diff --git a/data/.~lock.final_merge.csv# b/data/.~lock.final_merge.csv# new file mode 100644 index 0000000..c52aa27 --- /dev/null +++ b/data/.~lock.final_merge.csv# @@ -0,0 +1 @@ +,die,hoyganelpese,20.05.2024 09:59,file:///home/die/.config/libreoffice/4; \ No newline at end of file diff --git a/data/final_merge.csv b/data/final_merge.csv index b1bf0a3..4ec1057 100644 --- a/data/final_merge.csv +++ b/data/final_merge.csv @@ -1,139 +1,140 @@ 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b/results/plots/previous/testVsPredicted_lmplot_Total.N_lgbm_urineFlav.png similarity index 100% rename from results/plots/testVsPredicted_lmplot_Total.N_lgbm_urineFlav.png rename to results/plots/previous/testVsPredicted_lmplot_Total.N_lgbm_urineFlav.png diff --git a/results/plots/testVsPredicted_lmplot_Total.N_xgb_urineFlav.png b/results/plots/previous/testVsPredicted_lmplot_Total.N_xgb_urineFlav.png similarity index 100% rename from results/plots/testVsPredicted_lmplot_Total.N_xgb_urineFlav.png rename to results/plots/previous/testVsPredicted_lmplot_Total.N_xgb_urineFlav.png diff --git a/results/predicts/df_pred-test_lgbm_CA_plasm_post_CA_plasm_post.csv b/results/predicts/df_pred-test_lgbm_CA_plasm_post_CA_plasm_post.csv new file mode 100644 index 0000000..07fec7c --- /dev/null +++ b/results/predicts/df_pred-test_lgbm_CA_plasm_post_CA_plasm_post.csv @@ -0,0 +1,29 @@ +;CA_plasm_post_pred;CA_plasm_post +135;0.0729235795890992;0.3612482717756271 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b/results/predicts/df_pred-test_rf_CA_plasm_post_CA_plasm_post.csv @@ -0,0 +1,29 @@ +;CA_plasm_post_pred;CA_plasm_post +135;0.35903165864151454;0.3612482717756271 +66;0.26936704653433147;0.132530120481928 +31;0.319215060890585;0.253012048192771 +118;0.46335207290359154;0.674698795180723 +42;0.3560367611371535;0.3612482717756271 +12;0.28600173045440475;0.228915662650602 +51;0.2649475788535906;0.168674698795181 +68;0.47504493172416856;0.325301204819277 +126;0.32965331978773516;0.421686746987952 +55;0.42536031107445266;0.542168674698795 +112;0.46491894600815403;0.602409638554217 +130;0.35468404289273164;0.614457831325301 +80;0.5159611888507237;0.481927710843374 +19;0.25796996805026523;0.108433734939759 +86;0.33166579300636423;0.228915662650602 +69;0.45576911249248336;0.481927710843374 +11;0.45085212187253926;0.36144578313253 +27;0.46768514090147323;0.204819277108434 +134;0.36144735642447057;0.3612482717756271 +109;0.32044793539335076;0.204819277108434 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b/results/predicts/df_pred-test_xgb_CA_plasm_post_CA_plasm_post.csv new file mode 100644 index 0000000..1777e9b --- /dev/null +++ b/results/predicts/df_pred-test_xgb_CA_plasm_post_CA_plasm_post.csv @@ -0,0 +1,29 @@ +;CA_plasm_post_pred;CA_plasm_post +135;0.3925019;0.3612482717756271 +66;0.39276952;0.132530120481928 +31;0.39271295;0.253012048192771 +118;0.39278108;0.674698795180723 +42;0.39290622;0.3612482717756271 +12;0.3922817;0.228915662650602 +51;0.39232573;0.168674698795181 +68;0.3929823;0.325301204819277 +126;0.3925094;0.421686746987952 +55;0.3928393;0.542168674698795 +112;0.39326227;0.602409638554217 +130;0.3927222;0.614457831325301 +80;0.39282396;0.481927710843374 +19;0.39223745;0.108433734939759 +86;0.39261374;0.228915662650602 +69;0.39289227;0.481927710843374 +11;0.39267737;0.36144578313253 +27;0.3928673;0.204819277108434 +134;0.39246932;0.3612482717756271 +109;0.39240238;0.204819277108434 +36;0.39282528;0.421686746987952 +117;0.39265797;0.44578313253012 +78;0.39242294;0.144578313253012 +4;0.392605;0.216867469879518 +73;0.39271036;0.409638554216867 +26;0.39234754;0.228915662650602 +132;0.39249283;0.301204819277108 +60;0.3928383;0.72289156626506 diff --git a/results/predicts/df_pred-test_xgb_Total.N_post_Total.N_post.csv b/results/predicts/df_pred-test_xgb_Total.N_post_Total.N_post.csv new file mode 100644 index 0000000..1876d57 --- /dev/null +++ b/results/predicts/df_pred-test_xgb_Total.N_post_Total.N_post.csv @@ -0,0 +1,29 @@ +;Total.N_post_pred;Total.N_post +135;0.09163765;0.0494582035296564 +66;0.09162406;0.0108536878891862 +31;0.091618665;0.0691881205640523 +118;0.091645606;0.183032897318993 +42;0.09160828;0.0532739881108947 +12;0.09162833;0.0185480107164051 +51;0.091626026;0.0971008192457126 +68;0.09163941;0.171927275257926 +126;0.091624916;0.123281109909106 +55;0.091629095;0.0686441308014138 +112;0.09161635;0.118495142693544 +130;0.0916206;0.152465536794519 +80;0.09162345;0.008012454357685 +19;0.091666706;0.278571356376836 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+12;0.32837963;0.228915662650602 +51;0.32826132;0.168674698795181 +68;0.33619845;0.325301204819277 +126;0.32855675;0.421686746987952 +55;0.3344487;0.542168674698795 +112;0.34130353;0.602409638554217 +130;0.33062324;0.614457831325301 +80;0.33807716;0.481927710843374 +19;0.32763216;0.108433734939759 +86;0.33109218;0.228915662650602 +69;0.33346272;0.481927710843374 +11;0.3350421;0.36144578313253 +27;0.33836737;0.204819277108434 +134;0.3229889;0.0 +109;0.32576936;0.204819277108434 +36;0.33607948;0.421686746987952 +117;0.33268535;0.44578313253012 +78;0.32841596;0.144578313253012 +4;0.3264371;0.216867469879518 +73;0.33490095;0.409638554216867 +26;0.32439998;0.228915662650602 +132;0.3285762;0.301204819277108 +60;0.33743805;0.72289156626506 diff --git a/scripts/XGBFfit.py b/scripts/XGBFfit.py index 6b16e92..a26911f 100644 --- a/scripts/XGBFfit.py +++ b/scripts/XGBFfit.py @@ -15,7 +15,7 @@ def LGBMfit(X_train, X_test, X_val, y_train, y_test, y_val): def objective(trial): params = { 'n_estimators': trial.suggest_int('n_estimators', 10, 510, step=100), - 'max_depth': trial.suggest_int('max_depth', -1, 30), + 'max_depth': trial.suggest_int('max_depth', -1, 10), #'scale_pos_weight': trial.suggest_int('scale_pos_weight', 1, 5), 'learning_rate': trial.suggest_float('learning_rate', 0.0001, 0.01), #'reg_lambda': trial.suggest_float('reg_lambda', 0.0001, 0.1, log=True), @@ -23,12 +23,13 @@ def LGBMfit(X_train, X_test, X_val, y_train, y_test, y_val): #'colsample_bynode': trial.suggest_float('colsample_bynode', 0.1, 1), 'subsample': trial.suggest_float('subsample', 0.01, 1), 'boosting_type': trial.suggest_categorical('boosting_type', ['gbdt', 'dart']), + "num_leaves": trial.suggest_int("num_leaves", 1024, 2000) } model = LGBMRegressor( - eval_metric = 'rmse', n_jobs = -1, random_state = 42, + verbose = 0, #multi_strategy = "one_output_per_tree", **params ) @@ -46,9 +47,9 @@ def LGBMfit(X_train, X_test, X_val, y_train, y_test, y_val): # XGBoost con los mejores hiperparámetros encontrados # ============================================================================== lightGMB_model = LGBMRegressor( - eval_metric = 'rmse', n_jobs = -1, random_state = 42, + verbose = 0, #multi_strategy = "one_output_per_tree", **study.best_params ) diff --git a/scripts/__pycache__/XGBFfit.cpython-310.pyc b/scripts/__pycache__/XGBFfit.cpython-310.pyc index 8b409d9..44eb8dd 100644 Binary files a/scripts/__pycache__/XGBFfit.cpython-310.pyc and b/scripts/__pycache__/XGBFfit.cpython-310.pyc differ diff --git a/scripts/__pycache__/plotting.cpython-310.pyc b/scripts/__pycache__/plotting.cpython-310.pyc index af9a0d8..31434ad 100644 Binary files a/scripts/__pycache__/plotting.cpython-310.pyc and b/scripts/__pycache__/plotting.cpython-310.pyc differ diff --git a/scripts/modelSanoApp.py b/scripts/modelSanoApp.py index 3253e1e..eed6294 100644 --- a/scripts/modelSanoApp.py +++ b/scripts/modelSanoApp.py @@ -5,61 +5,162 @@ Created on Wed May 8 21:27:35 2024 @author: dres2 """ -from main_retrain import evaluateNPlot, RF_Fit -from utils import fullRead -# from fitEnsemble import fitRF -import re +from XGBFfit import XGBfit, LGBMfit +from RFfit import RFfit_lgbm +from sklearn.model_selection import train_test_split +from plotting import plotResiduals, plotTestVsPredicted +from sklearn.experimental import enable_iterative_imputer +from sklearn.impute import IterativeImputer +from xgboost import XGBRegressor import pandas as pd -import sys - -testeo = sys.argv[1] -ejecucion = sys.argv[2] -ejecucionPeque = sys.argv[3] - +import os +import time print(" ----------------- PACKAGES EXPORTING ENDED ----------------- ") -print(" ----------------- READING DATASETS ----------------- ") +print(" ----------------- READING DATASET ----------------- ") -paths = ["plasmFlav_ord.csv", "plasmAnt_ord.csv", "urineFlav_ord.csv", "urineAnt_ord.csv"] -paths = ["../data/" + s for s in paths] +main_df = pd.read_csv(os.path.join("..", "data", "final_merge.csv"), sep =";", decimal= ".") +main_df[['Sex', 'Sweetener']] = main_df[['Sex', 'Sweetener']].astype("category") +anthropometric_features = ["Weight_pre","Weight_post","Delta_Weight", + "Height","BMI_pre","BMI_post","Delta_BMI", + "Fat_pre","Fat_post","Delta_Fat","CVRI_post", + "CVRI_pre" ,"Bpmin_post","Bpmax_post", + "Bpmin_pre", "Bpmax_pre","Frec_post","Frec_pre"] -# execution -df, df_name = fullRead(paths[2], sep = ",", full = True) -df_name = re.sub("../", "", df_name) -df[['Weight', 'BMI']] = df[['Weight', 'BMI']].apply(pd.to_numeric) -RF_Fit(df, df_name, full = True, eval=True, plot = True) +study_features = ["Sex", "Sweetener"] -if testeo == True: +urine_ant_features = ['CA_urine_pre', 'CA.G_urine_pre', 'CA.S_urine_pre', + 'CA.GS_pre', 'Total.CA_urine_pre', 'DHPAA_urine_pre', + 'DHPAA.G_urine_pre', 'DHPAA.GG_urine_pre', 'DHPAA.GS_urine_pre', + 'DHPAA.SS_urine_pre', 'Total.DHPAA_urine_pre', 'TFA.G_urine_pre', + 'TFA.S_urine_pre', 'TFA.di.sulfate.1_pre', 'Total.TFA_urine_pre', + 'TIFA.Sulfate.1_pre', 'VA_urine_pre', 'VA.GG_urine_pre', + 'VA.GS_urine_pre', 'VA.SS_urine_pre', 'Total.VA_urine_pre', + 'CA_urine_post','CA.G_urine_post', 'CA.S_urine_post', 'CA.GS_post', + 'Total.CA_urine_post', 'DHPAA_urine_post', 'DHPAA.G_urine_post', + 'DHPAA.GG_urine_post', 'DHPAA.GS_urine_post', 'DHPAA.SS_urine_post', + 'Total.DHPAA_urine_post', 'TFA.G_urine_post', 'TFA.S_urine_post', + 'TFA.di.sulfate.1_post', 'Total.TFA_urine_post', + 'TIFA.Sulfate.1_post','VA_urine_post', 'VA.GG_urine_post', + 'VA.GS_urine_post','VA.SS_urine_post', 'Total.VA_urine_post'] - print(" ----------------- TEST MODE INCIATING ----------------- ") +urine_flav_features = ['E_urine_pre', 'EG.1_pre', 'E.S_urine_pre', 'Total.E_urine_pre', + 'HE_pre', 'HE.G_urine_pre', 'HE.GG_pre', 'Total.HE_pre', 'N_pre', + 'N.G_urine_pre', 'N.GG_pre', 'N.S_pre', 'Total.N_pre', 'E_urine_post', 'EG.1_post', + 'E.S_urine_post', 'Total.E_urine_post', 'HE_post', 'HE.G_urine_post', + 'HE.GG_post', 'Total.HE_post', 'N_post', 'N.G_urine_post', 'N.GG_post', + 'N.S_post', 'Total.N_post'] +plasm_flav_features = ['E_plasm_pre', 'E.S_plasm_pre', + 'Total.E_plasm_pre', 'HE.G_plasm_pre', 'N.G_plasm_pre', 'E_plasm_post', 'E.S_plasm_post', + 'Total.E_plasm_post', 'HE.G_plasm_post', 'N.G_plasm_post'] +plasm_ant_features = ['CA_plasm_pre', 'CA.G_plasm_pre', 'CA.S_plasm_pre', 'Total.CA_plasm_pre', + 'DHPAA_plasm_pre', 'DHPAA.G_plasm_pre', 'DHPAA.GG_plasm_pre', + 'DHPAA.GS_plasm_pre', 'DHPAA.SS_plasm_pre', 'Total.DHPAA_plasm_pre', + 'TFA.G_plasm_pre', 'TFA.S_plasm_pre', 'Total.TFA_plasm_pre', + 'VA_plasm_pre', 'VA.GG_plasm_pre', 'VA.S_pre', 'VA.GS_plasm_pre', + 'VA.SS_plasm_pre', 'Total.VA_plasm_pre', 'CA_plasm_post', 'CA.G_plasm_post', + 'CA.S_plasm_post', 'Total.CA_plasm_post', 'DHPAA_plasm_post', + 'DHPAA.G_plasm_post', 'DHPAA.GG_plasm_post', 'DHPAA.GS_plasm_post', + 'DHPAA.SS_plasm_post', 'Total.DHPAA_plasm_post', 'TFA.G_plasm_post', + 'TFA.S_plasm_post', 'Total.TFA_plasm_post', 'VA_plasm_post', + 'VA.GG_plasm_post', 'VA.S_post', 'VA.GS_plasm_post', 'VA.SS_plasm_post', + 'Total.VA_plasm_post'] - df, df_name = fullRead(paths[1], sep = ",", full = True) - df_name = re.sub("../", "", df_name) - df[['Weight', 'BMI']] = df[['Weight', 'BMI']].apply(pd.to_numeric) +def preproc (df, features, targets): - #y_test = pd.read_csv("/home/die/Documents/repositories/mSApp/doc/scripts/X_metTest_RF_Met_lightTrainplasmFlav.csv") - #X_test = pd.read_csv("/home/die/Documents/repositories/mSApp/doc/scripts/y_metTest_RF_Met_lightTrainplasmFlav.csv") + iimp = IterativeImputer( + estimator = XGBRegressor(), + random_state = 42, + verbose = 0, + ) - residPlot = False - evaluateNPlot(modelPath = "../models/RF_Full_lightTrainplasmAnt.pkl", df = df, df_name = df_name, pipeline = False) + iimp.set_output(transform="pandas") + targets_imp= iimp.fit_transform(pd.DataFrame(df[targets])) -else: - print(" ----------------- NO TESTING MODE ----------------- ") - -if ejecucion == True: + X_train, X_test, y_train, y_test = train_test_split( + df[features], + targets_imp, + train_size = 0.8, + random_state = 42, + shuffle = True + ) + + X_train, X_val, y_train, y_val = train_test_split( + X_train, + y_train, + train_size = 0.8, + random_state = 42, + shuffle = True + ) + + print("Observaciones en train:", X_train.shape) + print("Observaciones en validation:", X_val.shape) + print("Observaciones en test:", X_test.shape) - print(" ----------------- EXECUTION MODE INCIATING ----------------- ") - - for path in paths: - df, df_name = fullRead(path, sep = ",", full = True) - df_name = re.sub("../", "", df_name) - df[['Weight', 'BMI']] = df[['Weight', 'BMI']].apply(pd.to_numeric) - - RF_Fit(df, df_name, full = True, eval=True, plot = True) - RF_Fit(df, df_name, full = False, eval=True, plot = True) -else: - print(" ----------------- NO EXECUTION MODE ----------------- ") + return X_train, X_test, X_val, y_train, y_test, y_val +def model_training(X_train, X_test, X_val, y_train, y_test, y_val): + print(" ----------------- STARTING XGB ----------------- ") + start = time.time() + + xgb_model = XGBfit(X_train, X_test, X_val, y_train, y_test, y_val) + rf_model = RFfit_lgbm(X_train, X_test, X_val, y_train, y_test, y_val) + lgbm_model = LGBMfit(X_train, X_test, X_val, y_train, y_test, y_val) + + end = time.time() + + tiempo_entrenamiento_full = end - start + + print(f"Tiempo optimización + entrenamiento full: {tiempo_entrenamiento_full:.2f} segundos") + return(xgb_model, rf_model, lgbm_model) + +def post_model(model, X_test, y_test, targets, modelname, savePath): + + #modelname = "xgb_newDF_"+targets + + # saving predicted + print(" ----------------- PREDICTING AND JOINING PRED + TEST ----------------- ") + + # make predict + # y_pred_scaled = xgb_XGB.predict(X_test) + # y_pred = scaler.inverse_transform(y_pred_scaled) + # y_test = pd.DataFrame([i for i in scaler.inverse_transform(y_test)], index=y_test.index, columns= y_test.columns) + + y_test.name = targets+"_test" + y_pred = pd.Series(model.predict(X_test), index = y_test.index, name = targets+"_pred") + df_predTest = pd.concat([y_pred, y_test], axis = 1) + df_predTest.to_csv(savePath+"df_pred-test_"+modelname+"_"+targets+".csv", sep = ";") + + #df_predTest = pd.DataFrame([i for i in y_pred], index = y_test.index, columns= y_test.columns).add_suffix("_pred").join(y_test.add_suffix('_test')) + #df_predTest.reindex(sorted(df_predTest.columns), axis=1).to_csv("../results/predicts/df_pred-test_"+modelname+".csv", sep = ";") + + # plotting + #metabs = y_test.columns.drop(list(y_test.filter(regex='Sex|Sweetener'))) + + plotTestVsPredicted (metab=targets, df_predTest=df_predTest, modelname=modelname, savePath=savePath) + plotResiduals(metab=targets, df_predTest=df_predTest, modelname=modelname, savePath=savePath) + +post_targets = [i for i in main_df.columns if "_post" in i] + +for targets in post_targets: + + features = main_df.drop(targets, axis = 1).columns + + directory = os.path.join("..","results", "plots", "newPlots", targets) + # ["Sex", "Sweetener", "Delta_BMI", "Delta_Fat",'CA_plasm_pre', 'CA.G_plasm_pre', 'CA.S_plasm_pre','Total.CA_plasm_pre'] + # main_df["CA_plasm_post"] = main_df["CA_plasm_post"].fillna(0) + if not os.path.exists(directory): + os.mkdir(directory) + else: + os.mkdir(directory+"v1") + + X_train, X_test, X_val, y_train, y_test, y_val = preproc(df=main_df, targets=targets, features=features) + + xgb_model, rf_model, lgbm_model = model_training(X_train, X_test, X_val, y_train, y_test, y_val) + + post_model(xgb_model, X_test=X_test, y_test=y_test, targets = targets, modelname = "xgb_"+targets, savePath=directory) + post_model(rf_model, X_test=X_test, y_test=y_test, targets = targets, modelname ="rf_"+targets, savePath=directory) + post_model(lgbm_model, X_test=X_test, y_test=y_test, targets = targets, modelname ="lgbm_"+targets, savePath=directory) \ No newline at end of file diff --git a/scripts/plotting.py b/scripts/plotting.py index 00f48a4..f4a070c 100644 --- a/scripts/plotting.py +++ b/scripts/plotting.py @@ -3,7 +3,7 @@ import matplotlib.pyplot as plt import seaborn as sns -def plotTestVsPredicted (df_predTest, metab, modelname, df_name): +def plotTestVsPredicted (df_predTest, metab, modelname, savePath): print(" ----------------- STARTING PLOTTING ----------------- ") #for metab in metabs: @@ -12,18 +12,18 @@ def plotTestVsPredicted (df_predTest, metab, modelname, df_name): print(" ----------------- PREDICTED VS TEST " + metab + " ----------------- ") plt.figure() - sns.lmplot(data = df_predTest, x=metab+"_pred", y=metab+"_test", height=7, aspect = 1) + sns.lmplot(data = df_predTest, x=metab+"_pred", y=metab, height=7, aspect = 1) plt.title("TEST VS PREDICTED lmplot " + metab + " " + modelname) - plt.savefig("../results/plots/testVsPredicted_lmplot_"+ metab+"_"+modelname+"_"+df_name+".png") + plt.savefig(savePath+"testVsPredicted_lmplot_"+ metab+"_"+modelname+"_"".png") plt.close("all") -def plotResiduals (df_predTest, metab, modelname, df_name): +def plotResiduals (df_predTest, metab, modelname, savePath): #for metab in metabs: print(" ----------------- PREDICTED VS RESIDUALS " + metab + " ----------------- ") y_pred_metab = df_predTest[metab+"_pred"] - residuals = df_predTest[metab+"_test"]-df_predTest[metab+"_pred"] + residuals = df_predTest[metab]-df_predTest[metab+"_pred"] df_all = pd.concat([y_pred_metab, residuals], axis=1) df_all.columns = ["Predicted", "Residuals"] @@ -31,5 +31,5 @@ def plotResiduals (df_predTest, metab, modelname, df_name): sns.scatterplot(data = df_all, x="Predicted", y="Residuals") plt.axhline(y=0) plt.title("RESIDUALS VS PREDICTED " + metab + " " + modelname) - plt.savefig("../results/plots/residualsVsPredicted_"+ metab+"_"+modelname+"_"+df_name+".png") + plt.savefig(savePath+"residualsVsPredicted_"+ metab+"_"+modelname+"_"+".png") plt.close("all") diff --git a/scripts/pruebas.ipynb b/scripts/pruebas.ipynb index 676dcd9..fa0bda4 100644 --- a/scripts/pruebas.ipynb +++ b/scripts/pruebas.ipynb @@ -2,12 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "1078ac79", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", + "import numpy as np\n", "import os\n", "import re\n", "pd.options.display.max_seq_items = 4000" @@ -15,21 +16,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "3e24cea7", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(138, 149)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sep = \",\"\n", "\n", @@ -163,234 +153,128 @@ "df2 = pd.concat([df, pd.DataFrame.from_dict(prueba, orient='columns').set_index(df.index)], axis = 1)\n", "df2.iloc[0] = df2.iloc[0].fillna(df.iloc[138])\n", "df2.drop(138, axis = 0, inplace = True)\n", - "df2.to_csv(\"../data/final_merge.csv\", sep = \";\", decimal = \".\", index = False)" + "\n", + "anthropometric_features = [\"Weight_pre\",\"Weight_post\",\"Delta_Weight\",\n", + " \"Height\",\"BMI_pre\",\"BMI_post\",\"Delta_BMI\",\t\n", + " \"Fat_pre\",\"Fat_post\",\"Delta_Fat\",\"CVRI_post\",\t\n", + " \"CVRI_pre\",\"Bpmin_post\",\"Bpmax_post\",\n", + " \"Bpmin_pre\",\t\"Bpmax_pre\",\"Frec_post\",\"Frec_pre\"]\n", + "\n", + "tofill = [\"Fat_pre\", \"Delta_Fat\",\"CVRI_post\"]\n", + "\n", + "df.loc[78, tofill] = np.nan\n", + "df.loc[[104,121],[\"Bpmin_post\",\"Bpmax_post\",\"Frec_post\"]] = np.nan\n", + "df.loc[[3,10,12,15,51,63,64,65,69,93,102,109, 111, 113],\"Frec_pre\"] = np.nan\n", + "df.loc[102,]\n", + "\n", + "df[anthropometric_features] = df[anthropometric_features].apply(pd.to_numeric)\n", + "df[anthropometric_features].dtypes\n", + "df.to_csv(\"../data/final_merge.csv\", sep = \";\", decimal = \".\", index = False)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 77, "id": "b062aab9", "metadata": {}, + "outputs": [], + "source": [ + "df_path = \"/home/die/Documents/repositories/modelsanoapp/data/final_merge.csv\"\n", + "df = pd.read_csv(df_path, sep = \";\", decimal = \".\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "141cc6dc", + "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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5 rows × 149 columns

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" - ], "text/plain": [ - " numVol CA_urine_pre CA.G_urine_pre CA.S_urine_pre CA.GS_pre \\\n", - "0 1.0 0.019469 0.097610 NaN 0.009 \n", - "1 2.0 NaN 0.854582 NaN 0.008 \n", - "2 3.0 0.065487 0.020205 0.150113 0.007 \n", - "3 4.0 0.010619 0.018782 0.122460 0.002 \n", - "4 5.0 0.003540 0.017644 0.252257 0.004 \n", - "\n", - " Total.CA_urine_pre DHPAA_urine_pre DHPAA.G_urine_pre DHPAA.GG_urine_pre \\\n", - "0 0.367 NaN NaN NaN \n", - "1 3.018 0.179981 0.114795 0.01634 \n", - "2 0.381 0.054511 0.061518 0.01634 \n", - "3 0.299 0.049812 0.005538 NaN \n", - "4 0.518 NaN 0.030629 NaN \n", - "\n", - " DHPAA.GS_urine_pre ... Delta_TIFA.Sulfate.1 Delta_EG.1 Delta_HE \\\n", - "0 0.021025 ... NaN -0.000980 0.227074 \n", - "1 0.049934 ... 0.028075 0.005690 -0.028384 \n", - "2 0.030223 ... 0.045261 -0.193640 -0.096070 \n", - "3 0.065703 ... NaN -0.017008 0.072052 \n", - "4 0.022339 ... NaN 0.035669 0.037118 \n", - "\n", - " Delta_HE.GG Delta_Total.HE Delta_N Delta_N.GG Delta_N.S \\\n", - "0 0.062500 0.075191 0.175293 0.049477 0.001147 \n", - "1 -0.046196 -0.001654 0.144245 0.048081 NaN \n", - "2 -0.157609 -0.263931 0.015154 -0.008969 -0.156581 \n", - "3 0.084239 -0.000990 -0.019600 0.002243 -0.002049 \n", - "4 0.008152 -0.079794 -0.012041 -0.027384 0.012892 \n", - "\n", - " Delta_Total.N Delta_VA.S \n", - "0 0.003468 NaN \n", - "1 -0.004685 -0.001044 \n", - "2 -0.148903 NaN \n", - "3 0.016791 NaN \n", - "4 0.014465 -0.188935 \n", - "\n", - "[5 rows x 149 columns]" + "['CA_urine_post',\n", + " 'CA.G_urine_post',\n", + " 'CA.S_urine_post',\n", + " 'CA.GS_post',\n", + " 'Total.CA_urine_post',\n", + " 'DHPAA_urine_post',\n", + " 'DHPAA.G_urine_post',\n", + " 'DHPAA.GG_urine_post',\n", + " 'DHPAA.GS_urine_post',\n", + " 'DHPAA.SS_urine_post',\n", + " 'Total.DHPAA_urine_post',\n", + " 'TFA.G_urine_post',\n", + " 'TFA.S_urine_post',\n", + " 'TFA.di.sulfate.1_post',\n", + " 'Total.TFA_urine_post',\n", + " 'TIFA.Sulfate.1_post',\n", + " 'VA_urine_post',\n", + " 'VA.GG_urine_post',\n", + " 'VA.GS_urine_post',\n", + " 'VA.SS_urine_post',\n", + " 'Total.VA_urine_post',\n", + " 'E_urine_post',\n", + " 'EG.1_post',\n", + " 'E.S_urine_post',\n", + " 'Total.E_urine_post',\n", + " 'HE_post',\n", + " 'HE.G_urine_post',\n", + " 'HE.GG_post',\n", + " 'Total.HE_post',\n", + " 'N_post',\n", + " 'N.G_urine_post',\n", + " 'N.GG_post',\n", + " 'N.S_post',\n", + " 'Total.N_post',\n", + " 'CA_plasm_post',\n", + " 'CA.G_plasm_post',\n", + " 'CA.S_plasm_post',\n", + " 'Total.CA_plasm_post',\n", + " 'DHPAA_plasm_post',\n", + " 'DHPAA.G_plasm_post',\n", + " 'DHPAA.GG_plasm_post',\n", + " 'DHPAA.GS_plasm_post',\n", + " 'DHPAA.SS_plasm_post',\n", + " 'Total.DHPAA_plasm_post',\n", + " 'TFA.G_plasm_post',\n", + " 'TFA.S_plasm_post',\n", + " 'Total.TFA_plasm_post',\n", + " 'VA_plasm_post',\n", + " 'VA.GG_plasm_post',\n", + " 'VA.S_post',\n", + " 'VA.GS_plasm_post',\n", + " 'VA.SS_plasm_post',\n", + " 'Total.VA_plasm_post',\n", + " 'E_plasm_post',\n", + " 'E.S_plasm_post',\n", + " 'Total.E_plasm_post',\n", + " 'HE.G_plasm_post',\n", + " 'N.G_plasm_post',\n", + " 'Weight_post',\n", + " 'BMI_post',\n", + " 'Fat_post',\n", + " 'CVRI_post',\n", + " 'Bpmin_post',\n", + " 'Bpmax_post',\n", + " 'Frec_post']" ] }, - "execution_count": 11, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_path = \"/home/die/Documents/repositories/modelsanoapp/data/final_merge.csv\"\n", - "df = pd.read_csv(df_path, sep = \";\", decimal = \".\")\n", - "df.head()" + "\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18cd8802", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/scripts/pruebas_retrain.py b/scripts/pruebas_retrain.py index 345b05c..58504ad 100644 --- a/scripts/pruebas_retrain.py +++ b/scripts/pruebas_retrain.py @@ -70,7 +70,8 @@ for path in paths: ) iimp.set_output(transform="pandas") - + iimp.fit(df) + iimp.transform(df[features]) df_categorical = df_filtered[['Sex', 'Sweetener', 'Time']] df_imp = iimp.fit_transform(df_filtered.drop(['Sex', 'Sweetener', 'Time'],axis = 1)) df_filtered = pd.concat([df_imp, df_categorical], axis = 1)