etl-ml 1.0.7

etl_ml is a tools could etl origin excel or csv dirty data and send data to ftp or server and insert data to hive database and load data from jump hive make feature project machine learning model train and jump the jump machine to connect hive get hive data to pandas dataframe

DSTL
====

https://github.com/mullerhai/etl_ml

Note: this repo is not supported. License is MIT.


..

image:: etl_ml.jpg
.. image:: https://github.com/mullerhai/etl_ml/blob/master/img/logo.jpeg

.. contents::

Install [sorry Mircosoft Windows System cannot use it]
------------

: pip install -U etl_ml[Now Version is 0.0.1]

- python Version >= 3.5
- sasl>=0.2.1
- thrift>=0.11.0
- thrift-sasl>=0.3.0
- paramiko>=2.4.1
- selectors>=0.0.14



Use in Unix System Terminal[centos macos ubuntu]
------------

: jumps
- default param
: parameter:
- @click.option('-jh', '--jumphost', default="***", help='Jump Gateway Server host 跳板机ssh 主机名, 默认117.48.195.186')
- @click.option('-jp', '--jumpport', default=2222, help='Jump Gateway Server port跳板机ssh登录端口号, 默认2222')
- @click.option('-ju', '--jumpuser', default='dm', help='Jump Gateway Server login user 跳板机 ssh登录用户名')
- @click.option('-jpd', '--jumppwd', default="***", help='Jump Gateway Server login user password 跳板机登录用户密码')
- @click.option('-th', '--tunnelhost', default='172.16.16.32', help='ssh-tunnel 隧道 host ')
- @click.option('-tp', '--tunnelappport', default=10000, help='ssh-tunnel Application port隧道 目标程序的端口号 默认为 hive 10000 ')
- @click.option('-lh', '--localhost', default='127.0.0.1', help='localhost本机 host ,默认127.0.0.1 ')
- @click.option('-lp', '--localbindport', default="4230", help='localbindport 本机 被绑定的端口号')
- @click.option('-dt', '--daemonsecond', default="21600", help='ssh_tunnel_daemon_session_hold_on_second six hours, ssh 隧道 后台线程 保持时间 默认为六小时')

.. image:: https://github.com/mullerhai/sshjumphive/blob/master/img/runshell.jpeg


Use in Unix System Terminal Run GUI[centos macos ubuntu]
------------
: jumpgui
  - you will see the  GUI like this
.. image:: https://github.com/mullerhai/sshjumphive/blob/master/img/rungui.jpg


If you Buy the SSH_Tunnel for mac [maybe feel Expensive]
------------

.. image:: https://github.com/mullerhai/sshjumphive/blob/master/img/SSH_Tunnel_mac.jpg

Object types
------------

Note that ssh_jump_hive is an tools can jump the jump machine to connect hive get hive data to pandas dataframe:

- 0: hive_client for simple connect hive server with no jump server
- 1: Jump_Tunnel just for connect hive server with jump server separete
- 2: SSH_Tunnel for get ssh tunnel channel


General approach
----------------

if you want to use it ,you need to know some things
for example these parameters [ jumphost,jumpport,jumpuser,jumppwd,tunnelhost,tunnelAPPport,localhost,localbindport]
for hive server you also need to know params [localhost, hiveusername, hivepassword, localbindport,database, auth]
for query hive data you need to know params [ table, query_fileds_list, partions_param_dict, query_limit]

if your hive server has jump server separete， you need do like this
[
::
from ssh_jump_hive import Jump_Tunnel_HIVE
import pandas as pd
## get hive_tunnel_client_session
def gethive():
jumphost = '117.*****.176'
jumpport = 2222
jumpuser = 'dm'
jumppwd = '&&&&&&'
tunnelhost = '172.**.16.32'
tunnelhiveport = 10000
localhost = '127.0.0.1'
localbindport = 4800
username = 'muller'
auth = 'LDAP'
password = "abc123."
database = 'fkdb'
table = 'tab_client_label'
partions_param_dict = {'client_nmbr': 'AA75', 'batch': 'p1'}
query_fileds_list = ['gid', 'realname', 'card']
querylimit = 1000
jump = Jump_Tunnel_HIVE(jumphost, jumpport, jumpuser, jumppwd, tunnelhost, tunnelhiveport, localhost, localbindport,
username, password)
return jump

## query some fileds by table name and partitions params
def demo1():
table = 'tab_client_label'
partions_param_dict = {'client_nmbr': 'AA75', 'batch': 'p1'}
query_fileds_list = ['gid', 'realname', 'card']
querylimit = 1000
jump=gethive()
df2=jump.get_JumpTunnel_df(table,partions_param_dict,query_fileds_list,querylimit)
return df2
## query all fileds by table name and partitions params
def demo2():
table = 'tab_client_label'
partions_param_dict = {'client_nmbr': 'AA75', 'batch': 'p1'}
jump =gethive()
df2 = jump.get_JumpTunnel_table_partitions_df(table,partions_param_dict,1000)
return df2
## use hsql to query data
def demo3():
jump = gethive()
hsql="select * from fkdb.tab_client_label where client_nmbr= 'AA75' and batch= 'p1' limit 500"
df2=jump.get_JumpTunnel_hsql_df(hsql)
return df2
## initail the instance to query
df3=demo2()
print(df3.shape)
print(df3.columns)
print(df3.head(100))
]


UNet network with batch-normalization added, training with Adam optimizer with
a loss that is a sum of 0.1 cross-entropy and 0.9 dice loss.
Input for UNet was a 116 by 116 pixel patch, output was 64 by 64 pixels,
so there were 16 additional pixels on each side that just provided context for
the prediction.
Batch size was 128, learning rate was set to 0.0001
(but loss was multiplied by the batch size).
Learning rate was divided by 5 on the 25-th epoch
and then again by 5 on the 50-th epoch,
most models were trained for 70-100 epochs.
Patches that formed a batch were selected completely randomly across all images.
During one epoch, network saw patches that covered about one half
of the whole training set area. Best results for individual classes
were achieved when training on related classes, for example buildings
and structures, roads and tracks, two kinds of vehicles.

Augmentations included small rotations for some classes
(±10-25 degrees for houses, structures and both vehicle classes),
full rotations and vertical/horizontal flips
for other classes. Small amount of dropout (0.1) was used in some cases.
Alignment between channels was fixed with the help of
``cv2.findTransformECC``, and lower-resolution layers were upscaled to
match RGB size. In most cases, 12 channels were used (RGB, P, M),
while in some cases just RGB and P or all 20 channels made results
slightly better.


Validation
----------

Validation was very hard, especially for both water and both vehicle
classes. In most cases, validation was performed on 5 images
(6140_3_1, 6110_1_2, 6160_2_1, 6170_0_4, 6100_2_2), while other 20 were used
for training. Re-training the model with the same parameters on all 25 images
improved LB score.