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Update app.py
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app.py
CHANGED
@@ -14,7 +14,6 @@ from datetime import datetime
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import docx
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from docx.shared import Inches, Pt
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from docx.enum.text import WD_PARAGRAPH_ALIGNMENT
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-
from matplotlib.colors import to_hex
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import os
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# --- Data definition in global scope ---
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@@ -60,7 +59,77 @@ class RSM_BoxBehnken:
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self.x2_levels = x2_levels
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self.x3_levels = x3_levels
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-
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def fit_personalized_model(self, formula):
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"""
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@@ -92,14 +161,14 @@ class RSM_BoxBehnken:
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for fixed_variable in [self.x1_name, self.x2_name, self.x3_name]:
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for level in levels_to_plot_natural[fixed_variable]:
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fig_full = self.plot_rsm_individual(fixed_variable, level, model_type='full') # Pass model_type
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-
if fig_full:
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self.all_figures_full.append(fig_full)
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fig_simplified = self.plot_rsm_individual(fixed_variable, level, model_type='simplified') # Pass model_type
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if fig_simplified:
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self.all_figures_simplified.append(fig_simplified)
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if self.model_personalized is not None: # Generate personalized plots only if model exists
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fig_personalized = self.plot_rsm_individual(fixed_variable, level, model_type='personalized') # Pass model_type
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if fig_personalized:
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self.all_figures_personalized.append(fig_personalized)
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def plot_rsm_individual(self, fixed_variable, fixed_level, model_type='simplified'): # Added model_type parameter
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@@ -122,8 +191,101 @@ class RSM_BoxBehnken:
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print(f"Error: Ajusta el modelo {model_type} primero.") # More informative error message
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return None
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-
#
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fig.update_layout(
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scene=dict(
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xaxis_title=f"{varying_variables[0]} ({self.get_units(varying_variables[0])})",
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@@ -141,9 +303,20 @@ class RSM_BoxBehnken:
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# --- Funciones para la Interfaz de Gradio ---
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def load_data(data_str):
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return data.round(3), gr.update(visible=True)
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def fit_and_optimize_model():
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if 'rsm' not in globals():
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return [None]*11
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@@ -168,7 +341,7 @@ def fit_and_optimize_model():
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return (
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model_completo.summary().as_html(),
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pareto_completo,
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pareto_simplificado,
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equation_formatted,
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optimization_table,
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@@ -179,9 +352,9 @@ def fit_and_optimize_model():
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excel_path
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)
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def fit_custom_model(factor_checkboxes, interaction_checkboxes):
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if 'rsm' not in globals():
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return [None]*
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formula_parts = [rsm.x1_name, rsm.x2_name, rsm.x3_name] if "factors" in factor_checkboxes else []
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if "x1_sq" in factor_checkboxes: formula_parts.append(f'I({rsm.x1_name}**2)')
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@@ -199,7 +372,8 @@ def fit_custom_model(factor_checkboxes, interaction_checkboxes): # New function
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custom_model, pareto_custom = rsm.fit_personalized_model(formula) # Fit personalized model
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rsm.generate_all_plots() # Regenerate plots to include personalized model plots
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return
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def show_plot(current_index, all_figures, model_type): # Modified to accept model_type
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figure_list = []
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@@ -275,6 +449,29 @@ def download_all_plots_zip(model_type): # Modified to accept model_type
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return zip_path
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return None
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# --- Crear la interfaz de Gradio ---
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def create_gradio_interface():
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@@ -338,6 +535,7 @@ def create_gradio_interface():
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all_figures_state = gr.State([])
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current_model_type_state = gr.State('simplified') # State to track selected model type for plots
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# Cargar datos
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load_button.click(
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load_data,
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@@ -367,45 +565,42 @@ def create_gradio_interface():
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# Ajustar modelo personalizado
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custom_model_button.click( # New event for custom model fitting
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fit_custom_model,
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inputs=[factor_checkboxes, interaction_checkboxes],
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outputs=[model_personalized_output, pareto_personalized_output
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)
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# Generar y mostrar los gráficos
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plot_button.click(
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lambda fixed_var, fixed_lvl, model_type: (
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-
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model_type # Update model_type state
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),
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inputs=[fixed_variable_input, fixed_level_input, model_type_radio], # Added model_type_radio input
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outputs=[rsm_plot_output, plot_info, current_index_state, current_model_type_state] # Output model_type to state
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)
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# Navegación de gráficos
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left_button.click(
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lambda current_index, all_figures, model_type: navigate_plot('left', current_index, all_figures, model_type),
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inputs=[current_index_state, all_figures_state, current_model_type_state],
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outputs=[rsm_plot_output, plot_info, current_index_state]
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)
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right_button.click(
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lambda current_index, all_figures, model_type: navigate_plot('right', current_index, all_figures, model_type),
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inputs=[current_index_state, all_figures_state, current_model_type_state],
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outputs=[rsm_plot_output, plot_info, current_index_state]
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)
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# Descargar gráfico actual
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download_plot_button.click(
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download_current_plot,
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inputs=[all_figures_state, current_index_state, current_model_type_state],
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outputs=download_plot_button
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)
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# Descargar todos los gráficos en ZIP
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download_all_plots_button.click(
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lambda model_type: download_all_plots_zip(model_type),
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inputs=[current_model_type_state],
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outputs=download_all_plots_button
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)
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@@ -422,6 +617,7 @@ def create_gradio_interface():
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outputs=download_word_button
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)
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# Ejemplo de uso
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gr.Markdown("## Instrucciones:")
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gr.Markdown("""
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import docx
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from docx.shared import Inches, Pt
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from docx.enum.text import WD_PARAGRAPH_ALIGNMENT
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import os
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# --- Data definition in global scope ---
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self.x2_levels = x2_levels
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self.x3_levels = x3_levels
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def get_levels(self, variable_name):
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"""
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Obtiene los niveles para una variable específica.
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"""
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if variable_name == self.x1_name:
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return self.x1_levels
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elif variable_name == self.x2_name:
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return self.x2_levels
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elif variable_name == self.x3_name:
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return self.x3_levels
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else:
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raise ValueError(f"Variable desconocida: {variable_name}")
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def fit_model(self):
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"""
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Ajusta el modelo de segundo orden completo a los datos.
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"""
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formula = f'{self.y_name} ~ {self.x1_name} + {self.x2_name} + {self.x3_name} + ' \
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f'I({self.x1_name}**2) + I({self.x2_name}**2) + I({self.x3_name}**2) + ' \
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f'{self.x1_name}:{self.x2_name} + {self.x1_name}:{self.x3_name} + {self.x2_name}:{self.x3_name}'
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self.model = smf.ols(formula, data=self.data).fit()
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print("Modelo Completo:")
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print(self.model.summary())
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return self.model, self.pareto_chart(self.model, "Pareto - Modelo Completo")
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def fit_simplified_model(self):
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"""
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Ajusta el modelo de segundo orden a los datos, eliminando términos no significativos.
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"""
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formula = f'{self.y_name} ~ {self.x1_name} + {self.x2_name} + ' \
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f'I({self.x1_name}**2) + I({self.x2_name}**2) + I({self.x3_name}**2)' # Adjusted formula to include x3^2
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self.model_simplified = smf.ols(formula, data=self.data).fit()
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print("\nModelo Simplificado:")
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print(self.model_simplified.summary())
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return self.model_simplified, self.pareto_chart(self.model_simplified, "Pareto - Modelo Simplificado")
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def optimize(self, method='Nelder-Mead'):
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"""
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Encuentra los niveles óptimos de los factores para maximizar la respuesta usando el modelo simplificado.
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"""
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if self.model_simplified is None:
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print("Error: Ajusta el modelo simplificado primero.")
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return
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def objective_function(x):
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return -self.model_simplified.predict(pd.DataFrame({
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self.x1_name: [x[0]],
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self.x2_name: [x[1]],
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self.x3_name: [x[2]]
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})).values[0]
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bounds = [(-1, 1), (-1, 1), (-1, 1)]
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x0 = [0, 0, 0]
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self.optimized_results = minimize(objective_function, x0, method=method, bounds=bounds)
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self.optimal_levels = self.optimized_results.x
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# Convertir niveles óptimos de codificados a naturales
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optimal_levels_natural = [
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self.coded_to_natural(self.optimal_levels[0], self.x1_name),
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self.coded_to_natural(self.optimal_levels[1], self.x2_name),
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self.coded_to_natural(self.optimal_levels[2], self.x3_name)
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]
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# Crear la tabla de optimización
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optimization_table = pd.DataFrame({
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'Variable': [self.x1_name, self.x2_name, self.x3_name],
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'Nivel Óptimo (Natural)': optimal_levels_natural,
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'Nivel Óptimo (Codificado)': self.optimal_levels
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})
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return optimization_table.round(3) # Redondear a 3 decimales
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def fit_personalized_model(self, formula):
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"""
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for fixed_variable in [self.x1_name, self.x2_name, self.x3_name]:
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for level in levels_to_plot_natural[fixed_variable]:
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fig_full = self.plot_rsm_individual(fixed_variable, level, model_type='full') # Pass model_type
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if fig_full is not None:
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self.all_figures_full.append(fig_full)
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fig_simplified = self.plot_rsm_individual(fixed_variable, level, model_type='simplified') # Pass model_type
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if fig_simplified is not None:
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self.all_figures_simplified.append(fig_simplified)
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if self.model_personalized is not None: # Generate personalized plots only if model exists
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fig_personalized = self.plot_rsm_individual(fixed_variable, level, model_type='personalized') # Pass model_type
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if fig_personalized is not None:
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self.all_figures_personalized.append(fig_personalized)
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def plot_rsm_individual(self, fixed_variable, fixed_level, model_type='simplified'): # Added model_type parameter
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print(f"Error: Ajusta el modelo {model_type} primero.") # More informative error message
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return None
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# Determinar las variables que varían y sus niveles naturales
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varying_variables = [var for var in [self.x1_name, self.x2_name, self.x3_name] if var != fixed_variable]
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# Establecer los niveles naturales para las variables que varían
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x_natural_levels = self.get_levels(varying_variables[0])
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y_natural_levels = self.get_levels(varying_variables[1])
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# Crear una malla de puntos para las variables que varían (en unidades naturales)
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x_range_natural = np.linspace(x_natural_levels[0], x_natural_levels[-1], 100)
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y_range_natural = np.linspace(y_natural_levels[0], y_natural_levels[-1], 100)
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x_grid_natural, y_grid_natural = np.meshgrid(x_range_natural, y_range_natural)
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# Convertir la malla de variables naturales a codificadas
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x_grid_coded = self.natural_to_coded(x_grid_natural, varying_variables[0])
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y_grid_coded = self.natural_to_coded(y_grid_natural, varying_variables[1])
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# Crear un DataFrame para la predicción con variables codificadas
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prediction_data = pd.DataFrame({
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varying_variables[0]: x_grid_coded.flatten(),
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varying_variables[1]: y_grid_coded.flatten(),
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})
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prediction_data[fixed_variable] = self.natural_to_coded(fixed_level, fixed_variable)
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# Fijar la variable fija en el DataFrame de predicción
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fixed_var_levels = self.get_levels(fixed_variable)
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if len(fixed_var_levels) == 3: # Box-Behnken design levels
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prediction_data[fixed_variable] = self.natural_to_coded(fixed_level, fixed_variable)
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elif len(fixed_var_levels) > 0: # Use the closest level if not Box-Behnken
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closest_level_coded = self.natural_to_coded(min(fixed_var_levels, key=lambda x:abs(x-fixed_level)), fixed_variable)
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prediction_data[fixed_variable] = closest_level_coded
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# Calcular los valores predichos
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z_pred = model_to_use.predict(prediction_data).values.reshape(x_grid_coded.shape) # Use model_to_use here
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# Filtrar por el nivel de la variable fija (en codificado)
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fixed_level_coded = self.natural_to_coded(fixed_level, fixed_variable)
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subset_data = self.data[np.isclose(self.data[fixed_variable], fixed_level_coded)]
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# Filtrar por niveles válidos en las variables que varían
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valid_levels = [-1, 0, 1]
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experiments_data = subset_data[
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subset_data[varying_variables[0]].isin(valid_levels) &
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subset_data[varying_variables[1]].isin(valid_levels)
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]
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# Convertir coordenadas de experimentos a naturales
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experiments_x_natural = experiments_data[varying_variables[0]].apply(lambda x: self.coded_to_natural(x, varying_variables[0]))
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experiments_y_natural = experiments_data[varying_variables[1]].apply(lambda x: self.coded_to_natural(x, varying_variables[1]))
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# Crear el gráfico de superficie con variables naturales en los ejes y transparencia
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fig = go.Figure(data=[go.Surface(z=z_pred, x=x_grid_natural, y=y_grid_natural, colorscale='Viridis', opacity=0.7, showscale=True)])
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# --- Añadir cuadrícula a la superficie ---
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# Líneas en la dirección x
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for i in range(x_grid_natural.shape[0]):
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fig.add_trace(go.Scatter3d(
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x=x_grid_natural[i, :],
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y=y_grid_natural[i, :],
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z=z_pred[i, :],
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mode='lines',
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line=dict(color='gray', width=2),
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showlegend=False,
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+
hoverinfo='skip'
|
258 |
+
))
|
259 |
+
# Líneas en la dirección y
|
260 |
+
for j in range(x_grid_natural.shape[1]):
|
261 |
+
fig.add_trace(go.Scatter3d(
|
262 |
+
x=x_grid_natural[:, j],
|
263 |
+
y=y_grid_natural[:, j],
|
264 |
+
z=z_pred[:, j],
|
265 |
+
mode='lines',
|
266 |
+
line=dict(color='gray', width=2),
|
267 |
+
showlegend=False,
|
268 |
+
hoverinfo='skip'
|
269 |
+
))
|
270 |
+
|
271 |
+
# --- Fin de la adición de la cuadrícula ---
|
272 |
+
|
273 |
+
# Añadir los puntos de los experimentos en la superficie de respuesta con diferentes colores y etiquetas
|
274 |
+
colors = px.colors.qualitative.Safe
|
275 |
+
point_labels = [f"{row[self.y_name]:.3f}" for _, row in experiments_data.iterrows()]
|
276 |
+
|
277 |
+
fig.add_trace(go.Scatter3d(
|
278 |
+
x=experiments_x_natural,
|
279 |
+
y=experiments_y_natural,
|
280 |
+
z=experiments_data[self.y_name].round(3),
|
281 |
+
mode='markers+text',
|
282 |
+
marker=dict(size=4, color=colors[:len(experiments_x_natural)]),
|
283 |
+
text=point_labels,
|
284 |
+
textposition='top center',
|
285 |
+
name='Experimentos'
|
286 |
+
))
|
287 |
+
|
288 |
+
# Añadir etiquetas y título con variables naturales
|
289 |
fig.update_layout(
|
290 |
scene=dict(
|
291 |
xaxis_title=f"{varying_variables[0]} ({self.get_units(varying_variables[0])})",
|
|
|
303 |
# --- Funciones para la Interfaz de Gradio ---
|
304 |
|
305 |
def load_data(data_str):
|
306 |
+
global rsm, data
|
307 |
+
|
308 |
+
x1_name = "Glucosa_g_L"
|
309 |
+
x2_name = "Proteina_Pescado_g_L"
|
310 |
+
x3_name = "Sulfato_Manganeso_g_L"
|
311 |
+
y_name = "Abs_600nm"
|
312 |
+
x1_levels = sorted(list(set(data[x1_name])))
|
313 |
+
x2_levels = sorted(list(set(data[x2_name])))
|
314 |
+
x3_levels = sorted(list(set(data[x3_name])))
|
315 |
+
|
316 |
+
rsm = RSM_BoxBehnken(data, x1_name, x2_name, x3_name, y_name, x1_levels, x2_levels, x3_levels)
|
317 |
return data.round(3), gr.update(visible=True)
|
318 |
|
319 |
+
|
320 |
def fit_and_optimize_model():
|
321 |
if 'rsm' not in globals():
|
322 |
return [None]*11
|
|
|
341 |
return (
|
342 |
model_completo.summary().as_html(),
|
343 |
pareto_completo,
|
344 |
+
model_simplificado_output, # output_components are correctly referenced now
|
345 |
pareto_simplificado,
|
346 |
equation_formatted,
|
347 |
optimization_table,
|
|
|
352 |
excel_path
|
353 |
)
|
354 |
|
355 |
+
def fit_custom_model(factor_checkboxes, interaction_checkboxes, model_personalized_output_component, pareto_personalized_output_component):
|
356 |
if 'rsm' not in globals():
|
357 |
+
return [None]*2 # adjust output number
|
358 |
|
359 |
formula_parts = [rsm.x1_name, rsm.x2_name, rsm.x3_name] if "factors" in factor_checkboxes else []
|
360 |
if "x1_sq" in factor_checkboxes: formula_parts.append(f'I({rsm.x1_name}**2)')
|
|
|
372 |
custom_model, pareto_custom = rsm.fit_personalized_model(formula) # Fit personalized model
|
373 |
rsm.generate_all_plots() # Regenerate plots to include personalized model plots
|
374 |
|
375 |
+
return custom_model.summary().as_html(), pareto_custom # return values for outputs
|
376 |
+
|
377 |
|
378 |
def show_plot(current_index, all_figures, model_type): # Modified to accept model_type
|
379 |
figure_list = []
|
|
|
449 |
return zip_path
|
450 |
return None
|
451 |
|
452 |
+
def download_all_tables_excel():
|
453 |
+
"""
|
454 |
+
Descarga todas las tablas en un archivo Excel con múltiples hojas.
|
455 |
+
"""
|
456 |
+
if 'rsm' not in globals():
|
457 |
+
return None
|
458 |
+
excel_path = rsm.save_tables_to_excel()
|
459 |
+
if excel_path:
|
460 |
+
filename = f"Tablas_RSM_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
|
461 |
+
# Gradio no permite renombrar directamente, por lo que retornamos la ruta del archivo
|
462 |
+
return excel_path
|
463 |
+
return None
|
464 |
+
|
465 |
+
def exportar_word(rsm_instance, tables_dict):
|
466 |
+
"""
|
467 |
+
Función para exportar las tablas a un documento de Word.
|
468 |
+
"""
|
469 |
+
word_path = rsm_instance.export_tables_to_word(tables_dict)
|
470 |
+
if word_path and os.path.exists(word_path):
|
471 |
+
return word_path
|
472 |
+
return None
|
473 |
+
|
474 |
+
|
475 |
# --- Crear la interfaz de Gradio ---
|
476 |
|
477 |
def create_gradio_interface():
|
|
|
535 |
all_figures_state = gr.State([])
|
536 |
current_model_type_state = gr.State('simplified') # State to track selected model type for plots
|
537 |
|
538 |
+
|
539 |
# Cargar datos
|
540 |
load_button.click(
|
541 |
load_data,
|
|
|
565 |
# Ajustar modelo personalizado
|
566 |
custom_model_button.click( # New event for custom model fitting
|
567 |
fit_custom_model,
|
568 |
+
inputs=[factor_checkboxes, interaction_checkboxes, model_personalized_output, pareto_personalized_output], # pass output components
|
569 |
+
outputs=[model_personalized_output, pareto_personalized_output] # return values for outputs
|
570 |
)
|
571 |
|
572 |
+
|
573 |
# Generar y mostrar los gráficos
|
574 |
plot_button.click(
|
575 |
+
lambda fixed_var, fixed_lvl, model_type: show_plot(0, [], model_type) if not hasattr(rsm, 'all_figures_full') or not rsm.all_figures_full else show_plot(0, [], model_type) if model_type == 'full' and not rsm.all_figures_full else show_plot(0, [], model_type) if model_type == 'simplified' and not rsm.all_figures_simplified else show_plot(0, [], model_type) if model_type == 'personalized' and not rsm.all_figures_personalized else show_plot(0, rsm.all_figures_full if model_type == 'full' else rsm.all_figures_simplified if model_type == 'simplified' else rsm.all_figures_personalized, model_type),
|
576 |
+
inputs=[fixed_variable_input, fixed_level_input, model_type_radio],
|
577 |
+
outputs=[rsm_plot_output, plot_info, current_index_state, current_model_type_state]
|
|
|
|
|
|
|
|
|
578 |
)
|
579 |
|
580 |
|
581 |
# Navegación de gráficos
|
582 |
left_button.click(
|
583 |
+
lambda current_index, all_figures, model_type: navigate_plot('left', current_index, all_figures, model_type),
|
584 |
+
inputs=[current_index_state, all_figures_state, current_model_type_state],
|
585 |
outputs=[rsm_plot_output, plot_info, current_index_state]
|
586 |
)
|
587 |
right_button.click(
|
588 |
+
lambda current_index, all_figures, model_type: navigate_plot('right', current_index, all_figures, model_type),
|
589 |
+
inputs=[current_index_state, all_figures_state, current_model_type_state],
|
590 |
outputs=[rsm_plot_output, plot_info, current_index_state]
|
591 |
)
|
592 |
|
593 |
# Descargar gráfico actual
|
594 |
download_plot_button.click(
|
595 |
download_current_plot,
|
596 |
+
inputs=[all_figures_state, current_index_state, current_model_type_state],
|
597 |
outputs=download_plot_button
|
598 |
)
|
599 |
|
600 |
# Descargar todos los gráficos en ZIP
|
601 |
download_all_plots_button.click(
|
602 |
+
lambda model_type: download_all_plots_zip(model_type),
|
603 |
+
inputs=[current_model_type_state],
|
604 |
outputs=download_all_plots_button
|
605 |
)
|
606 |
|
|
|
617 |
outputs=download_word_button
|
618 |
)
|
619 |
|
620 |
+
|
621 |
# Ejemplo de uso
|
622 |
gr.Markdown("## Instrucciones:")
|
623 |
gr.Markdown("""
|