params = {'kernel': 'linear', 'gamma': 0.001} params_SVC = dict(params) param_grid_ = [{ 'n_components': [10, 30, 50, 100], 'n_iter': [8, 16], 'C': [1, 10, 100, 1000] }, { 'apply_svd': [False], 'C': [1, 10, 100, 1000] }] feature = [["mfcc", "delta", "power"]] results = [] x = Runner.load_x_train(feature) y = Runner.load_y_train() model = ModelSVC("SVC", **params_SVC) search = GridSearchCV(model, cv=5, param_grid=param_grid_, return_train_score=True) search.fit(x, y) results.append((search, feature)) logger.info( f'{feature} - bestscore : {search.best_score_} - result :{search.best_params_}' ) for search, name in results: logger.info(f'{name} - bestscore : {search.best_score_}')
ax.set_ylabel('Accuracy', size=14) ax.tick_params(labelsize=14) plt.savefig(f'../model/tuning/{NAME}-NB.png', dpi=300) if __name__ == '__main__': base_params = {'alpha': 1.0, 'fit_prior': True, 'class_prior': None} params_NB = dict(base_params) param_grid_ = {'alpha': [0.001, 0.01, 0.1, 1, 10, 100]} features = ["bow", "n-gram", "tf-idf", "n-gram-tf-idf"] results = [] NAME = ":".join(features) for name in features: x = Runner.load_x_train(name) y = Runner.load_y_train() model = ModelMultinomialNB(name, **dict(params_NB)) search = GridSearchCV(model, cv=6, param_grid=param_grid_, return_train_score=True, verbose=10, refit=True) search.fit(x, y) results.append((search, name)) logger.info( f'{name} - bestscore : {search.best_score_} - result :{search.cv_results_["mean_test_score"]}' ) res = pd.DataFrame.from_dict(
# Dataset1 mfccの主成分分析 import os, sys sys.path.append('../') import numpy as np import pandas as pd from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt import seaborn as sns from src.runner import Runner X = Runner.load_x_train(["mfcc"]) print(X.shape) #標準化 scaler = StandardScaler() scaler.fit(X) standard_X = scaler.transform(X) # 12次元 -> 6次元 へ落とす. dim = 6 params = { 'n_components': dim, 'random_state': 71, } # 主成分分析 clf = PCA(**params) clf.fit(standard_X) pca = clf.transform(standard_X)