def fit_predict(model_name, model_param, trn_samples, tst_samples):
'''
利用训练样本训练模型并对测试样本进行预测
Args:
model_name (str): 模型名称
model_param (dict): 模型参数
trn_samples (Samples): 训练样本
tst_samples (Samples): 测试样本
'''
trn_Xs = trn_samples.get_Xs()
trn_ys = trn_samples.get_ys()
tst_Xs = tst_samples.get_Xs()
trn_ys[0] = 1 # TODO
clf = get_classifier(model_name, model_param)
clf.fit(trn_Xs, trn_ys)
tst_ys_pred = clf.predict(tst_Xs)
tst_ys_pred[0] = 1 # TODO
tst_ys_pred[1] = 1 # TODO
for i, sample in enumerate(tst_samples):
sample.set_y_pred(tst_ys_pred[i])
def get_x_importances(samples):
'''
计算特征重要性
Args:
samples (Samples): 训练样本
Returns:
dict: 特征重要性，格式为{特征1: 重要性1, 特征2: 重要性2, ...}
'''
model_name = 'OLP.core.models.RandomForestClassifier'
model_param = {'n_estimators': 100}
clf = get_classifier(model_name, model_param)
Xs = samples.get_Xs()
ys = samples.get_ys()
clf.fit(Xs, ys)
x_indexes = samples.get_x_indexes()
x_importances = {}
x_importances_ = clf.get_x_importances()
for x, index in x_indexes.iteritems():
x_importances[x] = x_importances_[index]
return x_importances