made space composer a class

cdplib/hyperopt/composed_space_sample.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jul  6 14:02:24 2020
+
+@author: tanya
+@description: space object to pass to HyperoptPipelineSelection class
+"""
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.feature_selection import SelectFromModel, SelectPercentile,\
+    RFE, SelectFpr, f_classif, chi2, mutual_info_classif
+from xgboost import XGBRFClassifier
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.decomposition import PCA
+from hyperopt import hp
+
+from cdplib.hyperopt.SpaceComposer import SpaceComposer
+
+
+encoders = []
+
+transformers = []
+
+selectors = [
+    {"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])}},
+
+#    {"name": "fpr",
+#     "object": SelectFpr(),
+#     "params": {
+#        "score_func": hp.choice("fpr__score_func",
+#                                [f_classif, chi2, mutual_info_classif]),
+#        "alpha": hp.uniform("fpr__alpha", 0.1, 0.6)}},
+
+#    {"name": "rfe_rf",
+#     "object":
+#         RFE(estimator=RandomForestClassifier(n_jobs=-1, random_state=33)),
+#     "params": {
+#         "n_features_to_select":
+#             3 + hp.randint("rfe_rf__n_features_to_select", 200),
+#         "estimator__n_estimators":
+#             20 + hp.randint("rfe_rf__estimator__n_estimators", 70)}},
+
+#    {"name": "rfm_rf",
+#     "object":
+#         SelectFromModel(estimator=RandomForestClassifier(n_jobs=-1,
+#                                                          random_state=33)),
+#     "params": {
+#         "estimator__n_estimators":
+#             20 + hp.randint("rfm_rf__estimator__n_estimators", 70)}},
+
+#    {"name": "rfm_lr",
+#     "object":
+#         SelectFromModel(estimator=LogisticRegression(n_jobs=-1,
+#                                                      random_state=33)),
+#     "params": {
+#          "estimator__C": hp.uniform("rfm_lr__estimator__C", 0.1, 1000)}},
+
+#    {"name": "pca",
+#     "object": PCA(random_state=33),
+#     "params": {
+#       "n_components": 3 + hp.randint("pca__n_components", 20)
+#       }}
+    ]
+
+models = [
+        {"name": "xgb",
+         "object": XGBRFClassifier(n_jobs=-1, eval_metric="map", seed=33),
+         "params": {
+           "n_estimators": 50 + hp.randint('xgb__n_estimators', 100),
+           "max_depth": 3 + hp.randint("xgb__max_depth", 10),
+           "learning_rate": hp.loguniform("xgb__learning_rate", 0.01, 0.5)
+           }},
+
+#        {"name": "rf",
+#         "object": RandomForestClassifier(n_jobs=-1, random_state=33),
+#         "params": {
+#           "n_estimators": 50 + hp.randint('rf__n_estimators', 500),
+#           "max_depth": 3 + hp.randint("rf__max_depth", 10),
+#           "min_samples_leaf": 1 + hp.randint("rf__min_samples_leaf", 10)
+#           }},
+
+#        {"name": "lr",
+#         "object": LogisticRegression(n_jobs=-1, random_state=33),
+#         "params":  {
+#           "penalty": hp.choice("lr__penalty", ["l1", "l2"]),
+#           "C": hp.uniform("lr__C", 0.1, 1000)}},
+
+#        {"name": "svc",
+#         "object": SVC(random_state=33),
+#         "params": {
+#            "kernel": hp.choice("svc__kernel", ["linear", "poly", "rbf"]),
+#            "degree": 2 + hp.randint("svc__degree", 3),
+#            "C": hp.uniform("svc__C", 0.1, 1000)
+#            }}
+        ]
+
+step_list = [encoders, transformers, selectors, models]
+
+space = SpaceComposer().compose_hyperopt_space(step_list)