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01.PBMC_bulk.py

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+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 
+
+import random
+seed=4
+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)
+	return scale_x(raw_data[..., :263]), (raw_data[..., 263])
+
+
+data_dir = "../01.train_data/PBMC_bulk/"
+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.BatchNormalization())
+	model.add(tf.keras.layers.Dense(1))
+
+	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 = 245472
+
+
+
+model, history, loss, val_loss, example_batch, example_result, y_pre_batch = model_with_seed(seed)
+
+
+
+
+
+
+res_dir = "res/res_PBMC_bulk/"
+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({"real_y":test_y, "pred_y": tf.reshape(example_result, [-1])}).to_csv(res_dir + "03.res.csv")
+
+
+
+plt.scatter(test_y,tf.reshape(example_result, [-1]))
+plt.savefig("123.pdf")
+plt.savefig("123.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))

02.Placenta.4p.py

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+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=4
+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)
+	return scale_x(raw_data[..., :263]), (raw_data[..., 264: 267])
+
+
+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.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 = 769031
+
+
+
+
+model, history, loss, val_loss, example_batch, example_result, y_pre_batch = model_with_seed(seed)
+
+
+
+
+
+
+res_dir = "res/res_placenta_4p2/"
+if not os.path.exists(res_dir): os.mkdir(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))