pred

import pandas as pd
import pandas as pd
from joblib import load
import json
from datetime import datetime
import warnings


warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", message="Found null values in totalcalls.")

start_time = '2024-02-16'
end_time = '2024-02-17'

location_json = 'location_mapping.json'
customer_json = 'customer_mapping.json'
cluster_json = 'cluster_mapping.json'
project_json = 'project_mapping.json'

df_grouped = pd.read_csv('/opt/ML/final/data_augmentation/augmentation/merged_main.csv')

# Convert 'timestamp' to datetime
df_grouped['timestamp'] = pd.to_datetime(df_grouped['timestamp'])
# Convert object columns to category type
df_grouped['location'] = df_grouped['location'].astype('category')
df_grouped['customer'] = df_grouped['customer'].astype('category')
df_grouped['cluster'] = df_grouped['cluster'].astype('category')
df_grouped['project'] = df_grouped['project'].astype('category')

# Create a mapping of codes to labels for each category column dictionary
location_mapping_dict = dict(enumerate(df_grouped['location'].cat.categories))
customer_mapping_dict = dict(enumerate(df_grouped['customer'].cat.categories))
cluster_mapping_dict = dict(enumerate(df_grouped['cluster'].cat.categories))
project_mapping_dict = dict(enumerate(df_grouped['project'].cat.categories))

# Convert category columns to integer encoding
df_grouped['location'] = df_grouped['location'].cat.codes
df_grouped['customer'] = df_grouped['customer'].cat.codes
df_grouped['cluster'] = df_grouped['cluster'].cat.codes
df_grouped['project'] = df_grouped['project'].cat.codes

# two days data
#df_grouped = df_grouped[(df_grouped['timestamp'] >= start_time) & (df_grouped['timestamp'] <= end_time)]
df_grouped = df_grouped[(df_grouped['timestamp'] > start_time) & (df_grouped['timestamp'] < end_time)]

print("train head",df_grouped.head(10))
print("train tail",df_grouped.tail(10))


_horizon = len(df_grouped)


df_grouped['id_col'] = df_grouped['location'].astype(str) + '_' + df_grouped['customer'].astype(str) + '_' + df_grouped['cluster'].astype(str) + '_' + df_grouped['project'].astype(str)

# Load the model
model = load('test_train_1.joblib')

# Make predictions
pred = model.predict(horizon=1440,dynamic_dfs=[df_grouped], ids=df_grouped.id_col.unique().tolist())


# Splitting the numbers and creating two separate columns
pred[['location', 'customer', 'cluster', 'project']] = pred['id_col'].str.split('_', expand=True)
print("prediction head",pred.head(10))

# load json files
location_mapping_json = json.load(open(location_json))
customer_mapping_json = json.load(open(customer_json))
cluster_mapping_json = json.load(open(cluster_json))
project_mapping_json = json.load(open(project_json))

# Convert the keys back to integers
location_mapping_json = {int(k): v for k, v in location_mapping_json.items()}
customer_mapping_json = {int(k): v for k, v in customer_mapping_json.items()}
cluster_mapping_json = {int(k): v for k, v in cluster_mapping_json.items()}
project_mapping_json = {int(k): v for k, v in project_mapping_json.items()}

# Convert 'cat1' and 'customer' to integers
pred['location'] = pred['location'].astype(int)
pred['customer'] = pred['customer'].astype(int)
pred['cluster'] = pred['cluster'].astype(int)
pred['project'] = pred['project'].astype(int)

# Now, to convert back to original categories
pred['location'] = pred['location'].map(location_mapping_json)
pred['customer'] = pred['customer'].map(customer_mapping_json)
pred['cluster'] = pred['cluster'].map(cluster_mapping_json)
pred['project'] = pred['project'].map(project_mapping_json)

print("final pred data",pred.head(10))
pred.to_csv('test_pred_1,csv', index=False)