Jo-fai Chow woobe

## app.py
from h2o_wave import main, app, Q, ui


@app('/demo')
async def serve(q: Q):

    q.page["my_form_card"] = ui.form_card(
        box='1 1 -1 -1',
        items=[
            ui.text("My first app!")

## airline_sentiment_python_api.ipynb

      
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                woobe
                / airline_sentiment_python_api.ipynb
            
            
              Created
              September 11, 2018 19:27
            
              
                Example notebook for Airline Sentiment using Driverless AI's Python API
              
          
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## split_airline_sentiment.py
import numpy as np
import pandas as pd
from sklearn import model_selection

al = pd.read_csv("Airline-Sentiment-2-w-AA.csv", encoding='ISO-8859-1')
train_al, test_al = model_selection.train_test_split(al, test_size=0.2, random_state=2018)
train_al.to_csv("train_airline_sentiment.csv", index=False)
test_al.to_csv("test_airline_sentiment.csv", index=False)

## h2o_save_load_model.R
# Example: Saving and loading H2O models

# Using Iris for this example
d_train <- iris

# Initialize H2O
library(h2o)
h2o.init(nthreads = -1)

# Train a DRF model

## Readme.md

      
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                woobe
                / Readme.md
            
            
              Last active
              August 29, 2015 14:06
                — forked from timelyportfolio/Readme.md
            
          
    Introduction

This d3.js parallel coordinates plot is another experiment in how we might use interactive plots in Javascript to represent a partykit / rpart object from R.  The example builds on this d3.js collapsible tree plot.  Eventually, it would be nice to combine the tree and parallel coordinates into a layout with synced interactivity.

### Almost No [`rCharts`](http://rcharts.io)
Also, this is fairly different from most of my interactive plots from R in that it almost completely avoids [`rCharts`](http://rcharts.io) (almost because I did use its `publish` to make this gist).  I chose to exclude `rCharts` for two reasons:

demo how we can use htmltools to build html/js in R
help users understand some of the things rCharts does f


## link_data.R
## Convert Breast Cancer into H2O
dat <- BreastCancer[, -1]  # remove the ID column
dat_h2o <- as.h2o(localH2O, dat, key = 'dat')

## Import MNIST CSV as H2O
dat_h2o <- h2o.importFile(localH2O, path = ".../mnist_train.csv")

## code.R
## Load package
library(recharts) ## see https://github.com/taiyun/recharts

## Convert UKgas time series into a matrix
mat_UKgas <- matrix(UKgas, ncol = 4, byrow = TRUE)
rownames(mat_UKgas) <- rep(1960:1986, each = 1)
colnames(mat_UKgas) <- c("Q1", "Q2", "Q3", "Q4")

## Create the echarts plot object
e1 <- eArea(mat_UKgas,

## code.R
## Load Library
library(rCrimemap)  # devtools::install_github("woobe/rCrimemap")

## Create Crime Heatmap with Nokia map base
r1 <- rcmap_quick(period = "2014-01",
                  map_size = c(960, 500),
                  provider = "OpenStreetMap.BlackAndWhite",
                  zoom = 6)

## Save the Heatmap as a self-contained HTML

## code.R
library(rCharts)   ## devtools::install_github("ramnathv/rCharts@dev")
library(rPlotter)  ## devtools::install_github("woobe/rPlotter")

## Using Theoph as the demo data.
dat <- Theoph

## Initialise rCharts-parcoords
p1 <- rCharts$new()
p1$setLib(system.file('parcoords', package = 'rCharts'))

## index.html
<!doctype HTML>
<meta charset = 'utf-8'>
<html>
  <head>
    <link rel='stylesheet' href='http://cdn.jsdelivr.net/cal-heatmap/3.3.10/cal-heatmap.css'>

    <script src='http://d3js.org/d3.v3.min.js' type='text/javascript'></script>
    <script src='http://cdn.jsdelivr.net/cal-heatmap/3.3.10/cal-heatmap.min.js' type='text/javascript'></script>

    <style>
	from h2o_wave import main, app, Q, ui


	@app('/demo')
	async def serve(q: Q):

	q.page["my_form_card"] = ui.form_card(
	box='1 1 -1 -1',
	items=[
	ui.text("My first app!")
	import numpy as np
	import pandas as pd
	from sklearn import model_selection

	al = pd.read_csv("Airline-Sentiment-2-w-AA.csv", encoding='ISO-8859-1')
	train_al, test_al = model_selection.train_test_split(al, test_size=0.2, random_state=2018)
	train_al.to_csv("train_airline_sentiment.csv", index=False)
	test_al.to_csv("test_airline_sentiment.csv", index=False)
	# Example: Saving and loading H2O models

	# Using Iris for this example
	d_train <- iris

	# Initialize H2O
	library(h2o)
	h2o.init(nthreads = -1)

	# Train a DRF model
	## Convert Breast Cancer into H2O
	dat <- BreastCancer[, -1] # remove the ID column
	dat_h2o <- as.h2o(localH2O, dat, key = 'dat')

	## Import MNIST CSV as H2O
	dat_h2o <- h2o.importFile(localH2O, path = ".../mnist_train.csv")
	## Load package
	library(recharts) ## see https://github.com/taiyun/recharts

	## Convert UKgas time series into a matrix
	mat_UKgas <- matrix(UKgas, ncol = 4, byrow = TRUE)
	rownames(mat_UKgas) <- rep(1960:1986, each = 1)
	colnames(mat_UKgas) <- c("Q1", "Q2", "Q3", "Q4")

	## Create the echarts plot object
	e1 <- eArea(mat_UKgas,
	## Load Library
	library(rCrimemap) # devtools::install_github("woobe/rCrimemap")

	## Create Crime Heatmap with Nokia map base
	r1 <- rcmap_quick(period = "2014-01",
	map_size = c(960, 500),
	provider = "OpenStreetMap.BlackAndWhite",
	zoom = 6)

	## Save the Heatmap as a self-contained HTML
	library(rCharts) ## devtools::install_github("ramnathv/rCharts@dev")
	library(rPlotter) ## devtools::install_github("woobe/rPlotter")

	## Using Theoph as the demo data.
	dat <- Theoph

	## Initialise rCharts-parcoords
	p1 <- rCharts$new()
	p1$setLib(system.file('parcoords', package = 'rCharts'))
	<!doctype HTML>
	<meta charset = 'utf-8'>
	<html>
	<head>
	<link rel='stylesheet' href='http://cdn.jsdelivr.net/cal-heatmap/3.3.10/cal-heatmap.css'>

	<script src='http://d3js.org/d3.v3.min.js' type='text/javascript'></script>
	<script src='http://cdn.jsdelivr.net/cal-heatmap/3.3.10/cal-heatmap.min.js' type='text/javascript'></script>

	<style>