AdityaSoni19031997

1.251.423.0 1.279.441.0 1.257.387.0 1.275.1078.0 1.249.88.0 1.275.1080.0 1.257.401.0 1.275.1744.0 1.275.1346.0 1.249.715.0

AdityaSoni19031997

bbox Generation

• http://supervise.ly
• https://rectlabel.com/ https://www.kaggle.com/martinpiotte/bounding-box-data-for-the-whale-flukes
• https://github.com/Microsoft/VoTT
• https://github.com/wkentaro/labelme

AdityaSoni19031997

S.No	Slack Handler	NAME(Author)	Last Updated	NBS LINK	ODS MSG LINK
1	velavok	Alexander Kovalev	9th Dec	https://nbviewer.jupyter.org/github/Yorko/mlcourse.ai/blob/master/jupyter_english/tutorials/plotly_tutorial_for_interactive_plots_sankovalev.ipynb	https://opendatascience.slack.com/archives/C91N8TL83/p1543267284291700
2	altprof	Gleb Levitski	9th Dec	http://nbviewer.jupyter.org/github/Yorko/mlcourse.ai/blob/master/jupyter_english/tutorials/basic_semi-supervised_learning_models_altprof.ipynb	https://opendatascience.slack.com/archives/C91N8TL83/p1543848630231700
3	krotix	Aleksandr Korotkov	9th Dec	https://nbviewer.jupyter.org/github/Yorko/mlcourse.ai/blob/master/jupyter_english/tutorials/yet_another_ensemble_learning_helper_aleksandr_korotkov.ipynb	https://opendatascience.slack.com/archives/C91N8TL83/p1544203317384100
4	Archit Run

AdityaSoni19031997

#!/usr/bin/env python
# encoding: utf-8

import sys

def trace_calls_and_returns(frame, event, arg):
    co = frame.f_code
    func_name = co.co_name
    if func_name == 'write':
        # Ignore write() calls from print statements

AdityaSoni19031997

#from my repo 
#https://github.com/AdityaSoni19031997/Machine-Learning/blob/master/AV/AV_Enigma_NLP_functional_api.ipynb

def preprocess_word(word):
    # Remove punctuation
    word = word.strip('\'"?!,.():;')
    # Convert more than 2 letter repetitions to 2 letter
    # funnnnny --> funny
    word = re.sub(r'(.)\1+', r'\1\1', word)
    # Remove - & '

AdityaSoni19031997

AdityaSoni19031997

# Importing dataset
dataset <- read.csv('ASD.csv')

# Taking care of missing data
# Fixing ethnicity
dataset[dataset == "?"] <- NA
summary(dataset)

#install.packages('DescTools')
library(DescTools)

AdityaSoni19031997

# This way we have randomness and are able to reproduce the behaviour within this cell.
np.random.seed(13)

def impact_coding(data, feature, target='y'):
    '''
    In this implementation we get the values and the dictionary as two different steps.
    This is just because initially we were ignoring the dictionary as a result variable.
    
    In this implementation the KFolds use shuffling. If you want reproducibility the cv 
    could be moved to a parameter.

AdityaSoni19031997

AdityaSoni19031997

import seaborn as sns
from sklearn import preprocessing, ensemble
from scipy.stats import kendalltau
import pandas as pd
import random

#todo change module name
from tqdm import tqdm
import numpy as np
import pandas as pd