def train(self, features_file_path, save_dir_path):
train_features, train_labels, test_features, test_labels = data_loading.load_numerical_data(features_file_path, normalise=False)
MultiLabelBinarizer.set_params(range(0, 16))
mlb = MultiLabelBinarizer()
train_labels = np.array(train_labels)
# Used to create a baseline for random chance
# np.random.shuffle(train_labels)
train_labels = mlb.fit_transform(np.array(train_labels))
test_labels = mlb.fit_transform(np.array(test_labels))
# Reserve the first 4 tracks in test set for displaying predictions to dev
predict_features = test_features[:4]
predict_labels = test_labels[:4]
test_features = test_features[4:]
test_labels = test_labels[4:]
print('Training RF...')
model = sklearn.ensemble.RandomForestClassifier(verbose=1)
model.fit(train_features, train_labels)
print('Evaluating RF...')
print('Accuracy: ' + str(model.score(test_features, test_labels)))
print()
print('Model Predictions:')
print(np.array([[int(s) for s in x] for x in model.predict(predict_features)]))
print()
print('Correct Labels:')
print(predict_labels)
pickle.dump(model, open(os.path.join(save_dir_path, "model.pickle"), 'wb'))
class DataProcess(object): # 特征处理
def __init__(self, process_type):
self.process_type = process_type
if self.process_type == "Binary": # 二值化处理
self.processmodule = Binarizer(copy=True, threshold=0.0)
# 大于 threshold 的映射为1， 小于 threshold 的映射为0
elif self.process_type == "MinMax": # 归一化处理
self.processmodule = MinMaxScaler(feature_range=(0, 1), copy=True)
elif self.process_type == "Stand": # 标准化处理
self.processmodule = StandardScaler(copy=True, with_mean=True, with_std=True)
elif self.process_type == "Normal": # 正则化处理
self.processmodule = Normalizer(copy=True, norm="l2") # 可选择l1, max ,l2三种
elif self.process_type == "MultiLabelBinar": # 多标签二值化处理
self.processmodule = MultiLabelBinarizer(sparse_output=False) # 使用其他CRS格式使用True
else:
raise ValueError("please select a correct process_type")
def fit_transform(self, data):
return self.processmodule.fit_transform(data)
def fit(self, data):
self.processmodule.fit(data)
def transform(self, data):
self.processmodule.transform(data)
def set_params(self, params):
self.processmodule.set_params(**params)
def get_params(self):
return self.processmodule.get_params(deep=True)
def get_classes(self):
assert self.process_type in {"MultiLabelBinar"}
return self.processmodule.classes_ # 输出相关的classs有哪些不同的值
def invser_transform(self, data):
assert self.process_type in {"MultiLabelBinar", "MinMax", "Stand"}
return self.processmodule.inverse_transform(data)
def get_max(self): # 获取数组中所多有维度上的最大值与最小值
assert self.process_type in {"MinMax", "Stand"}
return self.processmodule.data_max_
def get_min(self):
assert self.process_type in {"MinMax", "Stand"}
return self.processmodule.data_min_
def partial_fit(self):
# 使用最后的一个缩放函数来在线计算最大值与最小值
assert self.process_type in {"MinMax", "Stand"}
return self.processmodule.partial_fit()
def classify(self, features):
model = pickle.load(open("classification\\numerical\\random_forest\\model\\model.pickle", 'rb'))
result = model.predict(features)
# todo put this functionality into the common classifier template
MultiLabelBinarizer.set_params(range(0, 16))
mlb = MultiLabelBinarizer()
mlb.fit([range(0, 16)])
genre_predictions_categorized = mlb.inverse_transform(result)
if len(genre_predictions_categorized) == 0 or not all(genre_predictions_categorized):
return ["Unclassifiable"]
genre_predictions_categorized = [x[0] for x in mlb.inverse_transform(result)] # this needs checkinf for which value o fthe tuple is the actual value
genre_predictions = []
lm = LabelManipulator()
for label in genre_predictions_categorized:
genre_predictions.append(lm.uncategorise_genre(label))
# convert the ids to names
return genre_predictions
return np.array(new_features), np.array(new_labels)
# Test accuracy with only best populated genres -> Experimental (38k), Electronic (34k), Rock (33k)
def cut_all_but_3_genres(train_features, train_labels, test_features, test_labels):
train_features, train_labels = cut_genres_from_list(train_features, train_labels)
test_features, test_labels = cut_genres_from_list(test_features, test_labels)
return train_features, train_labels, test_features, test_labels
train_features, train_labels, test_features, test_labels = data_loading.load_numerical_data(normalise=False)
# train_features, train_labels, test_features, test_labels = cut_all_but_3_genres(train_features, train_labels, test_features, test_labels)
MultiLabelBinarizer.set_params(range(0, 16))
mlb = MultiLabelBinarizer()
train_labels = np.array(train_labels)
np.random.shuffle(train_labels)
train_labels = mlb.fit_transform(np.array(train_labels))
test_labels = mlb.fit_transform(np.array(test_labels))
predict_features = test_features[:4]
predict_labels = test_labels[:4]
test_features = test_features[4:]
test_labels = test_labels[4:]
print('Training RF...')
model = RandomForestClassifier(verbose=1)
model.fit(train_features, train_labels)
