Update main.py

Author: Programmer-RD-AI

main.py

@@ -1,10 +1,19 @@
-from sklearn.decomposition import PCA, IncrementalPCA
 from DimRed import *
-if __name__ == "__main__":
-    param_grids = [{"n_components": [1, 2, 3],
-                    "random_state": [1, 42, 69, 100]}, {"n_components": [1, 2, 3]}]
+
+
+def config():
+    param_grids = [{"n_components": [2],
+                    "random_state": [42]}, {"n_components": [2]}]
     standard_pipeline = Pipeline([("StandardScalar", StandardScaler())])
     reduction_methods = [PCA, IncrementalPCA]
+    return param_grids, standard_pipeline, reduction_methods
+
+
+if __name__ == "__main__":
+    X_train, X_test, y_train, y_test = load_dataset()
+    param_grids, standard_pipeline, reduction_methods = config()
     all_possible_variations = Variations(param_grids=param_grids,
-                                         reduction_methods=reduction_methods, standard_pipeline=standard_pipeline).produce_variations()
-    print(all_possible_variations)
+                                         reduction_methods=reduction_methods, standard_pipeline=standard_pipeline, analysis_instance=Analysis(X_train, y_train)).produce_variations()
+    all_pipeline_performance, best_performances = Evaluation(_data={"X_train": X_train, "X_test": X_test, "y_train": y_train,
+                                                                    "y_test": y_test}, all_possible_variations=all_possible_variations, labels=np.unique(y_train)).evaluate()
+    pprint(best_performances)