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Type4Py: Deep Similarity Learning-Based Type Inference for Python
Type4Py: Deep Similarity Learning-Based Type Inference for Python
This repository contains the implementation of Type4Py and instructions for re-producing the results of the paper.
Dataset
For Type4Py, we use the ManyTypes4Py dataset. You can download the latest version of the dataset here. Also, note that the dataset is already de-duplicated.
Code De-deduplication
If you want to use your own dataset, it is essential to de-duplicate the dataset by using a tool like CD4Py.
Installation Guide
Requirements
Here are the recommended system requirements for training Type4Py on the MT4Py dataset:
- Linux-based OS (Ubuntu 18.04 or newer)
- Python 3.6 or newer
- A high-end NVIDIA GPU (w/ at least 8GB of VRAM)
- A CPU with 16 threads or higher (w/ at least 64GB of RAM)
Quick Install
git clone https://github.com/saltudelft/type4py.git && cd type4py
pip install .
Usage Guide
Follow the below steps to train and evaluate the Type4Py model.
1. Extraction
NOTE: Skip this step if you're using the ManyTypes4Py dataset.
$ type4py extract --c $DATA_PATH --o $OUTPUT_DIR --d $DUP_FILES --w $CORES
Description:
$DATA_PATH: The path to the Python corpus or dataset.$OUTPUT_DIR: The path to store processed projects.$DUP_FILES: The path to the duplicate files, i.e., the*.jsonl.gzfile produced by CD4Py. [Optional]$CORES: Number of CPU cores to use for processing projects.
2. Preprocessing
$ type4py preprocess --o $OUTPUT_DIR --l $LIMIT
Description:
$OUTPUT_DIR: The path that was used in the first step to store processed projects. For the MT4Py dataset, use the directory in which the dataset is extracted.$LIMIT: The number of projects to be processed. [Optional]
3. Vectorizing
$ type4py vectorize --o $OUTPUT_DIR
Description:
$OUTPUT_DIR: The path that was used in the previous step to store processed projects.
4. Learning
$ type4py learn --o $OUTPUT_DIR --c --p $PARAM_FILE
Description:
$OUTPUT_DIR: The path that was used in the previous step to store processed projects.
--c: Trains the complete model. Use type4py learn -h to see other configurations.
--p $PARAM_FILE: The path to user-provided hyper-parameters for the model. See this file as an example. [Optional]
5. Testing
$ type4py predict --o $OUTPUT_DIR --c
Description:
$OUTPUT_DIR: The path that was used in the first step to store processed projects.--c: Predicts using the complete model. Usetype4py predict -hto see other configurations.
6. Evaluating
$ type4py eval --o $OUTPUT_DIR --t c --tp 10
Description:
$OUTPUT_DIR: The path that was used in the first step to store processed projects.--t: Evaluates the model considering different prediction tasks. E.g.,--t cconsiders all predictions tasks, i.e., parameters, return, and variables. [Default: c]--tp 10: Considers Top-10 predictions for evaluation. For this argument, You can choose a positive integer between 1 and 10. [Default: 10]
Use type4py eval -h to see other options.
Reduce
To reduce the dimension of the created type clusters in step 5, run the following command:
Note: The reduced version of type clusters causes a slight performance loss in type prediction.
$ type4py reduce --o $OUTPUT_DIR --d $DIMENSION
Description:
$OUTPUT_DIR: The path that was used in the first step to store processed projects.$DIMENSION: Reduces the dimension of type clusters to the specified value [Default: 256]
Converting Type4Py to ONNX
To convert the pre-trained Type4Py model to the ONNX format, use the following command:
$ type4py to_onnx --o $OUTPUT_DIR
Description:
$OUTPUT_DIR: The path that was used in the usage section to store processed projects and the model.
Type4Py can be used in VSCode, which provides ML-based type auto-completion for Python files. The Type4Py's VSCode extension can be installed from the VS Marketplace here.
Using Local Pre-trained Model
Type4Py's pre-trained model can be queried locally by using provided Docker images. See here for usage info.
The Type4Py server is deployed on our server, which exposes a public API and powers the VSCode extension. However, if you would like to deploy the Type4Py server on your own machine, you can adapt the server code here. Also, please feel free to reach out to us for deployment, using the pre-trained Type4Py model and how to train your own model by creating an issue.
Citing Type4Py
@article{mir2021type4py,
title={Type4Py: Deep Similarity Learning-Based Type Inference for Python},
author={Mir, Amir M and Latoskinas, Evaldas and Proksch, Sebastian and Gousios, Georgios},
journal={arXiv preprint arXiv:2101.04470},
year={2021}
}Author: saltudelft
Source Code: https://github.com/saltudelft/type4py
License: Apache-2.0 license
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Basic Data Types in Python | Python Web Development For Beginners
At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Table of Contents hide
III Built-in data types in Python
Mutable objects
The Size and declared value and its sequence of the object can able to be modified called mutable objects.
Mutable Data Types are list, dict, set, byte array
Immutable objects
The Size and declared value and its sequence of the object can able to be modified.
Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.
id() and type() is used to know the Identity and data type of the object
a**=25+**85j
type**(a)**
output**:<class’complex’>**
b**={1:10,2:“Pinky”****}**
id**(b)**
output**:**238989244168
Built-in data types in Python
a**=str(“Hello python world”)****#str**
b**=int(18)****#int**
c**=float(20482.5)****#float**
d**=complex(5+85j)****#complex**
e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**
f**=tuple((“python”,“easy”,“learning”))****#tuple**
g**=range(10)****#range**
h**=dict(name=“Vidu”,age=36)****#dict**
i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**
j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**
k**=bool(18)****#bool**
l**=bytes(8)****#bytes**
m**=bytearray(8)****#bytearray**
n**=memoryview(bytes(18))****#memoryview**
Numbers (int,Float,Complex)
Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.
#signed interger
age**=**18
print**(age)**
Output**:**18
Python supports 3 types of numeric data.
int (signed integers like 20, 2, 225, etc.)
float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)
complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)
A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).
String
The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.
# String Handling
‘Hello Python’
#single (') Quoted String
“Hello Python”
# Double (") Quoted String
“”“Hello Python”“”
‘’‘Hello Python’‘’
# triple (‘’') (“”") Quoted String
In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.
The operator “+” is used to concatenate strings and “*” is used to repeat the string.
“Hello”+“python”
output**:****‘Hello python’**
"python "*****2
'Output : Python python ’
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Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services
Introduction
When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
Python Packages
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
- revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
- microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
- Anaconda 4.2 – Anaconda is an opensource Python package
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Learn Python - Full Course for Beginners [Tutorial]
This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
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⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
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⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3
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Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020
The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
Also Read: Why Deep Learning DevCon Comes At The Right Time
Adversarial Robustness in Deep Learning
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
Read an interview with Dipanjan Sarkar.
Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network
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About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
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