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)
