bankapp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "Importing necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "import boto3\n",
    "from sagemaker.amazon.amazon_estimator import get_image_uri \n",
    "from sagemaker.session import s3_input, Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Creating S3 bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "us-east-1\n"
     ]
    }
   ],
   "source": [
    "bucket_name = 'bankappproj' \n",
    "my_region = boto3.session.Session().region_name # set the region of the instance\n",
    "print(my_region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S3 bucket created successfully\n"
     ]
    }
   ],
   "source": [
    "s3 = boto3.resource('s3')\n",
    "try:\n",
    "    if  my_region == 'us-east-1':\n",
    "        s3.create_bucket(Bucket=bucket_name)\n",
    "    print('S3 bucket created successfully')\n",
    "except Exception as e:\n",
    "    print('S3 error: ',e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3://bankappproj/xgboost-as-a-built-in-algo/output\n"
     ]
    }
   ],
   "source": [
    "# set an output path where the trained model will be saved\n",
    "prefix = 'xgboost-as-a-built-in-algo'\n",
    "output_path ='s3://{}/{}/output'.format(bucket_name, prefix)\n",
    "print(output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Downloading The Dataset And Storing in S3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success: downloaded bank_clean.csv.\n",
      "Success: Data loaded into dataframe.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import urllib\n",
    "try:\n",
    "    urllib.request.urlretrieve (\"https://d1.awsstatic.com/tmt/build-train-deploy-machine-learning-model-sagemaker/bank_clean.27f01fbbdf43271788427f3682996ae29ceca05d.csv\", \"bank_clean.csv\")\n",
    "    print('Success: downloaded bank_clean.csv.')\n",
    "except Exception as e:\n",
    "    print('Data load error: ',e)\n",
    "\n",
    "try:\n",
    "    model_data = pd.read_csv('./bank_clean.csv',index_col=0)\n",
    "    print('Success: Data loaded into dataframe.')\n",
    "except Exception as e:\n",
    "    print('Data load error: ',e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train-Test split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28831, 61) (12357, 61)\n"
     ]
    }
   ],
   "source": [
    "### Train Test split\n",
    "\n",
    "import numpy as np\n",
    "train_data, test_data = np.split(model_data.sample(frac=1, random_state=1729), [int(0.7 * len(model_data))])\n",
    "print(train_data.shape, test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Saving Train And Test Into Buckets\n",
    "## We start with Train Data\n",
    "import os\n",
    "pd.concat([train_data['y_yes'], train_data.drop(['y_no', 'y_yes'], \n",
    "                                                axis=1)], \n",
    "                                                axis=1).to_csv('train.csv', index=False, header=False)\n",
    "boto3.Session().resource('s3').Bucket(bucket_name).Object(os.path.join(prefix, 'train/train.csv')).upload_file('train.csv')\n",
    "s3_input_train = sagemaker.inputs.TrainingInput(s3_data='s3://{}/{}/train'.format(bucket_name, prefix), content_type='csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test Data Into Buckets\n",
    "pd.concat([test_data['y_yes'], test_data.drop(['y_no', 'y_yes'], axis=1)], axis=1).to_csv('test.csv', index=False, header=False)\n",
    "boto3.Session().resource('s3').Bucket(bucket_name).Object(os.path.join(prefix, 'test/test.csv')).upload_file('test.csv')\n",
    "s3_input_test = sagemaker.inputs.TrainingInput(s3_data='s3://{}/{}/test'.format(bucket_name, prefix), content_type='csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Building Model with Xgboot- Inbuilt Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this line automatically looks for the XGBoost image URI and builds an XGBoost container.\n",
    "# specify the repo_version depending on your preference.\n",
    "container = sagemaker.image_uris.retrieve(\"xgboost\", my_region, \"1.2-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize hyperparameters\n",
    "hyperparameters = {\n",
    "        \"max_depth\":\"5\",\n",
    "        \"eta\":\"0.2\",\n",
    "        \"gamma\":\"4\",\n",
    "        \"min_child_weight\":\"6\",\n",
    "        \"subsample\":\"0.7\",\n",
    "        \"objective\":\"binary:logistic\",\n",
    "        \"num_round\":50\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct a SageMaker estimator that calls the xgboost-container\n",
    "estimator = sagemaker.estimator.Estimator(image_uri=container, \n",
    "                                          hyperparameters=hyperparameters,\n",
    "                                          role=sagemaker.get_execution_role(),\n",
    "                                          instance_count=1, \n",
    "                                          instance_type='ml.m5.2xlarge', \n",
    "                                          volume_size=5, # 5 GB \n",
    "                                          output_path=output_path,\n",
    "                                          use_spot_instances=True,\n",
    "                                          max_run=300,\n",
    "                                          max_wait=600)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-01-25 07:55:30 Starting - Starting the training job...\n",
      "2021-01-25 07:55:54 Starting - Launching requested ML instancesProfilerReport-1611561330: InProgress\n",
      "......\n",
      "2021-01-25 07:56:54 Starting - Preparing the instances for training......\n",
      "2021-01-25 07:57:55 Downloading - Downloading input data\n",
      "2021-01-25 07:57:55 Training - Downloading the training image...\n",
      "2021-01-25 07:58:29 Uploading - Uploading generated training model\n",
      "2021-01-25 07:58:29 Completed - Training job completed\n",
      "\u001b[34mINFO:sagemaker-containers:Imported framework sagemaker_xgboost_container.training\u001b[0m\n",
      "\u001b[34mINFO:sagemaker-containers:Failed to parse hyperparameter objective value binary:logistic to Json.\u001b[0m\n",
      "\u001b[34mReturning the value itself\u001b[0m\n",
      "\u001b[34mINFO:sagemaker-containers:No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34mINFO:sagemaker_xgboost_container.training:Running XGBoost Sagemaker in algorithm mode\u001b[0m\n",
      "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[34mINFO:root:Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[34mINFO:root:Single node training.\u001b[0m\n",
      "\u001b[34mINFO:root:Train matrix has 28831 rows\u001b[0m\n",
      "\u001b[34mINFO:root:Validation matrix has 12357 rows\u001b[0m\n",
      "\u001b[34m[0]#011train-error:0.10079#011validation-error:0.10528\u001b[0m\n",
      "\u001b[34m[1]#011train-error:0.09968#011validation-error:0.10456\u001b[0m\n",
      "\u001b[34m[2]#011train-error:0.10017#011validation-error:0.10375\u001b[0m\n",
      "\u001b[34m[3]#011train-error:0.09989#011validation-error:0.10310\u001b[0m\n",
      "\u001b[34m[4]#011train-error:0.09996#011validation-error:0.10286\u001b[0m\n",
      "\u001b[34m[5]#011train-error:0.09906#011validation-error:0.10261\u001b[0m\n",
      "\u001b[34m[6]#011train-error:0.09930#011validation-error:0.10286\u001b[0m\n",
      "\u001b[34m[7]#011train-error:0.09951#011validation-error:0.10261\u001b[0m\n",
      "\u001b[34m[8]#011train-error:0.09920#011validation-error:0.10286\u001b[0m\n",
      "\u001b[34m[9]#011train-error:0.09871#011validation-error:0.10294\u001b[0m\n",
      "\u001b[34m[10]#011train-error:0.09868#011validation-error:0.10294\u001b[0m\n",
      "\u001b[34m[11]#011train-error:0.09868#011validation-error:0.10326\u001b[0m\n",
      "\u001b[34m[12]#011train-error:0.09854#011validation-error:0.10358\u001b[0m\n",
      "\u001b[34m[13]#011train-error:0.09892#011validation-error:0.10342\u001b[0m\n",
      "\u001b[34m[14]#011train-error:0.09850#011validation-error:0.10342\u001b[0m\n",
      "\u001b[34m[15]#011train-error:0.09844#011validation-error:0.10326\u001b[0m\n",
      "\u001b[34m[16]#011train-error:0.09857#011validation-error:0.10318\u001b[0m\n",
      "\u001b[34m[17]#011train-error:0.09799#011validation-error:0.10318\u001b[0m\n",
      "\u001b[34m[18]#011train-error:0.09816#011validation-error:0.10383\u001b[0m\n",
      "\u001b[34m[19]#011train-error:0.09857#011validation-error:0.10383\u001b[0m\n",
      "\u001b[34m[20]#011train-error:0.09830#011validation-error:0.10350\u001b[0m\n",
      "\u001b[34m[21]#011train-error:0.09826#011validation-error:0.10318\u001b[0m\n",
      "\u001b[34m[22]#011train-error:0.09847#011validation-error:0.10399\u001b[0m\n",
      "\u001b[34m[23]#011train-error:0.09833#011validation-error:0.10407\u001b[0m\n",
      "\u001b[34m[24]#011train-error:0.09812#011validation-error:0.10415\u001b[0m\n",
      "\u001b[34m[25]#011train-error:0.09812#011validation-error:0.10399\u001b[0m\n",
      "\u001b[34m[26]#011train-error:0.09774#011validation-error:0.10375\u001b[0m\n",
      "\u001b[34m[27]#011train-error:0.09781#011validation-error:0.10375\u001b[0m\n",
      "\u001b[34m[28]#011train-error:0.09781#011validation-error:0.10391\u001b[0m\n",
      "\u001b[34m[29]#011train-error:0.09778#011validation-error:0.10367\u001b[0m\n",
      "\u001b[34m[30]#011train-error:0.09781#011validation-error:0.10383\u001b[0m\n",
      "\u001b[34m[31]#011train-error:0.09771#011validation-error:0.10358\u001b[0m\n",
      "\u001b[34m[32]#011train-error:0.09743#011validation-error:0.10391\u001b[0m\n",
      "\u001b[34m[33]#011train-error:0.09753#011validation-error:0.10342\u001b[0m\n",
      "\u001b[34m[34]#011train-error:0.09767#011validation-error:0.10342\u001b[0m\n",
      "\u001b[34m[35]#011train-error:0.09757#011validation-error:0.10350\u001b[0m\n",
      "\u001b[34m[36]#011train-error:0.09757#011validation-error:0.10342\u001b[0m\n",
      "\u001b[34m[37]#011train-error:0.09736#011validation-error:0.10342\u001b[0m\n",
      "\u001b[34m[38]#011train-error:0.09750#011validation-error:0.10342\u001b[0m\n",
      "\u001b[34m[39]#011train-error:0.09733#011validation-error:0.10350\u001b[0m\n",
      "\u001b[34m[40]#011train-error:0.09705#011validation-error:0.10358\u001b[0m\n",
      "\u001b[34m[41]#011train-error:0.09701#011validation-error:0.10383\u001b[0m\n",
      "\u001b[34m[42]#011train-error:0.09712#011validation-error:0.10407\u001b[0m\n",
      "\u001b[34m[43]#011train-error:0.09698#011validation-error:0.10375\u001b[0m\n",
      "\u001b[34m[44]#011train-error:0.09733#011validation-error:0.10342\u001b[0m\n",
      "\u001b[34m[45]#011train-error:0.09736#011validation-error:0.10367\u001b[0m\n",
      "\u001b[34m[46]#011train-error:0.09746#011validation-error:0.10350\u001b[0m\n",
      "\u001b[34m[47]#011train-error:0.09736#011validation-error:0.10358\u001b[0m\n",
      "\u001b[34m[48]#011train-error:0.09712#011validation-error:0.10334\u001b[0m\n",
      "\u001b[34m[49]#011train-error:0.09712#011validation-error:0.10318\u001b[0m\n",
      "Training seconds: 55\n",
      "Billable seconds: 22\n",
      "Managed Spot Training savings: 60.0%\n"
     ]
    }
   ],
   "source": [
    "estimator.fit({'train': s3_input_train,'validation': s3_input_test})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deployment of model as endpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------!"
     ]
    }
   ],
   "source": [
    "xgb_predictor = estimator.deploy(initial_instance_count=1,instance_type='ml.m4.xlarge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Prediction of the Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The csv_serializer has been renamed in sagemaker>=2.\n",
      "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12357,)\n"
     ]
    }
   ],
   "source": [
    "from sagemaker.predictor import csv_serializer\n",
    "test_data_array = test_data.drop(['y_no', 'y_yes'], axis=1).values #load the data into an array\n",
    "#xgb_predictor.content_type = 'text/csv' # set the data type for an inference\n",
    "xgb_predictor.serializer = csv_serializer # set the serializer type\n",
    "predictions = xgb_predictor.predict(test_data_array).decode('utf-8') # predict!\n",
    "predictions_array = np.fromstring(predictions[1:], sep=',') # and turn the prediction into an array\n",
    "print(predictions_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05214286, 0.05660191, 0.05096195, ..., 0.03436061, 0.02942475,\n",
       "       0.03715819])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Overall Classification Rate: 89.7%\n",
      "\n",
      "Predicted      No Purchase    Purchase\n",
      "Observed\n",
      "No Purchase    91% (10785)    34% (151)\n",
      "Purchase        9% (1124)     66% (297) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "cm = pd.crosstab(index=test_data['y_yes'], columns=np.round(predictions_array), rownames=['Observed'], colnames=['Predicted'])\n",
    "tn = cm.iloc[0,0]; fn = cm.iloc[1,0]; tp = cm.iloc[1,1]; fp = cm.iloc[0,1]; p = (tp+tn)/(tp+tn+fp+fn)*100\n",
    "print(\"\\n{0:<20}{1:<4.1f}%\\n\".format(\"Overall Classification Rate: \", p))\n",
    "print(\"{0:<15}{1:<15}{2:>8}\".format(\"Predicted\", \"No Purchase\", \"Purchase\"))\n",
    "print(\"Observed\")\n",
    "print(\"{0:<15}{1:<2.0f}% ({2:<}){3:>6.0f}% ({4:<})\".format(\"No Purchase\", tn/(tn+fn)*100,tn, fp/(tp+fp)*100, fp))\n",
    "print(\"{0:<16}{1:<1.0f}% ({2:<}){3:>7.0f}% ({4:<}) \\n\".format(\"Purchase\", fn/(tn+fn)*100,fn, tp/(tp+fp)*100, tp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deleting The Endpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The endpoint attribute has been renamed in sagemaker>=2.\n",
      "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
     ]
    },
    {
     "data": {
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       "    'x-amz-request-id': '4EF108F9D6E40E93',\n",
       "    'date': 'Mon, 25 Jan 2021 08:42:23 GMT',\n",
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       "   {'Key': 'xgboost-as-a-built-in-algo/test/test.csv'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/rule-output/ProfilerReport-1611561330/profiler-output/profiler-reports/OverallFrameworkMetrics.json'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/profiler-output/system/incremental/2021012507/1611561420.algo-1.json'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/rule-output/ProfilerReport-1611561330/profiler-output/profiler-reports/LoadBalancing.json'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/rule-output/ProfilerReport-1611561330/profiler-output/profiler-reports/LowGPUUtilization.json'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/rule-output/ProfilerReport-1611561330/profiler-output/profiler-reports/MaxInitializationTime.json'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/train/train.csv'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/rule-output/ProfilerReport-1611561330/profiler-output/profiler-report.ipynb'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/rule-output/ProfilerReport-1611561330/profiler-output/profiler-reports/BatchSize.json'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/profiler-output/system/training_job_end.ts'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/rule-output/ProfilerReport-1611561330/profiler-output/profiler-reports/Dataloader.json'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/rule-output/ProfilerReport-1611561330/profiler-output/profiler-reports/GPUMemoryIncrease.json'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/output/model.tar.gz'},\n",
       "   {'Key': 'xgboost-as-a-built-in-algo/output/sagemaker-xgboost-2021-01-25-07-55-30-035/profiler-output/framework/training_job_end.ts'}]}]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sagemaker.Session().delete_endpoint(xgb_predictor.endpoint)\n",
    "bucket_to_delete = boto3.resource('s3').Bucket(bucket_name)\n",
    "bucket_to_delete.objects.all().delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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