How to use the dython._private.convert function in dython

categories : list / NumPy ndarray / Pandas Series
        A sequence of categorical measurements
    measurements : list / NumPy ndarray / Pandas Series
        A sequence of continuous measurements
    nan_strategy : string, default = 'replace'
        How to handle missing values: can be either 'drop' to remove samples with missing values, or 'replace'
        to replace all missing values with the nan_replace_value. Missing values are None and np.nan.
    nan_replace_value : any, default = 0.0
        The value used to replace missing values with. Only applicable when nan_strategy is set to 'replace'.
    """
    if nan_strategy == REPLACE:
        categories, measurements = replace_nan_with_value(categories, measurements, nan_replace_value)
    elif nan_strategy == DROP:
        categories, measurements = remove_incomplete_samples(categories, measurements)
    categories = convert(categories, 'array')
    measurements = convert(measurements, 'array')
    fcat, _ = pd.factorize(categories)
    cat_num = np.max(fcat)+1
    y_avg_array = np.zeros(cat_num)
    n_array = np.zeros(cat_num)
    for i in range(0,cat_num):
        cat_measures = measurements[np.argwhere(fcat == i).flatten()]
        n_array[i] = len(cat_measures)
        y_avg_array[i] = np.average(cat_measures)
    y_total_avg = np.sum(np.multiply(y_avg_array,n_array))/np.sum(n_array)
    numerator = np.sum(np.multiply(n_array,np.power(np.subtract(y_avg_array,y_total_avg),2)))
    denominator = np.sum(np.power(np.subtract(measurements,y_total_avg),2))
    if numerator == 0:
        eta = 0.0
    else:
        eta = np.sqrt(numerator/denominator)
    return eta

----------
    y_true : list / NumPy ndarray
        The true classes of the predicted data
    y_pred : list / NumPy ndarray
        The predicted classes
    micro : Boolean, default = True
        Whether to calculate a Micro ROC graph (not applicable for binary cases)
    macro : Boolean, default = True
        Whether to calculate a Macro ROC graph (not applicable for binary cases)
    kwargs : any key-value pairs
        Different options and configurations
    """
    all_fpr = list()
    all_tpr = list()
    y_true = convert(y_true, 'array')
    y_pred = convert(y_pred, 'array')
    if y_pred.shape != y_true.shape:
        raise ValueError('y_true and y_pred must have the same shape')
    elif len(y_pred.shape) == 1 or y_pred.shape[1] <= 2:
        return binary_roc_graph(y_true, y_pred, **kwargs)
    else:
        colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
        n = y_pred.shape[1]
        plt.figure()
        kwargs['new_figure'] = False
        kwargs['show_graphs'] = False
        kwargs['return_pr'] = True
        for i in range(0,n):
            pr = binary_roc_graph(y_true[:,i], y_pred[:,i],
                                   color=colors[i % len(colors)],class_label=i, **kwargs)
            all_fpr.append(pr['fpr'])
            all_tpr.append(pr['tpr'])

0.8. In the first configuration, the input will be: y_true = [0,1], y_pred = [0.6,0.8]. In the second
    configuration, the input will be: y_true = [[1,0],[0,1]], y_pred = [[0.6,0.4],[0.2,0.8]].

    Based on sklearn examples (as was seen on April 2018):
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html

    Parameters
    ----------
    y_true : list / NumPy ndarray
        The true classes of the predicted data
    y_pred : list / NumPy ndarray
        The predicted classes
    kwargs : any key-value pairs
        Different options and configurations
    """
    y_true = convert(y_true, 'array')
    y_pred = convert(y_pred, 'array')
    if y_pred.shape != y_true.shape:
        raise ValueError('y_true and y_pred must have the same shape')
    elif len(y_pred.shape) == 1:
        y_t = y_true
        y_p = y_pred
    else:
        y_t = [np.argmax(x) for x in y_true]
        y_p = [x[1] for x in y_pred]
    fpr, tpr, _ = roc_curve(y_t, y_p)
    auc_score = auc(fpr,tpr)
    color = kwargs.get('color','darkorange')
    lw = kwargs.get('lw', 2)
    ls = kwargs.get('ls','-')
    ms = kwargs.get('ms', 10)
    fmt = kwargs.get('fmt','.2f')

plot : Boolean, default = True
        If True, plot a heat-map of the correlation matrix
    return_results : Boolean, default = False
        If True, the function will return a Pandas DataFrame of the computed associations
    nan_strategy : string, default = 'replace'
        How to handle missing values: can be either 'drop_samples' to remove samples with missing values,
        'drop_features' to remove features (columns) with missing values, or 'replace' to replace all missing
        values with the nan_replace_value. Missing values are None and np.nan.
    nan_replace_value : any, default = 0.0
        The value used to replace missing values with. Only applicable when nan_strategy is set to 'replace'
    ax : matplotlib ax, default = None
      Matplotlib Axis on which the heat-map will be plotted
    kwargs : any key-value pairs
        Arguments to be passed to used function and methods
    """
    dataset = convert(dataset, 'dataframe')
    if nan_strategy == REPLACE:
        dataset.fillna(nan_replace_value, inplace=True)
    elif nan_strategy == DROP_SAMPLES:
        dataset.dropna(axis=0, inplace=True)
    elif nan_strategy == DROP_FEATURES:
        dataset.dropna(axis=1, inplace=True)
    columns = dataset.columns
    if nominal_columns is None:
        nominal_columns = list()
    elif nominal_columns == 'all':
        nominal_columns = columns
    corr = pd.DataFrame(index=columns, columns=columns)
    for i in range(0, len(columns)):
        for j in range(i, len(columns)):
            if i == j:
                corr[columns[i]][columns[j]] = 1.0

configuration, the input will be: y_true = [[1,0],[0,1]], y_pred = [[0.6,0.4],[0.2,0.8]].

    Based on sklearn examples (as was seen on April 2018):
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html

    Parameters
    ----------
    y_true : list / NumPy ndarray
        The true classes of the predicted data
    y_pred : list / NumPy ndarray
        The predicted classes
    kwargs : any key-value pairs
        Different options and configurations
    """
    y_true = convert(y_true, 'array')
    y_pred = convert(y_pred, 'array')
    if y_pred.shape != y_true.shape:
        raise ValueError('y_true and y_pred must have the same shape')
    elif len(y_pred.shape) == 1:
        y_t = y_true
        y_p = y_pred
    else:
        y_t = [np.argmax(x) for x in y_true]
        y_p = [x[1] for x in y_pred]
    fpr, tpr, _ = roc_curve(y_t, y_p)
    auc_score = auc(fpr,tpr)
    color = kwargs.get('color','darkorange')
    lw = kwargs.get('lw', 2)
    ls = kwargs.get('ls','-')
    ms = kwargs.get('ms', 10)
    fmt = kwargs.get('fmt','.2f')
    if 'class_label' in kwargs:

----------
    categories : list / NumPy ndarray / Pandas Series
        A sequence of categorical measurements
    measurements : list / NumPy ndarray / Pandas Series
        A sequence of continuous measurements
    nan_strategy : string, default = 'replace'
        How to handle missing values: can be either 'drop' to remove samples with missing values, or 'replace'
        to replace all missing values with the nan_replace_value. Missing values are None and np.nan.
    nan_replace_value : any, default = 0.0
        The value used to replace missing values with. Only applicable when nan_strategy is set to 'replace'.
    """
    if nan_strategy == REPLACE:
        categories, measurements = replace_nan_with_value(categories, measurements, nan_replace_value)
    elif nan_strategy == DROP:
        categories, measurements = remove_incomplete_samples(categories, measurements)
    categories = convert(categories, 'array')
    measurements = convert(measurements, 'array')
    fcat, _ = pd.factorize(categories)
    cat_num = np.max(fcat)+1
    y_avg_array = np.zeros(cat_num)
    n_array = np.zeros(cat_num)
    for i in range(0,cat_num):
        cat_measures = measurements[np.argwhere(fcat == i).flatten()]
        n_array[i] = len(cat_measures)
        y_avg_array[i] = np.average(cat_measures)
    y_total_avg = np.sum(np.multiply(y_avg_array,n_array))/np.sum(n_array)
    numerator = np.sum(np.multiply(n_array,np.power(np.subtract(y_avg_array,y_total_avg),2)))
    denominator = np.sum(np.power(np.subtract(measurements,y_total_avg),2))
    if numerator == 0:
        eta = 0.0
    else:
        eta = np.sqrt(numerator/denominator)

Parameters
    ----------
    y_true : list / NumPy ndarray
        The true classes of the predicted data
    y_pred : list / NumPy ndarray
        The predicted classes
    micro : Boolean, default = True
        Whether to calculate a Micro ROC graph (not applicable for binary cases)
    macro : Boolean, default = True
        Whether to calculate a Macro ROC graph (not applicable for binary cases)
    kwargs : any key-value pairs
        Different options and configurations
    """
    all_fpr = list()
    all_tpr = list()
    y_true = convert(y_true, 'array')
    y_pred = convert(y_pred, 'array')
    if y_pred.shape != y_true.shape:
        raise ValueError('y_true and y_pred must have the same shape')
    elif len(y_pred.shape) == 1 or y_pred.shape[1] <= 2:
        return binary_roc_graph(y_true, y_pred, **kwargs)
    else:
        colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
        n = y_pred.shape[1]
        plt.figure()
        kwargs['new_figure'] = False
        kwargs['show_graphs'] = False
        kwargs['return_pr'] = True
        for i in range(0,n):
            pr = binary_roc_graph(y_true[:,i], y_pred[:,i],
                                   color=colors[i % len(colors)],class_label=i, **kwargs)
            all_fpr.append(pr['fpr'])