From 8ec896c19385818a83429a21ae2b1ece162f8247 Mon Sep 17 00:00:00 2001 From: "ryuta.yoshimatsu@databricks.com" Date: Fri, 7 Jun 2024 08:13:34 +0000 Subject: [PATCH] Added notebook markdowns --- README.md | 48 ++++---- .../chronos-example.py | 15 +-- .../moirai-example.py | 9 +- .../moment-example.py | 11 +- .../timesfm-example.py | 12 +- examples/foundation_daily.py | 113 +++++++++++------ examples/foundation_monthly.py | 94 +++++++++------ examples/global_daily.py | 114 ++++++++++++------ examples/global_external_regressors_daily.py | 97 +++++++++++---- examples/global_monthly.py | 94 +++++++++------ ...57.0124-101655-9l2fggt9-10-0-16-85.19427.0 | Bin 0 -> 21776 bytes .../lightning_logs/version_0/hparams.yaml | 68 +++++++++++ ...71.0124-101655-9l2fggt9-10-0-16-85.23709.0 | Bin 0 -> 21776 bytes .../lightning_logs/version_1/hparams.yaml | 68 +++++++++++ ...66.0124-101655-9l2fggt9-10-0-16-85.28436.2 | Bin 0 -> 1794 bytes .../lightning_logs/version_10/hparams.yaml | 67 ++++++++++ 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create mode 100644 examples/lightning_logs/version_12/hparams.yaml create mode 100644 examples/lightning_logs/version_13/events.out.tfevents.1717737248.0124-101655-9l2fggt9-10-0-16-85.40391.0 create mode 100644 examples/lightning_logs/version_13/hparams.yaml create mode 100644 examples/lightning_logs/version_14/events.out.tfevents.1717737252.0124-101655-9l2fggt9-10-0-16-85.37661.0 create mode 100644 examples/lightning_logs/version_14/hparams.yaml create mode 100644 examples/lightning_logs/version_15/events.out.tfevents.1717737254.0124-101655-9l2fggt9-10-0-16-85.37661.1 create mode 100644 examples/lightning_logs/version_15/hparams.yaml create mode 100644 examples/lightning_logs/version_16/events.out.tfevents.1717737255.0124-101655-9l2fggt9-10-0-16-85.37661.2 create mode 100644 examples/lightning_logs/version_16/hparams.yaml create mode 100644 examples/lightning_logs/version_17/events.out.tfevents.1717737264.0124-101655-9l2fggt9-10-0-16-85.37661.3 create mode 100644 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examples/lightning_logs/version_99/hparams.yaml diff --git a/README.md b/README.md index 7ecfe6d..d627918 100644 --- a/README.md +++ b/README.md @@ -4,21 +4,21 @@ Bootstrap your large-scale forecasting solutions on Databricks with the Many Models Forecasting (MMF) Solution Accelerator. -MMF expedites the development of sales and demand forecasting solutions on Databricks, including all critical steps: data preparation, training, backtesting, cross-validation, scoring, and deployment. Adopting a configuration-over-code approach, it minimizes the need for extensive coding. However, with its extensible architecture, MMF allows technically proficient users to incorporate new models and algorithms. We recommend reading though the source code and modify it to your specific requirements. +MMF accelerates the development of sales and demand forecasting solutions on Databricks, including critical phases of data preparation, training, backtesting, cross-validation, scoring, and deployment. Adopting a configuration-over-code approach, MMF minimizes the need for extensive coding. But with its extensible architecture, MMF allows technically proficient users to incorporate new models and algorithms. We recommend users to read through the documentation and the source code, and modify it to their specific requirements. -MMF integrates a variety of well-established and cutting-edge algorithms, including [local statistical models](https://github.com/databricks-industry-solutions/many-model-forecasting?tab=readme-ov-file#local-models), [global deep learning models](https://github.com/databricks-industry-solutions/many-model-forecasting?tab=readme-ov-file#global-models), and [foundation time series models](https://github.com/databricks-industry-solutions/many-model-forecasting?tab=readme-ov-file#foundation-models). MMF enables parallel modeling of hundreds or thousands of time series leveraging Spark's distributed computing power. Users can apply multiple models at once and select the best performing one for each time series based on their custom metrics. +MMF integrates a variety of well-established and cutting-edge algorithms, including [local statistical models](https://github.com/databricks-industry-solutions/many-model-forecasting?tab=readme-ov-file#local-models), [global deep learning models](https://github.com/databricks-industry-solutions/many-model-forecasting?tab=readme-ov-file#global-models), and [foundation time series models](https://github.com/databricks-industry-solutions/many-model-forecasting?tab=readme-ov-file#foundation-models). MMF enables parallel modeling of hundreds or thousands of time series leveraging Spark's distributed compute. Users can apply multiple models at once and select the best performing one for each time series based on their custom metrics. Get started now! ## Getting started -To run this solution on a public [M4](https://www.kaggle.com/datasets/yogesh94/m4-forecasting-competition-dataset) dataset, clone this MMF repo into [Databricks Repos](https://www.databricks.com/product/repos). +To run this solution on a public [M4](https://www.kaggle.com/datasets/yogesh94/m4-forecasting-competition-dataset) dataset, clone this MMF repo into your [Databricks Repos](https://www.databricks.com/product/repos). ### Local Models -Local models are used to model individual time series. We support models from [statsforecast](https://github.com/Nixtla/statsforecast), [r fable](https://cran.r-project.org/web/packages/fable/vignettes/fable.html) and [sktime](https://www.sktime.net/en/stable/). Covariates (i.e. exogenous regressors) are currently only supported for some statsforecast models. +Local models are used to model individual time series. They could be advantageous over other types of model for their capabilities to tailor fit to individual series, offer greater interpretability, and require lower data requirements. We support models from [statsforecast](https://github.com/Nixtla/statsforecast), [r fable](https://cran.r-project.org/web/packages/fable/vignettes/fable.html) and [sktime](https://www.sktime.net/en/stable/). Covariates (i.e. exogenous regressors) are currently only supported for some models from statsforecast. -To get started, attach the [examples/local_univariate_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_daily.py) notebook to a cluster running [DBR 14.3 ML](https://docs.databricks.com/en/release-notes/runtime/14.3lts-ml.html) or later runtime. The cluster can be either a single-node or multi-node CPU cluster. Make sure to set the following [Spark configurations](https://spark.apache.org/docs/latest/configuration.html) on the cluster before you start using MMF: ```spark.sql.execution.arrow.enabled true``` and ```spark.sql.adaptive.enabled false``` (more detailed explanation to follow). +To get started, attach the [examples/local_univariate_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_daily.py) notebook to a cluster running [DBR 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/14.3lts-ml.html) or later versions. The cluster can be either a single-node or multi-node CPU cluster. Make sure to set the following [Spark configurations](https://spark.apache.org/docs/latest/configuration.html) on the cluster before you start using MMF: ```spark.sql.execution.arrow.enabled true``` and ```spark.sql.adaptive.enabled false``` (more detailed explanation to follow). In this notebook, we will apply 20+ models to 100 time series. You can specify the models to use in a list: @@ -82,7 +82,7 @@ run_forecast( #### Parameters description: - ```train_data``` is a delta table name that stores the input dataset. -- ```scoring_data``` is a delta table name that stores the [future dynamical regressors](https://nixtlaverse.nixtla.io/neuralforecast/examples/exogenous_variables.html#3-training-with-exogenous-variables). If not provided or if the same name as ```train_data``` is provided, the models will ignore the future dynamical regressors. +- ```scoring_data``` is a delta table name that stores the [dynamic future regressors](https://nixtlaverse.nixtla.io/neuralforecast/examples/exogenous_variables.html#3-training-with-exogenous-variables). If not provided or if the same name as ```train_data``` is provided, the models will ignore the future dynamical regressors. - ```scoring_output``` is a delta table where you write your forecasting output. This table will be created if does not exist - ```evaluation_output``` is a delta table where you write the evalution results from all backtesting trials from all time series and all models. This table will be created if does not exist. - ```group_id``` is a column storing the unique id that groups your dataset to each time series. @@ -99,17 +99,17 @@ run_forecast( - ```experiment_path``` to keep metrics under the MLFlow. - ```use_case_name``` a new column will be created under the delta Table, in case you save multiple trials under 1 table. -To modify the model hyperparameters, directly change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values in [mmf_sa/base_forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/base_forecasting_conf.yaml). +To modify the model hyperparameters, change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). -MMF is fully integrated with MLflow, so once the training kicks off, the experiments will be visible in the MLflow Tracking UI with the corresponding metrics and parameters (note that we do not log all local models in MLFlow but we store the binary in the tables ```evaluation_output``` and ```scoring_output```). The metric you see in the MLflow Tracking UI is a simple mean over backtesting trials over all time series. +MMF is fully integrated with MLflow, so once the training kicks off, the experiments will be visible in the MLflow Tracking UI with the corresponding metrics and parameters (note that we do not log all local models in MLFlow but we store the binaries in the tables ```evaluation_output``` and ```scoring_output```). The metric you see in the MLflow Tracking UI is a simple mean over backtesting trials over all time series. -Other example notebooks for monthly forecasting and forecasting with exogenous regressors can be found in [examples/local_univariate_monthly.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_monthly.py) and [examples/local_univariate_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_external_regressors_daily.py). +We encourage you to reading through [examples/local_univariate_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_daily.py) notebook to better understand how local models can be applied to your time series using MMF. Other example notebooks for monthly forecasting and forecasting with exogenous regressors can be found in [examples/local_univariate_monthly.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_monthly.py) and [examples/local_univariate_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_external_regressors_daily.py). ### Global Models -Global models leverage patterns across multiple time series, enabling shared learning and improved predictions for each series. You typically train one big model for many or all time series. We support deep learning based models from [neuralforecast](https://nixtlaverse.nixtla.io/neuralforecast/index.html). Covariates (i.e. exogenous regressors) and hyperparameter tuning are both supported. +Global models leverage patterns across multiple time series, enabling shared learning and improved predictions for each series. You would typically train one big model for many or all time series. They can often deliver better performance and robustness for forecasting large and similar datasets. We support deep learning based models from [neuralforecast](https://nixtlaverse.nixtla.io/neuralforecast/index.html). Covariates (i.e. exogenous regressors) and hyperparameter tuning are both supported for some models. -To get started, attach the [examples/global_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_daily.py) notebook to a cluster running [DBR 14.3 ML](https://docs.databricks.com/en/release-notes/runtime/index.html) or later runtime. We recommend using a single-node cluster with multiple GPU instances such as [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. Multi-node setting is currently not supported. +To get started, attach the [examples/global_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_daily.py) notebook to a cluster running [DBR 14.3LTS for ML](https://docs.databricks.com/en/release-notes/runtime/index.html) or later version. We recommend using a single-node cluster with multiple GPU instances such as [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. Multi-node setting is currently not supported. You can choose the models to train and put them in a list: @@ -128,16 +128,16 @@ active_models = [ ] ``` -The models prefixed with "Auto" perform hyperparameter optimization within a specified range (see below for more detail). A comprehensive list of models currently supported by MMF is available in the [models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). +The models prefixed with "Auto" perform hyperparameter optimization within a specified range (see below for more detail). A comprehensive list of models currently supported by MMF is available in the [models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). -Now, with the following command, we run the [examples/run_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_daily.py) that will run the ```run_forecast``` function and loop through the ```active_models``` list . The reason why we iterate through the models this way is because once a neuralforecast model is loaded to the memory, we need to restart the python kernel to use another model. +Now, with the following command, we run the [examples/run_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_daily.py) notebook that will in turn call ```run_forecast``` function and loop through the ```active_models``` list . ```python for model in active_models: dbutils.notebook.run( "run_daily", timeout_seconds=0, - arguments={"catalog": catalog, "db": db, "model": model}) + arguments={"catalog": catalog, "db": db, "model": model, "run_id": run_id}) ``` Inside the [examples/run_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_daily.py), we have the ```run_forecast``` function specified as: @@ -163,6 +163,7 @@ run_forecast( active_models=[model], experiment_path="/Shared/mmf_experiment", use_case_name="m4_daily", + run_id=run_id, accelerator="gpu", ) ``` @@ -174,17 +175,17 @@ The parameters are all the same except: - ```use_case_name``` will be used to suffix the model name when registered to Unity Catalog. - ```accelerator``` tells MMF to use GPU instead of CPU. -To modify the model hyperparameters or reset the range of the hyperparameter optimization, directly change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values in [mmf_sa/base_forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/base_forecasting_conf.yaml). +To modify the model hyperparameters or reset the range of the hyperparameter search, change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). MMF is fully integrated with MLflow and so once the training kicks off, the experiments will be visible in the MLflow Tracking UI with the corresponding metrics and parameters. Once the training is complete the models will be logged to MLFlow and registered to Unity Catalog. -Other example notebooks for monthly forecasting and forecasting with exogenous regressors can be found in [examples/global_monthly.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_monthly.py) and [examples/global_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_external_regressors_daily.py) respectively. +We encourage you to reading through [examples/global_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_daily.py) notebook to better understand how global models can be applied to your time series using MMF. Other example notebooks for monthly forecasting and forecasting with exogenous regressors can be found in [examples/global_monthly.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_monthly.py) and [examples/global_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_external_regressors_daily.py) respectively. ### Foundation Models -Foundation time series models are large transformer based models pretrained on millions or billions of time series. These models can produce analysis (i.e. forecasting, anomaly detection, classfication) on an unforeseen time series without training or tuning. We support open source models from multiple sources: [chronos](https://github.com/amazon-science/chronos-forecasting), [moirai](https://blog.salesforceairesearch.com/moirai/), and [moment](https://github.com/moment-timeseries-foundation-model/moment). Covariates (i.e. exogenous regressors) and fine-tuning are currently not yet supported. This is a rapidly changing field, and we are working on updating the supported models and features as the field evolves. +Foundation time series models are transformer based models pretrained on millions or billions of time series. These models can produce analysis (i.e. forecasting, anomaly detection, classfication) on an unforeseen time series without training or tuning. We support open source models from multiple sources: [chronos](https://github.com/amazon-science/chronos-forecasting), [moirai](https://blog.salesforceairesearch.com/moirai/), and [moment](https://github.com/moment-timeseries-foundation-model/moment). Covariates (i.e. exogenous regressors) and fine-tuning are currently not yet supported. This is a rapidly changing field, and we are working on updating the supported models and new features as the field evolves. -To get started, attach the [examples/foundation_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_daily.py) notebook to a cluster running [DBR 14.3 ML](https://docs.databricks.com/en/release-notes/runtime/index.html) or later runtime. We recommend using a single-node cluster with multiple GPU instances such as [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. Multi-node setup is currently not supported. +To get started, attach the [examples/foundation_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_daily.py) notebook to a cluster running [DBR 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/index.html) or later versions. We recommend using a single-node cluster with multiple GPU instances such as [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. Multi-node setup is currently not supported. You can choose the models you want to evaluate and forecast by specifying them in a list: @@ -204,28 +205,27 @@ active_models = [ A comprehensive list of models currently supported by MMF is available in the [models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). -Now, with the following command, we run the [examples/run_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_daily.py) that will run the ```run_forecast``` function. We loop through the ```active_models``` list for the same reason mentioned above (see the global model section). +Now, with the following command, we run [examples/run_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_daily.py) notebook that will in turn run ```run_forecast``` function. We loop through the ```active_models``` list for the same reason mentioned above (see the global model section). ```python for model in active_models: dbutils.notebook.run( "run_daily", timeout_seconds=0, - arguments={"catalog": catalog, "db": db, "model": model}) + arguments={"catalog": catalog, "db": db, "model": model, "run_id": run_id}) ``` Inside the [examples/run_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_daily.py), we have the same ```run_forecast``` function as above. -To modify the model hyperparameters, directly change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values in [mmf_sa/base_forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/base_forecasting_conf.yaml). +To modify the model hyperparameters, change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). MMF is fully integrated with MLflow and so once the training kicks off, the experiments will be visible in the MLflow Tracking UI with the corresponding metrics and parameters. During the evaluation, the models are logged and registered to Unity Catalog. -An example notebook for monthly forecasting can be found in [examples/foundation_monthly.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_monthly.py). +We encourage you to reading through [examples/foundation_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_daily.py) notebook to better understand how foundation models can be applied to your time series using MMF. An example notebook for monthly forecasting can be found in [examples/foundation_monthly.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_monthly.py). #### Using Foundation Models on Databricks -If you just want to try out open source foundation models on Databricks, you can find example notebooks in [examples/foundation-model-examples](https://github.com/databricks-industry-solutions/many-model-forecasting/tree/main/examples/foundation-model-examples). These notebooks will show you how you can load a model, distribute the inference, register the model, deploy the model and generate online forecasts. We have a notebook for chronos, moirai, moment, and timesFM. - +If you want to try out open source foundation models on Databricks without MMF, you can find example notebooks in [examples/foundation-model-examples](https://github.com/databricks-industry-solutions/many-model-forecasting/tree/main/examples/foundation-model-examples). These notebooks will show you how you can load a model, distribute the inference, register the model, deploy the model and generate online forecasts. We have a notebook for chronos, moirai, moment, and timesFM. ## Project support Please note the code in this project is provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of these projects. The source in this project is provided subject to the Databricks License. All included or referenced third party libraries are subject to the licenses set forth below. diff --git a/examples/foundation-model-examples/chronos-example.py b/examples/foundation-model-examples/chronos-example.py index 63fd816..e570a16 100644 --- a/examples/foundation-model-examples/chronos-example.py +++ b/examples/foundation-model-examples/chronos-example.py @@ -13,6 +13,7 @@ # MAGIC %md # MAGIC ## Prepare Data +# MAGIC Make sure that the catalog and the schema already exist. # COMMAND ---------- @@ -23,16 +24,10 @@ # COMMAND ---------- # This cell will create tables: {catalog}.{db}.m4_daily_train, {catalog}.{db}.m4_monthly_train, {catalog}.{db}.rossmann_daily_train, {catalog}.{db}.rossmann_daily_test - dbutils.notebook.run("data_preparation", timeout_seconds=0, arguments={"catalog": catalog, "db": db, "n": n}) # COMMAND ---------- -# MAGIC %md -# MAGIC - -# COMMAND ---------- - from pyspark.sql.functions import collect_list # Make sure that the data exists @@ -44,6 +39,7 @@ # MAGIC %md # MAGIC ## Distribute Inference +# MAGIC We use [Pandas UDF](https://docs.databricks.com/en/udf/pandas.html#iterator-of-series-to-iterator-of-series-udf) to distribute the inference. # COMMAND ---------- @@ -184,6 +180,11 @@ def predict(self, context, input_data, params=None): # COMMAND ---------- +# MAGIC %md +# MAGIC ##Reload Model + +# COMMAND ---------- + from mlflow import MlflowClient client = MlflowClient() @@ -210,7 +211,7 @@ def get_latest_model_version(client, registered_model_name): # COMMAND ---------- # MAGIC %md -# MAGIC ## Deploy Model for Online Forecast +# MAGIC ## Deploy Model on Databricks Model Serving # COMMAND ---------- diff --git a/examples/foundation-model-examples/moirai-example.py b/examples/foundation-model-examples/moirai-example.py index 87134be..007f8e7 100644 --- a/examples/foundation-model-examples/moirai-example.py +++ b/examples/foundation-model-examples/moirai-example.py @@ -13,6 +13,7 @@ # MAGIC %md # MAGIC ## Prepare Data +# MAGIC Make sure that the catalog and the schema already exist. # COMMAND ---------- @@ -39,6 +40,7 @@ # MAGIC %md # MAGIC ## Distribute Inference +# MAGIC We use [Pandas UDF](https://docs.databricks.com/en/udf/pandas.html#iterator-of-series-to-iterator-of-series-udf) to distribute the inference. # COMMAND ---------- @@ -210,6 +212,11 @@ def predict(self, context, input_data, params=None): # COMMAND ---------- +# MAGIC %md +# MAGIC ##Reload Model + +# COMMAND ---------- + from mlflow import MlflowClient mlflow_client = MlflowClient() @@ -235,7 +242,7 @@ def get_latest_model_version(mlflow_client, registered_model_name): # COMMAND ---------- # MAGIC %md -# MAGIC ## Deploy Model for Online Forecast +# MAGIC ## Deploy Model on Databricks Model Serving # COMMAND ---------- diff --git a/examples/foundation-model-examples/moment-example.py b/examples/foundation-model-examples/moment-example.py index cfa41aa..e4b0d4f 100644 --- a/examples/foundation-model-examples/moment-example.py +++ b/examples/foundation-model-examples/moment-example.py @@ -13,6 +13,7 @@ # MAGIC %md # MAGIC ## Prepare Data +# MAGIC Make sure that the catalog and the schema already exist. # COMMAND ---------- @@ -38,7 +39,8 @@ # COMMAND ---------- # MAGIC %md -# MAGIC ## Distributed Inference +# MAGIC ## Distribute Inference +# MAGIC We use [Pandas UDF](https://docs.databricks.com/en/udf/pandas.html#iterator-of-series-to-iterator-of-series-udf) to distribute the inference. # COMMAND ---------- @@ -193,6 +195,11 @@ def predict(self, context, input_data, params=None): # COMMAND ---------- +# MAGIC %md +# MAGIC ##Reload Model + +# COMMAND ---------- + from mlflow import MlflowClient mlflow_client = MlflowClient() @@ -218,7 +225,7 @@ def get_latest_model_version(mlflow_client, registered_model_name): # COMMAND ---------- # MAGIC %md -# MAGIC ## Deploy Model for Online Forecast +# MAGIC ## Deploy Model on Databricks Model Serving # COMMAND ---------- diff --git a/examples/foundation-model-examples/timesfm-example.py b/examples/foundation-model-examples/timesfm-example.py index 7c7e256..ee686e1 100644 --- a/examples/foundation-model-examples/timesfm-example.py +++ b/examples/foundation-model-examples/timesfm-example.py @@ -4,7 +4,7 @@ # MAGIC # MAGIC The notebook loads the model, distributes the inference, registers the model, deploys the model and makes online forecasts. # MAGIC -# MAGIC As of today (June 5, 2024), TimesFM supports python version below [3.10](https://github.com/google-research/timesfm/issues/60). So make sure your cluster is below DBR ML 14.3. +# MAGIC As of June 5, 2024, TimesFM supports python version below [3.10](https://github.com/google-research/timesfm/issues/60). So make sure your cluster is below DBR ML 14.3. # COMMAND ---------- @@ -24,6 +24,7 @@ # MAGIC %md # MAGIC ## Prepare Data +# MAGIC Make sure that the catalog and the schema already exist. # COMMAND ---------- @@ -51,7 +52,7 @@ # COMMAND ---------- # MAGIC %md -# MAGIC See the [github repository](https://github.com/google-research/timesfm/tree/master?tab=readme-ov-file#initialize-the-model-and-load-a-checkpoint) of TimesFM for detailed description of the input parameters. +# MAGIC Distribution of the inference is managed by TimesFM so we don't need to use Pandas UDF. See the [github repository](https://github.com/google-research/timesfm/tree/master?tab=readme-ov-file#initialize-the-model-and-load-a-checkpoint) of TimesFM for detailed description of the input parameters. # COMMAND ---------- @@ -166,6 +167,11 @@ def __setstate__(self, state): # COMMAND ---------- +# MAGIC %md +# MAGIC ##Reload Model + +# COMMAND ---------- + from mlflow import MlflowClient client = MlflowClient() @@ -189,7 +195,7 @@ def get_latest_model_version(client, registered_model_name): # COMMAND ---------- # MAGIC %md -# MAGIC ## Deploy Model for Online Forecast +# MAGIC ## Deploy Model on Databricks Model Serving # COMMAND ---------- diff --git a/examples/foundation_daily.py b/examples/foundation_daily.py index 48c08ae..37fd281 100644 --- a/examples/foundation_daily.py +++ b/examples/foundation_daily.py @@ -1,42 +1,46 @@ # Databricks notebook source # MAGIC %md -# MAGIC # Many Models Forecasting SA (MMFSA) Demo -# MAGIC This demo highlights how to configure MMF SA to use M4 competition data +# MAGIC # Many Models Forecasting Demo +# MAGIC This notebook showcases how to run MMF with foundation models on multiple time series of daily resolution. We will use [M4 competition](https://www.sciencedirect.com/science/article/pii/S0169207019301128#sec5) data. # COMMAND ---------- -# MAGIC %pip install -r ../requirements.txt --quiet -# MAGIC dbutils.library.restartPython() +# MAGIC %md +# MAGIC ### Cluster setup +# MAGIC +# MAGIC We recommend using a cluster with [Databricks Runtime 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/14.3lts-ml.html) or above. The cluster should be single-node with one or more GPU instances: e.g. [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. MMF leverages [Pandas UDF](https://docs.databricks.com/en/udf/pandas.html) for distributing the inference tasks and utilizing all the available resource. # COMMAND ---------- # MAGIC %md -# MAGIC ### Data preparation steps -# MAGIC We are using `datasetsforecast` package to download M4 data. -# MAGIC -# MAGIC M4 dataset contains a set of time series which we use for testing of MMF SA. -# MAGIC -# MAGIC Below we have developed a number of functions to convert M4 time series to the expected format. +# MAGIC ### Install and import packages +# MAGIC Check out [requirements.txt](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/requirements.txt) if you're interested in the libraries we use. For foundation models, additional dependencies are installed and imported as per demand. See how this is done in `install` function defined in each model pipeline script: e.g. [mmf_sa/models/chronosforecast/ChronosPipeline.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/chronosforecast/ChronosPipeline.py). + +# COMMAND ---------- + +# MAGIC %pip install -r ../requirements.txt --quiet +# MAGIC dbutils.library.restartPython() # COMMAND ---------- -import pathlib -import pandas as pd import logging logger = spark._jvm.org.apache.log4j logging.getLogger("py4j.java_gateway").setLevel(logging.ERROR) logging.getLogger("py4j.clientserver").setLevel(logging.ERROR) -from datasetsforecast.m4 import M4 -import uuid # COMMAND ---------- -# Make sure that the catalog and the schema exist -catalog = "solacc_uc" # Name of the catalog we use to manage our assets -db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) +import uuid +import pathlib +import pandas as pd +from datasetsforecast.m4 import M4 +from mmf_sa import run_forecast -_ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") -_ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Prepare data +# MAGIC We are using [`datasetsforecast`](https://github.com/Nixtla/datasetsforecast/tree/main/) package to download M4 data. M4 dataset contains a set of time series which we use for testing MMF. Below we have written a number of custome functions to convert M4 time series to an expected format. # COMMAND ---------- @@ -68,6 +72,20 @@ def transform_group(df): return res_df +# COMMAND ---------- + +# MAGIC %md +# MAGIC We are going to save this data in a delta lake table. Provide catalog and database names where you want to store the data. + +# COMMAND ---------- + +catalog = "solacc_uc" # Name of the catalog we use to manage our assets +db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) + +# Making sure that the catalog and the schema exist +_ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") +_ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") + ( spark.createDataFrame(create_m4_daily()) .write.format("delta").mode("overwrite") @@ -76,15 +94,20 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now the dataset looks in the following way: +# MAGIC %md Let's take a peak at the dataset: # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.m4_daily_train where unique_id in ('D1', 'D2', 'D6', 'D7', 'D10') order by unique_id, ds +display( + spark.sql(f"select * from {catalog}.{db}.m4_daily_train where unique_id in ('D1', 'D2', 'D3', 'D4', 'D5') order by unique_id, ds") + ) # COMMAND ---------- -# MAGIC %md ### Let's configure the list of models we are going to use for training: +# MAGIC %md ### Models +# MAGIC Let's configure a list of models we are going to apply to our time series for evaluation and forecasting. A comprehensive list of all supported models is available in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Look for the models where `model_type: foundation`; these are the foundation models we import from [chronos](https://github.com/amazon-science/chronos-forecasting/tree/main), [uni2ts](https://github.com/SalesforceAIResearch/uni2ts) and [moment](https://github.com/moment-timeseries-foundation-model/moment). Check their documentation for the detailed description of each model. +# MAGIC +# MAGIC Foundation time series models are pretrained on millions or billions of time series. These models can produce analysis (i.e. forecasting, anomaly detection, classfication) on an unforeseen time series without training or tuning. You can modify the hyperparameters in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite the default values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). # COMMAND ---------- @@ -102,15 +125,19 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now we can run the forecasting process using `run_forecast` function. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC We have to loop through the model in the following way else cuda will throw an error. +# MAGIC %md ### Run MMF +# MAGIC +# MAGIC Now, we can run the evaluation and forecasting using `run_forecast` function defined in [mmf_sa/models/__init__.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/__init__.py). Refer to [README.md](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/README.md#parameters-description) for a comprehensive description of each parameter. +# MAGIC +# MAGIC While the following cell is running, you can check the status of your run on Experiments. Make sure you look for the experiments with the path you provided as `experiment_path` within `run_forecast`. On the Experiments page, you see one entry per one model (i.e. ChronosT5Large). The metric provided here is a simple average over all back testing trials and all time series. This is intended to give you an initial feeling of how good each model performs on your entire data mix. But we will look into how you can scrutinize the evaluation using the `evaluation_output` table in a bit. +# MAGIC +# MAGIC If you are interested in how MMF achieves distributed inference on these foundation models using Pandas UDF, have a look at the model pipeline scripts: e.g. [mmf_sa/models/chronosforecast/ChronosPipeline.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/chronosforecast/ChronosPipeline.py). +# MAGIC +# MAGIC One small difference here in running `run_forecast` from the local model case is that we have to iterate through the `active_models` and call the function written in a [separate notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_daily.py). This is to avoid the CUDA out of memory issue by freeing up the GPU memory after each model. Make sure to provide `accelerator="gpu"` as an input parameter to `run_forecast` function. # COMMAND ---------- +# The same run_id will be assigned to all the models. This makes it easier to run the post evaluation analysis later. run_id = str(uuid.uuid4()) for model in active_models: @@ -121,30 +148,44 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Evaluation output -# MAGIC In the evaluation output table, the evaluation for all backtest windows and all models are stored. This info can be used to monitor model performance or decide which models should be taken into the final aggregated forecast. +# MAGIC %md ### Evaluate +# MAGIC In `evaluation_output` table, the we store all evaluation results for all backtesting trials from all models. This information can be used to understand which models performed well on which time series on which periods of backtesting. This is very important for selecting the final model for forecasting or models for ensembling. Maybe, it's faster to take a look at the table: + +# COMMAND ---------- + +display(spark.sql(f"select * from {catalog}.{db}.daily_evaluation_output order by unique_id, model, backtest_window_start_date")) # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.daily_evaluation_output order by unique_id, model, backtest_window_start_date +# MAGIC %md +# MAGIC For foundation models, we use the same pre-trained model to produce the as-if forecasts for all back testing periods. See how MMF implements backtesting in `backtest` method in [mmf_sa/models/abstract_model.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/abstract_model.py). +# MAGIC +# MAGIC We store the as-if forecasts together with the actuals for each backtesting period, so you can construct any metric of your interest. We provide a few out-of-the-box metrics for you (e.g. smape), but the idea here is that you construct your own metrics reflecting your business requirements and evaluate models based on those. For example, maybe you care more about the accuracy of the near-horizon forecasts than the far-horizon ones. In such case, you can apply a decreasing wieght to compute weighted aggregated metrics. +# MAGIC +# MAGIC Note that if you run local and/or global models against the same time series with the same input parameters (except for those specifying global and foundation models), you will get the entries from those models in the same table and will be able to compare across all types models, which is the biggest benefit of having all models integrated in one solution. +# MAGIC +# MAGIC We also register the model in Unity Catalog and store each model's URI in this table (`model_uri`). You can use MLFlow to [load the models](https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html#mlflow.pyfunc.load_model) and access their specifications or produce forecasts. +# MAGIC +# MAGIC Once you have your foundation models registered in Unity Catalog, you can deploy them behind a real-time endpoint on [Model Serving](https://docs.databricks.com/en/machine-learning/model-serving/index.html). You can then generate a multi-step ahead forecast at any point in time as long as you provide the history with the right resolution. This could be a game changing feature for applications relying on real-time tracking and monitoring of time series data. See the notebooks in [examples/foundation-model-examples](https://github.com/databricks-industry-solutions/many-model-forecasting/tree/main/examples/foundation-model-examples) for examples of how to register and deploy a model, and make an online forecasting request on that model. # COMMAND ---------- -# MAGIC %md ### Forecast Output -# MAGIC In the Forecast output table, the final forecast for each model and each time series is stored. +# MAGIC %md ### Forecast +# MAGIC In `scoring_output` table, forecasts for each time series from each model are stored. Based on the evaluation exercised performed on `evaluation_output` table, you can select the forecasts from the best performing models or a mix of models. We again store each model's URI in this table (`model_uri`). You can use MLFlow to [load the models](https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html#mlflow.pyfunc.load_model) and access their specifications or produce forecasts. # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.daily_scoring_output order by unique_id, model, ds +display(spark.sql(f"select * from {catalog}.{db}.daily_scoring_output order by unique_id, model, ds")) # COMMAND ---------- # MAGIC %md ### Delete Tables +# MAGIC Let's clean up the tables. # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.daily_evaluation_output +display(spark.sql(f"delete from {catalog}.{db}.daily_evaluation_output")) # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.daily_scoring_output +display(spark.sql(f"delete from {catalog}.{db}.daily_scoring_output")) diff --git a/examples/foundation_monthly.py b/examples/foundation_monthly.py index cbe2ad8..c5f07ce 100644 --- a/examples/foundation_monthly.py +++ b/examples/foundation_monthly.py @@ -1,42 +1,46 @@ # Databricks notebook source # MAGIC %md -# MAGIC # Many Models Forecasting SA (MMFSA) Demo -# MAGIC This demo highlights how to configure MMF SA to use M4 competition data +# MAGIC # Many Models Forecasting Demo +# MAGIC This notebook showcases how to run MMF with foundation models on multiple time series of monthly resolution. We will use [M4 competition](https://www.sciencedirect.com/science/article/pii/S0169207019301128#sec5) data. The descriptions here are mostly the same as the case with the [daily resolution](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_daily.py), so we will skip the redundant parts and focus only on the essentials. # COMMAND ---------- -# MAGIC %pip install -r ../requirements.txt --quiet -# MAGIC dbutils.library.restartPython() +# MAGIC %md +# MAGIC ### Cluster setup +# MAGIC +# MAGIC We recommend using a cluster with [Databricks Runtime 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/14.3lts-ml.html) or above. The cluster should be single-node with one or more GPU instances: e.g. [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. MMF leverages [Pandas UDF](https://docs.databricks.com/en/udf/pandas.html) for distributing the inference tasks and utilizing all the available resource. # COMMAND ---------- # MAGIC %md -# MAGIC ### Data preparation steps -# MAGIC We are using `datasetsforecast` package to download M4 data. -# MAGIC -# MAGIC M4 dataset contains a set of time series which we use for testing of MMF SA. -# MAGIC -# MAGIC Below we have developed a number of functions to convert M4 time series to the expected format. +# MAGIC ### Install and import packages +# MAGIC Check out [requirements.txt](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/requirements.txt) if you're interested in the libraries we use. + +# COMMAND ---------- + +# MAGIC %pip install -r ../requirements.txt --quiet +# MAGIC dbutils.library.restartPython() # COMMAND ---------- -import pathlib -import pandas as pd import logging logger = spark._jvm.org.apache.log4j logging.getLogger("py4j.java_gateway").setLevel(logging.ERROR) logging.getLogger("py4j.clientserver").setLevel(logging.ERROR) -from datasetsforecast.m4 import M4 -import uuid # COMMAND ---------- -# Make sure that the catalog and the schema exist -catalog = "solacc_uc" # Name of the catalog we use to manage our assets -db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) +import uuid +import pathlib +import pandas as pd +from datasetsforecast.m4 import M4 +from mmf_sa import run_forecast -_ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") -_ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Prepare data +# MAGIC We are using [`datasetsforecast`](https://github.com/Nixtla/datasetsforecast/tree/main/) package to download M4 data. # COMMAND ---------- @@ -73,6 +77,20 @@ def transform_group(df): return _df +# COMMAND ---------- + +# MAGIC %md +# MAGIC We are going to save this data in a delta lake table. Provide catalog and database names where you want to store the data.__ + +# COMMAND ---------- + +catalog = "solacc_uc" # Name of the catalog we use to manage our assets +db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) + +# Making sure that the catalog and the schema exist +_ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") +_ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") + ( spark.createDataFrame(create_m4_monthly()) .write.format("delta").mode("overwrite") @@ -81,19 +99,22 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now the dataset looks in the following way: +# MAGIC %md Let's take a peak at the dataset: # COMMAND ---------- -# MAGIC %sql select unique_id, count(date) as count from solacc_uc.mmf.m4_monthly_train group by unique_id order by unique_id +display(spark.sql(f"select unique_id, count(date) as count from {catalog}.{db}.m4_monthly_train group by unique_id order by unique_id")) # COMMAND ---------- -# MAGIC %sql select count(distinct(unique_id)) from solacc_uc.mmf.m4_monthly_train +display( + spark.sql(f"select * from {catalog}.{db}.m4_monthly_train where unique_id in ('M1', 'M2', 'M3', 'M4', 'M5') order by unique_id, date") + ) # COMMAND ---------- -# MAGIC %md ### Let's configure the list of models we are going to use for training: +# MAGIC %md ### Models +# MAGIC Let's configure a list of models we are going to apply to our time series for evaluation and forecasting. A comprehensive list of all supported models is available in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Look for the models where `model_type: foundation`; these are the foundation models we import from [chronos](https://github.com/amazon-science/chronos-forecasting/tree/main), [uni2ts](https://github.com/SalesforceAIResearch/uni2ts) and [moment](https://github.com/moment-timeseries-foundation-model/moment). Check their documentation for the detailed description of each model. # COMMAND ---------- @@ -111,15 +132,13 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now we can run the forecasting process using `run_forecast` function. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC We have to loop through the model in the following way else cuda will throw an error. +# MAGIC %md ### Run MMF +# MAGIC +# MAGIC Now, we can run the evaluation and forecasting using `run_forecast` function defined in [mmf_sa/models/__init__.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/__init__.py). Refer to [README.md](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/README.md#parameters-description) for a comprehensive description of each parameter. # COMMAND ---------- +# The same run_id will be assigned to all the models. This makes it easier to run the post evaluation analysis later. run_id = str(uuid.uuid4()) for model in active_models: @@ -130,30 +149,31 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Evaluation Output -# MAGIC In the evaluation output table, the evaluation for all backtest windows and all models are stored. This info can be used to monitor model performance or decide which models should be taken into the final aggregated forecast. +# MAGIC %md ### Evaluate +# MAGIC In `evaluation_output` table, the we store all evaluation results for all backtesting trials from all models. This information can be used to understand which models performed well on which time series on which periods of backtesting. This is very important for selecting the final model for forecasting or models for ensembling. Maybe, it's faster to take a look at the table: # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.monthly_evaluation_output order by unique_id, model, backtest_window_start_date +display(spark.sql(f"select * from {catalog}.{db}.monthly_evaluation_output order by unique_id, model, backtest_window_start_date")) # COMMAND ---------- -# MAGIC %md ### Forecast Output -# MAGIC In the Forecast output table, the final forecast for each model and each time series is stored. +# MAGIC %md ### Forecast +# MAGIC In `scoring_output` table, forecasts for each time series from each model are stored. # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.monthly_scoring_output order by unique_id, model, date +display(spark.sql(f"select * from {catalog}.{db}.monthly_scoring_output order by unique_id, model, date")) # COMMAND ---------- # MAGIC %md ### Delete Tables +# MAGIC Let's clean up the tables. # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.monthly_evaluation_output +display(spark.sql(f"delete from {catalog}.{db}.monthly_evaluation_output")) # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.monthly_scoring_output +display(spark.sql(f"delete from {catalog}.{db}.monthly_scoring_output")) diff --git a/examples/global_daily.py b/examples/global_daily.py index 3c7af1c..3e9169f 100644 --- a/examples/global_daily.py +++ b/examples/global_daily.py @@ -1,42 +1,47 @@ # Databricks notebook source # MAGIC %md -# MAGIC # Many Models Forecasting SA (MMFSA) Demo -# MAGIC This demo highlights how to configure MMF SA to use M4 competition data +# MAGIC # Many Models Forecasting Demo +# MAGIC This notebook showcases how to run MMF with global models on multiple time series of daily resolution. We will use [M4 competition](https://www.sciencedirect.com/science/article/pii/S0169207019301128#sec5) data. # COMMAND ---------- -# MAGIC %pip install -r ../requirements.txt --quiet -# MAGIC dbutils.library.restartPython() +# MAGIC %md +# MAGIC ### Cluster setup +# MAGIC +# MAGIC We recommend using a cluster with [Databricks Runtime 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/14.3lts-ml.html) or above. The cluster should be single-node with one or more GPU instances: e.g. [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. MMF leverages [neuralforecast](https://nixtlaverse.nixtla.io/neuralforecast/index.html) which is built on top of [pytorch](https://lightning.ai/docs/pytorch/stable/common/trainer.html) and can therefore utilize all the [available resources](https://lightning.ai/docs/pytorch/stable/common/trainer.html). # COMMAND ---------- # MAGIC %md -# MAGIC ### Data preparation steps -# MAGIC We are using `datasetsforecast` package to download M4 data. -# MAGIC -# MAGIC M4 dataset contains a set of time series which we use for testing of MMF SA. -# MAGIC -# MAGIC Below we have developed a number of functions to convert M4 time series to the expected format. +# MAGIC ### Install and import packages +# MAGIC Check out [requirements.txt](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/requirements.txt) if you're interested in the libraries we use. + +# COMMAND ---------- + +# DBTITLE 1,Install the necessary libraries +# MAGIC %pip install -r ../requirements.txt --quiet +# MAGIC dbutils.library.restartPython() # COMMAND ---------- -import pathlib -import pandas as pd import logging logger = spark._jvm.org.apache.log4j logging.getLogger("py4j.java_gateway").setLevel(logging.ERROR) logging.getLogger("py4j.clientserver").setLevel(logging.ERROR) -from datasetsforecast.m4 import M4 -import uuid # COMMAND ---------- -# Make sure that the catalog and the schema exist -catalog = "solacc_uc" # Name of the catalog we use to manage our assets -db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) +import uuid +import pathlib +import pandas as pd +from datasetsforecast.m4 import M4 +from mmf_sa import run_forecast -_ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") -_ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Prepare data +# MAGIC We are using [`datasetsforecast`](https://github.com/Nixtla/datasetsforecast/tree/main/) package to download M4 data. M4 dataset contains a set of time series which we use for testing MMF. Below we have written a number of custome functions to convert M4 time series to an expected format. # COMMAND ---------- @@ -68,6 +73,20 @@ def transform_group(df): return res_df +# COMMAND ---------- + +# MAGIC %md +# MAGIC We are going to save this data in a delta lake table. Provide catalog and database names where you want to store the data. + +# COMMAND ---------- + +catalog = "solacc_uc" # Name of the catalog we use to manage our assets +db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) + +# Making sure that the catalog and the schema exist +_ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") +_ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") + ( spark.createDataFrame(create_m4_daily()) .write.format("delta").mode("overwrite") @@ -76,15 +95,20 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now the dataset looks in the following way: +# MAGIC %md Let's take a peak at the dataset: # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.m4_daily_train where unique_id in ('D1', 'D2', 'D6', 'D7', 'D10') order by unique_id, ds +display( + spark.sql(f"select * from {catalog}.{db}.m4_daily_train where unique_id in ('D1', 'D2', 'D3', 'D4', 'D5') order by unique_id, ds") + ) # COMMAND ---------- -# MAGIC %md ### Let's configure the list of models we are going to use for training: +# MAGIC %md ### Models +# MAGIC Let's configure a list of models we are going to apply to our time series for evaluation and forecasting. A comprehensive list of all supported models is available in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Look for the models where `model_type: global`; these are the global models we import from [neuralforecast](https://github.com/Nixtla/neuralforecast). Check their documentation for the detailed description of each model. +# MAGIC +# MAGIC Some of these models perform [hyperparameter optimization](https://nixtlaverse.nixtla.io/neuralforecast/examples/automatic_hyperparameter_tuning.html) on its own to search for the best parameters. You can specify the range of the search or fix the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite the default values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). # COMMAND ---------- @@ -103,15 +127,21 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now we can run the forecasting process using `run_forecast` function. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC We have to loop through the model in the following way else cuda will throw an error. +# MAGIC %md ### Run MMF +# MAGIC +# MAGIC Now, we can run the evaluation and forecasting using `run_forecast` function defined in [mmf_sa/models/__init__.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/__init__.py). Refer to [README.md](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/README.md#parameters-description) for a comprehensive description of each parameter. +# MAGIC +# MAGIC Note that we are not providing any covariate field (i.e. `static_features`, `dynamic_future` or `dynamic_historical`) yet in this example. We will look into how we can add exogenous regressors to help our models in a different notebook: [examples/global_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_external_regressors_daily.py). +# MAGIC +# MAGIC While the following cell is running, you can check the status of your run on Experiments. Make sure you look for the experiments with the path you provided as `experiment_path` within `run_forecast`. On the Experiments page, you see one entry per one model (i.e. NeuralForecastAutoNBEATSx). The metric provided here is a simple average over all back testing trials and all time series. This is intended to give you an initial feeling of how good each model performs on your entire data mix. But we will look into how you can scrutinize the evaluation using the `evaluation_output` table in a bit. +# MAGIC +# MAGIC If you are interested in how MMF achieves distributed training and inference, have a look at the two methods `evaluate_global_model` and `evaluate_global_model` defined in the source code [`Forecaster.py`](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/Forecaster.py). +# MAGIC +# MAGIC One small difference here in running `run_forecast` from the local model case is that we have to iterate through the `active_models` and call the function written in a [separate notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_daily.py). This is to avoid the CUDA out of memory issue by freeing up the GPU memory after each model. Make sure to provide `accelerator="gpu"` as an input parameter to `run_forecast` function. # COMMAND ---------- +# The same run_id will be assigned to all the models. This makes it easier to run the post evaluation analysis later. run_id = str(uuid.uuid4()) for model in active_models: @@ -122,30 +152,42 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Evaluation output -# MAGIC In the evaluation output table, the evaluation for all backtest windows and all models are stored. This info can be used to monitor model performance or decide which models should be taken into the final aggregated forecast. +# MAGIC %md ### Evaluate +# MAGIC In `evaluation_output` table, the we store all evaluation results for all backtesting trials from all models. This information can be used to understand which models performed well on which time series on which periods of backtesting. This is very important for selecting the final model for forecasting or models for ensembling. Maybe, it's faster to take a look at the table: + +# COMMAND ---------- + +display(spark.sql(f"select * from {catalog}.{db}.daily_evaluation_output order by unique_id, model, backtest_window_start_date")) # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.daily_evaluation_output order by unique_id, model, backtest_window_start_date +# MAGIC %md +# MAGIC For global models, we train the model once using the training dataset excluding `backtest_months`. We then use the same fitted model to produce the as-if forecasts for all back testing periods. We take this approach to make sure that there is no data leakage. See how MMF implements backtesting in `backtest` method in [mmf_sa/models/abstract_model.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/abstract_model.py). +# MAGIC +# MAGIC We store the as-if forecasts together with the actuals for each backtesting period, so you can construct any metric of your interest. We provide a few out-of-the-box metrics for you (e.g. smape), but the idea here is that you construct your own metrics reflecting your business requirements and evaluate models based on those. For example, maybe you care more about the accuracy of the near-horizon forecasts than the far-horizon ones. In such case, you can apply a decreasing wieght to compute weighted aggregated metrics. +# MAGIC +# MAGIC Note that if you run local and/or global models against the same time series with the same input parameters (except for those specifying global and foundation models), you will get the entries from those models in the same table and will be able to compare across all types models, which is the biggest benefit of having all models integrated in one solution. +# MAGIC +# MAGIC We also register the model in Unity Catalog and store each model's URI in this table (`model_uri`). You can use MLFlow to [load the models](https://mlflow.org/docs/latest/python_api/mlflow.sklearn.html#mlflow.sklearn.load_model) and access their specifications or produce forecasts. # COMMAND ---------- -# MAGIC %md ### Forecast Output -# MAGIC In the Forecast output table, the final forecast for each model and each time series is stored. +# MAGIC %md ### Forecast +# MAGIC In `scoring_output` table, forecasts for each time series from each model are stored. Based on the evaluation exercised performed on `evaluation_output` table, you can select the forecasts from the best performing models or a mix of models. We again store each model's URI in this table (`model_uri`). You can use MLFlow to [load the models](https://mlflow.org/docs/latest/python_api/mlflow.sklearn.html#mlflow.sklearn.load_model) and access their specifications or produce forecasts. # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.daily_scoring_output order by unique_id, model, ds +display(spark.sql(f"select * from {catalog}.{db}.daily_scoring_output order by unique_id, model, ds")) # COMMAND ---------- # MAGIC %md ### Delete Tables +# MAGIC Let's clean up the tables. # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.daily_evaluation_output +display(spark.sql(f"delete from {catalog}.{db}.daily_evaluation_output")) # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.daily_scoring_output +display(spark.sql(f"delete from {catalog}.{db}.daily_scoring_output")) diff --git a/examples/global_external_regressors_daily.py b/examples/global_external_regressors_daily.py index ddba67b..3487869 100644 --- a/examples/global_external_regressors_daily.py +++ b/examples/global_external_regressors_daily.py @@ -1,4 +1,24 @@ # Databricks notebook source +# MAGIC %md +# MAGIC # Many Models Forecasting Demo +# MAGIC +# MAGIC This notebook showcases how to run MMF with global models on multiple time series of daily resolution using exogenous regressors. We will use [Rossmann Store](https://www.kaggle.com/competitions/rossmann-store-sales/data) data. To be able to run this notebook, you need to register on [Kaggle](https://www.kaggle.com/) and download the dataset. The descriptions here are mostly the same as the case [without exogenous regressors](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_daily.py), so we will skip the redundant parts and focus only on the essentials. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Cluster setup +# MAGIC +# MAGIC We recommend using a cluster with [Databricks Runtime 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/14.3lts-ml.html) or above. The cluster should be single-node with one or more GPU instances: e.g. [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. MMF leverages [neuralforecast](https://nixtlaverse.nixtla.io/neuralforecast/index.html) which is built on top of [pytorch](https://lightning.ai/docs/pytorch/stable/common/trainer.html) and can therefore utilize all the [available resources](https://lightning.ai/docs/pytorch/stable/common/trainer.html). + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Install and import packages +# MAGIC Check out [requirements.txt](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/requirements.txt) if you're interested in the libraries we use. + +# COMMAND ---------- + # MAGIC %pip install -r ../requirements.txt --quiet # COMMAND ---------- @@ -12,21 +32,31 @@ # COMMAND ---------- -# Make sure that the catalog and the schema exist +import uuid +import pathlib +import pandas as pd +from datasetsforecast.m4 import M4 +from mmf_sa import run_forecast + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Prepare data +# MAGIC Before running this notebook, download the dataset from [Kaggle](https://www.kaggle.com/competitions/rossmann-store-sales/data) and store them in Unity Catalog as a [volume](https://docs.databricks.com/en/connect/unity-catalog/volumes.html). + +# COMMAND ---------- + catalog = "solacc_uc" # Name of the catalog we use to manage our assets db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) -volume = "rossmann" # Name of the schema where you have your rossmann dataset csv sotred +volume = "rossmann" # Name of the volume where you have your rossmann dataset csv sotred +# Make sure that the catalog and the schema exist _ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") _ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") _ = spark.sql(f"CREATE VOLUME IF NOT EXISTS {catalog}.{db}.{volume}") # COMMAND ---------- -# MAGIC %md Download the dataset from [Kaggle](kaggle.com/competitions/rossmann-store-sales/data) and store them in the volume. - -# COMMAND ---------- - # Randomly select 100 stores to forecast import random random.seed(7) @@ -43,13 +73,19 @@ train = train.filter(train.Store.isin(stores)) test = test.filter(test.Store.isin(stores)) +# COMMAND ---------- + +# MAGIC %md +# MAGIC We are going to save this data in a delta lake table. Provide catalog and database names where you want to store the data. + +# COMMAND ---------- + train.write.mode("overwrite").option("mergeSchema", "true").saveAsTable(f"{catalog}.{db}.rossmann_daily_train") test.write.mode("overwrite").option("mergeSchema", "true").saveAsTable(f"{catalog}.{db}.rossmann_daily_test") # COMMAND ---------- -# Set the number of shuffle partitions larger than the total number of cores -#sqlContext.setConf("spark.sql.shuffle.partitions", "1000") +# MAGIC %md Let's take a peak at the dataset: # COMMAND ---------- @@ -58,9 +94,17 @@ # COMMAND ---------- -import pathlib -import pandas as pd -from mmf_sa import run_forecast +# MAGIC %md +# MAGIC Note that in `rossmann_daily_train` we have our target variable `Sales` but not in `rossmann_daily_test`. This is because `rossmann_daily_test` is going to be used as our `scoring_data` that stores `dynamic_future` variables of the future dates. When you adapt this notebook to your use case, make sure to comply with these datasets formats. See neuralforecast's [documentation](https://nixtlaverse.nixtla.io/neuralforecast/examples/exogenous_variables.html) for more detail on exogenous regressors. + +# COMMAND ---------- + +# MAGIC %md ### Models +# MAGIC Let's configure a list of models we are going to apply to our time series for evaluation and forecasting. A comprehensive list of all supported models is available in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Look for the models where `model_type: global`; these are the global models we import from [neuralforecast](https://github.com/Nixtla/neuralforecast). Check their documentation for the detailed description of each model. +# MAGIC +# MAGIC Exogenous regressors are currently only supported for [some models](https://nixtlaverse.nixtla.io/neuralforecast/models.html) (e.g. `NeuralForecastAutoNBEATSx`). But including non-supported models in the active model list doesn't harm: models that can't use exogenous regressors will simply ignore them. + +# COMMAND ---------- active_models = [ "NeuralForecastRNN", @@ -77,15 +121,13 @@ # COMMAND ---------- -# MAGIC %md ### Now we can run the forecasting process using `run_forecast` function. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC We have to loop through the model in the following way else cuda will throw an error. +# MAGIC %md ### Run MMF +# MAGIC +# MAGIC Now, we run the evaluation and forecasting using `run_forecast` function. We are providing the training table and the scoring table names. If `scoring_data` is not provided or if the same name as `train_data` is provided, the models will ignore the `dynamic_future` regressors. Note that we are providing a covariate field (i.e. `dynamic_future`) this time in `run_forecast` function called in [examples/run_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_external_regressors_daily.py). There are also other convariate fields, namely `static_features`, and `dynamic_historical`, which you can provide. Read more about these covariates in [neuralforecast's documentation](https://nixtlaverse.nixtla.io/neuralforecast/examples/exogenous_variables.html). # COMMAND ---------- +# The same run_id will be assigned to all the models. This makes it easier to run the post evaluation analysis later. run_id = str(uuid.uuid4()) for model in active_models: @@ -96,20 +138,33 @@ # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.rossmann_daily_evaluation_output order by Store, model, backtest_window_start_date +# MAGIC %md ### Evaluate +# MAGIC In `evaluation_output` table, the we store all evaluation results for all backtesting trials from all models. + +# COMMAND ---------- + +display( + spark.sql(f"select * from {catalog}.{db}.rossmann_daily_evaluation_output order by Store, model, backtest_window_start_date") + ) + +# COMMAND ---------- + +# MAGIC %md ### Forecast +# MAGIC In `scoring_output` table, forecasts for each time series from each model are stored. # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.rossmann_daily_scoring_output order by Store, model +display(spark.sql(f"select * from {catalog}.{db}.rossmann_daily_scoring_output order by Store, model")) # COMMAND ---------- # MAGIC %md ### Delete Tables +# MAGIC Let's clean up the tables. # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.rossmann_daily_evaluation_output +display(spark.sql(f"delete from {catalog}.{db}.rossmann_daily_evaluation_output")) # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.rossmann_daily_scoring_output +display(spark.sql(f"delete from {catalog}.{db}.rossmann_daily_scoring_output")) diff --git a/examples/global_monthly.py b/examples/global_monthly.py index 88cece2..bf18389 100644 --- a/examples/global_monthly.py +++ b/examples/global_monthly.py @@ -1,42 +1,46 @@ # Databricks notebook source # MAGIC %md -# MAGIC # Many Models Forecasting SA (MMFSA) Demo -# MAGIC This demo highlights how to configure MMF SA to use M4 competition data +# MAGIC # Many Models Forecasting Demo +# MAGIC This notebook showcases how to run MMF with global models on multiple time series of monthly resolution. We will use [M4 competition](https://www.sciencedirect.com/science/article/pii/S0169207019301128#sec5) data. The descriptions here are mostly the same as the case with the [daily resolution](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/global_daily.py), so we will skip the redundant parts and focus only on the essentials. # COMMAND ---------- -# MAGIC %pip install -r ../requirements.txt --quiet -# MAGIC dbutils.library.restartPython() +# MAGIC %md +# MAGIC ### Cluster setup +# MAGIC +# MAGIC We recommend using a cluster with [Databricks Runtime 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/14.3lts-ml.html) or above. The cluster should be single-node with one or more GPU instances: e.g. [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. # COMMAND ---------- # MAGIC %md -# MAGIC ### Data preparation steps -# MAGIC We are using `datasetsforecast` package to download M4 data. -# MAGIC -# MAGIC M4 dataset contains a set of time series which we use for testing of MMF SA. -# MAGIC -# MAGIC Below we have developed a number of functions to convert M4 time series to the expected format. +# MAGIC ### Install and import packages +# MAGIC Check out [requirements.txt](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/requirements.txt) if you're interested in the libraries we use. + +# COMMAND ---------- + +# MAGIC %pip install -r ../requirements.txt --quiet +# MAGIC dbutils.library.restartPython() # COMMAND ---------- -import pathlib -import pandas as pd import logging logger = spark._jvm.org.apache.log4j logging.getLogger("py4j.java_gateway").setLevel(logging.ERROR) logging.getLogger("py4j.clientserver").setLevel(logging.ERROR) -from datasetsforecast.m4 import M4 -import uuid # COMMAND ---------- -# Make sure that the catalog and the schema exist -catalog = "solacc_uc" # Name of the catalog we use to manage our assets -db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) +import uuid +import pathlib +import pandas as pd +from datasetsforecast.m4 import M4 +from mmf_sa import run_forecast -_ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") -_ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Prepare data +# MAGIC We are using [`datasetsforecast`](https://github.com/Nixtla/datasetsforecast/tree/main/) package to download M4 data. # COMMAND ---------- @@ -73,6 +77,20 @@ def transform_group(df): return _df +# COMMAND ---------- + +# MAGIC %md +# MAGIC We are going to save this data in a delta lake table. Provide catalog and database names where you want to store the data.__ + +# COMMAND ---------- + +catalog = "solacc_uc" # Name of the catalog we use to manage our assets +db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) + +# Making sure that the catalog and the schema exist +_ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") +_ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") + ( spark.createDataFrame(create_m4_monthly()) .write.format("delta").mode("overwrite") @@ -81,19 +99,22 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now the dataset looks in the following way: +# MAGIC %md Let's take a peak at the dataset: # COMMAND ---------- -# MAGIC %sql select unique_id, count(date) as count from solacc_uc.mmf.m4_monthly_train group by unique_id order by unique_id +display(spark.sql(f"select unique_id, count(date) as count from {catalog}.{db}.m4_monthly_train group by unique_id order by unique_id")) # COMMAND ---------- -# MAGIC %sql select count(distinct(unique_id)) from solacc_uc.mmf.m4_monthly_train +display( + spark.sql(f"select * from {catalog}.{db}.m4_monthly_train where unique_id in ('M1', 'M2', 'M3', 'M4', 'M5') order by unique_id, date") + ) # COMMAND ---------- -# MAGIC %md ### Let's configure the list of models we are going to use for training: +# MAGIC %md ### Models +# MAGIC Let's configure a list of models we are going to apply to our time series for evaluation and forecasting. A comprehensive list of all supported models is available in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Look for the models where `model_type: global`; these are the global models we import from [neuralforecast](https://github.com/Nixtla/neuralforecast). Check their documentation for the detailed description of each model. # COMMAND ---------- @@ -112,15 +133,13 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now we can run the forecasting process using `run_forecast` function. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC We have to loop through the model in the following way else cuda will throw an error. +# MAGIC %md ### Run MMF +# MAGIC +# MAGIC Now, we can run the evaluation and forecasting using `run_forecast` function defined in [mmf_sa/models/__init__.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/__init__.py). Refer to [README.md](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/README.md#parameters-description) for a comprehensive description of each parameter. Make sure to set `freq="M"` in `run_forecast` function called in [examples/run_monthly.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/run_monthly.py). # COMMAND ---------- +# The same run_id will be assigned to all the models. This makes it easier to run the post evaluation analysis later. run_id = str(uuid.uuid4()) for model in active_models: @@ -131,30 +150,31 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Evaluation Output -# MAGIC In the evaluation output table, the evaluation for all backtest windows and all models are stored. This info can be used to monitor model performance or decide which models should be taken into the final aggregated forecast. +# MAGIC %md ### Evaluate +# MAGIC In `evaluation_output` table, the we store all evaluation results for all backtesting trials from all models. # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.monthly_evaluation_output order by unique_id, model, backtest_window_start_date +display(spark.sql(f"select * from {catalog}.{db}.monthly_evaluation_output order by unique_id, model, backtest_window_start_date")) # COMMAND ---------- -# MAGIC %md ### Forecast Output -# MAGIC In the Forecast output table, the final forecast for each model and each time series is stored. +# MAGIC %md ### Forecast +# MAGIC In `scoring_output` table, forecasts for each time series from each model are stored. # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.monthly_scoring_output order by unique_id, model, date +display(spark.sql(f"select * from 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+encoder_bias: true +encoder_dropout: 0.0 +encoder_hidden_size: 100 +encoder_n_layers: 2 +futr_exog_list: [] +h: 10 +hist_exog_list: [] +inference_input_size: -1 +input_size: 20 +learning_rate: 0.001 +loss: !!python/object:neuralforecast.losses.pytorch.SMAPE + _backward_hooks: !!python/object/apply:collections.OrderedDict + - [] + _backward_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _buffers: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_always_called: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _is_full_backward_hook: null + _load_state_dict_post_hooks: !!python/object/apply:collections.OrderedDict + - [] + _load_state_dict_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _modules: !!python/object/apply:collections.OrderedDict + - [] + _non_persistent_buffers_set: !!set {} + _parameters: !!python/object/apply:collections.OrderedDict + - [] + _state_dict_hooks: !!python/object/apply:collections.OrderedDict + - [] + _state_dict_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + horizon_weight: null + is_distribution_output: false + output_names: + - '' + outputsize_multiplier: 1 + training: true +max_steps: 200 +num_lr_decays: -1 +num_workers_loader: 0 +optimizer: null +optimizer_kwargs: null +random_seed: 1 +scaler_type: robust +stat_exog_list: [] +val_check_steps: 100 +valid_batch_size: null +valid_loss: null diff --git a/examples/lightning_logs/version_12/events.out.tfevents.1717737234.0124-101655-9l2fggt9-10-0-16-85.37983.0 b/examples/lightning_logs/version_12/events.out.tfevents.1717737234.0124-101655-9l2fggt9-10-0-16-85.37983.0 new file mode 100644 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null +batch_size: 32 +devices: -1 +drop_last_loader: false +dropout_prob_theta: 0.0 +early_stop_patience_steps: -1 +exclude_insample_y: false +futr_exog_list: [] +h: 10 +hist_exog_list: [] +inference_windows_batch_size: -1 +input_size: 20 +learning_rate: 0.001 +loss: !!python/object:neuralforecast.losses.pytorch.SMAPE + _backward_hooks: !!python/object/apply:collections.OrderedDict + - [] + _backward_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _buffers: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_always_called: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _is_full_backward_hook: null + _load_state_dict_post_hooks: 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_forward_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_always_called: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _is_full_backward_hook: null + _load_state_dict_post_hooks: !!python/object/apply:collections.OrderedDict + - [] + _load_state_dict_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _modules: !!python/object/apply:collections.OrderedDict + - [] + _non_persistent_buffers_set: !!set {} + _parameters: !!python/object/apply:collections.OrderedDict + - [] + _state_dict_hooks: !!python/object/apply:collections.OrderedDict + - [] + _state_dict_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + horizon_weight: null + is_distribution_output: false + output_names: + - '' 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+futr_exog_list: [] +h: 3 +hist_exog_list: [] +inference_input_size: -1 +input_size: 6 +learning_rate: 0.001 +loss: !!python/object:neuralforecast.losses.pytorch.SMAPE + _backward_hooks: !!python/object/apply:collections.OrderedDict + - [] + _backward_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _buffers: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_always_called: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _is_full_backward_hook: null + _load_state_dict_post_hooks: !!python/object/apply:collections.OrderedDict + - [] + _load_state_dict_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _modules: 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_buffers: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_always_called: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _is_full_backward_hook: null + _load_state_dict_post_hooks: !!python/object/apply:collections.OrderedDict + - [] + _load_state_dict_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _modules: !!python/object/apply:collections.OrderedDict + - [] + _non_persistent_buffers_set: !!set {} + _parameters: !!python/object/apply:collections.OrderedDict + - [] + _state_dict_hooks: !!python/object/apply:collections.OrderedDict + - [] + _state_dict_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + horizon_weight: null + is_distribution_output: false + output_names: + - '' + outputsize_multiplier: 1 + training: true +max_steps: 200 +mlp_units: +- - 512 + - 512 +- - 512 + - 512 +- - 512 + - 512 +n_blocks: +- 1 +- 1 +- 1 +n_freq_downsample: +- 4 +- 2 +- 1 +n_pool_kernel_size: +- 2 +- 2 +- 1 +num_lr_decays: 3 +num_workers_loader: 0 +optimizer: null +optimizer_kwargs: null +pooling_mode: MaxPool1d +random_seed: 1 +scaler_type: robust +stack_types: +- identity +- identity +- identity +start_padding_enabled: false +stat_exog_list: [] +step_size: 1 +val_check_steps: 100 +valid_batch_size: null +valid_loss: null +windows_batch_size: 1024 diff --git a/examples/lightning_logs/version_97/events.out.tfevents.1717742890.0124-101655-9l2fggt9-10-0-16-85.308659.1 b/examples/lightning_logs/version_97/events.out.tfevents.1717742890.0124-101655-9l2fggt9-10-0-16-85.308659.1 new file mode 100644 index 0000000000000000000000000000000000000000..04ed6f71901b3296b1252e89c6f9cae43bb6e586 GIT binary patch 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outputsize_multiplier: 1 + training: true +max_steps: 200 +mlp_units: +- - 512 + - 512 +- - 512 + - 512 +- - 512 + - 512 +n_blocks: +- 1 +- 1 +- 1 +n_freq_downsample: +- 4 +- 2 +- 1 +n_pool_kernel_size: +- 2 +- 2 +- 1 +num_lr_decays: 3 +num_workers_loader: 0 +optimizer: null +optimizer_kwargs: null +pooling_mode: MaxPool1d +random_seed: 1 +scaler_type: robust +stack_types: +- identity +- identity +- identity +start_padding_enabled: false +stat_exog_list: [] +step_size: 1 +val_check_steps: 100 +valid_batch_size: null +valid_loss: null +windows_batch_size: 1024 diff --git a/examples/lightning_logs/version_99/events.out.tfevents.1717742900.0124-101655-9l2fggt9-10-0-16-85.308659.3 b/examples/lightning_logs/version_99/events.out.tfevents.1717742900.0124-101655-9l2fggt9-10-0-16-85.308659.3 new file mode 100644 index 0000000000000000000000000000000000000000..bd1bdd25f87f48135a737ae68ee8fde35909e4a1 GIT binary patch literal 2203 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b/examples/lightning_logs/version_99/hparams.yaml new file mode 100644 index 0000000..428d408 --- /dev/null +++ b/examples/lightning_logs/version_99/hparams.yaml @@ -0,0 +1,95 @@ +accelerator: gpu +activation: ReLU +alias: null +batch_size: 32 +devices: -1 +drop_last_loader: false +dropout_prob_theta: 0.0 +early_stop_patience_steps: -1 +exclude_insample_y: false +futr_exog_list: +- DayOfWeek +- Open +- Promo +- SchoolHoliday +h: 10 +hist_exog_list: [] +inference_windows_batch_size: -1 +input_size: 20 +interpolation_mode: linear +learning_rate: 0.001 +loss: !!python/object:neuralforecast.losses.pytorch.SMAPE + _backward_hooks: !!python/object/apply:collections.OrderedDict + - [] + _backward_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _buffers: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_always_called: !!python/object/apply:collections.OrderedDict + - [] + _forward_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _forward_pre_hooks_with_kwargs: !!python/object/apply:collections.OrderedDict + - [] + _is_full_backward_hook: null + _load_state_dict_post_hooks: !!python/object/apply:collections.OrderedDict + - [] + _load_state_dict_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + _modules: !!python/object/apply:collections.OrderedDict + - [] + _non_persistent_buffers_set: !!set {} + _parameters: !!python/object/apply:collections.OrderedDict + - [] + _state_dict_hooks: !!python/object/apply:collections.OrderedDict + - [] + _state_dict_pre_hooks: !!python/object/apply:collections.OrderedDict + - [] + horizon_weight: null + is_distribution_output: false + output_names: + - '' + outputsize_multiplier: 1 + training: true +max_steps: 200 +mlp_units: +- - 512 + - 512 +- - 512 + - 512 +- - 512 + - 512 +n_blocks: +- 1 +- 1 +- 1 +n_freq_downsample: +- 4 +- 2 +- 1 +n_pool_kernel_size: +- 2 +- 2 +- 1 +num_lr_decays: 3 +num_workers_loader: 0 +optimizer: null +optimizer_kwargs: null +pooling_mode: MaxPool1d +random_seed: 1 +scaler_type: robust +stack_types: +- identity +- identity +- identity +start_padding_enabled: false +stat_exog_list: [] +step_size: 1 +val_check_steps: 100 +valid_batch_size: null +valid_loss: null +windows_batch_size: 1024 diff --git a/examples/local_univariate_daily.py b/examples/local_univariate_daily.py index d6c34f9..8771fdc 100644 --- a/examples/local_univariate_daily.py +++ b/examples/local_univariate_daily.py @@ -1,10 +1,24 @@ # Databricks notebook source # MAGIC %md -# MAGIC # Many Models Forecasting SA (MMFSA) Demo -# MAGIC This demo highlights how to configure MMF SA to use M4 competition data +# MAGIC # Many Models Forecasting +# MAGIC This notebook showcases how to run MMF with local models on multiple univariate time series of daily resolution. We will use [M4 competition](https://www.sciencedirect.com/science/article/pii/S0169207019301128#sec5) data. # COMMAND ---------- +# MAGIC %md +# MAGIC ### Cluster setup +# MAGIC +# MAGIC We recommend using a cluster with [Databricks Runtime 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/14.3lts-ml.html) or above. The cluster can be either a single-node or multi-node CPU cluster. MMF leverages [Pandas UDF](https://docs.databricks.com/en/udf/pandas.html) under the hood and utilizes all the available resource. Make sure to set the following Spark configurations before you start your cluster: [`spark.sql.execution.arrow.enabled true`](https://spark.apache.org/docs/3.0.1/sql-pyspark-pandas-with-arrow.html#enabling-for-conversion-tofrom-pandas) and [`spark.sql.adaptive.enabled false`](https://spark.apache.org/docs/latest/sql-performance-tuning.html#adaptive-query-execution). You can do this by specifying [Spark configuration](https://docs.databricks.com/en/compute/configure.html#spark-configuration) in the advanced options on the cluster creation page. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Install and import packages +# MAGIC Check out [requirements.txt](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/requirements.txt) if you're interested in the libraries we use. + +# COMMAND ---------- + +# DBTITLE 1,Install the necessary libraries # MAGIC %pip install -r ../requirements.txt --quiet # MAGIC dbutils.library.restartPython() @@ -25,12 +39,14 @@ # COMMAND ---------- # MAGIC %md -# MAGIC ### Data preparation steps -# MAGIC We are using `datasetsforecast` package to download M4 data. -# MAGIC -# MAGIC M4 dataset contains a set of time series which we use for testing of MMF SA. -# MAGIC -# MAGIC Below we have developed a number of functions to convert M4 time series to the expected format. +# MAGIC #### Install R packages +# MAGIC If you want to use the R fable models, you need to [install the R dependecies](https://docs.databricks.com/en/libraries/index.html#r-library-support). See [RUNME.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/RUNME.py) for the full list of required R libraries and their versions. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Prepare data +# MAGIC We are using [`datasetsforecast`](https://github.com/Nixtla/datasetsforecast/tree/main/) package to download M4 data. M4 dataset contains a set of time series which we use for testing MMF. Below we have written a number of custome functions to convert M4 time series to an expected format. # COMMAND ---------- @@ -64,10 +80,15 @@ def transform_group(df): # COMMAND ---------- -# Make sure that the catalog and the schema exist +# MAGIC %md +# MAGIC We are going to save this data in a delta lake table. Provide catalog and database names where you want to store the data. + +# COMMAND ---------- + catalog = "solacc_uc" # Name of the catalog we use to manage our assets db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) +# Making sure that the catalog and the schema exist _ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") _ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") @@ -79,15 +100,20 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now the dataset looks in the following way: +# MAGIC %md Let's take a peak at the dataset: # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.m4_daily_train where unique_id in ('D1', 'D2', 'D6', 'D7', 'D10') order by unique_id, ds +display( + spark.sql(f"select * from {catalog}.{db}.m4_daily_train where unique_id in ('D1', 'D2', 'D3', 'D4', 'D5') order by unique_id, ds") + ) # COMMAND ---------- -# MAGIC %md ### Let's configure the list of models we are going to use for training: +# MAGIC %md ### Models +# MAGIC Let's configure a list of models we are going to apply to our time series for evaluation and forecasting. A comprehensive list of all supported models is available in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Look for the models where `model_type: local`; these are the local models we import from [statsforecast](https://github.com/Nixtla/statsforecast), [r fable](https://cran.r-project.org/web/packages/fable/vignettes/fable.html) and [sktime](https://github.com/sktime/sktime). Check their documentations for the detailed description of each model. +# MAGIC +# MAGIC Some of these models perform hyperparameter optimization ([statsforecast Automatic Forecasting](https://nixtlaverse.nixtla.io/statsforecast/index.html#automatic-forecasting)) on itself to search for the best parameters. For other models, you can modify the model hyperparameters in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite the default values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). # COMMAND ---------- @@ -117,7 +143,15 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now we can run the forecasting process using `run_forecast` function. +# MAGIC %md ### Run MMF +# MAGIC +# MAGIC Now, we can run the evaluation and forecasting using `run_forecast` function defined in [mmf_sa/models/__init__.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/__init__.py). +# MAGIC +# MAGIC Refer to [README.md](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/README.md#parameters-description) for a comprehensive explanation of each parameter. Note that we are not providing any covariate field (i.e. `static_features`, `dynamic_future` or `dynamic_historical`) yet in this example. We will look into how we can add exogenous regressors to help our models in a different notebook: [examples/local_univariate_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_external_regressors_daily.py). +# MAGIC +# MAGIC While the following cell is running, you can check the status of your run on Experiments. Make sure you look for the experiments with the path you provided as `experiment_path` within `run_forecast`. On the Experiments page, you see one entry per one model (i.e. StatsForecastAutoArima). The metric provided here is a simple average over all back testing trials and all time series. This is intended to give you an initial feeling of how good each model performs on your entire data mix. But we will look into how you can scrutinize the evaluation using the `evaluation_output` table in a bit. +# MAGIC +# MAGIC If you are interested in how Pandas UDF achieves parallel fitting and forecasting of multiple time series by distributing them across multiple executors, have a look at the two methods `evaluate_local_model` and `score_local_model` defined in the source code [`Forecaster.py`](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/Forecaster.py). # COMMAND ---------- @@ -144,30 +178,46 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Evaluation Output -# MAGIC In the evaluation output table, the evaluations for all backtest windows and all models are stored. This info can be used to monitor model performance or decide which models should be taken into the final aggregated forecast. +# MAGIC %md ### Evaluate +# MAGIC In `evaluation_output` table, the we store all evaluation results for all backtesting trials from all models. This information can be used to understand which models performed well on which time series on which periods of backtesting. This is very important for selecting the final model for forecasting or models for ensembling. Maybe, it's faster to take a look at the table: # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.daily_evaluation_output order by unique_id, model, backtest_window_start_date +display(spark.sql(f"select * from {catalog}.{db}.daily_evaluation_output order by unique_id, model, backtest_window_start_date")) # COMMAND ---------- -# MAGIC %md ### Forecast Output -# MAGIC In the Forecast output table, the final forecast for each model and each time series is stored. +# MAGIC %md +# MAGIC For each backtesting trial, we train the model on the training dataset only up to `backtesting_window_start_date`. We then use that fitted model to generate the forecasts for that specific horizon. We then move `backtesting_window_start_date` forward by `stride` and do the same exercise. This continues until `backtesting_window_start_date` reaches the last day of the time series given in the training dataset. See how MMF implements backtesting in `backtest` method in [mmf_sa/models/abstract_model.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/abstract_model.py). +# MAGIC > +# MAGIC We store the **as-if** forecasts together with the actuals for each backtesting period, so you can construct any metric of your interest. We provide a few out-of-the-box metrics for you (e.g. smape), but the idea here is that you construct your own metrics reflecting your business requirements and evaluate models based on those. For example, maybe you care more about the accuracy of the near-horizon forecasts than the far-horizon ones. In such case, you can apply a decreasing wieght to compute weighted aggregated metrics. +# MAGIC +# MAGIC Note that if you run other global and foundation models against the same time series with the same input parameters (except for those specifying global and foundation models), you will get the entries from those models in the same table and will be able to compare across all types models, which is the biggest benefit of having all models integrated in one solution. +# MAGIC +# MAGIC We also store each model in a binary format in this table (`model_pickle`). You can unpickle the models and access their specifications or produce forecasts. + +# COMMAND ---------- + +# MAGIC %md ### Forecast +# MAGIC In `scoring_output` table, forecasts for each time series from each model are stored. Based on the evaluation exercised performed on `evaluation_output` table, you can select the forecasts from the best performing models or a mix of models. We are again storing each model in a binary format in this table. # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.daily_scoring_output order by unique_id, model, ds +display(spark.sql(f"select * from {catalog}.{db}.daily_scoring_output order by unique_id, model, ds")) # COMMAND ---------- # MAGIC %md ### Delete Tables +# MAGIC Let's clean up the tables. # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.daily_evaluation_output +display(spark.sql(f"delete from {catalog}.{db}.daily_evaluation_output")) # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.daily_scoring_output +display(spark.sql(f"delete from {catalog}.{db}.daily_scoring_output")) + +# COMMAND ---------- + + diff --git a/examples/local_univariate_external_regressors_daily.py b/examples/local_univariate_external_regressors_daily.py index eb68dca..e83a0da 100644 --- a/examples/local_univariate_external_regressors_daily.py +++ b/examples/local_univariate_external_regressors_daily.py @@ -1,5 +1,21 @@ # Databricks notebook source +# MAGIC %md +# MAGIC # Many Models Forecasting Demo +# MAGIC +# MAGIC This notebook showcases how to run MMF with local models on multiple time series of daily resolution using exogenous regressors. We will use [Rossmann Store](https://www.kaggle.com/competitions/rossmann-store-sales/data) data. To be able to run this notebook, you need to register on [Kaggle](https://www.kaggle.com/) and download the dataset. The descriptions here are mostly the same as the case [without exogenous regressors](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_daily.py), so we will skip the redundant parts and focus only on the essentials. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Cluster setup +# MAGIC +# MAGIC We recommend using a cluster with [Databricks Runtime 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/14.3lts-ml.html) or above. The cluster can be either a single-node or multi-node CPU cluster. Make sure to set the following Spark configurations before you start your cluster: [`spark.sql.execution.arrow.enabled true`](https://spark.apache.org/docs/3.0.1/sql-pyspark-pandas-with-arrow.html#enabling-for-conversion-tofrom-pandas) and [`spark.sql.adaptive.enabled false`](https://spark.apache.org/docs/latest/sql-performance-tuning.html#adaptive-query-execution). You can do this by specifying [Spark configuration](https://docs.databricks.com/en/compute/configure.html#spark-configuration) in the advanced options on the cluster creation page. + +# COMMAND ---------- + +# DBTITLE 1,Install the necessary libraries # MAGIC %pip install -r ../requirements.txt --quiet +# MAGIC dbutils.library.restartPython() # COMMAND ---------- @@ -11,21 +27,28 @@ # COMMAND ---------- -# Make sure that the catalog and the schema exist +import pandas as pd +from mmf_sa import run_forecast + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Prepare data +# MAGIC Before running this notebook, download the dataset from [Kaggle](https://www.kaggle.com/competitions/rossmann-store-sales/data) and store them in Unity Catalog as a [volume](https://docs.databricks.com/en/connect/unity-catalog/volumes.html). + +# COMMAND ---------- + catalog = "solacc_uc" # Name of the catalog we use to manage our assets db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) -volume = "rossmann" # Name of the schema where you have your rossmann dataset csv sotred +volume = "rossmann" # Name of the volume where you have your rossmann dataset csv sotred +# Make sure that the catalog and the schema exist _ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") _ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") _ = spark.sql(f"CREATE VOLUME IF NOT EXISTS {catalog}.{db}.{volume}") # COMMAND ---------- -# MAGIC %md Download the dataset from [Kaggle](kaggle.com/competitions/rossmann-store-sales/data) and store them in the volume. - -# COMMAND ---------- - # Randomly select 100 stores to forecast import random random.seed(7) @@ -42,13 +65,19 @@ train = train.filter(train.Store.isin(stores)) test = test.filter(test.Store.isin(stores)) +# COMMAND ---------- + +# MAGIC %md +# MAGIC We are going to save this data in a delta lake table. Provide catalog and database names where you want to store the data. + +# COMMAND ---------- + train.write.mode("overwrite").option("mergeSchema", "true").saveAsTable(f"{catalog}.{db}.rossmann_daily_train") test.write.mode("overwrite").option("mergeSchema", "true").saveAsTable(f"{catalog}.{db}.rossmann_daily_test") # COMMAND ---------- -# Set the number of shuffle partitions larger than the total number of cores -#sqlContext.setConf("spark.sql.shuffle.partitions", "1000") +# MAGIC %md Let's take a peak at the dataset: # COMMAND ---------- @@ -57,9 +86,17 @@ # COMMAND ---------- -import pathlib -import pandas as pd -from mmf_sa import run_forecast +# MAGIC %md +# MAGIC Note that in `rossmann_daily_train` we have our target variable `Sales` but not in `rossmann_daily_test`. This is because `rossmann_daily_test` is going to be used as our `scoring_data` that stores `dynamic_future` variables of the future dates. When you adapt this notebook to your use case, make sure to comply with these datasets formats. See statsforecast's [documentation](https://nixtlaverse.nixtla.io/statsforecast/docs/how-to-guides/exogenous.html) for more detail on exogenous regressors. + +# COMMAND ---------- + +# MAGIC %md ### Models +# MAGIC Let's configure a list of models we are going to apply to our time series for evaluation and forecasting. A comprehensive list of all supported models is available in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Look for the models where `model_type: local`; these are the local models we import from [statsforecast](https://github.com/Nixtla/statsforecast), [r fable](https://cran.r-project.org/web/packages/fable/vignettes/fable.html) and [sktime](https://github.com/sktime/sktime). Check their documentations for the description of each model. +# MAGIC +# MAGIC Exogenous regressors are currently only supported for [some models](https://nixtlaverse.nixtla.io/statsforecast/index.html#models) from statsforecast (e.g. `StatsForecastAutoArima`). But including non-supported models in the active model list doesn't harm: models that can't use exogenous regressors will simply ignore them. + +# COMMAND ---------- active_models = [ "StatsForecastBaselineWindowAverage", @@ -85,7 +122,15 @@ "SKTimeLgbmDsDt", ] -run_id = run_forecast( +# COMMAND ---------- + +# MAGIC %md ### Run MMF +# MAGIC +# MAGIC Now, we run the evaluation and forecasting using `run_forecast` function. We are providing the training table and the scoring table names. If `scoring_data` is not provided or if the same name as `train_data` is provided, the models will ignore the `dynamic_future` regressors. Note that we are providing a covariate field (i.e. `dynamic_future`) this time. There are also other convariate fields, namely `static_features`, and `dynamic_historical`, but these are only relevant with the global models. + +# COMMAND ---------- + +run_forecast( spark=spark, train_data=f"{catalog}.{db}.rossmann_daily_train", scoring_data=f"{catalog}.{db}.rossmann_daily_test", @@ -106,24 +151,34 @@ experiment_path=f"/Shared/mmf_rossmann", use_case_name="rossmann_daily", ) -print(run_id) # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.rossmann_daily_evaluation_output order by Store, model, backtest_window_start_date +# MAGIC %md ### Evaluate +# MAGIC In `evaluation_output` table, the we store all evaluation results for all backtesting trials from all models. + +# COMMAND ---------- + +display(spark.sql(f"select * from {catalog}.{db}.rossmann_daily_evaluation_output order by Store, model, backtest_window_start_date")) + +# COMMAND ---------- + +# MAGIC %md ### Forecast +# MAGIC In `scoring_output` table, forecasts for each time series from each model are stored. # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.rossmann_daily_scoring_output order by Store, model +display(spark.sql(f"select * from {catalog}.{db}.rossmann_daily_scoring_output order by Store, model")) # COMMAND ---------- # MAGIC %md ### Delete Tables +# MAGIC Let's clean up the tables. # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.rossmann_daily_evaluation_output +display(spark.sql(f"delete from {catalog}.{db}.rossmann_daily_evaluation_output")) # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.rossmann_daily_scoring_output +display(spark.sql(f"delete from {catalog}.{db}.rossmann_daily_scoring_output")) diff --git a/examples/local_univariate_monthly.py b/examples/local_univariate_monthly.py index e5f2007..44053fe 100644 --- a/examples/local_univariate_monthly.py +++ b/examples/local_univariate_monthly.py @@ -1,10 +1,24 @@ # Databricks notebook source # MAGIC %md -# MAGIC # Many Models Forecasting SA (MMFSA) Demo -# MAGIC This demo highlights how to configure MMF SA to use M4 competition data +# MAGIC # Many Models Forecasting Demo +# MAGIC +# MAGIC This notebook showcases how to run MMF with local models on multiple univariate time series of monthly resolution. We will use [M4 competition](https://www.sciencedirect.com/science/article/pii/S0169207019301128#sec5) data. The descriptions here are mostly the same as the case with the [daily resolution](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/local_univariate_daily.py), so we will skip the redundant parts and focus only on the essentials. # COMMAND ---------- +# MAGIC %md +# MAGIC ### Cluster setup +# MAGIC +# MAGIC We recommend using a cluster with [Databricks Runtime 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/14.3lts-ml.html) or above. The cluster can be either a single-node or multi-node CPU cluster. Make sure to set the following Spark configurations before you start your cluster: [`spark.sql.execution.arrow.enabled true`](https://spark.apache.org/docs/3.0.1/sql-pyspark-pandas-with-arrow.html#enabling-for-conversion-tofrom-pandas) and [`spark.sql.adaptive.enabled false`](https://spark.apache.org/docs/latest/sql-performance-tuning.html#adaptive-query-execution). You can do this by specifying [Spark configuration](https://docs.databricks.com/en/compute/configure.html#spark-configuration) in the advanced options on the cluster creation page. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Install and import packages + +# COMMAND ---------- + +# DBTITLE 1,Install the necessary libraries # MAGIC %pip install -r ../requirements.txt --quiet # MAGIC dbutils.library.restartPython() @@ -26,12 +40,14 @@ # COMMAND ---------- # MAGIC %md -# MAGIC ### Data preparation steps -# MAGIC We are using `datasetsforecast` package to download M4 data. -# MAGIC -# MAGIC M4 dataset contains a set of time series which we use for testing of MMF SA. -# MAGIC -# MAGIC Below we have developed a number of functions to convert M4 time series to the expected format. +# MAGIC #### Install R packages +# MAGIC If you want to use the R fable models, you need to [install the R dependecies](https://docs.databricks.com/en/libraries/index.html#r-library-support). See [RUNME.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/RUNME.py) for the full list of required R libraries and their versions. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ### Prepare data +# MAGIC We are using [`datasetsforecast`](https://github.com/Nixtla/datasetsforecast/tree/main/) package to download M4 data. # COMMAND ---------- @@ -70,10 +86,15 @@ def transform_group(df): # COMMAND ---------- -# Make sure that the catalog and the schema exist +# MAGIC %md +# MAGIC We are going to save this data in a delta lake table. Provide catalog and database names where you want to store the data. + +# COMMAND ---------- + catalog = "solacc_uc" # Name of the catalog we use to manage our assets db = "mmf" # Name of the schema we use to manage our assets (e.g. datasets) +# Making sure that the catalog and the schema exist _ = spark.sql(f"CREATE CATALOG IF NOT EXISTS {catalog}") _ = spark.sql(f"CREATE SCHEMA IF NOT EXISTS {catalog}.{db}") @@ -85,19 +106,22 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now the dataset looks in the following way: +# MAGIC %md Let's take a peak at the dataset: # COMMAND ---------- -# MAGIC %sql select unique_id, count(date) as count from solacc_uc.mmf.m4_monthly_train group by unique_id order by unique_id +display(spark.sql(f"select unique_id, count(date) as count from {catalog}.{db}.m4_monthly_train group by unique_id order by unique_id")) # COMMAND ---------- -# MAGIC %sql select count(distinct(unique_id)) from solacc_uc.mmf.m4_monthly_train +display( + spark.sql(f"select * from {catalog}.{db}.m4_monthly_train where unique_id in ('M1', 'M2', 'M3', 'M4', 'M5') order by unique_id, date") + ) # COMMAND ---------- -# MAGIC %md ### Let's configure the list of models we are going to use for training: +# MAGIC %md ### Models +# MAGIC Let's configure a list of models we are going to apply to our time series for evaluation and forecasting. A comprehensive list of all supported models is available in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml). Look for the models where `model_type: local`; these are the local models we import from [statsforecast](https://github.com/Nixtla/statsforecast), [r fable](https://cran.r-project.org/web/packages/fable/vignettes/fable.html) and [sktime](https://github.com/sktime/sktime). Check their documentations for the description of each model. # COMMAND ---------- @@ -127,7 +151,9 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Now we can run the forecasting process using `run_forecast` function. +# MAGIC %md ### Run MMF +# MAGIC +# MAGIC Now, we can run the evaluation and forecasting using `run_forecast` function defined in [mmf_sa/models/__init__.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/__init__.py). Make sure to set `freq="M"` in `run_forecast` function. # COMMAND ---------- @@ -154,30 +180,35 @@ def transform_group(df): # COMMAND ---------- -# MAGIC %md ### Evaluation Output -# MAGIC In the evaluation output table, the evaluation for all backtest windows and all models are stored. This info can be used to monitor model performance or decide which models should be taken into the final aggregated forecast. +# MAGIC %md ### Evaluate +# MAGIC In `evaluation_output` table, the we store all evaluation results for all backtesting trials from all models. # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.monthly_evaluation_output order by unique_id, model, backtest_window_start_date +display(spark.sql(f"select * from {catalog}.{db}.monthly_evaluation_output order by unique_id, model, backtest_window_start_date")) # COMMAND ---------- -# MAGIC %md ### Forecast Output -# MAGIC In the Forecast output table, the final forecast for each model and each time series is stored. +# MAGIC %md ### Forecast +# MAGIC In `scoring_output` table, forecasts for each time series from each model are stored. # COMMAND ---------- -# MAGIC %sql select * from solacc_uc.mmf.monthly_scoring_output order by unique_id, model, date +display(spark.sql(f"select * from {catalog}.{db}.monthly_scoring_output order by unique_id, model, date")) # COMMAND ---------- # MAGIC %md ### Delete Tables +# MAGIC Let's clean up the tables. + +# COMMAND ---------- + +display(spark.sql(f"delete from {catalog}.{db}.monthly_evaluation_output")) # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.monthly_evaluation_output +display(spark.sql(f"delete from {catalog}.{db}.monthly_scoring_output")) # COMMAND ---------- -# MAGIC %sql delete from solacc_uc.mmf.monthly_scoring_output +