%pip install -q validmind
Document a time series forecasting model
Use the FRED sample dataset to train a simple time series model and document that model with the ValidMind Developer Framework.
As part of the notebook, you will learn how to train a simple model while exploring how the documentation process works:
- Initializing the ValidMind Developer Framework
- Loading a sample dataset provided by the library to train a simple time series model
- Running a ValidMind test suite to quickly generate documentation about the data and model
Contents
About ValidMind
ValidMind is a platform for managing model risk, including risk associated with AI and statistical models.
You use the ValidMind Developer Framework to automate documentation and validation tests, and then use the ValidMind AI Risk Platform UI to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators.
Before you begin
This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.
If you encounter errors due to missing modules in your Python environment, install the modules with pip install
, and then re-run the notebook. For more help, refer to Installing Python Modules.
New to ValidMind?
If you haven’t already seen our Get started with the ValidMind Developer Framework, we recommend you explore the available resources for developers at some point. There, you can learn more about documenting models, find code samples, or read our developer reference.
Signing up is FREE — Register with ValidMind
Key concepts
Model documentation: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.
Documentation template: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.
Tests: A function contained in the ValidMind Developer Framework, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets, and can be run individually or as part of a suite defined by your model documentation template.
Metrics: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.
Custom metrics: Custom metrics are functions that you define to evaluate your model or dataset. These functions can be registered with ValidMind to be used in the platform.
Inputs: Objects to be evaluated and documented in the ValidMind framework. They can be any of the following:
- model: A single model that has been initialized in ValidMind with
vm.init_model()
. - dataset: Single dataset that has been initialized in ValidMind with
vm.init_dataset()
. - models: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom metric.
- datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom metric. See this example for more information.
Parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a metric, customize its behavior, or provide additional context.
Outputs: Custom metrics can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.
Test suites: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.
Example: the classifier_full_suite
test suite runs tests from the tabular_dataset
and classifier
test suites to fully document the data and model sections for binary classification model use-cases.
Install the client library
The client library provides Python support for the ValidMind Developer Framework. To install it:
Initialize the client library
ValidMind generates a unique code snippet for each registered model to connect with your developer environment. You initialize the client library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook.
In a browser, log in to ValidMind.
In the left sidebar, navigate to Model Inventory and click + Register new model.
Enter the model details and click Continue. (Need more help?)
For example, to register a model for use with this notebook, select:
- Documentation template:
Time Series Forecasting with ML
- Use case:
Analytics - Analytics
You can fill in other options according to your preference.
- Documentation template:
Go to Getting Started and click Copy snippet to clipboard.
Next, replace this placeholder with your own code snippet:
import validmind as vm
vm.init(= "https://api.prod.validmind.ai/api/v1/tracking",
api_host = "...",
api_key = "...",
api_secret = "..."
model )
Initialize the Python environment
Next, let’s import the necessary libraries and set up your Python environment for data analysis:
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
%matplotlib inline
Preview the documentation template
A template predefines sections for your model documentation and provides a general outline to follow, making the documentation process much easier.
You will upload documentation and test results into this template later on. For now, take a look at the structure that the template provides with the vm.preview_template()
function from the ValidMind library and note the empty sections:
vm.preview_template()
Load the sample dataset
The sample dataset used here is provided by the ValidMind library. To be able to use it, you need to import the dataset and load it into a pandas DataFrame, a two-dimensional tabular data structure that makes use of rows and columns:
from validmind.datasets.regression import fred_timeseries
= fred_timeseries.target_column
target_column
print(
f"Loaded demo dataset with: \n\n\t• Target column: '{target_column}'"
)
= fred_timeseries.load_data()
raw_df raw_df.head()
Document the model
As part of documenting the model with the ValidMind Developer Framework, you need to preprocess the raw dataset, initialize some training and test datasets, initialize a model object you can use for testing, and then run the full suite of tests.
Prepocess the raw dataset
Preprocessing performs a number of operations to get ready for the subsequent steps: - Split the dataset: Divide the original dataset into training and test sets for the primary model with an 80/20 split, without shuffling. - Difference the data: Calculate the first difference of the train and test datasets to remove trends and seasonality, then drop any resulting NaN values. - Extract features and target variables: Separate the feature columns (predictors) and the target variable from the differenced train and test datasets.
# Split the raw dataset into training and test sets
= train_test_split(raw_df, test_size=0.2, shuffle=False)
train_df, test_df
# Take the first difference of the training and test sets
= train_df.diff().dropna()
train_diff_df = test_df.diff().dropna()
test_diff_df
# Extract the features and target variable from the training set
= train_diff_df.drop(target_column, axis=1)
X_diff_train = train_diff_df[target_column]
y_diff_train
# Extract the features and target variable from the test set
= test_diff_df.drop(target_column, axis=1)
X_diff_test = test_diff_df[target_column] y_diff_test
Train random forests and gradient boosting regressor models
This section trains random forest and gradient boosting models on differenced data, transforms predictions back to the original scale, and evaluates model performance using Mean Squared Error (MSE) and R-squared (R²) scores.
The following helper functions are used to post-process predictions and evaluate model performance:
transform_to_levels
: Reconstructs the original values from differenced predictions by cumulatively summing them, starting from a given initial value.evaluate_model
: Calculates the Mean Squared Error (MSE) and R-squared (R²) score to evaluate the accuracy of the predictions against the true values.
def transform_to_levels(y_diff_pred, first_value=0):
= [first_value]
y_pred for pred in y_diff_pred:
-1] + pred)
y_pred.append(y_pred[return y_pred
def evaluate_model(y_true, y_pred):
= mean_squared_error(y_true, y_pred)
mse = r2_score(y_true, y_pred)
r2 return mse, r2
# Fit the random forest model
= RandomForestRegressor(n_estimators=1500, random_state=0)
model_rf
model_rf.fit(X_diff_train, y_diff_train)
# Make predictions on the training and test sets
= model_rf.predict(X_diff_train)
y_diff_train_pred = model_rf.predict(X_diff_test)
y_diff_test_pred
# Transform the predictions back to the original scale
= transform_to_levels(y_diff_train_pred, first_value=train_df[target_column].iloc[0])
y_train_rf_pred = transform_to_levels(y_diff_test_pred, first_value=test_df[target_column].iloc[0])
y_test_rf_pred
# Evaluate the model's performance on the training and test sets
= evaluate_model(train_df[target_column], y_train_rf_pred)
mse_train, r2_train = evaluate_model(test_df[target_column], y_test_rf_pred)
mse_test, r2_test
print(f"Train Mean Squared Error: {mse_train}")
print(f"Train R-Squared: {r2_train}")
print(f"Test Mean Squared Error: {mse_test}")
print(f"Test R-Squared: {r2_test}")
# Fit the gradient boost model
= GradientBoostingRegressor(n_estimators=1500, random_state=0)
model_gb
model_gb.fit(X_diff_train, y_diff_train)
# Make predictions on the training and test sets
= model_gb.predict(X_diff_train)
y_diff_train_pred = model_gb.predict(X_diff_test)
y_diff_test_pred
# Transform the predictions back to the original scale
= transform_to_levels(y_diff_train_pred, first_value=train_df[target_column].iloc[0])
y_train_gb_pred = transform_to_levels(y_diff_test_pred, first_value=test_df[target_column].iloc[0])
y_test_gb_pred
# Evaluate the model's performance on the training and test sets
= evaluate_model(train_df[target_column], y_train_gb_pred)
mse_train, r2_train = evaluate_model(test_df[target_column], y_test_gb_pred)
mse_test, r2_test
print(f"Train Mean Squared Error: {mse_train}")
print(f"Train R-Squared: {r2_train}")
print(f"Test Mean Squared Error: {mse_test}")
print(f"Test R-Squared: {r2_test}")
Initialize the ValidMind datasets
Before you can run tests, you must first initialize a ValidMind dataset object using the init_dataset
function from the ValidMind (vm
) module.
This function takes a number of arguments:
dataset
— the raw dataset that you want to provide as input to testsinput_id
- a unique identifier that allows tracking what inputs are used when running each individual testtarget_column
— a required argument if tests require access to true values. This is the name of the target column in the dataset
With all dataframes ready, you can now initialize the ValidMind datasets objects using vm.init_dataset()
:
vm_raw_ds
: contains the raw, unprocessed data with the specified target column.vm_train_diff_ds
: contains the training data with the differenced target column, excluding the first row to remove NaN values caused by differencing.vm_test_diff_ds
: contains the test data with the differenced target column, excluding the first row to remove NaN values caused by differencing.vm_train_ds
: contains the training data, excluding the first row to align with the differenced data.vm_test_ds
: includes the test data split from the raw dataset.
= vm.init_dataset(
vm_raw_ds ="raw_ds",
input_id=raw_df,
dataset=target_column,
target_column
)
= vm.init_dataset(
vm_train_diff_ds ="train_diff_ds",
input_id=train_diff_df,
dataset=target_column,
target_column
)
= vm.init_dataset(
vm_test_diff_ds ="test_diff_ds",
input_id=test_diff_df,
dataset=target_column,
target_column
)
= vm.init_dataset(
vm_train_ds ="train_ds",
input_id=train_df,
dataset=target_column,
target_column
)
= vm.init_dataset(
vm_test_ds ="test_ds",
input_id=test_df,
dataset=target_column,
target_column )
Initialize the model objects
Additionally, you need to initialize a ValidMind model object (vm_model
) for each model, that can be passed to other functions for analysis and tests on the data. You simply intialize this model object with vm.init_model()
:
= vm.init_model(
vm_model_rf
model_rf,="random_forests_model",
input_id
)
= vm.init_model(
vm_model_gb
model_gb,="gradient_boosting_model",
input_id )
Assign predictions to the datasets
We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:
vm_train_ds.assign_predictions(=vm_model_rf,
model=y_train_rf_pred,
prediction_values
)
vm_test_ds.assign_predictions(=vm_model_rf,
model=y_test_rf_pred,
prediction_values
)
vm_train_ds.assign_predictions(=vm_model_gb,
model=y_train_gb_pred,
prediction_values
)
vm_test_ds.assign_predictions(=vm_model_gb,
model=y_test_gb_pred,
prediction_values )
from validmind.utils import preview_test_config
= fred_timeseries.get_demo_test_config()
test_config preview_test_config(test_config)
Run data validation tests
= vm.tests.run_test(
test "validmind.data_validation.TimeSeriesDescription",
={
input_grid"dataset": ["raw_ds", "train_diff_ds", "test_diff_ds", "train_ds", "test_ds"],
},
) test.log()
= vm.tests.run_test(
test "validmind.data_validation.TimeSeriesLinePlot",
={
input_grid"dataset": ["raw_ds"],
},
) test.log()
= vm.tests.run_test(
test "validmind.data_validation.TimeSeriesMissingValues",
={
input_grid"dataset": ["raw_ds", "train_diff_ds", "test_diff_ds", "train_ds", "test_ds"],
},
) test.log()
= vm.tests.run_test(
test "validmind.data_validation.SeasonalDecompose",
={
input_grid"dataset": ["raw_ds"],
},
) test.log()
= vm.tests.run_test(
test "validmind.data_validation.TimeSeriesDescriptiveStatistics",
={
input_grid"dataset": ["train_diff_ds", "test_diff_ds"],
},
) test.log()
= vm.tests.run_test(
test "validmind.data_validation.TimeSeriesOutliers",
={
input_grid"dataset": ["train_diff_ds", "test_diff_ds"],
},={
params"zscore_threshold": 4
}
) test.log()
= vm.tests.run_test(
test "validmind.data_validation.TimeSeriesHistogram",
={
input_grid"dataset": [ "train_diff_ds", "test_diff_ds"],
},={
params"nbins": 100
}
) test.log()
= vm.tests.run_test(
test "validmind.data_validation.DatasetSplit",
={
inputs"datasets": ["train_diff_ds", "test_diff_ds"],
}
) test.log()
Run model validation tests
= vm.tests.run_test(
test "validmind.model_validation.ModelMetadata",
={
input_grid"model": ["random_forests_model", "gradient_boosting_model"],
}
) test.log()
= vm.tests.run_test(
test "validmind.model_validation.sklearn.RegressionErrors",
={
input_grid"dataset": ["train_ds", "test_ds"],
"model": ["random_forests_model", "gradient_boosting_model"],
}
) test.log()
= vm.tests.run_test(
test "validmind.model_validation.sklearn.RegressionR2Square",
={
input_grid"dataset": ["train_ds", "test_ds"],
"model": ["random_forests_model", "gradient_boosting_model"],
}
) test.log()
= vm.tests.run_test(
test "validmind.model_validation.TimeSeriesR2SquareBySegments:train_data",
={
input_grid"dataset": ["train_ds"],
"model": ["random_forests_model", "gradient_boosting_model"],
}
) test.log()
= vm.tests.run_test(
test "validmind.model_validation.TimeSeriesR2SquareBySegments:test_data",
={
input_grid"dataset": ["test_ds"],
"model": ["random_forests_model", "gradient_boosting_model"],
},={
params"segments":{
"start_date": ["2012-11-01","2018-02-01"],
"end_date": ["2018-01-01","2023-03-01"]
}
}
) test.log()
= vm.tests.run_test(
test "validmind.model_validation.TimeSeriesPredictionsPlot",
={
input_grid"dataset": ["train_ds", "test_ds"],
"model": ["random_forests_model", "gradient_boosting_model"],
}
) test.log()
= vm.tests.run_test(
test "validmind.model_validation.TimeSeriesPredictionWithCI",
={
input_grid"dataset": ["train_ds", "test_ds"],
"model": ["random_forests_model", "gradient_boosting_model"],
}
) test.log()
= vm.tests.run_test(
test "validmind.model_validation.ModelPredictionResiduals",
={
input_grid"dataset": ["train_ds", "test_ds"],
"model": ["random_forests_model", "gradient_boosting_model"],
}
) test.log()
= vm.tests.run_test(
test "validmind.model_validation.sklearn.FeatureImportance",
={
input_grid"dataset": ["train_ds", "test_ds"],
"model": ["random_forests_model", "gradient_boosting_model"],
}
) test.log()
= vm.tests.run_test(
test "validmind.model_validation.sklearn.PermutationFeatureImportance",
={
input_grid"dataset": ["train_ds", "test_ds"],
"model": ["random_forests_model", "gradient_boosting_model"],
}
) test.log()
Next steps
You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind platform to work with your model documentation.
Work with your model documentation
From the Model Inventory in the ValidMind Platform UI, go to the model you registered earlier. (Need more help?)
Click and expand the Model Development section.
What you see is the full draft of your model documentation in a more easily consumable version. From here, you can make qualitative edits to model documentation, view guidelines, collaborate with validators, and submit your model documentation for approval when it’s ready. Learn more …
Discover more learning resources
We offer many interactive notebooks to help you document models:
Or, visit our documentation to learn more about ValidMind.