renamed hyperparams to gscv

earlier_versions/GSCV_base

@@ -0,0 +1,128 @@
+# Logistic regression: 
+    pnca
+    input: numerical features
+    output: dm/om: target
+    
+grid search/base estimator with a single model with hyperparamter choices: gives you the best model based on a SINGLE metric!
+    -- question: which is the metric to optimise for?
+base estimator with multipe models and multiple hyperparams: returns the OVERALL best model-hyperparam combo, based on a single score?
+    -- question: which is the metric to optimise for?
+    
+    
+# Demonstration
+
+###################
+# Metric1: accuracy
+###################
+
+Best model:
+ {'clf__max_iter': 100, 'clf__solver': 'liblinear'}
+ 
+Best models score:
+ 0.7145320197044336
+ 
+ 
+###################
+# Metric2: F1
+###################
+Best model:
+ {'clf__max_iter': 100, 'clf__solver': 'saga'}
+ Best models score:
+ 0.7550294183111348
+ 
+ 
+###################
+# Metric3: Recall
+###################
+Best model:
+ {'clf__max_iter': 100, 'clf__solver': 'saga'}
+Best models score:
+ 0.8216666666666667
+ 
+ 
+###################
+# Metric4: ROC_AUC
+###################
+
+Best model:
+ {'clf__max_iter': 200, 'clf__solver': 'sag'}
+Best models score:
+ 0.7711904761904762
+ 
+###################
+# Metric5: MCC
+###################
+
+Best model:
+ {'clf__max_iter': 100, 'clf__solver': 'saga'}
+ Best models score:
+ 0.4322970173039572
+ 
+ sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
+ConvergenceWarning,
+
+#####################################
+# Same thing but using: CLFSwitcher()
+
+ 
+###################
+# Metric1: Accuracy
+###################
+
+Best model:
+ {'clf__estimator': LogisticRegression(random_state=42, solver='liblinear')
+ , 'clf__estimator__max_iter': 100, 'clf__estimator__solver': 'liblinear'}
+Best models score:
+ 0.7219298245614035
+ 
+###################
+# Metric2: F1
+###################
+Best model:
+ {'clf__estimator': LogisticRegression(random_state=42, solver='liblinear'), 'clf__estimator__max_iter': 100, 'clf__estimator__solver': 'liblinear'}
+
+print('Best models score:\n', gscv.best_score_)
+Best models score:
+ 0.7585724070894442
+
+###################
+# Metric3: Recall
+###################
+Best model:
+ {'clf__estimator': LogisticRegression(random_state=42, solver='liblinear')
+ , 'clf__estimator__max_iter': 100, 'clf__estimator__solver': 'liblinear'}
+Best models score:
+ 0.8198610213316095
+ 
+###################
+# Metric4: ROC_AUC
+###################
+Best model:
+ {'clf__estimator': LogisticRegression(solver='newton-cg')
+ , 'clf__estimator__max_iter': 100, 'clf__estimator__solver': 'newton-cg'}
+
+Best models score:
+ nan
+ 
+###################
+# Metric5: MCC
+###################
+Best model:
+ {'clf__estimator': LogisticRegression(random_state=42, solver='liblinear')
+ , 'clf__estimator__max_iter': 100, 'clf__estimator__solver': 'liblin
+ 
+Best models score:
+ 0.4480248700902755
+ 
+ 
+ 
+ 
+ 
+ 
+ print('Best model:\n', gs_dt.best_params_)
+Best model:
+ {'criterion': 'entropy', 'max_depth': 2, 'max_features': None, 'max_leaf_nodes': 10}
+
+print('Best models score:\n', gs_dt.best_score_)
+Best models score:
+ 0.43290518915746007