SHAP values for skforecast models¶

SHAP (SHapley Additive exPlanations) values are a popular method for explaining machine learning models, as they help to understand how variables and values influence predictions visually and quantitatively.

It is possible to generate SHAP-values explanations from Skforecast models with just two essential elements:

• The internal regressor of the forecaster.

• The training matrices created from the time series and used to fit the forecaster.

By leveraging these two components, users can create insightful and interpretable explanations for their skforecast models. These explanations can be used to verify the reliability of the model, identify the most significant factors that contribute to model predictions, and gain a deeper understanding of the underlying relationship between the input variables and the target variable.

  Note

See also: Feature importances

Libraries¶

In [1]:

# Libraries
# ==============================================================================
import pandas as pd
import shap
shap.initjs()
from sklearn.ensemble import HistGradientBoostingRegressor
from skforecast.ForecasterAutoreg import ForecasterAutoreg

# Libraries # ============================================================================== import pandas as pd import shap shap.initjs() from sklearn.ensemble import HistGradientBoostingRegressor from skforecast.ForecasterAutoreg import ForecasterAutoreg

Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)

Data¶

In [2]:

# Download data
# ==============================================================================
url = (
    'https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/'
    'data/h2o_exog.csv'
)
data = pd.read_csv(
            url, sep=',', header=0, names=['datetime', 'y', 'exog_1', 'exog_2']
       )

# Data preprocessing
# ==============================================================================
data['datetime'] = pd.to_datetime(data['datetime'], format='%Y-%m-%d')
data = data.set_index('datetime')
data = data.asfreq('MS')
data = data.sort_index()
data.head(3)

# Download data # ============================================================================== url = ( 'https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/' 'data/h2o_exog.csv' ) data = pd.read_csv( url, sep=',', header=0, names=['datetime', 'y', 'exog_1', 'exog_2'] ) # Data preprocessing # ============================================================================== data['datetime'] = pd.to_datetime(data['datetime'], format='%Y-%m-%d') data = data.set_index('datetime') data = data.asfreq('MS') data = data.sort_index() data.head(3)

Out[2]:

	y	exog_1	exog_2
datetime
1992-04-01	0.379808	0.958792	1.166029
1992-05-01	0.361801	0.951993	1.117859
1992-06-01	0.410534	0.952955	1.067942

Create and train forecaster¶

In [3]:

# Create recursive multi-step forecaster (ForecasterAutoreg)
# ==============================================================================
forecaster = ForecasterAutoreg(
                 regressor = HistGradientBoostingRegressor(random_state=123),
                 lags      = 5
             )

forecaster.fit(
    y    = data['y'],
    exog = data[['exog_1', 'exog_2']]
)

# Create recursive multi-step forecaster (ForecasterAutoreg) # ============================================================================== forecaster = ForecasterAutoreg( regressor = HistGradientBoostingRegressor(random_state=123), lags = 5 ) forecaster.fit( y = data['y'], exog = data[['exog_1', 'exog_2']] )

Training matrices¶

In [4]:

# Training matrices used by the forecaster to fit the internal regressor
# ==============================================================================
X_train, y_train = forecaster.create_train_X_y(
                        y    = data['y'],
                        exog = data[['exog_1', 'exog_2']]
                    )
display(X_train.head(3))
display(y_train.head(3))

# Training matrices used by the forecaster to fit the internal regressor # ============================================================================== X_train, y_train = forecaster.create_train_X_y( y = data['y'], exog = data[['exog_1', 'exog_2']] ) display(X_train.head(3)) display(y_train.head(3))

	lag_1	lag_2	lag_3	lag_4	lag_5	exog_1	exog_2
datetime
1992-09-01	0.475463	0.483389	0.410534	0.361801	0.379808	0.959610	1.153190
1992-10-01	0.534761	0.475463	0.483389	0.410534	0.361801	0.956205	1.194551
1992-11-01	0.568606	0.534761	0.475463	0.483389	0.410534	0.949715	1.231489

datetime
1992-09-01    0.534761
1992-10-01    0.568606
1992-11-01    0.595223
Freq: MS, Name: y, dtype: float64

Shap Values¶

Shap explainer¶

The python implementation of SHAP is built along the explainers. These explainers are appropriate only for certain types or classes of algorithms. For example, the TreeExplainer is used for tree-based models.

In [5]:

# Create SHAP explainer
# ==============================================================================
explainer = shap.TreeExplainer(forecaster.regressor)
shap_values = explainer.shap_values(X_train)

# Create SHAP explainer # ============================================================================== explainer = shap.TreeExplainer(forecaster.regressor) shap_values = explainer.shap_values(X_train)

SHAP Summary Plot¶

In [6]:

shap.summary_plot(shap_values, X_train, plot_type="bar")

shap.summary_plot(shap_values, X_train, plot_type="bar")

In [7]:

shap.summary_plot(shap_values, X_train)

shap.summary_plot(shap_values, X_train)

No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored

Explain predictions¶

Visualize a single prediction

In [8]:

# visualize the first prediction's explanation
shap.force_plot(explainer.expected_value, shap_values[0,:], X_train.iloc[0,:])

# visualize the first prediction's explanation shap.force_plot(explainer.expected_value, shap_values[0,:], X_train.iloc[0,:])

Out[8]:

Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.

Visualize many predictions

In [9]:

# visualize the first 200 training set predictions
shap.force_plot(explainer.expected_value, shap_values[:200,:], X_train.iloc[:200,:])

# visualize the first 200 training set predictions shap.force_plot(explainer.expected_value, shap_values[:200,:], X_train.iloc[:200,:])

Out[9]:

Visualization omitted, Javascript library not loaded!
Have you run `initjs()` in this notebook? If this notebook was from another user you must also trust this notebook (File -> Trust notebook). If you are viewing this notebook on github the Javascript has been stripped for security. If you are using JupyterLab this error is because a JupyterLab extension has not yet been written.

SHAP Dependence Plots¶

In [10]:

shap.dependence_plot("exog_2", shap_values, X_train)

shap.dependence_plot("exog_2", shap_values, X_train)

In [11]:

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