深度学习_数据读取到model模型存储

本文主要是介绍深度学习_数据读取到model模型存储，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

概要

应用场景：用户流失
本文将介绍模型调用预测的步骤，这里深度学习模型使用的是自定义的deepfm，并用机器学习lgb做比较

代码

导包

import pandas as pd
import numpy as npimport matplotlib.pyplot as plt
import seaborn as sns
from collections import defaultdict  
from scipy import stats
from scipy import signal
from tqdm import tqdm
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, f1_score
from scipy.spatial.distance import cosineimport lightgbm as lgbfrom sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler
from tensorflow.keras.layers import *
import tensorflow.keras.backend as K
import tensorflow as tf
from tensorflow.keras.models import Modelimport os,gc,re,warnings,sys,math
warnings.filterwarnings("ignore")pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)

读取数据

data = pd.read_csv('df_03m.csv')

区分稀疏及类别变量

sparse_cols = ['shop_id','sex']
dense_cols  = [c for c in data.columns if c not in sparse_cols + ['customer_id', 'flag', 'duartion_is_lm']]

dense特征处理

def process_dense_feats(data, cols):d = data.copy()for f in cols:d[f] = d[f].fillna(0)ss=StandardScaler()d[f] = ss.fit_transform(d[[f]])return ddata = process_dense_feats(data, dense_cols)

sparse稀疏特征处理

def process_sparse_feats(data, cols):d = data.copy()for f in cols:d[f] = d[f].fillna('-1').astype(str)label_encoder = LabelEncoder()d[f] = label_encoder.fit_transform(d[f])return ddata = process_sparse_feats(data, sparse_cols)

切分训练及测试集

X_train, X_test, _, _ = train_test_split(data, data, test_size=0.3, random_state=2024)y_train = X_train['flag']
y_test = X_test['flag']X_train1 = X_train.drop(['customer_id', 'flag', 'duartion_is_lm'], axis = 1)
X_test1 = X_test.drop(['customer_id', 'flag', 'duartion_is_lm'], axis = 1)

模型定义

def deepfm_model(sparse_columns, dense_columns, train, test):####### sparse features ##########sparse_input = []lr_embedding = []fm_embedding = []for col in sparse_columns:## lr_embedding_input = Input(shape=(1,))sparse_input.append(_input)nums = pd.concat((train[col], test[col])).nunique() + 1embed = Flatten()(Embedding(nums, 1, embeddings_regularizer=tf.keras.regularizers.l2(0.5))(_input))lr_embedding.append(embed)## fm_embeddingembed = Embedding(nums, 10, input_length=1, embeddings_regularizer=tf.keras.regularizers.l2(0.5))(_input)reshape = Reshape((10,))(embed)fm_embedding.append(reshape)####### fm layer ##########fm_square = Lambda(lambda x: K.square(x))(Add()(fm_embedding)) # square_fm = Add()([Lambda(lambda x:K.square(x))(embed)for embed in fm_embedding])snd_order_sparse_layer = subtract([fm_square, square_fm])snd_order_sparse_layer  = Lambda(lambda x: x * 0.5)(snd_order_sparse_layer)####### dense features ##########dense_input = []for col in dense_columns:_input = Input(shape=(1,))dense_input.append(_input)concat_dense_input = concatenate(dense_input)fst_order_dense_layer = Dense(4, activation='relu')(concat_dense_input)#     #######  NFM  ##########
#     inner_product = []
#     for i in range(field_cnt):
#         for j in range(i + 1, field_cnt):
#             tmp = dot([fm_embedding[i], fm_embedding[j]], axes=1)
#             # tmp = multiply([fm_embedding[i], fm_embedding[j]])
#             inner_product.append(tmp)
#     add_inner_product = add(inner_product)#     #######  PNN  ##########
#     for i in range(field_cnt):
#         for j in range(i+1,field_cnt):
#             tmp = dot([lr_embedding[i],lr_embedding[j]],axes=1)
#             product_list.append(temp)
#     inp = concatenate(lr_embedding+product_list)####### linear concat ##########fst_order_sparse_layer = concatenate(lr_embedding)linear_part = concatenate([fst_order_dense_layer, fst_order_sparse_layer])#     #######  DCN  ##########
#     linear_part = concatenate([fst_order_dense_layer, fst_order_sparse_layer])
#     x0 = linear_part
#     xl = x0
#     for i in range(3):
#         embed_dim = xl.shape[-1]
#         w = tf.Variable(tf.random.truncated_normal(shape=(embed_dim,), stddev=0.01))
#         b = tf.Variable(tf.zeros(shape=(embed_dim,)))
#         x_lw = tf.tensordot(tf.reshape(xl, [-1, 1, embed_dim]), w, axes=1)
#         cross = x0 * x_lw 
#         xl = cross + b + xl#######dnn layer##########concat_fm_embedding = concatenate(fm_embedding, axis=-1) # (None, 10*26)fc_layer = Dropout(0.2)(Activation(activation="relu")(BatchNormalization()(Dense(128)(concat_fm_embedding))))fc_layer = Dropout(0.2)(Activation(activation="relu")(BatchNormalization()(Dense(64)(fc_layer))))fc_layer = Dropout(0.2)(Activation(activation="relu")(BatchNormalization()(Dense(32)(fc_layer))))######## output layer ##########output_layer = concatenate([linear_part, snd_order_sparse_layer, fc_layer]) # (None, )output_layer = Dense(1, activation='sigmoid')(output_layer)model = Model(inputs=sparse_input+dense_input, outputs=output_layer)return model

model = deepfm_model(sparse_cols, dense_cols, X_train1, X_test1)
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["binary_crossentropy", tf.keras.metrics.AUC(name='auc')])

train_sparse_x = [X_train1[f].values for f in sparse_cols]
train_dense_x = [X_train1[f].values for f in dense_cols]
train_label = [y_train.values]test_sparse_x = [X_test1[f].values for f in sparse_cols]
test_dense_x = [X_test1[f].values for f in dense_cols]
test_label = [y_test.values]

test_sparse_x

训练模型

from keras.callbacks import *
# 回调函数
file_path = "deepfm_model_data.h5"
earlystopping = EarlyStopping(monitor="val_loss", patience=3)
checkpoint = ModelCheckpoint(file_path, save_weights_only=True, verbose=1, save_best_only=True)
callbacks_list = [earlystopping, checkpoint]hist = model.fit(train_sparse_x+train_dense_x, train_label,batch_size=4096,epochs=20,validation_data=(test_sparse_x+test_dense_x, test_label),callbacks=callbacks_list,shuffle=False)

模型存储

model.save('deepfm_model.h5')
loaded_model = tf.keras.models.load_model('deepfm_model.h5')

print("np.min(hist.history['val_loss']):", np.min(hist.history['val_loss']))
#np.min(hist.history['val_loss']):0.19

print("np.max(hist.history['val_auc']):", np.max(hist.history['val_auc']))
#np.max(hist.history['val_auc']):0.95

模型预测

deepfm_prob = model.predict(test_sparse_x+test_dense_x, batch_size=4096*4, verbose=1)
deepfm_prob.shape

deepfm_prob

df_submit          = pd.DataFrame()
df_submit          = X_test
df_submit['prob']  = deepfm_prob 
df_submit.head(3)

df_submit.shape

df_submit['y_pre'] = ''
df_submit['y_pre'].loc[(df_submit['prob']>=0.5)] = 1
df_submit['y_pre'].loc[(df_submit['prob']<0.5)]  = 0
df_submit.head(3)

df_submit = df_submit.reset_index()
df_submit.head(1)

df_submit = df_submit.drop('index', axis = 1)
df_submit.head(1)

df_submit.groupby(['flag', 'y_pre'])['customer_id'].count()

根据上述结果打印召回及精准

precision = 
recall  =

查看lgb结果做比较

from lightgbm import LGBMClassifier
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import f1_score, confusion_matrix, recall_score, precision_scoreparams = {'n_estimators': 1500, 'learning_rate': 0.1,'max_depth': 15,'metric': 'auc','verbose': -1, 'seed: 2023,'n_jobs':-1model=LGBMClarsifier(**params) 
model.fit(X_train, y_train,eval_set=[(X_train1, y_train), (X_test1, y_test)], eval_metric = 'auc', verbose=50,early_stopping_rounds = 100)
y_pred = model.predict(X_test1, num_iteration = model.best_iteration_)y_pred = model.predict(X_test1)
y_pred_proba = model.predict_proba(X_test1)
lgb_acc = model.score(X_test1, y_test) * 100
lgb_recall = recall_score(y_test, y_pred) * 100
lgb_precision = precision_score(y_test, y_pred) * 100 I 
lgb_f1 = f1_score(y_test, y_pred, pos_label=1) * 100
print("1gb 准确率:{:.2f}%".format(lgb_acc))
print("lgb 召回率:{:.2f}%".fornat(lgb_recall))
print("lgb 精准率:{:.2f}%".format(lgb_precision))
print("lgb F1分数:{:.2f}%".format(lgb_f1))#from sklearn.metrics import classification_report
#printf(classification_report(y_test, y_pred))# 混淆矩阵
plt.title("混淆矩阵", fontsize=21)
data_confusion_matrix = confusion_matrix(y_test, y_pred)
sns.heatmap(data_confusion_matrix, annot=True, cmap='Blues', fmt='d', cbar='False', annot_kws={'size': 28})
plt.xlabel('Predicted label') 
plt.ylabel('True label')from sklearn.metrics import roc_curve, auc
probs = model.predict_proba(X_test1)
preds = probs[:, 1]
fpr, tpr, threshold = roc_curve(y_test, preds)
# 绘制ROC曲线
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
plt.plot([0, 1], [0, 1], 'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive(TPR)')
plt.xlabel('False Positive(FPR)')
plt.title('ROC')
plt.legend(loc='lower right')
plt.show()