deep$wide keras

代码下载

# -*- 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 deep

import numpy as np
import pandas as pd
import os
import argparse
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler, StandardScaler

from 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_l2


def 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_test


def 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_c
    return crossed_columns


def 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_vals


def 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 datasets
    wide_cols   : columns to be used to fit the wide model
    x_cols      : columns to be "crossed"
    target      : the target feature
    model_type  : accepts "wide" and "wide_deep" (or anything that is not
    "wide"). If "wide_deep" the function will build and return the inputs
    but NOT run any model.
    method      : the fitting method. accepts regression, logistic and multiclass

    Returns:
    --------
    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'] = 1
    df_test['IS_TRAIN'] = 0
    df_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 easier
    cols = [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)

    # Scaling
    scaler = 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 dict
        if metrics:
            metrics = [metrics]

        # simply connecting the features to an output layer
        wide_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_test


def 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 datasets
    embedding_cols: columns to be passed as embeddings
    cont_cols     : numerical columns to be combined with the embeddings
    target        : the target feature
    model_type    : accepts "deep" and "wide_deep" (or anything that is not
    "wide"). If "wide_deep" the function will build and returns the inputs
    but NOT run any model
    method        : the fitting method. accepts regression, logistic and multiclass

    Returns:
    --------
    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 deep
    inp_embed, inp_layer: the embedding layers and the input tensors for Model()

    """

    df_train['IS_TRAIN'] = 1
    df_test['IS_TRAIN'] = 0
    df_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 = 8
    reg = 1e-3
    for 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 embeddings
        d = 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 model
        d = Dense(50, activation='relu')(d)
        # d = Dropout(0.5)(d) # Dropout don't seem to help in this model
        d = 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_layer


def 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 the
    wide 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_deep
    Y_tr_wd = y_train_deep  # wide or deep is the same here
    X_te_wd = [X_test_wide] + X_test_deep
    Y_te_wd = y_test_deep  # wide or deep is the same here

    activation, loss, metrics = fit_param[method]
    if metrics: metrics = [metrics]

    # WIDE
    w = Input(shape=(X_train_wide.shape[1],), dtype='float32', name='wide')

    # DEEP: the output of the 50 neurons layer will be the deep-side input
    print(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 + DEEP
    wd_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 example
    df_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 classification
    age_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 model
    wide_cols = ['age','hours_per_week','education', 'relationship', 'workclass',
                 'occupation','native_country','gender']
    x_cols = (['education', 'occupation'], ['native_country', 'occupation'])

    # columns for deep model
    embedding_cols = ['education', 'relationship', 'workclass', 'occupation',
                      'native_country']
    cont_cols = ["age","hours_per_week"]

    # target for logistic
    target = '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]