diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..6bc968a
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,7 @@
+*xlsx
+pics
+*zip
+*log
+*pkl
+*pyc
+*_dataset
diff --git a/LoaderClass.py b/LoaderClass.py
new file mode 100644
index 0000000..582cc99
--- /dev/null
+++ b/LoaderClass.py
@@ -0,0 +1,434 @@
+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import pickle
+import logging
+from sklearn.preprocessing import MinMaxScaler
+
+#self.logger.basicConfig(level=self.logger.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+class GasSensorDataLoader:
+    def __init__(self, label_file, force_overwrite=False, output_format="png", lower_limit=-0.01, threshold=0.7, target_list=['C2H2', 'CH4', 'C3H6', 'CO', 'C2H6', 'C3H8', 'C2H4', 'H2', 'O2'], source_channels=["MQ 8", "MQ 9", "MQ 135", "TGS 813", "TGS 821", "TGS 2600", "TGS 2602", "TGS 2611-0", "TGS 2612", "TGS 2620"], debug=False):
+        self.label_file = label_file
+        self.main_file = f"{self.label_file}/{self.label_file}.xlsx"
+        self.data_folder = os.path.splitext(label_file)[0]
+        self.state_file = f"{self.label_file}.pkl"
+        self.lower_limit = lower_limit
+        self.data = None
+        self.debug = debug
+        self.threshold = threshold
+        self.dataset = {}
+        self.dataset['threshold'] = self.threshold
+        self.dataset['range'] = {}
+
+        self.samples = {}
+        self.target_list = sorted(target_list)
+        self.target_len = len(self.target_list)
+        self.source_channels = sorted(source_channels)
+        self.force_overwrite = force_overwrite
+        self.output_format = output_format.lower()
+        self.pics_folder = self.create_pics_folder()
+
+        self.logger = logging.getLogger("GasSensorDataLoader")
+        if self.debug:
+            self.logger.setLevel(logging.DEBUG)
+        else:
+            self.logger.setLevel(logging.INFO)
+
+        if self.output_format not in ["png", "pdf"]:
+            raise ValueError("Invalid output format. Choose either 'png' or 'pdf'.")
+
+        if not os.path.isdir(self.data_folder):
+            raise FileNotFoundError(f"Data folder '{self.data_folder}' does not exist.")
+
+        if not os.path.exists(self.main_file):
+            raise FileNotFoundError(f"Main Excel file '{self.main_file}' not found.")
+
+        if not isinstance(threshold, (int, float)) or not 0 <= threshold <= 1:
+          raise ValueError("threshold must be a number between 0 and 1")
+
+        if os.path.exists(self.state_file):
+            if False:#not self.force_overwrite and not self._compare_state_with_main():
+                raise ValueError("State file differs from the main Excel file. Use 'force_overwrite=True' to overwrite.")
+            else:
+                self.load_state()
+        else:
+            self.logger.info("State file not found. Loading dataset.")
+            self.load_dataset()
+
+    def _compare_state_with_main(self):
+        try:
+            existing_labels = pd.read_excel(self.main_file)
+            with open(self.state_file, 'rb') as f:
+                saved_data = pickle.load(f)
+            saved_labels = pd.DataFrame([saved_data[key]['label'] for key in saved_data])
+            return existing_labels.equals(saved_labels)
+        except Exception as e:
+            self.logger.error(f"Error comparing state file: {e}")
+            return False
+
+    def load_dataset(self):
+        self.logger.info("Loading dataset from Excel files.")
+        labels = pd.read_excel(self.main_file)
+        data_dict = {}
+        samples, measuraments = 0, 0
+
+        for _, row in labels.iterrows():
+            file_path = os.path.join(self.data_folder, "Raw_data", '{}{}'.format(row['Raw_data'], '.xlsx'))
+            if os.path.exists(file_path):
+                self.logger.info(f"Loading data from {file_path}.")
+                df = pd.read_excel(file_path, header=0, usecols=self.source_channels)  # Ensure first row is used as column names
+                data_dict[row['Raw_data']] = {
+                    'label': row.to_dict(),
+                    'data': df,
+                    'sampleId': samples
+                }
+                samples += 1
+                measuraments += df.shape[0]
+            else:
+                raise FileNotFoundError(f"measurament file not found: {file_path}")
+
+        self.data = data_dict
+        self.save_state()
+        self.logger.info("Dataset loaded. {} samples in {} measuraments".format(samples, measuraments))
+
+    def save_state(self):
+        with open(self.state_file, 'wb') as f:
+            pickle.dump(self.data, f)
+        self.logger.info("State saved.")
+
+    def load_state(self):
+        with open(self.state_file, 'rb') as f:
+            self.data = pickle.load(f)
+        self.logger.info("State loaded.")
+
+    def create_pics_folder(self):
+        pics_folder = os.path.join(self.data_folder, "pics")
+        if not os.path.exists(pics_folder):
+            os.makedirs(pics_folder)
+            self.logger.info(f"Created folder: {pics_folder}")
+        return pics_folder
+
+    def init_delta(self):
+        self.logger.info("Initializing dataset delta values.")
+        data_copy = {key: {'label': value['label'], 'sampleId': value['sampleId'], 'data': value['data'].copy()} for key, value in self.data.items()}
+        lower_limit = pd.concat([data_copy[key]['data'] for key in data_copy], axis=0).max() * self.lower_limit
+        self.logger.debug("Lower limit {}.".format(lower_limit))
+
+        for key in data_copy:
+            data_copy[key]['data'] = data_copy[key]['data'] - data_copy[key]['data'].iloc[0]
+            for column in data_copy[key]['data'].columns:
+                data_copy[key]['data'][column] = data_copy[key]['data'][column].where(data_copy[key]['data'][column] >= lower_limit[column], other=lower_limit[column])
+        self.delta_data = data_copy
+
+    def init_minmax(self):
+        if not hasattr(self, 'delta_data'):
+            self.init_delta()
+
+        self.logger.info("Initializing dataset using MinMaxScaler.")
+        concatenated_data = pd.concat([self.delta_data[key]['data'] for key in self.delta_data], axis=0)
+        scaler = MinMaxScaler()
+        scaler.fit(concatenated_data)
+
+        self.scaled_data = {key: {'label': value['label'], 'sampleId': value['sampleId'], 'data': pd.DataFrame(scaler.transform(value['data']), columns=value['data'].columns)} for key, value in self.delta_data.items()}
+
+    def plot_channel(self, data_instance, channel_name, save=False, tag="", title=""):
+        self.logger.debug(f"{title}All measurament Sensor readings for: {channel_name}.")
+        plt.figure(figsize=(12, 6))
+        for key in data_instance:
+            if channel_name in data_instance[key]['data'].columns:
+                plt.plot(data_instance[key]['data'][channel_name])
+        plt.xlabel("Time")
+        plt.ylabel("Sensor Reading")
+        plt.title(f"{title} Sensor Channel: {channel_name}")
+
+        if save:
+            filename = os.path.join(self.pics_folder, f"{channel_name}_{tag}.{self.output_format}")
+            plt.savefig(filename, format=self.output_format)
+            self.logger.info(f"Saved plot as {filename}")
+        else:
+            plt.show()
+
+        plt.close()
+
+    def plot_measurament(self, data_instance, measurament_name, save=False, tag="", title="", limits=[]):
+        self.logger.debug(f"{title}All sensor readings for measurament: {measurament_name}.")
+        if measurament_name in data_instance:
+            data_instance[measurament_name]['data'].plot(figsize=(12, 6), title=f"{title} measurament: {measurament_name}")
+            plt.xlabel("Time")
+            plt.ylabel("Sensor Readings")
+            plt.legend(bbox_to_anchor=(0.95, 0.5), loc="center left")
+            for xvalue, txtcolor in limits:
+                plt.vlines(x=xvalue, ymin=0, ymax=1, colors=txtcolor)
+
+            if save:
+                filename = os.path.join(self.pics_folder, f"{measurament_name}_{tag}.{self.output_format}")
+                plt.savefig(filename, format=self.output_format)
+                self.logger.info(f"Saved plot as {filename}")
+            else:
+                plt.show()
+
+            plt.close()
+
+    def plotRawdata(self, save=True):
+        self.logger.debug("Plotting raw data for all measuraments and channels.")
+        for measurament in self.data:
+            self.plot_measurament(self.data, measurament, save=save, tag="raw", title="[Original] ")
+        for channel in self.data[next(iter(self.data))]['data'].columns:
+            self.plot_channel(self.data, channel, save=save, tag="raw", title="[Original] ")
+
+    def plotDeltadata(self, save=True):
+        if not hasattr(self, 'delta_data'):
+            self.init_delta()
+
+        self.logger.debug("Plotting raw data for all measuraments and channels.")
+        for measurament in self.delta_data:
+            self.plot_measurament(self.delta_data, measurament, save=save, tag="delta", title="[$\Delta V$] ")
+        for channel in self.delta_data[next(iter(self.delta_data))]['data'].columns:
+            self.plot_channel(self.delta_data, channel, save=save, tag="delta", title="[$\delta V$] ")
+
+    def plotScaleddata(self, save=True):
+        if not hasattr(self, 'scaled_data'):
+            self.init_minmax()
+
+        self.logger.debug("Plotting raw data for all measuraments and channels.")
+        for measurament in self.scaled_data:
+            self.plot_measurament(self.scaled_data, measurament, save=save, tag="scaled", title="[Scaled] ")
+        for channel in self.scaled_data[next(iter(self.scaled_data))]['data'].columns:
+            self.plot_channel(self.scaled_data, channel, save=save, tag="scaled", title="[Scaled] ")
+
+    def plotScaledBoundaries(self, save=True):
+        if not hasattr(self, 'scaled_data'):
+            self.init_minmax()
+
+        self.logger.debug("Plotting raw data for all measuraments and channels.")
+        for measurament in self.scaled_data:
+            r, l, m = self.findIndicesAbovethreshold(self.scaled_data[measurament]['data'])
+            self.plot_measurament(self.scaled_data, measurament, save=save, tag="train", title=f"[Interval {self.threshold} max] ", limits=[(r, 'blue'), (l, 'blue'), (m, 'red')])
+
+    def findIndicesAbovethreshold(self, df):
+        if not isinstance(df, pd.DataFrame):
+            raise TypeError("Input must be a pandas DataFrame.")
+
+        row_sums = df.sum(axis=1)
+        threshold = row_sums.max() * self.threshold
+
+        above_threshold_indices = row_sums[row_sums > threshold].index
+
+        if not above_threshold_indices.empty:
+            first_index = above_threshold_indices[0]
+            last_index = above_threshold_indices[-1]
+            return first_index, last_index, row_sums.idxmax()
+        else:
+            return None, None, None
+
+    def load_dataset_window(self, ws):
+
+        self.logger.info(f"Requested sample with threshold {self.threshold} and window size {ws}")
+
+        if not hasattr(self, 'scaled_data'):
+            self.init_minmax()
+
+        if not hasattr(self, 'dataset'):
+            self.logger.debug(f"Empty dataset")
+            self.dataset = {}
+
+        if 'threshold' in self.dataset:
+            self.logger.debug(f"threshold in dataset")
+            if self.dataset['threshold'] != self.threshold:
+                self.logger.info(f"wrong threshold {self.dataset['threshold']} {self.threshold}")
+                self.dataset = {}
+                self.dataset['threshold'] = self.threshold
+                self.dataset['range'] = {}
+                self.stats()
+            else:
+                self.logger.debug(f"correct threshold {self.dataset['threshold']} {self.threshold}")
+        else:
+            self.dataset['threshold'] = self.threshold
+            self.dataset['range'] = {}
+            self.stats()
+            self.logger.debug(f"No threshold {self.dataset['threshold']} {self.threshold}")
+
+        if ws in self.dataset:
+            return self.dataset[ws]
+
+        g_output = np.empty((0, 1))
+        y_output = np.empty((0, self.target_len))
+        x_output = np.empty((0, ws, len(self.source_channels)))
+        sample_size = self.min_sample - ws + 1
+        self.logger.info(f"Computing sample with threshold {self.threshold} and window size {ws}")
+        for measurament, (r, l) in self.dataset['range'].items():
+            self.logger.debug('{} | {} | {}-{} | {}'.format(measurament, self.scaled_data[measurament]['data'].shape, r, l, self.scaled_data[measurament]['data'].iloc[r:l].shape))
+            x_sample, y_sample, g_sample = self.create_conv1d_dataset(measurament, r, l, ws)
+
+            g_output = np.concatenate((g_output, g_sample))
+            x_output = np.concatenate((x_output, x_sample))
+            y_output = np.concatenate((y_output, y_sample))
+
+        self.dataset[ws] = (x_output, y_output, g_output)
+
+        return self.dataset[ws]
+
+    def load_dataset_xboost(self):
+
+        self.logger.info(f"Requested sample with threshold {self.threshold} for xboost")
+
+        if not hasattr(self, 'scaled_data'):
+            self.init_minmax()
+
+        if not hasattr(self, 'dataset'):
+            self.logger.debug(f"Empty dataset")
+            self.dataset = {}
+
+        if 'threshold' in self.dataset:
+            self.logger.debug(f"threshold in dataset")
+            if self.dataset['threshold'] != self.threshold:
+                self.logger.info(f"wrong threshold {self.dataset['threshold']} {self.threshold}")
+                self.dataset = {}
+                self.dataset['threshold'] = self.threshold
+                self.dataset['range'] = {}
+                self.stats()
+            else:
+                self.logger.debug(f"correct threshold {self.dataset['threshold']} {self.threshold}")
+        else:
+            self.dataset['threshold'] = self.threshold
+            self.dataset['range'] = {}
+            self.stats()
+            self.logger.debug(f"No threshold {self.dataset['threshold']} {self.threshold}")
+
+        if 'xboost' in self.dataset:
+            return self.dataset['xboost']
+
+        g_output = np.empty((0, 1))
+        y_output = np.empty((0, self.target_len))
+        x_output = np.empty((0, self.data_channels))
+
+        self.logger.info(f"Computing sample with threshold {self.threshold} for xboost")
+        for measurament, (r, l) in self.dataset['range'].items():
+            self.logger.debug('{} | {} | {}-{} | {}'.format(measurament, self.scaled_data[measurament]['data'].shape, r, l, self.scaled_data[measurament]['data'].iloc[r:l].shape))
+            x_sample, y_sample, g_sample = self.create_xboost_dataset(measurament, r, l)
+
+            g_output = np.concatenate((g_output, g_sample))
+            x_output = np.concatenate((x_output, x_sample))
+            y_output = np.concatenate((y_output, y_sample))
+
+        self.dataset['xboost'] = (x_output, y_output, g_output)
+
+        return self.dataset['xboost']
+
+    def create_xboost_dataset(self, measurament, r, l):
+
+      X_data = self.scaled_data[measurament]['data'].iloc[r:l]
+      Y_value = np.array([[self.scaled_data[measurament]['label'][key] for key in self.target_list]])
+      G_value = self.scaled_data[measurament]['sampleId']
+
+      total_samples = X_data.shape[0]
+      sample_size = self.min_sample
+
+      self.logger.debug(f"{measurament}: ({total_samples}) values, sampling ({sample_size}). (l-r {l-r}) (l {l}) (r {r}).")
+      if sample_size > total_samples:
+        self.logger.warn(f"sample_size ({sample_size}) exceeds available samples ({total_samples}). Using available samples.")
+        sample_size = total_samples
+
+      random_indices = np.random.choice(total_samples, size=sample_size, replace=False)
+      g_sample = np.zeros((sample_size, 1))
+      y_sample = np.zeros((sample_size, self.target_len))
+      x_samples = np.zeros((sample_size, self.data_channels))
+
+      for i, index in enumerate(random_indices):
+        x_samples[i] = X_data.iloc[index]
+        y_sample[i] = Y_value
+        g_sample[i] = G_value
+
+      return x_samples, y_sample, g_sample
+
+    def create_conv1d_dataset(self, measurament, r, l, window):
+
+      X_data = self.scaled_data[measurament]['data'].iloc[r:l]
+      Y_value = np.array([[self.scaled_data[measurament]['label'][key] for key in self.target_list]])
+      G_value = self.scaled_data[measurament]['sampleId']
+
+      total_samples = X_data.shape[0] - window + 1
+      sample_size = self.min_sample - window + 1
+
+      if sample_size > total_samples:
+        self.logger.warn(f"sample_size ({sample_size}) exceeds available samples ({total_samples}). Using available samples.")
+        sample_size = total_samples
+
+      random_indices = np.random.choice(total_samples, size=sample_size, replace=False)
+      g_sample = np.zeros((sample_size, 1))
+      y_sample = np.zeros((sample_size, self.target_len))
+      x_samples = np.zeros((sample_size, window, self.data_channels))
+
+
+      for i, index in enumerate(random_indices):
+        x_samples[i] = X_data.iloc[index:index + window].values
+        y_sample[i] = Y_value
+        g_sample[i] = G_value
+
+
+      return x_samples, y_sample, g_sample
+
+    def stats(self):
+        channel_columns = {}
+        sample_columns = {}
+        for key in self.data:
+            for col in self.data[key]['data'].columns:
+                if col in sample_columns:
+                    sample_columns[col].append(key)
+                else:
+                    sample_columns[col] = [key]
+            for col in self.data[key]['label']:
+                if col in channel_columns:
+                    channel_columns[col].append(key)
+                else:
+                    channel_columns[col] = [key]
+
+        self.data_channels = len(set(sample_columns))
+        sorted_channel = sorted(channel_columns.items(), key=lambda x: len(x[1]), reverse=True)
+        sorted_samples = sorted(sample_columns.items(), key=lambda x: len(x[1]), reverse=True)
+
+        self.logger.debug("{:=^60}".format("CHANNELS"))
+        for i, (col, keys) in enumerate(sorted_channel):
+            self.logger.debug(f"{i} | {col}: {len(keys)}")
+
+        self.logger.debug("{:=^60}".format("SAMPLES"))
+        for i, (col, keys) in enumerate(sorted_samples):
+            self.logger.debug(f"{i} | {col}: {len(keys)}")
+
+        if not hasattr(self, 'scaled_data'):
+            self.init_minmax()
+
+        self.logger.debug("{:=^60}".format(f"DATASET SIZE for {self.threshold}"))
+        valid_dataset = []
+
+        for measurament in self.scaled_data:
+            r, l, m = self.findIndicesAbovethreshold(self.scaled_data[measurament]['data'])
+            self.logger.debug(f"{measurament}: {l} - {r}  = {l-r}")
+            valid_dataset.append(l-r)
+            self.dataset['range'][measurament] = (r,l)
+
+        self.min_sample = np.min(valid_dataset)
+
+        self.logger.info("{:=^60}".format(f"DATASET STATS for {self.threshold}"))
+        self.logger.info(f"Min: {np.min(valid_dataset)}")
+        self.logger.info(f"Max: {np.max(valid_dataset)}")
+        self.logger.info(f"Mean: {np.mean(valid_dataset)}")
+        self.logger.info(f"Median: {np.median(valid_dataset)}")
+
+
+# Example usage:
+# loader = GasSensorDataLoader("enose_dataset", output_format="png", threshold=0.9)
+# loader.threshold = 0.8
+# loader.plotRawdata(save=True)
+# loader.plotDeltadata(save=True)
+# loader.plotScaledBoundaries(save=True)
+# # loader.threshold = 0.90
+# print(loader.load_dataset_window(128).shape)
+# loader.threshold = 0.85
+# print(loader.load_dataset_window(128).shape)
+# loader.threshold = 0.80
+# print(loader.load_dataset_window(128).shape)
+# loader.threshold = 0.75
+# print(loader.load_dataset_window(128).shape)
diff --git a/README.md b/README.md
index b55b1d8..ed24c20 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,15 @@
-# enose_2025
+# Codigo de entrenamiento del e-nose
 
+Sistema bio-inspirado para la detección de gases en diferentes fuentes.
+
+## Instalación del ambiente conda
+
+```
+./create_conda nombre-ambiente
+```
+
+## Ejecución de las Simulaciones
+
+```
+(nombre-ambiente)$ python train_sequence.py
+```
diff --git a/TrainerClass.py b/TrainerClass.py
new file mode 100644
index 0000000..097da09
--- /dev/null
+++ b/TrainerClass.py
@@ -0,0 +1,270 @@
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.rcParams['text.usetex'] = True
+
+from sklearn.model_selection import GroupShuffleSplit, ShuffleSplit, GridSearchCV
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+
+from xgboost import XGBRegressor
+
+# from ray import tune
+# import ray
+# from keras.callbacks import TensorBoard
+# from keras.models import Sequential
+# from keras.callbacks import EarlyStopping
+# from keras.layers import Dense, BatchNormalization, Dropout
+# from kerastuner.tuners import RandomSearch, Hyperband, GridSearch
+
+from datetime import datetime
+import enlighten
+import logging
+import zipfile
+import random
+import joblib
+import pickle
+import time
+import json
+import os
+
+
+def get_seed():
+  return random.randint(0, 2**32 - 1)
+
+class eNoseTrainer:
+    def __init__(self, loader, splits=1, test_size=0.2, debug=False):
+        self.ledger = pd.DataFrame(columns=["node", "ts", "Dataset", "Samples", "Target", "Train Size", "Train Ratio", "Model", "Params", "Ratio", "Train mse", "mse", "mae", "rmse"])
+        self.loader = loader
+        self.splits = splits
+        self.name = self.loader.label_file
+        self.target = '_'.join(self.loader.target_list)
+        self.state = dict()
+
+        os.makedirs(self.name, exist_ok=True)
+        self.start = int(time.time())
+
+        log_format = '%(asctime)s | %(levelname)-8s | %(name)-15s | %(message)s'
+        date_format = '%Y-%m-%d %H:%M:%S'
+        logging.basicConfig(format=log_format, datefmt=date_format)
+
+        target_log = '{}/load-{}.log'.format(self.name, self.start)
+        fh = logging.FileHandler(target_log)
+
+        self.debug = debug
+
+        self.logger = logging.getLogger("eNoseTrainer")
+        if self.debug:
+            self.logger.setLevel(logging.DEBUG)
+            fh.setLevel(logging.DEBUG)
+        else:
+            self.logger.setLevel(logging.INFO)
+            fh.setLevel(logging.INFO)
+        self.logger.addHandler(fh)
+
+        self.ratio = test_size
+
+        self.loader.stats()
+        self.loadCheckPoint()
+
+    def loadCheckPoint(self):
+        if not os.path.isfile('{}/Simulaciones.xlsx'.format(self.name)):
+            self.saveCheckPoint()
+
+        with pd.ExcelFile('{}/Simulaciones.xlsx'.format(self.name)) as xls:
+            self.ledger = pd.read_excel(xls, sheet_name='Historial')
+            self.trained = self.ledger.shape[0]
+
+        with open('{}/vars.pickle'.format(self.name), 'rb') as pfile:
+            self.ratio, self.splits, self.state  = pickle.load(pfile)
+
+    def saveCheckPoint(self):
+        with pd.ExcelWriter('{}/Simulaciones.xlsx'.format(self.name), engine='xlsxwriter') as xls:
+            self.ledger.to_excel(xls, sheet_name='Historial', index=False)
+
+        with open('{}/vars.pickle'.format(self.name), 'wb') as pfile:
+            pickle.dump((self.ratio, self.splits, self.state), pfile, protocol=pickle.HIGHEST_PROTOCOL)
+
+        self.trained = self.ledger.shape[0]
+
+    def wrap_and_save(self):
+        self.logger.info("{:=^60}".format(' Saving Summary and Wrap the output in a ZipFile '))
+
+        with pd.ExcelWriter('{}/Summary.xlsx'.format(self.name) , engine='xlsxwriter') as xls:
+            self.get_best_models().to_excel(xls, sheet_name='Results')
+
+        with zipfile.ZipFile('{}-{}.zip'.format(self.name, self.start), 'w', zipfile.ZIP_DEFLATED) as zipf:
+            for root, dirs, files in os.walk(self.name):
+                for file in files:
+                    zipf.write(os.path.join(root, file))
+
+    def row_exists(self, dataset, model):
+        return self.ledger[(self.ledger["Dataset"] == dataset) & (self.ledger["Target"] == self.target) & (self.ledger["Model"] == model) & (self.ledger["Ratio"] == self.ratio)].shape[0] > 0
+
+    def model_A(self, hp):
+
+        model = Sequential()
+        model.add(Dense(units=hp.Int('units_input', min_value=48, max_value=56, step=8), input_dim=self.nvars, activation='relu'))
+        model.add(BatchNormalization())
+        model.add(Dropout(rate=hp.Float('dropout_input', min_value=0.1, max_value=0.1, step=0.1)))
+
+        model.add(Dense(units=hp.Int('units_hidden', min_value=32, max_value=48, step=8), activation='relu'))
+        model.add(BatchNormalization())
+        model.add(Dropout(rate=hp.Float('dropout_hidden', min_value=0.4, max_value=0.4, step=0.1)))
+
+        model.add(Dense(1, activation='sigmoid'))
+
+        model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy', AUC()])
+
+        return model
+
+    def train_and_score_model_keras(self, X_train, X_test, y_train, y_test, seed, label):
+#        set_random_seed(seed)
+        ntrials = 6
+        tuner = RandomSearch(
+            self.get_model_train_keras,
+            objective='val_loss', #val_loss
+#            seed=seed,
+            max_trials=ntrials,
+#            executions_per_trial=1,  # Número de ejecuciones por cada configuración
+            directory=self.name,
+            project_name='{}-{}'.format(label,seed))
+
+        self.logger.info("{:~^60}".format(' {}-{} '.format(label,seed)))
+
+
+        search_dir = "{}/keras-tuner-{}/".format(self.name,label)
+        os.makedirs(search_dir, exist_ok=True)
+        search_callback = TensorBoard(log_dir=search_dir)
+        early_stopping_search = EarlyStopping(monitor='val_loss', patience=13, min_delta=0.005, start_from_epoch=7, restore_best_weights=True)
+        tuner.search(X_train, y_train, epochs=150, batch_size=10, validation_data=(X_test, y_test), callbacks=[early_stopping_search, search_callback])
+        best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
+
+        self.trained += 1
+        self.bar.update()
+        return mse, mae, rmse, optimized_model, model_params
+
+    def get_model_train(self):
+        return [
+            XGBRegressor(objective='reg:squarederror'),
+        ]
+
+    def get_tunable_params(self, model):
+        if isinstance(model, XGBRegressor):
+            return {
+                "n_estimators": [800, 1000, 1200],
+                "learning_rate": np.logspace(-1.5, -0.5, 3),
+                'max_depth': [5, 7, 9],
+                'subsample': [0.5, 0.75, 1.0],
+#                'colsample_bytree': [0.8, 0.9, 1.0],
+#                'gamma': [0, 0.1, 0.2],
+#                'min_child_weight': [1, 3, 5]
+            }
+        elif isinstance(model, RandomForestClassifier):
+            return {
+                "n_estimators": [50, 100, 200],
+                "max_depth": [5, 10, 15],
+                "max_features": [2, 5, 10] #['n', 'max_depth', 'max_features', 'max_leaf_nodes', 'max_samples', 'min_impurity_decrease', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'monotonic_cst', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start']
+            }
+        else:
+            return {}
+
+    def train_and_score_model(self, model, X_train, X_test, y_train, y_test):
+        param_dist = self.get_tunable_params(model)
+
+        cv = ShuffleSplit(n_splits=self.splits//2, test_size=0.2, random_state=get_seed())
+        grid_search = GridSearchCV(estimator=model, param_grid=param_dist, scoring='neg_mean_squared_error', cv=cv, verbose=10, n_jobs=-1)
+
+        grid_search.fit(X_train, y_train)
+
+        optimized_model = grid_search.best_estimator_
+        model_params = grid_search.best_params_
+
+        y_aux = optimized_model.predict(X_train)
+        tmse = mean_squared_error(y_train, y_aux)
+
+        y_pred = optimized_model.predict(X_test)
+        mse = mean_squared_error(y_test, y_pred)
+        mae = mean_absolute_error(y_test, y_pred)
+        rmse = np.sqrt(mse)
+
+        return tmse, mse, mae, rmse, optimized_model, model_params
+
+    def fit(self):
+        total_train_queue = self.splits*len(self.get_model_train())
+        self.logger.info("{:=^60}".format(f'Begin Fit {total_train_queue} Models'))
+        self.trained = 0
+        manager = enlighten.get_manager()
+        self.bar = manager.counter(total=total_train_queue, count=self.trained, desc='Tunning', unit='Models',
+                                format='{desc}{desc_pad}{percentage:3.0f}%|{bar}| {count:{len_total}d}/{total:d} [{elapsed}<{eta}, {rate:.2f}{unit_pad}{unit}/s]'
+                                )
+
+
+        node = os.uname()[1]
+        X_xboost, Y_xboost, G_xboost = self.loader.load_dataset_xboost()
+
+        gss = GroupShuffleSplit(n_splits=self.splits, test_size=self.ratio, random_state=get_seed())
+
+        dataset = 'Tabular'
+        os.makedirs('{}/{}/{}'.format(self.name, self.target, dataset), exist_ok=True)
+
+        for i, (train_index, test_index) in enumerate(gss.split(X_xboost, Y_xboost, G_xboost)):
+            X_train, X_test = X_xboost[train_index], X_xboost[test_index]
+            y_train, y_test = Y_xboost[train_index], Y_xboost[test_index]
+
+
+            for model in self.get_model_train():
+                model_id = "{}_{}".format(type(model).__name__, i)
+                self.trained += 1
+
+                if self.row_exists(dataset, model_id):
+                    self.bar.update()
+                    continue
+
+                model_file = '{}/{}/{}/{}'.format(self.name, self.target, dataset, model_id )
+
+                tmse, mse, mae, rmse, optimized_model, model_params = self.train_and_score_model(model, X_train, X_test, y_train, y_test)
+
+                ts = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+                joblib.dump(optimized_model, model_file)
+
+                newrow = pd.DataFrame( [{"node": node,
+                                    "ts": ts,
+                                    "Dataset": dataset,
+                                    "Samples": Y_xboost.shape[0],
+                                    "Target": self.target,
+                                    "Train Size": y_train.shape[0],
+                                    "Train Ratio": y_train.shape[0]/Y_xboost.shape[0],
+                                    "Ratio": self.ratio,
+                                    "Model": model_id,
+                                    "Params": json.dumps(model_params),
+                                    "Train mse": tmse,
+                                    "mse": mse,
+                                    "mae": mae,
+                                    "rmse": rmse
+                                    }] )
+                self.ledger = pd.concat([self.ledger, newrow], ignore_index=True)
+                self.bar.update()
+
+            self.saveCheckPoint()
+
+        # if self.dnn:
+        #     model_file = '{}/{}/DNN_{}'.format(self.name, label, seed )
+        #     model_label = "{}".format(label)
+        #
+        #     accuracy, specificity, recall, f1, roc_auc, optimized_model, parms = self.train_and_score_model_keras(X_train, X_test, y_train, y_test, seed, model_label)
+        #     ts = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+        #
+        #     newrow = pd.DataFrame( [{"node": node,
+        #                         "ts": ts,
+        #                         "Dataset": model_label,
+        #                         "Model": 'DNN',
+        #                         "Params": parms,
+        #                         "Seed": seed,
+        #                         "F1": f1,
+        #                         "ROC_AUC": roc_auc
+        #                         }] )
+        #     self.ledger = pd.concat([self.ledger, newrow], ignore_index=True)
+
+        self.bar.close()
diff --git a/create_conda.sh b/create_conda.sh
new file mode 100755
index 0000000..fee216c
--- /dev/null
+++ b/create_conda.sh
@@ -0,0 +1,21 @@
+# prompt: create a bash script that check if a single argument was given and install this conda packages: python scikit-learn keras pandas numpy matplotlib tensorflow  openpyxl enlighten
+# if the user dont input any argument or more than 1 print a help to use the script
+
+#!/bin/bash
+
+if [ $# -ne 1 ]; then
+    echo "Usage: $0 <environment_name>"
+    echo ""
+    echo "  Installs conda packages for a specified environment."
+    echo "  Example: $0 enose1"
+    exit 1
+fi
+
+env_name=$1
+
+conda create -n "$env_name" python scikit-learn==1.3.1 xgboost keras pandas numpy matplotlib tensorflow openpyxl xlsxwriter conda-forge::enlighten
+if [ $? -eq 0 ]; then
+  echo "Packages installed successfully in environment '$env_name'"
+else
+  echo "Aborted."
+fi
diff --git a/train_sequence.py b/train_sequence.py
new file mode 100644
index 0000000..f21ed93
--- /dev/null
+++ b/train_sequence.py
@@ -0,0 +1,21 @@
+from LoaderClass import GasSensorDataLoader
+from TrainerClass import eNoseTrainer
+
+import warnings
+warnings.filterwarnings("ignore")
+
+source_channels=["MQ 8", "MQ 9", "MQ 135", "TGS 813", "TGS 821", "TGS 2600", "TGS 2602", "TGS 2611-0", "TGS 2612", "TGS 2620"]
+#target_variables=['C2H2', 'CH4', 'C3H6', 'CO', 'C2H6', 'C3H8', 'C2H4', 'H2', 'O2']
+
+#target_variables=['C2H2']
+#eNoseLoaderC2H2 = GasSensorDataLoader("enose_dataset", threshold=0.85, source_channels=source_channels, target_list=target_variables, debug=False)
+#eNoseC2H2 = eNoseTrainer(eNoseLoaderC2H2, splits=3, test_size=0.2, debug=True)
+#eNoseC2H2.fit()
+
+target_variables=['CH4']
+eNoseLoaderCH4 = GasSensorDataLoader("enose_dataset", threshold=0.85, source_channels=source_channels, target_list=target_variables, debug=False)
+
+eNoseCH4 = eNoseTrainer(eNoseLoaderCH4, splits=3, test_size=0.2, debug=True)
+eNoseCH4.fit()
+
+#eNose.wrap_and_save()