Howto 04 - (Data) Store, plot, and save variables

Ver. 1.2.3 (2021-10-26)

This module demonstrates how to store, plot, save and load variables.

Prerequisites

Please install the following packages to run this examples properly:

• MLPro

• NumPy

• Matplotlib

Example Code

## -------------------------------------------------------------------------------------------------
## -- Project : MLPro - A Synoptic Framework for Standardized Machine Learning Tasks
## -- Package : mlpro
## -- Module  : Howto 04 - (Data) Store, plot, and save variables
## -------------------------------------------------------------------------------------------------
## -- History :
## -- yyyy-mm-dd  Ver.      Auth.    Description
## -- 2021-06-16  1.0.0     SY       Creation/Release
## -- 2021-06-21  1.1.0     SY       Adjustment to updated DataPlotting class
## -- 2021-07-01  1.2.0     SY       Adjustment due to extension in save and load data
## -- 2021-09-11  1.2.1     MRD      Change Header information to match our new library name
## -- 2021-10-25  1.2.2     SY       Adjustment due to improvement in DataPlotting
## -- 2021-10-26  1.2.3     SY       Rename module
## -------------------------------------------------------------------------------------------------

"""
Ver. 1.2.3 (2021-10-26)

This module demonstrates how to store, plot, save and load variables.
"""


from mlpro.bf.various import *
from mlpro.bf.data import *
from mlpro.bf.plot import *
import random


num_eps         = 10
num_cycles      = 10000
data_names      = ["reward","states_1","states_2","model_loss"]
data_printing   = {"reward":        [True,0,10],
                   "states_1":      [True,0,4],
                   "states_2":      [True,0,4],
                   "model_loss":    [True,0,-1]}

## 1. How to store data ##
mem = DataStoring(data_names)
for ep in range(num_eps):
    ep_id = ("ep. %s"%str(ep+1))
    mem.add_frame(ep_id)
    for i in range(num_cycles):
        mem.memorize("reward",ep_id,random.uniform(0+(ep*0.5),5+(ep*0.5)))
        mem.memorize("states_1",ep_id,random.uniform(2-(ep*0.2),4-(ep*0.2)))
        mem.memorize("states_2",ep_id,random.uniform(0+(ep*0.2),2+(ep*0.2)))
        mem.memorize("model_loss",ep_id,random.uniform(0.25-(ep*0.02),1-(ep*0.07)))

## 2. How to plot stored data ##        
# 2.1. Plotting data per cycle
# mem_plot    = DataPlotting(mem, p_type=DataPlotting.C_PLOT_TYPE_CY, p_window=100,
#                             p_showing=True, p_printing=data_printing, p_figsize=(7,7),
#                             p_color="darkblue")
# 2.2. Plotting data with continuous cycle
mem_plot    = DataPlotting(mem, p_type=DataPlotting.C_PLOT_TYPE_EP, p_window=1000,
                            p_showing=True, p_printing=data_printing, p_figsize=(7,7),
                            p_color="darkblue")
# 2.3. Plotting data per epsiode according to its mean value
# mem_plot    = DataPlotting(mem, p_type=DataPlotting.C_PLOT_TYPE_EP_M, p_window=1,
#                             p_showing=True, p_printing=data_printing, p_figsize=(7,7),
#                             p_color="darkblue")
mem_plot.get_plots()

## 3. How to save plots and data in binary file (variables, classes, etc.) ##
path_save   = input("Input path_save : ")
#Do not include quote-unquote ("" or '' ) into target path name
mem_plot.save_plots(path_save, "pdf")
mem_plot.save(path_save, "plot_memory")
mem.save(path_save, "data_memory")
mem.save_data(path_save, "data_storage", "\t")

## 4. How to load data from binary file ##
path_load   = path_save
mem_load    = DataStoring.load(path_load, "data_memory")
print("Comparison :")
print("Original data                : %.5f"%mem.memory_dict["reward"]["ep. 1"][0])
print("Loaded data from binary file : %.5f"%mem_load.memory_dict["reward"]["ep. 1"][0])

## 5. How to load data from csv file ##
data_names = []
mem_from_csv = DataStoring(data_names)
mem_from_csv.load_data(path_load, "data_storage.csv", "\t")
print("Comparison :")
print("Original data             : %.5f"%mem.memory_dict["reward"]["ep. 1"][0])
print("Loaded data from csv file : %.5f"%mem_from_csv.memory_dict["reward"]["ep. 1"][0])

Results

Descriptions, plots, images, screenshots of expected results.