JoshZastrow

import pandas as pd

def filter_columns_by_keyword(df: pd.DataFrame, keyword: str)-> pd.DataFrame:
    """Filters pandas columns by a keyword by searching the column index for a matching substring"""
    return df.loc[:, lambda d: d.columns.str.contains(keyword)]

def convert_monthly_ts(df):
    return df.reset_index().assign(ds_month = lambda x: x.ds + pd.offsets.MonthBegin()).groupby(["job_area", "ds_month"], as_index=False).y.mean()

JoshZastrow

from functools import wraps
import logging

def log_shape(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        result = func(*args, **kwargs)
        logging.info("%s,%s" % (func.__name__, result.shape))
        return result
    return wrapper

JoshZastrow

def setup_logger():    
    '''Sets up logger'''
    # formatter = logging.Formatter(
    #     '[%(asctime)s] {%(filename)s:%(lineno)d} %(levelname)s - %(message)s'
    # )
    logging.basicConfig(
        level=logging.INFO,
        format='[%(asctime)s] {%(filename)s:%(lineno)d} %(levelname)s - %(message)s')

    # handler = logging.StreamHandler()

JoshZastrow

import functools
import os


## Example 1: 
'''
Decorator functions are great ways to template-ize a piece of functional
code that should be run before / after any other function. For example,
checking permissions or connections to a DB could be put into a decorator
and reused before all other functions.

JoshZastrow

def load_dataset():
    train_dataset = h5py.File('datasets/train_happy.h5', "r")
    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels

    test_dataset = h5py.File('datasets/test_happy.h5', "r")
    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels

    classes = np.array(test_dataset["list_classes"][:]) # the list of classes

JoshZastrow

JoshZastrow

JoshZastrow

import numpy as np

class classifier():
    
    def __init__(self, lr=0.001, num_dims=6):
        self.lr = lr
        self.params = {}
        
        self.W = np.random.rand(num_dims, 1)
        self.b = np.ones((1,1))

JoshZastrow

import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import train_test_split
from torch.utils import data
from keras import preprocessing
from keras.datasets import imdb
import numpy as np

# Get Data

JoshZastrow

from __future__ import absolute_import
from __future__ import print_function

# from keras.preprocessing.image import ImageDataGenerator
import pandas as pd
import numpy as np
from scipy import misc
import matplotlib.pyplot as plt
import os, math, time
import csv