Add examples for wandb sweeps (#317)

.gitignore

@@ -123,4 +123,5 @@ config.env
 .devcontainer
 /docs/source/api/
 tmp
-wandb
+wandb
+!examples/wandb

examples/wandb/sweeps/README.md

@@ -0,0 +1,27 @@
+# Using WandB with ETNA library
+
+## Colab example
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EBSqqBPaYgLWCRdpC5vMy9RiLBsCEd7I?usp=sharing)  
+
+![](assets/etna-wandb.png)
+
+[Sweep Dashboard](https://wandb.ai/martins0n/wandb-etna-sweep/sweeps/c7e0r8sq/overview?workspace=user-martins0n)
+
+## Steps to start
+
+- Define your pipeline and hyperparameters in `pipeline.yaml`, in example we will optimize number of iterations `iterations` and `learning-rate`
+
+- Define WandB sweeps config `sweep.yaml` and push it to cloud:
+
+```bash
+WANDB_PROJECT=<project_name> WandB sweep sweep.yaml 
+```
+
+- You may change `dataloader` function and add additional parameters for WandB logger like tags for example in `run.py`
+
+- Run WandB agent for hyperparameters optimization start:
+
+```bash
+wandb agent <user_name>/<project_name>/<sweep_id>
+```

examples/wandb/sweeps/pipeline.yaml

@@ -0,0 +1,23 @@
+backtest:
+  n_folds: 3
+  n_jobs: 1
+  metrics:
+  - _target_: etna.metrics.MAE
+  - _target_: etna.metrics.MSE
+  - _target_: etna.metrics.MAPE
+  - _target_: etna.metrics.SMAPE
+  - _target_: etna.metrics.R2
+pipeline:
+  _target_: etna.pipeline.Pipeline
+  horizon: 10
+  model:
+    _target_: etna.models.CatBoostModelMultiSegment
+    iterations: ${iterations}
+    learning_rate: ${learning-rate}
+  transforms:
+  - _target_: etna.transforms.SegmentEncoderTransform
+  - _target_: etna.transforms.LagTransform
+    in_column: target
+    lags: [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]
+iterations: null
+learning-rate: null

examples/wandb/sweeps/run.py

@@ -0,0 +1,60 @@
+"""
+Example of using WandB with ETNA library.
+Current script could be used for sweeps and simple validation runs.
+"""
+
+import argparse
+import random
+from typing import Any, Dict
+
+import hydra_slayer
+import numpy as np
+from etna.datasets import TSDataset, generate_ar_df
+from etna.loggers import WandbLogger, tslogger
+from etna.pipeline import Pipeline
+from omegaconf import OmegaConf
+
+SEED = 11
+random.seed(SEED)
+np.random.seed(SEED)
+
+# Default config loading
+config = OmegaConf.load("pipeline.yaml")
+
+
+# Define arguments for WandB sweep parameters
+args = argparse.ArgumentParser()
+args.add_argument("--iterations", type=int)
+args.add_argument("--learning-rate", type=float)
+for key, value in vars(args.parse_args()).items():
+    if value:
+        config[key] = value
+
+# Config for Pipeline and backtesting pipeline
+config = OmegaConf.to_container(config, resolve=True)
+pipeline = config["pipeline"]
+backtest = config["backtest"]
+
+
+# Define WandbLogger and passing it to global library logger
+# It will not log child processes in case of `spawn` (OSX or Windows)
+wblogger = WandbLogger(project="test-run", config=pipeline)
+tslogger.add(wblogger)
+
+
+def dataloader() -> TSDataset:
+    df = generate_ar_df(periods=300, start_time="2021-01-02", n_segments=10)
+    df = TSDataset.to_dataset(df)
+    ts = TSDataset(df=df, freq="1D")
+    return ts
+
+
+if __name__ == "__main__":
+
+    ts = dataloader()
+
+    pipeline: Pipeline = hydra_slayer.get_from_params(**pipeline)
+
+    backtest_configs: Dict[str, Any] = hydra_slayer.get_from_params(**backtest)
+
+    metrics, forecast, info = pipeline.backtest(ts=ts, **backtest_configs)

examples/wandb/sweeps/sweep.yaml

@@ -0,0 +1,18 @@
+program:
+  run.py
+method: bayes
+parameters:
+  learning-rate:
+    min: 0.0001
+    max: 0.1
+  iterations:
+    distribution: int_uniform
+    min: 2
+    max: 30
+metric:
+  name: MAE_median
+  goal: minimize
+command:
+  - python
+  - run.py
+  - ${args}