XGB Sagemaker Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker-us-east-1-029864677731\n",
      "sagemaker-us-east-1-029864677731\n"
     ]
    }
   ],
   "source": [
    "import sagemaker\n",
    "import boto3\n",
    "import pandas as pd\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
    " \n",
    "region = boto3.Session().region_name\n",
    " \n",
    "session = sagemaker.Session()\n",
    "bucket = session.default_bucket()\n",
    "print(bucket)\n",
    "prefix = 'sagemaker-demo'\n",
    "\n",
    "sm = boto3.Session().client(service_name='sagemaker',region_name=region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-07-25 18:54:02--  https://gist.githubusercontent.com/michhar/2dfd2de0d4f8727f873422c5d959fff5/raw/fa71405126017e6a37bea592440b4bee94bf7b9e/titanic.csv\n",
      "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 151.101.248.133\n",
      "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|151.101.248.133|:443... connected.\n",
      "--2020-07-25 18:54:02--  https://gist.githubusercontent.com/michhar/2dfd2de0d4f8727f873422c5d959fff5/raw/fa71405126017e6a37bea592440b4bee94bf7b9e/titanic.csv\n",
      "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 151.101.248.133\n",
      "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|151.101.248.133|:443... connected.\n",
      "HTTP request sent, awaiting response... HTTP request sent, awaiting response... 200 OK\n",
      "Length: 60301 (59K) [text/plain]\n",
      "Saving to: ‘titanic.csv’\n",
      "\n",
      "titanic.csv         100%[===================>]  58.89K  --.-KB/s    in 0.002s  \n",
      "\n",
      "Last-modified header missing -- time-stamps turned off.\n",
      "2020-07-25 18:54:02 (28.3 MB/s) - ‘titanic.csv’ saved [60301/60301]\n",
      "\n",
      "200 OK\n",
      "Length: 60301 (59K) [text/plain]\n",
      "Saving to: ‘titanic.csv’\n",
      "\n",
      "titanic.csv         100%[===================>]  58.89K  --.-KB/s    in 0.002s  \n",
      "\n",
      "Last-modified header missing -- time-stamps turned off.\n",
      "2020-07-25 18:54:02 (28.3 MB/s) - ‘titanic.csv’ saved [60301/60301]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -N https://gist.githubusercontent.com/michhar/2dfd2de0d4f8727f873422c5d959fff5/raw/fa71405126017e6a37bea592440b4bee94bf7b9e/titanic.csv\n",
    "local_data_path = 'titanic.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(local_data_path)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare\n",
       "0         0       3    1  22.0      1      0   7.2500\n",
       "1         1       1    0  38.0      1      0  71.2833\n",
       "2         1       3    0  26.0      0      0   7.9250\n",
       "3         1       1    0  35.0      1      0  53.1000\n",
       "4         0       3    1  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>35.0</td>\n",
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       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare\n",
       "0         0       3    1  22.0      1      0   7.2500\n",
       "1         1       1    0  38.0      1      0  71.2833\n",
       "2         1       3    0  26.0      0      0   7.9250\n",
       "3         1       1    0  35.0      1      0  53.1000\n",
       "4         0       3    1  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['Name', 'Ticket', 'Cabin', 'Embarked', 'PassengerId'], axis = 1)\n",
    "data['Sex'] = data['Sex'].astype('category').cat.codes\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# XGBoost requires no headers\n",
    "# Target is first column\n",
    "train_data = data.sample(frac=0.8,random_state=200)\n",
    "test_data = data.drop(train_data.index)\n",
    "test_y = test_data['Survived']\n",
    "test_data = test_data.drop(columns=['Survived'])\n",
    "\n",
    "train_data = pd.DataFrame(train_data[:].values)\n",
    "test_data = pd.DataFrame(test_data[:].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset uploaded to: s3://sagemaker-us-east-1-029864677731/sagemaker-demo/train_data.csv\n",
      "Test dataset uploaded to: s3://sagemaker-us-east-1-029864677731/sagemaker-demo/test_data.csv\n",
      "Train dataset uploaded to: s3://sagemaker-us-east-1-029864677731/sagemaker-demo/train_data.csv\n",
      "Test dataset uploaded to: s3://sagemaker-us-east-1-029864677731/sagemaker-demo/test_data.csv\n"
     ]
    }
   ],
   "source": [
    "train_file = 'train_data.csv';\n",
    "train_data.to_csv(train_file, index=False, header=True)\n",
    "train_data_s3_path = session.upload_data(path=train_file, key_prefix=prefix)\n",
    "print('Train dataset uploaded to: ' + train_data_s3_path)\n",
    " \n",
    "test_file = 'test_data.csv';\n",
    "test_data.to_csv(test_file, index=False, header=False)\n",
    "test_data_s3_path = session.upload_data(path=test_file, key_prefix=prefix)\n",
    "print('Test dataset uploaded to: ' + test_data_s3_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'get_image_uri' method will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\n",
      "WARNING:root:There is a more up to date SageMaker XGBoost image. To use the newer image, please set 'repo_version'='1.0-1'. For example:\n",
      "\tget_image_uri(region, 'xgboost', '1.0-1').\n",
      "'get_image_uri' method will be deprecated in favor of 'ImageURIProvider' class in SageMaker Python SDK v2.\n",
      "WARNING:root:There is a more up to date SageMaker XGBoost image. To use the newer image, please set 'repo_version'='1.0-1'. For example:\n",
      "\tget_image_uri(region, 'xgboost', '1.0-1').\n"
     ]
    }
   ],
   "source": [
    "container = get_image_uri(session.boto_region_name, 'xgboost')\n",
    "role = get_execution_role()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Parameter image_name will be renamed to image_uri in SageMaker Python SDK v2.\n",
      "WARNING:root:Parameter image_name will be renamed to image_uri in SageMaker Python SDK v2.\n"
     ]
    }
   ],
   "source": [
    "xgb = sagemaker.estimator.Estimator(container, # The location of the container we wish to use\n",
    "                                    role,   # What is our current IAM Role\n",
    "                                    train_instance_count=1, # How many compute instances\n",
    "                                    train_instance_type='ml.m5.large', # What kind of compute instances\n",
    "                                    output_path='s3://{}/{}/output'.format(session.default_bucket(), prefix),\n",
    "                                    sagemaker_session=session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb.set_hyperparameters(max_depth=2,\n",
    "                        eta=0.2,\n",
    "                        gamma=4,\n",
    "                        min_child_weight=6,\n",
    "                        subsample=0.8,\n",
    "                        silent=0,\n",
    "                        objective='binary:logistic',\n",
    "                        early_stopping_rounds=10,\n",
    "                        num_round=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:sagemaker:'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\n",
      "WARNING:sagemaker:'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\n",
      "WARNING:sagemaker:'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\n",
      "WARNING:sagemaker:'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-25 18:54:03 Starting - Starting the training job.2020-07-25 18:54:03 Starting - Starting the training job.....\n",
      "2020-07-25 18:54:05 Starting - Launching requested ML instances.\n",
      "2020-07-25 18:54:05 Starting - Launching requested ML instances........."
     ]
    }
   ],
   "source": [
    "s3_input_train = sagemaker.s3_input(s3_data=train_data_s3_path, content_type='csv')\n",
    "s3_input_test = sagemaker.s3_input(s3_data=test_data_s3_path, content_type='csv')\n",
    "\n",
    "xgb.fit({'train': s3_input_train})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Batch transform\n",
    "\n",
    "# reloaded_xgb_model = sagemaker.estimator.Estimator.attach(model_job_name)\n",
    "\n",
    "xgb_transformer = xgb.transformer(instance_count = 1,\n",
    "                                  instance_type = 'ml.m5.large',\n",
    "                                  output_path='s3://{}/{}/out/'.format(bucket, prefix))\n",
    "                                  \n",
    "# Start the transform job\n",
    "xgb_transformer.transform(test_data_s3_path,\n",
    "                          content_type='text/csv', #pecify the content type \n",
    "                          split_type='Line') #split type of the test data\n",
    "\n",
    "\n",
    "xgb_transformer.wait()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.read_csv('s3://{}/{}/out/{}.out'.format(bucket, prefix, test_file), header=None)\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_predictor = xgb.deploy(initial_instance_count=1, instance_type='ml.m5.large')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.predictor import csv_serializer\n",
    "import numpy as np\n",
    "xgb_predictor.content_type = 'text/csv'\n",
    "xgb_predictor.serializer = csv_serializer\n",
    "\n",
    "Y_pred = xgb_predictor.predict(test_data.values).decode('utf-8')\n",
    "Y_pred = np.fromstring(Y_pred, sep=',')\n",
    "print(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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