import joblib
# from sklearn.neighbors import KNeighborsRegressor,KNeighborsClassifier
from sklearn.linear_model import LinearRegression
import os
os.chdir(r'E:\AI_big_model_learn\py_project\MLDL\model_creator')
print(os.getcwd())
# 1.准备数据
# 1.1 载入数据：pd.read_csv()
# 1.2 数据编码：文字 -> 数字 热独编码、标签编码
# 1.3 管制取值范围：0-100，0-10000 -> 0-1
X = [[80, 86], [82, 80], [85, 78], [90, 90], [86, 82], [82, 90], [78, 80], [92, 94]]
y = [84.2, 80.6, 80.1, 90, 83.2, 87.6, 79.4, 93.4]
# （搞笑镜头，拥抱镜头，打斗镜头） -> 电影类型：0-喜剧片 1-动作片 2-爱情片
# X = [[39, 0, 31], # 0 0
# [3, 2, 65], # 1 1
# [2, 3, 55], # 2 2
# [9, 38, 2], # 3 2
# [8, 34, 17], # 4 2
# [5, 2, 57], # 5 1
# [21, 17, 5], # 6 0
# [45, 2, 9]] # 7 0
# y = [0, 1, 2, 2, 2, 1, 0, 0]
# 2. 创建模型：model = KNNModel() / model = LinearRegression
# model = KNeighborsRegressor(n_neighbors=3)
# model = KNeighborsClassifier(n_neighbors=3)
model = LinearRegression()
# 3. 模型训练：model.fit(X,y)
model.fit(X,y)
print('model_LinearR:',model)
# 4. 模型预测：y_pred = model.predict(X_new)
y_pred = model.predict([[80,90],[90,80]])
print(y_pred)
# 5. 模型评估：score = evaluate(y_true,y_pred)
# 目测评估：还行
# 6. 模型保存
# joblib.dump(model,'linear_model.joblib')
# 1. 加载模型
model_loaded = joblib.load('model_creator/linear_model.joblib')
#
# # 2. 模型预测
y_pred_loaded = model_loaded.predict([[80,90],[90,80]])
print(y_pred_loaded)
