miracleyoo

# https://www.kaggle.com/qqgeogor/keras-lstm-attention-glove840b-lb-0-043
class Attention(Layer):
    def __init__(self, step_dim,
                 W_regularizer=None, b_regularizer=None,
                 W_constraint=None, b_constraint=None,
                 bias=True, **kwargs):
        self.supports_masking = True
        self.init = initializers.get('glorot_uniform')

        self.W_regularizer = regularizers.get(W_regularizer)

miracleyoo

from keras import Sequential
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
from keras.models import Sequential,Model
from keras.layers import LSTM, Dense, Bidirectional, Input,Dropout,BatchNormalization
from keras import backend as K
from keras.engine.topology import Layer
from keras import initializers, regularizers, constraints

# Definition of model

miracleyoo

from sklearn.model_selection import KFold
from sklearn.metrics import *

def kf_fit(model, x_train=X, y_train=y, test_data=test):
    kf = KFold(n_splits=10, shuffle=True, random_state=42069)
    preds = []
#     test_data = pad_sequences(test_data)
    fold = 0
    aucs = 0
    for train_idx, val_idx in kf.split(x_train):

miracleyoo

import random
import os
from shutil import copyfile

ori_path = './your-original-path'
out_path = './your-output-path'
dirs = [os.path.join(ori_path, adir) for adir in os.listdir(ori_path) if not adir.startswith('.')]
pure_dirs = [adir for adir in os.listdir(ori_path) if not adir.startswith('.')]
for adir, pure_adir in zip(dirs, pure_dirs):
    print("==> Now processing:", adir)

miracleyoo

import numpy as np
import torch
from models import miracle_net
from config import Config
opt=Config()
net = miracle_net.MiracleNet(opt)
import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
class Template(Dataset):

miracleyoo

with open(file_path) as f:
    for line in f:
        j_content = json.loads(line)

miracleyoo

import collections, numpy

a = numpy.array([0, 3, 0, 1, 0, 1, 2, 1, 0, 0, 0, 0, 1, 3, 4])
collections.Counter(a)

# [Out]: Counter({0: 7, 1: 4, 3: 2, 2: 1, 4: 1})

miracleyoo

from wordcloud import WordCloud, STOPWORDS

# Thanks : https://www.kaggle.com/aashita/word-clouds-of-various-shapes ##
def plot_wordcloud(text, mask=None, max_words=200, max_font_size=100, figure_size=(24.0,16.0), 
                   title = None, title_size=40, image_color=False):
    stopwords = set(STOPWORDS)
    more_stopwords = {'one', 'br', 'Po', 'th', 'sayi', 'fo', 'Unknown'}
    stopwords = stopwords.union(more_stopwords)

    wordcloud = WordCloud(background_color='black',

miracleyoo

import os
import seaborn as sns
import matplotlib.pyplot as plt

def plot_history(history, save_path:str=None, figsize = (20, 9)):
    f, axes = plt.subplots(1, 2, figsize=figsize)
    sns.lineplot(range(1, history.epochs+1), history.history['acc'], label='Train Accuracy', ax=axes[0])
    sns.lineplot(range(1, history.epochs+1), history.history['val_acc'], label='Val Accuracy', ax=axes[0])
    sns.lineplot(range(1, history.epochs+1), history.history['loss'], label='Train Loss', ax=axes[1])
    sns.lineplot(range(1, history.epochs+1), history.history['val_loss'], label='Val Loss', ax=axes[1])

miracleyoo

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)