{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Customer Churn Model Using XGBoost Framework" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Customer Retention Retail Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This dataset can be used to understand what are the various marketing strategy based on consumer behaviour that can be adopted to increase customer retention of a retail store.\n", "\n", "An online tea retail store which sells tea of different flavors across various cities in India. The dataset contains data about the store's customers, their orders, quantity ordered, order frequency, city,etc. This is a large dataset which will help in analysis.\n", "\n", "Reference: https://www.kaggle.com/uttamp/store-data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "text/html": [ "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%html\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| column | Description\n", "|--|--|\n", "| custid | Computer generated ID to identify customers throughout the database\n", "|retained |\t1, if customer is assumed to be active, 0 = otherwise\n", "|created | Date when the contact was created in the database - when the customer joined\n", "|firstorder | Date when the customer placed first order\n", "|lastorder | Date when the customer placed last order\n", "|esent |\tNumber of emails sent\n", "|eopenrate | Number of emails opened divided by number of emails sent\n", "|eclickrate | Number of emails clicked divided by number of emails sent\n", "|avgorder | Average order size for the customer\n", "|ordfreq | Number of orders divided by customer tenure\n", "|paperless | 1 if customer subscribed for paperless communication (only online)\n", "|refill | 1 if customer subscribed for automatic refill\n", "|doorstep | 1 if customer subscribed for doorstep delivery\n", "|train | 1 if customer is in the training database\n", "|favday | Customer's favorite delivery day\n", "|city | City where the customer resides in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Import Packages and Constants" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Install shap and smdebug packages if not already installed and restart kernel after installing the packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting package metadata (current_repodata.json): done\n", "Solving environment: done\n", "\n", "## Package Plan ##\n", "\n", " environment location: /opt/conda\n", "\n", " added / updated specs:\n", " - shap\n", "\n", "\n", "The following packages will be downloaded:\n", "\n", " package | build\n", " ---------------------------|-----------------\n", " _openmp_mutex-4.5 | 1_gnu 22 KB\n", " libgcc-ng-9.3.0 | h5101ec6_17 4.8 MB\n", " libgomp-9.3.0 | h5101ec6_17 311 KB\n", " libstdcxx-ng-11.1.0 | h56837e0_8 4.2 MB conda-forge\n", " openssl-1.1.1k | h7f98852_0 2.1 MB conda-forge\n", " shap-0.39.0 | py37h219a48f_0 543 KB conda-forge\n", " slicer-0.0.7 | pyhd8ed1ab_0 16 KB conda-forge\n", " ------------------------------------------------------------\n", " Total: 12.0 MB\n", "\n", "The following NEW packages will be INSTALLED:\n", "\n", " _openmp_mutex pkgs/main/linux-64::_openmp_mutex-4.5-1_gnu\n", " libgomp pkgs/main/linux-64::libgomp-9.3.0-h5101ec6_17\n", " shap conda-forge/linux-64::shap-0.39.0-py37h219a48f_0\n", " slicer conda-forge/noarch::slicer-0.0.7-pyhd8ed1ab_0\n", "\n", "The following packages will be UPDATED:\n", "\n", " libgcc-ng 9.1.0-hdf63c60_0 --> 9.3.0-h5101ec6_17\n", " libstdcxx-ng pkgs/main::libstdcxx-ng-9.1.0-hdf63c6~ --> conda-forge::libstdcxx-ng-11.1.0-h56837e0_8\n", "\n", "The following packages will be SUPERSEDED by a higher-priority channel:\n", "\n", " openssl pkgs/main::openssl-1.1.1k-h27cfd23_0 --> conda-forge::openssl-1.1.1k-h7f98852_0\n", "\n", "\n", "\n", "Downloading and Extracting Packages\n", "libstdcxx-ng-11.1.0 | 4.2 MB | ##################################### | 100% \n", "libgomp-9.3.0 | 311 KB | ##################################### | 100% \n", "shap-0.39.0 | 543 KB | ##################################### | 100% \n", "libgcc-ng-9.3.0 | 4.8 MB | ##################################### | 100% \n", "slicer-0.0.7 | 16 KB | ##################################### | 100% \n", "_openmp_mutex-4.5 | 22 KB | ##################################### | 100% \n", "openssl-1.1.1k | 2.1 MB | ##################################### | 100% \n", "Preparing transaction: done\n", "Verifying transaction: done\n", "Executing transaction: done\n", "/opt/conda/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n", " from cryptography.utils import int_from_bytes\n", "/opt/conda/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n", " from cryptography.utils import int_from_bytes\n", "Collecting smdebug\n", " Using cached smdebug-1.0.11-py2.py3-none-any.whl (269 kB)\n", "Requirement already satisfied: numpy>=1.16.0 in /opt/conda/lib/python3.7/site-packages (from smdebug) (1.21.1)\n", "Requirement already satisfied: boto3>=1.10.32 in /opt/conda/lib/python3.7/site-packages (from smdebug) (1.18.2)\n", "Requirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from smdebug) (20.1)\n", "Collecting pyinstrument>=3.1.3\n", " Downloading pyinstrument-4.0.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (114 kB)\n", "\u001b[K |████████████████████████████████| 114 kB 27.0 MB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: protobuf>=3.6.0 in /opt/conda/lib/python3.7/site-packages (from smdebug) (3.17.3)\n", "Requirement already satisfied: s3transfer<0.6.0,>=0.5.0 in /opt/conda/lib/python3.7/site-packages (from boto3>=1.10.32->smdebug) (0.5.0)\n", "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /opt/conda/lib/python3.7/site-packages (from boto3>=1.10.32->smdebug) (0.10.0)\n", "Requirement already satisfied: botocore<1.22.0,>=1.21.2 in /opt/conda/lib/python3.7/site-packages (from boto3>=1.10.32->smdebug) (1.21.2)\n", "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /opt/conda/lib/python3.7/site-packages (from botocore<1.22.0,>=1.21.2->boto3>=1.10.32->smdebug) (1.26.6)\n", "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /opt/conda/lib/python3.7/site-packages (from botocore<1.22.0,>=1.21.2->boto3>=1.10.32->smdebug) (2.8.1)\n", "Requirement already satisfied: six>=1.9 in /opt/conda/lib/python3.7/site-packages (from protobuf>=3.6.0->smdebug) (1.14.0)\n", "Requirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->smdebug) (2.4.6)\n", "Installing collected packages: pyinstrument, smdebug\n", "Successfully installed pyinstrument-4.0.3 smdebug-1.0.11\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n", "\u001b[33mWARNING: You are using pip version 21.1.3; however, version 21.2.4 is available.\n", "You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "!conda install -c conda-forge shap --yes\n", "!pip install smdebug --upgrade" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2021-08-25 16:08:41.034 datascience-1-0-ml-t3-medium-1abf3407f667f989be9d86559395:676 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\n" ] } ], "source": [ "import re\n", "import s3fs\n", "import shap\n", "import time\n", "import boto3\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from itertools import islice\n", "import matplotlib.pyplot as plt\n", "\n", "import sagemaker\n", "from sagemaker.xgboost.estimator import XGBoost\n", "from sagemaker.session import Session\n", "from sagemaker.inputs import TrainingInput\n", "from sagemaker.debugger import DebuggerHookConfig,CollectionConfig\n", "from sagemaker.debugger import rule_configs, Rule\n", "from smdebug.trials import create_trial\n", "from sagemaker.tuner import (\n", " IntegerParameter,\n", " CategoricalParameter,\n", " ContinuousParameter,\n", " HyperparameterTuner\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#Replace this value with the S3 Bucket Created\n", "\n", "default_bucket = \"customer-churn-sm-pipeline\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "sagemaker_session = sagemaker.Session()\n", "role = sagemaker.get_execution_role()\n", "region = sagemaker_session.boto_region_name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Preprocess Data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def preprocess_data(file_path):\n", " df = pd.read_csv(file_path)\n", " ## Convert to datetime columns\n", " df[\"firstorder\"]=pd.to_datetime(df[\"firstorder\"],errors='coerce')\n", " df[\"lastorder\"] = pd.to_datetime(df[\"lastorder\"],errors='coerce')\n", " ## Drop Rows with null values\n", " df = df.dropna()\n", " ## Create Column which gives the days between the last order and the first order\n", " df[\"first_last_days_diff\"] = (df['lastorder']-df['firstorder']).dt.days\n", " ## Create Column which gives the days between when the customer record was created and the first order\n", " df['created'] = pd.to_datetime(df['created'])\n", " df['created_first_days_diff']=(df['created']-df['firstorder']).dt.days\n", " ## Drop Columns\n", " df.drop(['custid','created','firstorder','lastorder'],axis=1,inplace=True)\n", " ## Apply one hot encoding on favday and city columns\n", " df = pd.get_dummies(df,prefix=['favday','city'],columns=['favday','city'])\n", " return df" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "storedata = preprocess_data(f\"s3://{default_bucket}/data/storedata_total.csv\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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retainedesenteopenrateeclickrateavgorderordfreqpaperlessrefilldoorstepfirst_last_days_diff...favday_Mondayfavday_Saturdayfavday_Sundayfavday_Thursdayfavday_Tuesdayfavday_Wednesdaycity_BLRcity_BOMcity_DELcity_MAA
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3000.0000000.00000054.960.0000000000...0001000100
413090.00000013.333333111.910.008850000791...1000000100
\n", "

5 rows × 22 columns

\n", "
" ], "text/plain": [ " retained esent eopenrate eclickrate avgorder ordfreq paperless \\\n", "0 0 29 100.000000 3.448276 14.52 0.000000 0 \n", "1 1 95 92.631579 10.526316 83.69 0.181641 1 \n", "2 0 0 0.000000 0.000000 33.58 0.059908 0 \n", "3 0 0 0.000000 0.000000 54.96 0.000000 0 \n", "4 1 30 90.000000 13.333333 111.91 0.008850 0 \n", "\n", " refill doorstep first_last_days_diff ... favday_Monday \\\n", "0 0 0 0 ... 1 \n", "1 1 1 1024 ... 0 \n", "2 0 0 217 ... 0 \n", "3 0 0 0 ... 0 \n", "4 0 0 791 ... 1 \n", "\n", " favday_Saturday favday_Sunday favday_Thursday favday_Tuesday \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 1 0 \n", "4 0 0 0 0 \n", "\n", " favday_Wednesday city_BLR city_BOM city_DEL city_MAA \n", "0 0 0 0 1 0 \n", "1 0 0 0 1 0 \n", "2 1 0 0 1 0 \n", "3 0 0 1 0 0 \n", "4 0 0 1 0 0 \n", "\n", "[5 rows x 22 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "storedata.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Split Train, Test and Validation Datasets" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def split_datasets(df):\n", " y=df.pop(\"retained\")\n", " X_pre = df\n", " y_pre = y.to_numpy().reshape(len(y),1)\n", " feature_names = list(X_pre.columns)\n", " X= np.concatenate((y_pre,X_pre),axis=1)\n", " np.random.shuffle(X)\n", " train,validation,test=np.split(X,[int(.7*len(X)),int(.85*len(X))])\n", " return feature_names,train,validation,test" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "feature_names,train,validation,test = split_datasets(storedata)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "pd.DataFrame(train).to_csv(f\"s3://{default_bucket}/data/train/train.csv\",header=False,index=False)\n", "pd.DataFrame(validation).to_csv(f\"s3://{default_bucket}/data/validation/validation.csv\",header=False,index=False)\n", "pd.DataFrame(test).to_csv(f\"s3://{default_bucket}/data/test/test.csv\",header=False,index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Hyperparameter Tuning HPO" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "s3_input_train = TrainingInput(\n", " s3_data=f\"s3://{default_bucket}/data/train/\",content_type=\"csv\")\n", "s3_input_validation = TrainingInput(\n", " s3_data=f\"s3://{default_bucket}/data/validation/\",content_type=\"csv\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "fixed_hyperparameters = {\n", " \"eval_metric\":\"auc\",\n", " \"objective\":\"binary:logistic\",\n", " \"num_round\":\"100\",\n", " \"rate_drop\":\"0.3\",\n", " \"tweedie_variance_power\":\"1.4\"\n", "}" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "sess = sagemaker.Session()\n", "container = sagemaker.image_uris.retrieve(\"xgboost\",region,\"0.90-2\")\n", "\n", "estimator = sagemaker.estimator.Estimator(\n", " container,\n", " role,\n", " instance_count=1,\n", " hyperparameters=fixed_hyperparameters,\n", " instance_type=\"ml.m4.xlarge\",\n", " output_path=\"s3://{}/output\".format(default_bucket),\n", " sagemaker_session=sagemaker_session\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "hyperparameter_ranges = {\n", " \"eta\": ContinuousParameter(0, 1),\n", " \"min_child_weight\": ContinuousParameter(1, 10),\n", " \"alpha\": ContinuousParameter(0, 2),\n", " \"max_depth\": IntegerParameter(1, 10),\n", "}" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "objective_metric_name = \"validation:auc\"" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "tuner = HyperparameterTuner(\n", " estimator,objective_metric_name,hyperparameter_ranges,max_jobs=10,max_parallel_jobs=2)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "....................................................................................................................................................................................................................................................................................!\n" ] } ], "source": [ "tuner.fit({\n", " \"train\":s3_input_train,\n", " \"validation\":s3_input_validation\n", " },include_cls_metadata=False)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "tuning_job_result = boto3.client(\"sagemaker\").describe_hyper_parameter_tuning_job(\n", " HyperParameterTuningJobName=tuner.latest_tuning_job.job_name\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 training jobs have completed\n" ] } ], "source": [ "job_count = tuning_job_result[\"TrainingJobStatusCounters\"][\"Completed\"]\n", "print(\"%d training jobs have completed\" %job_count)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Model found so far:\n", "{'CreationTime': datetime.datetime(2021, 8, 25, 16, 18, 23, tzinfo=tzlocal()),\n", " 'FinalHyperParameterTuningJobObjectiveMetric': {'MetricName': 'validation:auc',\n", " 'Value': 0.9807729721069336},\n", " 'ObjectiveStatus': 'Succeeded',\n", " 'TrainingEndTime': datetime.datetime(2021, 8, 25, 16, 22, 38, tzinfo=tzlocal()),\n", " 'TrainingJobArn': 'arn:aws:sagemaker:us-east-1:XXXXXXXXX:training-job/sagemaker-xgboost-210825-1608-006-6203e0f7',\n", " 'TrainingJobName': 'sagemaker-xgboost-210825-1608-006-6203e0f7',\n", " 'TrainingJobStatus': 'Completed',\n", " 'TrainingStartTime': datetime.datetime(2021, 8, 25, 16, 21, 20, tzinfo=tzlocal()),\n", " 'TunedHyperParameters': {'alpha': '1.1367818147326432',\n", " 'eta': '0.09468775702556441',\n", " 'max_depth': '10',\n", " 'min_child_weight': '4.206435039074789'}}\n" ] } ], "source": [ "from pprint import pprint\n", "\n", "if tuning_job_result.get(\"BestTrainingJob\",None):\n", " print(\"Best Model found so far:\")\n", " pprint(tuning_job_result[\"BestTrainingJob\"])\n", "else:\n", " print(\"No training jobs have reported results yet.\")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "best_hyperparameters = tuning_job_result[\"BestTrainingJob\"][\"TunedHyperParameters\"]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'alpha': '1.1367818147326432',\n", " 'eta': '0.09468775702556441',\n", " 'max_depth': '10',\n", " 'min_child_weight': '4.206435039074789'}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_hyperparameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7. XGBoost Model with SageMaker Debugger" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "hyperparameters = {**fixed_hyperparameters,**best_hyperparameters}\n", "save_interval = 5\n", "base_job_name = \"demo-smdebug-xgboost-churn-classification\"" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "container = sagemaker.image_uris.retrieve(\"xgboost\",region,\"0.90-2\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "estimator = sagemaker.estimator.Estimator(\n", " container,\n", " role,\n", " base_job_name=base_job_name,\n", " instance_count=1,\n", " instance_type=\"ml.m4.xlarge\",\n", " output_path=\"s3://{}/output\".format(default_bucket),\n", " sagemaker_session=sess,\n", " hyperparameters=hyperparameters,\n", " max_run=1800,\n", " debugger_hook_config = DebuggerHookConfig(\n", " s3_output_path=f\"s3://{default_bucket}/debugger/\", # Required\n", " collection_configs=[\n", " CollectionConfig(\n", " name=\"metrics\",\n", " parameters={\n", " \"save_interval\": \"5\"\n", " }),\n", " CollectionConfig(\n", " name=\"feature_importance\", parameters={\"save_interval\": \"5\"}\n", " ),\n", " CollectionConfig(name=\"full_shap\", parameters={\"save_interval\": \"5\"}),\n", " CollectionConfig(name=\"average_shap\", parameters={\"save_interval\": \"5\"}),\n", " ]\n", " ),\n", " rules=[\n", " Rule.sagemaker(\n", " rule_configs.loss_not_decreasing(),\n", " rule_parameters={\n", " \"collection_names\": \"metrics\",\n", " \"num_steps\": \"10\",\n", " },\n", " ),\n", " ]\n", ")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "estimator.fit(\n", " {\"train\":s3_input_train,\"validation\":s3_input_validation},wait=False\n", " )" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n", "Training job status: InProgress, Rule Evaluation Status: InProgress\n" ] } ], "source": [ "for _ in range(36):\n", " job_name = estimator.latest_training_job.name\n", " client = estimator.sagemaker_session.sagemaker_client\n", " description = client.describe_training_job(TrainingJobName=job_name)\n", " training_job_status = description[\"TrainingJobStatus\"]\n", " rule_job_summary = estimator.latest_training_job.rule_job_summary()\n", " rule_evaluation_status = rule_job_summary[0][\"RuleEvaluationStatus\"]\n", " print(\n", " \"Training job status: {}, Rule Evaluation Status: {}\".format(\n", " training_job_status, rule_evaluation_status\n", " )\n", " )\n", " if training_job_status in [\"Completed\", \"Failed\"]:\n", " break\n", " time.sleep(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 8. Analyze Debugger Output" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'RuleConfigurationName': 'LossNotDecreasing',\n", " 'RuleEvaluationJobArn': 'arn:aws:sagemaker:us-east-1:XXXXXXXXX:processing-job/demo-smdebug-xgboost-churn-lossnotdecreasing-e0a979ab',\n", " 'RuleEvaluationStatus': 'InProgress',\n", " 'LastModifiedTime': datetime.datetime(2021, 8, 25, 16, 42, 27, 682000, tzinfo=tzlocal())},\n", " {'RuleConfigurationName': 'ProfilerReport-1629909263',\n", " 'RuleEvaluationJobArn': 'arn:aws:sagemaker:us-east-1:XXXXXXXXX:processing-job/demo-smdebug-xgboost-churn-profilerreport-1629909263-cbdabd65',\n", " 'RuleEvaluationStatus': 'InProgress',\n", " 'LastModifiedTime': datetime.datetime(2021, 8, 25, 16, 42, 21, 461000, tzinfo=tzlocal())}]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "estimator.latest_training_job.rule_job_summary()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2021-08-25 16:42:41.256 datascience-1-0-ml-t3-medium-1abf3407f667f989be9d86559395:676 INFO s3_trial.py:42] Loading trial debug-output at path s3://customer-churn-sm-pipeline/debugger/demo-smdebug-xgboost-churn-classificati-2021-08-25-16-34-23-259/debug-output\n" ] } ], "source": [ "s3_output_path = estimator.latest_job_debugger_artifacts_path()\n", "trial = create_trial(s3_output_path)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2021-08-25 16:42:42.091 datascience-1-0-ml-t3-medium-1abf3407f667f989be9d86559395:676 INFO trial.py:198] Training has ended, will refresh one final time in 1 sec.\n", "[2021-08-25 16:42:43.112 datascience-1-0-ml-t3-medium-1abf3407f667f989be9d86559395:676 INFO trial.py:210] Loaded all steps\n" ] }, { "data": { "text/plain": [ "['average_shap/f0',\n", " 'average_shap/f1',\n", " 'average_shap/f10',\n", " 'average_shap/f11',\n", " 'average_shap/f12',\n", " 'average_shap/f13',\n", " 'average_shap/f14',\n", " 'average_shap/f15',\n", " 'average_shap/f16',\n", " 'average_shap/f17',\n", " 'average_shap/f18',\n", " 'average_shap/f19',\n", " 'average_shap/f2',\n", " 'average_shap/f20',\n", " 'average_shap/f3',\n", " 'average_shap/f4',\n", " 'average_shap/f5',\n", " 'average_shap/f6',\n", " 'average_shap/f7',\n", " 'average_shap/f8',\n", " 'average_shap/f9',\n", " 'feature_importance/cover/f0',\n", " 'feature_importance/cover/f1',\n", " 'feature_importance/cover/f10',\n", " 'feature_importance/cover/f11',\n", " 'feature_importance/cover/f12',\n", " 'feature_importance/cover/f13',\n", " 'feature_importance/cover/f14',\n", " 'feature_importance/cover/f15',\n", " 'feature_importance/cover/f16',\n", " 'feature_importance/cover/f17',\n", " 'feature_importance/cover/f18',\n", " 'feature_importance/cover/f19',\n", " 'feature_importance/cover/f2',\n", " 'feature_importance/cover/f20',\n", " 'feature_importance/cover/f3',\n", " 'feature_importance/cover/f4',\n", " 'feature_importance/cover/f5',\n", " 'feature_importance/cover/f6',\n", " 'feature_importance/cover/f7',\n", " 'feature_importance/cover/f8',\n", " 'feature_importance/cover/f9',\n", " 'feature_importance/gain/f0',\n", " 'feature_importance/gain/f1',\n", " 'feature_importance/gain/f10',\n", " 'feature_importance/gain/f11',\n", " 'feature_importance/gain/f12',\n", " 'feature_importance/gain/f13',\n", " 'feature_importance/gain/f14',\n", " 'feature_importance/gain/f15',\n", " 'feature_importance/gain/f16',\n", " 'feature_importance/gain/f17',\n", " 'feature_importance/gain/f18',\n", " 'feature_importance/gain/f19',\n", " 'feature_importance/gain/f2',\n", " 'feature_importance/gain/f20',\n", " 'feature_importance/gain/f3',\n", " 'feature_importance/gain/f4',\n", " 'feature_importance/gain/f5',\n", " 'feature_importance/gain/f6',\n", " 'feature_importance/gain/f7',\n", " 'feature_importance/gain/f8',\n", " 'feature_importance/gain/f9',\n", " 'feature_importance/total_cover/f0',\n", " 'feature_importance/total_cover/f1',\n", " 'feature_importance/total_cover/f10',\n", " 'feature_importance/total_cover/f11',\n", " 'feature_importance/total_cover/f12',\n", " 'feature_importance/total_cover/f13',\n", " 'feature_importance/total_cover/f14',\n", " 'feature_importance/total_cover/f15',\n", " 'feature_importance/total_cover/f16',\n", " 'feature_importance/total_cover/f17',\n", " 'feature_importance/total_cover/f18',\n", " 'feature_importance/total_cover/f19',\n", " 'feature_importance/total_cover/f2',\n", " 'feature_importance/total_cover/f20',\n", " 'feature_importance/total_cover/f3',\n", " 'feature_importance/total_cover/f4',\n", " 'feature_importance/total_cover/f5',\n", " 'feature_importance/total_cover/f6',\n", " 'feature_importance/total_cover/f7',\n", " 'feature_importance/total_cover/f8',\n", " 'feature_importance/total_cover/f9',\n", " 'feature_importance/total_gain/f0',\n", " 'feature_importance/total_gain/f1',\n", " 'feature_importance/total_gain/f10',\n", " 'feature_importance/total_gain/f11',\n", " 'feature_importance/total_gain/f12',\n", " 'feature_importance/total_gain/f13',\n", " 'feature_importance/total_gain/f14',\n", " 'feature_importance/total_gain/f15',\n", " 'feature_importance/total_gain/f16',\n", " 'feature_importance/total_gain/f17',\n", " 'feature_importance/total_gain/f18',\n", " 'feature_importance/total_gain/f19',\n", " 'feature_importance/total_gain/f2',\n", " 'feature_importance/total_gain/f20',\n", " 'feature_importance/total_gain/f3',\n", " 'feature_importance/total_gain/f4',\n", " 'feature_importance/total_gain/f5',\n", " 'feature_importance/total_gain/f6',\n", " 'feature_importance/total_gain/f7',\n", " 'feature_importance/total_gain/f8',\n", " 'feature_importance/total_gain/f9',\n", " 'feature_importance/weight/f0',\n", " 'feature_importance/weight/f1',\n", " 'feature_importance/weight/f10',\n", " 'feature_importance/weight/f11',\n", " 'feature_importance/weight/f12',\n", " 'feature_importance/weight/f13',\n", " 'feature_importance/weight/f14',\n", " 'feature_importance/weight/f15',\n", " 'feature_importance/weight/f16',\n", " 'feature_importance/weight/f17',\n", " 'feature_importance/weight/f18',\n", " 'feature_importance/weight/f19',\n", " 'feature_importance/weight/f2',\n", " 'feature_importance/weight/f20',\n", " 'feature_importance/weight/f3',\n", " 'feature_importance/weight/f4',\n", " 'feature_importance/weight/f5',\n", " 'feature_importance/weight/f6',\n", " 'feature_importance/weight/f7',\n", " 'feature_importance/weight/f8',\n", " 'feature_importance/weight/f9',\n", " 'full_shap/f0',\n", " 'full_shap/f1',\n", " 'full_shap/f10',\n", " 'full_shap/f11',\n", " 'full_shap/f12',\n", " 'full_shap/f13',\n", " 'full_shap/f14',\n", " 'full_shap/f15',\n", " 'full_shap/f16',\n", " 'full_shap/f17',\n", " 'full_shap/f18',\n", " 'full_shap/f19',\n", " 'full_shap/f2',\n", " 'full_shap/f20',\n", " 'full_shap/f3',\n", " 'full_shap/f4',\n", " 'full_shap/f5',\n", " 'full_shap/f6',\n", " 'full_shap/f7',\n", " 'full_shap/f8',\n", " 'full_shap/f9',\n", " 'train-auc',\n", " 'validation-auc']" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trial.tensor_names()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: array([0.00035465], dtype=float32),\n", " 5: array([0.00355713], dtype=float32),\n", " 10: array([0.00611376], dtype=float32),\n", " 15: array([0.01231118], dtype=float32),\n", " 20: array([0.01902173], dtype=float32),\n", " 25: array([0.02656582], dtype=float32),\n", " 30: array([0.03548843], dtype=float32),\n", " 35: array([0.04980956], dtype=float32),\n", " 40: array([0.06620862], dtype=float32),\n", " 45: array([0.08015627], dtype=float32),\n", " 50: array([0.08841099], dtype=float32),\n", " 55: array([0.09916104], dtype=float32),\n", " 60: array([0.11012504], dtype=float32),\n", " 65: array([0.11699083], dtype=float32),\n", " 70: array([0.1285478], dtype=float32),\n", " 75: array([0.13544343], dtype=float32),\n", " 80: array([0.14277798], dtype=float32),\n", " 85: array([0.14950742], dtype=float32),\n", " 90: array([0.15320371], dtype=float32),\n", " 95: array([0.1554056], dtype=float32)}" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trial.tensor(\"average_shap/f1\").values()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "MAX_PLOTS = 35\n", "\n", "\n", "def get_data(trial, tname):\n", " \"\"\"\n", " For the given tensor name, walks though all the iterations\n", " for which you have data and fetches the values.\n", " Returns the set of steps and the values.\n", " \"\"\"\n", " tensor = trial.tensor(tname)\n", " steps = tensor.steps()\n", " vals = [tensor.value(s) for s in steps]\n", " return steps, vals\n", "\n", "\n", "def match_tensor_name_with_feature_name(tensor_name, feature_names=feature_names):\n", " feature_tag = tensor_name.split(\"/\")\n", " for ifeat, feature_name in enumerate(feature_names):\n", " if feature_tag[-1] == \"f{}\".format(str(ifeat)):\n", " return feature_name\n", " return tensor_name\n", "\n", "\n", "def plot_collection(trial, collection_name, regex=\".*\", figsize=(8, 6)):\n", " \"\"\"\n", " Takes a `trial` and a collection name, and\n", " plots all tensors that match the given regex.\n", " \"\"\"\n", " fig, ax = plt.subplots(figsize=figsize)\n", " tensors = trial.collection(collection_name).tensor_names\n", " matched_tensors = [t for t in tensors if re.match(regex, t)]\n", " for tensor_name in islice(matched_tensors, MAX_PLOTS):\n", " steps, data = get_data(trial, tensor_name)\n", " ax.plot(steps, data, label=match_tensor_name_with_feature_name(tensor_name))\n", "\n", " ax.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5))\n", " ax.set_xlabel(\"Iteration\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_collection(trial, \"metrics\")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "def plot_feature_importance(trial, importance_type=\"weight\"):\n", " SUPPORTED_IMPORTANCE_TYPES = [\"weight\", \"gain\", \"cover\", \"total_gain\", \"total_cover\"]\n", " if importance_type not in SUPPORTED_IMPORTANCE_TYPES:\n", " raise ValueError(f\"{importance_type} is not one of the supported importance types.\")\n", " plot_collection(trial, \"feature_importance\", regex=f\"feature_importance/{importance_type}/.*\")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_feature_importance(trial, importance_type=\"cover\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SHAP" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_collection(trial, \"average_shap\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Global Explanations" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "shap_values = trial.tensor(\"full_shap/f0\").value(trial.last_complete_step)\n", "shap_no_base = shap_values[:, :-1]\n", "shap_base_value = shap_values[0, -1]\n", "shap.summary_plot(shap_no_base, plot_type=\"bar\", feature_names=feature_names)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.158564" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shap_base_value" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "train_shap = pd.DataFrame(train[:,1:],columns=feature_names)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "shap.summary_plot(shap_no_base, train_shap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Local Explanations" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "shap.initjs()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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\n", " Visualization omitted, Javascript library not loaded!
\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security. If you are using\n", " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shap.force_plot(\n", " shap_base_value,\n", " shap_no_base[100, :],\n", " train_shap.iloc[100, :],\n", " link=\"logit\",\n", " matplotlib=False,\n", ")" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "N_ROWS = shap_no_base.shape[0]\n", "N_SAMPLES = min(100, N_ROWS)\n", "sampled_indices = np.random.randint(N_ROWS, size=N_SAMPLES)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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\n", " Visualization omitted, Javascript library not loaded!
\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security. If you are using\n", " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shap.force_plot(\n", " shap_base_value,\n", " shap_no_base[sampled_indices, :],\n", " train_shap.iloc[sampled_indices, :],\n", " link=\"logit\",\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "instance_type": "ml.t3.medium", "kernelspec": { "display_name": "Python 3 (Data Science)", "language": "python", "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/datascience-1.0" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 4 }