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import torch |
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import torch.nn as nn |
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import pandas as pd |
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import numpy as np |
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from sklearn.preprocessing import StandardScaler, MinMaxScaler |
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from sklearn.model_selection import train_test_split |
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from torch.utils.data import DataLoader, TensorDataset |
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import plotly.express as px |
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import streamlit as st |
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from es_class_nn import SimplePlusNN2 |
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from prediction_analyzer import PredictionAnalyzer |
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version_name = 'CON_44' |
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c1, c2 = st.columns([6,6]) |
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with c2: |
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st.image('img/logo_vidad.png', width=300, caption='https://www.continental.edu.pe/') |
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st.title("Predicción de Abandono o Permanencia") |
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st.write("Cargue el archivo PKL para visualizar el análisis de su contenido.") |
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st.write('Cargue el archivo con datos nuevos aqui. Este archivo deberá seguir las pautas del diccionario de categorías y deberá estar en formato XLSX') |
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st.write('En caso no conozca el diccionario descarguelo aquí.') |
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st.link_button('Diccionario', 'https://huggingface.co/spaces/gestiodinamica/continental_predictivo/resolve/main/auxiliares/diccionario_variables_pkl_train.txt?download=true') |
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uploaded_file = st.file_uploader("Cargar archivo: ", type='xlsx') |
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if uploaded_file is not None: |
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df_2 = pd.read_excel(uploaded_file) |
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df_2 = df_2.dropna(axis=0) |
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df_2.index = df_2.DNI |
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df_2.drop(columns=['DNI'], inplace=True) |
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st.write('Dimensiones de archivo ingresado: ', df_2.shape) |
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cat_list = [] |
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num_list = [] |
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for col in df_2.columns: |
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if df_2[col].dtype == 'object': |
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if len(df_2[col].unique()) < 35: |
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cat_list.append(col) |
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else: |
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num_list.append(col) |
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df_a = df_2[cat_list] |
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df_b = pd.get_dummies(df_a) |
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df_b = df_b.set_index(df_2.index) |
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scaler = MinMaxScaler() |
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X_sc = scaler.fit_transform(df_b) |
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df_sc = pd.DataFrame(X_sc) |
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df_sc.columns = df_b.columns |
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df_n = df_2[num_list] |
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df_n = df_n.set_index(df_2.index) |
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df_sc.index = df_b.index |
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if (df_sc.index == df_n.index).sum()==len(df_2): |
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st.write('Indices Consistentes Verificados.') |
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df_r = pd.concat([df_sc, df_n], axis=1) |
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st.write('Dimensiones del archivo procesado: ', df_r.shape) |
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list_df_modelo = pd.read_excel('auxiliares/lista_categorias_CON_44_v0.xlsx') |
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lista_categorias = list_df_modelo[0].to_list() |
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cats_ad = [] |
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cats_res = [] |
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for c in df_r.columns: |
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if c not in lista_categorias: |
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cats_ad.append(c) |
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else: |
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cats_res.append(c) |
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cats_dis = [] |
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cats_res2 = [] |
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for c in lista_categorias: |
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if c not in df_r.columns: |
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cats_dis.append(c) |
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else: |
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cats_res2.append(c) |
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df_ad = pd.DataFrame(0, index=range(140), columns=cats_dis) |
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df_ad.index = df_r.index |
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df_base = df_r[cats_res] |
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df_base.index = df_r.index |
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df_tot = pd.concat([df_base, df_ad], axis=1) |
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cats_test = [] |
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for c in df_tot.columns: |
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if c in lista_categorias: |
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cats_test.append(c) |
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cats_final = [] |
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for c in lista_categorias: |
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if c[0:6]=='ESTADO': |
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print(c) |
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else: |
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cats_final.append(c) |
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df_ready = df_tot[cats_final] |
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st.write('Dimensiones del archivo para predicción: ', df_ready.shape) |
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DROPOUTX = 0.10 |
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version_name = 'CON_44' |
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X_train = df_ready.values |
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X_train_tensor = torch.tensor(X_train, dtype=torch.float32) |
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st.write('Dimensiones del tensor: ', X_train_tensor.shape) |
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input_size = X_train_tensor.shape[1] |
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num_classes = 2 |
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model = SimplePlusNN2(input_size, num_classes) |
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data_path = 'models/' |
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dict_name = f'edusights_20240702_state_dict_{version_name}.pth' |
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st.write(dict_name) |
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model.load_state_dict(torch.load(data_path+dict_name)) |
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model.eval() |
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outputs = model(X_train_tensor) |
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outputs_show = outputs.detach().numpy().flatten() |
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st.write('Salida: ', outputs_show.shape) |
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outputs_show[outputs_show > 0.51] = 1.0 |
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outputs_show[outputs_show < 0.49] = 0.0 |
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filtered_arr = outputs_show[(outputs_show == 0.0) | (outputs_show == 1.0)] |
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unique, counts = np.unique(filtered_arr, return_counts=True) |
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st.write(unique, counts) |
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pred_df = pd.DataFrame(filtered_arr) |
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st.write('Dimensión de predicciones totales: ', pred_df.shape) |
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df_ready_2 = df_ready.copy() |
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df_ready_2['Predicción'] = pred_df |
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st.write(df_ready_2.head()) |
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analyzer = PredictionAnalyzer(model, df_ready) |
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results_df = analyzer.predictions_loop() |
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st.write(results_df) |
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csv_out = df_ready_2.to_csv(encoding='iso-8859-1') |
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st.download_button( |
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label="Descargar Predicciones Totales", data=csv_out, |
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file_name='predicciones_carga.csv', mime='text/csv' |
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) |
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csv_out = results_df.to_csv(encoding='iso-8859-1') |
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st.download_button( |
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label="Descargar Predicciones por Categoría", data=csv_out, |
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file_name='predicciones_carga.csv', mime='text/csv' |
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) |
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c3, c4 = st.columns([6,6]) |
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with c3: |
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st.image('img/gdmklogo.png', width=100, caption='Powered by GestioDinámica 2024') |
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