Pretty-printing for the case where a hyperparameter to a higher-order…

… operator is a plain sklearn operator that has not been wrapped. (#330)

lale/operators.py

@@ -151,6 +151,30 @@ def visualize(self, ipython_display:bool=True):
 
     def pretty_print(self, show_imports:bool=True, combinators:bool=True, ipython_display:Union[bool,str]=False):
         """Returns the Python source code representation of the operator.
+
+        Parameters
+        ----------
+        show_imports : bool, default True
+
+            Whether to include import statements in the pretty-printed code.
+
+        combinators : bool, default True
+
+            If True, pretty-print with combinators (`>>`, `|`, `&`). Otherwise, pretty-print with functions (`make_pipeline`, `make_choice`, `make_union`) instead.
+
+        ipython_display : union type, default False
+
+            - False
+
+              Return the pretty-printed code as a plain old Python string.
+
+            - True:
+
+              Pretty-print in notebook cell output with syntax highlighting.
+
+            - 'input'
+
+              Create a new notebook cell with pretty-printed code as input.
         """
         result = lale.pretty_print.to_string(self, show_imports, combinators, call_depth=2)
         if ipython_display == False:

lale/pretty_print.py

@@ -90,6 +90,14 @@ def value_to_string(value):
                 else:
                     gen.imports.append(f'import {value.__module__} as {module}')
             return f'{module}.{value.__name__}'
+        elif hasattr(value, 'get_params'):
+            module = value.__module__
+            name = value.__class__.__name__
+            if gen is not None:
+                gen.imports.append(f'import {module}')
+            printed = pprint.pformat(value, width=10000, compact=True)
+            compacted = printed.replace('\n', ' ')
+            return f'{module}.{compacted}'
         else:
             return pprint.pformat(value, width=10000, compact=True)
     strings = [f'{k}={value_to_string(v)}' for k, v in hps.items()]

test/test_json_pretty_viz.py

@@ -332,7 +332,7 @@ def test_autoai_libs_numpy_replace_missing_values(self):
 pipeline = numpy_replace_missing_values >> LR()"""
         self._roundtrip(expected, lale.pretty_print.to_string(pipeline))
 
-    def test_autoai_libs_tam(self):
+    def test_autoai_libs_tam_1(self):
         from autoai_libs.cognito.transforms.transform_utils import TAM
         import autoai_libs.cognito.transforms.transform_extras
         import numpy as np
@@ -351,6 +351,29 @@ def test_autoai_libs_tam(self):
 pipeline = tam >> LR()"""
         self._roundtrip(expected, lale.pretty_print.to_string(pipeline))
 
+    def test_autoai_libs_tam_2(self):
+        from lale.lib.autoai_libs import TAM
+        import numpy as np
+        from lightgbm import LGBMClassifier
+        from sklearn.decomposition import PCA
+        from lale.operators import make_pipeline
+        pca = PCA(copy=False)
+        tam = TAM(tans_class=pca, name='pca', col_names=['a', 'b', 'c'], col_dtypes=[np.dtype('float32'), np.dtype('float32'), np.dtype('float32')])
+        lgbm_classifier = LGBMClassifier(class_weight='balanced', learning_rate=0.18)
+        pipeline = make_pipeline(tam, lgbm_classifier)
+        import lale.helpers
+        expected = \
+"""from lale.lib.autoai_libs import TAM
+import sklearn.decomposition.pca
+import numpy as np
+from lightgbm import LGBMClassifier
+from lale.operators import make_pipeline
+
+tam = TAM(tans_class=sklearn.decomposition.pca.PCA(copy=False, iterated_power='auto', n_components=None, random_state=None,   svd_solver='auto', tol=0.0, whiten=False), name='pca', col_names=['a', 'b', 'c'], col_dtypes=[np.dtype('float32'), np.dtype('float32'), np.dtype('float32')])
+lgbm_classifier = LGBMClassifier(class_weight='balanced', learning_rate=0.18)
+pipeline = make_pipeline(tam, lgbm_classifier)"""
+        self._roundtrip(expected, lale.pretty_print.to_string(pipeline, combinators=False))
+
     def test_autoai_libs_ta1(self):
         from autoai_libs.cognito.transforms.transform_utils import TA1
         import numpy as np