%matplotlib inline

import numpy as np 
import pandas as pd 
import matplotlib as mpl
import matplotlib.pyplot as plt 
import sklearn
import seaborn as sns
import mlflow 
import mlflow.sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, KFold
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_auc_score, plot_roc_curve, confusion_matrix 

print(f"numpy version {np.__version__}")
print(f"pandas version {pd.__version__}")
print(f"matplotlib version {mpl.__version__}")
print(f"seaborn version {sns.__version__}")
print(f"sklearn version {sklearn.__version__}")
print(f"MLFlow version {mlflow.__version__}")

DATA_PATH = "C:\\Users\\your_id\\Desktop\\mlops_tutorial\\data\\creditcard.csv"

df = pd.read_csv(DATA_PATH)
print(df.head())

normal = df[df['Class'] == 0].sample(frac=0.5, random_state=42).reset_index(drop=True)
anomaly = df[df['Class']==1]

normal_train, normal_test = train_test_split(normal, test_size = 0.2, random_state=42)
anomary_train, anomary_test = train_test_split(anomaly, test_size = 0.2, random_state=42)

normal_train, normal_validate = train_test_split(normal_train, test_size = 0.25, random_state=42)
anomary_train, anomary_validate = train_test_split(anomary_train, test_size = 0.25, random_state=42)

normal_train.shape, normal_validate.shape, anomary_train.shape, anomary_validate.shape

X_train = pd.concat((normal_train, anomary_train))
X_test = pd.concat((normal_test, anomary_test))
X_validate = pd.concat((normal_validate, anomary_validate))

y_train = np.array(X_train["Class"])
y_test = np.array(X_test["Class"])
y_validate = np.array(X_validate["Class"])

X_train = X_train.drop("Class", axis = 1)
X_test = X_test.drop("Class", axis = 1)
X_validate = X_validate.drop("Class", axis = 1)

X_train.shape, X_validate.shape, X_test.shape, y_train.shape, y_validate.shape, y_test.shape

scaler = StandardScaler()
scaler.fit(X_train)

X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
X_validate = scaler.transform(X_validate)

def train(sk_model, X_train, y_train):
    sk_model = sk_model.fit(X_train, y_train)
    train_acc = sk_model.score(X_train, y_train)
    mlflow.log_metric("train_acc", train_acc)
    
    print(f"Train Accuracy: (train_acc:.3%)")
    

def evaluate(sk_model, X_test, y_test):
    eval_acc = sk_model.score(X_test, y_test)
    preds = sk_model.predict(X_test)
    auc_score = roc_auc_score(y_test, preds)
    mlflow.log_metric("eval_acc", eval_acc)
    mlflow.log_metric("auc_score", auc_score)
    
    print(f"Auc Score : {auc_score:.3%}")
    print(f"Eval Score : {eval_acc:.3%}")
    roc_plot = plot_roc_curve(sk_model, X_test, y_test, name="Scikit-Learn ROC Curve")
    plt.savefig("sklearn_roc_plot.png")
    plt.show()
    plt.clf()
    conf_matrix = confusion_matrix(y_test, preds)
    ax = sns.heatmap(conf_matrix, annot=True, fmt='g')
    ax.invert_xaxis()
    ax.invert_yaxis()
    plt.ylabel("Actual")
    plt.xlabel("Predicted")
    plt.title("Confusion Matrix")
    plt.savefig("sklearn_conf_matrix.png")
    mlflow.log_artifact("sklearn_roc_plot.png")
    mlflow.log_artifact("sklearn_conf_matrix.png")

# 모델 설정
sk_model = LogisticRegression(random_state=None, max_iter=400, solver='newton-cg')

# 실험 이름 설정
mlflow.set_experiment("sklearn_experiment")

# 해당 이름으로 실행 배치
with mlflow.start_run():
    train(sk_model, X_train, y_train)
    evaluate(sk_model, X_test, y_test)
    
    # 하나의 MLFlow 실행 컨텍스트에서 모든 코드를 묶을 수 있음. 
    # 참조 : https://mlflow.org/docs/latest/models.html#model-customization
    mlflow.sklearn.log_model(sk_model, 'log_reg_model')
    
    # 본질적으로 모델과 지표가 로깅되는 현재 실행을 가져오고 출력함. 
    print("Model run: ", mlflow.active_run().info.run_uuid)
mlflow.end_run()

import numpy as np 
import pandas as pd 

np.random.seed(0) # 시드값 고정
intercept = 4 # 절편
slope = 3 # 기울기

# 변동성 주기 위해 노이즈 생성
noise = np.random.randn(50, 1)

# 50개의 x값 생성
x = 5 * np.random.rand(50, 1) # 0과 5사이의 실숫값 50개 생성
y = slope * x + intercept + noise

# 데이터 프레임 생성
data = pd.DataFrame({'X': x[:, 0], 'Y': y[:, 0]})
print(data.head())

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.scatter(data['X'], data['Y'])
plt.show()

$ virtualenv venv
created virtual environment CPython3.9.1.final.0-64 in 475ms
  creator CPython3Posix(dest=/Users/evan/Desktop/django-iris-tutorial/venv, clear=False, no_vcs_ignore=False, global=False)
  seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/Users/evan/Library/Application Support/virtualenv)
    added seed packages: pip==22.1.1, setuptools==62.3.2, wheel==0.37.1
  activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator

$ source venv/bin/activate
(venv) $

(venv) $ pip install jupyterlab sklearn django

(venv) $ python -m jupyterlab

# Module 불러오기
from pandas import read_csv
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
import pandas as pd

# 데이터셋 불러오기
df = pd.read_csv("data/iris.csv")

# 독립변수와 종속변수 분리
X = df[['sepal_length','sepal_width','petal_length','petal_width']]
y = df['classification']

# 훈련데이터와 종속데이터 분리
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.20, random_state=1)

# 모형 학습
model = SVC(gamma='auto')
model.fit(X_train, Y_train)

# 모형 예측
sepal_length = float(1.5)
sepal_width = float(5)
petal_length = float(4)
petal_width = float(3)

result = model.predict([[sepal_length,sepal_width,petal_length,petal_width]])  # input must be 2D array
print(result)

pd.to_pickle(model, r'models/svc_model.pickle')

(venv) $ django-admin startproject iris
(venv) $ cd iris
(venv) $ python manage.py startapp predict

.
.
# Application definition

INSTALLED_APPS = [
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
    'predict', 
]
.
.

from django.contrib import admin
from django.urls import path, include

urlpatterns = [
    path('admin/', admin.site.urls),
    path('', include('predict.urls', namespace='predict'))
]

from django.urls import path
from . import views

app_name = 'predict'

urlpatterns = [
    path('', views.predict, name='predict'),
]

from django.shortcuts import render

# Create your views here.
def predict(request):
    return render(request, 'predict.html', {})

hello

(venv) iris$ python manage.py runserver 

C:\Users\your_name>mysql -uroot -p
Enter password: ****
Welcome to the MySQL monitor.  Commands end with ; or \g.
Your MySQL connection id is 19
Server version: 8.0.28 MySQL Community Server - GPL

Copyright (c) 2000, 2022, Oracle and/or its affiliates.

Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

mysql>

mysql> CREATE DATABASE homestead;
Query OK, 1 row affected (0.01 sec)

mysql> CREATE USER '{username}'@'localhost' IDENTIFIED BY '{password}';
mysql> CREATE USER '{username}'@'%' IDENTIFIED BY '{password}';

mysql> CREATE USER 'homestead'@'localhost' IDENTIFIED BY 'secret';
Query OK, 0 rows affected (0.02 sec)

mysql> GRANT ALL PRIVILEGES ON homestead.* TO 'homestead'@'localhost';
Query OK, 0 rows affected (0.00 sec)
mysql> FLUSH PRIVILEGES;
Query OK, 0 rows affected (0.00 sec)

mysql> exit 
Bye

mysql -h127.0.0.1 -u{username} -p {database} # 예시
mysql -h127.0.0.1 -uhomestead -p homestead # 적용

C:\Users\your_name>mysql -h127.0.0.1 -uhomestead -p homestead
Enter password: ******
Welcome to the MySQL monitor.  Commands end with ; or \g.
Your MySQL connection id is 21
Server version: 8.0.28 MySQL Community Server - GPL

Copyright (c) 2000, 2022, Oracle and/or its affiliates.

Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

mysql> 

mysql> show databases;
+--------------------+
| Database           |
+--------------------+
| homestead          |
| information_schema |
+--------------------+
2 rows in set (0.00 sec)

import boto3
print(boto3.__version__)

bucket = boto3.resource('s3')

response = bucket.create_bucket(
    Bucket = "your_bucket_name",
    ACL="private", # public-read
    
    CreateBucketConfiguration = {
        'LocationConstraint' : 'ap-northeast-2'
    }
)

print(response)

client = boto3.client('s3')

response = client.create_bucket(
    Bucket = "your_bucket_name",
    ACL = "private",
    
    CreateBucketConfiguration = {
        'LocationConstraint' : 'ap-northeast-2'
    }

)

print(response)

import boto3
print(boto3.__version__)

def create_user(username):
    iam = boto3.client('iam')
    response = iam.create_user(UserName=username)
    print(response)

create_user('testuser2fromwsl2')

def all_users():
    iam = boto3.client('iam')
    paginator = iam.get_paginator('list_users')
    for response in paginator.paginate():
        for user in response['Users']:
            username = user['UserName']
            Arn = user['Arn']
            print('Username : {} Arn : {}'.format(username, Arn))

all_users()

def update_user(old_username, new_username):
    iam = boto3.client('iam')
    
    response = iam.update_user(
        UserName=old_username, 
        NewUserName=new_username
    )
    
    print(response)

update_user('testuser2fromwsl2', 'updatetest')
all_users()

import boto3
import json

def create_policy():
    iam = boto3.client('iam')

    user_policy = {
        "Version":"2012-10-17",
        "Statement":[
            {
                "Effect": "Allow",
                "Action": "*",
                "Resource": "*"
            }
        ]
    }

    response = iam.create_policy(
        PolicyName = 'pyFullAccess',
        PolicyDocument=json.dumps(user_policy)
    )
    print(response)


create_policy()

exec $SHELL

sudo apt update
sudo apt install software-properties-common
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.7

$ python3 --version
Python 3.8.10

ls /usr/bin/python* 

sudo apt install awscli

$ aws --version
aws-cli/1.18.69 Python/3.8.10 Linux/5.10.16.3-microsoft-standard-WSL2 botocore/1.16.19

C:\WINDOWS\system32>python --version
Python 3.7.4

C:\WINDOWS\system32>pip --version
pip 19.0.3 from c:\users\human\appdata\local\programs\python\python37-32\lib\site-packages\pip (python 3.7)

C:\Windows\System32> pip install awsebcli --upgrade --user