466 add feature importance cli

eis_toolkit/cli.py

@@ -871,6 +871,40 @@ def local_morans_i_cli(
     typer.echo(f"Local Moran's I completed, output vector saved to {output_vector}.")
 
 
+# FEATURE IMPORTANCE
+@app.command()
+def feature_importance_cli(
+    model_file: INPUT_FILE_OPTION,
+    input_rasters: INPUT_FILES_ARGUMENT,
+    target_labels: INPUT_FILE_OPTION,
+    n_repeats: int = 10,
+    random_state: Optional[int] = None,
+):
+    """Evaluate the feature importance of a sklearn classifier or regressor."""
+    from eis_toolkit.exploratory_analyses.feature_importance import evaluate_feature_importance
+    from eis_toolkit.prediction.machine_learning_general import load_model, prepare_data_for_ml
+
+    typer.echo("Progress: 10%")
+
+    model = load_model(model_file)
+    typer.echo("Progress: 20%")
+
+    X, y, _, _ = prepare_data_for_ml(input_rasters, target_labels)
+    typer.echo("Progress: 30%")
+
+    feature_names = [raster.name for raster in input_rasters]
+    typer.echo("Progress: 40%")
+
+    feature_importance, _ = evaluate_feature_importance(model, X, y, feature_names, n_repeats, random_state)
+    typer.echo("Progress: 80%")
+
+    results = dict(zip(feature_importance["Feature"], feature_importance["Importance"]))
+    json_str = json.dumps(results)
+    typer.echo("Progress: 100%")
+
+    typer.echo(f"Results: {json_str}")
+
+
 # --- RASTER PROCESSING ---
 
 

eis_toolkit/exploratory_analyses/feature_importance.py

@@ -5,28 +5,32 @@
 from beartype.typing import Optional, Sequence
 from sklearn.inspection import permutation_importance
 
-from eis_toolkit.exceptions import InvalidDatasetException, InvalidParameterValueException
+from eis_toolkit.exceptions import (
+    InvalidDatasetException,
+    InvalidParameterValueException,
+    NonMatchingParameterLengthsException,
+)
 
 
 @beartype
 def evaluate_feature_importance(
     model: sklearn.base.BaseEstimator,
-    x_test: np.ndarray,
-    y_test: np.ndarray,
+    X: np.ndarray,
+    y: np.ndarray,
     feature_names: Sequence[str],
-    n_repeats: int = 50,
+    n_repeats: int = 10,
     random_state: Optional[int] = None,
 ) -> tuple[pd.DataFrame, dict]:
     """
-    Evaluate the feature importance of a sklearn classifier or regressor.
+    Evaluate the feature importance of a Sklearn classifier or regressor.
 
     Args:
         model: A trained and fitted Sklearn model.
-        x_test: Testing feature data (X data need to be normalized / standardized).
-        y_test: Testing label data.
-        feature_names: Names of the feature columns.
-        n_repeats: Number of iteration used when calculate feature importance. Defaults to 50.
-        random_state: random state for repeatability of results. Optional parameter.
+        X: Feature data.
+        y: Target labels.
+        feature_names: Names of features in X.
+        n_repeats: Number of iteration used when calculating feature importance. Defaults to 10.
+        random_state:  Seed for random number generation. Defaults to None.
 
     Returns:
         A dataframe containing features and their importance.
@@ -37,18 +41,24 @@ def evaluate_feature_importance(
         InvalidParameterValueException: Value for 'n_repeats' is not at least one.
     """
 
-    if x_test.size == 0:
-        raise InvalidDatasetException("Array 'x_test' is empty.")
+    if X.size == 0:
+        raise InvalidDatasetException("Feature matrix X is empty.")
 
-    if y_test.size == 0:
-        raise InvalidDatasetException("Array 'y_test' is empty.")
+    if y.size == 0:
+        raise InvalidDatasetException("Target labels y is empty.")
 
     if n_repeats < 1:
         raise InvalidParameterValueException("Value for 'n_repeats' is less than one.")
 
-    result = permutation_importance(model, x_test, y_test.ravel(), n_repeats=n_repeats, random_state=random_state)
+    if len(X) != len(y):
+        raise NonMatchingParameterLengthsException("Feature matrix X and target labels y must have the same length.")
 
-    feature_importance = pd.DataFrame({"Feature": feature_names, "Importance": result.importances_mean * 100})
+    if len(feature_names) != X.shape[1]:
+        raise InvalidParameterValueException("Number of feature names must match the number of input features.")
+
+    result = permutation_importance(model, X, y.ravel(), n_repeats=n_repeats, random_state=random_state)
+
+    feature_importance = pd.DataFrame({"Feature": feature_names, "Importance": result.importances_mean})
 
     feature_importance = feature_importance.sort_values(by="Importance", ascending=False)
 

tests/exploratory_analyses/feature_importance_test.py

@@ -4,7 +4,11 @@
 from sklearn.neural_network import MLPClassifier
 from sklearn.preprocessing import StandardScaler
 
-from eis_toolkit.exceptions import InvalidDatasetException, InvalidParameterValueException
+from eis_toolkit.exceptions import (
+    InvalidDatasetException,
+    InvalidParameterValueException,
+    NonMatchingParameterLengthsException,
+)
 from eis_toolkit.exploratory_analyses.feature_importance import evaluate_feature_importance
 
 feature_names = [
@@ -42,43 +46,55 @@ def test_empty_data():
     empty_data = np.array([])
     empty_labels = np.array([])
     with pytest.raises(InvalidDatasetException):
-        _, _ = evaluate_feature_importance(
-            model=classifier, x_test=empty_data, y_test=labels, feature_names=feature_names
-        )
+        _, _ = evaluate_feature_importance(model=classifier, X=empty_data, y=labels, feature_names=feature_names)
 
     with pytest.raises(InvalidDatasetException):
-        _, _ = evaluate_feature_importance(
-            model=classifier, x_test=data, y_test=empty_labels, feature_names=feature_names
-        )
+        _, _ = evaluate_feature_importance(model=classifier, X=data, y=empty_labels, feature_names=feature_names)
 
 
 def test_invalid_n_repeats():
     """Test that invalid value for 'n_repeats' raises exception."""
     with pytest.raises(InvalidParameterValueException):
-        _, _ = evaluate_feature_importance(
-            model=classifier, x_test=data, y_test=labels, feature_names=feature_names, n_repeats=0
-        )
+        _, _ = evaluate_feature_importance(model=classifier, X=data, y=labels, feature_names=feature_names, n_repeats=0)
 
 
 def test_model_output():
     """Test that function output is as expected."""
     classifier.fit(data, labels.ravel())
     feature_importance, importance_results = evaluate_feature_importance(
-        model=classifier, x_test=data, y_test=labels, feature_names=feature_names, random_state=0
+        model=classifier, X=data, y=labels, feature_names=feature_names, n_repeats=50, random_state=0
     )
 
     np.testing.assert_almost_equal(
         feature_importance.loc[feature_importance["Feature"] == "EM_ratio", "Importance"].values[0],
-        desired=12.923077,
+        desired=0.129231,
         decimal=6,
     )
     np.testing.assert_almost_equal(
         feature_importance.loc[feature_importance["Feature"] == "EM_Qd", "Importance"].values[0],
-        desired=4.461538,
+        desired=0.044615,
         decimal=6,
     )
     np.testing.assert_equal(len(feature_importance), desired=len(feature_names))
     np.testing.assert_equal(
         tuple(importance_results.keys()),
         desired=("importances_mean", "importances_std", "importances"),
     )
+
+
+def test_invalid_input_lengths():
+    """Test that non matcing X and y lengths raises an exception."""
+    labels = np.random.randint(2, size=12)
+    with pytest.raises(NonMatchingParameterLengthsException):
+        _, _ = evaluate_feature_importance(model=classifier, X=data, y=labels, feature_names=feature_names)
+
+
+def test_invalid_number_of_feature_names():
+    """Test that invalid number of feature names raises an exception."""
+    with pytest.raises(InvalidParameterValueException):
+        _, _ = evaluate_feature_importance(
+            model=classifier,
+            X=data,
+            y=labels,
+            feature_names=["a", "b", "c"],
+        )