{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementing a Review classification model with Catboost and SageMaker" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Data set: Amazon Customer Reviews Dataset\n", " \n", " Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazon’s iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. Over 130+ million customer reviews are available to researchers as part of this dataset.\n", " \n", "#### Approach\n", " 1. The Review classficiation is NLP machine learning model to predict whether a review posted by the customer is positive or negative. For the sake of simplification, we have converted the ratings provided by the customer into a binary target variable with value equals to 1 when ratings were either 4 or 5. \n", "\n", " 2. In present work, after spilitting the data into train and test we created a feature engineerng pipeline on training data using natural language processing techiniques. The dataset was futher split into 10 chunks to demonstrate distributed learning, where during training not more than 1204 [batch_size] records are loaded onto memory at a time.\n", " \n", " \n", "#### Setup\n", "This notebook was created and tested on an ml.p2.xlarge notebook instance.\n", "\n", "Let's start by specifying:\n", "\n", "1. The S3 bucket and prefix that you want to use for training and model data. This should be within the same region as the Notebook Instance, training, and hosting.\n", "2. The IAM role arn used to give training and hosting access to your data. See the documentation for how to create these. Note, if more than one role is required for notebook instances, training, and/or hosting, please replace the get_execution_role() call with the appropriate full IAM role arn string(s)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from sagemaker import get_execution_role\n", "from sagemaker.session import Session\n", "import boto3\n", "\n", "bucket = 'custom-gpu-sagemaker-image'\n", "custom_code_upload_location = 's3://{}/sagemaker/DEMO-GPU-Catboost/customcode'.format(bucket)\n", "model_artifacts_location = 's3://{}/sagemaker/DEMO-GPU-Catboost/artifacts'.format(bucket)\n", "\n", "role = get_execution_role()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data\n", "##### Explore\n", "Let's start by bringing in our dataset from an S3 public bucket. As mentioned above, this contains 1 to 5 star ratings from over 2M Amazon customers on over 160K digital videos. More details on this dataset can be found at its AWS Public Datasets page.\n", "\n", "Note, because this dataset is over a half gigabyte, the load from S3 may take ~10 minutes. Also, since Amazon SageMaker Notebooks start with a 5GB persistent volume by default, and we don't need to keep this data on our instance for long, we'll bring it to the temporary volume (which has up to 20GB of storage).\n", "\n", "\n", "Let's read the data into a Pandas DataFrame so that we can begin to understand it.\n", "\n", "Note, we'll set error_bad_lines=False when reading the file in as there appear to be a very small number of records which would create a problem otherwise." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "b'Skipping line 92523: expected 15 fields, saw 22\\n'\n", "b'Skipping line 343254: expected 15 fields, saw 22\\n'\n", "b'Skipping line 524626: expected 15 fields, saw 22\\n'\n", "b'Skipping line 623024: expected 15 fields, saw 22\\n'\n", "b'Skipping line 977412: expected 15 fields, saw 22\\n'\n", "b'Skipping line 1496867: expected 15 fields, saw 22\\n'\n", "b'Skipping line 1711638: expected 15 fields, saw 22\\n'\n", "b'Skipping line 1787213: expected 15 fields, saw 22\\n'\n", "b'Skipping line 2395306: expected 15 fields, saw 22\\n'\n", "b'Skipping line 2527690: expected 15 fields, saw 22\\n'\n" ] } ], "source": [ "import pandas as pd\n", "import csv\n", "fname = 's3://amazon-reviews-pds/tsv/amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz'\n", "df = pd.read_csv(fname,sep='\\t',delimiter='\\t',error_bad_lines=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "We can see this dataset includes information like:\n", "\n", " 1. marketplace: 2-letter country code (in this case all \"US\").\n", " 2. customer_id: Random identifier that can be used to aggregate reviews written by a single author.\n", " 3. review_id: A unique ID for the review.\n", " 4. product_id: The Amazon Standard Identification Number (ASIN). http://www.amazon.com/dp/ links to the product's detail page.\n", " 5. product_parent: The parent of that ASIN. Multiple ASINs (color or format variations of the same product) can roll up into a single parent parent.\n", " 6. product_title: Title description of the product.\n", " 7. product_category: Broad product category that can be used to group reviews (in this case digital videos).\n", " 8. star_rating: The review's rating (1 to 5 stars).\n", " 9. helpful_votes: Number of helpful votes for the review.\n", " 10. total_votes: Number of total votes the review received.\n", " 11. vine: Was the review written as part of the Vine program?\n", " 12. verified_purchase: Was the review from a verified purchase?\n", " 13. review_headline: The title of the review itself.\n", " 14. review_body: The text of the review.\n", " 15. review_date: The date the review was written.\n", " \n", " \n", "For this example, let's limit ourselves 'verified_purchase','review_headline','review_body','product_title','helpful_votes and star_rating. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Pre-processing\n", "1. Replace any NaN values with empty string\n", "2. Create Target column based the star rating" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "def pre_process(df):\n", " df.fillna(\n", " value={'review_body': '', 'review_headline': ''}, inplace=True)\n", " df.fillna(\n", " value={'verified_purchase': 'Unk'}, inplace=True)\n", "\n", " df.fillna(0, inplace=True)\n", " return df\n", "\n", "df = pre_process(df)\n", "\n", "df.review_date = pd.to_datetime(df.review_date)\n", "df['target'] = np.where(df['star_rating']>=4,1,0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Split the data into Train, validation and Test set\n", "Note: We have used stratefied sampling as the criteria for splittng the data; stratefied sampling on the target variable insures same distribution of the varible across all the splits." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7920667165021078\n", "0.7920667275251041\n", "0.7920667165021078\n" ] } ], "source": [ "from sklearn.model_selection import StratifiedShuffleSplit\n", "sss = StratifiedShuffleSplit(n_splits=2, test_size=0.10, random_state=0)\n", "\n", "sss.get_n_splits(df, df['target'])\n", "for train_index, test_index in sss.split(df, df['target']):\n", " X_train_vallid , X_test = df.iloc[train_index], df.iloc[test_index]\n", " \n", " \n", "sss.get_n_splits(X_train_vallid, X_train_vallid['target']) \n", "for train_index, test_index in sss.split(X_train_vallid, X_train_vallid['target']):\n", " X_train , X_valid = X_train_vallid.iloc[train_index], X_train_vallid.iloc[test_index]\n", " \n", " \n", "print(X_train.target.value_counts()[1]/(X_train.target.value_counts().sum()))\n", "print(X_test.target.value_counts()[1]/(X_test.target.value_counts().sum()))\n", "print(X_valid.target.value_counts()[1]/(X_valid.target.value_counts().sum()))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['marketplace', 'customer_id', 'review_id', 'product_id',\n", " 'product_parent', 'product_title', 'product_category', 'star_rating',\n", " 'helpful_votes', 'total_votes', 'vine', 'verified_purchase',\n", " 'review_headline', 'review_body', 'review_date', 'target'],\n", " dtype='object')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.columns" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "! mkdir data data/train data/valid data/test" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "import csv\n", "cols = ['marketplace', 'customer_id', 'review_id', 'product_id','helpful_votes',\n", " 'verified_purchase','review_headline','review_body','product_title','target']\n", "X_train.to_csv('data/train/file_1', index=False,sep='\\t', quotechar='\"', quoting=csv.QUOTE_ALL)\n", "X_valid.to_csv('data/valid/file_1', index=False,sep='\\t', quotechar='\"', quoting=csv.QUOTE_ALL)\n", "X_test[cols].to_csv('data/test/file_1', index=False, header=False, sep='\\t',quotechar='\"', \n", " escapechar ='\\\\',quoting=csv.QUOTE_NONE)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "upload: data/test/file_1 to s3://custom-gpu-sagemaker-image/sagemaker/DEMO-GPU-Catboost/data/test/file_1\n", "upload: data/valid/file_1 to s3://custom-gpu-sagemaker-image/sagemaker/DEMO-GPU-Catboost/data/valid/file_1\n", "upload: data/train/file_1 to s3://custom-gpu-sagemaker-image/sagemaker/DEMO-GPU-Catboost/data/train/file_1\n" ] } ], "source": [ "### repalce with your bucket name\n", "! aws s3 cp ./data/ s3://custom-gpu-sagemaker-image/sagemaker/DEMO-GPU-Catboost/data/ --recursive --acl bucket-owner-full-control" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "\n", "X_test[cols].to_csv('data/test/file_1', index=False, header=False, sep='\\t',quotechar='\"', \n", " escapechar ='\\\\',quoting=csv.QUOTE_NONE)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "temp = pd.read_csv('data/test/file_1', names=cols ,sep='\\t',escapechar ='\\\\' , quoting=csv.QUOTE_NONE, \n", " lineterminator='\\n',quotechar='\"', keep_default_na=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Packaging and Uploading your Algorithm for use with Amazon SageMaker\n", "\n", "### An overview of Docker\n", "\n", "If you're familiar with Docker already, you can skip ahead to the next section.\n", "\n", "For many data scientists, Docker containers are a new concept, but they are not difficult, as you'll see here. \n", "\n", "Docker provides a simple way to package arbitrary code into an _image_ that is totally self-contained. Once you have an image, you can use Docker to run a _container_ based on that image. Running a container is just like running a program on the machine except that the container creates a fully self-contained environment for the program to run. Containers are isolated from each other and from the host environment, so the way you set up your program is the way it runs, no matter where you run it.\n", "\n", "Docker is more powerful than environment managers like conda or virtualenv because (a) it is completely language independent and (b) it comprises your whole operating environment, including startup commands, environment variable, etc.\n", "\n", "In some ways, a Docker container is like a virtual machine, but it is much lighter weight. For example, a program running in a container can start in less than a second and many containers can run on the same physical machine or virtual machine instance.\n", "\n", "Docker uses a simple file called a `Dockerfile` to specify how the image is assembled. We'll see an example of that below. You can build your Docker images based on Docker images built by yourself or others, which can simplify things quite a bit.\n", "\n", "Docker has become very popular in the programming and devops communities for its flexibility and well-defined specification of the code to be run. It is the underpinning of many services built in the past few years, such as [Amazon ECS].\n", "\n", "Amazon SageMaker uses Docker to allow users to train and deploy arbitrary algorithms.\n", "\n", "In Amazon SageMaker, Docker containers are invoked in a certain way for training and a slightly different way for hosting. The following sections outline how to build containers for the SageMaker environment.\n", "\n", "Some helpful links:\n", "\n", "* [Docker home page](http://www.docker.com)\n", "* [Getting started with Docker](https://docs.docker.com/get-started/)\n", "* [Dockerfile reference](https://docs.docker.com/engine/reference/builder/)\n", "* [`docker run` reference](https://docs.docker.com/engine/reference/run/)\n", "\n", "[Amazon ECS]: https://aws.amazon.com/ecs/\n", "\n", "### How Amazon SageMaker runs your Docker container\n", "\n", "Because you can run the same image in training or hosting, Amazon SageMaker runs your container with the argument `train` or `serve`. How your container processes this argument depends on the container:\n", "\n", "* In the example here, we don't define an `ENTRYPOINT` in the Dockerfile so Docker will run the command `train` at training time and `serve` at serving time. In this example, we define these as executable Python scripts, but they could be any program that we want to start in that environment.\n", "* If you specify a program as an `ENTRYPOINT` in the Dockerfile, that program will be run at startup and its first argument will be `train` or `serve`. The program can then look at that argument and decide what to do.\n", "* If you are building separate containers for training and hosting (or building only for one or the other), you can define a program as an `ENTRYPOINT` in the Dockerfile and ignore (or verify) the first argument passed in. \n", "\n", "#### Running your container during training\n", "\n", "When Amazon SageMaker runs training, your `train` script is run just like a regular Python program. A number of files are laid out for your use, under the `/opt/ml` directory:\n", "\n", " /opt/ml\n", " |-- input\n", " | |-- config\n", " | | |-- hyperparameters.json\n", " | | `-- resourceConfig.json\n", " | `-- data\n", " | `-- \n", " | `-- \n", " |-- model\n", " | `-- \n", " `-- output\n", " `-- failure\n", "\n", "##### The input\n", "\n", "* `/opt/ml/input/config` contains information to control how your program runs. `hyperparameters.json` is a JSON-formatted dictionary of hyperparameter names to values. These values will always be strings, so you may need to convert them. `resourceConfig.json` is a JSON-formatted file that describes the network layout used for distributed training. Since scikit-learn doesn't support distributed training, we'll ignore it here.\n", "* `/opt/ml/input/data//` (for File mode) contains the input data for that channel. The channels are created based on the call to CreateTrainingJob but it's generally important that channels match what the algorithm expects. The files for each channel will be copied from S3 to this directory, preserving the tree structure indicated by the S3 key structure. \n", "* `/opt/ml/input/data/_` (for Pipe mode) is the pipe for a given epoch. Epochs start at zero and go up by one each time you read them. There is no limit to the number of epochs that you can run, but you must close each pipe before reading the next epoch.\n", "\n", "##### The output\n", "\n", "* `/opt/ml/model/` is the directory where you write the model that your algorithm generates. Your model can be in any format that you want. It can be a single file or a whole directory tree. SageMaker will package any files in this directory into a compressed tar archive file. This file will be available at the S3 location returned in the `DescribeTrainingJob` result.\n", "* `/opt/ml/output` is a directory where the algorithm can write a file `failure` that describes why the job failed. The contents of this file will be returned in the `FailureReason` field of the `DescribeTrainingJob` result. For jobs that succeed, there is no reason to write this file as it will be ignored.\n", "\n", "#### Running your container during hosting\n", "\n", "Hosting has a very different model than training because hosting is reponding to inference requests that come in via HTTP. In this example, we use our recommended Python serving stack to provide robust and scalable serving of inference requests:\n", "\n", "![Request serving stack](stack.png)\n", "\n", "This stack is implemented in the sample code here and you can mostly just leave it alone. \n", "\n", "Amazon SageMaker uses two URLs in the container:\n", "\n", "* `/ping` will receive `GET` requests from the infrastructure. Your program returns 200 if the container is up and accepting requests.\n", "* `/invocations` is the endpoint that receives client inference `POST` requests. The format of the request and the response is up to the algorithm. If the client supplied `ContentType` and `Accept` headers, these will be passed in as well. \n", "\n", "The container will have the model files in the same place they were written during training:\n", "\n", " /opt/ml\n", " `-- model\n", " `-- \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The parts of the sample container\n", "\n", "In the `container` directory are all the components you need to package the sample algorithm for Amazon SageMager:\n", "\n", " .\n", " |-- Dockerfile\n", " `-- code\n", " |-- nginx.conf\n", " |-- predictor.py\n", " |-- serve\n", " |-- train\n", " `-- wsgi.py\n", "\n", "Let's discuss each of these in turn:\n", "\n", "* __`Dockerfile`__ describes how to build your Docker container image. More details below.\n", "* __`code`__ is the directory which contains the files that will be installed in the container.\n", "\n", "\n", "The files that we'll put in the container are:\n", "\n", "* __`nginx.conf`__ is the configuration file for the nginx front-end. Generally, you should be able to take this file as-is.\n", "* __`predictor.py`__ is the program that actually implements the Flask web server and the decision tree predictions for this app. You'll want to customize the actual prediction parts to your application. Since this algorithm is simple, we do all the processing here in this file, but you may choose to have separate files for implementing your custom logic.\n", "* __`serve`__ is the program started when the container is started for hosting. It simply launches the gunicorn server which runs multiple instances of the Flask app defined in `predictor.py`. You should be able to take this file as-is.\n", "* __`train`__ is the program that is invoked when the container is run for training. You will modify this program to implement your training algorithm.\n", "* __`wsgi.py`__ is a small wrapper used to invoke the Flask app. You should be able to take this file as-is.\n", "\n", "In summary, the two files you will probably want to change for your application are `train` and `predictor.py`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Dockerfile\n", "\n", "The Dockerfile describes the image that we want to build. You can think of it as describing the complete operating system installation of the system that you want to run. A Docker container running is quite a bit lighter than a full operating system, however, because it takes advantage of Linux on the host machine for the basic operations. \n", "\n", "For the Python science stack, we will start from a standard Ubuntu installation and run the normal tools to install the things needed by scikit-learn. Finally, we add the code that implements our specific algorithm to the container and set up the right environment to run under.\n", "\n", "Along the way, we clean up extra space. This makes the container smaller and faster to start.\n", "\n", "Let's look at the Dockerfile for the example:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FROM nvidia/cuda:11.7.0-base-centos7\r\n", "CMD nvidia-smi\r\n", "\r\n", "# Install the following:\r\n", "#1. Header files and static libraries for python dev\r\n", "#2. Required dependencies\r\n", "#3. Setup Deep Learning/Machine Learning libraries.\r\n", "# Note: Multiple statements are being combined into one RUN command to minimize the number of layers Docker creates\r\n", "#that translates into smaller docker image size\r\n", "\r\n", "RUN yum -y install epel-release && yum -y update && yum install wget -y\r\n", "RUN yum -y groupinstall \"Development Tools\" && yum -y install openssl-devel bzip2-devel libffi-devel xz-devel\r\n", "RUN gcc --version\r\n", "RUN wget https://www.python.org/ftp/python/3.8.12/Python-3.8.12.tgz\r\n", "RUN tar xvf Python-3.8.12.tgz -C /usr/bin/\r\n", "RUN pwd\r\n", "\r\n", "WORKDIR /usr/bin/Python-3.8.12/\r\n", "\r\n", "RUN ./configure --enable-optimizations\r\n", "RUN make altinstall\r\n", "RUN python3.8 --version\r\n", "RUN pip3.8 --version\r\n", "\r\n", "WORKDIR /\r\n", "\r\n", "RUN yum update -y && yum install -y wheel python3-devel python3-libs python3-tools python3-pip && \\\r\n", " yum install gcc tar make util-linux kmod man sudo git -y && \\\r\n", " yum install wget -y && yum install -y unzip && yum install gcc-c++ -y && yum clean all \\\r\n", " && yum install nginx -y\r\n", "\r\n", "RUN curl \"https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip\" -o \"awscliv2.zip\" \\\r\n", "&& unzip awscliv2.zip \\\r\n", "&& sudo ./aws/install \\\r\n", "&& /usr/local/bin/aws --version\r\n", "\r\n", "#RUN docker run --mount type=bind,source=\"~/.aws\",target=/root/.aws\r\n", "RUN pip3.8 install --no-cache-dir --upgrade pip && \\pip3.8 install --no-cache-dir --upgrade setuptools && \\\r\n", "pip3.8 install --no-cache-dir numpy==1.21.0 Cython\r\n", "\r\n", "RUN pip3.8 install --no-cache-dir scipy==1.7.3 scikit-learn==1.0.2 pandas==1.4.0 Pillow==9.0.0 \\\r\n", "flask gevent gunicorn boto3 s3fs joblib catboost==1.0.0\r\n", "\r\n", "##########################################################################\r\n", "# Download and install CUDA. Sagemaker requires CUDA and CuDNN environments built in the docker image. It will only\r\n", "# mount the NVIDIA driver from the host machine.\r\n", "\r\n", "RUN pip3.8 install shap==0.40.0\r\n", "\r\n", "# Create the directories required by Sagemaker. /opt/ml and all sub-directories are reserved by Amazon SageMaker training.\r\n", "RUN mkdir /opt/ml /opt/ml/input /opt/ml/input/config /opt/ml/input/data /opt/ml/input/data/training /opt/ml/model /opt/ml/output /opt/program\r\n", "\r\n", "# Set PYTHONPATH env. This is important for python to import python modules from codes present in sub-directories.\r\n", "ENV PYTHONPATH=/opt/program\r\n", "ENV PYTHONUNBUFFERED=TRUE\r\n", "ENV PYTHONDONTWRITEBYTECODE=TRUE\r\n", "ENV PATH=\"/opt/program:${PATH}\"\r\n", "\r\n", "# See https://github.com/NVIDIA/nvidia-docker/issues/1409\r\n", "ENV NVIDIA_DISABLE_REQUIRE=1\r\n", "\r\n", "# Update the Python version in PATH to python3\r\n", "RUN rm -f /usr/bin/python && rm -f /usr/bin/python3 && ln -s /usr/bin/Python-3.8.12/python /usr/bin/python3\r\n", "RUN ln -s /usr/bin/python3 /usr/bin/python\r\n", "\r\n", "# Set up the program in the image\r\n", "COPY code/* /opt/program/\r\n", "WORKDIR /opt/program/\r\n", "\r\n" ] } ], "source": [ "!cat Dockerfile" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Note:\n", "In the Dockerfile repalce wget <**virtual-hosted–style access path to the driver**> with path to driver file stored in previous step. Here is the link to get the virtual-hosted–style access path to the file in s3 < https://docs.aws.amazon.com/AmazonS3/latest/userguide/access-bucket-intro.html>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building and registering the container\n", "\n", "The following shell code shows how to build the container image using `docker build` and push the container image to ECR using `docker push` to build the image `sagemaker-catboost-github-gpu-img`. \n", "\n", "This code looks for an ECR repository in the account you're using and the current default region (if you're using a SageMaker notebook instance, this will be the region where the notebook instance was created). If the repository doesn't exist, the script will create it." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Login Succeeded\n", "\r\n", "Step 1/29 : FROM nvidia/cuda:11.7.0-base-centos7\n", " ---> 7b61ee5b64d1\n", "Step 2/29 : CMD nvidia-smi\n", " ---> Using cache\n", " ---> 4037c952aae9\n", "Step 3/29 : RUN yum -y install epel-release && yum -y update && yum install wget -y\n", " ---> Using cache\n", " ---> 43e739d21819\n", "Step 4/29 : RUN yum -y groupinstall \"Development Tools\" && yum -y install openssl-devel bzip2-devel libffi-devel xz-devel\n", " ---> Using cache\n", " ---> 90ff292981e7\n", "Step 5/29 : RUN gcc --version\n", " ---> Using cache\n", " ---> 919e3822d294\n", "Step 6/29 : RUN wget https://www.python.org/ftp/python/3.8.12/Python-3.8.12.tgz\n", " ---> Using cache\n", " ---> 63d1c13387a5\n", "Step 7/29 : RUN tar xvf Python-3.8.12.tgz -C /usr/bin/\n", " ---> Using cache\n", " ---> 0a5bb49b4015\n", "Step 8/29 : RUN pwd\n", " ---> Using cache\n", " ---> ef89f777d3a7\n", "Step 9/29 : WORKDIR /usr/bin/Python-3.8.12/\n", " ---> Using cache\n", " ---> d5f48b21e6b5\n", "Step 10/29 : RUN ./configure --enable-optimizations\n", " ---> Using cache\n", " ---> 33d72fbbea4e\n", "Step 11/29 : RUN make altinstall\n", " ---> Using cache\n", " ---> 7fcc76dd92e2\n", "Step 12/29 : RUN python3.8 --version\n", " ---> Using cache\n", " ---> 6ef1c45587f6\n", "Step 13/29 : RUN pip3.8 --version\n", " ---> Using cache\n", " ---> e72927cce9ad\n", "Step 14/29 : WORKDIR /\n", " ---> Using cache\n", " ---> fbcb04c64c1f\n", "Step 15/29 : RUN yum update -y && yum install -y wheel python3-devel python3-libs python3-tools python3-pip && yum install gcc tar make util-linux kmod man sudo git -y && yum install wget -y && yum install -y unzip && yum install gcc-c++ -y && yum clean all && yum install nginx -y\n", " ---> Using cache\n", " ---> 876a48fb334d\n", "Step 16/29 : RUN curl \"https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip\" -o \"awscliv2.zip\" && unzip awscliv2.zip && sudo ./aws/install && /usr/local/bin/aws --version\n", " ---> Using cache\n", " ---> e46a2d1704f4\n", "Step 17/29 : RUN pip3.8 install --no-cache-dir --upgrade pip && \\pip3.8 install --no-cache-dir --upgrade setuptools && pip3.8 install --no-cache-dir numpy==1.21.0 Cython\n", " ---> Using cache\n", " ---> bac6d93a0826\n", "Step 18/29 : RUN pip3.8 install --no-cache-dir scipy==1.7.3 scikit-learn==1.0.2 pandas==1.4.0 Pillow==9.0.0 flask gevent gunicorn boto3 s3fs joblib catboost==1.0.0\n", " ---> Using cache\n", " ---> a2c073702a98\n", "Step 19/29 : RUN pip3.8 install shap==0.40.0\n", " ---> Using cache\n", " ---> ac998a89dec7\n", "Step 20/29 : RUN mkdir /opt/ml /opt/ml/input /opt/ml/input/config /opt/ml/input/data /opt/ml/input/data/training /opt/ml/model /opt/ml/output /opt/program\n", " ---> Using cache\n", " ---> 6e96c7cbfe5f\n", "Step 21/29 : ENV PYTHONPATH=/opt/program\n", " ---> Using cache\n", " ---> 6744be881f30\n", "Step 22/29 : ENV PYTHONUNBUFFERED=TRUE\n", " ---> Using cache\n", " ---> 0be3f39929b0\n", "Step 23/29 : ENV PYTHONDONTWRITEBYTECODE=TRUE\n", " ---> Using cache\n", " ---> 9095fd686181\n", "Step 24/29 : ENV PATH=\"/opt/program:${PATH}\"\n", " ---> Using cache\n", " ---> b27cf479aea1\n", "Step 25/29 : ENV NVIDIA_DISABLE_REQUIRE=1\n", " ---> Using cache\n", " ---> bf760d22b6e7\n", "Step 26/29 : RUN rm -f /usr/bin/python && rm -f /usr/bin/python3 && ln -s /usr/bin/Python-3.8.12/python /usr/bin/python3\n", " ---> Using cache\n", " ---> f0d82beec60c\n", "Step 27/29 : RUN ln -s /usr/bin/python3 /usr/bin/python\n", " ---> Using cache\n", " ---> 23698a3850ac\n", "Step 28/29 : COPY code/* /opt/program/\n", " ---> ce7c335373d7\n", "Step 29/29 : WORKDIR /opt/program/\n", " ---> Running in 5d0934f52f7d\n", "Removing intermediate container 5d0934f52f7d\n", " ---> 1f1ec6a16f6e\n", "Successfully built 1f1ec6a16f6e\n", "Successfully tagged sagemaker-catboost-github-gpu-img:latest\n", "The push refers to repository [625789749037.dkr.ecr.us-east-1.amazonaws.com/sagemaker-catboost-github-gpu-img]\n", "87c4cd3dd593: Preparing\n", "0b0a8cd4d6e5: Preparing\n", "7a128ee919cd: Preparing\n", "bfc380767177: Preparing\n", "59ecd47d3bc1: Preparing\n", "3b9d7a3f08ef: Preparing\n", "9784cf8119c2: Preparing\n", "aa8a98a1abe1: Preparing\n", "95570d1ef54a: Preparing\n", "b6cca848cb08: Preparing\n", "680d011e6c42: Preparing\n", "61639f91bab2: Preparing\n", "bf07462a469c: Preparing\n", "56d6f9b336c9: Preparing\n", "454e17a9b783: Preparing\n", "126eac20ca51: Preparing\n", "0943531c27a8: Preparing\n", "696b24b61b9b: Preparing\n", "09f340bd8aab: Preparing\n", "a88e3bf02fda: Preparing\n", "174f56854903: Preparing\n", "95570d1ef54a: Waiting\n", "bf07462a469c: Waiting\n", "696b24b61b9b: Waiting\n", "56d6f9b336c9: Waiting\n", "09f340bd8aab: Waiting\n", "454e17a9b783: Waiting\n", "0943531c27a8: Waiting\n", "126eac20ca51: Waiting\n", "b6cca848cb08: Waiting\n", "a88e3bf02fda: Waiting\n", "680d011e6c42: Waiting\n", "174f56854903: Waiting\n", "3b9d7a3f08ef: Waiting\n", "9784cf8119c2: Waiting\n", "aa8a98a1abe1: Waiting\n", "bfc380767177: Layer already exists\n", "0b0a8cd4d6e5: Layer already exists\n", "59ecd47d3bc1: Layer already exists\n", "7a128ee919cd: Layer already exists\n", "3b9d7a3f08ef: Layer already exists\n", "aa8a98a1abe1: Layer already exists\n", "95570d1ef54a: Layer already exists\n", "9784cf8119c2: Layer already exists\n", "b6cca848cb08: Layer already exists\n", "680d011e6c42: Layer already exists\n", "61639f91bab2: Layer already exists\n", "bf07462a469c: Layer already exists\n", "56d6f9b336c9: Layer already exists\n", "454e17a9b783: Layer already exists\n", "126eac20ca51: Layer already exists\n", "0943531c27a8: Layer already exists\n", "696b24b61b9b: Layer already exists\n", "09f340bd8aab: Layer already exists\n", "a88e3bf02fda: Layer already exists\n", "174f56854903: Layer already exists\n", "87c4cd3dd593: Pushed\n", "latest: digest: sha256:c18556c542e4b5263752883ab62be1e285a7af03cecff4b6311ff1692567da77 size: 4737\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING! Using --password via the CLI is insecure. Use --password-stdin.\n", "WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.\n", "Configure a credential helper to remove this warning. See\n", "https://docs.docker.com/engine/reference/commandline/login/#credentials-store\n", "\n" ] } ], "source": [ "%%sh\n", "# The name of our algorithm\n", "algorithm_name=sagemaker-catboost-github-gpu-img\n", "\n", "chmod +x code/train\n", "chmod +x code/serve\n", "\n", "account=$(aws sts get-caller-identity --query Account --output text)\n", "\n", "# Get the region defined in the current configuration (default to us-west-2 if none defined)\n", "region=$(aws configure get region)\n", "region=${region:-us-east-1}\n", "\n", "fullname=\"${account}.dkr.ecr.${region}.amazonaws.com/${algorithm_name}:latest\"\n", "\n", "# If the repository doesn't exist in ECR, create it.\n", "aws ecr describe-repositories --repository-names \"${algorithm_name}\" > /dev/null 2>&1\n", "\n", "if [ $? -ne 0 ]\n", "then\n", " aws ecr create-repository --repository-name \"${algorithm_name}\" > /dev/null\n", "fi\n", "\n", "# Get the login command from ECR and execute it directly\n", "$(aws ecr get-login --region ${region} --no-include-email)\n", "\n", "# Build the docker image locally with the image name and then push it to ECR\n", "# with the full name.\n", "\n", "docker build -t ${algorithm_name} .\n", "docker tag ${algorithm_name} ${fullname}\n", "\n", "docker push ${fullname}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Submit a Hyper-Parameter Optimization job\n", "Below are the parameters and the respective ranges we would optimizing over\n", " * iterations': IntegerParameter(80000, 130000),\n", " * max_depth': IntegerParameter(6, 10), \n", " * max_ctr_complexity': IntegerParameter(4, 10),\n", " * learning_rate': ContinuousParameter(0.01, 0.5)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "train_volume_size has been renamed in sagemaker>=2.\n", "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n", "No finished training job found associated with this estimator. Please make sure this estimator is only used for building workflow config\n", "No finished training job found associated with this estimator. Please make sure this estimator is only used for building workflow config\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................_s" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Job ended with status 'Stopped' rather than 'Completed'. This could mean the job timed out or stopped early for some other reason: Consider checking whether it completed as you expect.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "## hyper-parameter optimization\n", "## HPO JOB\n", "import sagemaker as sage\n", "from time import gmtime, strftime\n", "sess = sage.Session()\n", "from sagemaker.tuner import IntegerParameter, CategoricalParameter, ContinuousParameter, HyperparameterTuner\n", "\n", "account = sess.boto_session.client('sts').get_caller_identity()['Account']\n", "region = sess.boto_session.region_name\n", "image = '{}.dkr.ecr.{}.amazonaws.com/sagemaker-catboost-github-gpu-img:latest'.format(account, region)\n", "tree_hpo = sage.estimator.Estimator(image,\n", " role, 1, \n", " 'ml.p3.16xlarge',\n", " train_volume_size = 100,\n", " output_path=\"s3://{}/sagemaker/DEMO-GPU-Catboost/output\".format(bucket),\n", " sagemaker_session=sess)\n", "\n", "hyperparameter_ranges = {'iterations': IntegerParameter(80000, 130000),\n", " 'max_depth': IntegerParameter(6, 10), \n", " 'max_ctr_complexity': IntegerParameter(4, 10),\n", " 'learning_rate': ContinuousParameter(0.01, 0.5)}\n", "\n", "objective_metric_name = 'auc'\n", "metric_definitions = [{'Name': 'auc',\n", " 'Regex': 'auc: ([0-9\\\\.]+)'}]\n", "\n", "tuner = HyperparameterTuner(tree_hpo,\n", " objective_metric_name,\n", " hyperparameter_ranges,\n", " metric_definitions,\n", " objective_type='Maximize',\n", " max_jobs=3,\n", " max_parallel_jobs=1)\n", "\n", "train_location = 's3://'+bucket+'/sagemaker/DEMO-GPU-Catboost/data/train/'\n", "valid_location = 's3://'+bucket+'/sagemaker/DEMO-GPU-Catboost/data/valid/'\n", "\n", "tuner.fit({'train': train_location,\n", " 'validation': valid_location })" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'sagemaker-catboost-g-220922-1938-002-c8121712'" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tuner.best_training_job()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Batch inference\n", "Run the batch inference on the test set" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Job name: sagemaker-catboost-g-220922-1938-002-c8121712\n", "\n", "2022-09-22 22:28:28 Starting - Found matching resource for reuse\n", "2022-09-22 22:28:28 Downloading - Downloading input data\n", "2022-09-22 22:28:28 Training - Training image download completed. Training in progress.\n", "2022-09-22 22:28:28 Uploading - Uploading generated training model\n", "2022-09-22 22:28:28 Completed - Resource reused by training job: sagemaker-catboost-g-220922-1938-003-36527ca7\n", ".....................................\u001b[34mStarting the inference server with 4 workers.\u001b[0m\n", "\u001b[34m[2022-09-23 16:31:08 +0000] [10] [INFO] Starting gunicorn 20.1.0\u001b[0m\n", "\u001b[34m[2022-09-23 16:31:08 +0000] [10] [INFO] Listening at: unix:/tmp/gunicorn.sock (10)\u001b[0m\n", "\u001b[34m[2022-09-23 16:31:08 +0000] [10] [INFO] Using worker: gevent\u001b[0m\n", "\u001b[34m[2022-09-23 16:31:08 +0000] [13] [INFO] Booting worker with pid: 13\u001b[0m\n", "\u001b[34m[2022-09-23 16:31:08 +0000] [14] [INFO] Booting worker with pid: 14\u001b[0m\n", "\u001b[34m[2022-09-23 16:31:08 +0000] [15] [INFO] Booting worker with pid: 15\u001b[0m\n", "\u001b[34m[2022-09-23 16:31:08 +0000] [16] [INFO] Booting worker with pid: 16\u001b[0m\n", "\u001b[34mi am here\u001b[0m\n", "\u001b[35mi am here\u001b[0m\n", "\u001b[34mModel is loaded:-\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:15 +0000] \"GET /ping HTTP/1.1\" 200 1 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:15 +0000] \"GET /execution-parameters HTTP/1.1\" 404 2 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mModel is loaded:-\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:15 +0000] \"GET /ping HTTP/1.1\" 200 1 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:15 +0000] \"GET /execution-parameters HTTP/1.1\" 404 2 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3825, 10)\u001b[0m\n", "\u001b[34mInvoked with 3825 records\u001b[0m\n", "\u001b[34mi am here\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3825, 10)\u001b[0m\n", "\u001b[35mInvoked with 3825 records\u001b[0m\n", "\u001b[35mi am here\u001b[0m\n", "\u001b[34mModel is loaded:-\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3825,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3825, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3825, 3) in time : 0.9225139617919922\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:17 +0000] \"POST /invocations HTTP/1.1\" 200 137480 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[35mModel is loaded:-\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3825,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3825, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3825, 3) in time : 0.9225139617919922\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:17 +0000] \"POST /invocations HTTP/1.1\" 200 137480 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3798, 10)\u001b[0m\n", "\u001b[34mInvoked with 3798 records\u001b[0m\n", "\u001b[34mi am here\u001b[0m\n", "\u001b[34mModel is loaded:-\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3798,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3798, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3798, 3) in time : 0.9042420387268066\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:18 +0000] \"POST /invocations HTTP/1.1\" 200 136458 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3798, 10)\u001b[0m\n", "\u001b[35mInvoked with 3798 records\u001b[0m\n", "\u001b[35mi am here\u001b[0m\n", "\u001b[35mModel is loaded:-\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3798,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3798, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3798, 3) in time : 0.9042420387268066\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:18 +0000] \"POST /invocations HTTP/1.1\" 200 136458 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3635, 10)\u001b[0m\n", "\u001b[34mInvoked with 3635 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3635, 10)\u001b[0m\n", "\u001b[35mInvoked with 3635 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[32m2022-09-23T16:31:15.338:[sagemaker logs]: MaxConcurrentTransforms=1, MaxPayloadInMB=1, BatchStrategy=MULTI_RECORD\u001b[0m\n", "\u001b[34mComplete inferencing on (3635,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3635, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3635, 3) in time : 0.7440066337585449\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:19 +0000] \"POST /invocations HTTP/1.1\" 200 130668 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[35mComplete inferencing on (3635,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3635, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3635, 3) in time : 0.7440066337585449\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:19 +0000] \"POST /invocations HTTP/1.1\" 200 130668 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3631, 10)\u001b[0m\n", "\u001b[34mInvoked with 3631 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3631, 10)\u001b[0m\n", "\u001b[35mInvoked with 3631 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3631,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3631, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3631, 3) in time : 0.714447021484375\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:19 +0000] \"POST /invocations HTTP/1.1\" 200 130446 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[35mComplete inferencing on (3631,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3631, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3631, 3) in time : 0.714447021484375\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:19 +0000] \"POST /invocations HTTP/1.1\" 200 130446 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3912, 10)\u001b[0m\n", "\u001b[34mInvoked with 3912 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3912, 10)\u001b[0m\n", "\u001b[35mInvoked with 3912 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3912,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3912, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3912, 3) in time : 0.7360846996307373\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:20 +0000] \"POST /invocations HTTP/1.1\" 200 140559 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3608, 10)\u001b[0m\n", "\u001b[34mInvoked with 3608 records\u001b[0m\n", "\u001b[35mComplete inferencing on (3912,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3912, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3912, 3) in time : 0.7360846996307373\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:20 +0000] \"POST /invocations HTTP/1.1\" 200 140559 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3608, 10)\u001b[0m\n", "\u001b[35mInvoked with 3608 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3608,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3608, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3608, 3) in time : 0.7409827709197998\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:21 +0000] \"POST /invocations HTTP/1.1\" 200 129639 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3642, 10)\u001b[0m\n", "\u001b[34mInvoked with 3642 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3608,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3608, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3608, 3) in time : 0.7409827709197998\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:21 +0000] \"POST /invocations HTTP/1.1\" 200 129639 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3642, 10)\u001b[0m\n", "\u001b[35mInvoked with 3642 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3642,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3642, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3642, 3) in time : 0.741976261138916\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:22 +0000] \"POST /invocations HTTP/1.1\" 200 130970 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[35mComplete inferencing on (3642,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3642, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3642, 3) in time : 0.741976261138916\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:22 +0000] \"POST /invocations HTTP/1.1\" 200 130970 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3883, 10)\u001b[0m\n", "\u001b[34mInvoked with 3883 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3883, 10)\u001b[0m\n", "\u001b[35mInvoked with 3883 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3883,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3883, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mComplete inferencing on (3883,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3883, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3883, 3) in time : 0.7602272033691406\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:22 +0000] \"POST /invocations HTTP/1.1\" 200 139581 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3766, 10)\u001b[0m\n", "\u001b[34mInvoked with 3766 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mTime to execute recs: (3883, 3) in time : 0.7602272033691406\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:22 +0000] \"POST /invocations HTTP/1.1\" 200 139581 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3766, 10)\u001b[0m\n", "\u001b[35mInvoked with 3766 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3766,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3766, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3766, 3) in time : 0.7478940486907959\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:23 +0000] \"POST /invocations HTTP/1.1\" 200 135390 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[35mComplete inferencing on (3766,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3766, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3766, 3) in time : 0.7478940486907959\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:23 +0000] \"POST /invocations HTTP/1.1\" 200 135390 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (4083, 10)\u001b[0m\n", "\u001b[34mInvoked with 4083 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (4083,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (4083, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (4083, 3) in time : 0.7699635028839111\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:24 +0000] \"POST /invocations HTTP/1.1\" 200 146717 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (4083, 10)\u001b[0m\n", "\u001b[35mInvoked with 4083 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (4083,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (4083, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (4083, 3) in time : 0.7699635028839111\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:24 +0000] \"POST /invocations HTTP/1.1\" 200 146717 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3843, 10)\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3843, 10)\u001b[0m\n", "\u001b[34mInvoked with 3843 records\u001b[0m\n", "\u001b[34mi am here\u001b[0m\n", "\u001b[34mModel is loaded:-\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3843,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3843, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3843, 3) in time : 0.9500284194946289\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:25 +0000] \"POST /invocations HTTP/1.1\" 200 138134 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mInvoked with 3843 records\u001b[0m\n", "\u001b[35mi am here\u001b[0m\n", "\u001b[35mModel is loaded:-\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3843,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3843, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3843, 3) in time : 0.9500284194946289\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:25 +0000] \"POST /invocations HTTP/1.1\" 200 138134 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3863, 10)\u001b[0m\n", "\u001b[34mInvoked with 3863 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3863,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3863, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3863, 3) in time : 0.7749016284942627\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:26 +0000] \"POST /invocations HTTP/1.1\" 200 138765 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3863, 10)\u001b[0m\n", "\u001b[35mInvoked with 3863 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3863,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3863, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3863, 3) in time : 0.7749016284942627\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:26 +0000] \"POST /invocations HTTP/1.1\" 200 138765 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3826, 10)\u001b[0m\n", "\u001b[34mInvoked with 3826 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3826, 10)\u001b[0m\n", "\u001b[35mInvoked with 3826 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3826,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3826, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3826, 3) in time : 0.7563540935516357\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:27 +0000] \"POST /invocations HTTP/1.1\" 200 137453 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[35mComplete inferencing on (3826,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3826, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3826, 3) in time : 0.7563540935516357\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:27 +0000] \"POST /invocations HTTP/1.1\" 200 137453 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3884, 10)\u001b[0m\n", "\u001b[34mInvoked with 3884 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3884, 10)\u001b[0m\n", "\u001b[35mInvoked with 3884 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3884,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3884, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3884, 3) in time : 0.7466793060302734\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:27 +0000] \"POST /invocations HTTP/1.1\" 200 139554 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[35mComplete inferencing on (3884,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3884, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3884, 3) in time : 0.7466793060302734\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:27 +0000] \"POST /invocations HTTP/1.1\" 200 139554 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3292, 10)\u001b[0m\n", "\u001b[34mInvoked with 3292 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3292, 10)\u001b[0m\n", "\u001b[35mInvoked with 3292 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3292,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3292, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3292, 3) in time : 0.7278680801391602\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:28 +0000] \"POST /invocations HTTP/1.1\" 200 118271 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[35mComplete inferencing on (3292,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3292, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3292, 3) in time : 0.7278680801391602\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:28 +0000] \"POST /invocations HTTP/1.1\" 200 118271 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3966, 10)\u001b[0m\n", "\u001b[34mInvoked with 3966 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3966,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3966, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3966, 3) in time : 0.7460732460021973\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:29 +0000] \"POST /invocations HTTP/1.1\" 200 142507 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3966, 10)\u001b[0m\n", "\u001b[35mInvoked with 3966 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3966,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3966, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3966, 3) in time : 0.7460732460021973\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:29 +0000] \"POST /invocations HTTP/1.1\" 200 142507 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3879, 10)\u001b[0m\n", "\u001b[34mInvoked with 3879 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3879, 10)\u001b[0m\n", "\u001b[35mInvoked with 3879 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3879,) records.\u001b[0m\n", "\u001b[35mComplete inferencing on (3879,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3879, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3879, 3) in time : 0.7513532638549805\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:30 +0000] \"POST /invocations HTTP/1.1\" 200 139293 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3913, 10)\u001b[0m\n", "\u001b[34mInvoked with 3913 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mShape of predictions : (3879, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3879, 3) in time : 0.7513532638549805\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:30 +0000] \"POST /invocations HTTP/1.1\" 200 139293 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3913, 10)\u001b[0m\n", "\u001b[35mInvoked with 3913 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3913,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3913, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3913, 3) in time : 0.7369349002838135\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:30 +0000] \"POST /invocations HTTP/1.1\" 200 140645 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[35mComplete inferencing on (3913,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3913, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3913, 3) in time : 0.7369349002838135\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:30 +0000] \"POST /invocations HTTP/1.1\" 200 140645 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3941, 10)\u001b[0m\n", "\u001b[34mInvoked with 3941 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3941, 10)\u001b[0m\n", "\u001b[35mInvoked with 3941 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3941,) records.\u001b[0m\n", "\u001b[35mComplete inferencing on (3941,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3941, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3941, 3) in time : 0.7497377395629883\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:31 +0000] \"POST /invocations HTTP/1.1\" 200 141620 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3920, 10)\u001b[0m\n", "\u001b[34mInvoked with 3920 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3920,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3920, 3)\u001b[0m\n", "\u001b[35mShape of predictions : (3941, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3941, 3) in time : 0.7497377395629883\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:31 +0000] \"POST /invocations HTTP/1.1\" 200 141620 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3920, 10)\u001b[0m\n", "\u001b[35mInvoked with 3920 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3920,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3920, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3920, 3) in time : 0.7482895851135254\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:32 +0000] \"POST /invocations HTTP/1.1\" 200 140833 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3902, 10)\u001b[0m\n", "\u001b[34mInvoked with 3902 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3920, 3) in time : 0.7482895851135254\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:32 +0000] \"POST /invocations HTTP/1.1\" 200 140833 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3902, 10)\u001b[0m\n", "\u001b[35mInvoked with 3902 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3902,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3902, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3902, 3) in time : 0.7266662120819092\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:33 +0000] \"POST /invocations HTTP/1.1\" 200 140237 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[35mComplete inferencing on (3902,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3902, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3902, 3) in time : 0.7266662120819092\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:33 +0000] \"POST /invocations HTTP/1.1\" 200 140237 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3943, 10)\u001b[0m\n", "\u001b[34mInvoked with 3943 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3943, 10)\u001b[0m\n", "\u001b[35mInvoked with 3943 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3943,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3943, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3943, 3) in time : 0.7668795585632324\u001b[0m\n", "\u001b[35mComplete inferencing on (3943,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3943, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3943, 3) in time : 0.7668795585632324\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:33 +0000] \"POST /invocations HTTP/1.1\" 200 141695 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3891, 10)\u001b[0m\n", "\u001b[34mInvoked with 3891 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:33 +0000] \"POST /invocations HTTP/1.1\" 200 141695 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3891, 10)\u001b[0m\n", "\u001b[35mInvoked with 3891 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3804, 10)\u001b[0m\n", "\u001b[34mInvoked with 3804 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3804,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3804, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3804, 3) in time : 0.732806921005249\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:35 +0000] \"POST /invocations HTTP/1.1\" 200 136648 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3804, 10)\u001b[0m\n", "\u001b[35mInvoked with 3804 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3804,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3804, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3804, 3) in time : 0.732806921005249\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:35 +0000] \"POST /invocations HTTP/1.1\" 200 136648 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3782, 10)\u001b[0m\n", "\u001b[34mInvoked with 3782 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3782, 10)\u001b[0m\n", "\u001b[35mInvoked with 3782 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3782,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3782, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3782, 3) in time : 0.7237586975097656\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:36 +0000] \"POST /invocations HTTP/1.1\" 200 135945 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[35mComplete inferencing on (3782,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3782, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3782, 3) in time : 0.7237586975097656\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:36 +0000] \"POST /invocations HTTP/1.1\" 200 135945 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3997, 10)\u001b[0m\n", "\u001b[34mInvoked with 3997 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3997, 10)\u001b[0m\n", "\u001b[35mInvoked with 3997 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3997,) records.\u001b[0m\n", "\u001b[35mComplete inferencing on (3997,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3997, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3997, 3) in time : 0.7513542175292969\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:36 +0000] \"POST /invocations HTTP/1.1\" 200 143509 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3798, 10)\u001b[0m\n", "\u001b[34mInvoked with 3798 records\u001b[0m\n", "\u001b[35mShape of predictions : (3997, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3997, 3) in time : 0.7513542175292969\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:36 +0000] \"POST /invocations HTTP/1.1\" 200 143509 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3798, 10)\u001b[0m\n", "\u001b[35mInvoked with 3798 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3798,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3798, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3798, 3) in time : 0.7294366359710693\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:37 +0000] \"POST /invocations HTTP/1.1\" 200 136549 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[35mComplete inferencing on (3798,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3798, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3798, 3) in time : 0.7294366359710693\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:37 +0000] \"POST /invocations HTTP/1.1\" 200 136549 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3950, 10)\u001b[0m\n", "\u001b[34mInvoked with 3950 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3950,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3950, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3950, 3) in time : 0.7478976249694824\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:38 +0000] \"POST /invocations HTTP/1.1\" 200 141981 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3950, 10)\u001b[0m\n", "\u001b[35mInvoked with 3950 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3950,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3950, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3950, 3) in time : 0.7478976249694824\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:38 +0000] \"POST /invocations HTTP/1.1\" 200 141981 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3925, 10)\u001b[0m\n", "\u001b[34mInvoked with 3925 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3925, 10)\u001b[0m\n", "\u001b[35mInvoked with 3925 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3925,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3925, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3925, 3) in time : 0.756028413772583\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:39 +0000] \"POST /invocations HTTP/1.1\" 200 141099 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[35mComplete inferencing on (3925,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3925, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3925, 3) in time : 0.756028413772583\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:39 +0000] \"POST /invocations HTTP/1.1\" 200 141099 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3863, 10)\u001b[0m\n", "\u001b[34mInvoked with 3863 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3863, 10)\u001b[0m\n", "\u001b[35mInvoked with 3863 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3863,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3863, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3863, 3) in time : 0.737419843673706\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:39 +0000] \"POST /invocations HTTP/1.1\" 200 138755 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[35mComplete inferencing on (3863,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3863, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3863, 3) in time : 0.737419843673706\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:39 +0000] \"POST /invocations HTTP/1.1\" 200 138755 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3901, 10)\u001b[0m\n", "\u001b[34mInvoked with 3901 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3901, 10)\u001b[0m\n", "\u001b[35mInvoked with 3901 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3901,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3901, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mComplete inferencing on (3901,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3901, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3901, 3) in time : 0.7349574565887451\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:40 +0000] \"POST /invocations HTTP/1.1\" 200 140181 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3829, 10)\u001b[0m\n", "\u001b[34mInvoked with 3829 records\u001b[0m\n", "\u001b[35mTime to execute recs: (3901, 3) in time : 0.7349574565887451\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:40 +0000] \"POST /invocations HTTP/1.1\" 200 140181 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3829, 10)\u001b[0m\n", "\u001b[35mInvoked with 3829 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3829,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3829, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3829, 3) in time : 0.7353544235229492\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:41 +0000] \"POST /invocations HTTP/1.1\" 200 137663 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3829,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3829, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3829, 3) in time : 0.7353544235229492\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:41 +0000] \"POST /invocations HTTP/1.1\" 200 137663 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3740, 10)\u001b[0m\n", "\u001b[34mInvoked with 3740 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3740,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3740, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3740, 3) in time : 0.7340347766876221\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:42 +0000] \"POST /invocations HTTP/1.1\" 200 134384 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3740, 10)\u001b[0m\n", "\u001b[35mInvoked with 3740 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3740,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3740, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3740, 3) in time : 0.7340347766876221\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:42 +0000] \"POST /invocations HTTP/1.1\" 200 134384 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3825, 10)\u001b[0m\n", "\u001b[34mInvoked with 3825 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3825, 10)\u001b[0m\n", "\u001b[35mInvoked with 3825 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3825,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3825, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mComplete inferencing on (3825,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3825, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3825, 3) in time : 0.7316923141479492\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:42 +0000] \"POST /invocations HTTP/1.1\" 200 137437 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3540, 10)\u001b[0m\n", "\u001b[34mInvoked with 3540 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mTime to execute recs: (3825, 3) in time : 0.7316923141479492\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:42 +0000] \"POST /invocations HTTP/1.1\" 200 137437 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3540, 10)\u001b[0m\n", "\u001b[35mInvoked with 3540 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3540,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3540, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3540, 3) in time : 0.7289621829986572\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:43 +0000] \"POST /invocations HTTP/1.1\" 200 127257 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[35mComplete inferencing on (3540,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3540, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3540, 3) in time : 0.7289621829986572\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:43 +0000] \"POST /invocations HTTP/1.1\" 200 127257 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3764, 10)\u001b[0m\n", "\u001b[34mInvoked with 3764 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3764,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3764, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3764, 3) in time : 0.7561616897583008\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:44 +0000] \"POST /invocations HTTP/1.1\" 200 135229 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3764, 10)\u001b[0m\n", "\u001b[35mInvoked with 3764 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3764,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3764, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3764, 3) in time : 0.7561616897583008\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:44 +0000] \"POST /invocations HTTP/1.1\" 200 135229 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (4050, 3) in time : 0.7496075630187988\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:45 +0000] \"POST /invocations HTTP/1.1\" 200 145519 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (4050, 3) in time : 0.7496075630187988\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:45 +0000] \"POST /invocations HTTP/1.1\" 200 145519 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3821, 10)\u001b[0m\n", "\u001b[34mInvoked with 3821 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3821, 10)\u001b[0m\n", "\u001b[35mInvoked with 3821 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3821,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3821, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mComplete inferencing on (3821,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3821, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3821, 3) in time : 0.7591428756713867\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:46 +0000] \"POST /invocations HTTP/1.1\" 200 137247 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget object\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3633, 10)\u001b[0m\n", "\u001b[34mInvoked with 3633 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mTime to execute recs: (3821, 3) in time : 0.7591428756713867\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:46 +0000] \"POST /invocations HTTP/1.1\" 200 137247 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget object\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3633, 10)\u001b[0m\n", "\u001b[35mInvoked with 3633 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3633,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3633, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3633, 3) in time : 0.7226240634918213\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:46 +0000] \"POST /invocations HTTP/1.1\" 200 130521 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id object\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[35mComplete inferencing on (3633,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3633, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3633, 3) in time : 0.7226240634918213\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:46 +0000] \"POST /invocations HTTP/1.1\" 200 130521 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id object\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes object\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget object\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3726, 10)\u001b[0m\n", "\u001b[34mInvoked with 3726 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3726,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3726, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3726, 3) in time : 0.7197742462158203\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:47 +0000] \"POST /invocations HTTP/1.1\" 200 133915 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[35mhelpful_votes object\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget object\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3726, 10)\u001b[0m\n", "\u001b[35mInvoked with 3726 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3726,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3726, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3726, 3) in time : 0.7197742462158203\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:47 +0000] \"POST /invocations HTTP/1.1\" 200 133915 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3926, 10)\u001b[0m\n", "\u001b[34mInvoked with 3926 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3926,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3926, 3)\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3926, 10)\u001b[0m\n", "\u001b[35mInvoked with 3926 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3926,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3926, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3926, 3) in time : 0.7476091384887695\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:48 +0000] \"POST /invocations HTTP/1.1\" 200 141208 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3806, 10)\u001b[0m\n", "\u001b[34mInvoked with 3806 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3926, 3) in time : 0.7476091384887695\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:48 +0000] \"POST /invocations HTTP/1.1\" 200 141208 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3806, 10)\u001b[0m\n", "\u001b[35mInvoked with 3806 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3806,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3806, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3806, 3) in time : 0.7612948417663574\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:49 +0000] \"POST /invocations HTTP/1.1\" 200 136926 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[35mComplete inferencing on (3806,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3806, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3806, 3) in time : 0.7612948417663574\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:49 +0000] \"POST /invocations HTTP/1.1\" 200 136926 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3930, 10)\u001b[0m\n", "\u001b[34mInvoked with 3930 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3930, 10)\u001b[0m\n", "\u001b[35mInvoked with 3930 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3930,) records.\u001b[0m\n", "\u001b[35mComplete inferencing on (3930,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3930, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3930, 3) in time : 0.7465355396270752\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:49 +0000] \"POST /invocations HTTP/1.1\" 200 141168 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3906, 10)\u001b[0m\n", "\u001b[34mInvoked with 3906 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mShape of predictions : (3930, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3930, 3) in time : 0.7465355396270752\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:49 +0000] \"POST /invocations HTTP/1.1\" 200 141168 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3906, 10)\u001b[0m\n", "\u001b[35mInvoked with 3906 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3906,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3906, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3906, 3) in time : 0.7860138416290283\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:50 +0000] \"POST /invocations HTTP/1.1\" 200 140369 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[35mComplete inferencing on (3906,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3906, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3906, 3) in time : 0.7860138416290283\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:50 +0000] \"POST /invocations HTTP/1.1\" 200 140369 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3821, 10)\u001b[0m\n", "\u001b[34mInvoked with 3821 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3821,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3821, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3821, 3) in time : 0.7737903594970703\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:51 +0000] \"POST /invocations HTTP/1.1\" 200 137336 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3982, 10)\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3821, 10)\u001b[0m\n", "\u001b[35mInvoked with 3821 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3821,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3821, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3821, 3) in time : 0.7737903594970703\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:51 +0000] \"POST /invocations HTTP/1.1\" 200 137336 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3982, 10)\u001b[0m\n", "\u001b[34mInvoked with 3982 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mInvoked with 3982 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3982,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3982, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3982, 3) in time : 0.7619829177856445\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:52 +0000] \"POST /invocations HTTP/1.1\" 200 143063 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[35mComplete inferencing on (3982,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3982, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3982, 3) in time : 0.7619829177856445\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:52 +0000] \"POST /invocations HTTP/1.1\" 200 143063 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3850, 10)\u001b[0m\n", "\u001b[34mInvoked with 3850 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3850, 10)\u001b[0m\n", "\u001b[35mInvoked with 3850 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3850,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3850, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mComplete inferencing on (3850,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3850, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3850, 3) in time : 0.7635679244995117\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:52 +0000] \"POST /invocations HTTP/1.1\" 200 138385 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3829, 10)\u001b[0m\n", "\u001b[34mInvoked with 3829 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mTime to execute recs: (3850, 3) in time : 0.7635679244995117\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:52 +0000] \"POST /invocations HTTP/1.1\" 200 138385 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3829, 10)\u001b[0m\n", "\u001b[35mInvoked with 3829 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3829,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3829, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3829, 3) in time : 0.7370517253875732\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:53 +0000] \"POST /invocations HTTP/1.1\" 200 137537 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[35mComplete inferencing on (3829,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3829, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3829, 3) in time : 0.7370517253875732\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:53 +0000] \"POST /invocations HTTP/1.1\" 200 137537 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3769, 10)\u001b[0m\n", "\u001b[34mInvoked with 3769 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3769,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3769, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3769, 3) in time : 0.74318528175354\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:54 +0000] \"POST /invocations HTTP/1.1\" 200 135425 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3769, 10)\u001b[0m\n", "\u001b[35mInvoked with 3769 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3769,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3769, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3769, 3) in time : 0.74318528175354\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:54 +0000] \"POST /invocations HTTP/1.1\" 200 135425 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (4000, 10)\u001b[0m\n", "\u001b[34mInvoked with 4000 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (4000, 10)\u001b[0m\n", "\u001b[35mInvoked with 4000 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3801, 10)\u001b[0m\n", "\u001b[34mInvoked with 3801 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3801, 10)\u001b[0m\n", "\u001b[35mInvoked with 3801 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3801,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3801, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3801, 3) in time : 0.7263987064361572\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:55 +0000] \"POST /invocations HTTP/1.1\" 200 136622 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[35mComplete inferencing on (3801,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3801, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3801, 3) in time : 0.7263987064361572\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:55 +0000] \"POST /invocations HTTP/1.1\" 200 136622 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3760, 10)\u001b[0m\n", "\u001b[34mInvoked with 3760 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3760, 10)\u001b[0m\n", "\u001b[35mInvoked with 3760 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3760,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3760, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3760, 3) in time : 0.7213115692138672\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:56 +0000] \"POST /invocations HTTP/1.1\" 200 135060 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[35mComplete inferencing on (3760,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3760, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3760, 3) in time : 0.7213115692138672\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:56 +0000] \"POST /invocations HTTP/1.1\" 200 135060 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3803, 10)\u001b[0m\n", "\u001b[34mInvoked with 3803 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3803,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3803, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3803, 3) in time : 0.720240592956543\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:57 +0000] \"POST /invocations HTTP/1.1\" 200 136519 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3803, 10)\u001b[0m\n", "\u001b[35mInvoked with 3803 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3803,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3803, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3803, 3) in time : 0.720240592956543\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:57 +0000] \"POST /invocations HTTP/1.1\" 200 136519 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3779, 10)\u001b[0m\n", "\u001b[34mInvoked with 3779 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3779, 10)\u001b[0m\n", "\u001b[35mInvoked with 3779 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3779,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3779, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3779, 3) in time : 0.7211973667144775\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:58 +0000] \"POST /invocations HTTP/1.1\" 200 135736 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[35mComplete inferencing on (3779,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3779, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3779, 3) in time : 0.7211973667144775\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:58 +0000] \"POST /invocations HTTP/1.1\" 200 135736 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3886, 10)\u001b[0m\n", "\u001b[34mInvoked with 3886 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3886, 10)\u001b[0m\n", "\u001b[35mInvoked with 3886 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3886,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3886, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3886, 3) in time : 0.7287158966064453\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:58 +0000] \"POST /invocations HTTP/1.1\" 200 139730 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[35mComplete inferencing on (3886,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3886, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3886, 3) in time : 0.7287158966064453\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:58 +0000] \"POST /invocations HTTP/1.1\" 200 139730 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3734, 10)\u001b[0m\n", "\u001b[34mInvoked with 3734 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3734, 10)\u001b[0m\n", "\u001b[35mInvoked with 3734 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3734,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3734, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3734, 3) in time : 0.7110311985015869\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:31:59 +0000] \"POST /invocations HTTP/1.1\" 200 134112 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3732, 10)\u001b[0m\n", "\u001b[35mComplete inferencing on (3734,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3734, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3734, 3) in time : 0.7110311985015869\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:31:59 +0000] \"POST /invocations HTTP/1.1\" 200 134112 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3732, 10)\u001b[0m\n", "\u001b[34mInvoked with 3732 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3732,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3732, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3732, 3) in time : 0.7035491466522217\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:00 +0000] \"POST /invocations HTTP/1.1\" 200 134047 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3707, 10)\u001b[0m\n", "\u001b[34mInvoked with 3707 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mInvoked with 3732 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3732,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3732, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3732, 3) in time : 0.7035491466522217\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:00 +0000] \"POST /invocations HTTP/1.1\" 200 134047 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3707, 10)\u001b[0m\n", "\u001b[35mInvoked with 3707 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3707,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3707, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3707, 3) in time : 0.7400839328765869\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:01 +0000] \"POST /invocations HTTP/1.1\" 200 133291 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[35mComplete inferencing on (3707,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3707, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3707, 3) in time : 0.7400839328765869\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:01 +0000] \"POST /invocations HTTP/1.1\" 200 133291 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3787, 10)\u001b[0m\n", "\u001b[34mInvoked with 3787 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3787, 10)\u001b[0m\n", "\u001b[35mInvoked with 3787 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3787,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3787, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mComplete inferencing on (3787,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3787, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3787, 3) in time : 0.776190996170044\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:01 +0000] \"POST /invocations HTTP/1.1\" 200 136190 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3903, 10)\u001b[0m\n", "\u001b[34mInvoked with 3903 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mTime to execute recs: (3787, 3) in time : 0.776190996170044\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:01 +0000] \"POST /invocations HTTP/1.1\" 200 136190 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3903, 10)\u001b[0m\n", "\u001b[35mInvoked with 3903 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3903,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3903, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3903, 3) in time : 0.7581231594085693\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:02 +0000] \"POST /invocations HTTP/1.1\" 200 140231 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[35mComplete inferencing on (3903,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3903, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3903, 3) in time : 0.7581231594085693\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:02 +0000] \"POST /invocations HTTP/1.1\" 200 140231 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3846, 10)\u001b[0m\n", "\u001b[34mInvoked with 3846 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3846,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3846, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3846, 3) in time : 0.735065221786499\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:03 +0000] \"POST /invocations HTTP/1.1\" 200 138302 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3846, 10)\u001b[0m\n", "\u001b[35mInvoked with 3846 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3846,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3846, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3846, 3) in time : 0.735065221786499\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:03 +0000] \"POST /invocations HTTP/1.1\" 200 138302 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (4029, 10)\u001b[0m\n", "\u001b[34mInvoked with 4029 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (4029, 10)\u001b[0m\n", "\u001b[35mInvoked with 4029 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (4029,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (4029, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (4029, 3) in time : 0.7469034194946289\u001b[0m\n", "\u001b[35mComplete inferencing on (4029,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (4029, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (4029, 3) in time : 0.7469034194946289\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:04 +0000] \"POST /invocations HTTP/1.1\" 200 144766 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3612, 10)\u001b[0m\n", "\u001b[34mInvoked with 3612 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:04 +0000] \"POST /invocations HTTP/1.1\" 200 144766 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3612, 10)\u001b[0m\n", "\u001b[35mInvoked with 3612 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3918, 10)\u001b[0m\n", "\u001b[34mInvoked with 3918 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3918, 10)\u001b[0m\n", "\u001b[35mInvoked with 3918 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3918,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3918, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3918, 3) in time : 0.802483081817627\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:05 +0000] \"POST /invocations HTTP/1.1\" 200 140848 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[35mComplete inferencing on (3918,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3918, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3918, 3) in time : 0.802483081817627\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:05 +0000] \"POST /invocations HTTP/1.1\" 200 140848 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3799, 10)\u001b[0m\n", "\u001b[34mInvoked with 3799 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3799,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3799, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3799, 3) in time : 0.7514183521270752\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:06 +0000] \"POST /invocations HTTP/1.1\" 200 136533 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3799, 10)\u001b[0m\n", "\u001b[35mInvoked with 3799 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3799,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3799, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3799, 3) in time : 0.7514183521270752\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:06 +0000] \"POST /invocations HTTP/1.1\" 200 136533 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (4024, 10)\u001b[0m\n", "\u001b[34mInvoked with 4024 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (4024,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (4024, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (4024, 10)\u001b[0m\n", "\u001b[35mInvoked with 4024 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (4024,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (4024, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (4024, 3) in time : 0.7549779415130615\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:07 +0000] \"POST /invocations HTTP/1.1\" 200 144524 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3802, 10)\u001b[0m\n", "\u001b[34mInvoked with 3802 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mTime to execute recs: (4024, 3) in time : 0.7549779415130615\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:07 +0000] \"POST /invocations HTTP/1.1\" 200 144524 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3802, 10)\u001b[0m\n", "\u001b[35mInvoked with 3802 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3802,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3802, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3802, 3) in time : 0.7206916809082031\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:08 +0000] \"POST /invocations HTTP/1.1\" 200 136672 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[35mComplete inferencing on (3802,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3802, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3802, 3) in time : 0.7206916809082031\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:08 +0000] \"POST /invocations HTTP/1.1\" 200 136672 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3824, 10)\u001b[0m\n", "\u001b[34mInvoked with 3824 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3824, 10)\u001b[0m\n", "\u001b[35mInvoked with 3824 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3824,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3824, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mComplete inferencing on (3824,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3824, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3824, 3) in time : 0.7134134769439697\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:08 +0000] \"POST /invocations HTTP/1.1\" 200 137410 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3620, 10)\u001b[0m\n", "\u001b[34mInvoked with 3620 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3620,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3620, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3620, 3) in time : 0.7020847797393799\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:09 +0000] \"POST /invocations HTTP/1.1\" 200 130117 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[35mTime to execute recs: (3824, 3) in time : 0.7134134769439697\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:08 +0000] \"POST /invocations HTTP/1.1\" 200 137410 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3620, 10)\u001b[0m\n", "\u001b[35mInvoked with 3620 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3620,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3620, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3620, 3) in time : 0.7020847797393799\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:09 +0000] \"POST /invocations HTTP/1.1\" 200 130117 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3794, 10)\u001b[0m\n", "\u001b[34mInvoked with 3794 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3794, 10)\u001b[0m\n", "\u001b[35mInvoked with 3794 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3794,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3794, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3794, 3) in time : 0.7446267604827881\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:10 +0000] \"POST /invocations HTTP/1.1\" 200 136428 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3658, 10)\u001b[0m\n", "\u001b[34mInvoked with 3658 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3794,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3794, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3794, 3) in time : 0.7446267604827881\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:10 +0000] \"POST /invocations HTTP/1.1\" 200 136428 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3658, 10)\u001b[0m\n", "\u001b[35mInvoked with 3658 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3658,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3658, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3658, 3) in time : 0.7240698337554932\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:10 +0000] \"POST /invocations HTTP/1.1\" 200 131588 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[35mComplete inferencing on (3658,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3658, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3658, 3) in time : 0.7240698337554932\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:10 +0000] \"POST /invocations HTTP/1.1\" 200 131588 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3877, 10)\u001b[0m\n", "\u001b[34mInvoked with 3877 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3877, 10)\u001b[0m\n", "\u001b[35mInvoked with 3877 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3877,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3877, 3)\u001b[0m\n", "\u001b[35mComplete inferencing on (3877,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3877, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3877, 3) in time : 0.71189284324646\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:11 +0000] \"POST /invocations HTTP/1.1\" 200 139282 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3935, 10)\u001b[0m\n", "\u001b[34mInvoked with 3935 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3935,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3935, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3935, 3) in time : 0.7271356582641602\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3877, 3) in time : 0.71189284324646\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:11 +0000] \"POST /invocations HTTP/1.1\" 200 139282 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3935, 10)\u001b[0m\n", "\u001b[35mInvoked with 3935 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3935,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3935, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3935, 3) in time : 0.7271356582641602\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:12 +0000] \"POST /invocations HTTP/1.1\" 200 141302 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3895, 10)\u001b[0m\n", "\u001b[34mInvoked with 3895 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:12 +0000] \"POST /invocations HTTP/1.1\" 200 141302 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3895, 10)\u001b[0m\n", "\u001b[35mInvoked with 3895 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3895,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3895, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3895, 3) in time : 0.735241174697876\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:13 +0000] \"POST /invocations HTTP/1.1\" 200 139869 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[35mComplete inferencing on (3895,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3895, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3895, 3) in time : 0.735241174697876\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:13 +0000] \"POST /invocations HTTP/1.1\" 200 139869 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3854, 10)\u001b[0m\n", "\u001b[34mInvoked with 3854 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3854, 10)\u001b[0m\n", "\u001b[35mInvoked with 3854 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3854,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3854, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3854, 3) in time : 0.7095065116882324\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:13 +0000] \"POST /invocations HTTP/1.1\" 200 138388 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[35mComplete inferencing on (3854,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3854, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3854, 3) in time : 0.7095065116882324\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:13 +0000] \"POST /invocations HTTP/1.1\" 200 138388 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3973, 10)\u001b[0m\n", "\u001b[34mInvoked with 3973 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3973, 10)\u001b[0m\n", "\u001b[35mInvoked with 3973 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3973,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3973, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3973, 3) in time : 0.7492384910583496\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:14 +0000] \"POST /invocations HTTP/1.1\" 200 142749 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[35mComplete inferencing on (3973,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3973, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3973, 3) in time : 0.7492384910583496\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:14 +0000] \"POST /invocations HTTP/1.1\" 200 142749 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3679, 10)\u001b[0m\n", "\u001b[34mInvoked with 3679 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3679,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3679, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3679, 3) in time : 0.7722535133361816\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:15 +0000] \"POST /invocations HTTP/1.1\" 200 132149 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3679, 10)\u001b[0m\n", "\u001b[35mInvoked with 3679 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3679,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3679, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3679, 3) in time : 0.7722535133361816\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:15 +0000] \"POST /invocations HTTP/1.1\" 200 132149 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3893, 10)\u001b[0m\n", "\u001b[34mInvoked with 3893 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3893, 10)\u001b[0m\n", "\u001b[35mInvoked with 3893 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3893,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3893, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3893, 3) in time : 0.7252733707427979\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:16 +0000] \"POST /invocations HTTP/1.1\" 200 139892 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mComplete inferencing on (3893,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3893, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3893, 3) in time : 0.7252733707427979\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:16 +0000] \"POST /invocations HTTP/1.1\" 200 139892 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (4012, 10)\u001b[0m\n", "\u001b[34mInvoked with 4012 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (4012, 10)\u001b[0m\n", "\u001b[35mInvoked with 4012 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (4012,) records.\u001b[0m\n", "\u001b[35mComplete inferencing on (4012,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (4012, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (4012, 3) in time : 0.7362587451934814\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:16 +0000] \"POST /invocations HTTP/1.1\" 200 144177 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3852, 10)\u001b[0m\n", "\u001b[34mInvoked with 3852 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mShape of predictions : (4012, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (4012, 3) in time : 0.7362587451934814\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:16 +0000] \"POST /invocations HTTP/1.1\" 200 144177 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3852, 10)\u001b[0m\n", "\u001b[35mInvoked with 3852 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3852,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3852, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3852, 3) in time : 0.7403268814086914\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:17 +0000] \"POST /invocations HTTP/1.1\" 200 138410 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[35mComplete inferencing on (3852,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3852, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3852, 3) in time : 0.7403268814086914\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:17 +0000] \"POST /invocations HTTP/1.1\" 200 138410 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3676, 10)\u001b[0m\n", "\u001b[34mInvoked with 3676 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3676,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3676, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3676, 3) in time : 0.7009508609771729\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:18 +0000] \"POST /invocations HTTP/1.1\" 200 132118 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3676, 10)\u001b[0m\n", "\u001b[35mInvoked with 3676 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3676,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3676, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3676, 3) in time : 0.7009508609771729\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:18 +0000] \"POST /invocations HTTP/1.1\" 200 132118 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3514, 10)\u001b[0m\n", "\u001b[34mInvoked with 3514 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3514, 10)\u001b[0m\n", "\u001b[35mInvoked with 3514 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3514,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3514, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3514, 3) in time : 0.7466397285461426\u001b[0m\n", "\u001b[35mComplete inferencing on (3514,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3514, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3514, 3) in time : 0.7466397285461426\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:19 +0000] \"POST /invocations HTTP/1.1\" 200 126289 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3911, 10)\u001b[0m\n", "\u001b[34mInvoked with 3911 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:19 +0000] \"POST /invocations HTTP/1.1\" 200 126289 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3911, 10)\u001b[0m\n", "\u001b[35mInvoked with 3911 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3852, 10)\u001b[0m\n", "\u001b[34mInvoked with 3852 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3852, 10)\u001b[0m\n", "\u001b[35mInvoked with 3852 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3852,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3852, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mComplete inferencing on (3852,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3852, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3852, 3) in time : 0.7352738380432129\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:20 +0000] \"POST /invocations HTTP/1.1\" 200 138232 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3983, 10)\u001b[0m\n", "\u001b[34mInvoked with 3983 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3983,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3983, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3983, 3) in time : 0.7352843284606934\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:21 +0000] \"POST /invocations HTTP/1.1\" 200 143069 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mTime to execute recs: (3852, 3) in time : 0.7352738380432129\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:20 +0000] \"POST /invocations HTTP/1.1\" 200 138232 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3983, 10)\u001b[0m\n", "\u001b[35mInvoked with 3983 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3983,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3983, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3983, 3) in time : 0.7352843284606934\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:21 +0000] \"POST /invocations HTTP/1.1\" 200 143069 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3857, 10)\u001b[0m\n", "\u001b[34mInvoked with 3857 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3857, 10)\u001b[0m\n", "\u001b[35mInvoked with 3857 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3857,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3857, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3857, 3) in time : 0.7337796688079834\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:22 +0000] \"POST /invocations HTTP/1.1\" 200 138575 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3930, 10)\u001b[0m\n", "\u001b[34mInvoked with 3930 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3857,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3857, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3857, 3) in time : 0.7337796688079834\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:22 +0000] \"POST /invocations HTTP/1.1\" 200 138575 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3930, 10)\u001b[0m\n", "\u001b[35mInvoked with 3930 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3930,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3930, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3930, 3) in time : 0.7278406620025635\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:22 +0000] \"POST /invocations HTTP/1.1\" 200 141221 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[35mComplete inferencing on (3930,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3930, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3930, 3) in time : 0.7278406620025635\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:22 +0000] \"POST /invocations HTTP/1.1\" 200 141221 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3559, 10)\u001b[0m\n", "\u001b[34mInvoked with 3559 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3559, 10)\u001b[0m\n", "\u001b[35mInvoked with 3559 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3559,) records.\u001b[0m\n", "\u001b[35mComplete inferencing on (3559,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3559, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3559, 3) in time : 0.7534174919128418\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:23 +0000] \"POST /invocations HTTP/1.1\" 200 128046 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[35mShape of predictions : (3559, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3559, 3) in time : 0.7534174919128418\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:23 +0000] \"POST /invocations HTTP/1.1\" 200 128046 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3878, 10)\u001b[0m\n", "\u001b[34mInvoked with 3878 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3878,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3878, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3878, 3) in time : 0.7393620014190674\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:24 +0000] \"POST /invocations HTTP/1.1\" 200 139348 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3878, 10)\u001b[0m\n", "\u001b[35mInvoked with 3878 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3878,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3878, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3878, 3) in time : 0.7393620014190674\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:24 +0000] \"POST /invocations HTTP/1.1\" 200 139348 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3684, 10)\u001b[0m\n", "\u001b[34mInvoked with 3684 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3684, 10)\u001b[0m\n", "\u001b[35mInvoked with 3684 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3684,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3684, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3684, 3) in time : 0.7183606624603271\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:25 +0000] \"POST /invocations HTTP/1.1\" 200 132429 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[35mComplete inferencing on (3684,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3684, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3684, 3) in time : 0.7183606624603271\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:25 +0000] \"POST /invocations HTTP/1.1\" 200 132429 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3875, 10)\u001b[0m\n", "\u001b[34mInvoked with 3875 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3875, 10)\u001b[0m\n", "\u001b[35mInvoked with 3875 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3875,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3875, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3875, 3) in time : 0.7285447120666504\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:25 +0000] \"POST /invocations HTTP/1.1\" 200 139263 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3923, 10)\u001b[0m\n", "\u001b[35mComplete inferencing on (3875,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3875, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3875, 3) in time : 0.7285447120666504\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:25 +0000] \"POST /invocations HTTP/1.1\" 200 139263 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3923, 10)\u001b[0m\n", "\u001b[34mInvoked with 3923 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mInvoked with 3923 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3923,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3923, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3923, 3) in time : 0.7283694744110107\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:26 +0000] \"POST /invocations HTTP/1.1\" 200 140926 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[35mComplete inferencing on (3923,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3923, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3923, 3) in time : 0.7283694744110107\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:26 +0000] \"POST /invocations HTTP/1.1\" 200 140926 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3812, 10)\u001b[0m\n", "\u001b[34mInvoked with 3812 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3812,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3812, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3812, 3) in time : 0.7272915840148926\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:27 +0000] \"POST /invocations HTTP/1.1\" 200 136949 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3812, 10)\u001b[0m\n", "\u001b[35mInvoked with 3812 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3812,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3812, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3812, 3) in time : 0.7272915840148926\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:27 +0000] \"POST /invocations HTTP/1.1\" 200 136949 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3814, 10)\u001b[0m\n", "\u001b[34mInvoked with 3814 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3814, 10)\u001b[0m\n", "\u001b[35mInvoked with 3814 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3814,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3814, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3814, 3) in time : 0.756608247756958\u001b[0m\n", "\u001b[35mComplete inferencing on (3814,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3814, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3814, 3) in time : 0.756608247756958\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:28 +0000] \"POST /invocations HTTP/1.1\" 200 136987 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3833, 10)\u001b[0m\n", "\u001b[34mInvoked with 3833 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:28 +0000] \"POST /invocations HTTP/1.1\" 200 136987 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3833, 10)\u001b[0m\n", "\u001b[35mInvoked with 3833 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3833,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3833, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3833, 3) in time : 0.7408621311187744\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:28 +0000] \"POST /invocations HTTP/1.1\" 200 137781 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[35mComplete inferencing on (3833,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3833, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3833, 3) in time : 0.7408621311187744\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:28 +0000] \"POST /invocations HTTP/1.1\" 200 137781 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3932, 10)\u001b[0m\n", "\u001b[34mInvoked with 3932 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3932, 10)\u001b[0m\n", "\u001b[35mInvoked with 3932 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3995, 10)\u001b[0m\n", "\u001b[34mInvoked with 3995 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3995,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3995, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3995, 3) in time : 0.7742941379547119\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:30 +0000] \"POST /invocations HTTP/1.1\" 200 143620 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3995, 10)\u001b[0m\n", "\u001b[35mInvoked with 3995 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3995,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3995, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3995, 3) in time : 0.7742941379547119\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:30 +0000] \"POST /invocations HTTP/1.1\" 200 143620 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3864, 10)\u001b[0m\n", "\u001b[34mInvoked with 3864 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3864, 10)\u001b[0m\n", "\u001b[35mInvoked with 3864 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3864,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3864, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3864, 3) in time : 0.7245891094207764\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:31 +0000] \"POST /invocations HTTP/1.1\" 200 138935 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[35mComplete inferencing on (3864,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3864, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3864, 3) in time : 0.7245891094207764\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:31 +0000] \"POST /invocations HTTP/1.1\" 200 138935 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3993, 10)\u001b[0m\n", "\u001b[34mInvoked with 3993 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3993, 10)\u001b[0m\n", "\u001b[35mInvoked with 3993 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3993,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3993, 3)\u001b[0m\n", "\u001b[35mComplete inferencing on (3993,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3993, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3993, 3) in time : 0.7275593280792236\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:32 +0000] \"POST /invocations HTTP/1.1\" 200 143522 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3165, 10)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3993, 3) in time : 0.7275593280792236\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:32 +0000] \"POST /invocations HTTP/1.1\" 200 143522 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3165, 10)\u001b[0m\n", "\u001b[34mInvoked with 3165 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mInvoked with 3165 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3165,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3165, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3165, 3) in time : 0.6972482204437256\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:32 +0000] \"POST /invocations HTTP/1.1\" 200 113621 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[35mComplete inferencing on (3165,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3165, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3165, 3) in time : 0.6972482204437256\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:32 +0000] \"POST /invocations HTTP/1.1\" 200 113621 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (4000, 10)\u001b[0m\n", "\u001b[34mInvoked with 4000 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (4000,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (4000, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (4000, 3) in time : 0.7474892139434814\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:33 +0000] \"POST /invocations HTTP/1.1\" 200 143791 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (4000, 10)\u001b[0m\n", "\u001b[35mInvoked with 4000 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (4000,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (4000, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (4000, 3) in time : 0.7474892139434814\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:33 +0000] \"POST /invocations HTTP/1.1\" 200 143791 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3731, 10)\u001b[0m\n", "\u001b[34mInvoked with 3731 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3731, 10)\u001b[0m\n", "\u001b[35mInvoked with 3731 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3731,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3731, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mComplete inferencing on (3731,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3731, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3731, 3) in time : 0.7140922546386719\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:34 +0000] \"POST /invocations HTTP/1.1\" 200 134027 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3709, 10)\u001b[0m\n", "\u001b[34mInvoked with 3709 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[35mTime to execute recs: (3731, 3) in time : 0.7140922546386719\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:34 +0000] \"POST /invocations HTTP/1.1\" 200 134027 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3709, 10)\u001b[0m\n", "\u001b[35mInvoked with 3709 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3709,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3709, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3709, 3) in time : 0.6862776279449463\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:34 +0000] \"POST /invocations HTTP/1.1\" 200 133182 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (3284, 10)\u001b[0m\n", "\u001b[34mInvoked with 3284 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (3284,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (3284, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mComplete inferencing on (3709,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3709, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (3709, 3) in time : 0.6862776279449463\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:34 +0000] \"POST /invocations HTTP/1.1\" 200 133182 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (3284, 10)\u001b[0m\n", "\u001b[35mInvoked with 3284 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (3284,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (3284, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (3284, 3) in time : 0.7083842754364014\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:35 +0000] \"POST /invocations HTTP/1.1\" 200 118082 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[34mI am here bby\u001b[0m\n", "\u001b[34mlength of all_Cols 10\u001b[0m\n", "\u001b[34mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[34mcustomer_id int64\u001b[0m\n", "\u001b[34mreview_id object\u001b[0m\n", "\u001b[34mproduct_id object\u001b[0m\n", "\u001b[34mhelpful_votes int64\u001b[0m\n", "\u001b[34mverified_purchase object\u001b[0m\n", "\u001b[34mreview_headline object\u001b[0m\n", "\u001b[34mreview_body object\u001b[0m\n", "\u001b[35mTime to execute recs: (3284, 3) in time : 0.7083842754364014\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:35 +0000] \"POST /invocations HTTP/1.1\" 200 118082 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mI am here bby\u001b[0m\n", "\u001b[35mlength of all_Cols 10\u001b[0m\n", "\u001b[35mColumn type: (Before Feature Engineering) marketplace object\u001b[0m\n", "\u001b[35mcustomer_id int64\u001b[0m\n", "\u001b[35mreview_id object\u001b[0m\n", "\u001b[35mproduct_id object\u001b[0m\n", "\u001b[35mhelpful_votes int64\u001b[0m\n", "\u001b[35mverified_purchase object\u001b[0m\n", "\u001b[35mreview_headline object\u001b[0m\n", "\u001b[35mreview_body object\u001b[0m\n", "\u001b[34mproduct_title object\u001b[0m\n", "\u001b[34mtarget int64\u001b[0m\n", "\u001b[34mdtype: object\u001b[0m\n", "\u001b[34mAfter pre-processing shape of the data: (2661, 10)\u001b[0m\n", "\u001b[34mInvoked with 2661 records\u001b[0m\n", "\u001b[34mRunning inference:-\u001b[0m\n", "\u001b[34mComplete inferencing on (2661,) records.\u001b[0m\n", "\u001b[34mShape of predictions : (2661, 3)\u001b[0m\n", "\u001b[34mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[34mTime to execute recs: (2661, 3) in time : 0.5063936710357666\u001b[0m\n", "\u001b[34m169.254.255.130 - - [23/Sep/2022:16:32:36 +0000] \"POST /invocations HTTP/1.1\" 200 95601 \"-\" \"Go-http-client/1.1\"\u001b[0m\n", "\u001b[35mproduct_title object\u001b[0m\n", "\u001b[35mtarget int64\u001b[0m\n", "\u001b[35mdtype: object\u001b[0m\n", "\u001b[35mAfter pre-processing shape of the data: (2661, 10)\u001b[0m\n", "\u001b[35mInvoked with 2661 records\u001b[0m\n", "\u001b[35mRunning inference:-\u001b[0m\n", "\u001b[35mComplete inferencing on (2661,) records.\u001b[0m\n", "\u001b[35mShape of predictions : (2661, 3)\u001b[0m\n", "\u001b[35mcolumns of predictions : Index(['review_id', 'target', 'score'], dtype='object')\u001b[0m\n", "\u001b[35mTime to execute recs: (2661, 3) in time : 0.5063936710357666\u001b[0m\n", "\u001b[35m169.254.255.130 - - [23/Sep/2022:16:32:36 +0000] \"POST /invocations HTTP/1.1\" 200 95601 \"-\" \"Go-http-client/1.1\"\u001b[0m\n" ] } ], "source": [ "\n", "#### prediction with 200,000 trees\n", "import sagemaker as sage\n", "from time import gmtime, strftime\n", "sess = sage.Session()\n", "\n", "best_job =tuner.best_training_job()\n", "print(\"Best Job name:\" , best_job)\n", "\n", "attached_estimator = sage.estimator.Estimator.attach(best_job)\n", "\n", "output_path ='s3://'+bucket+'/sagemaker/DEMO-GPU-Catboost/data/test-predictions/'\n", "input_path ='s3://'+bucket+'/sagemaker/DEMO-GPU-Catboost/data/test/'\n", "\n", "transformer = attached_estimator.transformer(instance_count=1, \n", " instance_type='ml.p2.xlarge', \n", " assemble_with='Line', \n", " accept='text/csv',\n", " max_payload=1,\n", " output_path=output_path,\n", " env = {'SAGEMAKER_MODEL_SERVER_TIMEOUT' : '3600' })\n", "transformer.transform(input_path, \n", " content_type='text/csv',\n", " split_type='Line')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluation\n", "1. read_predictions: Read results from the batch transform\n", "2. Analyze and plot the PR-AUC and ROC-AUC curve " ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "## Read predictions\n", "\n", "file_name = 's3://'+bucket+'/sagemaker/DEMO-GPU-Catboost/data/test-predictions/file_1.out'\n", "\n", "results = pd.read_csv(file_name, names=['review_id','target','score'] ,sep='\\t',escapechar ='\\\\' , quoting=csv.QUOTE_NONE, \n", " lineterminator='\\n',quotechar='\"').dropna()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/ipykernel/__main__.py:5: MatplotlibDeprecationWarning: The 'warn' parameter of use() is deprecated since Matplotlib 3.1 and will be removed in 3.3. If any parameter follows 'warn', they should be pass as keyword, not positionally.\n" ] } ], "source": [ "### Reading results and Analyzing\n", "from sklearn import metrics\n", "import matplotlib\n", "import pandas as pd\n", "matplotlib.use('agg', warn=False, force=True)\n", "from matplotlib import pyplot as plt\n", "\n", "%matplotlib inline \n", "\n", "def analyze_results(labels, predictions):\n", " precision, recall, thresholds = metrics.precision_recall_curve(labels, predictions)\n", " auc = metrics.auc(recall, precision)\n", " \n", " fpr, tpr, _ = metrics.roc_curve(labels, predictions)\n", " roc_auc_score = metrics.roc_auc_score(labels, predictions)\n", " \n", " print('Neural-Nets: ROC auc=%.3f' % ( roc_auc_score))\n", " \n", " plt.plot(fpr, tpr, label=\"data 1, auc=\" + str(roc_auc_score))\n", " plt.xlabel('1-Specificity')\n", " plt.ylabel('Sensitivity')\n", " plt.legend(loc=4)\n", " plt.show()\n", " \n", " \n", " lr_precision, lr_recall, _ = metrics.precision_recall_curve(labels, predictions)\n", " lr_auc = metrics.auc(lr_recall, lr_precision)\n", " # summarize scores\n", " print('Neural-Nets: PR auc=%.3f' % ( lr_auc))\n", " # plot the precision-recall curves\n", " no_skill = len(labels[labels==1.0]) / len(labels)\n", " plt.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')\n", " \n", " plt.plot(lr_recall, lr_precision, marker='.', label='Neural-Nets')\n", " # axis labels\n", " plt.xlabel('Recall')\n", " plt.ylabel('Precision')\n", " # show the legend\n", " plt.legend()\n", " # show the plot\n", " plt.show()\n", " \n", " \n", " return auc" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Neural-Nets: ROC auc=0.981\n" ] }, { "data": { "image/png": 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\n", 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marketplacecustomer_idreview_idproduct_idproduct_parentproduct_titleproduct_categorystar_ratinghelpful_votestotal_votesvineverified_purchasereview_headlinereview_bodyreview_datetarget
702738US14023492R2XLYL80Q1RGU3B00U7ZPFC649631571The CobblerDigital_Video_Download502NYFive StarsGreat movie with little violence.2015-05-201
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" ], "text/plain": [ " marketplace customer_id review_id product_id product_parent \\\n", "702738 US 14023492 R2XLYL80Q1RGU3 B00U7ZPFC6 49631571 \n", "\n", " product_title product_category star_rating helpful_votes \\\n", "702738 The Cobbler Digital_Video_Download 5 0 \n", "\n", " total_votes vine verified_purchase review_headline \\\n", "702738 2 N Y Five Stars \n", "\n", " review_body review_date target \n", "702738 Great movie with little violence. 2015-05-20 1 " ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test[X_test['review_id']=='R2XLYL80Q1RGU3']" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Great series. Superb acting and the settings are wonderful. I highly recommend it....\n" ] } ], "source": [ "top_bad_reviews =results.sort_values(by =['score'],ascending=True)['review_id'].iloc[0:5]\n", "print(df[df['review_id'].isin(top_bad_reviews)]['review_body'].iloc[4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Results\n", "1. Model achieved an ROC-AUC of 0.981 on the holdout set below are some reviews classified as good and bad by the model\n", "\n", "#### Reviews classified as Good by the model\n", " 1. I love anything Ken Burns does and this is an exceptionally great series. I highly, highly recommend it.\n", " 2. One of the all-time BEST shows ever! Can't wait for the next episode! Addictive!,\n", " 3. I highly recommend watching SOA. Great series can't wait for the movie\n", " 4. I absolutely love this show!!! I highly recommend it!!!\n", " 5. Great series. Superb acting and the settings are wonderful. I highly recommend it....\n", " \n", "#### Reviews classified as Bad by the model \n", " 1. Boring characters,bad acting,cheaply made just overall waste of time.\n", " 2. Boring, slow, waste of time.\n", " 3. Movie was stupid and really boring. I watched or I should say wasted 20 minutes before I turned it off. Terrible!!.\n", " 4.Why can't I give this no stars? Absolute garbage, stupid, boring, poorly written, bad acting, and terrible direction. I won’t waste another word on this trash. I already lost a couple of hours on my life. Stare at a wall for 2 hours instead of watching this.\n", " 5.By far the worst Iron Man movie, I've seen. The story line was predictable
and the acting was notorious. The one line jokes were not funny
and the movie made Iron Man look like a weak hero. I only wasted
five bucks on this movie. However the biggest waste was the 2+hrs
I spent watching this poor excuse of a movie. REAL BAD!!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "conda_tensorflow_p36", "language": "python", "name": "conda_tensorflow_p36" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.13" } }, "nbformat": 4, "nbformat_minor": 4 }