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Welcome to the Meridian end-to-end demo. This simplified demo showcases the fundamental functionalities and basic usage of the library, including working examples of the major modeling steps:
- Install
- Load the data
- Configure the model
- Run model diagnostics
- Generate model results & two-page output
- Run budget optimization & two-page output
- Save the model object
Note that this notebook skips all of the exploratory data analysis and preprocessing steps. It assumes that you have completed these tasks before reaching this point in the demo.
This notebook utilizes sample data. As a result, the numbers and results obtained might not accurately reflect what you encounter when working with a real dataset.
Step 0: Install
1. Make sure you are using one of the available GPU Colab runtimes which is required to run Meridian. You can change your notebook's runtime in Runtime > Change runtime type in the menu. All users can use the T4 GPU runtime which is sufficient to run the demo colab, free of charge. Users who have purchased one of Colab's paid plans have access to premium GPUs (such as V100, A100 or L4 Nvidia GPU).
2. Install the latest version of Meridian, and verify that GPU is available.
# Install meridian: from PyPI @ latest releasepip install --upgrade google-meridian[colab,and-cuda]# Install meridian: from PyPI @ specific version# !pip install google-meridian[colab,and-cuda]==1.0.3# Install meridian: from GitHub @HEAD# !pip install --upgrade "google-meridian[colab,and-cuda] @ git+https://github.com/google/meridian.git"
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Installing collected packages: scipy, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-nvcc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, ml-dtypes, tensorflow-probability, tensorboard, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, tensorflow, altair, tf-keras, google-meridian
Attempting uninstall: scipy
Found existing installation: scipy 1.13.1
Uninstalling scipy-1.13.1:
Successfully uninstalled scipy-1.13.1
Attempting uninstall: nvidia-nvjitlink-cu12
Found existing installation: nvidia-nvjitlink-cu12 12.5.82
Uninstalling nvidia-nvjitlink-cu12-12.5.82:
Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82
Attempting uninstall: nvidia-nccl-cu12
Found existing installation: nvidia-nccl-cu12 2.21.5
Uninstalling nvidia-nccl-cu12-2.21.5:
Successfully uninstalled nvidia-nccl-cu12-2.21.5
Attempting uninstall: nvidia-curand-cu12
Found existing installation: nvidia-curand-cu12 10.3.6.82
Uninstalling nvidia-curand-cu12-10.3.6.82:
Successfully uninstalled nvidia-curand-cu12-10.3.6.82
Attempting uninstall: nvidia-cufft-cu12
Found existing installation: nvidia-cufft-cu12 11.2.3.61
Uninstalling nvidia-cufft-cu12-11.2.3.61:
Successfully uninstalled nvidia-cufft-cu12-11.2.3.61
Attempting uninstall: nvidia-cuda-runtime-cu12
Found existing installation: nvidia-cuda-runtime-cu12 12.5.82
Uninstalling nvidia-cuda-runtime-cu12-12.5.82:
Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82
Attempting uninstall: nvidia-cuda-nvrtc-cu12
Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82
Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:
Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82
Attempting uninstall: nvidia-cuda-nvcc-cu12
Found existing installation: nvidia-cuda-nvcc-cu12 12.5.82
Uninstalling nvidia-cuda-nvcc-cu12-12.5.82:
Successfully uninstalled nvidia-cuda-nvcc-cu12-12.5.82
Attempting uninstall: nvidia-cuda-cupti-cu12
Found existing installation: nvidia-cuda-cupti-cu12 12.5.82
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Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82
Attempting uninstall: nvidia-cublas-cu12
Found existing installation: nvidia-cublas-cu12 12.5.3.2
Uninstalling nvidia-cublas-cu12-12.5.3.2:
Successfully uninstalled nvidia-cublas-cu12-12.5.3.2
Attempting uninstall: ml-dtypes
Found existing installation: ml-dtypes 0.4.1
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Successfully uninstalled ml-dtypes-0.4.1
Attempting uninstall: tensorflow-probability
Found existing installation: tensorflow-probability 0.25.0
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Found existing installation: tensorboard 2.18.0
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Attempting uninstall: nvidia-cusparse-cu12
Found existing installation: nvidia-cusparse-cu12 12.5.1.3
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Attempting uninstall: nvidia-cudnn-cu12
Found existing installation: nvidia-cudnn-cu12 9.3.0.75
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Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75
Attempting uninstall: nvidia-cusolver-cu12
Found existing installation: nvidia-cusolver-cu12 11.6.3.83
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Attempting uninstall: altair
Found existing installation: altair 5.5.0
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Found existing installation: tf_keras 2.18.0
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow-text 2.18.1 requires tensorflow<2.19,>=2.18.0, but you have tensorflow 2.16.2 which is incompatible.
dopamine-rl 4.1.2 requires tf-keras>=2.18.0, but you have tf-keras 2.16.0 which is incompatible.
torch 2.5.1+cu124 requires nvidia-cublas-cu12==12.4.5.8; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cublas-cu12 12.3.4.1 which is incompatible.
torch 2.5.1+cu124 requires nvidia-cuda-cupti-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cuda-cupti-cu12 12.3.101 which is incompatible.
torch 2.5.1+cu124 requires nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cuda-nvrtc-cu12 12.3.107 which is incompatible.
torch 2.5.1+cu124 requires nvidia-cuda-runtime-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cuda-runtime-cu12 12.3.101 which is incompatible.
torch 2.5.1+cu124 requires nvidia-cudnn-cu12==9.1.0.70; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cudnn-cu12 8.9.7.29 which is incompatible.
torch 2.5.1+cu124 requires nvidia-cufft-cu12==11.2.1.3; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cufft-cu12 11.0.12.1 which is incompatible.
torch 2.5.1+cu124 requires nvidia-curand-cu12==10.3.5.147; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-curand-cu12 10.3.4.107 which is incompatible.
torch 2.5.1+cu124 requires nvidia-cusolver-cu12==11.6.1.9; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cusolver-cu12 11.5.4.101 which is incompatible.
torch 2.5.1+cu124 requires nvidia-cusparse-cu12==12.3.1.170; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cusparse-cu12 12.2.0.103 which is incompatible.
torch 2.5.1+cu124 requires nvidia-nccl-cu12==2.21.5; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-nccl-cu12 2.19.3 which is incompatible.
torch 2.5.1+cu124 requires nvidia-nvjitlink-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-nvjitlink-cu12 12.3.101 which is incompatible.
Successfully installed altair-4.2.2 google-meridian-1.0.3 ml-dtypes-0.3.2 nvidia-cublas-cu12-12.3.4.1 nvidia-cuda-cupti-cu12-12.3.101 nvidia-cuda-nvcc-cu12-12.3.107 nvidia-cuda-nvrtc-cu12-12.3.107 nvidia-cuda-runtime-cu12-12.3.101 nvidia-cudnn-cu12-8.9.7.29 nvidia-cufft-cu12-11.0.12.1 nvidia-curand-cu12-10.3.4.107 nvidia-cusolver-cu12-11.5.4.101 nvidia-cusparse-cu12-12.2.0.103 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.3.101 scipy-1.12.0 tensorboard-2.16.2 tensorflow-2.16.2 tensorflow-probability-0.24.0 tf-keras-2.16.0
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_probability as tfp
import arviz as az
import IPython
from meridian import constants
from meridian.data import load
from meridian.data import test_utils
from meridian.model import model
from meridian.model import spec
from meridian.model import prior_distribution
from meridian.analysis import optimizer
from meridian.analysis import analyzer
from meridian.analysis import visualizer
from meridian.analysis import summarizer
from meridian.analysis import formatter
# check if GPU is available
from psutil import virtual_memory
ram_gb = virtual_memory().total / 1e9
print('Your runtime has {:.1f} gigabytes of available RAM\n'.format(ram_gb))
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
print("Num CPUs Available: ", len(tf.config.experimental.list_physical_devices('CPU')))
Your runtime has 54.8 gigabytes of available RAM Num GPUs Available: 1 Num CPUs Available: 1
Step 1: Load the data
Load the simulated dataset in CSV format as follows.
1. Map the column names to their corresponding variable types. For example, the column names 'GQV' and 'Competitor_Sales' are mapped to controls. The required variable types are time, controls, population, kpi, revenue_per_kpi, media and spend. If your data includes organic media or non-media treatments, you can add them using organic_media and non_media_treatments arguments. For the definition of each variable, see
Collect and organize your data.
coord_to_columns = load.CoordToColumns(
time='time',
geo='geo',
controls=['GQV', 'Competitor_Sales'],
population='population',
kpi='conversions',
revenue_per_kpi='revenue_per_conversion',
media=[
'Channel0_impression',
'Channel1_impression',
'Channel2_impression',
'Channel3_impression',
'Channel4_impression',
],
media_spend=[
'Channel0_spend',
'Channel1_spend',
'Channel2_spend',
'Channel3_spend',
'Channel4_spend',
],
organic_media=['Organic_channel0_impression'],
non_media_treatments=['Promo'],
)
2. Map the media variables and the media spends to the designated channel names intended for display in the two-page HTML output. In the following example, 'Channel0_impression' and 'Channel0_spend' are connected to the same channel, 'Channel0'.
correct_media_to_channel = {
'Channel0_impression': 'Channel_0',
'Channel1_impression': 'Channel_1',
'Channel2_impression': 'Channel_2',
'Channel3_impression': 'Channel_3',
'Channel4_impression': 'Channel_4',
}
correct_media_spend_to_channel = {
'Channel0_spend': 'Channel_0',
'Channel1_spend': 'Channel_1',
'Channel2_spend': 'Channel_2',
'Channel3_spend': 'Channel_3',
'Channel4_spend': 'Channel_4',
}
3. Load the CSV data using CsvDataLoader. Note that csv_path is the path to the data file location.
loader = load.CsvDataLoader(
csv_path="https://raw.githubusercontent.com/google/meridian/refs/heads/main/meridian/data/simulated_data/csv/geo_all_channels.csv",
kpi_type='non_revenue',
coord_to_columns=coord_to_columns,
media_to_channel=correct_media_to_channel,
media_spend_to_channel=correct_media_spend_to_channel,
)
data = loader.load()
/usr/local/lib/python3.11/dist-packages/meridian/data/load.py:1020: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
self.df[geo_column_name] = self.df[geo_column_name].replace(
/usr/local/lib/python3.11/dist-packages/meridian/data/load.py:201: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
if (constants.GEO) not in self.dataset.dims.keys():
/usr/local/lib/python3.11/dist-packages/meridian/data/load.py:231: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
if constants.MEDIA_TIME not in self.dataset.dims.keys():
Note that the simulated data here does not contain reach and frequency. We recommend including reach and frequency data whenever they are available. For information about the advantages of utilizing reach and frequency, see Bayesian Hierarchical Media Mix Model Incorporating Reach and Frequency Data. For code snippet for loading reach and frequency data, see Load geo-level data with reach and frequency
The documentation provides guidance for instances where reach and frequency data is accessible for specific channels. Additionally, for information about how to load other data types and formats, including data with reach and frequency, see Supported data types and formats.
Step 2: Configure the model
Meridian uses Bayesian framework and Markov Chain Monte Carlo (MCMC) algorithms to sample from the posterior distribution.
1. Inititalize the Meridian class by passing the loaded data and the customized model specification. One advantage of Meridian lies in its capacity to calibrate the model directly through ROI priors, as described in Media Mix Model Calibration With Bayesian Priors. In this particular example, the ROI priors for all media channels are identical, with each being represented as Lognormal(0.2, 0.9).
roi_mu = 0.2 # Mu for ROI prior for each media channel.
roi_sigma = 0.9 # Sigma for ROI prior for each media channel.
prior = prior_distribution.PriorDistribution(
roi_m=tfp.distributions.LogNormal(roi_mu, roi_sigma, name=constants.ROI_M)
)
model_spec = spec.ModelSpec(prior=prior)
mmm = model.Meridian(input_data=data, model_spec=model_spec)
2. Use the sample_prior() and sample_posterior() methods to obtain samples from the prior and posterior distributions of model parameters. If you are using the T4 GPU runtime this step may take about 10 minutes for the provided data set.
%%time
mmm.sample_prior(500)
mmm.sample_posterior(n_chains=7, n_adapt=500, n_burnin=500, n_keep=1000)
CPU times: user 13min 43s, sys: 25.5 s, total: 14min 9s Wall time: 13min 25s /usr/local/lib/python3.11/dist-packages/arviz/data/inference_data.py:157: UserWarning: trace group is not defined in the InferenceData scheme warnings.warn( /usr/local/lib/python3.11/dist-packages/arviz/data/inference_data.py:1655: UserWarning: trace group is not defined in the InferenceData scheme warnings.warn(
For more information about configuring the parameters and using a customized model specification, such as setting different ROI priors for each media channel, see Configure the model.
Step 3: Run model diagnostics
After the model is built, you must assess convergence, debug the model if needed, and then assess the model fit.
1. Assess convergence. Run the following code to generate r-hat statistics. R-hat close to 1.0 indicate convergence. R-hat < 1.2 indicates approximate convergence and is a reasonable threshold for many problems.
model_diagnostics = visualizer.ModelDiagnostics(mmm)
model_diagnostics.plot_rhat_boxplot()
/usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False)
2. Assess the model's fit by comparing the expected sales against the actual sales.
model_fit = visualizer.ModelFit(mmm)
model_fit.plot_model_fit()
/usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False)
For more information and additional model diagnostics checks, see Modeling diagnostics.
Step 4: Generate model results & two-page output
To export the two-page HTML summary output, initialize the Summarizer class with the model object. Then pass in the filename, filepath, start date, and end date to output_model_results_summary to run the summary for that time duration and save it to the specified file.
mmm_summarizer = summarizer.Summarizer(mmm)
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
filepath = '/content/drive/MyDrive'
start_date = '2021-01-25'
end_date = '2024-01-15'
mmm_summarizer.output_model_results_summary('summary_output.html', filepath, start_date, end_date)
/usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False)
Here is a preview of the two-page output based on the simulated data:
IPython.display.HTML(filename='/content/drive/MyDrive/summary_output.html')
For a customized two-page report, model results summary table, and individual visualizations, see Model results report and plot media visualizations.
Step 5: Run budget optimization & generate an optimization report
You can choose what scenario to run for the budget allocation. In default scenario, you find the optimal allocation across channels for a given budget to maximize the return on investment (ROI).
1. Instantiate the BudgetOptimizer class and run the optimize() method without any customization, to run the default library's Fixed Budget Scenario to maximize ROI.
%%time
budget_optimizer = optimizer.BudgetOptimizer(mmm)
optimization_results = budget_optimizer.optimize()
CPU times: user 30.8 s, sys: 1.61 s, total: 32.4 s Wall time: 37.9 s
2. Export the 2-page HTML optimization report, which contains optimized spend allocations and ROI.
filepath = '/content/drive/MyDrive'
optimization_results.output_optimization_summary('optimization_output.html', filepath)
/usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False) /usr/local/lib/python3.11/dist-packages/altair/utils/core.py:384: FutureWarning: the convert_dtype parameter is deprecated and will be removed in a future version. Do ``ser.astype(object).apply()`` instead if you want ``convert_dtype=False``. col = df[col_name].apply(to_list_if_array, convert_dtype=False)
IPython.display.HTML(filename='/content/drive/MyDrive/optimization_output.html')
For information about customized optimization scenarios, such as flexible budget scenarios, see Budget optimization scenarios. For more information about optimization results summary and individual visualizations, see optimization results output and optimization visualizations.
Step 6: Save the model object
We recommend that you save the model object for future use. This helps you to avoid repetitive model runs and saves time and computational resources. After the model object is saved, you can load it at a later stage to continue the analysis or visualizations without having to re-run the model.
Run the following codes to save the model object:
file_path='/content/drive/MyDrive/saved_mmm.pkl'
model.save_mmm(mmm, file_path)
Run the following codes to load the saved model:
mmm = model.load_mmm(file_path)