chore: run to predict with models notebook marimo
This commit is contained in:
Jesús Pérez Lorenzo
parent
fb1b3ab027
commit
39be50dd61
322
learning/python/run_perfspec.py
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322
learning/python/run_perfspec.py
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import marimo
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__generated_with = "0.10.16"
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app = marimo.App(width="medium")
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@app.cell(hide_code=True)
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def title():
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import marimo as mo
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notebook_name = 'run_perfspec.py'
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from lib_perfspec import perfspec_vars
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(_,_defs) = perfspec_vars.run()
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perfspec = _defs['perfspec']
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from lib_perfspec import perfspec_header
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(_,_defs) = perfspec_header.run()
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lib_header = _defs['header']
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lib_intro = _defs['intro']
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mo.md(
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f"""
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{lib_header(notebook_name)}
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### Use **{perfspec['app']['train_mode']}** trained model
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"""
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)
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return (
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lib_header,
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lib_intro,
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mo,
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notebook_name,
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perfspec,
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perfspec_header,
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perfspec_vars,
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)
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@app.cell
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def imports():
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from pathlib import Path
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return (Path,)
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@app.cell(hide_code=True)
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def intro_load(Path, lib_intro, mo, notebook_name, perfspec):
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verbose = perfspec['settings']['verbose']
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perfspec['vars'] = {}
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from lib_perfspec import perfspec_args
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(_,_defs) = perfspec_args.run()
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if not Path(perfspec['defaults']['models_dirpath']).exists():
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exit(f"Trained models dir path not found: {perfspec['defaults']['models_dirpath']}")
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if not Path(perfspec['defaults']['checkpoints_dirpath']).exists():
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exit(f"Trained checkpoints models dir path not found: {perfspec['defaults']['checkpoints_dirpath']}")
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if not Path(perfspec['defaults']['data_dirpath']).exists():
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exit(f"data dir path not found: {perfspec['defaults']['data_dirpath']}")
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verbose=perfspec['settings'].get('verbose')
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from lib_perfspec import perfspec_load_actions
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(_,_defs) = perfspec_load_actions.run()
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lib_load_actions = _defs['load_actions']
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from lib_perfspec import perfspec_input_sequence
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(_,_defs) = perfspec_input_sequence.run()
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lib_get_input_sequence = _defs['get_input_sequence']
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from lib_perfspec import perfspec_predict
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_, _defs = perfspec_predict.run()
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lib_predict_action = _defs['predict_action']
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perfspec['vars']['model'] = None
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perfspec['vars']['history'] = None
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(perfspec['vars']['actions'],
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perfspec['vars']['unique_actions'],
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perfspec['vars']['label_encoder'],
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perfspec['vars']['encoded_actions']
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) = lib_load_actions(
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actions_path=perfspec['settings'].get('actions_filepath'),
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verbose=None
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)
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perfspec['vars']['input_sequence'] = lib_get_input_sequence(
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input_str=perfspec['settings']['input_str'],
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unique_actions=perfspec['vars']['unique_actions']
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)
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from train_perfspec import perfspec_load_model_from_path
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(_,_defs) = perfspec_load_model_from_path.run()
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lib_load_model_from_path = _defs['load_model_from_path']
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mo.md(
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f"""
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{lib_intro(notebook_name)}
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"""
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)
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return (
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lib_get_input_sequence,
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lib_load_actions,
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lib_load_model_from_path,
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lib_predict_action,
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perfspec_args,
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perfspec_input_sequence,
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perfspec_load_actions,
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perfspec_load_model_from_path,
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perfspec_predict,
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verbose,
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)
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@app.cell(hide_code=True)
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def settings(mo, notebook_name):
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from lib_perfspec import perfspec_out_settings
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(_,_defs) = perfspec_out_settings.run()
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out_settings = _defs['out_settings']
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mo.md(out_settings(notebook_name))
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return out_settings, perfspec_out_settings
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@app.cell(hide_code=True)
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def command_line(mo, notebook_name):
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from lib_perfspec import perfspec_cli_ops
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(_,_defs) = perfspec_cli_ops.run()
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out_cli_ops = _defs['out_cli_ops']
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mo.accordion({
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"Mostrar command Line options ": out_cli_ops(notebook_name)
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})
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return out_cli_ops, perfspec_cli_ops
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@app.cell(hide_code=True)
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def load_trained_model(lib_load_model_from_path, mo, perfspec):
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def load_trained_model():
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if perfspec['vars']['model'] == None:
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_verbose = perfspec['settings']['verbose'] # if mo.running_in_notebook() else perfspec['settings']['verbose']
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perfspec['vars']['model'] = lib_load_model_from_path(perfspec,_verbose)
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if perfspec['vars']['model'] == None:
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print ("No model loaded !")
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mo.md(
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r"""
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## Load trained model
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"""
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)
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return (load_trained_model,)
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@app.cell(hide_code=True)
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def model_summary(load_trained_model, mo, perfspec):
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def model_sumary():
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load_trained_model()
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if perfspec['vars']['model'] != None:
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perfspec['vars']['model'].summary()
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if perfspec['settings']['verbose'] is not None or mo.running_in_notebook():
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model_sumary()
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mo.md(
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r"""
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## Model Summary
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"""
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)
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return (model_sumary,)
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@app.cell(hide_code=True)
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def perfspec_def_predict_input(
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input_multiselect,
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lib_get_input_sequence,
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lib_predict_action,
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load_trained_model,
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mo,
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model_sumary,
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perfspec,
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run_evaluate,
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):
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def predict_input(pred_input, verbose):
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#verbose = "1" if mo.running_in_notebook() else perfspec['settings']['verbose']
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if type(pred_input) == str and pred_input != '':
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input_sequence = lib_get_input_sequence(pred_input,perfspec['vars']['unique_actions'])
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elif type(pred_input) != str and len(pred_input) > 0:
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input_sequence = pred_input
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elif mo.running_in_notebook() and len(input_multiselect.value) > 0:
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input_sequence= input_multiselect.value
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elif perfspec['defaults']['pred_input'] != '':
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input_sequence = lib_get_input_sequence(perfspec['defaults']['pred_input'],perfspec['vars']['unique_actions'])
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else:
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print (f"No input found ! {input_sequence}")
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return
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if len(input_sequence) > 0:
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if verbose == "x":
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model_sumary()
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run_evaluate()
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print ("\nPrediction")
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if perfspec['vars']['model'] == None:
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load_trained_model()
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if perfspec['vars']['model'] != None:
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(encoded_input,predicted_probabilities) = lib_predict_action(
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perfspec['vars']['model'],
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perfspec['settings']['sequence_length'],
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input_sequence,
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perfspec['vars']['label_encoder'],
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verbose
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)
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return (encoded_input,predicted_probabilities)
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else:
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print (f"No Model found to predict {input_sequence}")
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return (None,None)
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return (predict_input,)
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@app.cell(hide_code=True)
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def multiselect_def(mo, perfspec):
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input_multiselect = mo.ui.multiselect(
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options=perfspec['vars']['unique_actions'],
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full_width=True,
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max_selections=perfspec['settings']['sequence_length'],
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)
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return (input_multiselect,)
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@app.cell(hide_code=True)
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def perfspec_def_show_value_selector(
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input_multiselect,
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mo,
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perfspec,
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predict_input,
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):
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def show_value():
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if len(input_multiselect.value) > 0:
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if len(input_multiselect.value) > perfspec['settings']['sequence_length']:
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return ""
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(_,prediction) = predict_input(input_multiselect.value, None)
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table_info = mo.md(f"""
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| desc. | value | % |
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| ---- | --- | --- |
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| input | {",".join(input_multiselect.value)}| |
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| prediction | {prediction['action'][0]} |{prediction['max_value']}|
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""")
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return mo.md(f"{mo.vstack(justify='center',items=[mo.md("<h3 style='margin-left: 7em'>Actions</h3>"),table_info])}")
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else:
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return ""
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return (show_value,)
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@app.cell(hide_code=True)
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def title_run_prediction(mo, perfspec):
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mo.md(
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f"""
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## Run Model Prediction
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Use **{perfspec['vars']['input_sequence']}** with trained model <u>created</u> or <u>loaded</u>
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<br>from {perfspec['settings']['model_filepath']}
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input value can be changed in **command-line** with **--input** `value` argument<br>
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with **--verbose** option more info is show in **command-line** mode
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"""
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)
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return
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@app.cell(hide_code=True)
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def _(mo, perfspec, predict_input):
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def run_prediction():
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_verbose = perfspec["settings"]["verbose"]
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if perfspec["settings"]["verbose"] is None and mo.running_in_notebook():
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_verbose=1
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predict_input("", _verbose)
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run_prediction()
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mo.md(
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"""
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### Test default prediction
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"""
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)
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return (run_prediction,)
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@app.cell(hide_code=True)
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def main(mo):
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mo.md("""<a id='main' />""")
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return
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@app.cell(hide_code=True)
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def perfspec_predictions_selector(
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input_multiselect,
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mo,
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perfspec,
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show_value,
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):
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def show_selector():
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if mo.running_in_notebook():
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if perfspec['settings']['sequence_length'] > 1:
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seq_msg = f"<br> <small>For better `prediction` use at least **{perfspec['settings']['sequence_length']}** options"
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else:
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seq_msg = ""
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return f"""
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{mo.hstack(widths="equal",gap=3,wrap=True,items=[
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mo.vstack(items=[mo.md("""
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## Predictions
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Select values to get prediction
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"""),input_multiselect]),
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show_value()]
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)}
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> <small>if notebook **autorun** is not set, use <u>click on cell to run</u></small>
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{seq_msg}
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"""
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else:
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return ""
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mo.md(show_selector())
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return (show_selector,)
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if __name__ == "__main__":
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app.run()
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