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run_doc.py
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run_doc.py
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# pylint: disable=C0321,C0103,C0301,E1305,E1121,C0302,C0330,C0111,W0613,W0611,R1705
# -*- coding: utf-8 -*-
"""
"""
import io
import os
import subprocess
import sys
from setuptools import find_packages, setup
######################################################################################
root = os.path.abspath(os.path.dirname(__file__))
##### Version #######################################################################
# from setup import version, entry_points
print("start Doc")
######################################################################################
#with open("README.md", "r") as fh:
# long_description = fh.read()
#################################################################################################
des1 = """
## In Jupyter
#### Model, data, ... definition
```python
model_uri = "model_tf.1_lstm.py"
model_pars = { "num_layers": 1,
"size": ncol_input, "size_layer": 128, "output_size": ncol_output, "timestep": 4,
}
data_pars = {"data_path": "/folder/myfile.csv" , "data_type": "pandas" }
compute_pars = { "learning_rate": 0.001, }
out_pars = { "path": "ztest_1lstm/", "model_path" : "ztest_1lstm/model/"}
save_pars = { "path" : "ztest_1lstm/model/" }
load_pars = { "path" : "ztest_1lstm/model/" }
```
#### Using local module (which contain the model)
```python
from mlmodels.models import module_load
module = module_load( model_uri= model_uri ) # Load file definition
model = module.Model(model_pars, data_pars, compute_pars) # Create Model instance
model, sess = module.fit(model, data_pars, compute_pars, out_pars) # fit the model
metrics_val = module.fit_metrics( model, sess, data_pars, compute_pars, out_pars) # get stats
module.save(model, sess, save_pars)
#### Inference
model, sess = module.load(load_pars) #Create Model instance
ypred = module.predict(model, sess, data_pars, compute_pars, out_pars) # predict pipeline
```
###### Using Generic API : Common to all models, models.py methods
```python
from mlmodels.models import module_load, create_model, fit, predict, stats
from mlmodels.models import load # Load model weights
module = module_load( model_uri= model_uri ) # Load file definition
model = model_create(module, model_pars, data_pars, compute_pars) # Create Model instance
model, sess = fit(model, data_pars, compute_pars, out_pars) # fit the model
metrics_val = fit_metrics( model, sess, data_pars, compute_pars, out_pars) # get stats
save(model, sess, save_pars)
#### Inference
load_pars = { "path" : "ztest_1lstm/model/", "model_type": "model_tf" }
model, sess = load( load_pars ) # Create Model instance
ypred = predict(model, module, sess, data_pars, compute_pars, out_pars)
```
"""
##########################################################################################
### Packages ####################################################
packages = ["mlmodels"] + ["mlmodels." + p for p in find_packages("mlmodels")]
#########################################################################################
def os_package_root_path(add_path="",n=0):
from pathlib import Path
add_path = os.path.join(Path(__file__).parent.absolute(), add_path)
# print("os_package_root_path,check", add_path)
return add_path
def get_recursive_files(folderPath, ext='/*model*/*.py'):
import glob
files = glob.glob( folderPath + ext, recursive=True)
return files
# Get all the model.py into folder
folder = None
folder = os_package_root_path() if folder is None else folder
# print(folder)
module_names = get_recursive_files(folder, r'/*model*//*model*/*.py' )
des = """
#### Model list
```
--model_uri
"""
for t in module_names :
t = t.replace(folder, "").replace("\\", ".")
if "__init__.py" in t :
des = des + "\n\n"
else :
if not 'util' in t and not 'preprocess' in t :
des = des + str(t) + "\n"
des = des + """
```
"""
#########################################################################################
################ Print on file
with open("README_model_list.md", mode="w") as f :
f.writelines(des1)
f.writelines(des)