dreamml 3.5.3

Framework for creating, running and validation of ML models on tabular data

DreamML - Self Machine Learning ❤️

The next stage of evalution DS-Template

About the DreamML

---

DreamML is a machine learning framework aimed at the industrial process. The main task is to choose a simple model, taking into account the balance of complexity, quality and metrics. We also suggest reviewing the quality of the models in special development reports, and for some tasks, a validation report created using the central bank's methodology.

*This is the first cycle of the project's release into open source, then we plan to publish more materials and improve the framework.

Installation

---

Step 1: Install Anaconda or Python 3.8

Step 2: Create environment

• Anaconda conda create --name dreamml_env python=3.8
• Python 3.8 python -m venv dreamml_env

Step 3: Activate environment

• Anaconda conda activate dreamml_env
• Python source dreamml_env/bin/activate

Step 4: Go to the dreamml root folder

Step 5: Install dreamml in your environment pip install -e .

Get started

---

To develop a model, you can use the notebooks located in the notebooks/1. Model Development and select the one you need depending on the type of your task.

To validate models, you can use the notebooks located in the notebooks/2. Validate Model

To calibration models, you can use the notebooks located in the notebooks/3. Calibration

How to Use

---

Information on notebooks for development notebooks/1. Model Development

1. First, you need to determine the pipeline configuration

   • For regression, binary, multiclass, multilabel tasks you can refer to this document docs/1_Model_Development_doc.md
   • For topic_modeling task you can refer to this document docs/1_Topic_Modeling_doc.md
   • For timeseries with (boosting) task you can refer to this document docs/1_TimeSeries_doc.md
   • For amts with (Prophet) task you can refer to this document docs/1_AltModeTimeSeries_forecast.md
   • If your dataset contains text features you should refer to this document docs/1_NLP_text_classification_doc.md
   • If you would like to learn more about quality metrics and loss functions, we recommend that you refer to the document docs/Binary_Classification_Metrics_doc.md

2. You should start building the configuration and preparing the data for modeling

config_storage = ConfigStorage(config=config)
transformer = DataTransformer(config_storage)
data_storage = transformer.transform()

3. Next, you should run the simulation pipeline

pipeline = MainPipeline(config_storage=config_storage, data_storage=data_storage)
pipeline.transform()

4. For some tasks, you can also use Light Auto M L as a model and calculate out of time potential

lama = add_lama_model(data_storage.get_eval_set(), config_storage)
oot_potential = calculate_oot_metrics(data_storage.get_eval_set(), config_storage)

5. You can also start the process of saving simulation artifacts if you need it

saver = pipeline.artifact_saver
models = pipeline.prepared_model_dict
pipeline.oot_potential = oot_potential
models.update(lama)
nb_name = saver.get_notebook_path_and_save()
saver.save_artifacts(
    models=models,
    other_models=pipeline.other_model_dict,
    encoder=transformer.cat_transformer,
    ipynb_name=nb_name,
    feature_threshold=config_storage.feature_threshold,
)
saver.save_data(data=data_storage.get_eval_set(), dropped_data=data_storage.get_dropped_data())

6. At the end, we can generate a development report. By default, it will be saved to the dreamml/results folder.

get_report(pipeline=pipeline, config_storage=config_storage, data_storage=data_storage, encoder=transformer.cat_transformer)

Authors

---

Author	Email
Nikita Buts	[e-mail]
Alexander Izyurov	[e-mail]
Ivan Plotnikov	[e-mail]
Maidari Tsydenov	[e-mail]
Evgeny Tkachenko	[e-mail]
Ilya Ivanov	[e-mail]
Nikita Varganov	-

LICENSE

---

This project is licensed under the Apache License, Version 2.0. See LICENSE for details.