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gm_strategy_cvr
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gm_strategy_cvr
Commits
4d681b0a
Commit
4d681b0a
authored
Jul 21, 2020
by
赵威
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main.py
src/main.py
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src/main.py
View file @
4d681b0a
...
...
@@ -20,17 +20,17 @@ from models.esmm.model import esmm_model_fn, model_export, model_predict
def
main
():
time_begin
=
time
.
time
()
#
device_df, diary_df, click_df, conversion_df = read_csv_data(Path("~/data/cvr_data/"))
#
#
print(diary_df.sample(1))
#
device_df = device_feature_engineering(device_df)
#
#
print(device_df.sample(1))
#
diary_df = diary_feature_engineering(diary_df)
#
#
print(diary_df.sample(1))
#
cc_df = click_feature_engineering(click_df, conversion_df)
#
df = join_features(device_df, diary_df, cc_df)
#
train_df, test_df = train_test_split(df, test_size=0.2)
#
train_df, val_df = train_test_split(train_df, test_size=0.2)
device_df
,
diary_df
,
click_df
,
conversion_df
=
read_csv_data
(
Path
(
"~/data/cvr_data/"
))
# print(diary_df.sample(1))
device_df
=
device_feature_engineering
(
device_df
)
# print(device_df.sample(1))
diary_df
=
diary_feature_engineering
(
diary_df
)
# print(diary_df.sample(1))
cc_df
=
click_feature_engineering
(
click_df
,
conversion_df
)
df
=
join_features
(
device_df
,
diary_df
,
cc_df
)
train_df
,
test_df
=
train_test_split
(
df
,
test_size
=
0.2
)
train_df
,
val_df
=
train_test_split
(
train_df
,
test_size
=
0.2
)
# all_features = build_features(df)
...
...
@@ -49,33 +49,34 @@ def main():
# save_path = model_export(model, all_features, model_export_path)
# print("save to: " + save_path)
# predict_fn = tf.contrib.predictor.from_saved_model(save_path)
# for i in range(10):
# test_300 = test_df.sample(300)
# model_predict(test_300, predict_fn)
print
(
"=============================="
)
device_id
=
"861601036552944"
diary_ids
=
[
"16195283"
,
"16838351"
,
"17161073"
,
"17297878"
,
"17307484"
,
"17396235"
,
"16418737"
,
"16995481"
,
"17312201"
,
"12237988"
]
df
=
get_device_df_from_redis
()
df2
=
get_diary_df_from_redis
()
redis_device_df
=
device_feature_engineering
(
df
)
redis_diary_df
=
diary_feature_engineering
(
df2
,
from_redis
=
True
)
print
(
list
(
redis_diary_df
[
"card_id"
]
.
values
)[:
10
])
time_1
=
timeit
.
default_timer
()
res
=
join_device_diary
(
device_id
,
diary_ids
,
redis_device_df
,
redis_diary_df
)
print
(
len
(
res
))
print
(
res
.
sample
(
1
),
"
\n
"
)
print
(
res
.
sample
(
1
))
# model_predict(res, predict_fn)
total_1
=
(
timeit
.
default_timer
()
-
time_1
)
print
(
"prediction total cost {:.5f}s"
.
format
(
total_1
))
save_path
=
"/home/gmuser/data/models/1595317247"
predict_fn
=
tf
.
contrib
.
predictor
.
from_saved_model
(
save_path
)
for
i
in
range
(
10
):
test_300
=
test_df
.
sample
(
300
)
model_predict
(
test_300
,
predict_fn
)
# print("==============================")
# device_id = "861601036552944"
# diary_ids = [
# "16195283", "16838351", "17161073", "17297878", "17307484", "17396235", "16418737", "16995481", "17312201", "12237988"
# ]
# df = get_device_df_from_redis()
# df2 = get_diary_df_from_redis()
# redis_device_df = device_feature_engineering(df)
# redis_diary_df = diary_feature_engineering(df2, from_redis=True)
# diary_ids = list(redis_diary_df["card_id"].values)[:300]
# time_1 = timeit.default_timer()
# res = join_device_diary(device_id, diary_ids, redis_device_df, redis_diary_df)
# print(len(res))
# print(res.sample(1), "\n")
# print(res.sample(1))
# # model_predict(res, predict_fn)
# total_1 = (timeit.default_timer() - time_1)
# print("prediction total cost {:.5f}s".format(total_1))
total_time
=
(
time
.
time
()
-
time_begin
)
/
60
print
(
"cost {:.2f} mins at {}"
.
format
(
total_time
,
datetime
.
now
()))
...
...
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