1597745880
Level Up with Google’s Public Datasets
I remember my first data science project, analyzing a 6.5MB file on ten thousand movies. It was magical to quickly find patterns from data from a few keystrokes and soon I was on the hunt to find other datasets to analyze. Going through Kaggle and eventually generating unique data through web crawling, I found that most downloadable datasets were small (<1GB) and that collecting and working with large datasets required a different type of skill: data engineering.
Most aspiring data scientists only know how to analyze clean, comma separated files that fit on their RAM (usually <8GB). In practice, data science is much more unpredictable when you deal with terabytes of data in different formats from different data streams. So how does an aspiring data scientist gain exposure to big data?
To take you to big data, we will explore:
- Diving into Big Data with Google Cloud Public Datasets
- What is BigQuery?
- How to Connect Your Jupyter Notebook to BigQuery
Diving into Big Data with Google Cloud Public Datasets
It turns out that Google has a repository of most publicly available data such as air quality, US census, Reddit, flight. If there’s a public dataset, Google probably has it hosted, available for you to query. What’s more is that new datasets (i.e. Covid-19) are constantly being added and updated.
With the data hosted on BigQuery, you can easily query the data to see if it’s interesting. For example, I can query and answer the question: “How many political ad campaigns on Google, targeted women in Florida?”
#jupyter-notebook #data-science #bigquery #data analysis
What is GEEK
Buddha Community
1656151740
Test_cov_console: Flutter Console Coverage Test
Flutter Console Coverage Test
This small dart tools is used to generate Flutter Coverage Test report to console
How to install
Add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):
dev_dependencies:
test_cov_console: ^0.2.2
How to run
run the following command to make sure all flutter library is up-to-date
flutter pub get
Running "flutter pub get" in coverage... 0.5s
run the following command to generate lcov.info on coverage directory
flutter test --coverage
00:02 +1: All tests passed!
run the tool to generate report from lcov.info
flutter pub run test_cov_console
---------------------------------------------|---------|---------|---------|-------------------|
File |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
print_cov_constants.dart | 0.00 | 0.00 | 0.00 | no unit testing|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
Optional parameter
If not given a FILE, "coverage/lcov.info" will be used.
-f, --file=<FILE> The target lcov.info file to be reported
-e, --exclude=<STRING1,STRING2,...> A list of contains string for files without unit testing
to be excluded from report
-l, --line It will print Lines & Uncovered Lines only
Branch & Functions coverage percentage will not be printed
-i, --ignore It will not print any file without unit testing
-m, --multi Report from multiple lcov.info files
-c, --csv Output to CSV file
-o, --output=<CSV-FILE> Full path of output CSV file
If not given, "coverage/test_cov_console.csv" will be used
-t, --total Print only the total coverage
Note: it will ignore all other option (if any), except -m
-p, --pass=<MINIMUM> Print only the whether total coverage is passed MINIMUM value or not
If the value >= MINIMUM, it will print PASSED, otherwise FAILED
Note: it will ignore all other option (if any), except -m
-h, --help Show this help
example run the tool with parameters
flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock
---------------------------------------------|---------|---------|---------|-------------------|
File |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
report for multiple lcov.info files (-m, --multi)
It support to run for multiple lcov.info files with the followings directory structures:
1. No root module
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
2. With root module
<root>/coverage/lcov.info
<root>/lib/src
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
You must run test_cov_console on <root> dir, and the report would be grouped by module, here is
the sample output for directory structure 'with root module':
flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock --multi
---------------------------------------------|---------|---------|---------|-------------------|
File |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_a - |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_b - |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
Output to CSV file (-c, --csv, -o, --output)
flutter pub run test_cov_console -c --output=coverage/test_coverage.csv
#### sample CSV output file:
File,% Branch,% Funcs,% Lines,Uncovered Line #s
lib/,,,,
test_cov_console.dart,0.00,0.00,0.00,no unit testing
lib/src/,,,,
parser.dart,100.00,100.00,97.22,"97"
parser_constants.dart,100.00,100.00,100.00,""
print_cov.dart,100.00,100.00,82.91,"29,49,51,52,171,174,177,180,183,184,185,186,187,188,279,324,325,387,388,389,390,391,392,393,394,395,398"
print_cov_constants.dart,0.00,0.00,0.00,no unit testing
All files with unit testing,100.00,100.00,86.07,""Installing
Use this package as an executable
Install it
You can install the package from the command line:
dart pub global activate test_cov_consoleUse it
The package has the following executables:
$ test_cov_console
Use this package as a library
Depend on it
Run this command:
With Dart:
$ dart pub add test_cov_consoleWith Flutter:
$ flutter pub add test_cov_consoleThis will add a line like this to your package's pubspec.yaml (and run an implicit dart pub get):
dependencies:
test_cov_console: ^0.2.2Alternatively, your editor might support dart pub get or flutter pub get. Check the docs for your editor to learn more.
Import it
Now in your Dart code, you can use:
import 'package:test_cov_console/test_cov_console.dart';example/lib/main.dart
import 'package:flutter/material.dart';
void main() {
runApp(MyApp());
}
class MyApp extends StatelessWidget {
// This widget is the root of your application.
@override
Widget build(BuildContext context) {
return MaterialApp(
title: 'Flutter Demo',
theme: ThemeData(
// This is the theme of your application.
//
// Try running your application with "flutter run". You'll see the
// application has a blue toolbar. Then, without quitting the app, try
// changing the primarySwatch below to Colors.green and then invoke
// "hot reload" (press "r" in the console where you ran "flutter run",
// or simply save your changes to "hot reload" in a Flutter IDE).
// Notice that the counter didn't reset back to zero; the application
// is not restarted.
primarySwatch: Colors.blue,
// This makes the visual density adapt to the platform that you run
// the app on. For desktop platforms, the controls will be smaller and
// closer together (more dense) than on mobile platforms.
visualDensity: VisualDensity.adaptivePlatformDensity,
),
home: MyHomePage(title: 'Flutter Demo Home Page'),
);
}
}
class MyHomePage extends StatefulWidget {
MyHomePage({Key? key, required this.title}) : super(key: key);
// This widget is the home page of your application. It is stateful, meaning
// that it has a State object (defined below) that contains fields that affect
// how it looks.
// This class is the configuration for the state. It holds the values (in this
// case the title) provided by the parent (in this case the App widget) and
// used by the build method of the State. Fields in a Widget subclass are
// always marked "final".
final String title;
@override
_MyHomePageState createState() => _MyHomePageState();
}
class _MyHomePageState extends State<MyHomePage> {
int _counter = 0;
void _incrementCounter() {
setState(() {
// This call to setState tells the Flutter framework that something has
// changed in this State, which causes it to rerun the build method below
// so that the display can reflect the updated values. If we changed
// _counter without calling setState(), then the build method would not be
// called again, and so nothing would appear to happen.
_counter++;
});
}
@override
Widget build(BuildContext context) {
// This method is rerun every time setState is called, for instance as done
// by the _incrementCounter method above.
//
// The Flutter framework has been optimized to make rerunning build methods
// fast, so that you can just rebuild anything that needs updating rather
// than having to individually change instances of widgets.
return Scaffold(
appBar: AppBar(
// Here we take the value from the MyHomePage object that was created by
// the App.build method, and use it to set our appbar title.
title: Text(widget.title),
),
body: Center(
// Center is a layout widget. It takes a single child and positions it
// in the middle of the parent.
child: Column(
// Column is also a layout widget. It takes a list of children and
// arranges them vertically. By default, it sizes itself to fit its
// children horizontally, and tries to be as tall as its parent.
//
// Invoke "debug painting" (press "p" in the console, choose the
// "Toggle Debug Paint" action from the Flutter Inspector in Android
// Studio, or the "Toggle Debug Paint" command in Visual Studio Code)
// to see the wireframe for each widget.
//
// Column has various properties to control how it sizes itself and
// how it positions its children. Here we use mainAxisAlignment to
// center the children vertically; the main axis here is the vertical
// axis because Columns are vertical (the cross axis would be
// horizontal).
mainAxisAlignment: MainAxisAlignment.center,
children: <Widget>[
Text(
'You have pushed the button this many times:',
),
Text(
'$_counter',
style: Theme.of(context).textTheme.headline4,
),
],
),
),
floatingActionButton: FloatingActionButton(
onPressed: _incrementCounter,
tooltip: 'Increment',
child: Icon(Icons.add),
), // This trailing comma makes auto-formatting nicer for build methods.
);
}
}Author: DigitalKatalis
Source Code: https://github.com/DigitalKatalis/test_cov_console
License: BSD-3-Clause license
1619247660
Google's TPU's being primed for the Quantum Jump
The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.
The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.
As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.
Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.
The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.
#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus
1624516500
Inside ABCD, A Dataset To Build In-Depth Task-Oriented Dialogue Systems
According to a recent study, call centre agents’ spend approximately 82 percent of their total time looking at step-by-step guides, customer data, and knowledge base articles.
Traditionally, dialogue state tracking (DST) has served as a way to determine what a caller wants at a given point in a conversation. Unfortunately, these aspects are not accounted for in popular DST benchmarks. DST is the core part of a spoken dialogue system. It estimates the beliefs of possible user’s goals at every dialogue turn.
To reduce the burden on call centre agents and improve the SOTA of task-oriented dialogue systems, AI-powered customer service company ASAPP recently launched an action-based conversations dataset (ABCD). The dataset is designed to help develop task-oriented dialogue systems for customer service applications. ABCD consists of a fully labelled dataset with over 10,000 human dialogues containing 55 distinct user intents requiring sequences of actions constrained by company policies to accomplish tasks.
https://twitter.com/asapp/status/1397928363923177472
The dataset is currently available on GitHub.
#developers corner #asapp abcd dataset #asapp new dataset #build enterprise chatbot #chatbot datasets latest #customer support datasets #customer support model training #dataset for chatbots #dataset for customer datasets
1598383290
What Are Google Compute Engine ? - Explained
What Are Google Compute Engine ? - Explained
The Google computer engine exchanges a large number of scalable virtual machines to serve as clusters used for that purpose. GCE can be managed through a RESTful API, command line interface, or web console. The computing engine is serviced for a minimum of 10-minutes per use. There is no up or front fee or time commitment. GCE competes with Amazon’s Elastic Compute Cloud (EC2) and Microsoft Azure.
https://www.mrdeluofficial.com/2020/08/what-are-google-compute-engine-explained.html
#google compute engine #google compute engine tutorial #google app engine #google cloud console #google cloud storage #google compute engine documentation
1596830700
Embedding your <image> in google colab <markdown>
This article is a quick guide to help you embed images in google colab markdown without mounting your google drive!
Just a quick intro to google colab
Google colab is a cloud service that offers FREE python notebook environments to developers and learners, along with FREE GPU and TPU. Users can write and execute Python code in the browser itself without any pre-configuration. It offers two types of cells: text and code. The ‘code’ cells act like code editor, coding and execution in done this block. The ‘text’ cells are used to embed textual description/explanation along with code, it is formatted using a simple markup language called ‘markdown’.
Embedding Images in markdown
If you are a regular colab user, like me, using markdown to add additional details to your code will be your habit too! While working on colab, I tried to embed images along with text in markdown, but it took me almost an hour to figure out the way to do it. So here is an easy guide that will help you.
STEP 1:
The first step is to get the image into your google drive. So upload all the images you want to embed in markdown in your google drive.
Step 2:
Google Drive gives you the option to share the image via a sharable link. Right-click your image and you will find an option to get a sharable link.
On selecting ‘Get shareable link’, Google will create and display sharable link for the particular image.
#google-cloud-platform #google-collaboratory #google-colaboratory #google-cloud #google-colab #cloud