A timer for ML functions

"A timer for ML functions"

• toc:true- branch: master- badges: true- comments: true
• author: David Kearney
• categories: [timer, jupyter]
• description: A timer for ML functions
• title: A timer for ML functions

#collapse-hide

from functools import wraps
import time


def timer(func):
    """[This decorator is a timer for functions]

    Args:
        func ([function]): [This decorator takes a function as argument]

    Returns:
        [string]: [states the duration of time between the function begining and ending]
    """
    @wraps(func)
    def wrapper(*args, **kwargs):
        print(f"{func.__name__!r} begins")
        start_time = time.time()
        result = func(*args, **kwargs)
        print(f"{func.__name__!r} ends in {time.time()-start_time}  secs")
        return result
    return wrapper

@timer
def model_metrics(*args, **kwargs):
    """[This is a function to print model metrics of interest]
    """
    print("Model ID Number:", args)
    print("Metric of Interest:", kwargs)


model_metrics(1, 2, 10, key="word", key2="word2", numtrees="200")

from collections import Counter
import math, random

#
# data splitting
#

def split_data(data, prob):
    """split data into fractions [prob, 1 - prob]"""
    results = [], []
    for row in data:
        results[0 if random.random() < prob else 1].append(row)
    return results

def train_test_split(x, y, test_pct):
    data = list(zip(x, y))                        # pair corresponding values
    train, test = split_data(data, 1 - test_pct)  # split the dataset of pairs
    x_train, y_train = list(zip(*train))          # magical un-zip trick
    x_test, y_test = list(zip(*test))
    return x_train, x_test, y_train, y_test

#
# correctness
#

def accuracy(tp, fp, fn, tn):
    correct = tp + tn
    total = tp + fp + fn + tn
    return correct / total

def precision(tp, fp, fn, tn):
    return tp / (tp + fp)

def recall(tp, fp, fn, tn):
    return tp / (tp + fn)

def f1_score(tp, fp, fn, tn):
    p = precision(tp, fp, fn, tn)
    r = recall(tp, fp, fn, tn)

    return 2 * p * r / (p + r)

if __name__ == "__main__":

    print("accuracy(70, 4930, 13930, 981070)", accuracy(70, 4930, 13930, 981070))
    print("precision(70, 4930, 13930, 981070)", precision(70, 4930, 13930, 981070))
    print("recall(70, 4930, 13930, 981070)", recall(70, 4930, 13930, 981070))
    print("f1_score(70, 4930, 13930, 981070)", f1_score(70, 4930, 13930, 981070))

favorite_number = 7


def add(a, b):
    return a + b


def sub(a, b):
    return a - b


def multiply(a, b):
    return a * b


def divide(a, b):
    return a / b


def count_vowels(word):
    count = 0
    for letter in word.lower():
        count += letter in 'aeiou'

    return count

# import example_module as sm

# print(sm.favorite_number)

# # add two numbers together
# print(sm.add(3, 8))

# # count the number of vowels in a string
# print(sm.count_vowels('Testing'))

import pandas as pd
from alive_progress import alive_bar, showtime, show_bars, show_spinners, config_handler
config_handler.set_global(theme='ascii', spinner='notes', bar='solid')

with alive_bar(3) as bar:
    df = pd.read_csv('https://gist.githubusercontent.com/davidrkearney/bb461ba351da484336a19bd00a2612e2/raw/18dd90b57fec46a247248d161ffd8085de2a00db/china_province_economicdata_1996_2007.csv')
    bar('file read, printing file')
    print(df.head)
    bar('data printed ok, printing methods of data')
    print(dir(df))
    bar('process complete')

from functools import wraps
import time


def timer(func):
    """[This decorator is a timer for functions]

    Args:
        func ([function]): [This decorator takes a function as argument]

    Returns:
        [string]: [states the duration of time between the function begining and ending]
    """
    @wraps(func)
    def wrapper(*args, **kwargs):
        print(f"{func.__name__!r} begins")
        start_time = time.time()
        result = func(*args, **kwargs)
        print(f"{func.__name__!r} ends in {time.time()-start_time}  secs")
        return result
    return wrapper



@timer
def model_metrics(*args, **kwargs):
    """[This is a function to print model metrics of interest]
    """
    print("Model ID Number:", args)
    print("Metric of Interest:", kwargs)


model_metrics(1, 2, 10, key="word", key2="word2", numtrees="200")

This post includes code adapted from Data Science from Scratch