snakers4

import gc
import gzip
import time
import json
import shutil
import os,sys
import tldextract
import collections
import pandas as pd
from tqdm import tqdm

snakers4

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"vA0-1iElD-wr","executionInfo":{"status":"ok","timestamp":1681303032533,"user_tz":-180,"elapsed":10963,"user":{"displayName":"Senhor Maestro","userId":"08176940519269874318"}}},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import torch\n","\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","from sklearn.model_selection import train_test_split\n","from sklearn.feature_selection import mutual_info_regression\n","from sklearn.metrics import accuracy_score\n","#from catboost import CatBoostClassifier"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2rjmy9OpEYEr","executionInfo":{"status":"ok","timestamp":1681303100348,"user_tz":-180,"elapsed":19790,"user":{"displayName":"Senhor Maestro","userId":"08176940519269874318"}},"outputId":"cac0e451-2165-406b-cba1-f8c3bf6d480f"},"execution_count":

snakers4

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "1XEMm5oo36Sm"
      },
      "outputs": [],
      "source": [

snakers4

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import torch
import torch.nn as nn
import torch.nn.functional as F

# Focal loss implementation inspired by
# https://github.com/c0nn3r/RetinaNet/blob/master/focal_loss.py
# https://github.com/doiken23/pytorch_toolbox/blob/master/focalloss2d.py
class MultiClassBCELoss(nn.Module):
    def __init__(self,
                 use_weight_mask=False,

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import os
import re
import sys
import glob
import nltk
import gensim
import numpy as np
import pandas as pd
from tqdm import tqdm
from uuid import uuid4

snakers4

import gc
import gzip
import time
import json
import shutil
import os,sys
import tldextract
import collections
import pandas as pd
from tqdm import tqdm

snakers4

# Unsophisticated corr analysis to deal w variable bias
data_corr = sDf.corr()
size = data_corr.shape[0] - 1 


# Set the threshold to select only highly correlated attributes
threshold = 0.5

# List of pairs along with correlation above threshold
corr_list = []

snakers4

# https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
#Import libraries:
import pandas as pd
import numpy as np
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn import cross_validation, metrics   #Additional scklearn functions
from sklearn.grid_search import GridSearchCV   #Perforing grid search

import matplotlib.pylab as plt

snakers4

Original	Model
She heard Missis Gibson talking on in a sweet monotone, and wished to attend to what she was saying, but the Squires visible annoyance struck sharper on her mind.	She heard Missis Gibson talking on in a sweet monotone and wished to attend to what she was saying, but the squires visible ann