show_simple.py

import pandas as pd
import codecs
import os
import numpy as np
import csv
import json
import random
from configs import ModelConfig, ActiveConfig
import matplotlib.pyplot as plt

def read_from_csv(path):
    with open(path, "r") as f:
        reader = csv.reader(f)
        stats = []
        for line in reader:
            stats.append(line)
        return stats

def read_file_simple_torch(path):
    experiments = []
    loginfo = read_from_csv(path)
    for line in loginfo:
        if len(line) != 0:
            if line[0] == "BEGIN":
                stat = {"budget": float(line[2])
                         }
            if line[0] == "result":
                stat.update({"f1": float(line[10])*100, "precision": float(line[6])*100, "recall": float(line[8])*100})
                experiments.append(stat)
    return pd.DataFrame(experiments)

def read_file_simple_self_new(path):
    experiments = []
    loginfo = read_from_csv(path)
    for line in loginfo:
        if len(line) != 0:
            if line[0] == "initbudget":
                stat = {"budget": float(line[1]),"f1": float(line[11])*100, "precision": float(line[12])*100, "recall": float(line[13])*100
                         }
                experiments.append(stat)
    return pd.DataFrame(experiments)

def read_file_simple(path):
    experiments = []
    loginfo = read_from_csv(path)
    for line in loginfo:
        if len(line) != 0:
            if line[0] == "BEGIN":
                stat = {"budget": float(line[2])
                         }
            if line[0] == "result":
                stat.update({"f1": float(line[2])})
                experiments.append(stat)
    return pd.DataFrame(experiments)

def find_new_number(directory):
    result = 0
    for filename in os.listdir(directory):
        try:
            num = int(filename[:2])
            result = num if num > result else result
        except Exception:
            pass

    if result+1<10:
        result = "0"+str(result+1)
    else:
        result = str(result+1)
    return result
def random_color():
    levels = range(32,256,32)
    return tuple(random.choice(levels) for _ in range(3))

def read_file_simple_old(path):
    experiments = []
    loginfo = read_from_csv(path)
    for line in loginfo:
        if len(line) != 0:
            if line[0] == "Begin":
                stat = {"budget": float(line[3]), "iter":[]
                         }
            if line[0] == "EndIter":
                stat["iter"].append([line[14],line[6],line[8],line[10],line[16],line[17],line[18]])
            if line[0] == "Results":
                stat.update({"f1": float(line[10])*100, "precision": float(line[6])*100, "recall": float(line[8])*100, "epoch":int(line[14])})
                experiments.append(stat)
    return pd.DataFrame(experiments)


if __name__ == '__main__':
    model_config = ModelConfig()
    directory_report = "report/simple/"
    if not os.path.exists(directory_report):
        os.makedirs(directory_report)
    new_plot_num = find_new_number(directory_report)

    path_simple = "logs/simple/paper_simple_learning_dev.csv"
    mode = 'simple'

    experiments = read_file_simple(path_simple)
    print(experiments)
    experiments_simple = experiments.groupby('budget', as_index=False).agg({'f1': ['mean', 'std']})

    print(experiments_simple)

    plt.figure(figsize=(10,9))
    plt.plot(experiments_simple['budget'], experiments_simple[('f1','mean')], marker="o", label="PAPER")
    plt.fill_between(experiments_simple['budget'],experiments_simple[('f1','mean')]+experiments_simple[('f1','std')],experiments_simple[('f1','mean')]- experiments_simple[('f1','std')],alpha=.2)


    experiments_old = read_file_simple_old("logs/simple/tf_old_simple_batch_8.csv")
    experiments_simple = experiments_old.groupby('budget',as_index=False).agg({'f1': ['mean', 'std'],'precision': ['mean', 'std'],'recall': ['mean', 'std'],"epoch":['mean','std']})
    plt.plot(experiments_simple['budget'], experiments_simple[('f1','mean')], marker="o", label="TF")
    plt.fill_between(experiments_simple['budget'],experiments_simple[('f1','mean')]+experiments_simple[('f1','std')],experiments_simple[('f1','mean')]- experiments_simple[('f1','std')],alpha=.2)


    experiments_self_paper = read_file_simple_self_new("/home/roman/PycharmProjects/biomedner/logs/self_results_simple.csv")
    experiments_simple = experiments_self_paper.groupby('budget',as_index=False).agg({'f1': ['mean', 'std'],'precision': ['mean', 'std'],'recall': ['mean', 'std']})
    plt.plot(experiments_simple['budget'], experiments_simple[('f1','mean')], marker="o", label="SELF_PAPER")
    plt.fill_between(experiments_simple['budget'],experiments_simple[('f1','mean')]+experiments_simple[('f1','std')],experiments_simple[('f1','mean')]- experiments_simple[('f1','std')],alpha=.5)

    experiments_self_paper = read_file_simple_self_new("/home/roman/PycharmProjects/biomedner/logs/self_results_simple_lazy.csv")
    experiments_simple = experiments_self_paper.groupby('budget',as_index=False).agg({'f1': ['mean', 'std'],'precision': ['mean', 'std'],'recall': ['mean', 'std']})
    plt.plot(experiments_simple['budget'], experiments_simple[('f1','mean')], marker="o", label="SELF_PAPER_lazy_adam")
    plt.fill_between(experiments_simple['budget'],experiments_simple[('f1','mean')]+experiments_simple[('f1','std')],experiments_simple[('f1','mean')]- experiments_simple[('f1','std')],alpha=.5)

    plt.legend(loc='best')
    plt.xlabel('budget')
    plt.ylabel('f1')
    plt.savefig(directory_report+new_plot_num+'simple.png')