Python read_pickle示例

示例#1

def main(args):

    os.environ["CUDA_VISIBLE_DEVICES"] = ""

    # maybe create dir
    if not os.path.isdir(SAVE_DIR):
        os.makedirs(SAVE_DIR)

    # load results if already exist
    if os.path.isfile(SAVE_PATH):
        results = log_utils.read_pickle(SAVE_PATH)
    else:
        results = {}

    for num_experience in NUM_EXPERIENCE_LIST:

        for split_threshold in SPLIT_THRESHOLD_LIST:

            for min_conf in CONF_THRESHOLD_LIST:

                for run_idx in range(NUM_RUNS):

                    key = (num_experience, split_threshold, min_conf, run_idx)

                    # skip if this setting is already in results
                    if key in results:
                        continue

                    accuracy = run(num_experience, split_threshold, min_conf)
                    results[key] = accuracy

                    # save results after each run
                    log_utils.write_pickle(SAVE_PATH, results)

示例#2

def main(args):

    os.environ["CUDA_VISIBLE_DEVICES"] = ""

    # maybe create dir
    if not os.path.isdir(SAVE_DIR):
        os.makedirs(SAVE_DIR)

    # load results if already exist
    if os.path.isfile(SAVE_PATH):
        results = log_utils.read_pickle(SAVE_PATH)
    else:
        results = {}

    for resolution in RESOLUTION_LIST:

        for run_idx in range(NUM_RUNS):

            key = (resolution, run_idx)

            # skip if this setting is already in results
            if key in results:
                continue

            accuracy = run(resolution)
            results[key] = accuracy

            # save results after each run
            log_utils.write_pickle(SAVE_PATH, results)

示例#3

def main(args):

    os.environ["CUDA_VISIBLE_DEVICES"] = ""

    NUM_RUNS = 50
    NUM_EXPERIENCE = 2000
    SPLIT_THRESHOLD_LIST = [50, 100, 200, 500]
    SAVE_DIR = "results/homo_g/balanced_mlp"
    SAVE_FILE = "continuous_3_evaluation_3.pickle"
    SAVE_PATH = os.path.join(SAVE_DIR, SAVE_FILE)

    # maybe create dir
    if not os.path.isdir(SAVE_DIR):
        os.makedirs(SAVE_DIR)

    # load results if already exist
    if os.path.isfile(SAVE_PATH):
        results = log_utils.read_pickle(SAVE_PATH)
    else:
        results = {}

    for t1 in SPLIT_THRESHOLD_LIST:

        for t2 in SPLIT_THRESHOLD_LIST:

            for run_idx in range(NUM_RUNS):

                key = (t1, t2, run_idx)

                # skip if this setting is already in results
                if key in results:
                    continue

                accuracy = run(NUM_EXPERIENCE, t1, t2)
                results[key] = accuracy

                # save results after each run
                log_utils.write_pickle(SAVE_PATH, results)

示例#4

文件： experiment_1_thresholds.py 项目： ondrejba/mdp_abstraction

def main(args):

    os.environ["CUDA_VISIBLE_DEVICES"] = ""

    NUM_RUNS = 200
    NUM_EXPERIENCE_LIST = [200, 500, 1000]
    SPLIT_THRESHOLD_LIST = [50, 100, 200]
    SAVE_DIR = "results/homo_g/balanced_mlp"
    SAVE_FILE = "experiment_1_thresholds.pickle"
    SAVE_PATH = os.path.join(SAVE_DIR, SAVE_FILE)

    # maybe create dir
    if not os.path.isdir(SAVE_DIR):
        os.makedirs(SAVE_DIR)

    # load results if already exist
    if os.path.isfile(SAVE_PATH):
        results = log_utils.read_pickle(SAVE_PATH)
    else:
        results = {}

    for num_experience in NUM_EXPERIENCE_LIST:

        for split_threshold in SPLIT_THRESHOLD_LIST:

            for run_idx in range(NUM_RUNS):

                key = (num_experience, split_threshold, run_idx)

                # skip if this setting is already in results
                if key in results:
                    continue

                accuracy = run(num_experience, split_threshold)
                results[key] = accuracy

                # save results after each run
                log_utils.write_pickle(SAVE_PATH, results)

示例#5

文件： report_1_thresholds_12th_percentile.py 项目： ondrejba/mdp_abstraction

import os
import numpy as np
import log_utils
import seaborn as sns
import matplotlib.pyplot as plt

LOAD_DIR = "results/homo_g_conf/balanced_mlp"
LOAD_FILE = "experiment_1_thresholds_12th_percentile.pickle"
LOAD_PATH = os.path.join(LOAD_DIR, LOAD_FILE)

NUM_RUNS = 200
NUM_EXPERIENCE_LIST = [200, 500, 1000]
SPLIT_THRESHOLD_LIST = [10, 20, 50]
CONF_THRESHOLD_LIST = [0.0, 0.5, 0.7, 0.8, 0.85, 0.9]

results = log_utils.read_pickle(LOAD_PATH)
results_array = np.zeros((len(NUM_EXPERIENCE_LIST), len(SPLIT_THRESHOLD_LIST),
                          len(CONF_THRESHOLD_LIST)))

for i, num_experience in enumerate(NUM_EXPERIENCE_LIST):

    for j, split_threshold in enumerate(SPLIT_THRESHOLD_LIST):

        for k, min_conf in enumerate(CONF_THRESHOLD_LIST):

            accuracies = []

            for run_idx in range(NUM_RUNS):

                key = (num_experience, split_threshold, min_conf, run_idx)

示例#6

文件： compare_1_25th_sort_blocks.py 项目： ondrejba/mdp_abstraction

import matplotlib.pyplot as plt

LOAD_DIR_1 = "results/homo_g_conf/balanced_mlp"
LOAD_FILE_1 = "experiment_1_thresholds_25th_percentile_sort_blocks.pickle"
LOAD_PATH_1 = os.path.join(LOAD_DIR_1, LOAD_FILE_1)

LOAD_DIR_2 = "results/homo_g_conf/balanced_mlp"
LOAD_FILE_2 = "experiment_1_thresholds_25th_percentile.pickle"
LOAD_PATH_2 = os.path.join(LOAD_DIR_2, LOAD_FILE_2)

NUM_RUNS = 200
NUM_EXPERIENCE_LIST = [200, 500, 1000]
SPLIT_THRESHOLD_LIST = [10, 20, 50]
CONF_THRESHOLD_LIST = [0.0, 0.5, 0.7, 0.8, 0.85, 0.9]

results_1 = log_utils.read_pickle(LOAD_PATH_1)
results_2 = log_utils.read_pickle(LOAD_PATH_2)

results_array = np.zeros((len(NUM_EXPERIENCE_LIST), len(SPLIT_THRESHOLD_LIST),
                          len(CONF_THRESHOLD_LIST)))

for i, num_experience in enumerate(NUM_EXPERIENCE_LIST):

    for j, split_threshold in enumerate(SPLIT_THRESHOLD_LIST):

        for k, min_conf in enumerate(CONF_THRESHOLD_LIST):

            accuracies_1 = []
            accuracies_2 = []

            for run_idx in range(NUM_RUNS):