Upload grid_search.py

grid_search.py

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+"""
+A script to grid search all parameters provided in parameters.py
+including both classifiers and regressors.
+Note that the execution of this script may take hours to search the 
+best possible model parameters for various algorithms, feel free
+to edit parameters.py on your need ( e.g remove some parameters for 
+faster search )
+"""
+
+import pickle
+
+from emotion_recognition import EmotionRecognizer
+from parameters import classification_grid_parameters, regression_grid_parameters
+
+# emotion classes you want to perform grid search on
+emotions = ['sad', 'neutral', 'happy']
+# number of parallel jobs during the grid search
+n_jobs = 4
+
+best_estimators = []
+
+for model, params in classification_grid_parameters.items():
+    if model.__class__.__name__ == "KNeighborsClassifier":
+        # in case of a K-Nearest neighbors algorithm
+        # set number of neighbors to the length of emotions
+        params['n_neighbors'] = [len(emotions)]
+    d = EmotionRecognizer(model, emotions=emotions)
+    d.load_data()
+    best_estimator, best_params, cv_best_score = d.grid_search(params=params, n_jobs=n_jobs)
+    best_estimators.append((best_estimator, best_params, cv_best_score))
+    print(f"{emotions} {best_estimator.__class__.__name__} achieved {cv_best_score:.3f} cross validation accuracy score!")
+
+print(f"[+] Pickling best classifiers for {emotions}...")
+pickle.dump(best_estimators, open(f"grid/best_classifiers.pickle", "wb"))
+
+best_estimators = []
+
+for model, params in regression_grid_parameters.items():
+    if model.__class__.__name__ == "KNeighborsRegressor":
+        # in case of a K-Nearest neighbors algorithm
+        # set number of neighbors to the length of emotions
+        params['n_neighbors'] = [len(emotions)]
+    d = EmotionRecognizer(model, emotions=emotions, classification=False)
+    d.load_data()
+    best_estimator, best_params, cv_best_score = d.grid_search(params=params, n_jobs=n_jobs)
+    best_estimators.append((best_estimator, best_params, cv_best_score))
+    print(f"{emotions} {best_estimator.__class__.__name__} achieved {cv_best_score:.3f} cross validation MAE score!")
+
+print(f"[+] Pickling best regressors for {emotions}...")
+pickle.dump(best_estimators, open(f"grid/best_regressors.pickle", "wb"))
+
+
+
+# Best for SVC: C=0.001, gamma=0.001, kernel='poly'
+# Best for AdaBoostClassifier: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 60}
+# Best for RandomForestClassifier: {'max_depth': 7, 'max_features': 0.5, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 40}
+# Best for GradientBoostingClassifier: {'learning_rate': 0.3, 'max_depth': 7, 'max_features': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 70, 'subsample': 0.7}
+# Best for DecisionTreeClassifier: {'criterion': 'entropy', 'max_depth': 7, 'max_features': None, 'min_samples_leaf': 1, 'min_samples_split': 2}
+# Best for KNeighborsClassifier: {'n_neighbors': 5, 'p': 1, 'weights': 'distance'}
+# Best for MLPClassifier: {'alpha': 0.005, 'batch_size': 256, 'hidden_layer_sizes': (300,), 'learning_rate': 'adaptive', 'max_iter': 500}