修改response
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zhaojinghao
parent
365c194e82
commit
2e64b83e0d
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@ -5,15 +5,19 @@ import numpy as np
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from sklearn.model_selection import train_test_split
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from logzero import logger
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import os
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current_path = os.path.dirname(__file__)
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def load_data():
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logger.info(f"读取本地数据")
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logger.info(current_path)
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train_data = pd.read_csv(f'{current_path}/data/train.csv')
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train_data.drop(train_data[(train_data["GrLivArea"]>4000)&(train_data["SalePrice"]<300000)].index,inplace=True)#pandas 里面的条件索引
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train_data.drop(train_data[(train_data["GrLivArea"] > 4000) & (train_data["SalePrice"] < 300000)].index,
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inplace=True) # pandas 里面的条件索引
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return train_data
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def load_model():
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logger.info(f"读取本地模型")
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model = xgb.XGBModel()
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@ -43,7 +47,8 @@ def preprocessing(local_train: pd.DataFrame, new_data: pd.DataFrame):
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all_data.loc[na_index, 'GarageYrBlt'] = None
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all_data.GarageYrBlt.fillna(all_data.YearBuilt, inplace=True)
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cols1 = ["GarageQual", "GarageCond", "GarageFinish", "GarageType", "BsmtExposure", "BsmtCond", "BsmtQual", "BsmtFinType2", "BsmtFinType1", "MasVnrType"]
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cols1 = ["GarageQual", "GarageCond", "GarageFinish", "GarageType", "BsmtExposure", "BsmtCond", "BsmtQual",
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"BsmtFinType2", "BsmtFinType1", "MasVnrType"]
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for col in cols1:
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all_data[col].fillna("None", inplace=True)
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@ -53,11 +58,13 @@ def preprocessing(local_train: pd.DataFrame, new_data: pd.DataFrame):
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all_data[col].fillna(0, inplace=True)
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all_data["LotFrontage"].fillna(np.mean(all_data["LotFrontage"]), inplace=True)
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cols3 = ["MSZoning", "BsmtFullBath", "BsmtHalfBath", "Utilities", "Functional", "Electrical", "KitchenQual", "SaleType","Exterior1st", "Exterior2nd"]
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cols3 = ["MSZoning", "BsmtFullBath", "BsmtHalfBath", "Utilities", "Functional", "Electrical", "KitchenQual",
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"SaleType", "Exterior1st", "Exterior2nd"]
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for col in cols3:
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all_data[col].fillna(all_data[col].mode()[0], inplace=True)
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numeric_cols = [x for x in all_data.select_dtypes(exclude=['object']).columns.tolist() if x != 'Id' and x != 'SalePrice']
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numeric_cols = [x for x in all_data.select_dtypes(exclude=['object']).columns.tolist() if
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x != 'Id' and x != 'SalePrice']
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object_cols = [x for x in all_data.select_dtypes(include=['object']).columns.tolist()]
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for col in numeric_cols:
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@ -67,6 +74,7 @@ def preprocessing(local_train: pd.DataFrame, new_data: pd.DataFrame):
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dataset = pd.get_dummies(all_data, columns=object_cols)
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return dataset
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def build_dataset(dataset):
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dataset.SalePrice = np.log1p(dataset.SalePrice)
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train = dataset[~dataset.SalePrice.isna()].copy()
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@ -84,7 +92,8 @@ def build_dataset(dataset):
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def build_model(dtrain, dvalid, watchlist, num_iter=5000, early_stop=200, **params):
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logger.info('开始本地建模')
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model = xgb.train(params, dtrain, evals=watchlist, num_boost_round=num_iter, early_stopping_rounds=early_stop, verbose_eval=True)
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model = xgb.train(params, dtrain, evals=watchlist, num_boost_round=num_iter, early_stopping_rounds=early_stop,
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verbose_eval=True)
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return model
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@ -1,4 +1,5 @@
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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import tensorflow as tf
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import numpy as np
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@ -12,6 +13,7 @@ loss = []
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jiance = []
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current_path = os.path.dirname(__file__)
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def readData(filePath):
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data = pd.read_csv(filePath, header=0)
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x = data.iloc[:len(data), 5:12].values
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@ -19,11 +21,13 @@ def readData(filePath):
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return x, y
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# 标准化函数
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def Z_ScoreNormalization(x, mean, sigma):
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x = (x - mean) / sigma
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return x
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# 特征标准化
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def featureScore(x):
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for i in range(7):
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@ -34,6 +38,7 @@ def featureScore(x):
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return x
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# 划分数据集
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def dataDivision(x, y, train_scale, val_scale):
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train_volumn = int(len(x) * train_scale)
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@ -52,6 +57,7 @@ def dataDivision(x, y, train_scale, val_scale):
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return x_train, x_val, x_test, y_train, y_val, y_test
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# 创建模型
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def createModel(neure, activation, learning_rate, loss):
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model = tf.keras.models.Sequential([
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@ -63,21 +69,25 @@ def createModel(neure, activation, learning_rate, loss):
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return model
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# 训练模型
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def trainModel(model, x_train, y_train, x_val, y_val, epochs):
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history = LossHistory()
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model.fit(x_train, y_train, batch_size=32, epochs=epochs, validation_data=(x_val, y_val), validation_freq=1, callbacks=[history])
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model.fit(x_train, y_train, batch_size=32, epochs=epochs, validation_data=(x_val, y_val), validation_freq=1,
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callbacks=[history])
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model.summary()
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return model
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# 模型预测
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def predictModel(x_test, model):
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predicted_data = model.predict(x_test)
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return predicted_data
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# 训练
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def train(csv_file, neure, activation, learning_rate, loss, epochs):
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x, y = readData(csv_file)
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@ -98,21 +108,25 @@ def train(csv_file, neure, activation, learning_rate, loss, epochs):
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# model.save(os.path.abspath("./appweb/self_model/ocean_wave_mlp.h5"))
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return model, mse, mae, r
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def drawLoss(logs):
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loss.append(logs['loss'])
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print(loss[-1])
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def load_model():
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logger.info(f"{current_path}/pretrain_models/ocean_wave_mlp.h5")
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return keras_load_model(f"{current_path}/pretrain_models/ocean_wave_mlp.h5")
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class LossHistory(tf.keras.callbacks.Callback):
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def on_epoch_end(self, epoch, logs={}):
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# print(logs['loss'])
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drawLoss(logs)
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# return logs['loss'], logs['mae']
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def predict_wave_height(csv_file, num_units:int, activation:str, lr:float, loss:str, epochs:int, x_test:list):
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def predict_wave_height(csv_file, num_units: int, activation: str, lr: float, loss: str, epochs: int, x_test: [np.ndarray, list]):
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"""_summary_
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Args:
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@ -4,6 +4,7 @@ import datetime as dt
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from get_holiday_cn.client import getHoliday
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from logzero import logger
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def run_prophet(data: pd.DataFrame, period: int = 1, freq: str = 'D'):
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"""_summary_
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65
run.py
65
run.py
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@ -9,10 +9,12 @@ from house_price.house_price_predcition import run_boston_price
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from ocean_wave.wave_height_mlp import predict_wave_height
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from prophet_predict.prophet_predict import run_prophet
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TEXT = "text"
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app = Flask(__name__)
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@app.route('/house_price', methods=["POST"])
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def predict_price():
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resp_info = dict()
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if request.method == 'POST':
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eta = request.form.get('eta', 0.05)
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max_depth = request.form.get('max_depth', 10)
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@ -22,7 +24,6 @@ def predict_price():
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early_stopping_rounds = int(request.form.get('early_stopping_rounds', 200))
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train_data = request.files.get('train_data', None)
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test_data = request.files.get('test_data', None)
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resp = make_response()
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logger.info(train_data)
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params = {
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"eta": float(eta),
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@ -34,9 +35,10 @@ def predict_price():
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train_data = None
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else:
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train_data = pd.read_csv(train_data)
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if test_data is None:
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resp.status_code = '406'
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resp.response = json.dumps({"text": "test data is None"})
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if test_data is None or pd.read_csv(test_data).shape[0] == 0:
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resp_info["msg"] = "测试数据为空"
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resp_info["code"] = 406
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else:
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test_data = pd.read_csv(test_data)
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try:
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if train_data is None:
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@ -45,19 +47,20 @@ def predict_price():
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rst = run_boston_price(test_data, train_data, num_boost_round, early_stopping_rounds, **params)
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except Exception as e:
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logger.error(f"Error: {e}")
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resp.status_code = '406'
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resp.response = json.dumps({'text': str(e)})
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return resp
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resp.status_code=200
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resp.response = json.dumps({"Id": rst["Id"].values.tolist(), "price": rst["SalePrice"].values.tolist()})
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return resp
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resp_info["msg"] = str(e)
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resp_info["code"] = 406
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else:
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resp.status_code=405
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resp_info["code"] = 200
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resp_info["data"] = rst.to_csv()
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resp_info["dtype"] = "csv"
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resp = make_response(json.dumps(resp_info))
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resp.status_code = 200
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return resp
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@app.route('/ocean_wave_height', methods=["POST"])
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def predict_height():
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resp_info = dict()
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if request.method == 'POST':
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num_units = int(request.form.get('num_units', 8))
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activation = request.form.get('activation', 'relu')
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@ -75,7 +78,6 @@ def predict_height():
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x_test = [WVHT_1, WDIR_1, WSPD_1, WDIR_2, WSPD_2, WDIR, WSPD]
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x_test = np.array([x_test])
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logger.info(f"test data: {x_test}")
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resp = make_response()
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if not train_data:
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train_data = None
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else:
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@ -83,27 +85,27 @@ def predict_height():
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train_data = pd.read_csv(train_data)
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except Exception as e:
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logger.error(f"Error: {e}")
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resp.status_code = '406'
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resp.response = json.dumps({'text': str(e)})
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return resp
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resp_info["msg"] = str(e)
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resp_info["code"] = 406
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train_data = None
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try:
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rst = predict_wave_height(train_data, num_units, activation, lr, loss, epochs, x_test)
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except Exception as e:
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logger.error(f"Error: {e}")
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resp.status_code = '406'
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resp.response = json.dumps({'text': "上传数据不符合海浪高度预测的规定文件示例，请检查"})
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return resp
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resp.status_code=200
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resp.response = json.dumps({"result": str(rst)})
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return resp
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resp_info["msg"] = "上传数据不符合海浪高度预测的规定文件示例，请检查"
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resp_info["code"] = 406
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else:
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resp.status_code=405
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resp_info["code"] = 200
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resp_info["data"] = rst
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resp_info["dtype"] = TEXT
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resp = make_response(json.dumps(resp_info))
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resp.status_code = 200
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return resp
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@app.route("/prophet/", methods=["POST"])
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def run_ts_predict():
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resp = make_response()
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resp_info = dict()
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if request.method == "POST":
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data_file = request.files.get("data")
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freq = request.form.get('freq')
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@ -117,20 +119,19 @@ def run_ts_predict():
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logger.info(rest.columns)
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rest['ds'] = rest['ds'].apply(str)
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rest['yhat'] = rest['yhat'].apply(str)
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resp.data = json.dumps({'ds':rest.ds.values.tolist(), 'yhat':rest.yhat.values.tolist()})
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resp.status_code = 200
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return resp
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except Exception as e:
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logger.error(f"Error: {e}")
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resp.status_code = '406'
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resp.response = json.dumps({'text': str(e)})
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return resp
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resp_info["msg"] = str(e)
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resp_info["code"] = 406
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else:
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resp.status_code=405
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resp_info["code"] = 200
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resp_info["data"] = rest.to_csv()
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resp_info["dtype"] = "csv"
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resp = make_response(json.dumps(resp_info))
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resp.status_code = 200
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return resp
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=8901, debug=True)
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