def main():
URL = 'https://storage.googleapis.com/applied-dl/heart.csv'
dataframe = pd.read_csv(URL)
print(dataframe.head())
train, test = train_test_split(dataframe, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
print(len(train), 'train examples')
print(len(val), 'validation examples')
print(len(test), 'test examples')
batch_size = 5 # 小批量大小用于演示
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
for feature_batch, label_batch in train_ds.take(1):
print('Every feature:', list(feature_batch.keys()))
print('A batch of ages:', feature_batch['age'])
print('A batch of targets:', label_batch)
# 我们将使用该批数据演示几种特征列
example_batch = next(iter(train_ds))[0]
# 用于创建一个特征列
# 并转换一批次数据的一个实用程序方法
def demo(feature_column):
feature_layer = layers.DenseFeatures(feature_column)
print(feature_layer(example_batch).numpy())
age = feature_column.numeric_column("age")
demo(age)
age_buckets = feature_column.bucketized_column(
age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
demo(age_buckets)
thal = feature_column.categorical_column_with_vocabulary_list(
'thal', ['fixed', 'normal', 'reversible'])
thal_one_hot = feature_column.indicator_column(thal)
demo(thal_one_hot)
# 注意到嵌入列的输入是我们之前创建的类别列
thal_embedding = feature_column.embedding_column(thal, dimension=8)
demo(thal_embedding)
thal_hashed = feature_column.categorical_column_with_hash_bucket(
'thal', hash_bucket_size=1000)
demo(feature_column.indicator_column(thal_hashed))
crossed_feature = feature_column.crossed_column([age_buckets, thal],
hash_bucket_size=1000)
demo(feature_column.indicator_column(crossed_feature))
feature_columns = []
# 数值列
for header in [
'age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca'
]:
feature_columns.append(feature_column.numeric_column(header))
# 分桶列
age_buckets = feature_column.bucketized_column(
age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
feature_columns.append(age_buckets)
# 分类列
thal = feature_column.categorical_column_with_vocabulary_list(
'thal', ['fixed', 'normal', 'reversible'])
thal_one_hot = feature_column.indicator_column(thal)
feature_columns.append(thal_one_hot)
# 嵌入列
thal_embedding = feature_column.embedding_column(thal, dimension=8)
feature_columns.append(thal_embedding)
# 组合列
crossed_feature = feature_column.crossed_column([age_buckets, thal],
hash_bucket_size=1000)
crossed_feature = feature_column.indicator_column(crossed_feature)
feature_columns.append(crossed_feature)
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
batch_size = 32
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
model = tf.keras.Sequential([
feature_layer,
layers.Dense(128, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'],
run_eagerly=True)
model.fit(train_ds, validation_data=val_ds, epochs=5)
loss, accuracy = model.evaluate(test_ds)
print("Accuracy", accuracy)
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
dataframe = dataframe.copy()
labels = dataframe.pop('species')
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
if shuffle:
ds = ds.shuffle(buffer_size=len(dataframe))
ds = ds.batch(batch_size)
return ds
feature_columns = []
for header in ['sl', 'sw', 'pl', 'pw']:
feature_columns.append(feature_column.numeric_column(header))
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
batch_size = 32
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
model = tf.keras.Sequential([
feature_layer,
layers.Dense(128, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(1, activation='sigmoid')
])
def create_feature_columns():
# user feature
gender = fc.indicator_column(
fc.categorical_column_with_identity("gender",
num_buckets=3,
default_value=0))
age_class = fc.indicator_column(
fc.categorical_column_with_identity("age_class",
num_buckets=7,
default_value=0))
has_baby = fc.indicator_column(
fc.categorical_column_with_identity("has_baby",
num_buckets=2,
default_value=0))
baby_gender = fc.indicator_column(
fc.categorical_column_with_identity("baby_gender",
num_buckets=3,
default_value=0))
baby_age = fc.indicator_column(
fc.categorical_column_with_identity("baby_age",
num_buckets=7,
default_value=0))
grade = fc.indicator_column(
fc.categorical_column_with_identity("grade",
num_buckets=7,
default_value=0))
rfm_type = fc.indicator_column(
fc.categorical_column_with_identity("bi_rfm_type",
num_buckets=12,
default_value=0))
cate1_price_prefer = fc.indicator_column(
fc.categorical_column_with_identity("cate1_price_prefer",
num_buckets=6,
default_value=0))
cate2_price_prefer = fc.indicator_column(
fc.categorical_column_with_identity("cate2_price_prefer",
num_buckets=6,
default_value=0))
cate3_price_prefer = fc.indicator_column(
fc.categorical_column_with_identity("cate3_price_prefer",
num_buckets=6,
default_value=0))
city_id = fc.categorical_column_with_hash_bucket("city", 700)
city = fc.embedding_column(city_id, 16)
shop_visit_cnt = fc.indicator_column(
fc.categorical_column_with_identity("shop_visit_cnt_rank",
num_buckets=20,
default_value=19))
shop_visit_usr = fc.indicator_column(
fc.categorical_column_with_identity("shop_visit_usr_rank",
num_buckets=20,
default_value=19))
# item feature
c2id = fc.categorical_column_with_hash_bucket("cate2Id",
10000,
dtype=tf.int64)
c2id_embed = fc.embedding_column(c2id, 32)
modified_time = fc.numeric_column("modified_time", default_value=0.0)
modified_time_sqrt = fc.numeric_column("modified_time_sqrt",
default_value=0.0)
modified_time_square = fc.numeric_column("modified_time_square",
default_value=0.0)
props_sex = fc.indicator_column(
fc.categorical_column_with_vocabulary_list("props_sex",
["男", "女", "通用", "情侣"],
default_value=0))
brand_grade = fc.indicator_column(
fc.categorical_column_with_vocabulary_list(
"brand_grade", ["A类品牌", "B类品牌", "C类品牌", "D类品牌"], default_value=0))
shipment_rate = fc.numeric_column("shipment_rate", default_value=0.0)
shipping_rate = fc.numeric_column("shipping_rate", default_value=0.0)
ipv_ntile = fc.bucketized_column(
fc.numeric_column("ipv_ntile", dtype=tf.int64, default_value=99),
boundaries=[1, 2, 3, 4, 5, 10, 20, 50, 80])
pay_ntile = fc.bucketized_column(
fc.numeric_column("pay_ntile", dtype=tf.int64, default_value=99),
boundaries=[1, 2, 3, 4, 5, 10, 20, 50, 80])
price = fc.numeric_column("price_norm", default_value=0.0)
ctr_1d = fc.numeric_column("ctr_1d", default_value=0.0)
cvr_1d = fc.numeric_column("cvr_1d", default_value=0.0)
uv_cvr_1d = fc.numeric_column("uv_cvr_1d", default_value=0.0)
ctr_1w = fc.numeric_column("ctr_1w", default_value=0.0)
cvr_1w = fc.numeric_column("cvr_1w", default_value=0.0)
uv_cvr_1w = fc.numeric_column("uv_cvr_1w", default_value=0.0)
ctr_2w = fc.numeric_column("ctr_2w", default_value=0.0)
cvr_2w = fc.numeric_column("cvr_2w", default_value=0.0)
uv_cvr_2w = fc.numeric_column("uv_cvr_2w", default_value=0.0)
ctr_1m = fc.numeric_column("ctr_1m", default_value=0.0)
cvr_1m = fc.numeric_column("cvr_1m", default_value=0.0)
uv_cvr_1m = fc.numeric_column("uv_cvr_1m", default_value=0.0)
pay_qty_1d = fc.numeric_column("pay_qty_1d", default_value=0.0)
pay_qty_1w = fc.numeric_column("pay_qty_1w", default_value=0.0)
pay_qty_2w = fc.numeric_column("pay_qty_2w", default_value=0.0)
pay_qty_1m = fc.numeric_column("pay_qty_1m", default_value=0.0)
cat2_pay_qty = fc.numeric_column("cat2_pay_qty_1d", default_value=0.0)
cat1_pay_qty = fc.numeric_column("cat1_pay_qty_1d", default_value=0.0)
brd_pay_qty = fc.numeric_column("brd_pay_qty_1d", default_value=0.0)
slr_pay_qty_1d = fc.numeric_column("slr_pay_qty_1d", default_value=0.0)
slr_pay_qty_1w = fc.numeric_column("slr_pay_qty_1w", default_value=0.0)
slr_pay_qty_2w = fc.numeric_column("slr_pay_qty_2w", default_value=0.0)
slr_pay_qty_1m = fc.numeric_column("slr_pay_qty_1m", default_value=0.0)
slr_brd_pay_qty_1d = fc.numeric_column("slr_brd_pay_qty_1d",
default_value=0.0)
slr_brd_pay_qty_1w = fc.numeric_column("slr_brd_pay_qty_1w",
default_value=0.0)
slr_brd_pay_qty_2w = fc.numeric_column("slr_brd_pay_qty_2w",
default_value=0.0)
slr_brd_pay_qty_1m = fc.numeric_column("slr_brd_pay_qty_1m",
default_value=0.0)
weighted_ipv = fc.numeric_column("weighted_ipv", default_value=0.0)
cat1_weighted_ipv = fc.numeric_column("cat1_weighted_ipv",
default_value=0.0)
cate_weighted_ipv = fc.numeric_column("cate_weighted_ipv",
default_value=0.0)
slr_weighted_ipv = fc.numeric_column("slr_weighted_ipv", default_value=0.0)
brd_weighted_ipv = fc.numeric_column("brd_weighted_ipv", default_value=0.0)
cms_scale = fc.numeric_column("cms_scale", default_value=0.0)
cms_scale_sqrt = fc.numeric_column("cms_scale_sqrt", default_value=0.0)
# context feature
matchScore = fc.numeric_column("matchScore", default_value=0.0)
popScore = fc.numeric_column("popScore", default_value=0.0)
brandPrefer = fc.numeric_column("brandPrefer", default_value=0.0)
cate2Prefer = fc.numeric_column("cate2Prefer", default_value=0.0)
catePrefer = fc.numeric_column("catePrefer", default_value=0.0)
sellerPrefer = fc.numeric_column("sellerPrefer", default_value=0.0)
matchType = fc.indicator_column(
fc.categorical_column_with_identity("matchType", 9, default_value=0))
position = fc.bucketized_column(fc.numeric_column("position",
dtype=tf.int64,
default_value=301),
boundaries=[
1, 2, 3, 4, 5, 6, 7, 8, 9, 15, 20, 30,
40, 50, 80, 100, 150, 200, 300
])
triggerNum = fc.indicator_column(
fc.categorical_column_with_identity("triggerNum", 41,
default_value=40))
triggerRank = fc.indicator_column(
fc.categorical_column_with_identity("triggerRank",
41,
default_value=40))
sceneType = fc.indicator_column(
fc.categorical_column_with_identity("type", 2, default_value=0))
hour = fc.indicator_column(
fc.categorical_column_with_identity("hour", 24, default_value=0))
phoneBrandId = fc.categorical_column_with_hash_bucket("phoneBrand", 1000)
phoneResolutionId = fc.categorical_column_with_hash_bucket(
"phoneResolution", 500)
phoneBrand = fc.embedding_column(phoneBrandId, 20)
phoneResolution = fc.embedding_column(phoneResolutionId, 10)
phoneOs = fc.indicator_column(
fc.categorical_column_with_vocabulary_list("phoneOs",
["android", "ios"],
default_value=0))
global my_feature_columns
my_feature_columns = [
matchScore, matchType, position, triggerNum, triggerRank, sceneType,
hour, phoneBrand, phoneResolution, phoneOs, popScore, sellerPrefer,
brandPrefer, cate2Prefer, catePrefer, gender, age_class, has_baby,
baby_gender, baby_age, grade, rfm_type, city, price, props_sex,
brand_grade, cate1_price_prefer, cate2_price_prefer,
cate3_price_prefer, modified_time, modified_time_sqrt,
modified_time_square, shipment_rate, shipping_rate, ipv_ntile,
pay_ntile, shop_visit_cnt, shop_visit_usr, c2id_embed, uv_cvr_1d,
uv_cvr_1w, uv_cvr_2w, uv_cvr_1m, ctr_1d, ctr_1w, ctr_2w, ctr_1m,
cvr_1d, cvr_1w, cvr_2w, cvr_1m, pay_qty_1d, pay_qty_1w, pay_qty_2w,
pay_qty_1m, cat2_pay_qty, cat1_pay_qty, brd_pay_qty, slr_pay_qty_1d,
slr_pay_qty_1w, slr_pay_qty_2w, slr_pay_qty_1m, slr_brd_pay_qty_1d,
slr_brd_pay_qty_1w, slr_brd_pay_qty_2w, slr_brd_pay_qty_1m,
weighted_ipv, cat1_weighted_ipv, cate_weighted_ipv, slr_weighted_ipv,
brd_weighted_ipv, cms_scale, cms_scale_sqrt
]
return my_feature_columns
def transform_from_code_gen(source_inputs):
education_hash_fc = fc.categorical_column_with_hash_bucket(
"education", hash_bucket_size=education_hash.hash_bucket_size)
occupation_hash_fc = fc.categorical_column_with_hash_bucket(
"occupation", hash_bucket_size=occupation_hash.hash_bucket_size)
native_country_hash_fc = fc.categorical_column_with_hash_bucket(
"native_country",
hash_bucket_size=native_country_hash.hash_bucket_size)
workclass_lookup_fc = fc.categorical_column_with_vocabulary_list(
"workclass", vocabulary_list=workclass_lookup.vocabulary_list)
marital_status_lookup_fc = fc.categorical_column_with_vocabulary_list(
"marital_status",
vocabulary_list=marital_status_lookup.vocabulary_list)
relationship_lookup_fc = fc.categorical_column_with_vocabulary_list(
"relationship", vocabulary_list=relationship_lookup.vocabulary_list)
race_lookup_fc = fc.categorical_column_with_vocabulary_list(
"race", vocabulary_list=race_lookup.vocabulary_list)
sex_lookup_fc = fc.categorical_column_with_vocabulary_list(
"sex", vocabulary_list=sex_lookup.vocabulary_list)
age_bucketize_fc = fc.bucketized_column(
fc.numeric_column("age"), boundaries=age_bucketize.boundaries)
capital_gain_bucketize_fc = fc.bucketized_column(
fc.numeric_column("capital_gain"),
boundaries=capital_gain_bucketize.boundaries,
)
capital_loss_bucketize_fc = fc.bucketized_column(
fc.numeric_column("capital_loss"),
boundaries=capital_loss_bucketize.boundaries,
)
hours_per_week_bucketize_fc = fc.bucketized_column(
fc.numeric_column("hours_per_week"),
boundaries=hours_per_week_bucketize.boundaries,
)
group1_fc = edl_fc.concatenated_categorical_column(categorical_columns=[
workclass_lookup_fc,
hours_per_week_bucketize_fc,
capital_gain_bucketize_fc,
capital_loss_bucketize_fc,
])
group2_fc = edl_fc.concatenated_categorical_column(categorical_columns=[
education_hash_fc,
marital_status_lookup_fc,
relationship_lookup_fc,
occupation_hash_fc,
])
group3_fc = edl_fc.concatenated_categorical_column(categorical_columns=[
age_bucketize_fc,
sex_lookup_fc,
race_lookup_fc,
native_country_hash_fc,
])
group1_wide_embedding_fc = fc.embedding_column(
group1_fc,
dimension=group1_embedding_wide.output_dim,
)
group2_wide_embedding_fc = fc.embedding_column(
group2_fc,
dimension=group2_embedding_wide.output_dim,
)
group1_deep_embedding_fc = fc.embedding_column(
group1_fc,
dimension=group1_embedding_deep.output_dim,
)
group2_deep_embedding_fc = fc.embedding_column(
group2_fc,
dimension=group2_embedding_deep.output_dim,
)
group3_deep_embedding_fc = fc.embedding_column(
group3_fc,
dimension=group3_embedding_deep.output_dim,
)
wide_feature_columns = [
group1_wide_embedding_fc,
group2_wide_embedding_fc,
]
deep_feature_columns = [
group1_deep_embedding_fc,
group2_deep_embedding_fc,
group3_deep_embedding_fc,
]
return (
tf.keras.layers.DenseFeatures(wide_feature_columns)(source_inputs),
tf.keras.layers.DenseFeatures(deep_feature_columns)(source_inputs),
)
label_key='charged_off',
num_epochs=5,
shuffle=True,
batch_size=20000) #300000 #230934
#val_inpf = functools.partial(easy_input_function, val_df, label_key='charged_off', num_epochs=1, shuffle=False, batch_size=val_df.shape[0]) #200000
test_inpf = functools.partial(easy_input_function,
test_df,
label_key='charged_off',
num_epochs=1,
shuffle=False,
batch_size=test_df.shape[0]) #200000
###################################################################
#DEFINE ALL NUMERIC COLUMNS
loan_amnt = fc.numeric_column('loan_amnt')
term = fc.numeric_column('term')
installment = fc.numeric_column('installment')
emp_length = fc.numeric_column('emp_length')
dti = fc.numeric_column('dti')
earliest_cr_line = fc.numeric_column('earliest_cr_line')
open_acc = fc.numeric_column('open_acc')
pub_rec = fc.numeric_column('pub_rec')
revol_util = fc.numeric_column('revol_util')
total_acc = fc.numeric_column('total_acc')
mort_acc = fc.numeric_column('mort_acc')
pub_rec_bankruptcies = fc.numeric_column('pub_rec_bankruptcies')
log_annual_inc = fc.numeric_column('log_annual_inc')
fico_score = fc.numeric_column('fico_score')
log_revol_bal = fc.numeric_column('log_revol_bal')
if isinstance(x_train, np.ndarray):
print("data have been loaded as numpy array")
features = [' CRIM', 'ZN', 'INDUS', ' CHAS', ' NOX', ' RM', ' AGE', 'DIS', 'RAD', 'TAX',
'PTRATIO', 'B', 'LSTAT']
x_train_df = pd.DataFrame(data=x_train, columns=features)
x_test_df = pd.DataFrame(data=x_test, columns=features)
y_train_df = pd.DataFrame(data=y_train, columns=['price'])
y_test_df = pd.DataFrame(data=y_test, columns=['price'])
print(x_train_df.head())
feature_columns = []
for feature_name in features:
feature_columns.append(feature_column.numeric_column(feature_name, dtype=tf.float32))
"""
Have to create an input pipeline using tf.data
"""
def estimator_input_fn(df_data, df_label, epochs=10, shuffle=True, batch_size=32):
def input_function():
ds = tf.data.Dataset.from_tensor_slices((dict(df_data), df_label))
if shuffle:
ds = ds.shuffle(100)
ds = ds.batch(batch_size).repeat(epochs)
return ds
return input_function
labels_one_hot = to_categorical(labels, num_classes=40)
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels_one_hot))
if shuffle:
ds = ds.shuffle(buffer_size=len(dataframe))
ds = ds.batch(batch_size)
return ds
batch_size = 20
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
feature_columns = []
fc_age = feature_column.numeric_column('age')
fc_age_buckets = feature_column.bucketized_column(fc_age, boundaries=[20, 30])
feature_columns.append(fc_age_buckets)
fc_gender = feature_column.categorical_column_with_vocabulary_list(
'gender', ['Male', 'Female'])
fc_gender_one_hot = feature_column.indicator_column(fc_gender)
feature_columns.append(fc_gender_one_hot)
fc_emotion = feature_column.categorical_column_with_vocabulary_list(
'emotion', ['Happy', 'Sad', 'Fear', 'Disgust', 'Anger', 'Surprice'])
fc_emotion_one_hot = feature_column.indicator_column(fc_emotion)
feature_columns.append(fc_emotion_one_hot)
fc_color = feature_column.categorical_column_with_vocabulary_list(
'color', ['Red', 'Blue', 'Green', 'White', 'Gray'])
def create_feature_columns(self):
feature_columns = []
numeric_cols = [
'owner_influence', 'is_commented_by_connections', 'is_liked_by_me',
'is_liked_by_connections', 'poster_gender', 'poster_influence',
'participant1_gender', 'participant1_influence',
'participant2_gender', 'participant2_influence',
'participant3_gender', 'participant3_influence'
]
# numeric cols
for header in numeric_cols:
feature_columns.append(feature_column.numeric_column(header))
# bucketized columns
# age
step = len(self.df.age) // 8
sorted_ages = sorted(self.df.age)
age_boundaries = [sorted_ages[i * step] for i in range(1, 8)]
age = feature_column.numeric_column("age")
age_buckets = feature_column.bucketized_column(
age, boundaries=age_boundaries)
feature_columns.append(age_buckets)
# number_of_likes
likes_num = feature_column.numeric_column("number_of_likes")
likes_num_buckets = feature_column.bucketized_column(
likes_num, boundaries=[2, 5, 10, 20, 50, 100])
feature_columns.append(likes_num_buckets)
# number_of_comments
comments_num = feature_column.numeric_column("number_of_comments")
comments_num_buckets = feature_column.bucketized_column(
comments_num, boundaries=[1, 2, 5, 10, 20, 50, 100])
feature_columns.append(comments_num_buckets)
# indicator columns for categorical features
app_type = feature_column.categorical_column_with_vocabulary_list(
'app_type', self.df.app_type.unique())
app_type_1hot = feature_column.indicator_column(app_type)
feature_columns.append(app_type_1hot)
owner_type = feature_column.categorical_column_with_vocabulary_list(
'owner_type', self.df.owner_type.unique())
owner_type_1hot = feature_column.indicator_column(owner_type)
feature_columns.append(owner_type_1hot)
poster_focus = feature_column.categorical_column_with_vocabulary_list(
'poster_focus', [
'engineering', 'sales', 'marketing', 'management', 'financial',
'other'
])
poster_focus_1hot = feature_column.indicator_column(poster_focus)
feature_columns.append(poster_focus_1hot)
# functions to reduce code duplication
def participant_action(part_action):
participant_action = feature_column.categorical_column_with_vocabulary_list(
part_action, ['commented', 'liked', 'viewed'])
return participant_action
def participant_focus(part_f):
participant_focus = feature_column.categorical_column_with_vocabulary_list(
part_f, [
'engineering', 'sales', 'marketing', 'management',
'financial', 'other', 'none'
])
return participant_focus
participant1_action = participant_action("participant1_action")
participant2_action = participant_action("participant2_action")
participant3_action = participant_action("participant3_action")
participant1_focus = participant_focus("participant1_focus")
participant2_focus = participant_focus("participant2_focus")
participant3_focus = participant_focus("participant3_focus")
feature_columns.append(
feature_column.indicator_column(participant1_action))
feature_columns.append(
feature_column.indicator_column(participant1_focus))
feature_columns.append(
feature_column.indicator_column(participant2_action))
feature_columns.append(
feature_column.indicator_column(participant2_focus))
feature_columns.append(
feature_column.indicator_column(participant3_action))
feature_columns.append(
feature_column.indicator_column(participant3_focus))
# feature crosses for participant action and focus
crossed_feature1 = feature_column.crossed_column(
[participant1_action, participant1_focus], hash_bucket_size=1000)
crossed_feature1 = feature_column.indicator_column(crossed_feature1)
feature_columns.append(crossed_feature1)
crossed_feature2 = feature_column.crossed_column(
[participant2_action, participant2_focus], hash_bucket_size=1000)
crossed_feature2 = feature_column.indicator_column(crossed_feature2)
feature_columns.append(crossed_feature2)
crossed_feature3 = feature_column.crossed_column(
[participant3_action, participant3_focus], hash_bucket_size=1000)
crossed_feature3 = feature_column.indicator_column(crossed_feature3)
feature_columns.append(crossed_feature3)
self.feature_columns = feature_columns
print("Every feature:", list(feature_batch.keys()))
print("A batch of ages", feature_batch["Age"])
print("A batch of targets", label_batch)
example_batch = next(iter(train_ds))[0] # 一个批量的数据
def demo(feature_column): # 显示相关数据 ＃ 进行显示
feature_layer = layers.DenseFeatures(feature_columns=feature_column)
print(feature_layer(example_batch).numpy())
feature_columns = []
# ---------------------------------------数值列--------------------------------------------
for header in ["PhotoAmt", "Fee", "Age"]:
feature_columns.append(feature_column.numeric_column(header))
# 测试
# photo_count = feature_column.numeric_column('PhotoAmt')
# demo(photo_count)
# --------------------------------------分桶列--------------------------------------------------
age = feature_column.numeric_column(key="Age")
age_buckets = feature_column.bucketized_column(source_column=age,
boundaries=[1, 2, 3, 4, 5])
# 测试
# demo(age_buckets)
feature_columns.append(age_buckets)
# --------------------------------------种类列--------------------------------------------------
animal_type = feature_column.categorical_column_with_vocabulary_list(
key="Type", vocabulary_list=["Cat", "Dog"])
if options['distribute']:
return dataset
else:
return dataset.make_one_shot_iterator().get_next()
return _input_fn
#
# The input layer: See Feature_Engineering.ipynb for explanations
#
from tensorflow.feature_column import numeric_column
from tensorflow.feature_column import crossed_column
from tensorflow.feature_column import indicator_column
from tensorflow.feature_column import categorical_column_with_identity
from tensorflow_transform.tf_metadata import dataset_schema
beta1 = numeric_column('beta1')
beta2 = numeric_column('beta2')
weekday = categorical_column_with_identity('weekday', num_buckets=7)
hour = categorical_column_with_identity('hour', num_buckets=24)
hour_of_week = indicator_column(crossed_column([weekday, hour], 24 * 7))
all_feature_columns = [beta1, beta2, hour_of_week]
def input_layer(features):
return tf.feature_column.input_layer(features,
feature_columns=all_feature_columns)
# ax.scatter3D(data_train["Temp"], data_train["QDot"], data_train["HTC"], c='k', marker='x', label="Train")
# ax.set_xlabel("Temp")
# ax.set_ylabel("QDot")
# ax.set_zlabel("HTC")
# plt.legend()
#plt.show()
data_train.head()
#%%
# Feature Columns: As for any other TF estimator, data needs to be passed to the estimator,
# which is typically via an input_fn and parsed using FeatureColumns.
feature_columns = [
fc.numeric_column("Temp"),
fc.numeric_column("QDot")
]
# creating input_fn: As for any other estimator, you can use an input_fn to feed data to the
# model for training and evaluation. TFL estimators automatically calculate quantiles of
# the features and use them as input keypoints for the PWL calibration layer. To do so,
# they require passing a feature_analysis_input_fn, which is similar to the training
# input_fn but with a single epoch or a subsample of the data.
train_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
x=data_train[data_train.columns[:2]],
y=data_train["HTC"],
shuffle=False,
batch_size=BATCH_SIZE,
num_epochs=NUM_EPOCHS,
num_threads=1
#demo(feature_column.indicator_column(crossed_feature))
feature_columns = []
#Age,Gender,Total_Bilirubin,Direct_Bilirubin,Alkaline_Phosphotase,Alamine_Aminotransferase,Aspartate_Aminotransferase,Total_Protiens,Albumin,Albumin_and_Globulin_Ratio,Dataset
#feature_columns.append(gender_one_hot)
#feature_columns.append(age_buckets)
#feature_columns.append(age)
# numeric cols
for header in numeric_columns:
print('Printing header')
print(header)
feature_columns.append(
feature_column.numeric_column(header, dtype=tf.float64))
# bucketized cols
age = feature_column.numeric_column('Age')
age_buckets = feature_column.bucketized_column(
age,
boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90])
gender = feature_column.categorical_column_with_vocabulary_list(
'Gender', ['Male', 'Female'])
gender_one_hot = feature_column.indicator_column(gender)
feature_columns.append(age_buckets)
feature_columns.append(gender_one_hot)
train_ds = df_to_dataset(train, shuffle=True, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=True, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=True, batch_size=batch_size)
for feature_batch, label_batch in train_ds.take(1):
print("dnesity feature", feature_batch['density'])
example_batch = next(iter(train_ds))[0]
def demo(feature_column):
feature_layer = layers.DenseFeatures(feature_column)
print(feature_layer(example_batch).numpy())
dioxide = feature_column.numeric_column('free_sulfur_dioxide')
demo(dioxide)
dioxide_buckets = feature_column.bucketized_column(
dioxide, boundaries=[2, 6, 10, 14, 18, 24, 30, 50])
demo(dioxide_buckets)
# fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
#0 7.4 0.700 0.00 1.9 0.076 11.0 34.0 0.9978 3.51 0.56 9.4 5
feature_columns = []
for header in [
'fixed_acidity', 'volatile_acidity', 'residual_sugar', 'chlorides',
'free_sulfur_dioxide', 'total_sulfur_dioxide', 'density', 'pH',
'sulphates', 'alcohol'
print('Grupo de objetivos: ', label_batch)
# We will use this batch to demonstrate several types of feature columns
example_batch = next(iter(train_ds))[0]
# A utility method to create a feature column
# and to transform a batch of data
def demo(feature_column):
feature_layer = layers.DenseFeatures(feature_column)
print(feature_layer(example_batch).numpy())
feature_columns = []
# Creamos diferentes columnas para ver resultados de cada caracteristica
age = feature_column.numeric_column("edad")
demo(age)
age_buckets = feature_column.bucketized_column(age, boundaries=[16, 25, 30, 35, 40, 45, 50])
demo(age_buckets)
feature_columns.append(age_buckets)
genero = feature_column.categorical_column_with_vocabulary_list(
'genero', ['Hombre', 'Mujer'])
genero_one_hot = feature_column.indicator_column(genero)
demo(genero_one_hot)
feature_columns.append(genero_one_hot)
ubicacion = feature_column.categorical_column_with_vocabulary_list(
'ubicacion', ['Pueblo', 'Ciudad'])
def get_compiled_model(headers,targetGroup,denseNum):
# model = tf.keras.Sequential([
# tf.keras.layers.Dense(10, activation='relu'),
# tf.keras.layers.Dense(10, activation='relu'),
# tf.keras.layers.Dense(1, activation='sigmoid')
# ])
# model.compile(optimizer='adam',
# loss='binary_crossentropy',
# metrics=['accuracy'])
# model = keras.models.Sequential()
# for _ in range(20):
# model.add(keras.layers.Dense(10, activation="relu"))
# # model.add(keras.layers.AlphaDropout(rate=0.5))
# # AlphaDropout: 1. 均值和方差不变 2. 归一化性质也不变
# model.add(keras.layers.Dense(1, activation="sigmoid"))
feature_columns = []
for header in headers:
feature_columns.append(feature_column.numeric_column(header))
feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
model = keras.models.Sequential()
# model.add(keras.layers.Flatten(input_shape=(0,1, len(headers))),)
# model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(, return_sequences=True)))
# for _ in range(20):
# model.add(keras.layers.Dense(len(headers), activation='relu'))
# model.add(keras.layers.Dense(10))
# # model.add(keras.layers.Dense(10, activation="selu"))
# # model.add(keras.layers.Dense(len(headers), activation="relu"))
# model.add(keras.layers.Dense(len(headers), activation="relu"))
# model.add(keras.layers.AlphaDropout(rate=0.5))
# model.add(keras.layers.Dense(10, activation="relu"))
# model.add(tf.keras.layers.Dense(128,activation='relu'))
# model.add(tf.keras.layers.Flatten())
# AlphaDropout: 1. 均值和方差不变 2. 归一化性质也不变
# model.add(keras.layers.Dense(len(headers), activation='relu'))
# model.add(keras.layers.Dense(3))
if len(targetGroup) > 2:
print('222222222222222222222222222222222222222222')
denseCount =len(targetGroup)
# model.add(keras.layers.Dense(denseCount, activation="softmax"))
# model.add(keras.layers.Flatten())
# model.add(keras.layers.Dense(len(headers), activation='relu'))
# model.add(keras.layers.Dense(len(headers), activation='relu'))
# model.add(keras.layers.Flatten())
# model.add(keras.layers.Dense(len(headers), activation='relu'))
# for _ in range(2):
# model.add(keras.layers.Dense(10*len(headers), activation='relu'))
# for _ in range(2):
# model.add(keras.layers.Dense(5*len(headers), activation='relu'))
# model.add(keras.layers.Dense(10*len(headers), activation='relu'))
# for _ in range(denseNum):
# model.add(keras.layers.Dense(len(headers), activation='relu'))
# # model.add(keras.layers.Dense(len(headers), activation='relu'))
# model.add(keras.layers.AlphaDropout(rate=0.5))
# # model.add(keras.layers.Dense(denseCount))
# model.add(tf.keras.layers.Dense(denseCount, activation='softmax'))
# model.add(tf.keras.layers.Dense(denseCount))
# model.compile(optimizer='adam',
# loss='sparse_categorical_crossentropy',
# metrics=['accuracy'])
# model.compile(optimizer= tf.keras.optimizers.Adam(),
# loss= tf.keras.losses.SparseCategoricalCrossentropy(),
# metrics=['accuracy'])
model = keras.Sequential([
# keras.layers.Dense(64, activation='relu',input_dim=len(headers)),
keras.layers.Dense(64, activation='relu',input_dim=len(headers)),
keras.layers.Dense(64, activation='relu'),
keras.layers.AlphaDropout(rate=0.5),
# keras.layers.Dense(len(headers), activation='relu'),
keras.layers.Dense(denseCount, activation='softmax')
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# model.compile(optimizer=keras.optimizers.Adam(lr=1e-3),
# loss=keras.losses.BinaryCrossentropy(),
# metrics=[keras.metrics.SparseCategoricalAccuracy()])
# model.add(tf.keras.layers.Dense(denseCount))
# model.add(tf.keras.layers.Dense(denseCount))
# model.add(tf.keras.layers.Dense(denseCount))
# model.compile(optimizer='adam',
# loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
# metrics=["accuracy"])
# model.compile(optimizer= tf.keras.optimizers.Adam(lr=1e-3),
# loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
# metrics=["accuracy"])
# model.compile( optimizer=keras.optimizers.Adam(lr=1e-3),
# loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
# metrics=["accuracy"])
# model.compile(optimizer=tf.keras.optimizers.Adam(),
# loss='sparse_categorical_crossentropy',
# metrics=["accuracy"])
# model.add(keras.layers.Dense(denseCount))
# model.compile(optimizer='adam',
# loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
# metrics=['accuracy'])
else:
# for _ in range(denseNum):
# model.add(keras.layers.Dense(len(headers), activation='relu'))
# # model.add(keras.layers.Dense(10, activation='relu'))
# # model.add(keras.layers.Dense(len(headers), activation='relu'))
# # model.add(keras.layers.Dense(len(headers), activation='relu'))
# # model.add(keras.layers.Dense(len(headers), activation='relu'))
# model.add(keras.layers.AlphaDropout(rate=0.5))
# model.add(keras.layers.Dense(1, activation="sigmoid"))
model = keras.Sequential([
# keras.layers.Dense(64, activation='relu',input_dim=len(headers)),
keras.layers.Dense(64, activation='relu',input_dim=len(headers)),
keras.layers.Dense(64, activation='relu'),
keras.layers.AlphaDropout(rate=0.5),
# keras.layers.Dense(len(headers), activation='relu'),
keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'],
run_eagerly=True)
# model.summary()
# model.compile(
# optimizer='adam',
# loss='categorical_crossentropy',
# metrics=['accuracy'],
# run_eagerly=True,
# # optimizer='sgd'
# )
# model.add(keras.layers.Dense(denseCount, activation="sigmoid"))
# 配置 SGD，学习率为 0.1
# optimizer = tf.keras.optimizers.SGD(0.1)
# model.compile(optimizer=optimizer,
# loss = loss,
# metrics=['accuracy'])
# model.compile(optimizer = 'adam' , loss = 'sparse_categorical_crossentropy',metrics = ['accuracy'])
# model = tf.keras.Sequential([
# feature_layer,
# keras.layers.Dense(128, activation='relu'),
# keras.layers.Dense(128, activation='relu'),
# keras.layers.Dense(1, activation='sigmoid')
# ])
# layer0 = tf.keras.layers.Dense(class_num, input_shape=(x_data.shape[1],), activation='softmax')
# model = tf.keras.Sequential([layer0])
# model.compile(loss='categorical_crossentropy', optimizer='adam')
# #首先先将数据集归一化
# train_image = train_image/255
# test_image = test_image/255
# #创建model
# model = tf.keras.Sequential()
# #添加隐含层
# model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
# model.add(tf.keras.layers.Dense(128,activation = 'relu'))
# model.add(tf.keras.layers.Dense(10,activation = 'softmax'))
# #编译model
# model.compile(optimizer = 'adam' , loss = 'sparse_categorical_crossentropy',metrics = ['acc'])
# model.compile(loss='categorical_crossentropy', optimizer='adam')
# #训练model
# model.fit(train_image , train_label,epochs = 5)
# #进行model的预测
# model.predict(test_image , test_label)
# model.compile(
# optimizer='adam',
# loss='categorical_crossentropy',
# metrics=['accuracy'],
# run_eagerly=True,
# # optimizer='sgd'
# )
# model.compile(optimizer=tf.keras.optimizers.RMSprop(),
# loss=tf.keras.losses.SparseCategoricalCrossentropy(),
# metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
# model.compile(optimizer='adam',
# loss='binary_crossentropy',
# metrics=['accuracy'],
# run_eagerly=True)
return model
print(numeric_data)
#import pdb; pdb.set_trace()
#demo(numeric_data)
#pdb.set_trace()
print('GENDER============>')
gender = feature_column.categorical_column_with_vocabulary_list(
'Gender', ['Male', 'Female'])
gender_one_hot = feature_column.indicator_column(gender)
print('Gender one hot')
print(gender_one_hot)
pdb.set_trace()
print('AGE==============>')
age = feature_column.numeric_column('Age')
print("Demo AGE")
demo(age)
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90])
print(age_buckets)
pdb.set_trace()
numeric_data['Age_Buckets']= pd.DataFrame(age_buckets)
numeric_data['Gender'] = pd.DataFrame(gender_one_hot)
print('DEBUG==========>')
print(numeric_data)
print(age_buckets_df)
print('Gender one hot')
print(gender_one_hot)
def main(_):
# Parse configs updates from command line flags.
config_updates = []
for update in FLAGS.config_updates:
config_updates.extend(re.findall(r'(\S*)\s*=\s*(\S*)', update))
# UCI Statlog (Heart) dataset.
csv_file = tf.keras.utils.get_file(
'heart.csv',
'http://storage.googleapis.com/download.tensorflow.org/data/heart.csv')
df = pd.read_csv(csv_file)
target = df.pop('target')
train_size = int(len(df) * 0.8)
train_x = df[:train_size]
train_y = target[:train_size]
test_x = df[train_size:]
test_y = target[train_size:]
# feature_analysis_input_fn is used to collect statistics about the input
# features, thus requiring only one loop of the dataset.
#
# feature_analysis_input_fn is required if you have at least one FeatureConfig
# with "pwl_calibration_input_keypoints='quantiles'". Note that 'quantiles' is
# default keypoints configuration so most likely you'll need it.
feature_analysis_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
x=train_x,
y=train_y,
shuffle=False,
batch_size=FLAGS.batch_size,
num_epochs=1,
num_threads=1)
# prefitting_input_fn is used to prefit an initial ensemble that is used to
# estimate feature interactions. This prefitting step does not need to fully
# converge and thus requiring fewer epochs than the main training.
#
# prefitting_input_fn is only required if your model_config is
# CalibratedLatticeEnsembleConfig with "lattices='crystals'"
prefitting_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
x=train_x,
y=train_y,
shuffle=True,
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.prefitting_num_epochs,
num_threads=1)
train_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
x=train_x,
y=train_y,
shuffle=True,
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs,
num_threads=1)
test_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
x=test_x,
y=test_y,
shuffle=False,
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs,
num_threads=1)
# Feature columns.
# - age
# - sex
# - cp chest pain type (4 values)
# - trestbps resting blood pressure
# - chol serum cholestoral in mg/dl
# - fbs fasting blood sugar > 120 mg/dl
# - restecg resting electrocardiographic results (values 0,1,2)
# - thalach maximum heart rate achieved
# - exang exercise induced angina
# - oldpeak ST depression induced by exercise relative to rest
# - slope the slope of the peak exercise ST segment
# - ca number of major vessels (0-3) colored by flourosopy
# - thal 3 = normal; 6 = fixed defect; 7 = reversable defect
feature_columns = [
fc.numeric_column('age', default_value=-1),
fc.categorical_column_with_vocabulary_list('sex', [0, 1]),
fc.numeric_column('cp'),
fc.numeric_column('trestbps', default_value=-1),
fc.numeric_column('chol'),
fc.categorical_column_with_vocabulary_list('fbs', [0, 1]),
fc.categorical_column_with_vocabulary_list('restecg', [0, 1, 2]),
fc.numeric_column('thalach'),
fc.categorical_column_with_vocabulary_list('exang', [0, 1]),
fc.numeric_column('oldpeak'),
fc.categorical_column_with_vocabulary_list('slope', [0, 1, 2]),
fc.numeric_column('ca'),
fc.categorical_column_with_vocabulary_list(
'thal', ['normal', 'fixed', 'reversible']),
]
# Feature configs are used to specify how each feature is calibrated and used.
feature_configs = [
configs.FeatureConfig(
name='age',
lattice_size=3,
# By default, input keypoints of pwl are quantiles of the feature.
pwl_calibration_num_keypoints=5,
monotonicity='increasing',
pwl_calibration_clip_max=100,
),
configs.FeatureConfig(
name='cp',
pwl_calibration_num_keypoints=4,
# Keypoints can be uniformly spaced.
pwl_calibration_input_keypoints='uniform',
monotonicity='increasing',
),
configs.FeatureConfig(
name='chol',
# Explicit input keypoint initialization.
pwl_calibration_input_keypoints=[
126.0, 210.0, 247.0, 286.0, 564.0
],
monotonicity='increasing',
pwl_calibration_clip_min=130,
# Calibration can be forced to span the full output range by clamping.
pwl_calibration_clamp_min=True,
pwl_calibration_clamp_max=True,
# Per feature regularization.
regularizer_configs=[
configs.RegularizerConfig(name='calib_hessian', l2=1e-4),
],
),
configs.FeatureConfig(
name='fbs',
# Monotonicity: output for 1 should be larger than output for 0.
monotonicity=[(0, 1)],
),
configs.FeatureConfig(
name='trestbps',
pwl_calibration_num_keypoints=5,
monotonicity='decreasing',
),
configs.FeatureConfig(
name='thalach',
pwl_calibration_num_keypoints=5,
monotonicity='decreasing',
),
configs.FeatureConfig(
name='restecg',
# Categorical monotonicity can be partial order.
monotonicity=[(0, 1), (0, 2)],
),
configs.FeatureConfig(
name='exang',
monotonicity=[(0, 1)],
),
configs.FeatureConfig(
name='oldpeak',
pwl_calibration_num_keypoints=5,
monotonicity='increasing',
),
configs.FeatureConfig(
name='slope',
monotonicity=[(0, 1), (1, 2)],
),
configs.FeatureConfig(
name='ca',
pwl_calibration_num_keypoints=4,
monotonicity='increasing',
),
configs.FeatureConfig(
name='thal',
monotonicity=[('normal', 'fixed'), ('normal', 'reversible')],
),
]
# Serving input fn is used to create saved models.
serving_input_fn = (
tf.estimator.export.build_parsing_serving_input_receiver_fn(
feature_spec=fc.make_parse_example_spec(feature_columns)))
# Model config defines the model strcutre for the estimator.
# This is calibrated linear model with outputput calibration: Inputs are
# calibrated, linearly combined and the output of the linear layer is
# calibrated again using a PWL function.
model_config = configs.CalibratedLinearConfig(
feature_configs=feature_configs,
use_bias=True,
output_calibration=True,
regularizer_configs=[
# Regularizer for the output calibrator.
configs.RegularizerConfig(name='output_calib_hessian', l2=1e-4),
])
# Update model configuration.
# See tfl.configs.apply_updates for details.
configs.apply_updates(model_config, config_updates)
estimator = estimators.CannedClassifier(
feature_columns=feature_columns,
model_config=model_config,
feature_analysis_input_fn=feature_analysis_input_fn,
optimizer=tf.keras.optimizers.Adam(FLAGS.learning_rate))
estimator.train(input_fn=train_input_fn)
results = estimator.evaluate(input_fn=test_input_fn)
print('Calibrated linear results: {}'.format(results))
print('Calibrated linear model exported to {}'.format(
estimator.export_saved_model(estimator.model_dir, serving_input_fn)))
# This is calibrated lattice model: Inputs are calibrated, then combined
# non-linearly using a lattice layer.
model_config = configs.CalibratedLatticeConfig(
feature_configs=feature_configs,
regularizer_configs=[
# Torsion regularizer applied to the lattice to make it more linear.
configs.RegularizerConfig(name='torsion', l2=1e-4),
# Globally defined calibration regularizer is applied to all features.
configs.RegularizerConfig(name='calib_hessian', l2=1e-4),
])
estimator = estimators.CannedClassifier(
feature_columns=feature_columns,
model_config=model_config,
feature_analysis_input_fn=feature_analysis_input_fn,
optimizer=tf.keras.optimizers.Adam(FLAGS.learning_rate))
estimator.train(input_fn=train_input_fn)
results = estimator.evaluate(input_fn=test_input_fn)
print('Calibrated lattice results: {}'.format(results))
print('Calibrated lattice model exported to {}'.format(
estimator.export_saved_model(estimator.model_dir, serving_input_fn)))
# This is random lattice ensemble model with separate calibration:
# model output is the average output of separately calibrated lattices.
model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=feature_configs,
num_lattices=6,
lattice_rank=5,
separate_calibrators=True,
regularizer_configs=[
# Torsion regularizer applied to the lattice to make it more linear.
configs.RegularizerConfig(name='torsion', l2=1e-4),
# Globally defined calibration regularizer is applied to all features.
configs.RegularizerConfig(name='calib_hessian', l2=1e-4),
])
configs.apply_updates(model_config, config_updates)
estimator = estimators.CannedClassifier(
feature_columns=feature_columns,
model_config=model_config,
feature_analysis_input_fn=feature_analysis_input_fn,
optimizer=tf.keras.optimizers.Adam(FLAGS.learning_rate))
estimator.train(input_fn=train_input_fn)
results = estimator.evaluate(input_fn=test_input_fn)
print('Random ensemble results: {}'.format(results))
print('Random ensemble model exported to {}'.format(
estimator.export_saved_model(estimator.model_dir, serving_input_fn)))
# This is Crystals ensemble model with separate calibration: model output is
# the average output of separately calibrated lattices.
# Crystals algorithm first trains a prefitting model and uses the interactions
# between features to form the final lattice ensemble.
model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=feature_configs,
# Using Crystals algorithm.
lattices='crystals',
num_lattices=6,
lattice_rank=5,
separate_calibrators=True,
regularizer_configs=[
# Torsion regularizer applied to the lattice to make it more linear.
configs.RegularizerConfig(name='torsion', l2=1e-4),
# Globally defined calibration regularizer is applied to all features.
configs.RegularizerConfig(name='calib_hessian', l2=1e-4),
])
configs.apply_updates(model_config, config_updates)
estimator = estimators.CannedClassifier(
feature_columns=feature_columns,
model_config=model_config,
feature_analysis_input_fn=feature_analysis_input_fn,
# prefitting_input_fn is required to train the prefitting model.
prefitting_input_fn=prefitting_input_fn,
optimizer=tf.keras.optimizers.Adam(FLAGS.learning_rate))
estimator.train(input_fn=train_input_fn)
results = estimator.evaluate(input_fn=test_input_fn)
print('Crystals ensemble results: {}'.format(results))
print('Crystals ensemble model exported to {}'.format(
estimator.export_saved_model(estimator.model_dir, serving_input_fn)))
print('A batch of targets:', label_batch)
# We will use this batch to demonstrate several types of feature columns
example_batch = next(iter(train_ds))[0]
# A utility method to create a feature column
# and to transform a batch of data
def demo(feature_column):
feature_layer = layers.DenseFeatures(feature_column)
print(feature_layer(example_batch).numpy())
#Age,Gender,Total_Bilirubin,Direct_Bilirubin,Alkaline_Phosphotase,Alamine_Aminotransferase,Aspartate_Aminotransferase,Total_Protiens,Albumin,Albumin_and_Globulin_Ratio,Dataset
age = feature_column.numeric_column('Age')
print("Demo AGE")
demo(age)
age_buckets = feature_column.bucketized_column(
age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65, 75, 85, 90])
print('Demo AGE buckets')
demo(age_buckets)
gender = feature_column.categorical_column_with_vocabulary_list(
'Gender', ['Male', 'Female'])
gender_one_hot = feature_column.indicator_column(gender)
print('Gender one hot')
demo(gender_one_hot)
#for feature_batch, label_batch in train_ds.take(1):
#print('Every feature:', list(feature_batch.keys()))
#print('A batch of YTG:', feature_batch['YardsToGo'])
#print('A batch of targets:', label_batch)
#print(tf.shape(label_batch))
example_batch = next(iter(train_ds))[0]
def demo(feature_column):
feature_layer = layers.DenseFeatures(feature_column)
print(feature_layer(example_batch).numpy())
feature_columns = []
for header in ['Down', 'ScoreDifferential', 'CurrentQuarter', 'OffenseSPRank', 'RushRank', 'PassRank', 'OpponentPlay', 'YardsToGo', 'YardsToGoal', 'Location']:
feature_columns.append(feature_column.numeric_column(header))
Down = feature_column.numeric_column("Down")
Quarter = feature_column.numeric_column("CurrentQuarter")
Score = feature_column.numeric_column("ScoreDifferential")
SP = feature_column.numeric_column("OffenseSPRank")
RushR = feature_column.numeric_column("RushRank")
PassR = feature_column.numeric_column("PassRank")
YTGo = feature_column.numeric_column("YardsToGo")
YTGoal = feature_column.numeric_column("YardsToGoal")
Play = feature_column.categorical_column_with_vocabulary_list('OpponentPlay', ['Pass', 'Run'])
Location = feature_column.categorical_column_with_vocabulary_list('Location', ['Home', 'Neutral', 'Away'])
Down_buckets = feature_column.bucketized_column(Down, boundaries=[4])
feature_columns.append(Down_buckets)
Quarter_buckets = feature_column.bucketized_column(Quarter, boundaries=[2, 3, 4])
def create_feature_columns():
c2id = fc.categorical_column_with_hash_bucket("cate2Id",
5000,
dtype=tf.int64)
modified_time = fc.numeric_column("modified_time", default_value=0.0)
modified_time_sqrt = fc.numeric_column("modified_time_sqrt",
default_value=0.0)
modified_time_square = fc.numeric_column("modified_time_square",
default_value=0.0)
props_sex = fc.indicator_column(
fc.categorical_column_with_vocabulary_list("props_sex",
["男", "女", "通用", "情侣"],
default_value=0))
brand_grade = fc.indicator_column(
fc.categorical_column_with_vocabulary_list(
"brand_grade", ["A类品牌", "B类品牌", "C类品牌", "D类品牌"], default_value=0))
shipment_rate = fc.numeric_column("shipment_rate", default_value=0.0)
shipping_rate = fc.numeric_column("shipping_rate", default_value=0.0)
ipv_ntile = fc.bucketized_column(
fc.numeric_column("ipv_ntile", dtype=tf.int64, default_value=99),
boundaries=[1, 2, 3, 4, 5, 10, 20, 50, 80])
pay_ntile = fc.bucketized_column(
fc.numeric_column("pay_ntile", dtype=tf.int64, default_value=99),
boundaries=[1, 2, 3, 4, 5, 10, 20, 50, 80])
price = fc.numeric_column("price_norm", default_value=0.0)
ctr_1d = fc.numeric_column("ctr_1d", default_value=0.0)
cvr_1d = fc.numeric_column("cvr_1d", default_value=0.0)
uv_cvr_1d = fc.numeric_column("uv_cvr_1d", default_value=0.0)
ctr_1w = fc.numeric_column("ctr_1w", default_value=0.0)
cvr_1w = fc.numeric_column("cvr_1w", default_value=0.0)
uv_cvr_1w = fc.numeric_column("uv_cvr_1w", default_value=0.0)
ctr_2w = fc.numeric_column("ctr_2w", default_value=0.0)
cvr_2w = fc.numeric_column("cvr_2w", default_value=0.0)
uv_cvr_2w = fc.numeric_column("uv_cvr_2w", default_value=0.0)
ctr_1m = fc.numeric_column("ctr_1m", default_value=0.0)
cvr_1m = fc.numeric_column("cvr_1m", default_value=0.0)
uv_cvr_1m = fc.numeric_column("uv_cvr_1m", default_value=0.0)
pay_qty_1d = fc.numeric_column("pay_qty_1d", default_value=0.0)
pay_qty_1w = fc.numeric_column("pay_qty_1w", default_value=0.0)
pay_qty_2w = fc.numeric_column("pay_qty_2w", default_value=0.0)
pay_qty_1m = fc.numeric_column("pay_qty_1m", default_value=0.0)
cat2_pay_qty = fc.numeric_column("cat2_pay_qty_1d", default_value=0.0)
cat1_pay_qty = fc.numeric_column("cat1_pay_qty_1d", default_value=0.0)
brd_pay_qty = fc.numeric_column("brd_pay_qty_1d", default_value=0.0)
slr_pay_qty_1d = fc.numeric_column("slr_pay_qty_1d", default_value=0.0)
slr_pay_qty_1w = fc.numeric_column("slr_pay_qty_1w", default_value=0.0)
slr_pay_qty_2w = fc.numeric_column("slr_pay_qty_2w", default_value=0.0)
slr_pay_qty_1m = fc.numeric_column("slr_pay_qty_1m", default_value=0.0)
slr_brd_pay_qty_1d = fc.numeric_column("slr_brd_pay_qty_1d",
default_value=0.0)
slr_brd_pay_qty_1w = fc.numeric_column("slr_brd_pay_qty_1w",
default_value=0.0)
slr_brd_pay_qty_2w = fc.numeric_column("slr_brd_pay_qty_2w",
default_value=0.0)
slr_brd_pay_qty_1m = fc.numeric_column("slr_brd_pay_qty_1m",
default_value=0.0)
weighted_ipv = fc.numeric_column("weighted_ipv", default_value=0.0)
cat1_weighted_ipv = fc.numeric_column("cat1_weighted_ipv",
default_value=0.0)
cate_weighted_ipv = fc.numeric_column("cate_weighted_ipv",
default_value=0.0)
slr_weighted_ipv = fc.numeric_column("slr_weighted_ipv", default_value=0.0)
brd_weighted_ipv = fc.numeric_column("brd_weighted_ipv", default_value=0.0)
cms_scale = fc.numeric_column("cms_scale", default_value=0.0)
cms_scale_sqrt = fc.numeric_column("cms_scale_sqrt", default_value=0.0)
# context feature
matchScore = fc.numeric_column("matchScore", default_value=0.0)
popScore = fc.numeric_column("popScore", default_value=0.0)
brandPrefer = fc.numeric_column("brandPrefer", default_value=0.0)
cate2Prefer = fc.numeric_column("cate2Prefer", default_value=0.0)
catePrefer = fc.numeric_column("catePrefer", default_value=0.0)
sellerPrefer = fc.numeric_column("sellerPrefer", default_value=0.0)
matchType = fc.indicator_column(
fc.categorical_column_with_identity("matchType", 9, default_value=0))
position = fc.bucketized_column(fc.numeric_column("position",
dtype=tf.int64,
default_value=301),
boundaries=[
1, 2, 3, 4, 5, 6, 7, 8, 9, 15, 20, 30,
40, 50, 80, 100, 150, 200, 300
])
triggerNum = fc.indicator_column(
fc.categorical_column_with_identity("triggerNum", 41,
default_value=40))
triggerRank = fc.indicator_column(
fc.categorical_column_with_identity("triggerRank",
41,
default_value=40))
sceneType = fc.indicator_column(
fc.categorical_column_with_identity("type", 2, default_value=0))
hour = fc.indicator_column(
fc.categorical_column_with_identity("hour", 24, default_value=0))
phoneBrandId = fc.categorical_column_with_hash_bucket("phoneBrand", 1000)
phoneBrand = fc.shared_embedding_columns([phoneBrandId], 20)
phoneResolutionId = fc.categorical_column_with_hash_bucket(
"phoneResolution", 500)
phoneResolution = fc.shared_embedding_columns([phoneResolutionId], 10)
phoneOs = fc.indicator_column(
fc.categorical_column_with_vocabulary_list("phoneOs",
["android", "ios"],
default_value=0))
tab = fc.indicator_column(
fc.categorical_column_with_vocabulary_list("tab", [
"ALL", "TongZhuang", "XieBao", "MuYing", "NvZhuang", "MeiZhuang",
"JuJia", "MeiShi"
],
default_value=0))
c2id_embed = fc.shared_embedding_columns(
[c2id], 16, shared_embedding_collection_name="c2id")
feature_columns = [
matchScore, matchType, position, triggerNum, triggerRank, sceneType,
hour, phoneOs, tab, popScore, sellerPrefer, brandPrefer, cate2Prefer,
catePrefer, price, props_sex, brand_grade, modified_time,
modified_time_sqrt, modified_time_square, shipment_rate, shipping_rate,
ipv_ntile, pay_ntile, uv_cvr_1d, uv_cvr_1w, uv_cvr_2w, uv_cvr_1m,
ctr_1d, ctr_1w, ctr_2w, ctr_1m, cvr_1d, cvr_1w, cvr_2w, cvr_1m,
pay_qty_1d, pay_qty_1w, pay_qty_2w, pay_qty_1m, cat2_pay_qty,
cat1_pay_qty, brd_pay_qty, slr_pay_qty_1d, slr_pay_qty_1w,
slr_pay_qty_2w, slr_pay_qty_1m, slr_brd_pay_qty_1d, slr_brd_pay_qty_1w,
slr_brd_pay_qty_2w, slr_brd_pay_qty_1m, weighted_ipv,
cat1_weighted_ipv, cate_weighted_ipv, slr_weighted_ipv,
brd_weighted_ipv, cms_scale, cms_scale_sqrt
]
feature_columns += c2id_embed
feature_columns += phoneResolution
feature_columns += phoneBrand
return feature_columns
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import feature_column as fc
from tensorflow.python.lib.io import file_io
from tensorflow.python.ops import init_ops
from .eval_metrics import AverageNClass, HitAtOne
N_CLASS = 3862
BATCH_SIZE = 1024
VOCAB_FILE = "data/vocabulary.csv"
# Exclude audio feature since we didn't implement audio feature extraction.
# Even if the model can be trained on audio feature,
# they won't be available for inference on new video.
FEAT_COL_VIDEO = [
fc.numeric_column(key="mean_rgb", shape=(1024, ), dtype=tf.float32),
#fc.numeric_column(key="mean_audio", shape=(128,), dtype=tf.float32),
fc.indicator_column(
fc.categorical_column_with_identity(key="labels", num_buckets=N_CLASS))
]
FEAT_X = ["mean_rgb"]
FEAT_SPEC_VIDEO = fc.make_parse_example_spec(FEAT_COL_VIDEO)
MULTI_HOT_ENCODER = tf.keras.layers.DenseFeatures(FEAT_COL_VIDEO[-1])
# If we'd like to use a custom serving input function, we need to use the estimator API.
# There is no document on how a keras model can use a custom serving input function.
KERAS_TO_ESTIMATOR = True
def calc_class_weight(infile, scale=1):
"""Calculate class weight to re-balance label distribution.
The class weight for class i (w_i) is determined by:
def _sparse_fc(sparse_feature: SparseFeature):
return fc.numeric_column(key=sparse_feature.feature_name,
dtype=tf.int64)
def create_feature_columns(self):
_NUMERIC_COLUMNS = ['posted_time', 'owner_influence', 'poster_influence', 'participant1_influence',
'participant2_influence', 'participant3_influence', 'participant4_influence',
'participant5_influence']
_BINARY_COLUMNS = ["is_mentions_me", "is_mentions_connections", "is_commented_by_me",
"is_commented_by_connections", "is_liked_by_me", "is_liked_by_connections",
"poster_is_employee","poster_is_in_connections",
"participant1_is_employee", "participant1_is_in_connections",
"participant2_is_employee", "participant2_is_in_connections",
"participant3_is_employee", "participant3_is_in_connections",
"participant4_is_employee", "participant4_is_in_connections",
"participant5_is_employee", "participant5_is_in_connections"]
_GENDER_COLUMNS = ["poster_gender", "participant1_gender", "participant2_gender", "participant3_gender",
"participant4_gender", "participant5_gender"]
self.real = {
colname: feature_column.numeric_column(colname) \
for colname in _NUMERIC_COLUMNS
}
self.sparse = dict()
app_type = feature_column.categorical_column_with_vocabulary_list(
'app_type', self.df.app_type.unique())
app_type_1hot = feature_column.indicator_column(app_type)
self.sparse["app_type"] = app_type_1hot
owner_type = feature_column.categorical_column_with_vocabulary_list(
'owner_type', self.df.owner_type.unique())
owner_type_1hot = feature_column.indicator_column(owner_type)
self.sparse["owner_type"] = owner_type_1hot
poster_focus = feature_column.categorical_column_with_vocabulary_list(
'poster_focus', ['engineering', 'sales', 'marketing', 'management', 'financial', 'other'])
poster_focus_1hot = feature_column.indicator_column(poster_focus)
self.sparse["poster_focus"] = poster_focus_1hot
for col in _GENDER_COLUMNS:
feature = feature_column.categorical_column_with_vocabulary_list(col, self.df[col].unique())
feature_1hot = feature_column.indicator_column(feature)
self.sparse[col] = feature_1hot
participant1_action = participant_action("participant1_action")
participant2_action = participant_action("participant2_action")
participant3_action = participant_action("participant3_action")
participant4_action = participant_action("participant4_action")
participant5_action = participant_action("participant5_action")
participant1_focus = participant_focus("participant1_focus")
participant2_focus = participant_focus("participant2_focus")
participant3_focus = participant_focus("participant3_focus")
participant4_focus = participant_focus("participant4_focus")
participant5_focus = participant_focus("participant5_focus")
self.sparse["participant2_action"] = feature_column.indicator_column(participant2_action)
self.sparse["participant3_action"] = feature_column.indicator_column(participant3_action)
self.sparse["participant1_action"] = feature_column.indicator_column(participant1_action)
self.sparse["participant4_action"] = feature_column.indicator_column(participant4_action)
self.sparse["participant5_action"] = feature_column.indicator_column(participant5_action)
self.sparse["participant1_focus"] = feature_column.indicator_column(participant1_focus)
self.sparse["participant2_focus"] = feature_column.indicator_column(participant2_focus)
self.sparse["participant3_focus"] = feature_column.indicator_column(participant3_focus)
self.sparse["participant4_focus"] = feature_column.indicator_column(participant4_focus)
self.sparse["participant5_focus"] = feature_column.indicator_column(participant5_focus)
self.inputs = {
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='float32') \
for colname in self.real.keys()
}
self.inputs.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='string') \
for colname in self.sparse.keys()
})
for col in _BINARY_COLUMNS:
feature = feature_column.categorical_column_with_vocabulary_list(
col, self.df[col].unique())
feature_1hot = feature_column.indicator_column(feature)
self.sparse[col] = feature_1hot
likes_num = feature_column.numeric_column("number_of_likes")
likes_num_buckets = feature_column.bucketized_column(likes_num, boundaries=[2, 5, 10, 20, 50, 100])
self.sparse["number_of_likes"] = likes_num_buckets
comments_num = feature_column.numeric_column("number_of_comments")
comments_num_buckets = feature_column.bucketized_column(comments_num, boundaries=[1, 2, 5, 10, 20, 50, 100])
self.sparse["number_of_comments"] = comments_num_buckets
age_boundaries = [30 * _ONE_MIN, _ONE_HOUR, 2 * _ONE_HOUR, 3 * _ONE_HOUR, 4 * _ONE_HOUR, 24 * _ONE_HOUR]
age = feature_column.numeric_column("effective_age_long")
age_buckets = feature_column.bucketized_column(age, boundaries=age_boundaries)
self.sparse["effective_age_long"] = age_buckets
daytime = feature_column.numeric_column("daytime")
daytime_buckets = feature_column.bucketized_column(daytime, boundaries=[8, 12, 16, 24])
self.sparse["daytime"] = daytime_buckets
weekday = feature_column.categorical_column_with_vocabulary_list(
'weekday', self.df.weekday.unique())
weekday_1hot = feature_column.indicator_column(weekday)
self.sparse["weekday"] = weekday_1hot
self.inputs.update({
colname: tf.keras.layers.Input(name=colname, shape=(), dtype='int64') \
for colname in
_BINARY_COLUMNS + ["number_of_likes", "number_of_comments", "effective_age_long", "daytime", "weekday"]
})
# hash_bucket_size=30 because there are 7 possible values in weekday and 4 in daytime, all possible combinations will be 28 ~ 30
weekday_x_daytime = feature_column.crossed_column([weekday, daytime_buckets], hash_bucket_size=30)
self.sparse["weekday_x_daytime"] = feature_column.indicator_column(weekday_x_daytime)
# 6 bins in likes and 7 in comments
likes_x_comments = feature_column.crossed_column([likes_num_buckets, comments_num_buckets], hash_bucket_size=45)
self.sparse["likes_x_comments"] = feature_column.indicator_column(likes_x_comments)
# 6 bins in likes, 3 in action and 7 in focus, 6*3*7=126~130
likes_x_participant1_focus_n_action = feature_column.crossed_column(
[likes_num_buckets, participant1_action, participant1_focus], hash_bucket_size=130)
self.sparse["likes_x_participant1_focus_n_action"] = feature_column.indicator_column(
likes_x_participant1_focus_n_action)
likes_x_participant2_focus_n_action = feature_column.crossed_column(
[likes_num_buckets, participant2_action, participant2_focus], hash_bucket_size=130)
self.sparse["likes_x_participant2_focus_n_action"] = feature_column.indicator_column(
likes_x_participant2_focus_n_action)
likes_x_participant3_focus_n_action = feature_column.crossed_column(
[likes_num_buckets, participant3_action, participant3_focus], hash_bucket_size=130)
self.sparse["likes_x_participant3_focus_n_action"] = feature_column.indicator_column(
likes_x_participant3_focus_n_action)
likes_x_participant4_focus_n_action = feature_column.crossed_column(
[likes_num_buckets, participant4_action, participant4_focus], hash_bucket_size=130)
self.sparse["likes_x_participant4_focus_n_action"] = feature_column.indicator_column(
likes_x_participant4_focus_n_action)
likes_x_participant5_focus_n_action = feature_column.crossed_column(
[likes_num_buckets, participant5_action, participant5_focus], hash_bucket_size=130)
self.sparse["likes_x_participant5_focus_n_action"] = feature_column.indicator_column(
likes_x_participant5_focus_n_action)
def _dense_fc(dense_feature: DenseFeature):
return fc.numeric_column(key=dense_feature.feature_name)
def create_feature_columns(df, which_lanes):
transformed_df = df.copy()
all_lanes = which_lanes.copy()
champion_list = champ_info.id.tolist()
feature_columns = []
good_numeric = list(map(lambda x: "{}_good_numeric".format(x), all_lanes))
for header in good_numeric:
feature_columns.append(
feature_column.numeric_column(header, shape=(22, )))
bad_numeric = list(map(lambda x: "{}_bad_numeric".format(x), all_lanes))
for header in bad_numeric:
feature_columns.append(
feature_column.numeric_column(header, shape=(3, )))
good_categ = list(map(lambda x: "{}_good_categ".format(x), all_lanes))
for header in good_categ:
feature_columns.append(
feature_column.numeric_column(header, shape=(4, )))
bad_categ = list(map(lambda x: "{}_bad_categ".format(x), all_lanes))
for header in bad_categ:
feature_columns.append(
feature_column.numeric_column(header, shape=(2, )))
TOP100 = feature_column.categorical_column_with_vocabulary_list(
"TOP100_champ", champion_list)
TOP200 = feature_column.categorical_column_with_vocabulary_list(
"TOP200_champ", champion_list)
TOP_crossed = feature_column.crossed_column([TOP100, TOP200],
hash_bucket_size=1000)
TOP_crossed = feature_column.indicator_column(TOP_crossed)
feature_columns.append(TOP_crossed)
JUNGLE100 = feature_column.categorical_column_with_vocabulary_list(
"JUNGLE100_champ", champion_list)
JUNGLE200 = feature_column.categorical_column_with_vocabulary_list(
"JUNGLE200_champ", champion_list)
JUNGLE_crossed = feature_column.crossed_column([JUNGLE100, JUNGLE200],
hash_bucket_size=1000)
JUNGLE_crossed = feature_column.indicator_column(JUNGLE_crossed)
feature_columns.append(JUNGLE_crossed)
MID100 = feature_column.categorical_column_with_vocabulary_list(
"MID100_champ", champion_list)
MID200 = feature_column.categorical_column_with_vocabulary_list(
"MID200_champ", champion_list)
MID_crossed = feature_column.crossed_column([MID100, MID200],
hash_bucket_size=1000)
MID_crossed = feature_column.indicator_column(MID_crossed)
feature_columns.append(MID_crossed)
ADC100 = feature_column.categorical_column_with_vocabulary_list(
"ADC100_champ", champion_list)
ADC200 = feature_column.categorical_column_with_vocabulary_list(
"ADC200_champ", champion_list)
ADC_crossed = feature_column.crossed_column([ADC100, ADC200],
hash_bucket_size=1000)
ADC_crossed = feature_column.indicator_column(ADC_crossed)
feature_columns.append(ADC_crossed)
SUPPORT100 = feature_column.categorical_column_with_vocabulary_list(
"SUPPORT100_champ", champion_list)
SUPPORT200 = feature_column.categorical_column_with_vocabulary_list(
"SUPPORT200_champ", champion_list)
SUPPORT_crossed = feature_column.crossed_column([SUPPORT100, SUPPORT200],
hash_bucket_size=1000)
SUPPORT_crossed = feature_column.indicator_column(SUPPORT_crossed)
feature_columns.append(SUPPORT_crossed)
return feature_columns
#Configure the train_inpf to iterate over the data twice:
import functools
train_inpf = functools.partial(census_dataset.input_fn, train_file, num_epochs=2, shuffle=True, batch_size=64)
test_inpf = functools.partial(census_dataset.input_fn, test_file, num_epochs=1, shuffle=False, batch_size=64)
##Selecting and Engineering Features for the Model
### Base Feature Columns
####Numericals
age = fc.numeric_column('age')
#Train and evaluate a model using only the age feature:
classifier = tf.estimator.LinearClassifier(feature_columns=[age])
classifier.train(train_inpf)
result = classifier.evaluate(test_inpf)
clear_output() # used for display in notebook
print(result)
#We define a NumericColumn for each continuous feature column that we want to use in the model:
education_num = tf.feature_column.numeric_column('education_num')
capital_gain = tf.feature_column.numeric_column('capital_gain')
capital_loss = tf.feature_column.numeric_column('capital_loss')
hours_per_week = tf.feature_column.numeric_column('hours_per_week')
ds = ds.batch(batch_size)
return ds
example_batch = next(iter(df_to_dataset(dataframe)))[0]
# A utility method to create a feature column
# and to transform a batch of data
def demo(feature_column):
feature_layer = layers.DenseFeatures(feature_column)
print(feature_layer(example_batch).numpy())
print('Pre processing age==========================>')
age = feature_column.numeric_column("age")
demo(age)
age_buckets = feature_column.bucketized_column(
age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
#demo(age_buckets)
#type(age_buckets)
#dataframe.pop('age')
#age_buckets_series = pd.Series(age_buckets)
print('age buckets series====================>')
demo(age_buckets)
age_buckets_df = pd.DataFrame({'age': age_buckets})
print('age buckets series====================>')
#dataframe.join(age_buckets_column)
def create_feature_columns():
# user feature
bids = fc.categorical_column_with_hash_bucket("behaviorBids",
10240,
dtype=tf.int64)
c1ids = fc.categorical_column_with_hash_bucket("behaviorC1ids",
100,
dtype=tf.int64)
cids = fc.categorical_column_with_hash_bucket("behaviorCids",
10240,
dtype=tf.int64)
sids = fc.categorical_column_with_hash_bucket("behaviorSids",
10240,
dtype=tf.int64)
pids = fc.categorical_column_with_hash_bucket("behaviorPids",
1000000,
dtype=tf.int64)
bids_weighted = fc.weighted_categorical_column(bids, "bidWeights")
c1ids_weighted = fc.weighted_categorical_column(c1ids, "c1idWeights")
cids_weighted = fc.weighted_categorical_column(cids, "cidWeights")
sids_weighted = fc.weighted_categorical_column(sids, "sidWeights")
pids_weighted = fc.weighted_categorical_column(pids, "pidWeights")
# item feature
pid = fc.categorical_column_with_hash_bucket("productId",
1000000,
dtype=tf.int64)
sid = fc.categorical_column_with_hash_bucket("sellerId",
10240,
dtype=tf.int64)
bid = fc.categorical_column_with_hash_bucket("brandId",
10240,
dtype=tf.int64)
c1id = fc.categorical_column_with_hash_bucket("cate1Id",
100,
dtype=tf.int64)
cid = fc.categorical_column_with_hash_bucket("cateId",
10240,
dtype=tf.int64)
# context feature
matchScore = fc.numeric_column("matchScore", default_value=0.0)
popScore = fc.numeric_column("popScore", default_value=0.0)
brandPrefer = fc.numeric_column("brandPrefer", default_value=0.0)
cate2Prefer = fc.numeric_column("cate2Prefer", default_value=0.0)
catePrefer = fc.numeric_column("catePrefer", default_value=0.0)
sellerPrefer = fc.numeric_column("sellerPrefer", default_value=0.0)
matchType = fc.indicator_column(
fc.categorical_column_with_identity("matchType", 9, default_value=0))
postition = fc.indicator_column(
fc.categorical_column_with_identity("position", 201,
default_value=200))
triggerNum = fc.indicator_column(
fc.categorical_column_with_identity("triggerNum", 51,
default_value=50))
triggerRank = fc.indicator_column(
fc.categorical_column_with_identity("triggerRank",
51,
default_value=50))
sceneType = fc.indicator_column(
fc.categorical_column_with_identity("type", 2, default_value=0))
hour = fc.indicator_column(
fc.categorical_column_with_identity("hour", 24, default_value=0))
phoneBrand = fc.indicator_column(
fc.categorical_column_with_hash_bucket("phoneBrand", 1000))
phoneResolution = fc.indicator_column(
fc.categorical_column_with_hash_bucket("phoneResolution", 500))
phoneOs = fc.indicator_column(
fc.categorical_column_with_vocabulary_list("phoneOs",
["android", "ios"],
default_value=0))
tab = fc.indicator_column(
fc.categorical_column_with_vocabulary_list("tab", [
"ALL", "TongZhuang", "XieBao", "MuYing", "NvZhuang", "MeiZhuang",
"JuJia", "MeiShi"
],
default_value=0))
pid_embed = fc.shared_embedding_columns(
[pids_weighted, pid],
64,
combiner='sum',
shared_embedding_collection_name="pid")
bid_embed = fc.shared_embedding_columns(
[bids_weighted, bid],
32,
combiner='sum',
shared_embedding_collection_name="bid")
cid_embed = fc.shared_embedding_columns(
[cids_weighted, cid],
32,
combiner='sum',
shared_embedding_collection_name="cid")
c1id_embed = fc.shared_embedding_columns(
[c1ids_weighted, c1id],
10,
combiner='sum',
shared_embedding_collection_name="c1id")
sid_embed = fc.shared_embedding_columns(
[sids_weighted, sid],
32,
combiner='sum',
shared_embedding_collection_name="sid")
global my_feature_columns
my_feature_columns = [
matchScore, matchType, postition, triggerNum, triggerRank, sceneType,
hour, phoneBrand, phoneResolution, phoneOs, tab, popScore,
sellerPrefer, brandPrefer, cate2Prefer, catePrefer
]
my_feature_columns += pid_embed
my_feature_columns += sid_embed
my_feature_columns += bid_embed
my_feature_columns += cid_embed
my_feature_columns += c1id_embed
print("feature columns:", my_feature_columns)
return my_feature_columns
print('Every feature:', list(feature_batch.keys()))
print('A batch of ages:', feature_batch['Age'])
print('A batch of targets:', label_batch )
# We will use this batch to demonstrate several types of feature columns
example_batch = next(iter(train_ds))[0]
# A utility method to create a feature column
# and to transform a batch of data
def demo(feature_column):
feature_layer = layers.DenseFeatures(feature_column)
print(feature_layer(example_batch).numpy())
#Age,Gender,Total_Bilirubin,Direct_Bilirubin,Alkaline_Phosphotase,Alamine_Aminotransferase,Aspartate_Aminotransferase,Total_Protiens,Albumin,Albumin_and_Globulin_Ratio,Dataset
age = feature_column.numeric_column('Age')
print("Demo AGE")
demo(age)
ronaldo = feature_column.numeric_column('Age')
print('Demo Ronaldo')
demo(ronaldo)
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65, 75, 85, 90])
print('Demo AGE buckets')
demo(age_buckets)
gender = feature_column.categorical_column_with_vocabulary_list(
'Gender', ['Male', 'Female'])
gender_one_hot = feature_column.indicator_column(gender)
def official_census_feature_columns_config_demo():
# categorical_column
gender = fc.categorical_column_with_vocabulary_list(
'gender', ['Female', 'Male'])
education = fc.categorical_column_with_vocabulary_list(
'education', [
'Bachelors', 'HS-grad', '11th', 'Masters', '9th', 'Some-college',
'Assoc-acdm', 'Assoc-voc', '7th-8th', 'Doctorate', 'Prof-school',
'5th-6th', '10th', '1st-4th', 'Preschool', '12th'
])
marital_status = fc.categorical_column_with_vocabulary_list(
'marital_status', [
'Married-civ-spouse', 'Divorced', 'Married-spouse-absent',
'Never-married', 'Separated', 'Married-AF-spouse', 'Widowed'
])
relationship = fc.categorical_column_with_vocabulary_list(
'relationship', [
'Husband', 'Not-in-family', 'Wife', 'Own-child', 'Unmarried',
'Other-relative'
])
workclass = fc.categorical_column_with_vocabulary_list(
'workclass', [
'Self-emp-not-inc', 'Private', 'State-gov', 'Federal-gov',
'Local-gov', '?', 'Self-emp-inc', 'Without-pay', 'Never-worked'
])
# To show an example of hashing:
native_country = fc.categorical_column_with_hash_bucket(
'native_country', hash_bucket_size=1000)
occupation = fc.categorical_column_with_hash_bucket('occupation',
hash_bucket_size=1000)
# Continuous feature columns.
age = fc.numeric_column('age')
education_num = fc.numeric_column('education_num')
capital_gain = fc.numeric_column('capital_gain')
capital_loss = fc.numeric_column('capital_loss')
hours_per_week = fc.numeric_column('hours_per_week')
# bucketized transformations.
age_buckets = fc.bucketized_column(
age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
# Wide columns and deep columns.
base_columns = [
gender, education, marital_status, relationship, workclass, occupation,
native_country, age_buckets
]
crossed_columns = [
fc.crossed_column(['education', 'occupation'], hash_bucket_size=1000),
fc.crossed_column([age_buckets, 'education', 'occupation'],
hash_bucket_size=1000),
fc.crossed_column(['native_country', 'occupation'],
hash_bucket_size=1000)
]
feature_columns = [
fc.indicator_column(workclass),
fc.indicator_column(education),
fc.indicator_column(gender),
fc.indicator_column(relationship),
fc.embedding_column(native_country, dimension=32),
fc.embedding_column(occupation, dimension=32),
age,
education_num,
capital_gain,
capital_loss,
hours_per_week,
]
return feature_columns, base_columns, crossed_columns
