Muhammad4hmed

import re, string, unicodedata
import nltk
import contractions
import inflect
from nltk import word_tokenize, sent_tokenize
from nltk.corpus import stopwords
from nltk.stem import LancasterStemmer, WordNetLemmatizer

def clean_text(s):
    """

Muhammad4hmed

from sklearn.feature_selection import chi2
from sklearn.feature_selection import f_classif
from sklearn.feature_selection import f_regression
from sklearn.feature_selection import mutual_info_classif
from sklearn.feature_selection import mutual_info_regression
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import SelectPercentile 

class UnivariateFeatureSelction:
    def __init__(self, n_features, problem_type, scoring):

Muhammad4hmed

def create_date_featues(df, col):
    df['Year'] = pd.to_datetime(df[col]).dt.year
    df['Month'] = pd.to_datetime(df[col]).dt.month
    df['Day'] = pd.to_datetime(df[col]).dt.day
    df['Dayofweek'] = pd.to_datetime(df[col]).dt.dayofweek
    df['DayOfyear'] = pd.to_datetime(df[col]).dt.dayofyear
    df['Week'] = pd.to_datetime(df[col]).dt.week
    df['Quarter'] = pd.to_datetime(df[col]).dt.quarter 
    df['Is_month_start'] = pd.to_datetime(df[col]).dt.is_month_start
    df['Is_month_end'] = pd.to_datetime(df[col]).dt.is_month_end

Muhammad4hmed

def rounding(num):
    round_num, round_num2 = 0, 0
    uniques = np.sort(np.unique(train['price']))
    for i,n in enumerate(uniques):
        if n > num:
            break
        round_num = n
        round_num2 = uniques[i+1]
    return (round_num+round_num2)/2

Muhammad4hmed

# dataset.py
import torch
import numpy as np 
from PIL import Image
from PIL import ImageFile 
from torch.utils.data import DataLoader, Dataset
# sometimes, you will have images without anending bit# this takes care of those kind of (corrupt) images
ImageFile.LOAD_TRUNCATED_IMAGES = True

class Model(nn.Module):

Muhammad4hmed

# config.py
# we define all the configuration here
MAX_LEN = 128
TRAIN_BATCH_SIZE = 16
VALID_BATCH_SIZE = 8
EPOCHS = 10

# EMB CNN LSTM
import torch
import torch.nn.functional as F

Muhammad4hmed

# config.py
import transformers
# this is the maximum number of tokens in the sentence
MAX_LEN = 512
# batch sizes is small because model is huge!
TRAIN_BATCH_SIZE = 8
VALID_BATCH_SIZE = 4
# let's train for a maximum of 10 epochs
EPOCHS = 10
# define path to BERT model files

Muhammad4hmed

class AttentionModel(nn.Module):
    def __init__(self, num_classes = 5,
                 embed_size = 2560, LSTM_UNITS = 64, pretrained = True, BATCH_SIZE = 4):
        super().__init__()
        self.batch_size = BATCH_SIZE
        
        self.cnn = timm.create_model('efficientnet_b7', pretrained=pretrained)
        
        self.avgpool = torch.nn.AdaptiveAvgPool2d(1)
        

Muhammad4hmed

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        backbone = timm.create_model(TIMM_MODEL, pretrained=True)
        n_features = backbone.head.in_features
        self.backbone = nn.Sequential(*backbone.children())[:-2]
        self.classifier = nn.Linear(n_features, 5)
        self.pool = nn.AdaptiveAvgPool2d((1, 1))

    def forward_features(self, x):

Muhammad4hmed

import os

from PIL import Image
import pandas as pd
from tqdm.auto import tqdm

import numpy as np
import pydicom
from pydicom.pixel_data_handlers.util import apply_voi_lut