#!/usr/bin/env python
# coding: utf-8

# In[1]:


import os 
import pandas as pd
import numpy as np


# In[6]:


def get_file_stem(path):
    base=os.path.basename(path)
    return os.path.splitext(base)[0]

def read_metadata(df_path):  
    #read df
    df = pd.read_csv(df_path,sep=" ",header= None)
    df.columns = ["video_path","frames","label"]
    return df

def df_to_txt(df,dir_path):
    df.to_csv(dir_path, header=None, index=None, sep=' ', mode='a')


# In[2]:


file = r"C:\Users\jeuux\Desktop\Carrera\MoAI\TFM\AnnotatedData\FinalDatasets\Datasets\HAR_Video\Base_Dataset\Train_encodded.txt"


# In[7]:


df = read_metadata(file)
freq = df.label.value_counts(normalize=True)
weights  = np.empty(len(freq))
for idx,class_freq in zip(freq.index,freq.values):
    weights[idx]  = 1/class_freq


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# In[13]:


freq.index


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freq.values


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# In[21]:


weights


# In[24]:


import joblib
encoder_file = r"C:\Users\jeuux\Desktop\Carrera\MoAI\TFM\AnnotatedData\FinalDatasets\Datasets\HAR_dataset_v1\encoder_train.pkl"
encoder_file_2  =r"C:\Users\jeuux\Downloads\encoder_train (1).pkl"
encoder = joblib.load(encoder_file_2)


# In[25]:


len(encoder.classes_)