Project Definition
Project Name and Description
Describing and naming your project is essential for version tracking, .
Setup
Project description and core documentation.
Project assets that are used to create a module.
Importing modules as projects.
Properties
Settings
Here’s a snapshot of what a comprehensive project configuration might look like, including many of the options and features to be discussed.
{
"project_collections": [
{
"database": "master",
"collection": "bank_customer"
},
{
"database": "master",
"collection": "bank_transactions"
},
{
"database": "master",
"collection": "bank_transactions_personality"
},
{
"database": "master",
"collection": "bank_transactions_personality_bydate"
},
{
"database": "master",
"collection": "bank_transactions_personality_options"
},
{
"database": "master",
"collection": "bank_transactions_money"
},
{
"database": "master",
"collection": "spend_personality__network"
},
{
"database": "master",
"collection": "spend_personality__network_config"
},
{
"database": "master",
"collection": "spend_personality__network_selector"
},
{
"database": "master",
"collection": "spend_personality__network_selector_options"
},
{
"database": "master",
"collection": "bank_transactions_reliability"
}
],
"project_files": [
{
"name": "bank_transactions.csv",
"absolutePath": "/data/bank_transactions.csv",
"lastModified": 1711192602491,
"size": 121.429,
"canRead": true,
"isDirectory": false
},
{
"name": "bank_customer.csv",
"absolutePath": "/data/bank_customer.csv",
"lastModified": 1711192601868,
"size": 0.18,
"canRead": true,
"isDirectory": false
},
{
"name": "spend_personality.txt",
"absolutePath": "/data/spend_personality.txt"
},
{
"name": "money_personality.txt",
"absolutePath": "/data/money_personality.txt"
},
{
"name": "config",
"date": "2024-04-28",
"absolutePath": "/data/config"
},
{
"name": "bank_customer__list.csv",
"date": "2024-04-28",
"absolutePath": "/data/bank_customer__list.csv"
}
],
"project_networks": [
{
"date": "2023-06-15",
"uuid": "ab823cdf-9ddd-404a-90f8-19d5fe68639c",
"description": "bank_customer"
},
{
"date": "2024-03-19",
"uuid": "0acb461e-ceb6-45fc-b097-719ce415a5a1",
"description": "bank_transactions"
}
],
"purpose": "intervention",
"configuration": "\"Spend personality\" refers to the characterization of an individual's spending habits based on their bank transactions. This type of analysis, leveraging AI and Machine Learning, provides valuable insights into customer behavior, preferences, and spending patterns over time. By analyzing the frequency, amount, and type of purchases, a bank can categorize their customers into different spending personality types. \n\nFor \"Money Personality\" analyzing customer transaction patterns against profiles, banks can better understand their customers’ money habits and financial management strategies. This can then drive personalized product recommendations, improve customer engagement, and help in more effectively managing financial risks.\n\nFor example, some customers may be identified as big spenders, frequent shoppers, savers, or even impulse buyers based on their demonstrated spending behaviors. These spending personalities can then be utilized for developing personalized marketing strategies, providing tailored financial advice, or enhancing customer service. It can help banks understand their customers better, predict their needs, and build stronger, more profitable relationships with them.\n\nProcess spending personality using Feature Engineering enrichment option:\n1. Use customer transaction data and perform category enrichment.\n2. Then run Ecosystem Spending Personality to generate new collections with scores assigned to customers.\n3. API returns scores per customer.\n\nNotes on 0.09.3:\n1. Network selector configured to allow for different usage options.\n2. Updated Text to SQL engine.\n3. Updates across all configurations for consistency.",
"project_notebooks": [
{
"name": "Spend Personality 0.09",
"date": "2024-04-15"
}
],
"project_calendars": [
{
"name": "master",
"date": "NA"
}
],
"project_data_wizards": [
{
"title": "Spend Personality",
"date": "NA"
}
],
"project_predictors": [
{
"date": "2024-03-20",
"predict_id": "bank_customer__personality_auto"
},
{
"date": "2024-03-23",
"predict_id": "bank_transactions__personality_auto"
}
],
"userid": "ecosystem",
"uuid": "20bdf5c6-be30-4fdd-8c43-4ec1663e6571",
"project_end_date": "2026-05-02",
"undefined": [
{
"name": "Spend Personality 0.09.1",
"date": "2024-04-15"
}
],
"project_cpr_client_analytics": [
{
"name": "spend_personality_590",
"date": "2024-04-16"
}
],
"project_id": "Spend Personality 0.09.4",
"project_owner": "ecosystem",
"presto_connections": [
{
"name": "Postgres-bank_transactions-select",
"date": "2024-03-30"
},
{
"name": "Postgres-bank_transactions",
"date": "2024-03-30"
},
{
"name": "Postgres-bank_transactions-drop",
"date": "2024-03-30"
}
],
"project_models": [
{
"parameters[21].actual_value": "---",
"output.validation_metrics.model_category": "Multinomial",
"output.validation_metrics.logloss": 0.4969054048712993,
"output.validation_metrics.RMSE": 0.3961064072904806,
"response_column_name": "personality",
"output.validation_metrics.mean_per_class_error": 0.4,
"output.end_time": 1710851747225,
"model_id.name": "GLM_1_AutoML_2_20240319_123546",
"output.validation_metrics.MSE": 0.15690028589657207,
"data_frame.name": "AutoML_2_20240319_123546_training_bank_customer0_8.hex",
"output.model_summary.data[3][0]": "Ridge ( lambda = 0.01456 )",
"algo": "glm",
"parameters[22].actual_value": "---",
"output.validation_metrics.r2": 0.04324034294718049
},
{
"parameters[21].actual_value": 20,
"output.validation_metrics.model_category": "Multinomial",
"output.validation_metrics.logloss": 0.5139734681851796,
"output.validation_metrics.RMSE": 0.4038291760955808,
"response_column_name": "personality",
"output.validation_metrics.mean_per_class_error": 0.3984280701754386,
"output.end_time": 1710851749153,
"model_id.name": "XRT_1_AutoML_2_20240319_123546",
"output.validation_metrics.MSE": 0.16307800346603557,
"data_frame.name": "AutoML_2_20240319_123546_training_bank_customer0_8.hex",
"output.model_summary.data[3][0]": 163134,
"algo": "drf",
"parameters[22].actual_value": 1,
"output.validation_metrics.r2": 0.0055693412064614645
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{
"parameters[21].actual_value": 15,
"output.validation_metrics.model_category": "Multinomial",
"output.validation_metrics.logloss": 0.00504091424119842,
"output.validation_metrics.RMSE": 0.03350626288016139,
"response_column_name": "personality_description",
"output.validation_metrics.mean_per_class_error": 0.0012288059914334056,
"output.end_time": 1711196621263,
"model_id.name": "GBM_1_AutoML_1_20240323_121956",
"output.validation_metrics.MSE": 0.001122669652194481,
"data_frame.name": "AutoML_1_20240323_121956_training_bank_transactions__enriched.hex",
"output.model_summary.data[3][0]": 1591898,
"algo": "gbm",
"parameters[22].actual_value": 100,
"output.validation_metrics.r2": 0.9987902818019092
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{
"parameters[21].actual_value": true,
"output.validation_metrics.model_category": "Multinomial",
"output.validation_metrics.logloss": 0.5281917248961325,
"output.validation_metrics.RMSE": 0.4061316271799821,
"response_column_name": "personality",
"output.validation_metrics.mean_per_class_error": 0.39896140350877196,
"output.end_time": 1710851749497,
"model_id.name": "DeepLearning_1_AutoML_2_20240319_123546",
"output.validation_metrics.MSE": 0.16494289859585998,
"data_frame.name": "AutoML_2_20240319_123546_training_bank_customer0_8.hex",
"output.model_summary.data[3][0]": "Input",
"algo": "deeplearning",
"parameters[22].actual_value": true,
"output.validation_metrics.r2": -0.005802571946242674
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{
"parameters[21].actual_value": 20,
"output.validation_metrics.model_category": "Multinomial",
"output.validation_metrics.logloss": 0.54451602038775,
"output.validation_metrics.RMSE": 0.4116196202612879,
"response_column_name": "personality",
"output.validation_metrics.mean_per_class_error": 0.3879578947368421,
"output.end_time": 1710851747910,
"model_id.name": "DRF_1_AutoML_2_20240319_123546",
"output.validation_metrics.MSE": 0.16943071178404687,
"data_frame.name": "AutoML_2_20240319_123546_training_bank_customer0_8.hex",
"output.model_summary.data[3][0]": 394707,
"algo": "drf",
"parameters[22].actual_value": 1,
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},
{
"parameters[21].actual_value": 16,
"output.validation_metrics.model_category": "Multinomial",
"output.validation_metrics.logloss": 0.509847958643211,
"output.validation_metrics.RMSE": 0.40153470072547653,
"response_column_name": "personality",
"output.validation_metrics.mean_per_class_error": 0.3989894736842105,
"output.end_time": 1710851749737,
"model_id.name": "GBM_grid_1_AutoML_2_20240319_123546_model_1",
"output.validation_metrics.MSE": 0.16123011588669803,
"data_frame.name": "AutoML_2_20240319_123546_training_bank_customer0_8.hex",
"output.model_summary.data[3][0]": 87275,
"algo": "gbm",
"parameters[22].actual_value": 100,
"output.validation_metrics.r2": 0.016837544298485163
}
],
"project_start_date": "2024-05-02",
"module_metadata": {
"reviewed_by": "ecosystem.Ai",
"image_path": "https://ecosystem.ai/wp-content/uploads/2022/05/ecosystem_black-transparent.png",
"icon_path": "https://ecosystem.ai/wp-content/uploads/2022/05/ecosystem_black-transparent.png",
"name": "Spend_Personality_0.09.4",
"module_owner": "ecosystem.Ai",
"description": "\"Spend personality\" refers to the characterization of an individual's spending habits based on their bank transactions. This type of analysis, leveraging AI and Machine Learning, provides valuable insights into customer behavior, preferences, and spending patterns over time. By analyzing the frequency, amount, and type of purchases, a bank can categorize their customers into different spending personality types. \n\nFor \"Money Personality\" analyzing customer transaction patterns against profiles, banks can better understand their customers’ money habits and financial management strategies. This can then drive personalized product recommendations, improve customer engagement, and help in more effectively managing financial risks.\n\nFor example, some customers may be identified as big spenders, frequent shoppers, savers, or even impulse buyers based on their demonstrated spending behaviors. These spending personalities can then be utilized for developing personalized marketing strategies, providing tailored financial advice, or enhancing customer service. It can help banks understand their customers better, predict their needs, and build stronger, more profitable relationships with them.\n\nProcess spending personality using Feature Engineering enrichment option:\n1. Use customer transaction data and perform category enrichment.\n2. Then run Ecosystem Spending Personality to generate new collections with scores assigned to customers.\n3. API returns scores per customer.",
"categories": "banking, personality, spend, spending, money, transactions",
"created_by": "ecosystem.Ai",
"version": "0.09.4",
"fact_sheet_path": "https://ecosystem.ai/modules",
"contact_email": "amy@ecosystem.ai",
"status": "released"
},
"project_type": "predict",
"deployment_step": [
{
"budget_tracker": {
"budget_parameters_database": "",
"budget_parameters_datasource": "mongodb",
"budget_id": "",
"description": "",
"budget_parameters_table_collection": "",
"x_axis_datasource": "offer_matrix",
"x_axis_name": "",
"acc_namesource": "",
"y_axis_name": "",
"y_axis_namesource": "",
"acc_name": "",
"budget_strategy": "",
"x_axis_namesource": "",
"acc_datasource": "offer_matrix",
"y_axis_datasource": "offer_matrix"
},
"date": "2024-04-08",
"complexity": "medium",
"plugins": {
"post_score_class_text": "PostScoreBasic.java",
"post_score_class_code": "",
"api_endpoint_code": "",
"pre_score_class_text": "",
"pre_score_class_code": ""
},
"model_configuration": {
"model_note": "",
"models_load": "GBM_1_AutoML_1_20240323_121956.zip",
"model_outline": "predict"
},
"setup_offer_matrix": {
"offer_lookup_id": "",
"database": "",
"table_collection": "",
"datasource": "mongodb"
},
"project_status": "experiment",
"description": "Model driven personality detection: (http://ecosystem-runtime2:8092)",
"multi_armed_bandit": {
"epsilon": "",
"duration": 0,
"pulse_responder_uuid": ""
},
"whitelist": {
"table_collection": "",
"datasource": "mongodb",
"database": ""
},
"version": "0.1",
"model_selector": {
"selector_column": "",
"lookup": "",
"database": "",
"selector": "",
"table_collection": "",
"datasource": "mongodb"
},
"performance_expectation": "high",
"pattern_selector": {
"pattern": "",
"duration": ""
},
"paths": {
"logging_collection_response": "ecosystemruntime_response",
"logging_collection": "ecosystemruntime",
"logging_database": "logging",
"mongo_server_port": "ecosystem-server:54445",
"scoring_engine_path_prod": "http://ecosystem-runtime3:8091",
"models_path": "/data/deployed/",
"mongo_connect": "mongodb://ecosystem_user:EcoEco321@ecosystem-server:54445/?authSource=admin",
"data_path": "/data/",
"build_server_path": "http://ecosystem:EcoEco321@build.ecosystem.ai:8080/job/ecosystem-runtime/buildWithParameters?token=114a827a8ada36685a1f3958a6059cd677&BRANCH=spendpersonality&EMAIL=user@ecosystem.ai&CONTAINER_NAME=ecosystem-runtime-spendpersonality",
"scoring_engine_path_dev": "http://ecosystem-runtime2:8092",
"aws_container_resource": "",
"scoring_engine_path_test": "http://ecosystem-runtime2:8091",
"git_repo_path_branch": "spendpersonality",
"download_path": "https://hub.docker.com/u/ecosystemai",
"mongo_ecosystem_password": "EcoEco321",
"mongo_ecosystem_user": "ecosystem_user",
"git_repo_path": "https://github.com/ecosystemai/ecosystem-runtime.git"
},
"updated_by": "admin@ecosystem.ai",
"options": {
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"is_multi_armed_bandit": false,
"is_enable_plugins": true,
"is_whitelist": false,
"is_corpora": false,
"is_custom_api": false,
"is_budget_tracking": false,
"is_params_from_data_source": true,
"is_model_selector": false,
"is_generate_dashboards": false,
"is_pattern_selector": false,
"is_prediction_model": true
},
"corpora": {
"corpora": ""
},
"parameter_access": {
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"key": "customer"
},
"create_virtual_variables": false,
"database": "master",
"datasource": "mongodb",
"lookup_fields": [
"age",
"education",
"gender",
"language",
"maritalStatus",
"numberOfAddresses",
"numberOfChildren",
"numberOfProducts",
"proprtyOwnership"
],
"lookup_defaults": "",
"virtual_variables": [],
"table_collection": "bank_customer",
"fields": "proprtyOwnership,numberOfChildren,maritalStatus,language,numberOfProducts,age,numberOfAddresses,education,gender"
},
"updated_date": "2024-06-10T13:19:34.099Z",
"deployment_id": "spend_personality_model"
},
{
"budget_tracker": {
"budget_parameters_database": "",
"budget_parameters_datasource": "mongodb",
"budget_id": "",
"description": "",
"budget_parameters_table_collection": "",
"x_axis_datasource": "offer_matrix",
"x_axis_name": "",
"acc_namesource": "",
"y_axis_name": "",
"y_axis_namesource": "",
"acc_name": "",
"budget_strategy": "",
"x_axis_namesource": "",
"acc_datasource": "offer_matrix",
"y_axis_datasource": "offer_matrix"
},
"date": "2024-04-15",
"complexity": "low",
"plugins": {
"post_score_class_text": "PostScoreMoneyPersonality.java",
"post_score_class_code": "",
"api_endpoint_code": "",
"pre_score_class_text": "",
"pre_score_class_code": ""
},
"model_configuration": {},
"setup_offer_matrix": {
"offer_lookup_id": "",
"database": "",
"table_collection": "",
"datasource": "mongodb"
},
"project_status": "experiment",
"description": "Money personality detection.",
"multi_armed_bandit": {
"epsilon": "",
"duration": 0,
"pulse_responder_uuid": ""
},
"whitelist": {
"table_collection": "",
"datasource": "mongodb",
"database": ""
},
"version": "0.04",
"model_selector": {
"selector_column": "",
"lookup": "",
"database": "",
"selector": "",
"table_collection": "",
"datasource": "mongodb"
},
"performance_expectation": "high",
"pattern_selector": {
"pattern": "",
"duration": ""
},
"paths": {
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"logging_collection": "ecosystemruntime",
"logging_database": "logging",
"mongo_server_port": "ecosystem-server:54445",
"scoring_engine_path_prod": "http://ecosystem-runtime3:8091",
"models_path": "/data/deployed/",
"mongo_connect": "mongodb://ecosystem_user:EcoEco321@ecosystem-server:54445/?authSource=admin",
"data_path": "/data/",
"build_server_path": "http://ecosystem:EcoEco321@build.ecosystem.ai:8080/job/ecosystem-runtime/buildWithParameters?token=114a827a8ada36685a1f3958a6059cd677&BRANCH=spendpersonality&EMAIL=user@ecosystem.ai&CONTAINER_NAME=ecosystem-runtime-spendpersonality",
"scoring_engine_path_dev": "http://ecosystem-runtime2:8092",
"aws_container_resource": "",
"scoring_engine_path_test": "http://ecosystem-runtime2:8091",
"git_repo_path_branch": "spendpersonality",
"download_path": "https://hub.docker.com/u/ecosystemai",
"mongo_ecosystem_password": "EcoEco321",
"mongo_ecosystem_user": "ecosystem_user",
"git_repo_path": "https://github.com/ecosystemai/ecosystem-runtime.git"
},
"updated_by": "admin@ecosystem.ai",
"options": {
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"is_multi_armed_bandit": false,
"is_enable_plugins": true,
"is_whitelist": false,
"is_corpora": false,
"is_custom_api": false,
"is_budget_tracking": false,
"is_params_from_data_source": true,
"is_model_selector": false,
"is_generate_dashboards": false,
"is_pattern_selector": false,
"is_prediction_model": false
},
"corpora": {
"corpora": ""
},
"parameter_access": {
"lookup": {
"value": 123,
"key": "customer"
},
"create_virtual_variables": false,
"database": "master",
"datasource": "mongodb",
"lookup_fields": [
"adventurer",
"adventurous",
"borrower",
"carefree",
"cautious",
"conservative",
"customer",
"indulger",
"item",
"personality",
"personality_score",
"prudent",
"total_spend",
"trait"
],
"lookup_defaults": "",
"virtual_variables": [],
"table_collection": "bank_transactions_money",
"fields": "customer,total_spend,conservative,indulger,adventurer,borrower,cautious,carefree,personality,personality_score,prudent,adventurous,trait,item"
},
"updated_date": "2024-06-10T10:51:57.726Z",
"deployment_id": "money_personality"
},
{
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"budget_parameters_datasource": "mongodb",
"budget_id": "",
"description": "",
"budget_parameters_table_collection": "",
"x_axis_datasource": "offer_matrix",
"x_axis_name": "",
"acc_namesource": "",
"y_axis_name": "",
"y_axis_namesource": "",
"acc_name": "",
"budget_strategy": "",
"x_axis_namesource": "",
"acc_datasource": "offer_matrix",
"y_axis_datasource": "offer_matrix"
},
"date": "2024-04-01",
"complexity": "medium",
"plugins": {
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"post_score_class_code": "",
"api_endpoint_code": "",
"pre_score_class_text": "",
"pre_score_class_code": ""
},
"model_configuration": {},
"setup_offer_matrix": {
"offer_lookup_id": "",
"database": "",
"table_collection": "",
"datasource": "mongodb"
},
"project_status": "experiment",
"description": "Dynamic personality based on base behaviors: (http://ecosystem-runtime3:8093)",
"multi_armed_bandit": {
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"duration": 0,
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},
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"datasource": "mongodb",
"database": ""
},
"version": "0.2",
"model_selector": {
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"lookup": "",
"database": "",
"selector": "",
"table_collection": "",
"datasource": "mongodb"
},
"performance_expectation": "high",
"pattern_selector": {
"pattern": "",
"duration": ""
},
"paths": {
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"logging_collection": "ecosystemruntime",
"logging_database": "logging",
"mongo_server_port": "ecosystem-server:54445",
"scoring_engine_path_prod": "http://ecosystem-runtime3:8093",
"models_path": "/data/deployed/",
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"data_path": "/data/",
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"scoring_engine_path_dev": "http://ecosystem-runtime3:8093",
"aws_container_resource": "",
"scoring_engine_path_test": "http://ecosystem-runtime3:8093",
"git_repo_path_branch": "spendpersonality",
"download_path": "https://hub.docker.com/u/ecosystemai",
"mongo_ecosystem_password": "EcoEco321",
"mongo_ecosystem_user": "ecosystem_user",
"git_repo_path": "https://github.com/ecosystemai/ecosystem-runtime.git"
},
"updated_by": "admin@ecosystem.ai",
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"is_multi_armed_bandit": true,
"is_enable_plugins": true,
"is_whitelist": false,
"is_corpora": false,
"is_custom_api": false,
"is_budget_tracking": false,
"is_params_from_data_source": true,
"is_model_selector": false,
"is_generate_dashboards": false,
"is_pattern_selector": false,
"is_prediction_model": false
},
"corpora": {
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},
"parameter_access": {
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"key": "customer"
},
"create_virtual_variables": false,
"database": "master",
"datasource": "mongodb",
"lookup_fields": [
"Extrovert",
"Introvert",
"segment_enum"
],
"lookup_defaults": "{proprtyOwnership:1,numberOfChildren:1,maritalStatus:1,numberOfProducts:1,age:1,education:1,language:1}",
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},
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},
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],
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},
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},
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},
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}
],
"project_data": "ecosystem",
"project_dashboards": [
{
"name": "Customer Banking 005.1",
"date": "2024-04-25"
}
],
"project_fact_injection_configs": [
{
"name": "bank_customer",
"date": "2024-03-30",
"description": "Chat with bank customer personality. Copy all transactions from MongoDB to PostgresQL using Feature Engineering tools. Use query: to see SQL statements. Example chat sequence:\nuse: bank_customer\nHow many customer transactions are there?\nquery: How many customer transactions are there?\nquery: How many female customer own homes?"
},
{
"name": "spend",
"date": "2024-04-09",
"description": "Use knowledge to contain the LLM chat performance."
},
{
"name": "customer_chat",
"date": "2024-04-09",
"description": "Agent chat about customer spend and money personalities. This config requires that you provide the customer number first and then can ask about the actions that are configured. Example chat sequence:\nuse: customer_chat\nUse number is 851\nObtain the spend personality\nObtain money personality"
},
{
"name": "customer_local",
"date": "2024-04-30",
"description": "Use Presto and the ecosystem.Ai text to sql engine."
}
],
"created_by": "admin@ecosystem.ai",
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{
"name": "spend_personality_selector",
"date": "2024-04-25"
},
{
"name": "spend_personality_model",
"date": "2024-04-25"
},
{
"name": "spend_personality_dynamic",
"date": "2024-04-25"
},
{
"name": "spend_personality_master",
"date": "2024-04-25"
}
],
"preview_detail": {
"summary": "Generic predictors based on banking data.",
"image": "/data/xyz.png",
"active": true,
"detail": "intervention",
"heading": "Spend Personality"
},
"project_dynamic_interactions": [
{
"name": "spend_personality_dynamic",
"date": "2024-04-28"
},
{
"name": "spend_personality_selector",
"date": "2024-04-28"
}
],
"project_description": "Extract customer spending and money personalities with Algorithm, Model and Dynamic options using the network selector.",
"project_simulations": [
{
"name": "spend_personality_master",
"date": "2024-04-28"
},
{
"name": "spend_personality_dynamic",
"date": "2024-04-28"
},
{
"name": "bank_customer__personality",
"date": "2024-05-02"
}
],
"updated_by": "admin@ecosystem.ai",
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{
"name": "spend_personality",
"date": "2024-04-15"
},
{
"name": "spend_personality_dynamic",
"date": "2024-04-28"
}
],
"_id": {
"date": "Wed Jun 12 13:12:18 UTC 2024",
"timestamp": 1718197938
},
"updated_date": "2024-06-12T13:12:18.000933Z",
"created_date": "2024-05-02T09:07:30.757Z",
"userid_login": "ecosystem",
"feature_stores": [
{
"date": "2024-03-19",
"frame_id": "bank_customer"
},
{
"date": "2024-03-23",
"frame_id": "bank_transactions"
}
]
}