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mppt-inference-module
Commit cec9a86f authored by aakash.bedi's avatar aakash.bedi
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updated inference module

parent f063dfef
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    *.egg-info/
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    *.egg
    MANIFEST
    # PyInstaller
    # Usually these files are written by a python script from a template
    # before PyInstaller builds the exe, so as to inject date/other infos into it.
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    # pyenv
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    # Environments
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    .gitlab-ci.yml 0 → 100644
    View file @ cec9a86f
    stages:
    - auto-tagging
    - validate
    - scan
    - build
    - deploy
    - update
    variables:
    MYSQL_CONNECTION: "mysql -h $MYSQL_HOST -u $MYSQL_USER -p$MYSQL_PASS "
    STATUS_SCRIPT: /home/gitlab-runner/monitor/deployment-status.sh
    HELM_CHART: /home/gitlab-runner/kubernetes/ilens/$QA_ENV/ilens-modules
    VARIABLES_YML: variables.yml
    DEPLOYMENT_YML: $CI_PROJECT_NAME.yml
    TIMEOUT: 960s
    before_script:
    - val=`echo $($MYSQL_CONNECTION -e "SELECT COUNT(*) FROM $VERSION_DB.$DB_TABLE WHERE category='Server' AND type='Service' AND os='docker' AND module_name='$CI_PROJECT_NAME' ") | cut -d " " -f2`
    - if [ $val == 0 ]; then $MYSQL_CONNECTION -e "INSERT INTO $VERSION_DB.$DB_TABLE values('Server','Service','$CI_PROJECT_NAME','docker', '2', '0', '0', '0')";fi
    - QA=$($MYSQL_CONNECTION -N -e "SELECT qa FROM $VERSION_DB.$DB_TABLE where module_name = '$CI_PROJECT_NAME' AND type = 'Service' AND category = 'Server' AND os = 'docker'")
    - DEV=$($MYSQL_CONNECTION -N -e "SELECT dev FROM $VERSION_DB.$DB_TABLE where module_name = '$CI_PROJECT_NAME' AND type = 'Service' AND category = 'Server' AND os = 'docker'")
    - UAT=$(mysql -h $MYSQL_HOST -u $MYSQL_USER -p$MYSQL_PASS -N -e "SELECT uat FROM $VERSION_DB.$DB_TABLE where module_name = '$CI_PROJECT_NAME' AND type = 'Service' AND category = 'Server' AND os = 'docker'")
    - PROD=$($MYSQL_CONNECTION -N -e "SELECT prod FROM $VERSION_DB.$DB_TABLE where module_name = '$CI_PROJECT_NAME' AND type = 'Service' AND category = 'Server' AND os = 'docker'")
    auto-tagging:
    stage: auto-tagging
    before_script:
    - val=`echo $($MYSQL_CONNECTION -e "SELECT COUNT(*) FROM $VERSION_DB.$VERSION_RELEASE_TABLE WHERE module_name='$CI_PROJECT_NAME' ") | cut -d " " -f2`
    - if [ $val == 0 ]; then $MYSQL_CONNECTION -N -e "INSERT INTO $VERSION_DB.$VERSION_RELEASE_TABLE values('$CI_PROJECT_NAME', 'iLens', '6', '3', '0', '0')";fi
    - ILENS=$($MYSQL_CONNECTION -N -e "SELECT ilens_version FROM "$VERSION_DB.$VERSION_RELEASE_TABLE" where module_name = '$CI_PROJECT_NAME'")
    - RELEASE=$($MYSQL_CONNECTION -N -e "SELECT release_version FROM "$VERSION_DB.$VERSION_RELEASE_TABLE" where module_name = '$CI_PROJECT_NAME'")
    - FEATURE=$($MYSQL_CONNECTION -N -e "SELECT feature_version FROM "$VERSION_DB.$VERSION_RELEASE_TABLE" where module_name = '$CI_PROJECT_NAME'")
    - PATCH=$($MYSQL_CONNECTION -N -e "SELECT patch_version FROM "$VERSION_DB.$VERSION_RELEASE_TABLE" where module_name = '$CI_PROJECT_NAME'")
    script:
    - SOURCE_BRANCH=$(echo $CI_COMMIT_TITLE | cut -f 3 -d " " | cut -f 1 -d "/" | cut -f 2 -d "'")
    - >
    if [ "$SOURCE_BRANCH" = "QA" ]; then
    ((RELEASE=RELEASE+1)) && FEATURE=0 && PATCH=0;
    TAG_NAME=v$ILENS.$RELEASE.$FEATURE
    IMAGE_URL=azrilensprod.azurecr.io/ilens/release/versions/v"$ILENS.$RELEASE:$CI_PROJECT_NAME-$TAG_NAME"
    PROD=$RELEASE; QA=0; DEV=0;
    $MYSQL_CONNECTION -e "UPDATE $VERSION_DB.$DB_TABLE SET prod='$PROD' ,qa='$QA', dev='$DEV' WHERE module_name='$CI_PROJECT_NAME' AND type='Service' AND category='Server' AND os='docker'"
    elif [ $SOURCE_BRANCH == "feature" ]; then
    ((FEATURE=FEATURE+1)) && PATCH=0;
    TAG_NAME=v$ILENS.$RELEASE.$FEATURE
    IMAGE_URL=azrilensprod.azurecr.io/ilens/release/versions/v"$ILENS.$RELEASE:$CI_PROJECT_NAME-$TAG_NAME"
    elif [ $SOURCE_BRANCH == "patch" ]; then
    ((PATCH=PATCH+1));
    TAG_NAME=v$ILENS.$RELEASE.$FEATURE.$PATCH
    IMAGE_URL=azrilensprod.azurecr.io/ilens/release/versions/v"$ILENS.$RELEASE:$CI_PROJECT_NAME-$TAG_NAME"
    else
    exit 1
    fi
    - echo -e "\n\nImage:" $IMAGE_URL >> ReleaseNote.txt
    - sed -i "1s|^|Version":" $TAG_NAME\n|" ReleaseNote.txt
    - sed -i "1s|^|Module Name":" $CI_PROJECT_NAME\n|" ReleaseNote.txt
    - docker build -t $IMAGE_URL .
    - docker push $IMAGE_URL
    - docker rmi --force $IMAGE_URL
    - URL=$(echo $CI_PROJECT_URL | sed 's|https://||')
    - git remote set-url origin https://$GIT_USRNAME:$GIT_USRPASSWD@$URL
    - git config user.email "devopsilens@gmail.com"
    - git config user.name "$GIT_USRNAME"
    - git tag -a $TAG_NAME -F ReleaseNote.txt
    - git push origin $TAG_NAME
    - $MYSQL_CONNECTION -e "UPDATE $VERSION_DB.$VERSION_RELEASE_TABLE SET release_version='$RELEASE', feature_version='$FEATURE', patch_version='$PATCH' WHERE module_name = '$CI_PROJECT_NAME' "
    - $MYSQL_CONNECTION -e "INSERT INTO $HISTORY_DB.$VERSION_RELEASE_TABLE values('$CI_JOB_ID', '$CI_PROJECT_NAME','iLens', '$ILENS.$RELEASE.$FEATURE', '$CI_COMMIT_SHA', '$GITLAB_USER_NAME', '$CI_COMMIT_REF_NAME')"
    tags:
    - shell
    only:
    - master
    #~~~~~| Requirements.txt version check |~~~~~#
    package-version-check:
    stage: validate
    script:
    - REQUIREMENTS=$(cat requirements.txt)
    - FAILED=0
    - >
    for REQ in ${REQUIREMENTS[@]};
    do
    PKG=$(echo $REQ | tr = " " | awk '{print $1}')
    VER=$(echo $REQ | tr = " " | awk '{print $2}')
    VER=${VER//[^[:alnum:]]/}
    if [ ! -z "${VER//[0-9]}" ] || [ -z $VER ]; then
    echo " Package version not specified for: $PKG "
    FAILED=`expr $FAILED + 1`
    fi
    done
    - if [ $FAILED -gt 0 ]; then exit 1; fi
    only:
    - QA
    tags:
    - shell
    #~~~~~| QA K8 |~~~~~#
    qa-k8-deployment:
    stage: deploy
    script:
    - REGISTRY_URL=azacrknowledgelens.azurecr.io/knowledgelens/products/ilens/qa
    - export KUBECONFIG=/home/gitlab-runner/.kube/$QA_ENV
    - NAMESPACE=ilens-core
    - QA=`expr $QA + 1` && DEV=0
    - docker build -t $REGISTRY_URL/$CI_PROJECT_NAME:v$PROD.$QA.$DEV .
    - docker push $REGISTRY_URL/$CI_PROJECT_NAME:v$PROD.$QA.$DEV
    - echo "Deploying $CI_PROJECT_NAME"
    - >
    for YML in ${DEPLOYMENT_YML[@]};
    do
    FILE_PATH=$HELM_CHART/$YML
    SERVICE=$(echo $YML | cut -f 1 -d "." )
    CURR_VERSION=$(cat $FILE_PATH | grep "imageName:" )
    CURR_VERSION=$(echo $CURR_VERSION | cut -f 3 -d ":")
    echo " Deploying $SERVICE"
    echo " $SERVICE Version: $CURR_VERSION"
    sed -E -i'' "s|(.*imageName:.*"$REGISTRY_URL"/).*|\1"$CI_PROJECT_NAME":v"$PROD.$QA.$DEV"|" $FILE_PATH
    helm upgrade --install $SERVICE $HELM_CHART -f $FILE_PATH -f $VARIABLES_YML -n $NAMESPACE --history-max 1
    if ! sh $STATUS_SCRIPT $SERVICE $NAMESPACE $TIMEOUT ; then
    sed -E -i'' "s|(.*imageName:.*"$REGISTRY_URL"/).*|\1"$CI_PROJECT_NAME":"$CURR_VERSION"|" $FILE_PATH
    helm upgrade --install $SERVICE $HELM_CHART -f $FILE_PATH -f $VARIABLES_YML -n $NAMESPACE --history-max 1
    echo " $SERVICE Reverted to the previous version..."
    exit 1
    fi
    UI_POD=$(kubectl get pods -n $NAMESPACE | grep ilens-ui | awk '{print $1}')
    kubectl delete pod $UI_POD -n $NAMESPACE
    done
    only:
    - QA
    tags:
    - shell
    #~~~~~| Vulnerability Scanner |~~~~~#
    vulnerability-scanner:
    stage: scan
    script:
    - QA=`expr $QA + 1` && DEV=0
    - DOCKER_IMAGE=$CI_PROJECT_NAME:vulnarable-scan
    - docker build -t $DOCKER_IMAGE --no-cache .
    - trivy image --format template --template "@/home/gitlab-runner/image-scanner/templates/html.tpl" -o imageScanner-$CI_PROJECT_NAME.html $DOCKER_IMAGE
    - trivy image --format json -o imageScanner-$CI_PROJECT_NAME.json $DOCKER_IMAGE
    - docker rmi --force $DOCKER_IMAGE
    - mv imageScanner-$CI_PROJECT_NAME.html /data0/email-util/module/reports/
    - >
    if ! /home/gitlab-runner/image-scanner/severity_check imageScanner-$CI_PROJECT_NAME.json ; then
    cd /home/gitlab-runner/image-scanner/
    ./mail imageScanner-$CI_PROJECT_NAME.html $DOCKER_IMAGE
    fi
    only:
    - QA
    tags:
    - shell
    tag-update-qa:
    stage: update
    script:
    - QA=`expr $QA + 1` && DEV=0
    - REGISTRY_URL=azacrknowledgelens.azurecr.io/knowledgelens/products/ilens/qa
    - docker rmi --force $REGISTRY_URL/$CI_PROJECT_NAME:v$PROD.$QA.$DEV
    - $MYSQL_CONNECTION -e "INSERT INTO $HISTORY_DB.$DB_TABLE values('$CI_JOB_ID','Server','Service', '$CI_PROJECT_NAME','docker', '$PROD.$QA.$DEV', '$CI_COMMIT_SHA', '$GITLAB_USER_NAME', '$CI_COMMIT_REF_NAME')"
    - $MYSQL_CONNECTION -e "UPDATE $VERSION_DB.$DB_TABLE SET prod='$PROD' ,qa='$QA', dev='$DEV' WHERE module_name = '$CI_PROJECT_NAME' AND type = 'Service' AND category = 'Server' AND os = 'docker'"
    dependencies:
    - qa-k8-deployment
    only:
    - QA
    tags:
    - shell
    #~~~~~| DEV 220 |~~~~~#
    dev-deployment-220:
    stage: deploy
    script:
    - tar czvf $CI_PROJECT_NAME.tar.gz *
    - echo "Deploying to the dev 220 server..."
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "mkdir -p /tmp/$CI_PROJECT_NAME/tar/"
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "mkdir -p /tmp/$CI_PROJECT_NAME/untar/"
    - sshpass -p $OFC_PASSWD scp $CI_PROJECT_NAME.tar.gz $OFC_USERNAME@$OFC_HOSTNAME:/tmp/$CI_PROJECT_NAME/tar/
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "tar xzvf /tmp/$CI_PROJECT_NAME/tar/$CI_PROJECT_NAME.tar.gz -C /tmp/$CI_PROJECT_NAME/untar/"
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "rsync -r /tmp/$CI_PROJECT_NAME/untar/* /opt/services/ilens2.0/$CI_PROJECT_NAME/"
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "/home/svc-ilens/.miniconda3/envs/ilens37/bin/pip install -r /opt/services/ilens2.0/$CI_PROJECT_NAME/requirements.txt"
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "sudo systemctl restart ilens_2.0_dev_$CI_PROJECT_NAME.service"
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "sudo systemctl status ilens_2.0_dev_$CI_PROJECT_NAME.service"
    after_script:
    - sshpass -p $OFC_PASSWD ssh $OFC_USERNAME@$OFC_HOSTNAME "rm -rf /tmp/$CI_PROJECT_NAME"
    - rm -f $CI_PROJECT_NAME.tar.gz
    only:
    - develop
    tags:
    - shell
    #~~~~~| CODE QUALITY |~~~~~#
    codequality:
    stage: deploy
    image: azacrknowledgelens.azurecr.io/knowledgelens/klit-operation/devops/gitlab-runner:ubuntu-sonarscanner
    script:
    - /opt/sonar-scanner/bin/sonar-scanner -Dsonar.projectKey=$CI_PROJECT_NAME -Dsonar.projectName=$CI_PROJECT_NAME -Dsonar.typescript.node=./node/node -Dsonar.login=admin -Dsonar.password=admin -Dsonar.sources=.
    - sleep 5
    - python3 /opt/code_quality_report/static_code_quality_report_csv_v2.py $CI_PROJECT_NAME $GITLAB_USER_EMAIL,$EMAIL_TO $EMAIL_FROM $EMAIL_PASSWD False admin admin
    only:
    - develop
    tags:
    - docker
    Dockerfile 0 → 100644
    View file @ cec9a86f
    FROM python:3.9-buster
    RUN apt-get update -y && \
    apt install -y openjdk-11-jre && \
    apt install -y libtiff5-dev libjpeg62-turbo-dev && \
    apt install -y zlib1g-dev libfreetype6-dev liblcms2-dev libwebp-dev tcl8.6-dev && \
    apt install -y tk8.6-dev python-tk pdftk libmagickwand-dev
    COPY . /code
    WORKDIR /code
    RUN pip install -r requirements.txt
    CMD [ "python","app.py" ]
    \ No newline at end of file
    app.py 0 → 100644
    View file @ cec9a86f
    if __name__ == "__main__":
    from dotenv import load_dotenv
    load_dotenv(dotenv_path='config.env')
    import warnings
    from loguru import logger
    import tracemalloc
    import gc
    import numpy as np
    from scripts.core.engine.mppt_data import GetData
    from scripts.core.engine.tags_data import get_tags_data
    from scripts.utils.start_end_date import KairosStartEndDate
    from scripts.core.engine.final_tags import GetFinalDf
    from scripts.core.engine.data_training_and_inference import Inference
    from scripts.core.engine.final_predictions import ai_modelling
    from scripts.core.engine.raw_predicted_tags import get_raw_predicted_tags
    from scripts.core.data_puller_push.kafka_push import CalculatedDataPush
    warnings.filterwarnings("ignore")
    start_date, end_date, start_timestamp, end_timestamp = KairosStartEndDate().start_end_date()
    def orchestrator():
    try:
    tracemalloc.start()
    print(f'memory allocation - {tracemalloc.get_traced_memory()}')
    gc.collect()
    print(f'memory allocation - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces()
    print(f'memory allocation - {tracemalloc.get_traced_memory()}')
    df_raw_tags, df_predicted_tags, df_coefficients = get_raw_predicted_tags()
    logger.info(f'raw tags dataframe shape - {df_raw_tags.shape}')
    logger.info(f'predicted tags dataframe shape - {df_predicted_tags.shape}')
    for inv_id in list(df_raw_tags['inv_id'].unique()):
    df = df_raw_tags[df_raw_tags['inv_id'] == inv_id]
    for mppt_id in list(df_raw_tags['mppt_id'].unique()):
    print(f'1st memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    gc.collect()
    print(f'2nd memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces()
    print(f'3rd memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    df_mppt_level = df[df['mppt_id'] == mppt_id]
    df_kairos_data = get_tags_data(df_input_tags=df_mppt_level, start_timestamp=start_timestamp,
    end_timestamp=end_timestamp, inv_id=inv_id, mppt_id=mppt_id)
    logger.info(f'Shape of final df - {df_kairos_data.shape}')
    print(f'4th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    gc.collect()
    print(f'5th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces()
    print(f'6th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    mppt_data = GetData()
    df_mppt = mppt_data.associate_inv_mppt_id(df=df_kairos_data)
    print(f'7th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    gc.collect()
    print(f'8th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces()
    print(f'9th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    df_coefficient_multiply = mppt_data.multiply_mppt_coefficients(df_mppt=df_mppt,
    df_coefficients=df_coefficients)
    print(f'10th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    gc.collect()
    print(f'11th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces()
    print(f'12th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    x_test, y_test, predictions = Inference(df=df_coefficient_multiply).data_inference(inv_id=inv_id, mppt_id=mppt_id)
    get_final_df = GetFinalDf()
    df_result = get_final_df.get_final_data(x_test=x_test,
    y_test=y_test,
    predictions=predictions)
    final_dict = get_final_df.get_final_predicted_tags(df_predicted_current_tags=df_predicted_tags,
    inv_id=inv_id, mppt_id=mppt_id)
    df_result["timestamp"] = df_result["datetime"].values.astype(np.int64) / 10 ** 9
    df_result["timestamp"] = df_result["timestamp"].astype('int')
    CalculatedDataPush(df_result=df_result, final_tags_dict=final_dict).kafka_data_push()
    logger.info(f'{final_dict}')
    del df_kairos_data
    del df_mppt
    del df_coefficient_multiply
    print(f'16th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    gc.collect()
    print(f'17th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces()
    print(f'18th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    print(f'final allocation for {inv_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.stop()
    logger.info(f'data inference has been completed !')
    except Exception as e:
    logger.exception(f'Exception - {e}')
    if __name__ == '__main__':
    orchestrator()
    catboost_info/catboost_training.json 0 → 100644
    View file @ cec9a86f
    {
    "meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"RMSE"}],"launch_mode":"Train","parameters":"","iteration_count":1000,"learn_sets":["learn"],"name":"experiment"},
    "iterations":[
    {"learn":[0.2716334562],"iteration":0,"passed_time":0.01072461139,"remaining_time":10.71388678},
    {"learn":[0.2591449693],"iteration":1,"passed_time":0.01402836638,"remaining_time":7.000154822},
    {"learn":[0.2472572955],"iteration":2,"passed_time":0.02063816517,"remaining_time":6.858750226},
    {"learn":[0.2359791925],"iteration":3,"passed_time":0.02737476344,"remaining_time":6.816316095},
    {"learn":[0.2255066505],"iteration":4,"passed_time":0.03922844912,"remaining_time":7.806461376},
    {"learn":[0.2154587847],"iteration":5,"passed_time":0.06108355729,"remaining_time":10.11950932},
    {"learn":[0.2062675881],"iteration":6,"passed_time":0.07117975382,"remaining_time":10.09735651},
    {"learn":[0.1974915883],"iteration":7,"passed_time":0.08150786157,"remaining_time":10.10697483},
    {"learn":[0.189124107],"iteration":8,"passed_time":0.08493989151,"remaining_time":9.352825832},
    {"learn":[0.1813406563],"iteration":9,"passed_time":0.09418001656,"remaining_time":9.323821639},
    {"learn":[0.1738157701],"iteration":10,"passed_time":0.1001065841,"remaining_time":9.000491973},
    {"learn":[0.1669904429],"iteration":11,"passed_time":0.1033227528,"remaining_time":8.506906649},
    {"learn":[0.1602706547],"iteration":12,"passed_time":0.1048648935,"remaining_time":7.961665374},
    {"learn":[0.1542243536],"iteration":13,"passed_time":0.1090489775,"remaining_time":7.680163698},
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    catboost_info/time_left.tsv 0 → 100644
    View file @ cec9a86f
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    950 1943 100
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    969 1971 60
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    975 1979 48
    976 1981 46
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    978 1984 42
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    980 1987 38
    981 1988 36
    982 1989 34
    983 1991 32
    984 1992 30
    985 1994 28
    986 1995 26
    987 1997 24
    988 1998 22
    989 1999 20
    990 2001 18
    991 2002 16
    992 2003 14
    993 2005 12
    994 2006 10
    995 2007 8
    996 2009 6
    997 2010 4
    998 2011 2
    999 2013 0
    4 4383 66
    985 4384 62
    986 4386 57
    987 4387 53
    988 4389 48
    989 4390 44
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    995 4399 17
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    998 4403 4
    999 4405 0
    conf/application.conf 0 → 100644
    View file @ cec9a86f
    [LOGGING]
    level = $LOG_LEVEL
    traceback = $LOG_TRACEBACK
    [MODULE]
    name = $APP_NAME
    [KAIROS_DB]
    uri=$KAIROS_URI
    metric_name=$KAIROS_METRIC
    aggregator=$AGGREGATOR
    aggregator_value=$AGGREGATOR_VALUE
    aggregator_unit=$AGGREGATOR_UNIT
    [KAFKA]
    kafka_host=$KAFKA_HOST
    kafka_port=$KAFKA_PORT
    kafka_topic=$KAFKA_TOPIC
    [DATE_RANGE]
    start_date=$START_DATE
    end_date=$END_DATE
    start_relative_days=$START_RELATIVE
    end_relative_days=$END_RELATIVE
    [EMAIL_DETAILS]
    email_reciever=$RECIEVER_EMAIL
    email_sender=$SENDER_EMAIL
    [MONGO]
    mongo_uri=$MONGO_URI
    project_id=$PROJECT_ID
    query_filter=$QUERY_FILTER
    [TIMEZONE]
    required_tz=$REQUIRED_TZ
    [MLFLOW]
    mlflow_tracking_uri=$MLFLOW_TRACKING_URI
    mlflow_tracking_username=$MLFLOW_TRACKING_USERNAME
    mlflow_tracking_password=$MLFLOW_TRACKING_PASSWORD
    azure_storage_connection_string=$AZURE_STORAGE_CONNECTION_STRING
    azure_storage_access_key=$AZURE_STORAGE_ACCESS_KEY
    user=$USER
    experiment_name=$EXPERIMENT_NAME
    run_name=$RUN_NAME
    model_name=$MODEL_NAME
    check_param=$CHECK_PARAM
    model_check_param=$MODEL_CHECK_PARAM
    [PYCARET]
    models_list=$MODELS_LIST
    selected_metric=$SELECTED_METRIC
    hyperparameter_tuning_method=$HYPERPARAMETER_TUNING_METHOD
    conf/data.yml 0 → 100644
    View file @ cec9a86f
    inv_list:
    - inv_1
    - inv_2
    - inv_3
    - inv_4
    - inv_5
    - inv_6
    - inv_7
    - inv_8
    - inv_9
    - inv_10
    - inv_11
    - inv_12
    - inv_13
    - inv_14
    - inv_15
    - inv_16
    - inv_17
    - inv_18
    - inv_19
    - inv_20
    - inv_21
    - inv_22
    - inv_23
    - inv_24
    - inv_25
    - inv_26
    - inv_27
    - inv_28
    - inv_29
    - inv_30
    - inv_31
    - inv_32
    - inv_33
    - inv_34
    - inv_35
    - inv_36
    - inv_37
    - inv_38
    - inv_39
    - inv_40
    - inv_41
    - inv_42
    conf/engine.yml 0 → 100644
    View file @ cec9a86f
    tag_heirarcy:
    D1D001_consumptions_Opening_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6452
    source: calculated
    dependency:
    - D1D001_consumptions_Closing_DPR
    D1D001_consumptions_Day_Cons_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6456
    source: calculated
    dependency:
    - D1D001_consumptions_Opening_DPR
    - D1D001_consumptions_Day_Receipt_DPR
    - D1D001_consumptions_Day_Recovery_DPR
    - D1D001_consumptions_Closing_DPR
    7302011030_Consumptions_Opening_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6487
    source: calculated
    dependency:
    - 7302011030_Consumptions_Closing_DPR
    7302011030_Consumptions_Day_Cons_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6490
    source: calculated
    dependency:
    - D1D001_consumptions_Opening_DPR
    - D1D001_consumptions_Day_Receipt_DPR
    - D1D001_consumptions_Day_Recovery_DPR
    - D1D001_consumptions_Closing_DPR
    7302011030_Consumptions_Closing_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6491
    source: manual
    dependency:
    - None
    7302011061_Consumption_Opening_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6492
    source: calculated
    dependency:
    - 7302011061_Consumption_Closing_DPR
    Crude_Prod_Day_Prod_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4305$tag_6460
    source: calculated
    dependency:
    - D1D001_consumptions_Day_Cons_DPR
    - D1D001Readings_Conv_DPR
    Pure_Production_Opening_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6497
    source: calculated
    dependency:
    - Pure_Production_Closing_of_Pure_Tanks_only_DPR
    Pure_Production_Day_Prod_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6496
    source: calculated
    dependency:
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Power_Norms:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6470
    source: calculated
    dependency:
    - Utility_report_Day_Power
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Steam_Norms:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6471
    source: calculated
    dependency:
    - Utility_report_Day_Steam
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Raffinate_Norms:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6472
    source: calculated
    dependency:
    - Utility_report_Day_Raffinate
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Raffinate_Vent_Gas:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6473
    source: calculated
    dependency:
    - Utility_report_Vent_Gas_Raffinate
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Raw_Water_Norms:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6474
    source: calculated
    dependency:
    - Utility_report_Day_Treated_Water
    - Crude_Prod_Day_Prod_DPR
    Utility_report_per_hr_burn_rate:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6483
    source: calculated
    dependency:
    - Utility_report_Raffinate_Incinerated
    D1D001Readings_T_2703_A_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6447
    source: manual
    dependency: None
    D1D001Readings_T_2703_B_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6448
    source: manual
    dependency: None
    D1D001Readings_Conv_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6451
    source: manual
    dependency: None
    D1D001_consumptions_Day_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6453
    source: manual
    dependency: None
    D1D001_consumptions_Day_Recovery_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6454
    source: calculated_depends_previous_value
    dependency: None
    7302011030_Consumptions_Day_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6488
    source: manual
    dependency: None
    7302011061_Consumption_Day_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6493
    source: manual
    dependency: None
    7302011061_Consumption_Day_Cons_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6459
    source: manual
    dependency: None
    Pure_Production_Day_Nia_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6462
    source: manual
    dependency: None
    Pure_Production_Day_Drum_Filling_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6464
    source: manual
    dependency: None
    Pure_Production_Pure_tank_Dead_Volumes_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6469
    source: manual
    dependency: None
    Utility_report_Day_Power:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6475
    source: manual
    dependency: None
    Utility_report_Day_Steam:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6477
    source: manual
    dependency: None
    Utility_report_Day_Raffinate:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6478
    source: manual
    dependency: None
    Utility_report_Vent_Gas_Raffinate:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6479
    source: manual
    dependency: None
    Utility_report_Day_DM:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6480
    source: manual
    dependency: None
    Utility_report_Day_Treated_Water:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6481
    source: manual
    dependency: None
    Utility_report_Raffinate_Incinerated:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6482
    source: manual
    dependency: None
    D1D001Readings_T_2101_A_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6449
    source: dcs
    dependency: None
    D1D001Readings_T_2101_B_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6450
    source: dcs
    dependency: None
    Pure_Production_LT_2701_A_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6466
    source: dcs
    dependency: None
    Pure_Production_LT_2701_B_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6467
    source: dcs
    dependency: None
    D1D001_consumptions_Closing_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6457
    source: calculated
    dependency:
    - D1D001Readings_T_2703_A_DPR
    - D1D001Readings_T_2703_B_DPR
    - D1D001Readings_T_2101_A_DPR
    - D1D001Readings_T_2101_B_DPR
    D1D001_consumptions_Total_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4298$tag_6455
    source: calculated_depends_previous_value
    dependency:
    - D1D001_consumptions_Total_Receipt_DPR
    - D1D001_consumptions_Day_Receipt_DPR
    7302011030_Consumptions_Total_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6489
    source: calculated_depends_previous_value
    dependency:
    - 7302011030_Consumptions_Total_Receipt_DPR
    - 7302011030_Consumptions_Day_Receipt_DPR
    7302011030_Consumptions_Total_Cons_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4299$tag_6458
    source: calculated_depends_previous_value
    dependency:
    - 7302011030_Consumptions_Total_Cons_DPR
    - 7302011030_Consumptions_Day_Cons_DPR
    7302011061_Consumption_Closing_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6499
    source: calculated_depends_previous_value
    dependency:
    - 7302011061_Consumption_Opening_DPR
    - 7302011061_Consumption_Day_Receipt_DPR
    - 7302011061_Consumption_Day_Cons_DPR
    7302011061_Consumption_Total_Receipt_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6495
    source: calculated_depends_previous_value
    dependency:
    - 7302011061_Consumption_Total_Receipt_DPR
    - 7302011030_Consumptions_Day_Receipt_DPR
    7302011061_Consumption_Total_Cons_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4302$tag_6498
    source: calculated_depends_previous_value
    dependency:
    - 7302011061_Consumption_Total_Cons_DPR
    - 7302011061_Consumption_Day_Cons_DPR
    Crude_Prod_Total_Prod_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4305$tag_6461
    source: calculated_depends_previous_value
    dependency:
    - Crude_Prod_Total_Prod_DPR
    - Crude_Prod_Day_Prod_DPR
    Pure_Production_Closing_of_Pure_Tanks_only_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6468
    source: calculated_depends_previous_value
    dependency:
    - Pure_Production_LT_2701_A_DPR
    - Pure_Production_LT_2701_B_DPR
    - Pure_Production_Pure_tank_Dead_Volumes_DPR
    Pure_Production_Total_Prod_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6494
    source: calculated_depends_previous_value
    dependency:
    - Pure_Production_Total_Prod_DPR
    - Pure_Production_Day_Prod_DPR
    Pure_Production_Total_Nia_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6463
    source: calculated_depends_previous_value
    dependency:
    - Pure_Production_Total_Nia_DPR
    - Pure_Production_Day_Nia_DPR
    Pure_Production_Total_Drum_Filling_DPR:
    column_tag: site_116$dept_134$line_350$equipment_4306$tag_6465
    source: calculated_depends_previous_value
    dependency:
    - Pure_Production_Total_Drum_Filling_DPR
    - Pure_Production_Day_Drum_Filling_DPR
    Utility_report_Actual_Ammonia_Norms:
    column_tag: site_116$dept_134$line_350$equipment_4179$tag_6486
    source: calculated
    dependency:
    - 7302011030_Consumptions_Day_Cons_DPR
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Actual_Beta_Norms:
    column_tag: site_116$dept_134$line_350$equipment_4179$tag_6724
    source: calculated
    dependency:
    - D1D001_consumptions_Day_Cons_DPR
    - D1D001_consumptions_Day_Recovery_DPR
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Actual_Benzene_Norms:
    column_tag: site_116$dept_134$line_350$equipment_4179$tag_6725
    source: calculated
    dependency:
    - 7302011061_Consumption_Day_Cons_DPR
    - Crude_Prod_Day_Prod_DPR
    Utility_report_Day_DM_norm:
    column_tag: site_116$dept_135$line_366$equipment_4307$tag_6915
    source: calculated
    dependency: Utility_report_Day_DM
    Beta_Purification_Column_C_2409_Outlet_Flow_TZ:
    column_tag: site_116$dept_134$line_351$equipment_4209$tag_5372
    source: manual
    dependency: None
    config.env 0 → 100644
    View file @ cec9a86f
    APP_NAME=dalmia-solar-degradation
    KAIROS_URI=https://iLens:iLensDAL$456@dalmia.ilens.io/kairos/
    KAIROS_METRIC=ilens.live_data.raw
    AGGREGATOR=max
    AGGREGATOR_VALUE=15
    AGGREGATOR_UNIT=minutes
    KAFKA_HOST=192.168.0.220
    KAFKA_PORT=9092
    KAFKA_TOPIC=ilens_dev
    START_RELATIVE=1
    END_RELATIVE=1
    REQUIRED_TZ="Asia/Kolkata"
    MONGO_URI=mongodb://admin:iLensDevMongo783@192.168.0.220:2717/
    PROJECT_ID=project_101
    QUERY_FILTER=dalmia_string_level_tags
    MLFLOW_TRACKING_URI=https://qa.unifytwin.com/mlflow/
    MLFLOW_TRACKING_USERNAME=mlflow
    MLFLOW_TRACKING_PASSWORD=MlFlOwQA#4321
    AZURE_STORAGE_CONNECTION_STRING=DefaultEndpointsProtocol=https;AccountName=azrmlilensqa006382180551;AccountKey=tDGOKfiZ2svfoMvVmS0Fbpf0FTHfTq4wKYuDX7cAxlhve/3991QuzdvJHm9vWc+lo6mtC+x9yPSghWNR4+gacg==;EndpointSuffix=core.windows.net
    AZURE_STORAGE_ACCESS_KEY=tDGOKfiZ2svfoMvVmS0Fbpf0FTHfTq4wKYuDX7cAxlhve/3991QuzdvJHm9vWc+lo6mtC+x9yPSghWNR4+gacg==
    USER=Dalmia_degradation
    EXPERIMENT_NAME=Dalmia Solar Degradation2
    RUN_NAME=Degradation
    MODEL_NAME=versioning
    CHECK_PARAM=hours
    MODEL_CHECK_PARAM=480
    MODELS_LIST=lr,knn,gbr,rf,catboost,lightgbm,ada,et,xgboost,dt,en,par,huber
    SELECTED_METRIC=R2
    HYPERPARAMETER_TUNING_METHOD=scikit-learn
    \ No newline at end of file
    data_folder/df_merged.csv 0 → 100644
    View file @ cec9a86f
    This source diff could not be displayed because it is too large. You can view the blob instead.
    scripts/constants/app_configuration.py 0 → 100644
    View file @ cec9a86f
    if __name__ == "__main__":
    from dotenv import load_dotenv
    load_dotenv(dotenv_path='config.env')
    import os
    import os.path
    import sys
    from configparser import ConfigParser, BasicInterpolation
    import yaml
    from loguru import logger
    # Configuring file constants
    data_conf = "./conf/data.yml"
    class EnvInterpolation(BasicInterpolation):
    """
    Interpolation which expands environment variables in values.
    """
    def before_get(self, parser, section, option, value, defaults):
    value = super().before_get(parser, section, option, value, defaults)
    if not os.path.expandvars(value).startswith("$"):
    return os.path.expandvars(value)
    else:
    return
    try:
    config = ConfigParser(interpolation=EnvInterpolation())
    config.read(f"conf/application.conf")
    except Exception as e:
    print(f"Error while loading the config: {e}")
    print("Failed to Load Configuration. Exiting!!!")
    sys.exit()
    class KairosDb:
    uri = config["KAIROS_DB"]["uri"]
    metric_name = config['KAIROS_DB']['metric_name']
    aggregator = config['KAIROS_DB']['aggregator']
    aggregator_value = config['KAIROS_DB']['aggregator_value']
    aggregator_unit = config['KAIROS_DB']['aggregator_unit']
    class Kafka:
    kafka_host = config["KAFKA"]["kafka_host"]
    kafka_port = config["KAFKA"]["kafka_port"]
    kafka_topic = config["KAFKA"]["kafka_topic"]
    class DateRange:
    start_date = config.get("DATE_RANGE", "start_date")
    end_date = config.get("DATE_RANGE", "end_date")
    start_relative_days = config.get("DATE_RANGE", "start_relative_days")
    end_relative_days = config.get("DATE_RANGE", "end_relative_days")
    class ReqTimeZone:
    required_tz = config.get('TIMEZONE', 'required_tz')
    class MlFlow:
    mlflow_tracking_uri = config['MLFLOW']['mlflow_tracking_uri']
    mlflow_tracking_username = config['MLFLOW']['mlflow_tracking_username']
    mlflow_tracking_password = config['MLFLOW']['mlflow_tracking_password']
    azure_storage_connection_string = config['MLFLOW']['azure_storage_connection_string']
    azure_storage_access_key = config['MLFLOW']['azure_storage_access_key']
    user = config['MLFLOW']['user']
    experiment_name = config['MLFLOW']['experiment_name']
    run_name = config['MLFLOW']['run_name']
    model_name = config['MLFLOW']['model_name']
    check_param = config['MLFLOW']['check_param']
    model_check_param = config['MLFLOW']['model_check_param']
    class PycaretParams:
    model_list = config['PYCARET']['models_list']
    selected_metric = config['PYCARET']['selected_metric']
    hyperparameter_tuning_method= config['PYCARET']['hyperparameter_tuning_method']
    class Mongo:
    mongo_uri = config["MONGO"]["mongo_uri"]
    project_id = config["MONGO"]["project_id"]
    query_filter = config["MONGO"]["query_filter"]
    scripts/constants/app_constants.py 0 → 100644
    View file @ cec9a86f
    json_file_path = "scripts/utils/"
    class MongoConstants:
    # DB
    db = "ilens_ai"
    # collections
    collection = "dalmiaStringTags"
    scripts/core/analytics_notebook/Untitled.ipynb 0 → 100644
    View file @ cec9a86f
    This source diff could not be displayed because it is too large. You can view the blob instead.
    scripts/core/analytics_notebook/catboost_info/catboost_training.json 0 → 100644
    View file @ cec9a86f
    {
    "meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"RMSE"}],"launch_mode":"Train","parameters":"","iteration_count":1000,"learn_sets":["learn"],"name":"experiment"},
    "iterations":[
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    scripts/core/analytics_notebook/catboost_info/time_left.tsv 0 → 100644
    View file @ cec9a86f
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    scripts/core/analytics_notebook/current_analytics.ipynb 0 → 100644
    View file @ cec9a86f
    This source diff could not be displayed because it is too large. You can view the blob instead.
    scripts/core/analytics_notebook/tags_configuration.ipynb 0 → 100644
    View file @ cec9a86f
    This source diff could not be displayed because it is too large. You can view the blob instead.
    scripts/core/analytics_notebook/voltage_analytics.ipynb 0 → 100644
    View file @ cec9a86f
    This source diff could not be displayed because it is too large. You can view the blob instead.
    scripts/core/data_puller_push/data_puller.py 0 → 100644
    View file @ cec9a86f
    import json
    import pandas as pd
    import requests
    from loguru import logger
    from scripts.constants.app_configuration import KairosDb
    class KairosQuery:
    def __init__(self, start_timestamp, end_timestamp, tag_dict):
    self.start_timestamp = start_timestamp
    self.end_timestamp = end_timestamp
    self.kairos_host = KairosDb.uri
    self.kairos_url = "{kairos_host}/api/v1/datapoints/query".format(kairos_host=self.kairos_host)
    self.tag_dict = tag_dict
    def kairos_query(self):
    try:
    return {
    "metrics": [
    {
    "tags": {
    "c3": list(self.tag_dict.keys())
    },
    "name": KairosDb.metric_name,
    "group_by": [
    {
    "name": "tag",
    "tags": ["c3"]
    }
    ],
    "aggregators": [
    {
    "name": KairosDb.aggregator,
    "sampling": {
    "value": KairosDb.aggregator_value,
    "unit": KairosDb.aggregator_unit
    },
    "align_sampling": True,
    "align_start_time": True
    }
    ]
    }
    ],
    "plugins": [],
    "cache_time": 0,
    "time_zone": "Asia/Calcutta",
    "start_absolute": self.start_timestamp,
    "end_absolute": self.end_timestamp,
    }
    except Exception as e:
    logger.exception(f"Exception - {e}")
    def get_data(self, data_query):
    try:
    logger.info("Data for the parameters being pulled from Kairos Database")
    response = requests.post(self.kairos_url, data=json.dumps(data_query))
    response_data = response.json()
    output_data = response_data["queries"]
    logger.debug("Data pull complete")
    df_final = pd.DataFrame()
    for i in range(len(output_data)):
    grouped_output_data = output_data[i]["results"]
    for each_grouped_data in grouped_output_data:
    value = (each_grouped_data["values"])
    tag_id = each_grouped_data["group_by"][0]["group"]["c3"]
    try:
    logger.debug("Renamed {} to {} in Data".format(tag_id, self.tag_dict[tag_id]))
    column_name = self.tag_dict[tag_id]
    except KeyError as e:
    logger.exception(f'Exception - {e}')
    logger.debug("Column Renaming Logic not found for {}".format(tag_id))
    column_name = tag_id
    df_column_data = pd.DataFrame(data=value, columns=["timestamp", column_name])
    if df_final.empty:
    df_final = df_column_data
    else:
    df_final = df_final.merge(df_column_data, how="outer", left_on="timestamp",
    right_on="timestamp")
    df_final['datetime'] = pd.to_datetime(df_final['timestamp'], unit="ms").dt.tz_localize('UTC').\
    dt.tz_convert('Asia/Kolkata')
    logger.debug("Final number of columns : {}".format(str(len(list(df_final.columns)))))
    return df_final
    except Exception as e:
    logger.exception(f"Exception occurred - {e}", exc_info=True)
    def kairos_data_import(self):
    try:
    logger.debug("Fetching live data")
    query_live = self.kairos_query()
    logger.info(f"query_live = {query_live}")
    df = self.get_data(data_query=query_live)
    return df
    except Exception as e:
    logger.exception(f"Exception - {e}")
    scripts/core/data_puller_push/kafka_push.py 0 → 100644
    View file @ cec9a86f
    from loguru import logger
    from scripts.core.data_puller_push.push_data import insert_values_dalmia
    class CalculatedDataPush:
    def __init__(self, df_result, final_tags_dict):
    self.df_result = df_result
    self.final_tags_dict = final_tags_dict
    def kafka_data_push(self):
    try:
    logger.info(f"final_tags_dict = {self.final_tags_dict}")
    logger.info(f"df result shape = {self.df_result.shape}")
    for i, j in self.df_result.iterrows():
    my_dict = {v: j[k] for k, v in self.final_tags_dict.items()}
    logger.info(f"{j['timestamp'], j['datetime'], my_dict}")
    insert_values_dalmia(j['timestamp'], my_dict)
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/core/data_puller_push/push_data.py 0 → 100644
    View file @ cec9a86f
    from json import dumps
    from kafka import KafkaProducer
    from loguru import logger
    from scripts.constants.app_configuration import Kafka
    def insert_values_dalmia(ts, my_dict):
    kairos_writer = KairosWriter()
    kairos_writer.write_data(
    {
    ts: my_dict
    },
    Kafka.kafka_topic
    )
    logger.info("Data pushed successfully!")
    class KafkaProducerUtil:
    def __init__(self):
    try:
    self.host = Kafka.kafka_host
    self.port = Kafka.kafka_port
    logger.debug(f"Connecting to Kafka with details: {self.host}, {self.port}")
    kafka_broker = [self.host + ":" + str(self.port)]
    self.producer = KafkaProducer(
    bootstrap_servers=kafka_broker,
    value_serializer=lambda v: v.encode('utf-8'),
    api_version=(0, 10, 1))
    self.producer.flush()
    except Exception as e:
    logger.error(f"Kafka connection error: {e}")
    def publish(self, topic, data):
    try:
    kafka_response = self.producer.send(topic, data)
    self.producer.flush()
    logger.debug(f" Message sent to kafka with response: {kafka_response}")
    return True
    except Exception as e:
    logger.error(e)
    return False
    class KairosWriter(KafkaProducerUtil):
    def write_data(self, data_json, topic):
    site_id = "site_107"
    logger.debug(f"Data being pushed to kafka topic: {topic}")
    msg_counter = 0
    for k, v in data_json.items():
    timestamp, data = self.data_validator(k, v)
    timestamp = timestamp * 1000
    write_json = {
    "data": data,
    "site_id": site_id,
    "gw_id": "gw_{}".format(site_id.lstrip("site_")), # The lstrip(s) removes leading whitespace (on the left)
    "pd_id": "pd_{}".format(site_id.lstrip("site_")), # The rstrip(s) removes the trailing whitespace (on the right)
    "timestamp": timestamp,
    "msg_id": msg_counter,
    "partition": "",
    "retain_flag": False
    }
    logger.debug(f"Timestamp: {timestamp}, Values: {data}")
    self.publish(topic, dumps(write_json))
    msg_counter += 1
    return msg_counter
    def audit_data(self, data_json, topic):
    logger.debug(f"Audit Data being pushed to kafka topic: {topic}")
    msg_counter = len(data_json)
    for each in data_json:
    self.publish(topic, dumps(each))
    return msg_counter
    @staticmethod
    def data_validator(timestamp, data):
    logger.debug("Validating the data to remove Nan values")
    __temp__ = {}
    for k, v in data.items():
    if not k.startswith("site"):
    continue
    # This function will return True if the "v" is one of the types in the tuple
    if isinstance(v, (int, float)) and str(v) not in ('nan', 'inf'):
    __temp__[k] = v
    return int(timestamp), __temp__
    scripts/core/engine/data_training_and_inference.py 0 → 100644
    View file @ cec9a86f
    from loguru import logger
    from scripts.utils.mlflow_util import ModelLoad
    class Inference:
    def __init__(self, df):
    self.df = df
    def data_inference(self, inv_id, mppt_id):
    try:
    df_test_mppt = self.df[['datetime', 'tilt_irradiance', 'voltage_mppt', 'hour', 'current_mppt']]
    df_test_mppt.reset_index(drop=True, inplace=True)
    x_test = df_test_mppt[['datetime', 'tilt_irradiance', 'voltage_mppt', 'hour']]
    y_test = df_test_mppt[['current_mppt']]
    logger.debug(f'shape of x_test for {inv_id} & {mppt_id} - {x_test.shape}')
    logger.debug(f'shape of y_test for {inv_id} & {mppt_id} - {y_test.shape}')
    inv_mppt_id = f'{inv_id}_{mppt_id}'
    model = ModelLoad().model_manager(inv_mppt_id=inv_mppt_id)
    predictions = model.predict(x_test.drop(['datetime'], axis=1)).reshape(1, -1)
    return x_test, y_test, predictions
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/core/engine/final_predictions.py 0 → 100644
    View file @ cec9a86f
    import numpy as np
    from loguru import logger
    from scripts.core.engine.final_tags import GetFinalDf
    from scripts.core.data_puller_push.kafka_push import CalculatedDataPush
    import tracemalloc
    def ai_modelling(df_train, get_training_inference, df_predicted_tags):
    try:
    get_final_df = GetFinalDf()
    for inv_id in list(df_train.inv_id.unique()):
    for mppt_id in list(df_train.mppt_id.unique()):
    try:
    model, scaler_x, scaler_y = get_training_inference.data_training(inv_id=inv_id, mppt_id=mppt_id)
    print(f'4th memory usage - {tracemalloc.get_traced_memory()}')
    x_test, y_test, predictions = get_training_inference.data_inference(scaler_x=scaler_x,
    scaler_y=scaler_y,
    model=model,
    inv_id=inv_id,
    mppt_id=mppt_id)
    df_result = get_final_df.get_final_data(x_test=x_test,
    y_test=y_test,
    predictions=predictions)
    final_dict = get_final_df.get_final_predicted_tags(df_predicted_current_tags=df_predicted_tags,
    inv_id=inv_id, mppt_id=mppt_id)
    df_result["timestamp"] = df_result["datetime"].values.astype(np.int64) / 10 ** 9
    df_result["timestamp"] = df_result["timestamp"].astype('int')
    df_result['hour'] = df_result['datetime'].dt.hour
    df_result.loc[df_result['hour'].between(left=18, right=23, inclusive=True),
    'predicted_current_mppt'] = 0
    df_result.loc[df_result['hour'].between(left=0, right=5, inclusive=True),
    'predicted_current_mppt'] = 0
    df_result.drop(['hour'], axis=1, inplace=True)
    CalculatedDataPush(df_result=df_result, final_tags_dict=final_dict).kafka_data_push()
    logger.info(f'{final_dict}')
    print(f'5th memory allocation - {tracemalloc.get_traced_memory()}')
    except Exception as e:
    logger.exception(f'Exception - {e}')
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/core/engine/final_tags.py 0 → 100644
    View file @ cec9a86f
    import pandas as pd
    from loguru import logger
    class GetFinalDf:
    @staticmethod
    def get_final_data(x_test, y_test, predictions):
    try:
    df_result = pd.DataFrame(index=[i for i in range(len(y_test))])
    df_result['datetime'] = x_test['datetime']
    df_result['actual_current_mppt'] = y_test
    df_result['predicted_current_mppt'] = predictions.reshape(-1, 1)
    df_result.drop(['actual_current_mppt'], axis=1, inplace=True)
    df_result['hour'] = df_result['datetime'].dt.hour
    df_result.loc[df_result['hour'].between(18, 23, inclusive='both'),
    'predicted_current_mppt'] = 0
    df_result.loc[df_result['hour'].between(0, 5, inclusive='both'),
    'predicted_current_mppt'] = 0
    df_result.drop(['hour'], inplace=True, axis=1)
    df_result = df_result.round(2)
    df_result.reset_index(drop=True, inplace=True)
    logger.info(f'{df_result.shape}')
    return df_result
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def get_final_predicted_tags(df_predicted_current_tags, inv_id, mppt_id):
    try:
    df = df_predicted_current_tags[df_predicted_current_tags['inv_id'] == inv_id]
    df = df[df['mppt_id'] == mppt_id]
    df.reset_index(drop=True, inplace=True)
    final_dict = {}
    for index in range(df.shape[0]):
    tag_id = df.iloc[index, df.columns.get_loc('tag_id')]
    parameter_name = df.iloc[index, df.columns.get_loc('parameter_name')]
    final_dict['predicted_current_mppt'] = tag_id
    logger.debug(f'tag_id - {tag_id} & parameter name - {parameter_name}')
    return final_dict
    except Exception as e:
    logger.exception(f'Exception - {e}')
    \ No newline at end of file
    scripts/core/engine/mppt_data.py 0 → 100644
    View file @ cec9a86f
    import pandas as pd
    from loguru import logger
    class GetData:
    @staticmethod
    def associate_inv_mppt_id(df):
    try:
    current_column_list = [col_name for col_name in df if 'current' in col_name]
    voltage_column_list = [col_name for col_name in df if 'voltage' in col_name]
    current_column_list.sort()
    voltage_column_list.sort()
    df_mppt = pd.DataFrame()
    for n_mppt in range(len(current_column_list)):
    df_temp = df[['datetime', 'inv_id', 'hour', 'tilt_irradiance', voltage_column_list[n_mppt],
    current_column_list[n_mppt]]]
    df_temp['mppt_id'] = current_column_list[n_mppt]
    df_temp['mppt_id'] = df_temp['mppt_id'].str.replace('current_', '')
    df_temp.rename(columns={voltage_column_list[n_mppt]: 'voltage_mppt',
    current_column_list[n_mppt]: 'current_mppt'}, inplace=True)
    if df_mppt.empty:
    df_mppt = df_temp
    else:
    df_mppt = pd.concat([df_mppt, df_temp], axis=0)
    df_mppt.reset_index(drop=True, inplace=True)
    df_mppt.sort_values(['inv_id', 'mppt_id'], inplace=True)
    df_mppt['inv_id_mppt_id'] = df_mppt['inv_id'] + '_' + df_mppt['mppt_id']
    df_mppt.dropna(axis=0, inplace=True)
    df_mppt.reset_index(drop=True, inplace=True)
    return df_mppt
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def multiply_mppt_coefficients(df_mppt, df_coefficients):
    try:
    df = pd.merge(df_mppt, df_coefficients, on='inv_id_mppt_id', how='left')
    logger.debug(f'null rows after mppt coefficient multiplication - {df.isnull().sum()}')
    df["coefficient"].fillna(1, inplace=True)
    df.reset_index(drop=True, inplace=True)
    df['current_mppt'] = df['current_mppt'] * df["coefficient"]
    df.drop(['coefficient', 'inv_id_mppt_id'], axis=1, inplace=True)
    return df
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/core/engine/raw_predicted_tags.py 0 → 100644
    View file @ cec9a86f
    import pandas as pd
    from loguru import logger
    import warnings
    import tracemalloc, gc
    from scripts.utils.mongo_utils import MongoConnect
    from scripts.constants.app_configuration import Mongo
    from scripts.constants.app_constants import MongoConstants
    warnings.filterwarnings('ignore')
    def get_raw_predicted_tags():
    try:
    mongo_conn = MongoConnect(uri=Mongo.mongo_uri, database=MongoConstants.db,
    collection=MongoConstants.collection)
    if mongo_conn is None:
    logger.info(f'mongodb is not connected, please check')
    else:
    logger.info(f'mongodb is connected')
    logger.debug(f'mongo conn - {mongo_conn}')
    raw_tags_dict = mongo_conn.find_one({"$and": [{"id": "dalmia_string_level_tags"}, {"city": "ariyalur"},
    {"tags_property": "raw"}]})
    req_tags = raw_tags_dict['input_data']
    logger.info(f'req raw tags length - {len(req_tags)}')
    df_raw_tags = pd.DataFrame.from_dict(req_tags, orient='index')
    predicted_tags_dict = mongo_conn.find_one({"$and": [{"id": "dalmia_string_level_tags"}, {"city": "ariyalur"},
    {"tags_property": "predicted"}]})
    predicted_tags = predicted_tags_dict['input_data']
    logger.info(f'req predicted tags length - {len(predicted_tags)}')
    df_predicted_tags = pd.DataFrame.from_dict(predicted_tags, orient='index')
    df_raw_tags.reset_index(inplace=True)
    df_raw_tags.rename(columns={'index': 'tag_name'}, inplace=True)
    df_predicted_tags.reset_index(inplace=True)
    df_predicted_tags.rename(columns={'index': 'tag_name'}, inplace=True)
    mppt_coefficients = mongo_conn.find_one({"$and": [{"id": "dalmia_string_level_tags"}, {"city": "ariyalur"},
    {"tags_property": "mppt_coefficients"}]})
    coefficients_dict = mppt_coefficients['input_data']
    logger.info(f'req coefficients dict length - {len(coefficients_dict)}')
    df_coefficients = pd.DataFrame.from_dict(coefficients_dict, orient='index')
    df_coefficients.reset_index(inplace=True)
    df_coefficients.rename(columns={'index': 'inv_id_mppt_id'}, inplace=True)
    raw_tags_dict.clear()
    predicted_tags_dict.clear()
    coefficients_dict.clear()
    del req_tags
    del predicted_tags
    print(f'memory allocation - {tracemalloc.get_traced_memory()}')
    gc.collect()
    print(f'memory allocation - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces()
    print(f'memory allocation - {tracemalloc.get_traced_memory()}')
    return df_raw_tags, df_predicted_tags, df_coefficients
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/core/engine/tags_data.py 0 → 100644
    View file @ cec9a86f
    import pandas as pd
    from loguru import logger
    from scripts.core.data_puller_push.data_puller import KairosQuery
    def get_tags_data(df_input_tags, start_timestamp, end_timestamp, inv_id, mppt_id):
    try:
    df_tags_id = df_input_tags[['tag_id', 'tag_name', 'inv_id', 'parameter_name', 'mppt_id']]
    df_tags_id.reset_index(drop=True, inplace=True)
    current_voltage_tags_only = [data for data in df_tags_id['parameter_name']
    if any([x in data for x in ['current', 'voltage']])]
    req_data_list = [data for data in current_voltage_tags_only if 'Potential' not in data]
    req_data_list = [data for data in req_data_list if 'Degradation' not in data]
    df_req_tags_id = df_tags_id.loc[df_tags_id['parameter_name'].isin(req_data_list)]
    df_req_tags_id.reset_index(drop=True, inplace=True)
    tags_dict = df_req_tags_id[['tag_id', 'parameter_name']].set_index('tag_id').T.to_dict(orient="records")[0]
    tags_dict['site_107$dept_140$line_371$equipment_4115$tag_15828'] = 'tilt_irradiance'
    df_data = KairosQuery(start_timestamp=start_timestamp,
    end_timestamp=end_timestamp,
    tag_dict=tags_dict).kairos_data_import()
    df_data['inv_id'] = inv_id
    df_data['mppt_id'] = mppt_id
    logger.info(f' for inv- {inv_id}, {df_data.shape}')
    df_data['date'] = df_data['datetime'].dt.date
    df_data['hour'] = df_data['datetime'].dt.hour
    df_data.drop(['date'], axis=1, inplace=True)
    logger.info(f'Final shape of merged dataframe = {df_data.shape}')
    df_data.reset_index(drop=True, inplace=True)
    return df_data
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/utils/mlflow_util.py 0 → 100644
    View file @ cec9a86f
    import mlflow
    from loguru import logger
    import pandas as pd
    import re
    import os
    import pytz
    from datetime import datetime
    from scripts.constants.app_configuration import MlFlow, ReqTimeZone
    from scripts.utils.pycaret_util import PycaretUtil
    mlflow_tracking_uri = MlFlow.mlflow_tracking_uri
    os.environ["MLFLOW_TRACKING_USERNAME"] = MlFlow.mlflow_tracking_username
    os.environ["MLFLOW_TRACKING_PASSWORD"] = MlFlow.mlflow_tracking_password
    os.environ["AZURE_STORAGE_CONNECTION_STRING"] = MlFlow.azure_storage_connection_string
    os.environ["AZURE_STORAGE_ACCESS_KEY"] = MlFlow.azure_storage_access_key
    mlflow.set_tracking_uri(mlflow_tracking_uri)
    mlflow.set_registry_uri(mlflow_tracking_uri)
    client = mlflow.tracking.MlflowClient()
    class ModelLoad(object):
    def model_manager(self, inv_mppt_id):
    try:
    experiment_id = self.create_experiment(experiment_name=MlFlow.experiment_name)
    days, latest_run_id = self.fetch_latest_model(experiment_id=experiment_id,
    run_name=MlFlow.run_name + '_' + inv_mppt_id)
    if days < int(MlFlow.model_check_param):
    logger.debug(f'Using the pretrained model !')
    energy_model = self.load_model_pyfunc(
    model_path=self.forming_loading_path(latest_run_id=latest_run_id))
    else:
    logger.debug(f'Model is not present')
    return energy_model
    except Exception as e:
    logger.exception(str(e))
    @staticmethod
    def create_experiment(experiment_name):
    """
    Function is to create an experiment by passing experiment name
    :param experiment_name: Name of the experiment
    :return: Experiment id, Run id if any parent run is existing
    """
    try:
    experiment = mlflow.get_experiment_by_name(experiment_name)
    if experiment:
    exp_id = experiment.experiment_id
    else:
    mlflow.set_experiment(experiment_name)
    experiment = mlflow.get_experiment_by_name(experiment_name)
    exp_id = experiment.experiment_id
    return exp_id
    except Exception as e:
    logger.exception(str(e))
    def fetch_latest_model(self, experiment_id, run_name):
    """
    Function is to fetch the latest run
    :param experiment_id: Experiment Id
    :return: return the difference in the days/Hours/Minutes of current and run time, latest run id
    """
    try:
    days = int(MlFlow.model_check_param) + 1
    model_history = ""
    latest_run_id = ""
    if experiment_id:
    run_id = self.get_parent_run_id(experiment_id, run_name)
    run_info = mlflow.search_runs([experiment_id],
    filter_string="tags.mlflow.parentRunId='{run_id}'".format(
    run_id=run_id))
    if not run_info.empty:
    for ind in run_info.index:
    model_history, days, latest_run_id = self.check_model_existing(run_info=run_info,
    index=ind)
    if model_history is not None:
    break
    if model_history is None:
    days = int(MlFlow.model_check_param) + 1
    logger.info("No Model is existing with this experiment")
    return days, latest_run_id
    except Exception as e:
    logger.exception(f"Exception while fetching the latest model - {e}")
    @staticmethod
    def get_parent_run_id(experiment_id, run_name):
    """
    Function is to fetch latest parent run id if available else latest run id
    :param experiment_id: Experiment Id
    :param run_name: Name of the run
    :return: latest parent run id
    """
    try:
    result_run_id = None
    df = mlflow.search_runs([experiment_id])
    for index, row in df.iterrows():
    parent_run_name = row.get("tags.mlflow.runName")
    if parent_run_name == run_name:
    result_run_id = row.get("run_id")
    else:
    logger.info(f"No Run is existing with this Experiment id - {experiment_id}")
    return result_run_id
    except Exception as e:
    logger.exception(f"Exception while fetching the latest run_id - {e}")
    def check_model_existing(self, run_info, index):
    """
    Function is to check if model is existing or not
    :param run_info: Dataframe of run details
    :param index: index of which run from the dataframe
    :return:
    """
    try:
    model_history = None
    date_param = MlFlow.check_param
    # Difference between the current date and latest available model date
    days = self.format_mlflow_time(run_info=run_info, index=index, date_param=date_param)
    latest_run_id = run_info.loc[index, 'run_id']
    if 'tags.mlflow.log-model.history' in run_info:
    model_history = run_info['tags.mlflow.log-model.history'][index]
    if model_history:
    model_history_list = model_history.split(":")
    model_history = model_history_list[2].split(",")[0]
    else:
    logger.info("No Model is existing")
    return model_history, days, latest_run_id
    except Exception as e:
    logger.exception(f"Exception while checking the model name - {e}")
    @staticmethod
    def forming_loading_path(latest_run_id):
    """
    Function is to form the loading path
    :param latest_run_id: Run id
    :return : Return the loading path
    """
    try:
    model_name = MlFlow.model_name
    model_path = f"runs:/{latest_run_id}/{model_name}"
    return model_path
    except Exception as e:
    logger.exception(f"Exception while forming loading path - {e}")
    @staticmethod
    def format_mlflow_time(run_info, index, date_param):
    """
    Formatting mlflow time
    :param run_info: details of the runs
    :param index: index of the run in the dataframe
    :param: What type of the date param
    :return: calculate the time difference between the mlflow time and the current time zone
    """
    try:
    df_time = run_info.copy()
    df_time['end_time'] = pd.to_datetime(df_time['end_time']).dt.tz_convert(ReqTimeZone.required_tz)
    df_time["days"] = df_time['end_time'].dt.date
    df_time["hours"] = df_time['end_time'].dt.hour
    df_required = df_time.iloc[index:index + 1:, :]
    df_required.reset_index(drop=True, inplace=True)
    last_model_time = df_required['end_time'][0].to_pydatetime()
    central_current = datetime.now(pytz.utc).astimezone(pytz.timezone(ReqTimeZone.required_tz))
    time_diff = central_current - last_model_time
    if date_param.lower() == "days":
    days_diff = int(time_diff.days)
    return days_diff
    elif date_param.lower() == "hours":
    hours_diff = int(time_diff.total_seconds() // 3600)
    return hours_diff
    elif date_param.lower() == "minutes":
    minutes_diff = int(time_diff.total_seconds() // 60)
    return minutes_diff
    else:
    logger.info("No Valid Date format was given")
    except Exception as e:
    logger.exception(f"Exception while Loading the model - {e}")
    @staticmethod
    def set_tag(run_id, key, value):
    """
    Function is to set the tag for a particular run
    :param run_id: Run id in which the tags need to be added
    :param key: Name of the key
    :param value: what needs to tagged in the value
    """
    try:
    client.set_tag(run_id=run_id, key=key, value=value)
    logger.debug(f'set the tag for the model')
    except Exception as e:
    logger.exception(f"Exception while setting the tag - {e}")
    @staticmethod
    def load_model_pyfunc(model_path):
    """
    Function is load the sklearn model
    :param model_path: path of the model
    :return: boolen value
    """
    try:
    model = mlflow.pyfunc.load_model(model_path)
    logger.info("loading the model")
    return model
    except Exception as e:
    logger.exception(str(e))
    scripts/utils/mongo_utils.py 0 → 100644
    View file @ cec9a86f
    from typing import Dict
    from loguru import logger
    from pymongo import MongoClient
    from scripts.constants.app_configuration import Mongo
    class MongoConnect:
    def __init__(self, uri, database, collection):
    try:
    self.uri = uri
    self.client = MongoClient(self.uri, connect=False)
    self.database = database
    self.collection = collection
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def data_dict(data, city):
    try:
    req_dict = dict()
    req_dict['project_id'] = Mongo.project_id
    req_dict['id'] = Mongo.query_filter
    req_dict['city'] = city
    req_dict['input_data'] = data
    return req_dict
    except Exception as e:
    logger.exception(f'Exception - {e}')
    def insert_one(self, data, city):
    try:
    db = self.client[self.database]
    collection = db[self.collection]
    req_dict = self.data_dict(data=data, city=city)
    response = collection.insert_one(req_dict)
    return response.inserted_id
    except Exception as e:
    logger.exception(f'Exception - {e}')
    def find_one(self, query, filter_dict=None):
    try:
    if filter_dict is None:
    filter_dict = {"_id": 0}
    db = self.client[self.database]
    collection = db[self.collection]
    response = collection.find_one(query, filter_dict)
    return response
    except Exception as e:
    logger.exception(f'Exception - {e}')
    scripts/utils/pycaret_util.py 0 → 100644
    View file @ cec9a86f
    from loguru import logger
    from pycaret import regression
    from scripts.constants.app_configuration import MlFlow, PycaretParams
    class PycaretUtil:
    def __init__(self):
    self.model_list = PycaretParams.model_list.split(",")
    self.selected_metric = PycaretParams.selected_metric
    self.hyperparameter_tuning_method = PycaretParams.hyperparameter_tuning_method
    def get_best_model(self, df, target):
    try:
    regression.setup(data=df, target=target)
    best_model = regression.compare_models(include=self.model_list, sort=self.selected_metric,
    n_select=1)
    tuned_model = regression.tune_model(best_model, optimize=self.selected_metric,
    search_library=self.hyperparameter_tuning_method)
    results = regression.pull()
    results.sort_values(self.selected_metric, ascending=False, inplace=True)
    results.reset_index(drop=True, inplace=True)
    get_best_model_row = results.iloc[0]
    best_metrics = get_best_model_row.to_dict()
    best_metrics.pop('Model', None)
    return tuned_model, best_metrics
    except Exception as e:
    logger.exception(f'Exception - {e}')
    @staticmethod
    def get_model_name(model):
    try:
    model_name = str(model).split('(')[0]
    return model_name
    except Exception as e:
    logger.info(f"Unable to get the model name - {e}")
    def get_auto_ml_model(self, df, target):
    try:
    model, metrics = self.get_best_model(df=df, target=target)
    model_name = self.get_model_name(model)
    hyper_params = model.get_params()
    return model, model_name, metrics, hyper_params
    except Exception as e:
    logger.info(f"Unable to get the model name - {e}")
    scripts/utils/start_end_date.py 0 → 100644
    View file @ cec9a86f
    from datetime import datetime, timedelta
    from scripts.constants.app_configuration import DateRange,ReqTimeZone
    from loguru import logger
    import pytz
    class KairosStartEndDate:
    @staticmethod
    def start_end_date():
    try:
    local_timezone = pytz.timezone(ReqTimeZone.required_tz)
    start_date_timestamp = DateRange.start_date
    end_date_timestamp = DateRange.end_date
    if (start_date_timestamp is not None) and (start_date_timestamp.lower() != "none"):
    start_date = datetime.fromtimestamp((int(start_date_timestamp)/1000)).strftime('%Y-%m-%d %H:%M:%S')
    start_date = datetime.strptime(start_date, "%Y-%m-%d %H:%M:%S")
    start_date = start_date.astimezone(local_timezone).replace(hour=5, minute=0, second=0, microsecond=0)
    else:
    start_date = datetime.now(pytz.utc) - timedelta(days=int(DateRange.start_relative_days))
    start_date = start_date.astimezone(local_timezone).replace(hour=0, minute=0, second=0, microsecond=0)
    if (end_date_timestamp is not None) and (end_date_timestamp.lower() != "none"):
    end_date = datetime.fromtimestamp((int(end_date_timestamp)/1000)).strftime('%Y-%m-%d %H:%M:%S')
    end_date = datetime.strptime(end_date, "%Y-%m-%d %H:%M:%S")
    end_date = end_date.astimezone(local_timezone).replace(hour=5, minute=0, second=0, microsecond=0)
    else:
    end_date = datetime.now(pytz.utc) - timedelta(days=int(DateRange.end_relative_days))
    end_date = end_date.astimezone(local_timezone).replace(hour=23, minute=59, second=59, microsecond=0)
    start_date = start_date.replace(hour=0, minute=0, second=0)
    end_date = end_date.replace(hour=23, minute=59, second=59)
    start_timestamp = int(start_date.timestamp())*1000
    end_timestamp = int(end_date.timestamp())*1000
    return start_date, end_date, start_timestamp, end_timestamp
    except Exception as e:
    logger.exception(f"Exception - {e}")
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