Delete data_generation.py

data_generation.py

@@ -1,171 +0,0 @@
-from ast import Raise
-from re import S
-import re
-import gym
-
-import matplotlib.pyplot as plt
-
-from citylearn.citylearn import CityLearnEnv
-import numpy as np
-import pandas as pd
-import os
-
-from collections import deque
-import argparse 
-import random
-# import logger
-import logging
-from sys import stdout
-from copy import deepcopy
-
-
-class Constants:
-    episodes = 3
-    schema_path = '/home/aicrowd/data/citylearn_challenge_2022_phase_1/schema.json'
-    variables_to_forecast = ['solar_generation', 'non_shiftable_load', 'electricity_pricing', 'carbon_intensity', "electricity_consumption_crude",
-                                     'hour', 'month']
-
-    additional_variable = ['hour', "month"]
-
-
-# create env from citylearn
-env = CityLearnEnv(schema=Constants.schema_path)
-
-def action_space_to_dict(aspace):
-    """ Only for box space """
-    return { "high": aspace.high,
-             "low": aspace.low,
-             "shape": aspace.shape,
-             "dtype": str(aspace.dtype)
-    }
-
-def env_reset(env):
-    observations = env.reset()
-    action_space = env.action_space
-    observation_space = env.observation_space
-    building_info = env.get_building_information()
-    building_info = list(building_info.values())
-    action_space_dicts = [action_space_to_dict(asp) for asp in action_space]
-    observation_space_dicts = [action_space_to_dict(osp) for osp in observation_space]
-    obs_dict = {"action_space": action_space_dicts,
-                "observation_space": observation_space_dicts,
-                "building_info": building_info,
-                "observation": observations }
-    return obs_dict
-
-## env wrapper for stable baselines
-class EnvCityGym(gym.Env):
-    """
-    Env wrapper coming from the gym library.
-    """
-    def __init__(self, env):
-        self.env = env
-
-        # get the number of buildings
-        self.num_buildings = len(env.action_space)
-        print("num_buildings: ", self.num_buildings)
-
-        self.action_space = gym.spaces.Box(low=np.array([-0.2]), high=np.array([0.2]), dtype=np.float32)
-
-        self.observation_space = gym.spaces.MultiDiscrete(np.array([25, 13]))
-
-    def reset(self):
-        obs_dict = env_reset(self.env)
-        obs = self.env.reset()
-
-        observation = [o for o in obs]
-
-        return observation
-
-    def step(self, action):
-        """
-        we apply the same action for all the buildings
-        """
-        obs, reward, done, info = self.env.step(action)
-
-        observation = [o for o in obs]
-
-        return observation, reward, done, info
-        
-    def render(self, mode='human'):
-        return self.env.render(mode)
-
-
-
-
-def env_run_without_action(actions_all=None):
-    """
-    This function is used to run the environment without applying any action.
-    and return the dataset
-    """
-    # create env from citylearn
-    env = CityLearnEnv(schema=Constants.schema_path)
-
-    # get the number of buildings
-    num_buildings = len(env.action_space)
-    print("num_buildings: ", num_buildings)
-
-    # create env wrapper
-    env = EnvCityGym(env)
-
-    # reset the environment
-    obs = env.reset()
-
-    infos = []
-
-    for id_building in range(num_buildings):
-        # run the environment
-        obs = env.reset()
-
-        for i in range(8759):
-
-            info_tmp = env.env.buildings[id_building].observations.copy()
-
-            if actions_all is not None:
-
-                action = [[actions_all[i + 8759 * b]] for b in range(num_buildings)]
-
-            else:
-                # we get the action
-                action = np.zeros((5, )) # 5 is the number of buildings
-
-                # reshape action into form like [[0], [0], [0], [0], [0]]
-                action = [[a] for a in action]
-
-            #print(action)
-
-            obs, reward, done, info = env.step(action)
-
-            info_tmp['reward'] = reward[id_building]
-            info_tmp['building_id'] = id_building
-            infos.append(info_tmp)
-
-            if done:
-                obs = env.reset()
-
-    # create the data
-    data_pd = {}
-
-    for info in infos:
-        for i, v in info.items():
-            try:
-                data_pd[i].append(v)
-            except:
-                data_pd[i] = [v]
-
-    data = pd.DataFrame(infos)
-
-    return data
-
-if __name__ == "__main__":
-
-    # data generation
-    data = env_run_without_action()
-
-    # we only normalize month and hour
-    data['hour'] = data['hour']/24
-    data['month'] = data['month']/12
-
-    # save the data into the data_histo folder into parquet format
-    data.to_parquet("data_histo/data.parquet")
-