playing with gridsearchCV and base estimator

2022-03-17 18:19:43 +00:00 · 2022-03-17 18:19:43 +00:00 · 5138036d8b
commit 5138036d8b
parent 458a933d73
2 changed files with 218 additions and 0 deletions
--- a/hyperparams.py
+++ b/hyperparams.py
@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar 16 16:55:06 2022
+
+@author: tanu
+"""
+# https://stackoverflow.com/questions/57248072/gridsearchcv-gives-different-result
+
+mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
+
+scoring_refit = {'scoring': 'recall'
+                 ,'refit': 'recall'}
+
+scoring_refit = {'scoring': mcc_score_fn
+                 ,'refit': 'mcc'}
+#n_jobs = 10 # my desktop has 12 cores
+njobs = {'n_jobs': 10}
+skf_cv = StratifiedKFold(n_splits=10,shuffle  = True)
+#cv = {'cv': 10}
+
+
+gs_dt = GridSearchCV(estimator=DecisionTreeClassifier(**rs
+                                                      #,class_weight = {1:10, 0:1}
+                                                      ),
+                  param_grid=[{'max_depth': [ 2,  4,  6, 8, 10]
+                               , 'criterion':['gini','entropy']
+                               , "max_features":["auto", None]
+                               , "max_leaf_nodes":[10,20,30,40]}]
+                  #, **cv
+                  , cv = skf_cv
+                  , **scoring_refit)
+                  #, scoring = mcc_score_fn
+                  #, refit = 'mcc')
+
+#gs_dt.fit(X_train, y_train)
+#gs_dt_fit = gs_dt.fit(X_train, y_train)
+gs_dt.fit(num_df_wtgt[numerical_FN]
+          , num_df_wtgt['mutation_class'])
+
+gs_dt_fit = gs_dt.fit(num_df_wtgt[numerical_FN]
+          , num_df_wtgt['mutation_class'])
+
+gs_dt_fit_res  = gs_dt_fit.cv_results_
+
+print('Best model:\n', gs_dt.best_params_)
+print('Best models score:\n', gs_dt.best_score_)
+print('Check best models score:\n', mean(gs_dt_fit_res['mean_test_mcc']))
+
+#%% Check the scores:
+#  https://stackoverflow.com/questions/44947574/what-is-the-meaning-of-mean-test-score-in-cv-result
+print([(len(train), len(test)) for train, test in skf_cv.split(num_df_wtgt[numerical_FN]
+                                                               , num_df_wtgt['mutation_class'])])
+gs_dt_fit.cv_results_
+#%%
+gs_rf = GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1, oob_score = True
+                                                      ,class_weight = {1: 10/11, 0: 1/11})
+                     , param_grid=[{'max_depth': [4, 6, 8, 10, 12, 16, 20, None]
+                                 , 'max_features': ['auto', 'sqrt']
+                                 , 'min_samples_leaf': [2, 4, 8]
+                                 , 'min_samples_split': [10, 20]}]
+                    , **cv
+                    , **njobs
+                    #, **scoring_refit
+                    , scoring = scoring_fn
+                    #, refit  = T
+                    )
+gs_rf.fit(X_train, y_train)
+#gs_rf_fit = gs_rf.fit(X_train, y_train)
+#res_rf = res_dt_fit.cv_results_
+
+print('Best model:\n', gs_rf.best_params_)
+print('Best models score:\n', gs_rf.best_score_)
+print('Check best models score:\n', mean(res_rf['mean_test_score']))
+
+#%%
+gs_lr = GridSearchCV(estimator=LogisticRegression(multi_class='ovr',random_state=42,class_weight={1:10, 0:1}),
+              param_grid=[{'C': [0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1 ,1],
+                          'penalty':['l1','l2']}],
+              scoring=scoring,
+              cv=10,
+              refit='recall')
+
+gs_gb = GridSearchCV(estimator=XGBClassifier(n_jobs=-1),
+                    param_grid=[{'learning_rate': [0.01, 0.05, 0.1, 0.2],
+                                 'max_depth': [4, 6, 8, 10, 12, 16, 20],
+                                 'min_samples_leaf': [4, 8, 12, 16, 20], 
+                                 'max_features': ['auto', 'sqrt']}],
+                    scoring=scoring,
+                    cv=10,
+                    n_jobs=4,
+                    refit='recall')
+#%%
+
+
+
+#%%
+gs_mlp = MLPClassifier(max_iter=600)
+clf = GridSearchCV(gs_mlp, parameter_space, n_jobs= 10, cv = 10)
+
+#clf.fit(X_train, y_train.values.ravel())
+
+parameter_space = {
+    'hidden_layer_sizes': [(1), (2), (3)],
+}
+
+print('Best parameters found:\n', clf.best_params_)
+
+means = clf.cv_results_['mean_test_score']
+stds = clf.cv_results_['std_test_score']
+for mean, std, params in zip(means, stds, clf.cv_results_['params']):
+    print("%0.3f (+/-%0.03f) for %r" % (mean, std * 2, params))