updated docs

README.md

@@ -46,18 +46,19 @@ and download the files from the data folder to your computer.
 ```python
 import pandas as pd
 
-from enfobench.dataset import Dataset, DemandDataset
+from enfobench import Dataset
+from enfobench.datasets import ElectricityDemandDataset
 from enfobench.evaluation import cross_validate, evaluate_metrics
 from enfobench.evaluation.metrics import mean_bias_error, mean_absolute_error, root_mean_squared_error
 
 # Load the dataset from the folder that you downloaded the files to.
-ds = DemandDataset("/path/to/the/dataset/folder/that/contains/all/subsets")
+ds = ElectricityDemandDataset("/path/to/the/dataset/folder/that/contains/all/subsets")
 
 # List all meter ids
-ds.metadata_subset.list_unique_ids()
+ds.list_unique_ids()
 
 # Get one of the meter ids
-unique_id = ds.metadata_subset.list_unique_ids()[0]
+unique_id = ds.list_unique_ids()[0]
 
 # Get dataset for a specific meter id
 target, past_covariates, metadata = ds.get_data_by_unique_id(unique_id)

docs/docs/examples.md

@@ -12,11 +12,11 @@ The examples models are found in the `models/` folder, and the example notebooks
 The repository follows this structure:
 
 ```
-├── README.md                       <- The top-level README for getting started.
 ├── data
-│   ├── demand.parquet              <- Demand data subset.
-│   ├── metadata.parquet            <- Metadata subset.
-│   └── weather.parquet             <- Weather data subset.
+│   └── electricity-demand          <- Electricity demand dataset.
+│       ├── demand.parquet          <- Demand data subset.
+│       ├── metadata.parquet        <- Metadata subset.
+│       └── weather.parquet         <- Weather data subset.
 │
 ├── models                          <- Example models each in its own subfolder.
 │   ├── sf-naive-seasonal           <- Naive seasonal model based on statsforecast package.
@@ -28,9 +28,9 @@ The repository follows this structure:
 ├── notebooks                       <- Jupyter notebooks, should be read in order.
 │   ├── 01. Univariate.ipynb        <- Simple univariate forecast model benchmarking example.
 │   ├── 02. Multivariate.ipynb      <- Multivariate forecast model benchmarking example.
-│   └── 02. ForecastClient.ipynb    <- Benchmarking using the ForecastClient example.
+│   └── 03. ForecastClient.ipynb    <- Benchmarking using the ForecastClient example.
 │
-└── requirements.txt                <- Overall requirements to run all the example notebooks.
+└── README.md                       <- The top-level README for getting started.
 ```
 
 ## Requirements
@@ -52,7 +52,7 @@ pip install notebook enfobench
 ```
 
 To run the notebooks, you also need the HuggingFace dataset [attila-balint-kul/electricity-demand](https://huggingface.co/datasets/attila-balint-kul/electricity-demand).
-Download all three files from the `data/` folder into the `data/` folder of this repository.
+Download all three files from the `data/` folder into the `data/electicity-demand/` folder of this repository.
 
 Run the example notebooks in the `notebooks` folder.
 

docs/docs/index.md

@@ -33,7 +33,7 @@ Load your own data and create a dataset.
 ```python
 import pandas as pd
 
-from enfobench.dataset import Dataset
+from enfobench import Dataset
 
 # Load your datasets
 data = pd.read_csv("../path/to/your/data.csv", parse_dates=['timestamp'], index_col='timestamp')
@@ -46,7 +46,7 @@ past_covariates = data.loc[:, ['covariate_1', 'covariate_2']]
 
 # As sometimes it can be challenging to access historical forecasts to use future covariates, 
 # the package also has a helper function to create perfect historical forecasts from the past covariates.
-from enfobench.dataset.utils import create_perfect_forecasts_from_covariates
+from enfobench.datasets.utils import create_perfect_forecasts_from_covariates
 
 # The example below creates simulated perfect historical forecasts with a horizon of 24 hours and a step of 1 day.
 future_covariates = create_perfect_forecasts_from_covariates(
@@ -56,7 +56,7 @@ future_covariates = create_perfect_forecasts_from_covariates(
 )
 
 dataset = Dataset(
-    target=data['target_column'],
+    target=target,
     past_covariates=past_covariates,
     future_covariates=future_covariates,
 )
@@ -66,13 +66,14 @@ The package integrates with the HuggingFace Dataset ['attila-balint-kul/electric
 To use this, just download all the files from the data folder to your computer.
 
 ```python
-from enfobench.dataset import Dataset, DemandDataset
+from enfobench import Dataset
+from enfobench.datasets import ElectricityDemandDataset
 
 # Load the dataset from the folder that you downloaded the files to.
-ds = DemandDataset("/path/to/the/dataset/folder/that/contains/all/subsets")
+ds = ElectricityDemandDataset("/path/to/the/dataset/folder/that/contains/all/subsets")
 
 # List all meter ids
-ds.metadata_subset.list_unique_ids()
+ds.list_unique_ids()
 
 # Get dataset for a specific meter id
 target, past_covariates, metadata = ds.get_data_by_unique_id("unique_id_of_the_meter")
@@ -101,8 +102,8 @@ model = MyModel()
 cv_results = cross_validate(
     model,
     dataset,
-    start_date=pd.Timestamp("2018-01-01"),
-    end_date=pd.Timestamp("2018-01-31"),
+    start_date=pd.Timestamp("2018-01-01T00:00:00"),
+    end_date=pd.Timestamp("2018-01-31T00:00:00"),
     horizon=pd.Timedelta("24 hours"),
     step=pd.Timedelta("1 day"),
 )
@@ -153,7 +154,7 @@ metrics = evaluate_metrics(
 )
 ```
 
-In order to serve your model behind an API, you can use the built in server factory.
+In order to serve your model behind an API, you can use the built-in server factory.
 
 ```python
 import uvicorn

pyproject.toml

@@ -93,7 +93,7 @@ python = ["3.10", "3.11"]
 [tool.hatch.envs.lint]
 detached = true
 dependencies = [
-    "black>=23.1.0",
+    "black[jupyter]>=23.1.0",
     "mypy>=1.6.0",
     "ruff>=0.1.5",
 ]