C2MV commited on
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4662cb9
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1 Parent(s): 07dcaa0

Update app.py

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  1. app.py +4 -1
app.py CHANGED
@@ -2,6 +2,7 @@ import os
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  os.system('pip install gradio seaborn scipy scikit-learn openpyxl networkx pydantic==1.10.0')
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  from pydantic import BaseModel, ConfigDict
 
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  import numpy as np
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  import networkx as nx
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  import matplotlib.pyplot as plt
@@ -102,9 +103,11 @@ class GraphTheoryAnalysis:
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  # Identificar los 3 factores más relevantes
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  factor_r2 = {}
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  for factor in self.all_factors:
 
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  edges = [data['weight'] for u, v, data in self.graph.edges(data=True) if u == factor or v == factor]
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  factor_r2[factor] = sum(edges) / len(edges) if edges else 0
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  top_factors = sorted(factor_r2, key=factor_r2.get, reverse=True)[:3]
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  # Añadir únicamente las interacciones entre los 3 factores seleccionados
@@ -190,8 +193,8 @@ def generate_box_behnken(factors):
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  # Definición de la interfaz de Gradio
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  def analyze_design(level, pb_design, bb_design, variable1_values, variable2_values, style):
 
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  try:
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- # Validar matrices de diseño
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  pb_design_np = np.array(pb_design, dtype=float)
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  bb_design_np = np.array(bb_design, dtype=float)
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  except ValueError:
 
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  os.system('pip install gradio seaborn scipy scikit-learn openpyxl networkx pydantic==1.10.0')
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  from pydantic import BaseModel, ConfigDict
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+
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  import numpy as np
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  import networkx as nx
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  import matplotlib.pyplot as plt
 
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  # Identificar los 3 factores más relevantes
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  factor_r2 = {}
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  for factor in self.all_factors:
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+ # Filtrar las aristas que contienen el factor
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  edges = [data['weight'] for u, v, data in self.graph.edges(data=True) if u == factor or v == factor]
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  factor_r2[factor] = sum(edges) / len(edges) if edges else 0
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+ # Seleccionar los 3 factores con mayor R²
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  top_factors = sorted(factor_r2, key=factor_r2.get, reverse=True)[:3]
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  # Añadir únicamente las interacciones entre los 3 factores seleccionados
 
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  # Definición de la interfaz de Gradio
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  def analyze_design(level, pb_design, bb_design, variable1_values, variable2_values, style):
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+ # Validar y convertir matrices de diseño a numpy arrays
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  try:
 
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  pb_design_np = np.array(pb_design, dtype=float)
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  bb_design_np = np.array(bb_design, dtype=float)
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  except ValueError: