Detailed explanation of three methods of parsing parameters in Python

This article brings you relevant knowledge about Python, which mainly organizes issues related to three methods of parsing parameters. The first option is to use argparse, which is a popular Python module, specifically used for command line parsing; another method is to read a JSON file, where we can place all hyperparameters; the third and little-known method is to use a YAML file. Let’s take a look at it together. I hope it will help Everyone is helpful.

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The main purpose of what we share today is to use the command line and Configuration files to improve code efficiency

Let's go!

We use the parameter adjustment process in machine learning to practice. There are three ways to choose from. The first option is to use argparse, which is a popular Python module dedicated to command line parsing; the other is to read a JSON file where we can put all the hyperparameters; the third is also less known The solution is to use YAML files! Curious, let’s get started!

Prerequisites

In the code below, I will use Visual Studio Code, which is a very efficient integrated Python development environment. The beauty of this tool is that it supports every programming language by installing extensions, integrates the terminal and allows working with a large number of Python scripts and Jupyter notebooks

datasets, using the Shared Bike Dataset on Kaggle

Using argparse

As shown in the picture above, we have a standard structure to organize our small project:

• Contains our data Set a folder named data
• train.py file
• options.py file for specifying hyperparameters

First, we can create a file train.py, in which we have the basic procedure to import the data, train the model on the training data and evaluate it on the test set:

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error

from options import train_options

df = pd.read_csv('data\hour.csv')
print(df.head())
opt = train_options()

X=df.drop(['instant','dteday','atemp','casual','registered','cnt'],axis=1).values
y =df['cnt'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

if opt.normalize == True:
    scaler = StandardScaler()
    X = scaler.fit_transform(X)
    
rf = RandomForestRegressor(n_estimators=opt.n_estimators,max_features=opt.max_features,max_depth=opt.max_depth)
model = rf.fit(X_train,y_train)
y_pred = model.predict(X_test)
rmse = np.sqrt(mean_squared_error(y_pred, y_test))
mae = mean_absolute_error(y_pred, y_test)
print("rmse: ",rmse)
print("mae: ",mae)

In the code, we also import the files contained in options.py train_options function in the file. The latter file is a Python file from which we can change the hyperparameters considered in train.py:

import argparse

def train_options():
    parser = argparse.ArgumentParser()
    parser.add_argument("--normalize", default=True, type=bool, help='maximum depth')
    parser.add_argument("--n_estimators", default=100, type=int, help='number of estimators')
    parser.add_argument("--max_features", default=6, type=int, help='maximum of features',)
    parser.add_argument("--max_depth", default=5, type=int,help='maximum depth')
    opt = parser.parse_args()
    return opt

In this example, we use the argparse library, which is very popular when parsing command line arguments. First, we initialize the parser, then, we can add the parameters we want to access.

Here is an example of running code:

python train.py

To change the default values ​​of hyperparameters, there are two ways. The first option is to set different default values ​​in the options.py file. Another option is to pass the hyperparameter value from the command line:

python train.py --n_estimators 200

We need to specify the name of the hyperparameter we want to change and the corresponding value.

python train.py --n_estimators 200 --max_depth 7

Using JSON files

As before, we can keep a similar file structure. In this case, we replace the options.py file with a JSON file. In other words, we want to specify the values ​​of the hyperparameters in a JSON file and pass them to the train.py file. JSON files can be a fast and intuitive alternative to the argparse library, leveraging key-value pairs to store data. Next we create an options.json file that contains the data we need to pass to other code later.

{
"normalize":true,
"n_estimators":100,
"max_features":6,
"max_depth":5 
}

As you can see above, it is very similar to a Python dictionary. But unlike a dictionary, it contains data in text/string format. Additionally, there are some common data types with slightly different syntax. For example, Boolean values ​​are false/true, while Python recognizes False/True. Other possible values ​​in JSON are arrays, which are represented as Python lists using square brackets.

The beauty of working with JSON data in Python is that it can be converted into a Python dictionary via the load method:

f = open("options.json", "rb")
parameters = json.load(f)

To access a specific item, we just need to quote it within square brackets Key name:

if parameters["normalize"] == True:
    scaler = StandardScaler()
    X = scaler.fit_transform(X)
rf=RandomForestRegressor(n_estimators=parameters["n_estimators"],max_features=parameters["max_features"],max_depth=parameters["max_depth"],random_state=42)
model = rf.fit(X_train,y_train)
y_pred = model.predict(X_test)

Using YAML files

The last option is to take advantage of the potential of YAML. As with JSON files, we read the YAML file in Python code as a dictionary to access the values ​​of the hyperparameters. YAML is a human-readable data representation language in which hierarchies are represented using double-space characters instead of parentheses like in JSON files. Below we show what the options.yaml file will contain:

normalize: True 
n_estimators: 100
max_features: 6
max_depth: 5

In train.py, we open the options.yaml file, which will always be converted to a Python dictionary using the load method, this time from the yaml library Imported in:

import yaml
f = open('options.yaml','rb')
parameters = yaml.load(f, Loader=yaml.FullLoader)

As before, we can access the value of the hyperparameter using the syntax required by the dictionary.

Final Thoughts

Configuration files compile very quickly, whereas argparse requires writing a line of code for each argument we want to add.

So we should choose the most appropriate method according to our different situations

For example, if we need to add comments to parameters, JSON is not suitable because it does not allow comments, and YAML and argparse might be a good fit.

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