moved SpaceComposer out of the hyperopt folder

cdplib/hyperopt/SpaceComposer.py

@@ -1,47 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Sep 30 13:54:04 2020
-
-@author: tanya
-@description: a function that from a given list of pipeline steps
- composes a space to be passed in the HyperoptPipelineSelection class.
- A classic list of steps would be: [encoders, transformers, selectors, models]
-"""
-from sklearn.pipeline import Pipeline
-from hyperopt import hp
-from itertools import product
-
-
-def space_composer(step_list: list) -> hp.choice:
-    """
-    :param step_list: list of pipeline steps
-     of the form [encoders, transformers, selectors, models]
-     each element of step_list is a list of dictionaries
-     of the form {"name": NAME, "object": OBJECT, "params": PARAMS}
-    :return: hp.choice object of pipelines to choose from
-     when passed to the HyperoptPipelineSelection class
-    """
-
-    pipelines = []
-
-    step_combinations = product(*[step for step in
-                                  step_list if len(step) > 0])
-
-    for step_combination in step_combinations:
-
-        pipeline_dist = {}
-
-        pipeline_dist["name"] = "_".join([step["name"]
-                                          for step in step_combination])
-        pipeline_dist["pipeline"] = Pipeline([(step["name"], step["object"])
-                                              for step in step_combination]),
-
-        pipeline_dist["params"] = {step["name"] + "__" + param_name: param_dist
-                                   for step in step_combination
-                                   for param_name, param_dist
-                                   in step["params"].items()}
-
-        pipelines.append(pipeline_dist)
-
-    return hp.choice("pipelines", pipelines)

cdplib/space_composer/SpaceComposer.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Sep 30 13:54:04 2020
+
+@author: tanya
+@description: a class that from a given list of pipeline steps
+ composes a space to be passed in the GridsearchPipelineSelector
+ or HyperoptPipelineSelector classes.
+ A classic list of steps would be: [encoders, transformers, selectors, models]
+"""
+from sklearn.pipeline import Pipeline
+from hyperopt import hp
+from itertools import product
+
+
+class SpaceComposer:
+    """
+    A class that from a given list of pipeline steps
+    composes a space to be passed to GridsearchPipelineSelector
+    or HyperoptPipelineSelector.
+    """
+    def compose_gridsearch_space(self, step_list: list) -> list:
+        """
+        Composes a hyperparameter space for input to the
+        GridsearchPipelineSelector class.
+
+        :param step_list: a classic list of steps would be
+        [encoders, transformers, selectors, models],
+        where, for example, selectors is a list
+        of sklearn feature selectors, each selector given as a dict:
+        for example {"name": "kbest",
+                     "object": SelectPercentile(),
+                     "params": {
+                             "percentile":
+                                 [5, 10, 20],
+                             "score_func":
+                                 [f_classif, chi2, mutual_info_classif]}}
+
+        :return: a list of dictionaries of form
+            {"name": NAME, "pipeline": PIPELINE, "params": PARAMS}
+        """
+        space = []
+
+        step_combinations = product(*[step for step in
+                                      step_list if len(step) > 0])
+
+        for step_combination in step_combinations:
+
+            space_element = {}
+
+            space_element["name"] = "_".join([step["name"]
+                                              for step in step_combination])
+
+            space_element["pipeline"] = Pipeline(
+                    [(step["name"], step["object"])
+                     for step in step_combination])
+
+            space_element["params"] =\
+                {step["name"] + "__" + param_name: param_dist
+                 for step in step_combination
+                 for param_name, param_dist
+                 in step["params"].items()}
+
+            space.append(space_element)
+
+        return space
+
+    def compose_hyperopt_space(self, step_list: list) -> hp.choice:
+        """
+        Composes a hyperopt space from a list of steps.
+        A classic list of steps would be
+        [encoders, transformers, selectors, models],
+        where, for example, selectors is a list
+        of sklearn feature selectors, each selector given as a dict:
+        for example {"name": "kbest",
+                     "object": SelectPercentile(),
+                     "params": {
+                             "percentile":
+                                 3 + hp.randint("kbest__percentile", 200),
+                             "score_func":
+                                 hp.choice("kbest__score_func",
+                                    [f_classif, chi2, mutual_info_classif])}}
+        """
+        return hp.choise("pipelines", self.compose_gridsearch_space(step_list))