):
return stats.pearsonr(x,y)[1]
def min_dis_range(x):
x=x[['x','y']].values
nn=NearestNeighbors(n_neighbors=2,algorithm='ball_tree').fit(x)
distances,indices=nn.kneighbors(x)
return distances[:,1]
def longest_below_median(x):#中位值以下最大连续数
x=x.values
x=(x<np.median(x))
x=x.astype(int)
res=[len(list(group)) for value,group in itertools.groupby(x) if value==1]
if len(res)>0:
return np.max(res)
else:
return 0
def peak_nums(x):
x=x.values
x=(x>np.median(x))
x=x.astype(int)
res=[len(list(group)) for value,group in itertools.groupby(x) if value==1]
if len(res)>0:
return len(res)
else:
return 0
def geometric_median(x,eps=1):
x=x[['x','y']].values
y=np.mean(x,0)
while True:
D=cdist(x,[y])
nonzeros=(D!=0)[:,0]
Dinv=1/D[nonzeros]
Dinvs=np.sum(Dinv)
W=Dinv/Dinvs
T=np.sum(W*x[nonzeros],0)
num_zeros=len(x)-np.sum(nonzeros)
if num_zeros==0:
y1=T
elif num_zeros==len(x):
return y
else:
R=(T-y)*Dinvs
r=np.linalg.norm(R)
rinv=0 if r == 0 else num_zeros/r
y1=max(0,1-rinv)*T+min(1,rinv)*y
if euclidean(y,y1)<eps:
return y1
y=y1
def quantile_min(x):
return x.quantile(0.02)
def quantile_max(x):
return x.quantile(0.98)
def quantile25(x):
return x.quantile(0.25)
def quantile75(x):
return x.quantile(0.75)
def get_stat_features(data):
temp=data.copy()
temp['time']=pd.to_datetime(temp['time'],format='%m%d %H:%M:%S')
temp.sort_values(['ship','group','time'],inplace=True)
temp['r']=(temp['x']**2+temp['y']**2)**0.5
temp['the']=temp.apply(lambda x:math.atan2(x.y,x.x)*180/math.pi,axis=1)
features=pd.DataFrame()
for col in ['x','y','speed','direction']:
feature=temp.groupby('group',as_index=False)[col].agg({
'{}_min'.format(col):quantile_min,
'{}_max'.format(col):quantile_max,
'{}_mean'.format(col):'mean',
'{}_median'.format(col):'median',
'{}_std'.format(col):'std',
'{}_skew'.format(col):'skew',
'{}_sum'.format(col):'sum',
'{}_diff_abs_mean'.format(col):diff_abs_mean,
'{}_mode'.format(col):lambda x:x.value_counts().index[0],
'{}_quantile25'.format(col):quantile25,#stat+
'{}_quantile75'.format(col):quantile75,#stat+
'count':'count'})
if features.shape[0]==0:
features=feature
else:
feature=feature.drop('count',axis=1)
features=features.merge(feature,on='group',how='left')
for col in ['speed','direction']:
feature=temp.groupby('group',as_index=False)[col].agg({
'{}_cid_ce'.format(col):cid_ce,
'{}_longest_above_median'.format(col):longest_above_median,})
features=features.merge(feature,on='group',how='left')
first_feature=temp.groupby('group').first()
first_feature=first_feature[['direction','speed','x','y']]
first_feature.columns=[c+'_first' for c in first_feature.columns]
first_feature=first_feature.reset_index()
features=features.merge(first_feature,on='group',how='left')
last_feature=temp.groupby('group').last()
last_feature=last_feature[['direction','speed','x','y']]
last_feature.columns=[c+'_last' for c in last_feature.columns]
last_feature=last_feature.reset_index()
features=features.merge(last_feature,on='group',how='left')
features['x_gap_max_min']=features['x_max']-features['x_min']
features['y_gap_max_min']=features['y_max']-features['y_min']
features['xy_gap_max_min']=features['x_max']-features['y_min']
features['yx_gap_max_min']=features['y_max']-features['x_min']
features['angle_max_min']=features['y_gap_max_min']/(features['x_gap_max_min']+0.00001)
features['area_max_min']=features['x_gap_max_min']*features['y_gap_max_min']
features['dis_max_min']=(features['x_gap_max_min']**2+features['y_gap_max_min']**2)**0.5
features['x_gap_last_first']=features['x_last']-features['x_first']
features['y_gap_last_first']=features['y_last']-features['y_first']
features['xy_gap_last_first']=features['x_last']-features['y_first']
features['yx_gap_last_first']=features['y_last']-features['x_first']
features['angle_last_first']=features['y_gap_last_first']/(features['x_gap_last_first']+0.00001)
features['area_last_first']=abs(features['x_gap_last_first']*features['y_gap_last_first'])
features['dis_last_first']=(features['x_gap_last_first']**2+features['y_gap_last_first']**2)**0.5
temp['x_gap']=temp.groupby('group')['x'].diff()#stat+
temp['x_gap_shift']=temp.groupby('group')['x_gap'].shift(-1)
temp['y_gap']=temp.groupby('group')['y'].diff()
temp['y_gap_shift']=temp.groupby('group')['y_gap'].shift(-1)
temp['angle']=temp.apply(lambda x:angle(x.x_gap,x.x_gap_shift,x.y_gap,x.y_gap_shift),axis=1)
temp['time_gap']=temp.groupby('group')['time'].diff()
temp['time_gap']=temp.apply(lambda x:x.time_gap.total_seconds()/3600,axis=1)
temp['dis']=(temp.x_gap**2+temp.y_gap**2)**0.5
temp['area']=abs(temp.x_gap*temp.y_gap)
feature_temp=temp.groupby('group',as_index=False).agg({
'time_gap':'sum',
'dis':'sum',
'area':'sum',
'angle':'sum'})
features=features.merge(feature_temp,on='group',how='left')
features['dis_max_min_ration']=features['dis']/(features['dis_max_min']+0.00001)
features['dis_last_first_ration']=features['dis']/(features['dis_last_first']+0.00001)
features['area_max_min_ration']=features['area']/(features['area_max_min']+0.00001)
features['area_last_first_ration']=features['area']/(features['area_last_first']+0.00001)
features['area_max_min_dis_ration']=features['area_max_min']/(features['dis_max_min']+0.00001)
features['area_last_first_dis_ration']=features['area_last_first']/(features['dis_last_first']+0.00001)
features['area_dis_ration']=features['area']/(features['dis']+0.00001)
features['area_max_min_time_ration']=features['area_max_min']/(features['time_gap']+0.00001)
features['area_last_first_time_ration']=features['area_last_first']/(features['time_gap']+0.00001)
features['area_time_ration']=features['area']/(features['time_gap']+0.00001)
features['dis_max_min_time_ration']=features['dis_max_min']/(features['time_gap']+0.00001)
features['dis_last_first_time_ration']=features['dis_last_first']/(features['time_gap']+0.00001)
features['dis_time_ration']=features['dis']/(features['time_gap']+0.00001)
features['angle_ration_dis']=features['angle']/(features['dis']+0.00001)
features['angle_ration_time']=features['angle']/(features['time_gap']+0.00001)
dis_range=data.groupby('group').apply(min_dis_range)#stat+
dis_range=dis_range.reset_index().rename(columns={0:'dis_range'})
dis_range['dis_q25']= dis_range.apply(lambda x:np.percentile(x.dis_range,25),axis=1)
dis_range['dis_q50']= dis_range.apply(lambda x:np.percentile(x.dis_range,50),axis=1)
dis_range['dis_q75']= dis_range.apply(lambda x:np.percentile(x.dis_range,75),axis=1)
dis_range=dis_range[['group','dis_q25','dis_q50','dis_q75']]
features=features.merge(dis_range,on='group',how='left')
points=temp.groupby('group').apply(geometric_median)#stat+
points=points.reset_index().rename(columns={0:'points'})
points['x_geometric_median']=points.points.apply(lambda x:x[0])
points['y_geometric_median']=points.points.apply(lambda x:x[1])
points=points[['group','x_geometric_median','y_geometric_median']]
features=features.merge(points,on='group',how='left')
temp=temp.merge(points,on='group',how='left')
temp['dis_geometric_median']=((temp.x-temp.x_geometric_median)**2+(temp.y-temp.y_geometric_median)**2)**0.5
feature=temp.groupby('group',as_index=False)['dis_geometric_median'].agg({
'dis_geometric_median_min':quantile_min,
'dis_geometric_median_max':quantile_max,
'dis_geometric_median_mean':'mean',
'dis_geometric_median_median':'median',
'dis_geometric_median_std':'std',
'dis_geometric_median_quantile25':quantile25,
'dis_geometric_median_quantile75':quantile75,
})
features=features.merge(feature,on='group',how='left')
return features
def get_stat_features_2(data):
temp=data.copy()
temp['time']=pd.to_datetime(temp['time'],format='%m%d %H:%M:%S')
temp.sort_values(['ship','group','time'],inplace=True)
temp['r']=(temp['x']**2+temp['y']**2)**0.5
temp['the']=temp.apply(lambda x:math.atan2(x.y,x.x)*180/math.pi,axis=1)
features=pd.DataFrame()
for col in ['x','y','speed','direction','r','the']:
feature=temp.groupby('group',as_index=False)[col].agg({
'{}_min'.format(col):quantile_min,
'{}_max'.format(col):quantile_max,
'{}_mean'.format(col):'mean',
'{}_median'.format(col):'median',
'{}_std'.format(col):'std',
'{}_skew'.format(col):'skew',
'{}_sum'.format(col):'sum',
'{}_diff_abs_mean'.format(col):diff_abs_mean,
'{}_mode'.format(col):lambda x:x.value_counts().index[0],
'{}_quantile25'.format(col):quantile25,#stat+
'{}_quantile75'.format(col):quantile75,#stat+
'count':'count'})
if features.shape[0]==0:
features=feature
else:
feature=feature.drop('count',axis=1)
features=features.merge(feature,on='group',how='left')
for col in ['speed','direction']:
feature=temp.groupby('group',as_index=False)[col].agg({
'{}_cid_ce'.format(col):cid_ce,
'{}_longest_above_median'.format(col):longest_above_median,})
features=features.merge(feature,on='group',how='left')
first_feature=temp.groupby('group').first()
first_feature=first_feature[['direction','speed','x','y']]
first_feature.columns=[c+'_first' for c in first_feature.columns]
first_feature=first_feature.reset_index()
features=features.merge(first_feature,on='group',how='left')
last_feature=temp.groupby('group').last()
last_feature=last_feature[['direction','speed','x','y']]
last_feature.columns=[c+'_last' for c in last_feature.columns]
last_feature=last_feature.reset_index()
features=features.merge(last_feature,on='group',how='left')
features['x_gap_max_min']=features['x_max']-features['x_min']
features['y_gap_max_min']=features['y_max']-features['y_min']
features['xy_gap_max_min']=features['x_max']-features['y_min']
features['yx_gap_max_min']=features['y_max']-features['x_min']
features['angle_max_min']=features['y_gap_max_min']/(features['x_gap_max_min']+0.00001)
features['area_max_min']=features['x_gap_max_min']*features['y_gap_max_min']
features['dis_max_min']=(features['x_gap_max_min']**2+features['y_gap_max_min']**2)**0.5
features['x_gap_last_first']=features['x_last']-features['x_first']
features['y_gap_last_first']=features['y_last']-features['y_first']
features['xy_gap_last_first']=features['x_last']-features['y_first']
features['yx_gap_last_first']=features['y_last']-features['x_first']
features['angle_last_first']=features['y_gap_last_first']/(features['x_gap_last_first']+0.00001)
features['area_last_first']=abs(features['x_gap_last_first']*features['y_gap_last_first'])
features['dis_last_first']=(features['x_gap_last_first']**2+features['y_gap_last_first']**2)**0.5
temp['x_gap']=temp.groupby('group')['x'].diff()#stat+
temp['x_gap_shift']=temp.groupby('group')['x_gap'].shift(-1)
temp['y_gap']=temp.groupby('group')['y'].diff()
temp['y_gap_shift']=temp.groupby('group')['y_gap'].shift(-1)
temp['angle']=temp.apply(lambda x:angle(x.x_gap,x.x_gap_shift,x.y_gap,x.y_gap_shift),axis=1)
temp['time_gap']=temp.groupby('group')['time'].diff()
temp['time_gap']=temp.apply(lambda x:x.time_gap.total_seconds()/3600,axis=1)
temp['dis']=(temp.x_gap**2+temp.y_gap**2)**0.5
temp['area']=abs(temp.x_gap*temp.y_gap)
feature_temp=temp.groupby('group',as_index=False).agg({
'time_gap':'sum',
'dis':'sum',
'area':'sum',
'angle':'sum'})
features=features.merge(feature_temp,on='group',how='left')
features['dis_max_min_ration']=features['dis']/(features['dis_max_min']+0.00001)
features['dis_last_first_ration']=features['dis']/(features['dis_last_first']+0.00001)
features['area_max_min_ration']=features['area']/(features['area_max_min']+0.00001)
features['area_last_first_ration']=features['area']/(features['area_last_first']+0.00001)
features['area_max_min_dis_ration']=features['area_max_min']/(features['dis_max_min']+0.00001)
features['area_last_first_dis_ration']=features['area_last_first']/(features['dis_last_first']+0.00001)
features['area_dis_ration']=features['area']/(features['dis']+0.00001)
features['area_max_min_time_ration']=features['area_max_min']/(features['time_gap']+0.00001)
features['area_last_first_time_ration']=features['area_last_first']/(features['time_gap']+0.00001)
features['area_time_ration']=features['area']/(features['time_gap']+0.00001)
features['dis_max_min_time_ration']=features['dis_max_min']/(features['time_gap']+0.00001)
features['dis_last_first_time_ration']=features['dis_last_first']/(features['time_gap']+0.00001)
features['dis_time_ration']=features['dis']/(features['time_gap']+0.00001)
features['angle_ration_dis']=features['angle']/(features['dis']+0.00001)
features['angle_ration_time']=features['angle']/(features['time_gap']+0.00001)
dis_range=data.groupby('group').apply(min_dis_range)#stat+
dis_range=dis_range.reset_index().rename(columns={0:'dis_range'})
dis_range['dis_q25']= dis_range.apply(lambda x:np.percentile(x.dis_range,25),axis=1)
dis_range['dis_q50']= dis_range.apply(lambda x:np.percentile(x.dis_range,50),axis=1)
dis_range['dis_q75']= dis_range.apply(lambda x:np.percentile(x.dis_range,75),axis=1)
dis_range=dis_range[['group','dis_q25','dis_q50','dis_q75']]
features=features.merge(dis_range,on='group',how='left')
points=temp.groupby('group').apply(geometric_median)#stat+
points=points.reset_index().rename(columns={0:'points'})
points['x_geometric_median']=points.points.apply(lambda x:x[0])
points['y_geometric_median']=points.points.apply(lambda x:x[1])
points=points[['group','x_geometric_median','y_geometric_median']]
features=features.merge(points,on='group',how='left')
temp=temp.merge(points,on='group',how='left')
temp['dis_geometric_median']=((temp.x-temp.x_geometric_median)**2+(temp.y-temp.y_geometric_median)**2)**0.5
feature=temp.groupby('group',as_index=False)['dis_geometric_median'].agg({
'dis_geometric_median_min':quantile_min,
'dis_geometric_median_max':quantile_max,
'dis_geometric_median_mean':'mean',
'dis_geometric_median_median':'median',
'dis_geometric_median_std':'std',
'dis_geometric_median_quantile25':quantile25,
'dis_geometric_median_quantile75':quantile75,
})
features=features.merge(feature,on='group',how='left')
return features
#stat_features=get_stat_features_multi(num,data,'group',get_stat_features,'stop')
def get_stat_features_multi(num,data,col,func,name):
if os.path.exists('./data/stat_{}.pkl'.format(name)):
features=pd.read_pickle('./data/stat_{}.pkl'.format(name))
else:
features=multi_func(num,data,col,func)
features.to_pickle('./data/stat_{}.pkl'.format(name))
print('stat_feature done_{}'.format(name))
return features
def get_corr_features(data,name):
if os.path.exists('./data/corr_{}.pkl'.format(name)):
features=pd.read_pickle('./data/corr_{}.pkl'.format(name))
else:
temp=data.copy()
temp['time']=pd.to_datetime(temp['time'],format='%m%d %H:%M:%S')
temp.sort_values(['ship','group','time'],inplace=True)
features=pd.DataFrame()
feature=temp.groupby('group').apply(lambda x:corr(x.x,x.y)).reset_index().rename(columns={0:'x_y_corr'})
if features.shape[0]==0:
features=feature
else:
features=features.merge(feature,on='group',how='left')
feature=temp.groupby('group').apply(lambda x:p(x.x,x.y)).reset_index().rename(columns={0:'x_y_p'})
features=features.merge(feature,on='group',how='left')
feature=temp.groupby('group').apply(lambda x:corr(x.speed,x.direction)).reset_index().rename(columns={0:'speed_direction_corr'})
features=features.merge(feature,on='group',how='left')
feature=temp.groupby('group').apply(lambda x:p(x.speed,x.direction)).reset_index().rename(columns={0:'speed_direction_p'})
features=features.merge(feature,on='group',how='left')
features.to_pickle('./data/corr_{}.pkl'.format(name))
print('corr feature done_{}'.format(name))
return features
def get_count_features(data,name):
if os.path.exists('./data/count_{}.pkl'.format(name)):
features=pd.read_pickle('./data/count_{}.pkl'.format(name))
else:
temp=data.copy()
features=pd.DataFrame()
for col in ['speed','direction']:
le=LabelEncoder()
temp['{}_cut'.format(col)]=le.fit_transform(pd.cut(temp[col],10))
feature=temp.groupby(['group','{}_cut'.format(col)])['time'].count()
feature=feature.unstack().fillna(0)
feature.columns=['{}_cut_{}_count'.format(col,c) for c in feature.columns]
feature=feature.reset_index()
if features.shape[0]==0:
features=feature
else:
features=features.merge(feature,on='group',how='left')
features=features.fillna(0)
features.to_pickle('./data/count_{}.pkl'.format(name))
print('count feature done_{}'.format(name))
return features
def get_time_features(data,name):
if os.path.exists('./data/time_{}.pkl'.format(name)):
features=pd.read_pickle('./data/time_{}.pkl'.format(name))
else:
temp=data.copy()
features=pd.DataFrame()
temp['time']=pd.to_datetime(temp['time'],format='%m%d %H:%M:%S')
temp.sort_values(['ship','group','time'],inplace=True)
temp['time_gap']=temp.groupby('group')['time'].diff()
temp['time_gap']=temp.apply(lambda x:x.time_gap.total_seconds()/3600,axis=1)
temp['hour']=temp.time.dt.hour
temp['hour_day']=temp['hour'].apply(lambda x:'day' if (x>=6)*(x<18) else 'night')
feature=temp.groupby('group',as_index=False)['hour'].agg({
'hour_first':'first',
'hour_last':'last',
'hour_min':'min',
'hour_max':'max',
'hour_mean':'mean',
'hour_median':'median',
'hour_quantile25':quantile25,
'hour_quantile25':quantile75,
'hour_std':'std'})
if features.shape[0]==0:
features=feature
else:
features=features.merge(feature,on='group',how='left')
features.to_pickle('./data/time_{}.pkl'.format(name))
print('time feature done_{}'.format(name))
return features
def get_coastline_features(data):
temp=data.copy()
features=pd.DataFrame()
coastline=pd.read_csv('./data/coastline.csv')
coastline['xy']=coastline.apply(lambda x:get_xy(base[0],base[1],x.lat,x.lon),axis=1)
coastline['x']=coastline['xy'].apply(lambda x:x[0])
coastline['y']=coastline['xy'].apply(lambda x:x[1])
coastline_line=geom.LineString(coastline[['x','y']].values)
coastline_Polygon=geom.Polygon(coastline[['x','y']].values)
temp['point']=temp.apply(lambda x:geom.Point(x.x,x.y),axis=1)
temp['coastline_dis']=temp.apply(lambda x:x.point.distance(coastline_line),axis=1)
temp['is_in']=temp.apply(lambda x:coastline_Polygon.contains(x.point),axis=1)
temp['is_in']=temp['is_in'].apply(lambda x:-1 if x else 1)
temp['coastline_dis']=temp['coastline_dis']*temp['is_in']
feature=temp.groupby('group',as_index=False)['coastline_dis'].agg({
'coastline_dis_first':'first',
'coastline_dis_last':'last',
'coastline_dis_min':quantile_min,
'coastline_dis_max':quantile_max,
'coastline_dis_mean':'mean',
'coastline_dis_median':'median',
'coastline_dis_quantile25':quantile25,
'coastline_dis_quantile25':quantile75,
'coastline_dis_std':'std'})
if features.shape[0]==0:
features=feature
else:
features=features.merge(feature,on='group',how='left')
return features
def get_coastline_features_multi(num,data,col,func,name):
if os.path.exists('./data/coastline_{}.pkl'.format(name)):
features=pd.read_pickle('./data/coastline_{}.pkl'.format(name))
else:
features=multi_func(num,data,col,func)
features.to_pickle('./data/coastline_{}.pkl'.format(name))
print('coastline feature done_{}'.format(name))
return features
def get_port():
data=pd.read_pickle('./data/data_3.pkl')
temp=data.copy()
coastline=pd.read_csv('./data/coastline.csv')
coastline['xy']=coastline.apply(lambda x:get_xy(base[0],base[1],x.lat,x.lon),axis=1)
coastline['x']=coastline['xy'].apply(lambda x:x[0])
coastline['y']=coastline['xy'].apply(lambda x:x[1])
coastline_line=geom.LineString(coastline[['x','y']].values)
coastline_Polygon=geom.Polygon(coastline[['x','y']].values)
temp_train=temp[~temp.type.isnull()]
temp_train=temp_train[temp_train.stop==1]
temp_train=temp_train.groupby('group',as_index=False).agg({'x':'mean','y':'mean'})
temp_train['point']=temp_train.apply(lambda x:geom.Point(x.x,x.y),axis=1)
temp_train['coastline_dis']=temp_train.apply(lambda x:x.point.distance(coastline_line),axis=1)
temp_train['is_in']=temp_train.apply(lambda x:coastline_Polygon.contains(x.point),axis=1)
temp_train['is_in']=temp_train['is_in'].apply(lambda x:-1 if x else 1)
temp_train['coastline_dis']=temp_train['coastline_dis']*temp_train['is_in']
temp_train=temp_train[temp_train.coastline_dis<=15000]
dbscan=DBSCAN(eps=10000,min_samples=8)
temp_train['port']=dbscan.fit_predict(temp_train[['x','y']].values)
port=temp_train.groupby('port',as_index=False).agg({'x':'mean','y':'mean'})
port=port[port.port!=-1].rename(columns={'x':'x_port','y':'y_port'})
ports=list(port.port)
coastline_port={}
for p in ports:
coastline_port[p]=geom.Point(port[port['port']==p][['x_port']].values[0],port[port['port']==p][['y_port']].values[0])
def min_port_dis(point):
coastline_port_dis={}
for (k,v) in coastline_port.items():
coastline_port_dis[k]=point.distance(v)
return (
min(zip(coastline_port_dis.values(),coastline_port_dis.keys()))[0],
min(zip(coastline_port_dis.values(),coastline_port_dis.keys()))[1])
temp.sort_values(['ship','time'],inplace=True)
temp_first=temp.groupby('ship',as_index=False).first()
temp_first['point']=temp_first.apply(lambda x:geom.Point(x.x,x.y),axis=1)
temp_first['prot_dis']=temp_first.apply(lambda x:min_port_dis(x.point),axis=1)
temp_first['port']=temp_first['prot_dis'].apply(lambda x:x[1])
temp_first=temp_first[['ship','port']]
temp_first=temp_first.merge(port,on='port',how='left')
return temp_first
def get_port_features(data,name):
if os.path.exists('./data/port_{}.pkl'.format(name)):
features=pd.read_pickle('./data/port_{}.pkl'.format(name))
else:
temp=data.copy()
temp['time']=pd.to_datetime(temp['time'],format='%m%d %H:%M:%S')
temp.sort_values(['ship','group','time'],inplace=True)
port=get_port()
temp=temp.merge(port,on='ship',how='left')
temp['port_dis']=((temp['x']-temp['x_port'])**2+(temp['y']-temp['y_port'])**2)**0.5
features=temp.groupby(['group','port','x_port','y_port'],as_index=False)['port_dis'].agg({
'port_dis_first':'first',
'port_dis_last':'last',
'port_dis_min':'min',
'port_dis_max':'max',
'port_dis_mean':'mean',
'port_dis_median':'median',
'port_dis_quantile25':quantile25,
'port_dis_quantile25':quantile75,
'port_dis_std':'std'})
features=features[['group','port','x_port','y_port']]
features.to_pickle('./data/port_{}.pkl'.format(name))
print('port feature done_{}'.format(name))
return features
def get_province(data):
temp=data.copy()
features=pd.DataFrame()
base=[25.173215180565872, 119.27677013681826]
coastline=pd.read_csv('./data/coastline.csv')
coastline['xy']=coastline.apply(lambda x:get_xy(base[0],base[1],x.lat,x.lon),axis=1)
coastline['x']=coastline['xy'].apply(lambda x:x[0])
coastline['y']=coastline['xy'].apply(lambda x:x[1])
provinces=list(set(coastline.province))
coastline_province={}
for province in provinces:
if province!='other':
coastline_province[province]=geom.LineString(coastline[coastline.province==province][['x','y']].values)
def min_province_dis(point):
coastline_province_dis={}
for (k,v) in coastline_province.items():
coastline_province_dis[k]=point.distance(v)
return (
min(zip(coastline_province_dis.values(),coastline_province_dis.keys()))[0],
min(zip(coastline_province_dis.values(),coastline_province_dis.keys()))[1])
temp['point']=temp.apply(lambda x:geom.Point(x.x,x.y),axis=1)
temp['pro_dis']=temp.apply(lambda x:min_province_dis(x.point),axis=1)
temp['province']=temp['pro_dis'].apply(lambda x:x[1])
temp['min_dis']=temp['pro_dis'].apply(lambda x:x[0])
temp.sort_values(['ship','min_dis'],inplace=True)
temp=temp.drop_duplicates('ship')
le=LabelEncoder()
temp['province']=le.fit_transform(temp['province'])
province=temp[['ship','province']]
return province
def get_city(data):
temp=data.copy()
features=pd.DataFrame()
coastcity=pd.read_csv('./data/coastcity.csv')
coastcity['xy']=coastcity.apply(lambda x:get_xy(base[0],base[1],x.lat,x.lon),axis=1)
coastcity['x']=coastcity['xy'].apply(lambda x:x[0])
coastcity['y']=coastcity['xy'].apply(lambda x:x[1])
cities=list(set(coastcity.city))
coastcity_city={}
for city in cities:
coastcity_city[city]=geom.LineString(coastcity_city[coastcity_city.city==city][['x','y']].values)
def min_city_dis(point):
coastcity_city_dis={}
for (k,v) in coastcity_city.items():
coastcity_city_dis[k]=point.distance(v)
return (
min(zip(coastcity_city_dis.values(),coastcity_city_dis.keys()))[0],
min(zip(coastcity_city_dis.values(),coastcity_city_dis.keys()))[1])
temp['point']=temp.apply(lambda x:geom.Point(x.x,x.y),axis=1)
temp['city_dis']=temp.apply(lambda x:min_city_dis(x.point),axis=1)
temp['city']=temp['city_dis'].apply(lambda x:x[1])
temp['min_city_dis']=temp['city_dis'].apply(lambda x:x[0])
temp.sort_values(['ship','min_city_dis'],inplace=True)
temp=temp.drop_duplicates('ship')
le=LabelEncoder()
temp['city']=le.fit_transform(temp['city'])
province=temp[['ship','city','min_city_dis']]
return province
def get_city_features(data,name):
temp=data.copy()
features=pd.DataFrame()
base=[25.173215180565872, 119.27677013681826]
coastcity=pd.read_csv('./data/coastcity.csv')
coastcity['xy']=coastcity.apply(lambda x:get_xy(base[0],base[1],x.lat,x.lon),axis=1)
coastcity['x']=coastcity['xy'].apply(lambda x:x[0])
coastcity['y']=coastcity['xy'].apply(lambda x:x[1])
cities=list(coastcity.city)
for city in cities:
city_x=coastcity[coastcity.city==city]['x'].values[0]
city_y=coastcity[coastcity.city==city]['y'].values[0]
temp['{}_min_ids'.format(city)]=((temp.x-city_x)**2+(temp.y-city_y)**2)**0.5
feature=temp.groupby('group',as_index=False)['{}_min_ids'.format(city)].min()
if features.shape[0]==0:
features=feature
else:
features=features.merge(feature,on='group',how='left')
return features
def direction_diff_change(x):
if x<=-180:
x=x+360
elif x>180:
x=x-360
else:
x=x
return x
def get_diff_features(data,name):
if os.path.exists('./diff_{}.pkl'.format(name)):
features=pd.read_pickle('./diff_{}.pkl'.format(name))
else:
temp=data.copy()
features=pd.DataFrame()
temp['time']=pd.to_datetime(temp['time'],format='%m%d %H:%M:%S')
temp.sort_values(['ship','group','time'],inplace=True)
temp['time_gap']=temp.groupby('group')['time'].diff()
temp['time_gap']=temp.apply(lambda x:x.time_gap.total_seconds()/3600,axis=1)
temp['x_gap']=temp.groupby('group')['x'].diff()
temp['y_gap']=temp.groupby('group')['y'].diff()
temp['x_gap_after']=temp.groupby('group')['x_gap'].shift(-1)
temp['y_gap_after']=temp.groupby('group')['y_gap'].shift(-1)
temp['x_after_2']=temp.groupby('group')['x'].shift(-2)
temp['y_after_2']=temp.groupby('group')['y'].shift(-2)
temp['angle']=temp.apply(lambda x:angle(x.x_gap,x.x_gap_after,x.y_gap,x.y_gap_after),axis=1)
temp['angle']=temp.apply(lambda x:x.angle*1 if -x.x_gap*x.y_gap_after+x.y_gap*x.x_gap_after>=0 else x.angle*(-1),axis=1)
temp['dis_gap']=(temp['x_gap']**2+temp['y_gap']**2)**0.5
temp['dis_gap_after']=(temp['x_gap_after']**2+temp['y_gap_after']**2)**0.5
temp['dis_gap_line']=((temp['x']-temp['x_after_2'])**2+(temp['y']-temp['y_after_2'])**2)**0.5
temp['curvity']=(temp['dis_gap']+temp['dis_gap_after'])/(temp['dis_gap_line']+0.00001)
temp['speed_gap']=temp['dis_gap']/temp['time_gap']
temp['speed_gap_after']=temp.groupby('group')['speed_gap'].shift(-1)
temp['acceleration']=(temp['speed_gap_after']-temp['speed_gap'])/temp['time_gap']
temp.to_csv('diff.csv',index=False)
for col in ['angle','curvity','acceleration']:
feature=temp.groupby('group',as_index=False)[col].agg({
'{}_min'.format(col):'min',
'{}_max'.format(col):'max',
'{}_mean'.format(col):'mean',
'{}_median'.format(col):'median',
'{}_std'.format(col):'std',
'{}_skew'.format(col):'skew',
'{}_quantile25'.format(col):quantile25,
'{}_quantile75'.format(col):quantile75})
if features.shape[0]==0:
features=feature
else:
features=features.merge(feature,on='group',how='left')
for col in ['angle','curvity','acceleration']:
le=LabelEncoder()
feature_temp=temp[~temp[col].isnull()]
if col=='curvity':
bins=[-0.1,0.5,1,1.5,2,2.5,3,3.5,4,4.5,feature_temp[col].max()]
feature_temp['{}_cut'.format(col)]=le.fit_transform(pd.cut(feature_temp[col],bins))
elif col=='acceleration':
bins=[feature_temp[col].min()-1,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,feature_temp[col].max()]
feature_temp['{}_cut'.format(col)]=le.fit_transform(pd.cut(feature_temp[col],bins))
else:
feature_temp['{}_cut'.format(col)]=le.fit_transform(pd.cut(feature_temp[col],10))
feature=feature_temp.groupby(['group','{}_cut'.format(col)])['time'].count()
feature=feature.unstack().fillna(0)
feature.columns=['{}_cut_{}_count'.format(col,c) for c in feature.columns]
feature=feature.reset_index()
if features.shape[0]==0:
features=feature
else:
features=features.merge(feature,on='group',how='left')
features.to_pickle('./diff_{}.pkl'.format(name))
print('diff feature done_{}'.format(name))
return features
def get_diff_features_multi(num,data,col,func,name):
if os.path.exists('./data/diff_{}.pkl'.format(name)):
features=pd.read_pickle('./data/diff_{}.pkl'.format(name))
else:
features=multi_func(num,data,col,func)
features.to_pickle('./data/diff_{}.pkl'.format(name))
print('diff feature done_{}'.format(name))
return features
def get_w2v_features(data,name):
if os.path.exists('./w2v_{}.pkl'.format(name)):
features=pd.read_pickle('./w2v_{}.pkl'.format(name))
else:
temp=data.copy()
features=pd.DataFrame()
temp['time']=pd.to_datetime(temp['time'],format='%m%d %H:%M:%S')
temp.sort_values(['ship','group','time'],inplace=True)
temp['r']=(temp['x']**2+temp['y']**2)**0.5
temp['the']=temp.apply(lambda x:math.atan2(x.y,x.x)*180/math.pi,axis=1)
le=LabelEncoder()
temp['x_cut']=le.fit_transform(pd.cut(temp['x'],80))
temp['y_cut']=le.fit_transform(pd.cut(temp['y'],100))
temp['x_y_cut']=le.fit_transform(temp['x_cut'].astype(str)+'_'+temp['y_cut'].astype(str))
temp['x_y_cut']=temp['x_y_cut'].astype(str)
x_y_list=temp.groupby('group').agg({'x_y_cut':lambda x:x.tolist()}).reset_index()
model=Word2Vec(list(x_y_list['x_y_cut']),size=50,window=5,min_count=1,seed=2020,workers=1)
temp['x_y_cut']=temp['x_y_cut'].apply(lambda x:model[x])
temp_=temp.groupby('group').agg({'x_y_cut':lambda x:x.tolist()}).reset_index()
temp_['emb']=temp_['x_y_cut'].apply(lambda x:sum(x))
feature=pd.DataFrame(np.vstack(temp_['emb']))
feature.columns=['xy_w2v_feat{}'.format(i) for i in range(feature.shape[1])]
feature['group']=temp_['group']
if features.shape[0]==0:
features=feature
else:
features=features.merge(feature,on='group',how='left')
temp['s_cut']=le.fit_transform(pd.cut(temp['speed'],20))
temp['d_cut']=le.fit_transform(pd.cut(temp['direction'],36))
temp['s_d_cut']=le.fit_transform(temp['s_cut'].astype(str)+'_'+temp['d_cut'].astype(str))
temp['s_d_cut']=temp['s_d_cut'].astype(str)
s_d_list=temp.groupby('group').agg({'s_d_cut':lambda x:x.tolist()}).reset_index()
model=Word2Vec(list(s_d_list['s_d_cut']),size=50,window=5,min_count=1,seed=2020,workers=1)
temp['s_d_cut']=temp['s_d_cut'].apply(lambda x:model[x])
temp_=temp.groupby('group').agg({'s_d_cut':lambda x:x.tolist()}).reset_index()
temp_['emb']=temp_['s_d_cut'].apply(lambda x:sum(x))
feature=pd.DataFrame(np.vstack(temp_['emb']))
feature.columns=['sd_w2v_feat{}'.format(i) for i in range(feature.shape[1])]
feature['group']=temp_['group']
if features.shape[0]==0:
features=feature
else:
features=features.merge(feature,on='group',how='left')
features.to_pickle('./w2v_{}.pkl'.format(name))
print('w2v feature done_{}'.format(name))
return features
def stdbscan_cluster(x):
stdbscan=STDBSCAN(
col_lat='y',
col_lon='x',
col_time='time',
spatial_threshold=10000,
temporal_threshold=6000,
min_neighbors=3)
results=stdbscan.run(x)
return results['cluster'].values
def stdbscan_cluster_stop(x):
if x['x'].std()<=200 and x['y'].std()<=200:
return np.ones(shape=(x.shape[0]))
else:
stdbscan=STDBSCAN(
col_lat='y',
col_lon='x',
col_time='time',
spatial_threshold=500,
temporal_threshold=9000,
min_neighbors=21)
results=stdbscan.run(x)
return results['cluster'].values
def prepare_data_1(data):
print('prepare_data_1_before:',data.shape)
if os.path.exists('./data/data_1.pkl'):
data=pd.read_pickle('./data/data_1.pkl')
else:
train_data=data[~data.type.isnull()]
base=[train_data.lat.mean(),train_data.lon.mean()]
print(base)
data['xy']=data.apply(lambda x:get_xy(base[0],base[1],x.lat,x.lon),axis=1)
data['x']=data['xy'].apply(lambda x:x[0])
data['y']=data['xy'].apply(lambda x:x[1])
data=data.drop(['xy'],axis=1)
data.to_pickle('./data/data_1.pkl')
print('prepare_data_1_after:',data.shape)
return data
def prepare_data_2(data):
cols=list(data.columns)
data['time']=pd.to_datetime(data['time'],format='%m%d %H:%M:%S')
data.sort_values(['ship','time'],inplace=True)
data=data[~((data.lat>54)+(data.lat<3)+(data.lon>136)+(data.lon<73))]
data['count']=1
data['index']=data.groupby('ship')['count'].cumsum()
cluster=data.groupby('ship').apply(stdbscan_cluster)
data['cluster']=data['ship'].map(cluster)
data['cluster_preds']=data.apply(lambda x:x['cluster'][x['index']-1],axis=1)
outer=data.groupby('ship',as_index=False)['cluster_preds'].mean()
outer_ships=list(outer[outer.cluster_preds==-1]['ship'])
print('outer_ships',outer_ships)
data.loc[data.ship.isin(outer_ships),'cluster_preds']=1
data=data[data.cluster_preds!=-1]
data=data[cols]
data['speed']=data['speed'].apply(lambda x:20 if x>20 else x)#stat+
data['direction']=data['direction'].apply(lambda x:x-360 if x>180 else x)
return data
def prepare_data_2_multi(num,data,col,func):
print('prepare_data_2_before:',data.shape)
if os.path.exists('./data/data_2.pkl'):
data=pd.read_pickle('./data/data_2.pkl')
else:
data=multi_func(num,data,col,func)
data.to_pickle('./data/data_2.pkl')
print('prepare_data_2_after:',data.shape)
return data
def prepare_data_3(data):
cols=list(data.columns)
data['time']=pd.to_datetime(data['time'],format='%m%d %H:%M:%S')
data.sort_values(['ship','time'],inplace=True)
data['count']=1
data['index']=data.groupby('ship')['count'].cumsum()
cluster=data.groupby('ship').apply(stdbscan_cluster_stop)
data['cluster']=data['ship'].map(cluster)
data['stop']=data.apply(lambda x:x['cluster'][x['index']-1],axis=1)
data=data.drop('cluster',axis=1)
data['stop']=data['stop'].apply(lambda x:1 if x>0 else 0)
data['diff']=data.groupby('ship')['stop'].diff(1)
data['count']=data['diff'].apply(lambda x:1 if x!=0 else 0)
data['group']=data['count'].cumsum()
temp=data.groupby(['ship','group','stop'],as_index=False)['time'].agg({'max':'max','min':'min'})
temp['time_gap']=temp.apply(lambda x:(x['max']-x['min']).total_seconds()/3600,axis=1)
groups=list(temp[(temp.time_gap<=1)]['group'])
data=data[~data.group.isin(groups)]
data['diff']=data.groupby('ship')['stop'].diff(1)
data['count']=data['diff'].apply(lambda x:1 if x!=0 else 0)
data['group']=data['count'].cumsum()
data=data[cols+['group','stop']]
return data
def prepare_data_3_multi(num,data,col,func):
print('prepare_data_3_before:',data.shape)
if os.path.exists('./data/data_3.pkl'):
data=pd.read_pickle('./data/data_3.pkl')
else:
data=multi_func(num,data,col,func)
data.to_pickle('./data/data_3.pkl')
print('prepare_data_3_after:',data.shape)
return data
def get_f1_score(y_true,y_pred):
y_pred=y_pred.reshape(3,-1)
y_pred=np.argmax(y_pred,axis=0)
score=f1_score(y_true,y_pred,average='macro')
f1s=f1_score(y_true,y_pred,average=None)
return 'f1_score',score,True
def get_samples():
train=prepare_data(train_path,'train')
train['type']=train['type'].map(type_map)
train_ships=list(train[['ship']].drop_duplicates().sample(frac=0.85,random_state=0)['ship'])
test=train[~train.ship.isin(train_ships)]
train=train[train.ship.isin(train_ships)]
y_test=test.drop_duplicates('ship')[['ship','type']].rename(columns={'type':'type_true'})
test['type']=np.nan
data=pd.concat([train,test],axis=0)
num=cpu_count()-4