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Issue看板
前往新版Gitcode,体验更适合开发者的 AI 搜索 >>
提交
6e9224a5
编写于
7月 23, 2020
作者:
P
PyCaret
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
pycaret-nightly==0.34 part 3
上级
db4876c4
变更
5
显示空白变更内容
内联
并排
Showing
5 changed file
with
168 addition
and
66 deletion
+168
-66
pycaret/anomaly.py
pycaret/anomaly.py
+28
-22
pycaret/classification.py
pycaret/classification.py
+46
-2
pycaret/clustering.py
pycaret/clustering.py
+33
-28
pycaret/nlp.py
pycaret/nlp.py
+14
-8
pycaret/regression.py
pycaret/regression.py
+47
-6
未找到文件。
pycaret/anomaly.py
浏览文件 @
6e9224a5
...
...
@@ -2,7 +2,7 @@
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
# Release: PyCaret 2.0x
# Last modified : 2
1
/07/2020
# Last modified : 2
3
/07/2020
def
setup
(
data
,
categorical_features
=
None
,
...
...
@@ -1096,7 +1096,9 @@ def setup(data,
mlflow
.
set_tag
(
"Run ID"
,
RunID
)
# Log the transformation pipeline
logger
.
info
(
"SubProcess save_model() called =================================="
)
save_model
(
prep_pipe
,
'Transformation Pipeline'
,
verbose
=
False
)
logger
.
info
(
"SubProcess save_model() end =================================="
)
mlflow
.
log_artifact
(
'Transformation Pipeline'
+
'.pkl'
)
size_bytes
=
Path
(
'Transformation Pipeline.pkl'
).
stat
().
st_size
size_kb
=
np
.
round
(
size_bytes
/
1000
,
2
)
...
...
@@ -1130,7 +1132,8 @@ def setup(data,
mlflow
.
log_artifact
(
"input.txt"
)
os
.
remove
(
'input.txt'
)
logger
.
info
(
"setup() succesfully completed"
)
logger
.
info
(
str
(
prep_pipe
))
logger
.
info
(
"setup() succesfully completed......................................"
)
return
X
,
data_
,
seed
,
prep_pipe
,
prep_param
,
experiment__
,
\
n_jobs_param
,
html_param
,
exp_name_log
,
logging_param
,
log_plots_param
,
USI
...
...
@@ -1442,11 +1445,6 @@ def create_model(model = None,
mlflow
.
log_params
(
params
)
# Log internal parameters
mlflow
.
log_param
(
"create_model_model"
,
model
)
mlflow
.
log_param
(
"create_model_fraction"
,
fraction
)
mlflow
.
log_param
(
"create_model_verbose"
,
verbose
)
#set tag of compare_models
mlflow
.
set_tag
(
"Source"
,
"create_model"
)
...
...
@@ -1462,6 +1460,9 @@ def create_model(model = None,
# Log AUC and Confusion Matrix plot
if
log_plots_param
:
logger
.
info
(
"SubProcess plot_model() called =================================="
)
try
:
plot_model
(
model
,
plot
=
'tsne'
,
save
=
True
,
system
=
False
)
mlflow
.
log_artifact
(
'TSNE.html'
)
...
...
@@ -1469,8 +1470,12 @@ def create_model(model = None,
except
:
pass
logger
.
info
(
"SubProcess plot_model() end =================================="
)
# Log model and transformation pipeline
logger
.
info
(
"SubProcess save_model() called =================================="
)
save_model
(
model
,
'Trained Model'
,
verbose
=
False
)
logger
.
info
(
"SubProcess save_model() end =================================="
)
mlflow
.
log_artifact
(
'Trained Model'
+
'.pkl'
)
size_bytes
=
Path
(
'Trained Model.pkl'
).
stat
().
st_size
size_kb
=
np
.
round
(
size_bytes
/
1000
,
2
)
...
...
@@ -1482,7 +1487,8 @@ def create_model(model = None,
if
verbose
:
clear_output
()
logger
.
info
(
"create_models() succesfully completed"
)
logger
.
info
(
str
(
model
))
logger
.
info
(
"create_models() succesfully completed......................................"
)
return
model
...
...
@@ -1678,7 +1684,8 @@ def assign_model(model,
if
verbose
:
clear_output
()
logger
.
info
(
"assign_model() succesfully completed"
)
logger
.
info
(
str
(
data__
.
shape
))
logger
.
info
(
"assign_model() succesfully completed......................................"
)
return
data__
...
...
@@ -2986,14 +2993,6 @@ def tune_model(model=None,
mlflow
.
log_params
(
params
)
# Log internal parameters
mlflow
.
log_param
(
"tune_model_model"
,
model
)
mlflow
.
log_param
(
"tune_model_supervised_target"
,
supervised_target
)
mlflow
.
log_param
(
"tune_model_estimator"
,
estimator
)
mlflow
.
log_param
(
"tune_model_optimize"
,
optimize
)
mlflow
.
log_param
(
"tune_model_fold"
,
fold
)
mlflow
.
log_param
(
"tune_model_verbose"
,
verbose
)
#set tag of compare_models
mlflow
.
set_tag
(
"Source"
,
"tune_model"
)
...
...
@@ -3013,14 +3012,17 @@ def tune_model(model=None,
os
.
remove
(
'Iterations.html'
)
# Log model and transformation pipeline
logger
.
info
(
"SubProcess save_model() called =================================="
)
save_model
(
best_model
,
'Trained Model'
,
verbose
=
False
)
logger
.
info
(
"SubProcess save_model() end =================================="
)
mlflow
.
log_artifact
(
'Trained Model'
+
'.pkl'
)
size_bytes
=
Path
(
'Trained Model.pkl'
).
stat
().
st_size
size_kb
=
np
.
round
(
size_bytes
/
1000
,
2
)
mlflow
.
set_tag
(
"Size KB"
,
size_kb
)
os
.
remove
(
'Trained Model.pkl'
)
logger
.
info
(
"tune_model() succesfully completed"
)
logger
.
info
(
str
(
best_model
))
logger
.
info
(
"tune_model() succesfully completed......................................"
)
return
best_model
...
...
@@ -3234,7 +3236,8 @@ def save_model(model, model_name, verbose=True):
print
(
'Transformation Pipeline and Model Succesfully Saved'
)
logger
.
info
(
str
(
model_name
)
+
' saved in current working directory'
)
logger
.
info
(
"save_model() succesfully completed"
)
logger
.
info
(
str
(
model_
))
logger
.
info
(
"save_model() succesfully completed......................................"
)
def
load_model
(
model_name
,
platform
=
None
,
...
...
@@ -3509,7 +3512,9 @@ def deploy_model(model,
import
boto3
logger
.
info
(
"Saving model in current working directory"
)
logger
.
info
(
"SubProcess save_model() called =================================="
)
save_model
(
model
,
model_name
=
model_name
,
verbose
=
False
)
logger
.
info
(
"SubProcess save_model() end =================================="
)
#initiaze s3
logger
.
info
(
"Initializing S3 client"
)
...
...
@@ -3520,8 +3525,9 @@ def deploy_model(model,
s3
.
upload_file
(
filename
,
bucket_name
,
key
)
clear_output
()
os
.
remove
(
filename
)
logger
.
info
(
"deploy_model() succesfully completed"
)
print
(
"Model Succesfully Deployed on AWS S3"
)
logger
.
info
(
str
(
model
))
logger
.
info
(
"deploy_model() succesfully completed......................................"
)
def
get_outliers
(
data
,
model
=
None
,
...
...
@@ -3779,7 +3785,7 @@ def get_config(variable):
global_var
=
USI
logger
.
info
(
"Global variable: "
+
str
(
variable
)
+
' returned'
)
logger
.
info
(
"get_config() succesfully completed"
)
logger
.
info
(
"get_config() succesfully completed
......................................
"
)
return
global_var
...
...
@@ -3859,7 +3865,7 @@ def set_config(variable,value):
USI
=
value
logger
.
info
(
"Global variable: "
+
str
(
variable
)
+
' updated'
)
logger
.
info
(
"set_config() succesfully completed"
)
logger
.
info
(
"set_config() succesfully completed
......................................
"
)
def
get_system_logs
():
...
...
pycaret/classification.py
浏览文件 @
6e9224a5
...
...
@@ -2,7 +2,7 @@
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
# Release: PyCaret 2.0x
# Last modified : 2
2
/07/2020
# Last modified : 2
3
/07/2020
def
setup
(
data
,
target
,
...
...
@@ -2079,6 +2079,10 @@ def setup(data,
mlflow
.
log_artifact
(
"input.txt"
)
os
.
remove
(
'input.txt'
)
logger
.
info
(
"create_model_container "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
prep_pipe
))
logger
.
info
(
"setup() succesfully completed......................................"
)
...
...
@@ -2600,6 +2604,10 @@ def create_model(estimator = None,
if
verbose
:
clear_output
()
logger
.
info
(
"create_model_container "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model
))
logger
.
info
(
"create_models() succesfully completed......................................"
)
...
...
@@ -2952,7 +2960,7 @@ def create_model(estimator = None,
display_container
.
append
(
model_results
.
data
)
#storing results in master_model_container
logger
.
info
(
"Uploading model into container"
)
logger
.
info
(
"Uploading model into container
now
"
)
master_model_container
.
append
(
model
)
if
verbose
:
...
...
@@ -2963,6 +2971,10 @@ def create_model(estimator = None,
else
:
print
(
model_results
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model
))
logger
.
info
(
"create_model() succesfully completed......................................"
)
return
model
...
...
@@ -3712,6 +3724,10 @@ def ensemble_model(estimator,
else
:
clear_output
()
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model
))
logger
.
info
(
"ensemble_model() succesfully completed......................................"
)
...
...
@@ -5138,6 +5154,10 @@ def compare_models(blacklist = None,
#store in display container
display_container
.
append
(
compare_models_
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model_store_final
))
logger
.
info
(
"compare_models() succesfully completed......................................"
)
...
...
@@ -6361,6 +6381,10 @@ def tune_model(estimator = None,
else
:
print
(
model_results
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
best_model
))
logger
.
info
(
"tune_model() succesfully completed......................................"
)
...
...
@@ -7178,6 +7202,10 @@ def blend_models(estimator_list = 'All',
else
:
print
(
model_results
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model
))
logger
.
info
(
"blend_models() succesfully completed......................................"
)
...
...
@@ -7962,6 +7990,10 @@ def stack_models(estimator_list,
else
:
print
(
model_results
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
models_
))
logger
.
info
(
"stack_models() succesfully completed......................................"
)
...
...
@@ -8805,6 +8837,10 @@ def create_stacknet(estimator_list,
else
:
print
(
model_results
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
models_
))
logger
.
info
(
"create_stacknet() succesfully completed......................................"
)
...
...
@@ -9631,6 +9667,10 @@ def calibrate_model(estimator,
else
:
print
(
model_results
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model
))
logger
.
info
(
"calibrate_model() succesfully completed......................................"
)
...
...
@@ -10014,6 +10054,10 @@ def finalize_model(estimator):
mlflow
.
set_tag
(
"Size KB"
,
size_kb
)
os
.
remove
(
'Trained Model.pkl'
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model_final
))
logger
.
info
(
"finalize_model() succesfully completed......................................"
)
...
...
pycaret/clustering.py
浏览文件 @
6e9224a5
...
...
@@ -2,7 +2,7 @@
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
# Release: PyCaret 2.0x
# Last modified : 2
2
/07/2020
# Last modified : 2
3
/07/2020
def
setup
(
data
,
categorical_features
=
None
,
...
...
@@ -1099,7 +1099,9 @@ def setup(data,
mlflow
.
set_tag
(
"Run ID"
,
RunID
)
# Log the transformation pipeline
logger
.
info
(
"SubProcess save_model() called =================================="
)
save_model
(
prep_pipe
,
'Transformation Pipeline'
,
verbose
=
False
)
logger
.
info
(
"SubProcess save_model() end =================================="
)
mlflow
.
log_artifact
(
'Transformation Pipeline'
+
'.pkl'
)
size_bytes
=
Path
(
'Transformation Pipeline.pkl'
).
stat
().
st_size
size_kb
=
np
.
round
(
size_bytes
/
1000
,
2
)
...
...
@@ -1133,7 +1135,8 @@ def setup(data,
mlflow
.
log_artifact
(
"input.txt"
)
os
.
remove
(
'input.txt'
)
logger
.
info
(
"setup() succesfully completed"
)
logger
.
info
(
str
(
prep_pipe
))
logger
.
info
(
"setup() succesfully completed......................................"
)
return
X
,
data_
,
seed
,
prep_pipe
,
prep_param
,
experiment__
,
\
n_jobs_param
,
html_param
,
exp_name_log
,
logging_param
,
log_plots_param
,
USI
...
...
@@ -1508,12 +1511,6 @@ def create_model(model = None,
mlflow
.
log_params
(
params
)
# Log internal parameters
mlflow
.
log_param
(
"create_model_model"
,
model
)
mlflow
.
log_param
(
"create_model_num_clusters"
,
num_clusters
)
mlflow
.
log_param
(
"create_model_verbose"
,
verbose
)
mlflow
.
log_param
(
"create_model_system"
,
system
)
#set tag of compare_models
mlflow
.
set_tag
(
"Source"
,
"create_model"
)
...
...
@@ -1533,6 +1530,9 @@ def create_model(model = None,
# Log Cluster, Distribution Plot and Elbow Plot
if
log_plots_param
:
logger
.
info
(
"SubProcess plot_model() called =================================="
)
try
:
plot_model
(
model
,
plot
=
'cluster'
,
save
=
True
,
system
=
False
)
mlflow
.
log_artifact
(
'Cluster.html'
)
...
...
@@ -1554,8 +1554,12 @@ def create_model(model = None,
except
:
pass
logger
.
info
(
"SubProcess plot_model() end =================================="
)
# Log model and transformation pipeline
logger
.
info
(
"SubProcess save_model() called =================================="
)
save_model
(
model
,
'Trained Model'
,
verbose
=
False
)
logger
.
info
(
"SubProcess save_model() end =================================="
)
mlflow
.
log_artifact
(
'Trained Model'
+
'.pkl'
)
size_bytes
=
Path
(
'Trained Model.pkl'
).
stat
().
st_size
size_kb
=
np
.
round
(
size_bytes
/
1000
,
2
)
...
...
@@ -1571,7 +1575,8 @@ def create_model(model = None,
except
:
pass
logger
.
info
(
"create_models() succesfully completed"
)
logger
.
info
(
str
(
model
))
logger
.
info
(
"create_models() succesfully completed......................................"
)
return
model
...
...
@@ -1744,7 +1749,8 @@ def assign_model(model,
if
verbose
:
clear_output
()
logger
.
info
(
"assign_model() succesfully completed"
)
logger
.
info
(
done__
.
shape
)
logger
.
info
(
"assign_model() succesfully completed......................................"
)
return
data__
...
...
@@ -2330,17 +2336,17 @@ def tune_model(model=None,
#create and assign the model to dataset d
model_fit_start
=
time
.
time
()
logger
.
info
(
"SubProcess create_model() called"
)
logger
.
info
(
"SubProcess create_model() called
==================================
"
)
m
=
create_model
(
model
=
model
,
num_clusters
=
i
,
verbose
=
False
,
system
=
False
)
logger
.
info
(
"SubProcess create_model() end"
)
logger
.
info
(
"SubProcess create_model() end
==================================
"
)
model_fit_end
=
time
.
time
()
model_fit_time
=
np
.
array
(
model_fit_end
-
model_fit_start
).
round
(
2
)
model_fit_time_list
.
append
(
model_fit_time
)
logger
.
info
(
"Generating labels"
)
logger
.
info
(
"SubProcess assign_model() called"
)
logger
.
info
(
"SubProcess assign_model() called
==================================
"
)
d
=
assign_model
(
m
,
transformation
=
True
,
verbose
=
False
)
logger
.
info
(
"SubProcess assign_model() ends"
)
logger
.
info
(
"SubProcess assign_model() ends
==================================
"
)
d
[
str
(
supervised_target
)]
=
target_
master
.
append
(
m
)
...
...
@@ -3037,14 +3043,6 @@ def tune_model(model=None,
mlflow
.
log_params
(
params
)
# Log internal parameters
mlflow
.
log_param
(
"tune_model_model"
,
model
)
mlflow
.
log_param
(
"tune_model_supervised_target"
,
supervised_target
)
mlflow
.
log_param
(
"tune_model_estimator"
,
estimator
)
mlflow
.
log_param
(
"tune_model_optimize"
,
optimize
)
mlflow
.
log_param
(
"tune_model_fold"
,
fold
)
mlflow
.
log_param
(
"tune_model_verbose"
,
verbose
)
#set tag of compare_models
mlflow
.
set_tag
(
"Source"
,
"tune_model"
)
...
...
@@ -3064,14 +3062,17 @@ def tune_model(model=None,
os
.
remove
(
'Iterations.html'
)
# Log model and transformation pipeline
logger
.
info
(
"SubProcess save_model() called =================================="
)
save_model
(
best_model
,
'Trained Model'
,
verbose
=
False
)
logger
.
info
(
"SubProcess save_model() end =================================="
)
mlflow
.
log_artifact
(
'Trained Model'
+
'.pkl'
)
size_bytes
=
Path
(
'Trained Model.pkl'
).
stat
().
st_size
size_kb
=
np
.
round
(
size_bytes
/
1000
,
2
)
mlflow
.
set_tag
(
"Size KB"
,
size_kb
)
os
.
remove
(
'Trained Model.pkl'
)
logger
.
info
(
"tune_model() succesfully completed"
)
logger
.
info
(
str
(
best_model
))
logger
.
info
(
"tune_model() succesfully completed......................................"
)
return
best_model
...
...
@@ -3453,7 +3454,8 @@ def save_model(model, model_name, verbose=True):
print
(
'Transformation Pipeline and Model Succesfully Saved'
)
logger
.
info
(
str
(
model_name
)
+
' saved in current working directory'
)
logger
.
info
(
"save_model() succesfully completed"
)
logger
.
info
(
str
(
model_
))
logger
.
info
(
"save_model() succesfully completed......................................"
)
def
load_model
(
model_name
,
platform
=
None
,
...
...
@@ -3734,7 +3736,9 @@ def deploy_model(model,
import
boto3
logger
.
info
(
"Saving model in current working directory"
)
logger
.
info
(
"SubProcess save_model() called =================================="
)
save_model
(
model
,
model_name
=
model_name
,
verbose
=
False
)
logger
.
info
(
"SubProcess save_model() end =================================="
)
#initiaze s3
logger
.
info
(
"Initializing S3 client"
)
...
...
@@ -3745,8 +3749,9 @@ def deploy_model(model,
s3
.
upload_file
(
filename
,
bucket_name
,
key
)
clear_output
()
os
.
remove
(
filename
)
logger
.
info
(
"deploy_model() succesfully completed"
)
print
(
"Model Succesfully Deployed on AWS S3"
)
logger
.
info
(
str
(
model
))
logger
.
info
(
"deploy_model() succesfully completed......................................"
)
def
get_clusters
(
data
,
model
=
None
,
...
...
@@ -3997,7 +4002,7 @@ def get_config(variable):
global_var
=
USI
logger
.
info
(
"Global variable: "
+
str
(
variable
)
+
' returned'
)
logger
.
info
(
"get_config() succesfully completed"
)
logger
.
info
(
"get_config() succesfully completed
......................................
"
)
return
global_var
...
...
@@ -4077,7 +4082,7 @@ def set_config(variable,value):
USI
=
value
logger
.
info
(
"Global variable: "
+
str
(
variable
)
+
' updated'
)
logger
.
info
(
"set_config() succesfully completed"
)
logger
.
info
(
"set_config() succesfully completed
......................................
"
)
def
get_system_logs
():
...
...
pycaret/nlp.py
浏览文件 @
6e9224a5
...
...
@@ -2,7 +2,7 @@
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
# Release: PyCaret 2.0x
# Last modified : 2
1
/07/2020
# Last modified : 2
3
/07/2020
def
setup
(
data
,
target
=
None
,
...
...
@@ -749,7 +749,9 @@ def setup(data,
else
:
print
(
functions_
.
data
)
logger
.
info
(
"setup() succesfully completed"
)
logger
.
info
(
'Corpus: '
+
str
(
len
(
corpus
)))
logger
.
info
(
'Vocab: '
+
str
(
len
(
id2word
.
keys
())))
logger
.
info
(
"setup() succesfully completed......................................"
)
return
text
,
data_
,
corpus
,
id2word
,
seed
,
target_
,
experiment__
,
\
exp_name_log
,
logging_param
,
log_plots_param
,
USI
,
html_param
...
...
@@ -1126,7 +1128,8 @@ def create_model(model=None,
if
verbose
:
clear_output
()
logger
.
info
(
"create_model() succesfully completed"
)
logger
.
info
(
str
(
model
))
logger
.
info
(
"create_model() succesfully completed......................................"
)
return
model
...
...
@@ -1449,7 +1452,8 @@ def assign_model(model,
if
verbose
:
clear_output
()
logger
.
info
(
"assign_model() succesfully completed"
)
logger
.
info
(
str
(
bb_
.
shape
))
logger
.
info
(
"assign_model() succesfully completed......................................"
)
return
bb_
...
...
@@ -3065,7 +3069,8 @@ def tune_model(model=None,
p
=
'Best Model: '
+
topic_model_name
+
' |'
+
' # Topics: '
+
str
(
best_k
)
+
' | '
+
str
(
optimize
)
+
' : '
+
str
(
best_m
)
print
(
p
)
logger
.
info
(
"tune_model() succesfully completed"
)
logger
.
info
(
str
(
best_model
))
logger
.
info
(
"tune_model() succesfully completed......................................"
)
return
best_model
...
...
@@ -3194,7 +3199,8 @@ def save_model(model, model_name,
if
verbose
:
print
(
'Model Succesfully Saved'
)
logger
.
info
(
"save_model() succesfully completed"
)
logger
.
info
(
str
(
model
))
logger
.
info
(
"save_model() succesfully completed......................................"
)
def
load_model
(
model_name
,
verbose
=
True
):
#added in pycaret==2.0.0
...
...
@@ -3403,7 +3409,7 @@ def get_config(variable):
global_var
=
USI
logger
.
info
(
"Global variable: "
+
str
(
variable
)
+
' returned'
)
logger
.
info
(
"get_config() succesfully completed"
)
logger
.
info
(
"get_config() succesfully completed
......................................
"
)
return
global_var
...
...
@@ -3479,7 +3485,7 @@ def set_config(variable,value):
USI
=
value
logger
.
info
(
"Global variable: "
+
str
(
variable
)
+
' updated'
)
logger
.
info
(
"set_config() succesfully completed"
)
logger
.
info
(
"set_config() succesfully completed
......................................
"
)
def
get_system_logs
():
...
...
pycaret/regression.py
浏览文件 @
6e9224a5
...
...
@@ -2,7 +2,7 @@
# Author: Moez Ali <moez.ali@queensu.ca>
# License: MIT
# Release: PyCaret 2.0x
# Last modified : 2
2
/07/2020
# Last modified : 2
3
/07/2020
def
setup
(
data
,
target
,
...
...
@@ -2020,6 +2020,10 @@ def setup(data,
mlflow
.
log_artifact
(
"input.txt"
)
os
.
remove
(
'input.txt'
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
"setup() succesfully completed......................................"
)
return
X
,
y
,
X_train
,
X_test
,
y_train
,
y_test
,
seed
,
prep_pipe
,
target_inverse_transformer
,
\
...
...
@@ -2542,6 +2546,10 @@ def create_model(estimator = None,
if
verbose
:
clear_output
()
logger
.
info
(
"create_model_container "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model
))
logger
.
info
(
"create_models() succesfully completed......................................"
)
return
model
...
...
@@ -2851,6 +2859,10 @@ def create_model(estimator = None,
else
:
print
(
model_results
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model
))
logger
.
info
(
"create_model() succesfully completed......................................"
)
return
model
...
...
@@ -3531,6 +3543,10 @@ def ensemble_model(estimator,
else
:
clear_output
()
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model
))
logger
.
info
(
"ensemble_model() succesfully completed......................................"
)
...
...
@@ -4398,6 +4414,10 @@ def compare_models(blacklist = None,
#store in display container
display_container
.
append
(
compare_models_
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model_store_final
))
logger
.
info
(
"compare_models() succesfully completed......................................"
)
...
...
@@ -5151,6 +5171,10 @@ def blend_models(estimator_list = 'All',
else
:
print
(
model_results
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model
))
logger
.
info
(
"blend_models() succesfully completed......................................"
)
...
...
@@ -6488,6 +6512,10 @@ def tune_model(estimator,
else
:
clear_output
()
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
best_model
))
logger
.
info
(
"tune_model() succesfully completed......................................"
)
...
...
@@ -7198,6 +7226,10 @@ def stack_models(estimator_list,
else
:
print
(
model_results
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
models_
))
logger
.
info
(
"stack_models() succesfully completed......................................"
)
...
...
@@ -7964,6 +7996,10 @@ def create_stacknet(estimator_list,
else
:
print
(
model_results
.
data
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
models_
))
logger
.
info
(
"create_stacknet() succesfully completed......................................"
)
...
...
@@ -8830,7 +8866,12 @@ def finalize_model(estimator):
mlflow
.
set_tag
(
"Size KB"
,
size_kb
)
os
.
remove
(
'Trained Model.pkl'
)
logger
.
info
(
"create_model_container: "
+
str
(
len
(
create_model_container
)))
logger
.
info
(
"master_model_container: "
+
str
(
len
(
master_model_container
)))
logger
.
info
(
"display_container: "
+
str
(
len
(
display_container
)))
logger
.
info
(
str
(
model_final
))
logger
.
info
(
"finalize_model() succesfully completed......................................"
)
return
model_final
...
...
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