/getting-started/index.html This section helps you install Abacus and run your first model. Start here if you want to: set up a local environment from this repository fit PanelMMM directly from Python build a model from YAML run the structured pipeline against one of the bundled demo configs Pages Installation — Set up Abacus from a local checkout with Conda or pip, add optional extras, and verify the install. Quickstart: Python API — Fit PanelMMM directly from a pandas dataset in Python. Quickstart: YAML Builder — Construct a model from a YAML config and combined dataset. Quickstart: Pipeline Runner — Run the staged pipeline from Python, the thin CLI, or the bundled demo launcher. Hugo en-gb /getting-started/installation/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/installation/index.html These instructions assume you are working from a local checkout of the Abacus repository. Prerequisites Item Notes Python The package requires Python 3.11 or later. The repo development environment uses Python 3.12. Local checkout Install from the repository root, not from a published package index. Writable temp/cache directory Useful for PyTensor compiledir and local verification commands. Recommended setup: Conda + editable install This is the supported local development path for the repository. /getting-started/quickstart-python/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/quickstart-python/index.html This page shows the fastest direct path from a pandas dataset to a fitted PanelMMM. If you have not prepared your dataset yet, read Data Preparation first. Load a dataset The repository includes bundled demo datasets under data/demo/. The timeseries bundle is the simplest starting point because it has no extra panel dimensions. import pandas as pd dataset = pd.read_csv("data/demo/timeseries/dataset.csv") dataset["date"] = pd.to_datetime(dataset["date"]) X = dataset.drop(columns=["revenue"]) y = dataset["revenue"].rename("revenue") Construct PanelMMM from abacus.mmm import GeometricAdstock, LogisticSaturation from abacus.mmm.panel import PanelMMM mmm = PanelMMM( date_column="date", target_column="revenue", channel_columns=[ "channel_1", "channel_2", "channel_3", "channel_4", "channel_5", "channel_6", ], yearly_seasonality=2, adstock=GeometricAdstock(l_max=4), saturation=LogisticSaturation(), ) This example uses a plain timeseries. If your dataset has panel dimensions such as geo or brand, add them with dims=(...) and keep those columns in X. /getting-started/quickstart-yaml/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/quickstart-yaml/index.html Use the YAML builder when you want the model specification to live in a config file instead of Python code. The builder entry point is: from abacus.mmm.builders.yaml import build_mmm_from_yaml Smallest useful workflow The bundled demo config at data/demo/timeseries/config.yml is a working starting point. It already points to a combined dataset with data.dataset_path. import pandas as pd from abacus.mmm.builders.yaml import build_mmm_from_yaml dataset = pd.read_csv("data/demo/timeseries/dataset.csv") X = dataset.drop(columns=["revenue"]) y = dataset["revenue"].rename("revenue") mmm = build_mmm_from_yaml( "data/demo/timeseries/config.yml", X=X, y=y, ) build_mmm_from_yaml(...) returns a PanelMMM instance with the PyMC graph already built. /getting-started/quickstart-runner/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/quickstart-runner/index.html Use the pipeline runner when you want a full staged run instead of only an in-memory model fit. The runner writes: a run manifest copied and resolved config files fitted model artefacts posterior predictive assessment outputs decomposition, diagnostics, and response-curve artefacts Fastest first run: bundled demo From the repository root, the quickest way to see a real structured run is the demo launcher: