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eda

compare_two_tree_models(model1, model2, headers=['Parameter', 'Default', 'Spot'])

Compares two tree models. Args: model1 (object): A tree model. model2 (object): A tree model. headers (list): A list with the headers of the table.

Returns:

Type Description
str

A table with the comparison of the two models.

Examples:

>>> from spotPython.utils.eda import compare_two_tree_models
>>> from spotPython.hyperparameters.values import get_default_values
>>> fun_control = {
...     "x1": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x2": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x3": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x4": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x5": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x6": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x7": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x8": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x9": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x10": {"type": "int", "default": 1, "lower": 1, "upper": 10},
... }
>>> default_values = get_default_values(fun_control)
>>> model1 = spot_tuner.get_model("rf", default_values)
>>> model2 = spot_tuner.get_model("rf", default_values)
>>> compare_two_tree_models(model1, model2)
Source code in spotPython/utils/eda.py 
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def compare_two_tree_models(model1, model2, headers=["Parameter", "Default", "Spot"]):
    """Compares two tree models.
    Args:
        model1 (object):
            A tree model.
        model2 (object):
            A tree model.
        headers (list):
            A list with the headers of the table.

    Returns:
        (str):
            A table with the comparison of the two models.

    Examples:
        >>> from spotPython.utils.eda import compare_two_tree_models
        >>> from spotPython.hyperparameters.values import get_default_values
        >>> fun_control = {
        ...     "x1": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x2": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x3": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x4": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x5": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x6": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x7": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x8": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x9": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x10": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ... }
        >>> default_values = get_default_values(fun_control)
        >>> model1 = spot_tuner.get_model("rf", default_values)
        >>> model2 = spot_tuner.get_model("rf", default_values)
        >>> compare_two_tree_models(model1, model2)
    """
    keys = model1.summary.keys()
    values1 = model1.summary.values()
    values2 = model2.summary.values()
    tbl = []
    for key, value1, value2 in zip(keys, values1, values2):
        tbl.append([key, value1, value2])
    return tabulate(tbl, headers=headers, numalign="right", tablefmt="github")

count_missing_data(df)

Counts the number of missing values in each column of the given DataFrame.

Parameters:

Name Type Description Default
df DataFrame

DataFrame containing the data to be counted.

 required

Returns:

Type Description
DataFrame

DataFrame containing the number of missing values in each column.
Example

import pandas as pd df = pd.DataFrame({‘A’: [1, 2, None], ‘B’: [4, None, 6], ‘C’: [7, 8, 9]}) count_missing_data(df) column_name missing_count 0 A 1 1 B 1

Source code in spotPython/utils/eda.py 
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def count_missing_data(df) -> pd.DataFrame:
    """
    Counts the number of missing values in each column of the given DataFrame.

    Args:
        df (pd.DataFrame): DataFrame containing the data to be counted.

    Returns:
        (pd.DataFrame): DataFrame containing the number of missing values in each column.

    Example:
        >>> import pandas as pd
        >>> df = pd.DataFrame({'A': [1, 2, None], 'B': [4, None, 6], 'C': [7, 8, 9]})
        >>> count_missing_data(df)
           column_name  missing_count
        0           A              1
        1           B              1
    """
    missing_df = df.isnull().sum(axis=0).reset_index()
    missing_df.columns = ["column_name", "missing_count"]
    missing_df = missing_df.loc[missing_df["missing_count"] > 0]
    missing_df = missing_df.sort_values(by="missing_count")

    return missing_df

filter_highly_correlated(df, sorted=True, threshold=1 - 1e-05)

Return a new DataFrame with only those columns that are highly correlated.

Parameters:

Name Type Description Default
df DataFrame

The input DataFrame.

 required
threshold float

The correlation threshold.

 1 - 1e-05
sorted bool

If True, the columns are sorted by name.

 True

Returns:

Name Type Description
DataFrame DataFrame

A new DataFrame with only highly correlated columns.

Examples:

>>> df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
    df = filter_highly_correlated(df, sorted=True, threshold=0.99)
Source code in spotPython/utils/eda.py 
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def filter_highly_correlated(df: pd.DataFrame, sorted: bool = True, threshold: float = 1 - 1e-5) -> pd.DataFrame:
    """
    Return a new DataFrame with only those columns that are highly correlated.

    Args:
        df (DataFrame): The input DataFrame.
        threshold (float): The correlation threshold.
        sorted (bool): If True, the columns are sorted by name.

    Returns:
        DataFrame: A new DataFrame with only highly correlated columns.

    Examples:
        >>> df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
            df = filter_highly_correlated(df, sorted=True, threshold=0.99)

    """
    corr_matrix = df.corr()
    # Find pairs of columns with correlation greater than threshold
    corr_pairs = corr_matrix.abs().unstack()
    corr_pairs = corr_pairs[corr_pairs < 1]  # Remove self-correlations
    high_corr = corr_pairs[corr_pairs > threshold]
    high_corr = high_corr[high_corr < 1]  # Remove self-correlations

    # Get the column names of highly correlated columns
    high_corr_cols = list(set([col[0] for col in high_corr.index]))

    # Create new DataFrame with only highly correlated columns
    new_df = df[high_corr_cols]
    # sort the columns by name
    if sorted:
        new_df = new_df.sort_index(axis=1)

    return new_df

gen_design_table(fun_control, spot=None, tablefmt='github')

Generates a table with the design variables and their bounds. Args: fun_control (dict): A dictionary with function design variables. spot (object): A spot object. Defaults to None. Returns: (str): a table with the design variables, their default values, and their bounds. If a spot object is provided, the table will also include the value and the importance of each hyperparameter. Use the print function to display the table.

Examples:

>>> from spotPython.utils.eda import gen_design_table
>>> from spotPython.hyperparameters.values import get_default_values
>>> fun_control = {
...     "x1": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x2": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x3": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x4": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x5": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x6": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x7": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x8": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x9": {"type": "int", "default": 1, "lower": 1, "upper": 10},
...     "x10": {"type": "int", "default": 1, "lower": 1, "upper": 10},
... }
Source code in spotPython/utils/eda.py 
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def gen_design_table(fun_control: dict, spot: object = None, tablefmt="github") -> str:
    """Generates a table with the design variables and their bounds.
    Args:
        fun_control (dict):
            A dictionary with function design variables.
        spot (object):
            A spot object. Defaults to None.
    Returns:
        (str):
            a table with the design variables, their default values, and their bounds.
            If a spot object is provided,
            the table will also include the value and the importance of each hyperparameter.
            Use the `print` function to display the table.

    Examples:
        >>> from spotPython.utils.eda import gen_design_table
        >>> from spotPython.hyperparameters.values import get_default_values
        >>> fun_control = {
        ...     "x1": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x2": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x3": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x4": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x5": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x6": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x7": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x8": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x9": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ...     "x10": {"type": "int", "default": 1, "lower": 1, "upper": 10},
        ... }
    """
    default_values = get_default_values(fun_control)
    defaults = list(default_values.values())
    if spot is None:
        tab = tabulate(
            {
                "name": get_var_name(fun_control),
                "type": get_var_type(fun_control),
                "default": defaults,
                "lower": get_bound_values(fun_control, "lower", as_list=True),
                "upper": get_bound_values(fun_control, "upper", as_list=True),
                "transform": get_transform(fun_control),
            },
            headers="keys",
            tablefmt=tablefmt,
        )
    else:
        res = spot.print_results(print_screen=False, dict=fun_control)
        tuned = [item[1] for item in res]
        # imp = spot.print_importance(threshold=0.0, print_screen=False)
        # importance = [item[1] for item in imp]
        importance = spot.get_importance()
        stars = get_stars(importance)
        tab = tabulate(
            {
                "name": get_var_name(fun_control),
                "type": get_var_type(fun_control),
                "default": defaults,
                "lower": get_bound_values(fun_control, "lower", as_list=True),
                "upper": get_bound_values(fun_control, "upper", as_list=True),
                "tuned": tuned,
                "transform": get_transform(fun_control),
                "importance": importance,
                "stars": stars,
            },
            headers="keys",
            numalign="right",
            floatfmt=("", "", "", "", "", "", "", ".2f"),
            tablefmt=tablefmt,
        )
    return tab

generate_config_id(config, hash=False, timestamp=False)

Generates a unique id for a configuration.

Parameters:

Name Type Description Default
config dict

A dictionary with the configuration.

 required
hash bool

If True, the id is hashed.

 False
timestamp bool

If True, the id is appended with a timestamp. Defaults to False.

 False

Returns:

Type Description
str

A unique id for the configuration.

Examples:

>>> from spotPython.hyperparameters.values import get_one_config_from_X
>>> X = spot_tuner.to_all_dim(spot_tuner.min_X.reshape(1,-1))
>>> config = get_one_config_from_X(X, fun_control)
>>> generate_config_id(config)
Source code in spotPython/utils/eda.py 
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def generate_config_id(config, hash=False, timestamp=False):
    """Generates a unique id for a configuration.

    Args:
        config (dict):
            A dictionary with the configuration.
        hash (bool):
            If True, the id is hashed.
        timestamp (bool):
            If True, the id is appended with a timestamp. Defaults to False.

    Returns:
        (str):
            A unique id for the configuration.

    Examples:
        >>> from spotPython.hyperparameters.values import get_one_config_from_X
        >>> X = spot_tuner.to_all_dim(spot_tuner.min_X.reshape(1,-1))
        >>> config = get_one_config_from_X(X, fun_control)
        >>> generate_config_id(config)
    """
    config_id = ""
    for key in config:
        # if config[key] is a number, round it to 4 digits after the decimal point
        if isinstance(config[key], float):
            config_id += str(round(config[key], 4)) + "_"
        else:
            config_id += str(config[key]) + "_"
    # hash the config_id to make it shorter and unique
    if hash:
        config_id = str(hash(config_id)) + "_"
    # remove () and , from the string
    config_id = config_id.replace("(", "")
    config_id = config_id.replace(")", "")
    config_id = config_id.replace(",", "")
    config_id = config_id.replace(" ", "")
    config_id = config_id.replace(":", "")
    if timestamp:
        config_id = get_timestamp(only_int=True) + "_" + config_id
    return config_id[:-1]

get_stars(input_list)

Converts a list of values to a list of stars, which can be used to visualize the importance of a variable.

Parameters:

Name Type Description Default
input_list list

A list of values.

 required

Returns:

Type Description
list

A list of strings.

Examples:

>>> from spotPython.utils.eda import convert_list
>>> get_stars([100, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
[***, '', '', '', '', '', '', '', '']
Source code in spotPython/utils/eda.py 
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def get_stars(input_list) -> list:
    """Converts a list of values to a list of stars, which can be used to
        visualize the importance of a variable.

    Args:
        input_list (list): A list of values.

    Returns:
        (list):
            A list of strings.

    Examples:
        >>> from spotPython.utils.eda import convert_list
        >>> get_stars([100, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
        [***, '', '', '', '', '', '', '', '']
    """
    output_list = []
    for value in input_list:
        if value > 95:
            output_list.append("***")
        elif value > 50:
            output_list.append("**")
        elif value > 1:
            output_list.append("*")
        elif value > 0.1:
            output_list.append(".")
        else:
            output_list.append("")
    return output_list

plot_missing_data(df, relative=False, figsize=(7, 5), color='grey', xlabel='Missing Data', title='Missing Data')

Plots a horizontal bar chart of the number of missing values in each column of the given DataFrame.

Parameters:

Name Type Description Default
df DataFrame

DataFrame containing the data to be plotted.

 required
relative bool

Whether to plot relative values (percentage) or absolute values.

 False
figsize tuple

Size of the figure to be plotted.

 (7, 5)
color str

Color of the bars in the bar chart.

 'grey'
xlabel str

Label for the x-axis.

 'Missing Data'
title str

Title for the plot.

 'Missing Data'

Returns:

Type Description
NoneType

None
Example

import pandas as pd df = pd.DataFrame({‘A’: [1, 2, np.nan], ‘B’: [4, np.nan, 6], ‘C’: [7, 8, 9]}) plot_missing_data(df)

Source code in spotPython/utils/eda.py 
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def plot_missing_data(
    df, relative=False, figsize=(7, 5), color="grey", xlabel="Missing Data", title="Missing Data"
) -> None:
    """
    Plots a horizontal bar chart of the number of missing values in each column of the given DataFrame.

    Args:
        df (pd.DataFrame): DataFrame containing the data to be plotted.
        relative (bool): Whether to plot relative values (percentage) or absolute values.
        figsize (tuple): Size of the figure to be plotted.
        color (str): Color of the bars in the bar chart.
        xlabel (str): Label for the x-axis.
        title (str): Title for the plot.

    Returns:
        (NoneType): None

    Example:
        >>> import pandas as pd
        >>> df = pd.DataFrame({'A': [1, 2, np.nan], 'B': [4, np.nan, 6], 'C': [7, 8, 9]})
        >>> plot_missing_data(df)
    """
    missing_df = count_missing_data(df)

    if relative:
        missing_df["missing_count"] = missing_df["missing_count"] / df.shape[0]
        xlabel = "Percentage of " + xlabel
        title = "Percentage of " + title

    ind = np.arange(missing_df.shape[0])
    _, ax = plt.subplots(figsize=figsize)
    _ = ax.barh(ind, missing_df.missing_count.values, color=color)
    ax.set_yticks(ind)
    ax.set_yticklabels(missing_df.column_name.values, rotation="horizontal")
    ax.set_xlabel(xlabel)
    ax.set_title(title)
    plt.vlines(1, 0, missing_df.shape[0])
    plt.vlines(0.97, 0, missing_df.shape[0])
    plt.vlines(0.5, 0, missing_df.shape[0])
    plt.show()

plot_sns_heatmap(df_heat, figsize=(16, 12), cmap='vlag', vmin=-1, vmax=1, annot=True, fmt='.5f', linewidths=0.5, annot_kws={'size': 8})

Plots a heatmap of the correlation matrix of the given DataFrame.

Parameters:

Name Type Description Default
df_heat DataFrame

DataFrame containing the data to be plotted.

 required
figsize tuple

Size of the figure to be plotted.

 (16, 12)
cmap str

Color map to be used for the heatmap.

 'vlag'
vmin int

Minimum value for the color scale.

 -1
vmax int

Maximum value for the color scale.

 1
annot bool

Whether to display annotations on the heatmap.

 True
fmt str

Format string for annotations.

 '.5f'
linewidths float

Width of lines separating cells in the heatmap.

 0.5
annot_kws dict

Keyword arguments for annotations.

 {'size': 8}

Returns:

Type Description
NoneType

None
Example

import pandas as pd df = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6], ‘C’: [7, 8, 9]}) plot_heatmap(df)

Source code in spotPython/utils/eda.py 
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def plot_sns_heatmap(
    df_heat,
    figsize=(16, 12),
    cmap="vlag",
    vmin=-1,
    vmax=1,
    annot=True,
    fmt=".5f",
    linewidths=0.5,
    annot_kws={"size": 8},
) -> None:
    """
    Plots a heatmap of the correlation matrix of the given DataFrame.

    Args:
        df_heat (pd.DataFrame): DataFrame containing the data to be plotted.
        figsize (tuple): Size of the figure to be plotted.
        cmap (str): Color map to be used for the heatmap.
        vmin (int): Minimum value for the color scale.
        vmax (int): Maximum value for the color scale.
        annot (bool): Whether to display annotations on the heatmap.
        fmt (str): Format string for annotations.
        linewidths (float): Width of lines separating cells in the heatmap.
        annot_kws (dict): Keyword arguments for annotations.

    Returns:
        (NoneType): None

    Example:
        >>> import pandas as pd
        >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
        >>> plot_heatmap(df)
    """
    plt.figure(figsize=figsize)
    matrix = np.triu(np.ones_like(df_heat.corr()))
    sns.heatmap(
        data=df_heat.corr(),
        cmap=cmap,
        vmin=vmin,
        vmax=vmax,
        annot=annot,
        fmt=fmt,
        linewidths=linewidths,
        annot_kws=annot_kws,
        mask=matrix,
    )
    plt.show()
    plt.gcf().clear()