03.Placenta.4p_and_PBMC.py

#
# 这个加上了PBMC作为参数
# 这个加上了PBMC作为参数
# 这个加上了PBMC作为参数
# 这个加上了PBMC作为参数



import tensorflow as tf
import utils
# import functools
import numpy as np
from sklearn import preprocessing
import keras_tuner as kt
from  matplotlib import  pyplot as plt
import pandas as pd
from scipy.stats import spearmanr, pearsonr
import os 

os.chdir(r"E:\Documents\bio\10.nucleosome\01.scripts\09.train_scripts")


import random
seed=7


def scale_x(x):
    # 预处理
    # 先对非0特征取log
    # 然后进行归一化
    min_max_scaler = preprocessing.MinMaxScaler()
    standar_scaler = preprocessing.StandardScaler()
    x[x!=0] = np.log(x[x!=0])
    x = standar_scaler.fit_transform(x)
    x = min_max_scaler.fit_transform(x)
    return x

def load_data(path):
    raw_data = np.loadtxt(path, dtype=np.float32, delimiter=",", skiprows=1)
    raw_data[..., 264] = raw_data[..., 264]
    return (raw_data[..., :264]), (raw_data[..., 264: 268])


data_dir = "../01.train_data/4p/"
train_x, train_y = load_data(data_dir + "train.csv")
valid_x, valid_y = load_data(data_dir + "valid.csv")
test_x, test_y = load_data(data_dir + "test.csv")




def model_with_seed(seed):
    random.seed(seed)# 为python设置随机种子
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)# 为numpy设置随机种子
    tf.compat.v1.set_random_seed(seed)# tf cpu fix seed
    os.environ['TF_DETERMINISTIC_OPS'] = '1'  # tf gpu fix seed


    # 263 -> TPM



    model = tf.keras.Sequential()

    model.add(tf.keras.layers.Dense(32, activation="relu"))

    for _ in range(4):
        model.add(tf.keras.layers.BatchNormalization())
        model.add(tf.keras.layers.Dense(32, activation="relu"
            )
        )
        # model.add(tf.keras.layers.AlphaDropout(rate=0.5))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.Dense(3))

    model.compile(
        loss = "mse",
        optimizer = "adam"
    )


    stop_early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)

    y_pre_batch = []
    class logPredictPer100epoch(tf.keras.callbacks.Callback):
        def on_epoch_end(self, batch, logs=None):
            example_result = self.model.predict(test_x)
            y_pre_batch.append(tf.reshape(example_result,[-1]))


    history = model.fit(x=train_x,
              y=train_y,
              batch_size=512,
              validation_data=(valid_x, valid_y),
              epochs=40,
              callbacks=[stop_early, logPredictPer100epoch()]
              )
              
    loss = history.history['loss']
    val_loss = history.history['val_loss']

    example_batch = test_x
    example_result = model.predict(example_batch)
    
    return (model, history, loss, val_loss, example_batch, example_result, y_pre_batch)


seed = 588228




model, history, loss, val_loss, example_batch, example_result, y_pre_batch = model_with_seed(seed)






res_dir = "res/res_placenta_4p_and_PBMC/"
if not os.path.exists(res_dir):
    os.makedirs(res_dir)


np.savetxt(res_dir + "01.y_pre_history.csv", y_pre_batch, delimiter=",")
pd.DataFrame({"loss": loss, "val_loss": val_loss}).to_csv(res_dir + "02.loss_history.csv")
pd.DataFrame(example_result).to_csv(res_dir + "03.res.csv")
pd.DataFrame(test_y).to_csv(res_dir + "04.real_y.csv")



# plt.scatter(test_y,tf.reshape(example_result, [-1]))
# plt.savefig("res_placenta_4p.pdf")
# plt.savefig("res_placenta_4p.tiff")


# y_pre = pd.Series(tf.reshape(example_result, [-1]))
# y = pd.Series(test_y)

# print(y.corr(y_pre))
# print(spearmanr(y_pre, y))
# print(pearsonr(y_pre, y))