sagemaker train and serve titanic

sklearn-titanic-train-job.py

import argparse
import os
import numpy as np
import pandas as pd
import boto3
from io import StringIO

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.externals import joblib
from sagemaker_containers.beta.framework import (
    content_types, encoders, env, modules, transformer, worker, server)
from sagemaker_inference import content_types, decoder, encoder

if __name__ =='__main__':

    parser = argparse.ArgumentParser()

    # Data, model, and output directories
    parser.add_argument('--output-data-dir', type=str, default=os.environ.get('SM_OUTPUT_DATA_DIR'))
    parser.add_argument('--model-dir', type=str, default=os.environ.get('SM_MODEL_DIR'))
    parser.add_argument('--train', type=str, default=os.environ.get('SM_CHANNEL_TRAIN'))

    args, _ = parser.parse_known_args()
    file = os.path.join(args.train, "train.csv")
    
    #manipulate
    titanic_train_data = pd.read_csv(file, engine="python")
    titanic_train_data = titanic_train_data.drop(columns='Cabin', axis=1)
    titanic_train_data['Age'].fillna(titanic_train_data['Age'].mean(), inplace=True)
    titanic_train_data['Embarked'].fillna(titanic_train_data['Embarked'].mode()[0], inplace=True)
    titanic_train_data.replace({'Sex':{'male':0,'female':1}, 'Embarked':{'S':0,'C':1,'Q':2}}, inplace=True)
    
    #train
    X = titanic_train_data.drop(columns = ['PassengerId','Name','Ticket','Survived'],axis=1)
    Y = titanic_train_data['Survived']
    X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, random_state=2)
    model = LogisticRegression(solver='lbfgs', max_iter=400)
    model.fit(X_train, Y_train)
    
    # Print the coefficients of the trained classifier, and save the coefficients
    joblib.dump(model, os.path.join(args.model_dir, "model.joblib"))
    
def model_fn(model_dir):
    regressor = joblib.load(os.path.join(model_dir, "model.joblib"))
    return regressor


def input_fn(input_data, content_type):
    print("input_fn")
    if content_type == 'text/csv':
        # Read the raw input data as CSV.
        df = pd.read_csv(StringIO(input_data), header=None)
        return df.to_numpy()
    elif content_type == 'application/x-npy':
        np_array = encoders.decode(input_data, content_type)
        result = np_array.astype(np.float32) if content_type in content_types.UTF8_TYPES else np_array
        return result
    else:
        raise ValueError("{} not supported by script!".format(content_type))