代碼下載

# -*- coding: utf-8 -*- # to run: # python wide_and_deep_keras.py --method method --model_type model_type # --train_data train_path --test_data test_path # Examples: # 1_. wide and deep model for logistic regression (defaults) # python wide_and_deep_keras.py # 2_. deep model for multiclass classification # python wide_and_deep_keras.py --method multiclass --model_type deepimport numpy as np import pandas as pd import os import argparse from sklearn.preprocessing import OneHotEncoder, MinMaxScaler, StandardScalerfrom keras.models import Sequential from keras.layers import Dense from keras.optimizers import Adam from keras.layers import Input, concatenate, Embedding, Reshape,concatenate #from keras.layers import Merge, Flatten, merge, Lambda, Dropout from keras.layers import Flatten, merge, Lambda, Dropout from keras.layers.normalization import BatchNormalization from keras.models import Model from keras.regularizers import l2, l1_l2def maybe_download(train_data,test_data):"""if adult data "train.csv" and "test.csv" are not in your directory,download them."""COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num","marital_status", "occupation", "relationship", "race", "gender","capital_gain", "capital_loss", "hours_per_week", "native_country","income_bracket"]if not os.path.exists(train_data): # print "downloading training data..."df_train = pd.read_csv("http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.data",names=COLUMNS, skipinitialspace=True)else:df_train = pd.read_csv("train.csv")if not os.path.exists(test_data): # print "downloading testing data..."df_test = pd.read_csv("http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.test",names=COLUMNS, skipinitialspace=True, skiprows=1)else:df_test = pd.read_csv("test.csv")return df_train, df_testdef cross_columns(x_cols):"""simple helper to build the crossed columns in a pandas dataframe"""crossed_columns = dict()colnames = ['_'.join(x_c) for x_c in x_cols]for cname, x_c in zip(colnames, x_cols):crossed_columns[cname] = x_creturn crossed_columnsdef val2idx(df, cols):"""helper to index categorical columns before embeddings."""val_types = dict()for c in cols:val_types[c] = df[c].unique()val_to_idx = dict()for k, v in val_types.items():val_to_idx[k] = {o: i for i, o in enumerate(val_types[k])}for k, v in val_to_idx.items():df[k] = df[k].apply(lambda x: v[x])unique_vals = dict()for c in cols:unique_vals[c] = df[c].nunique()return df, unique_valsdef onehot(x):return np.array(OneHotEncoder().fit_transform(x).todense())def embedding_input(name, n_in, n_out, reg):inp = Input(shape=(1,), dtype='int64', name=name)return inp, Embedding(n_in, n_out, input_length=1, embeddings_regularizer=l2(reg))(inp)def continous_input(name):inp = Input(shape=(1,), dtype='float32', name=name)return inp, Reshape((1, 1))(inp)def wide(df_train, df_test, wide_cols, x_cols, target, model_type, method):"""Run the wide (linear) model.Params:-------df_train, df_test: train and test datasetswide_cols : columns to be used to fit the wide modelx_cols : columns to be "crossed"target : the target featuremodel_type : accepts "wide" and "wide_deep" (or anything that is not"wide"). If "wide_deep" the function will build and return the inputsbut NOT run any model.method : the fitting method. accepts regression, logistic and multiclassReturns:--------if "wide":print the results obtained on the test set in the terminal.if "wide_deep":X_train, y_train, X_test, y_test: the inputs required to build wide and deep"""df_train['IS_TRAIN'] = 1df_test['IS_TRAIN'] = 0df_wide = pd.concat([df_train, df_test])# my understanding on how to replicate what layers.crossed_column does. One# can read here: https://www.tensorflow.org/tutorials/linear.crossed_columns_d = cross_columns(x_cols)categorical_columns = list(df_wide.select_dtypes(include=['object']).columns)wide_cols += crossed_columns_d.keys()for k, v in crossed_columns_d.items():df_wide[k] = df_wide[v].apply(lambda x: '-'.join(x), axis=1)df_wide = df_wide[wide_cols + [target] + ['IS_TRAIN']]dummy_cols = [c for c in wide_cols if c in categorical_columns + list(crossed_columns_d.keys())]df_wide = pd.get_dummies(df_wide, columns=[x for x in dummy_cols])train = df_wide[df_wide.IS_TRAIN == 1].drop('IS_TRAIN', axis=1)test = df_wide[df_wide.IS_TRAIN == 0].drop('IS_TRAIN', axis=1)# make sure all columns are in the same order and life is easiercols = [target] + [c for c in train.columns if c != target]train = train[cols]test = test[cols]X_train = train.values[:, 1:]y_train = train.values[:, 0].reshape(-1, 1)X_test = test.values[:, 1:]y_test = test.values[:, 0].reshape(-1, 1)if method == 'multiclass':y_train = onehot(y_train)y_test = onehot(y_test)# Scalingscaler = MinMaxScaler()X_train = scaler.fit_transform(X_train)X_test = scaler.fit_transform(X_test)if model_type == 'wide':activation, loss, metrics = fit_param[method]# metrics parameter needs to be passed as a list or dictif metrics:metrics = [metrics]# simply connecting the features to an output layerwide_inp = Input(shape=(X_train.shape[1],), dtype='float32', name='wide_inp')w = Dense(y_train.shape[1], activation=activation)(wide_inp)wide = Model(wide_inp, w)wide.compile(Adam(0.01), loss=loss, metrics=metrics)wide.fit(X_train, y_train, nb_epoch=10, batch_size=64)results = wide.evaluate(X_test, y_test)print ("\n", results)else:return X_train, y_train, X_test, y_testdef deep(df_train, df_test, embedding_cols, cont_cols, target, model_type, method):"""Run the deep model. Two layers of 100 and 50 neurons. In a decent,finished code these would be tunable.Params:-------df_train, df_test: train and test datasetsembedding_cols: columns to be passed as embeddingscont_cols : numerical columns to be combined with the embeddingstarget : the target featuremodel_type : accepts "deep" and "wide_deep" (or anything that is not"wide"). If "wide_deep" the function will build and returns the inputsbut NOT run any modelmethod : the fitting method. accepts regression, logistic and multiclassReturns:--------if "deep":print the results obtained on the test set in the terminal.if "wide_deep":X_train, y_train, X_test, y_test: the inputs required to build wide and deepinp_embed, inp_layer: the embedding layers and the input tensors for Model()"""df_train['IS_TRAIN'] = 1df_test['IS_TRAIN'] = 0df_deep = pd.concat([df_train, df_test])deep_cols = embedding_cols + cont_cols# I 'd say that adding numerical columns to embeddings can be done in two ways:# 1_. normalise the values in the dataframe and pass them to the network# 2_. add BatchNormalization() layer. (I am not entirely sure this is right)# I'd say option 1 is the correct one. 2 performs better, which does not say much, but...# 1_. Scaling in the dataframe# scaler = MinMaxScaler()# cont_df = df_deep[cont_cols]# cont_norm_df = pd.DataFrame(scaler.fit_transform(df_train[cont_cols]))# cont_norm_df.columns = cont_cols# for c in cont_cols: df_deep[c] = cont_norm_df[c]df_deep, unique_vals = val2idx(df_deep, embedding_cols)train = df_deep[df_deep.IS_TRAIN == 1].drop('IS_TRAIN', axis=1)test = df_deep[df_deep.IS_TRAIN == 0].drop('IS_TRAIN', axis=1)embeddings_tensors = []n_factors = 8reg = 1e-3for ec in embedding_cols:layer_name = ec + '_inp't_inp, t_build = embedding_input(layer_name, unique_vals[ec], n_factors, reg)embeddings_tensors.append((t_inp, t_build))del(t_inp, t_build)continuous_tensors = []for cc in cont_cols:layer_name = cc + '_in't_inp, t_build = continous_input(layer_name)continuous_tensors.append((t_inp, t_build))del(t_inp, t_build)X_train = [train[c] for c in deep_cols]y_train = np.array(train[target].values).reshape(-1, 1)X_test = [test[c] for c in deep_cols]y_test = np.array(test[target].values).reshape(-1, 1)if method == 'multiclass':y_train = onehot(y_train)y_test = onehot(y_test)inp_layer = [et[0] for et in embeddings_tensors]inp_layer += [ct[0] for ct in continuous_tensors]inp_embed = [et[1] for et in embeddings_tensors]inp_embed += [ct[1] for ct in continuous_tensors]if model_type == 'deep':activation, loss, metrics = fit_param[method]if metrics:metrics = [metrics]d = merge(inp_embed, mode='concat')d = Flatten()(d)# 2_. layer to normalise continous columns with the embeddingsd = BatchNormalization()(d)d = Dense(100, activation='relu', kernel_regularizer=l1_l2(l1=0.01, l2=0.01))(d)# d = Dropout(0.5)(d) # Dropout don't seem to help in this modeld = Dense(50, activation='relu')(d)# d = Dropout(0.5)(d) # Dropout don't seem to help in this modeld = Dense(y_train.shape[1], activation=activation)(d)deep = Model(inp_layer, d)deep.compile(Adam(0.01), loss=loss, metrics=metrics)deep.fit(X_train, y_train, batch_size=64, nb_epoch=10)results = deep.evaluate(X_test, y_test)print ("\n", results)else:return X_train, y_train, X_test, y_test, inp_embed, inp_layerdef wide_deep(df_train, df_test, wide_cols, x_cols, embedding_cols, cont_cols, method):"""Run the wide and deep model. Parameters are the same as those for thewide and deep functions"""# Default model_type is "wide_deep"X_train_wide, y_train_wide, X_test_wide, y_test_wide = \wide(df_train, df_test, wide_cols, x_cols, target, model_type, method)X_train_deep, y_train_deep, X_test_deep, y_test_deep, deep_inp_embed, deep_inp_layer = \deep(df_train, df_test, embedding_cols,cont_cols, target, model_type, method)X_tr_wd = [X_train_wide] + X_train_deepY_tr_wd = y_train_deep # wide or deep is the same hereX_te_wd = [X_test_wide] + X_test_deepY_te_wd = y_test_deep # wide or deep is the same hereactivation, loss, metrics = fit_param[method]if metrics: metrics = [metrics]# WIDEw = Input(shape=(X_train_wide.shape[1],), dtype='float32', name='wide')# DEEP: the output of the 50 neurons layer will be the deep-side inputprint(deep_inp_embed) # d = merge(deep_inp_embed, mode='concat')d=concatenate(deep_inp_embed, axis=-1)d = Flatten()(d)d = BatchNormalization()(d)d = Dense(100, activation='relu', kernel_regularizer=l1_l2(l1=0.01, l2=0.01))(d)d = Dense(50, activation='relu', name='deep')(d)# WIDE + DEEPwd_inp = concatenate([w, d])wd_out = Dense(Y_tr_wd.shape[1], activation=activation, name='wide_deep')(wd_inp)wide_deep = Model(inputs=[w] + deep_inp_layer, outputs=wd_out)wide_deep.compile(optimizer=Adam(lr=0.01), loss=loss, metrics=metrics)wide_deep.fit(X_tr_wd, Y_tr_wd, nb_epoch=10, batch_size=128)# Maybe you want to schedule a second search with lower learning rate# wide_deep.optimizer.lr = 0.0001# wide_deep.fit(X_tr_wd, Y_tr_wd, nb_epoch=10, batch_size=128)results = wide_deep.evaluate(X_te_wd, Y_te_wd)print ("\n", results)if __name__ == '__main__':ap = argparse.ArgumentParser()ap.add_argument("--method", type=str, default="logistic",help="fitting method")ap.add_argument("--model_type", type=str, default="wide_deep",help="wide, deep or both")ap.add_argument("--train_data", type=str, default="train.csv")ap.add_argument("--test_data", type=str, default="test.csv")args = vars(ap.parse_args())method = args["method"]model_type = args['model_type']train_data = args['train_data']test_data = args['test_data']fit_param = dict()fit_param['logistic'] = ('sigmoid', 'binary_crossentropy', 'accuracy')fit_param['regression'] = (None, 'mse', None)fit_param['multiclass'] = ('softmax', 'categorical_crossentropy', 'accuracy')df_train, df_test = maybe_download(train_data, test_data)# Add a feature to illustrate the logistic regression exampledf_train['income_label'] = (df_train["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)df_test['income_label'] = (df_test["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)# Add a feature to illustrate multiclass classificationage_groups = [0, 25, 65, 90]age_labels = range(len(age_groups) - 1)df_train['age_group'] = pd.cut(df_train['age'], age_groups, labels=age_labels)df_test['age_group'] = pd.cut(df_test['age'], age_groups, labels=age_labels)# columns for wide modelwide_cols = ['age','hours_per_week','education', 'relationship', 'workclass','occupation','native_country','gender']x_cols = (['education', 'occupation'], ['native_country', 'occupation'])# columns for deep modelembedding_cols = ['education', 'relationship', 'workclass', 'occupation','native_country']cont_cols = ["age","hours_per_week"]# target for logistictarget = 'income_label'# # A set-up for multiclass classification would be:# # change method to multiclass# wide_cols = ["gender", "native_country", "education", "occupation", "workclass",# "relationship"]# x_cols = (['education', 'occupation'], ['native_country', 'occupation'])# # columns for deep model# embedding_cols = ['education', 'relationship', 'workclass', 'occupation',# 'native_country']# cont_cols = ["hours_per_week"]# # target# target = 'age_group'if model_type == 'wide':wide(df_train, df_test, wide_cols, x_cols, target, model_type, method)elif model_type == 'deep':deep(df_train, df_test, embedding_cols, cont_cols, target, model_type, method)else:wide_deep(df_train, df_test, wide_cols, x_cols, embedding_cols, cont_cols, method) Epoch 1/10 32561/32561 [==============================] - 4s 108us/step - loss: 0.6511 - acc: 0.8268 Epoch 2/10 32561/32561 [==============================] - 2s 61us/step - loss: 0.4134 - acc: 0.8376 Epoch 3/10 32561/32561 [==============================] - 2s 48us/step - loss: 0.4030 - acc: 0.8393 Epoch 4/10 32561/32561 [==============================] - 2s 55us/step - loss: 0.4014 - acc: 0.8399 Epoch 5/10 32561/32561 [==============================] - 2s 53us/step - loss: 0.4003 - acc: 0.8415 Epoch 6/10 32561/32561 [==============================] - 2s 50us/step - loss: 0.3988 - acc: 0.8400 Epoch 7/10 32561/32561 [==============================] - 2s 57us/step - loss: 0.3973 - acc: 0.8407 Epoch 8/10 32561/32561 [==============================] - 2s 55us/step - loss: 0.3951 - acc: 0.8409 Epoch 9/10 32561/32561 [==============================] - 2s 47us/step - loss: 0.3948 - acc: 0.8409 Epoch 10/10 32561/32561 [==============================] - 2s 53us/step - loss: 0.3928 - acc: 0.8413 16281/16281 [==============================] - 1s 61us/step[0.4065274388873894, 0.8361280019911081]

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