LSTM思路

Dense_LSTM_embedding_128_dim.py

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+# %%
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
+from sklearn.model_selection import cross_val_score
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import StratifiedKFold
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from keras.utils import to_categorical
+from tensorflow.keras.layers import LSTM
+# import keras
+from tensorflow.keras import layers
+from tensorflow.keras.utils import multi_gpu_model
+import gc
+from mail import mail
+# %%
+samples = 900000
+columns = ['c'+str(n) for n in range(128)]
+train_data = pd.read_csv('word2vec/creative_id.csv',
+                         nrows=samples, skiprows=None)
+X_train = train_data[columns].values
+del train_data
+# %%
+test_data = pd.read_csv('word2vec/creative_id.csv',
+                        names=columns,
+                        skiprows=900001)
+gc.collect()
+X_test = test_data[columns].values
+del test_data
+gc.collect()
+# %%
+user_train = pd.read_csv(
+    'data/train_preliminary/user.csv').sort_values(['user_id'], ascending=(True,))
+Y_gender = user_train['gender'].values
+Y_age = user_train['age'].values
+# %%
+user_id_test = pd.read_csv(
+    'data/test/clicklog_ad_user_test.csv').sort_values(['user_id'], ascending=(True,)).user_id.unique()
+ans = pd.DataFrame({'user_id': user_id_test})
+# %%
+# %%
+
+
+def gender_lstm():
+    X_train = X_train.reshape(-1, 128, 1)
+    Y_gender = Y_gender[:samples] - 1
+    model = keras.Sequential(
+        [
+            LSTM(1024),
+            Dense(1, activation='sigmoid')
+        ]
+    )
+    model.compile(loss='binary_crossentropy',
+                  optimizer='adam', metrics=['accuracy'])
+
+    model.fit(X_train, Y_gender, validation_split=0.1,
+              epochs=150, batch_size=1024)
+    mail('train lstm done!')
+# %%
+
+
+def create_gender_model():
+    model = keras.Sequential(
+        [
+            keras.Input(shape=(128,)),
+            layers.Dense(256, activation="relu"),
+            # layers.Dense(512, activation="relu"),
+            layers.Dense(1024, activation="relu"),
+            # layers.Dense(512, activation="relu"),
+            # layers.Dense(256, activation="relu"),
+            layers.Dense(128, activation='relu'),
+            # layers.Dense(2, activation='softmax', name='classifier')
+            Dense(1, activation='sigmoid')
+        ]
+    )
+    model.compile(loss='binary_crossentropy',
+                  #   optimizer='sgd', metrics=['accuracy'])
+                  optimizer='adam', metrics=['accuracy'])
+
+    # model.summary()
+    return model
+
+# %%
+
+
+def create_age_model():
+    model = keras.Sequential(
+        [
+            keras.Input(shape=(128,)),
+            layers.Dense(256, activation="relu"),
+            # layers.Dense(512, activation="relu"),
+            # layers.Dense(1024, activation="relu"),
+            # layers.Dense(512, activation="relu"),
+            # layers.Dense(256, activation="relu"),
+            layers.Dense(128, activation='relu'),
+            Dense(10, activation='softmax')
+        ]
+    )
+    model.compile(loss='categorical_crossentropy',
+                  #   optimizer='rmsprop', metrics=['accuracy'])
+                  optimizer='adam', metrics=['accuracy'])
+
+    # model.summary()
+    return model
+
+# %%
+
+
+def train_gender(X, Y, X_test, train=True, epoch=10, batch_size=1024):
+    # 类别转换为0和1
+    encoder = LabelEncoder()
+    encoder.fit(Y)
+    Y_encoded = encoder.transform(Y)
+    if train:
+        scaler = StandardScaler()
+        scaler.fit(X)
+        X = scaler.transform(X)
+        model = create_gender_model()
+        model.fit(X, Y_encoded, validation_split=0.1,
+                  batch_size=batch_size, epochs=epoch)
+
+        X_test = scaler.transform(X_test)
+        y_pre = model.predict(X_test)
+        threshold = 0.5
+        y_pred_gender = np.where(y_pre > threshold, 1, 0)
+        return y_pred_gender
+    else:
+        estimators = []
+        estimators.append(('standardize', StandardScaler()))
+        estimators.append(('mlp', KerasClassifier(
+            build_fn=create_gender_model, epochs=epoch, batch_size=batch_size, verbose=0)))
+        pipeline = Pipeline(estimators)
+        kfold = StratifiedKFold(n_splits=5, shuffle=True)
+        results = cross_val_score(pipeline, X, Y_encoded, cv=kfold)
+        print("Baseline: %.2f%% (%.2f%%)" %
+              (results.mean()*100, results.std()*100))
+
+
+y_gender = train_gender(X_train, Y_gender, X_test,
+                        train=True, epoch=300, batch_size=4096)
+# parallel_model = multi_gpu_model(model, gpus=2)
+# parallel_model = model
+# parallel_model.fit(X, Y, epochs=10, batch_size=batch_size)
+# gender_pred = parallel_model.predict(X_test, batch_size=batch_size)
+# return gender_pred
+# %%
+
+
+def train_age(X, Y, X_test, train=True, epoch=10, batch_size=1024):
+        # 类别转换为0和1
+    encoder = LabelEncoder()
+    encoder.fit(Y)
+    Y = encoder.transform(Y)
+    if train:
+        scaler = StandardScaler()
+        scaler.fit(X)
+        X = scaler.transform(X)
+        Y = to_categorical(Y)
+        model = create_age_model()
+        model.fit(X, Y, validation_split=0.1,
+                  batch_size=batch_size, epochs=epoch)
+
+        X_test = scaler.transform(X_test)
+        y_pre = model.predict(X_test)
+        y_pred_age = np.argmax(y_pre, axis=1)
+
+        return y_pred_age
+    else:
+        # estimator = KerasClassifier(
+        #     build_fn=create_gender_model, epochs=epoch, batch_size=batch_size, verbose=0)
+        estimators = []
+        estimators.append(('standardize', StandardScaler()))
+        estimators.append(('mlp', KerasClassifier(
+            build_fn=create_age_model, epochs=epoch, batch_size=batch_size, verbose=0)))
+        pipeline = Pipeline(estimators)
+        kfold = StratifiedKFold(n_splits=10, shuffle=True)
+        results = cross_val_score(pipeline, X, Y, cv=kfold)
+        print("Baseline: %.2f%% (%.2f%%)" %
+              (results.mean()*100, results.std()*100))
+
+
+# %%
+y_gender = train_gender(X_train, Y_gender, X_test,
+                        train=True, epoch=300, batch_size=4096)
+# %%
+y_age = train_age(X_train, Y_age, X_test,
+                  train=True, epoch=500, batch_size=4096)
+# %%
+ans['predicted_age'] = y_age+1
+ans['predicted_gender'] = y_gender+1
+# %%
+ans.to_csv('data/ans/word2vec.csv',
+           columns=['user_id', 'predicted_age', 'predicted_gender'],
+           header=True,
+           index=False,
+           )
+
+# %%
+# %%
+# df_train = df_train.sort_values(
+#     ["user_id"], ascending=(True,))
+
+# # %%
+
+
+# def get_batch(file_name,):
+#     for row in open(file_name, "r"):
+#         yield 1
+
+
+# for line in get_batch('data/train_data.csv'):
+# for line in get_batch('test.py'):
+# print(line)
+# break
+# %%
+# 合成用户embedding
+# path = "word2vec/wordvectors.kv"
+# wv = KeyedVectors.load(path, mmap='r')
+# with open('word2vec/userid_creativeids.txt', 'r')as f:
+#     lines = f.readlines()
+# lines = [[int(e) for e in line.split(' ')] for line in lines]
+# number_train_user = 900000
+# number_test_user = 1000000
+# user_train = lines[:number_train_user]
+# user_test = lines[number_train_user:]
+# columns = ['c'+str(i) for i in range(128)]
+# data = {}
+# for col_name in columns:
+#     data[col_name] = pd.Series([], dtype='float')
+# df_user_train = pd.DataFrame(data)
+# df_user_test = pd.DataFrame(data)
+# # %%
+# for line in tqdm.tqdm(user_train):
+#     user_embedding_train = np.zeros(128)
+#     for creative_id in line:
+#         user_embedding_train += wv[str(creative_id)]
+#     user_embedding_train = user_embedding_train / len(line)
+#     tmp = pd.DataFrame(user_embedding_train.reshape(-1,
+#                                                     len(user_embedding_train)), columns=columns)
+#     df_user_train = df_user_train.append(tmp)
+# # %%
+# for line in tqdm.tqdm(user_test):
+#     user_embedding_test = np.zeros(128)
+#     for creative_id in line:
+#         user_embedding_test += wv[str(creative_id)]
+#     user_embedding_test = user_embedding_test / len(line)
+#     tmp = pd.DataFrame(user_embedding_test.reshape(-1,
+#                                                    len(user_embedding_train)), columns=columns)
+#     df_user_test = df_user_test.append(tmp)
+# # %%
+# # 将同一个用户creative_id相加平均后即为一个用户的Embedding
+# all_train_data = pd.read_csv(
+#     'data/train_preliminary/clicklog_ad_user_train_eval_test.csv')
+# all_train_data = all_train_data.sort_values(
+#     ["user_id"], ascending=(True))
+# # %%
+# all_test_data = pd.read_csv(
+#     'data/test/clicklog_ad_user_test.csv')
+# all_test_data = all_test_data.sort_values(
+#     ["user_id"], ascending=(True))
+# # %%
+# assert df_user_train.shape[0] == all_train_data.shape[0]
+# df_user_train['user_id'] = all_train_data['user_id']
+# df_user_train['gender'] = all_train_data['gender']
+# df_user_train['age'] = all_train_data['age']
+# df_user_train.to_hdf('word2vec/df_user_train_test.h5',
+#                      key='df_user_train', mode='w')
+# # %%
+# assert df_user_test.shape[0] == all_test_data.shape[0]
+# df_user_test['user_id'] = all_test_data['user_id']
+# df_user_test.to_hdf('word2vec/df_user_train_test.h5',
+#                     key='df_user_test', mode='a')
+
+
+# %%