Experiment Management#

Introduction#

This guide illustrates how to track experiments using MLFlow and how to serve models.

# %%capture
# !pip install -U onetick-ml

from onetick import ml
import onetick.py as otp
'otml:', ml.__version__, ' otp:', otp.__version__

('otml:', '1.0.8', ' otp:', '1.131.0')

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yaml


start = otp.dt(2022, 5, 10, 9, 30)
end = otp.dt(2022, 11, 10, 16, 0)


class VolumePrediction(ml.Experiment):
    target_columns = ["VOLUME"]
    features_columns = ["VOLUME_LAG_.*"]

    # DATA
    datafeeds = [
        ml.OneTickBarsDatafeed(
            db="NYSE_TAQ_BARS",
            tick_type="TRD_1M",
            symbols=["SPY"],
            start=start,
            end=end,
            bucket=600,
        )
    ]

    splitters = [
        ml.PercentageSplitter(test_size=0.15, val_size=0.15)
    ]

    pipeline = [
        ml.CalcLags(periods=[1, 2, 3, 39, 40], columns=["VOLUME"]),
    ]

    # MODEL
    models = [
        ml.LGBMRegressor()
    ]

    train_params = {"verbose": 0}

    # EVALUATION
    evaluators = [ml.MAPEEvaluator(),
                  ml.MAEEvaluator(),
                  ml.RMSEEvaluator(),
                  ml.R2Evaluator()]

Run full experiment cycle, get resulted metrics and predictions#

exp = VolumePrediction()
config = exp.serialize_config()
metrics, predictions = exp.run()

exp.x_test
VOLUME_LAG_1 VOLUME_LAG_2 VOLUME_LAG_3 VOLUME_LAG_39 VOLUME_LAG_40
4276 1332339.0 1118539.0 1042534.0 1903358.0 2712928.0
4277 1346291.0 1332339.0 1118539.0 1262840.0 1903358.0
4278 730047.0 1346291.0 1332339.0 1339100.0 1262840.0
4279 1117967.0 730047.0 1346291.0 1386015.0 1339100.0
4280 1162810.0 1117967.0 730047.0 1382818.0 1386015.0
... ... ... ... ... ...
5026 1680948.0 1553053.0 784539.0 1282829.0 1377165.0
5027 2092322.0 1680948.0 1553053.0 1193638.0 1282829.0
5028 1929999.0 2092322.0 1680948.0 1460215.0 1193638.0
5029 2503589.0 1929999.0 2092322.0 2799459.0 1460215.0
5030 4004600.0 2503589.0 1929999.0 4517255.0 2799459.0

755 rows × 5 columns

pd.DataFrame([metrics])
VOLUME_MAPE VOLUME_MAE VOLUME_RMSE VOLUME_R2
0 0.230435 359508.613699 615230.295819 0.609123

Local saving and loading models#

You can save the trained model simply by calling a function save_model() of the experiment:

exp.save_model('./model.cbm')

metrics = exp.calc_metrics()
metrics

{'VOLUME_MAPE': 0.23043529978382501,
 'VOLUME_MAE': 359508.6136989899,
 'VOLUME_RMSE': 615230.2958193648,
 'VOLUME_R2': 0.6091232005321459}

To restore a model, we first initialize the experiment and prepare the data, and then call load_model() instead of going through a model training stage.

exp = ml.build_experiment(config)

# data stage
exp.get_data()
exp.prepare_data()

# model load stage
model = exp.load_model(ml.LGBMRegressor(), './model.cbm')

# evaluate stage
predictions = exp.predict(model=model)
metrics = exp.calc_metrics()
metrics

{'VOLUME_MAPE': 0.23043529978382501,
 'VOLUME_MAE': 359508.6136989899,
 'VOLUME_RMSE': 615230.2958193648,
 'VOLUME_R2': 0.6091232005321459}

MLFlow usage#

Save experiment to MLFlow#

Special attributes in experiment define MLFlow tracking capabilities.

  • log_models : bool – enable logging of the trained model.

  • experiment_name : str – the name of the experiment.

  • mlflow_url : str – MLFlow tracking URL used to log parameters, metrics and artifacts.

After running the whole cycle of an experiment, you can save it to MLFlow by calling .save_mlflow_run() method.

class MLFlowLoggedExperiment(VolumePrediction):
    general = {'log_models': True, 
               'experiment_name': 'example-experiment', 
               'mlflow_url': 'http://172.16.1.89:5000/'}

experiment = MLFlowLoggedExperiment()
metrics, predictions = experiment.run()
run_id = experiment.save_mlflow_run()

metrics

Registered model 'LGBMRegressor' already exists. Creating a new version of this model...
2024/12/03 18:44:50 INFO mlflow.tracking._model_registry.client: Waiting up to 300 seconds for model version to finish creation.                     Model name: LGBMRegressor, version 386

Created version '386' of model 'LGBMRegressor'.

2024/12/03 18:44:54 WARNING mlflow.utils.requirements_utils: The following packages were not found in the public PyPI package index as of 2022-12-21; if these packages are not present in the public PyPI index, you must install them manually before loading your model: {'onetick-ml', 'onetick-py-test'}

Registered model 'wrapped_LGBMRegressor' already exists. Creating a new version of this model...
2024/12/03 18:44:55 INFO mlflow.tracking._model_registry.client: Waiting up to 300 seconds for model version to finish creation.                     Model name: wrapped_LGBMRegressor, version 386

Created version '386' of model 'wrapped_LGBMRegressor'.

{'VOLUME_MAPE': 0.23043529978382501,
 'VOLUME_MAE': 359508.6136989899,
 'VOLUME_RMSE': 615230.2958193648,
 'VOLUME_R2': 0.6091232005321459}

Restore experiment from MLFlow#

We use run_id produced in the previous step to call restore_experiment_from_mlflow() utility function. This function reconstructs the experiment and restores the trained model.

# Load experiment from MLflow
experiment = ml.restore_experiment_from_mlflow(mlflow_url='http://172.16.1.89:5000/',
                                               run_id=run_id)

# data stage
experiment.get_data()
experiment.prepare_data()

# we can skip model stage and go directly to prediction and metrics calculation
predictions = experiment.predict()
metrics = experiment.calc_metrics()

# metrics are the same, as in previous step
metrics

/builds/solutions/ml-ops/ds-framework/onetick/ml/utils/func.py:109: FutureWarning: ``mlflow.tracking.client.MlflowClient.download_artifacts`` is deprecated since 2.0. This method will be removed in a future release. Use ``mlflow.artifacts.download_artifacts`` instead.
  config_path = client.download_artifacts(run_id, "config.yaml", tmpdirname)
/builds/solutions/ml-ops/ds-framework/onetick/ml/utils/func.py:112: FutureWarning: ``mlflow.tracking.client.MlflowClient.download_artifacts`` is deprecated since 2.0. This method will be removed in a future release. Use ``mlflow.artifacts.download_artifacts`` instead.
  hashes_file = client.download_artifacts(run_id, "datahashes.yaml", tmpdirname)

{'VOLUME_MAPE': 0.23043529978382501,
 'VOLUME_MAE': 359508.6136989899,
 'VOLUME_RMSE': 615230.2958193648,
 'VOLUME_R2': 0.6091232005321459}