<jupyter_start><jupyter_text>Number of experiments- Total in this machine: 5,128,336- New data in this machine: 26532- In siren so far: 26570  Looking around the parameters<jupyter_code>df = pd.read_csv(pathlib.Path("../_old_parameters/main.csv"))
len(df)
df_new = pd.read_csv(pathlib.Path("../data/_parameters/main.csv"))
len(df_new)<jupyter_output><empty_output><jupyter_text>Summary<jupyter_code>summary = df.describe()
summary
summary_new = df_new.describe()
summary_new<jupyter_output><empty_output><jupyter_text>Separation of set of parameters<jupyter_code>df["buffer_capacity_2"][5039999]
df["buffer_capacity_2"][5040000]<jupyter_output><empty_output><jupyter_text>Get hash of given set of parameters<jupyter_code>def get_index_of_values(problem_parameters, data, atol=1e-08, rtol=1e-05):
    """
    Get the indices of the rows that match the given parameters' values
    """
    index = data.index
    condition = (
        np.isclose(data["lambda_2"], problem_parameters["lambda_2"], atol=atol, rtol=rtol)
        & np.isclose(data["lambda_1_1"], problem_parameters["lambda_1_1"], atol=atol, rtol=rtol)
        & np.isclose(data["lambda_1_2"], problem_parameters["lambda_1_2"], atol=atol, rtol=rtol)
        & np.isclose(data["mu_1"], problem_parameters["mu_1"], atol=atol, rtol=rtol)
        & np.isclose(data["mu_2"], problem_parameters["mu_2"], atol=atol, rtol=rtol)
        & np.isclose(data["num_of_servers_1"], problem_parameters["num_of_servers_1"])
        & np.isclose(data["num_of_servers_2"], problem_parameters["num_of_servers_2"])
        & np.isclose(data["system_capacity_1"], problem_parameters["system_capacity_1"])
        & np.isclose(data["system_capacity_2"], problem_parameters["system_capacity_2"])
        & np.isclose(data["buffer_capacity_1"], problem_parameters["buffer_capacity_1"])
        & np.isclose(data["buffer_capacity_2"], problem_parameters["buffer_capacity_2"])
        & np.isclose(data["alpha"], problem_parameters["alpha"])
        & np.isclose(data["target"], problem_parameters["target"], atol=atol, rtol=rtol)
    )
    indices = index[condition]
    return indices
def create_directory_with_notebook(data, index_value):
    """
    Create the directory with main.ipynb that contains a brief analysis for that scenario
    """
    hash_value = data[index_value:index_value + 1]["hash_value"]
    dirname = hash_value.to_string(index=False)[1:]
    new_dir = pathlib.Path() / dirname
    new_dir.mkdir(exist_ok=True)
    command = 'copy _main\main.ipynb ' + dirname + '\\'
    os.system(command)
    return dirname<jupyter_output><empty_output><jupyter_text>Experiments with small games<jupyter_code>data = df<jupyter_output><empty_output><jupyter_text>1) Changing the target  1.1) R = 5.2<jupyter_code>problem_parameters = {
        "alpha": 0.95,
        "buffer_capacity_1": 2,
        "buffer_capacity_2": 2,
        "lambda_1_1": 1.8,
        "lambda_1_2": 1.2,
        "lambda_2": 4.2,
        "mu_1": 2,
        "mu_2": 2,
        "num_of_servers_1": 1,
        "num_of_servers_2": 1,
        "system_capacity_1": 2,
        "system_capacity_2": 2,
        "target": 5.25,
    }
index = get_index_of_values(
    problem_parameters=problem_parameters, data=data, atol=1e-01
)
data.iloc[list(index)]
create_directory_with_notebook(data=data, index_value=index[0])<jupyter_output><empty_output><jupyter_text>1.2) R = 0.5<jupyter_code>problem_parameters = {
        "alpha": 0.95,
        "buffer_capacity_1": 2,
        "buffer_capacity_2": 2,
        "lambda_1_1": 1.8,
        "lambda_1_2": 1.2,
        "lambda_2": 4.2,
        "mu_1": 2,
        "mu_2": 2,
        "num_of_servers_1": 1,
        "num_of_servers_2": 1,
        "system_capacity_1": 2,
        "system_capacity_2": 2,
        "target": 0.5,
    }
index = get_index_of_values(
    problem_parameters=problem_parameters, data=data, atol=1e-01
)
data.iloc[list(index)]
create_directory_with_notebook(data=data, index_value=index[0])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14027603513174405, 'fraction_numerical': 0.037390213299874527, 'mean_word_length': 4.45280437756498, 'pattern_counts': {'":': 26, '<': 29, '<?xml version=': 0, '>': 29, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '25342', 'license': 'MIT', 'n_tokens_mistral': 1507, 'n_tokens_neox': 1442, 'n_words': 217, 'path': 'data/game_data/nbs/initial_exploration.ipynb', 'repo_name': '11michalis11/AmbulanceDecisionGame', 'size': '3985'}