--- a/ml_visualization_ex.py	Thu Oct 01 20:11:05 2020 +0000
+++ b/ml_visualization_ex.py	Tue Apr 13 17:36:50 2021 +0000
@@ -22,16 +22,16 @@
 
 # plotly default colors
 default_colors = [
-    '#1f77b4',  # muted blue
-    '#ff7f0e',  # safety orange
-    '#2ca02c',  # cooked asparagus green
-    '#d62728',  # brick red
-    '#9467bd',  # muted purple
-    '#8c564b',  # chestnut brown
-    '#e377c2',  # raspberry yogurt pink
-    '#7f7f7f',  # middle gray
-    '#bcbd22',  # curry yellow-green
-    '#17becf'   # blue-teal
+    "#1f77b4",  # muted blue
+    "#ff7f0e",  # safety orange
+    "#2ca02c",  # cooked asparagus green
+    "#d62728",  # brick red
+    "#9467bd",  # muted purple
+    "#8c564b",  # chestnut brown
+    "#e377c2",  # raspberry yogurt pink
+    "#7f7f7f",  # middle gray
+    "#bcbd22",  # curry yellow-green
+    "#17becf",  # blue-teal
 ]
 
 
@@ -52,46 +52,31 @@
         y_true = df1.iloc[:, idx].values
         y_score = df2.iloc[:, idx].values
 
-        precision, recall, _ = precision_recall_curve(
-            y_true, y_score, pos_label=pos_label)
-        ap = average_precision_score(
-            y_true, y_score, pos_label=pos_label or 1)
+        precision, recall, _ = precision_recall_curve(y_true, y_score, pos_label=pos_label)
+        ap = average_precision_score(y_true, y_score, pos_label=pos_label or 1)
 
         trace = go.Scatter(
             x=recall,
             y=precision,
-            mode='lines',
-            marker=dict(
-                color=default_colors[idx % len(default_colors)]
-            ),
-            name='%s (area = %.3f)' % (idx, ap)
+            mode="lines",
+            marker=dict(color=default_colors[idx % len(default_colors)]),
+            name="%s (area = %.3f)" % (idx, ap),
         )
         data.append(trace)
 
     layout = go.Layout(
-        xaxis=dict(
-            title='Recall',
-            linecolor='lightslategray',
-            linewidth=1
-        ),
-        yaxis=dict(
-            title='Precision',
-            linecolor='lightslategray',
-            linewidth=1
-        ),
+        xaxis=dict(title="Recall", linecolor="lightslategray", linewidth=1),
+        yaxis=dict(title="Precision", linecolor="lightslategray", linewidth=1),
         title=dict(
-            text=title or 'Precision-Recall Curve',
+            text=title or "Precision-Recall Curve",
             x=0.5,
             y=0.92,
-            xanchor='center',
-            yanchor='top'
+            xanchor="center",
+            yanchor="top",
         ),
-        font=dict(
-            family="sans-serif",
-            size=11
-        ),
+        font=dict(family="sans-serif", size=11),
         # control backgroud colors
-        plot_bgcolor='rgba(255,255,255,0)'
+        plot_bgcolor="rgba(255,255,255,0)",
     )
     """
     legend=dict(
@@ -112,45 +97,47 @@
 
     plotly.offline.plot(fig, filename="output.html", auto_open=False)
     # to be discovered by `from_work_dir`
-    os.rename('output.html', 'output')
+    os.rename("output.html", "output")
 
 
 def visualize_pr_curve_matplotlib(df1, df2, pos_label, title=None):
-    """visualize pr-curve using matplotlib and output svg image
-    """
+    """visualize pr-curve using matplotlib and output svg image"""
     backend = matplotlib.get_backend()
     if "inline" not in backend:
         matplotlib.use("SVG")
-    plt.style.use('seaborn-colorblind')
+    plt.style.use("seaborn-colorblind")
     plt.figure()
 
     for idx in range(df1.shape[1]):
         y_true = df1.iloc[:, idx].values
         y_score = df2.iloc[:, idx].values
 
-        precision, recall, _ = precision_recall_curve(
-            y_true, y_score, pos_label=pos_label)
-        ap = average_precision_score(
-            y_true, y_score, pos_label=pos_label or 1)
+        precision, recall, _ = precision_recall_curve(y_true, y_score, pos_label=pos_label)
+        ap = average_precision_score(y_true, y_score, pos_label=pos_label or 1)
 
-        plt.step(recall, precision, 'r-', color="black", alpha=0.3,
-                 lw=1, where="post", label='%s (area = %.3f)' % (idx, ap))
+        plt.step(
+            recall,
+            precision,
+            "r-",
+            color="black",
+            alpha=0.3,
+            lw=1,
+            where="post",
+            label="%s (area = %.3f)" % (idx, ap),
+        )
 
     plt.xlim([0.0, 1.0])
     plt.ylim([0.0, 1.05])
-    plt.xlabel('Recall')
-    plt.ylabel('Precision')
-    title = title or 'Precision-Recall Curve'
+    plt.xlabel("Recall")
+    plt.ylabel("Precision")
+    title = title or "Precision-Recall Curve"
     plt.title(title)
     folder = os.getcwd()
     plt.savefig(os.path.join(folder, "output.svg"), format="svg")
-    os.rename(os.path.join(folder, "output.svg"),
-              os.path.join(folder, "output"))
+    os.rename(os.path.join(folder, "output.svg"), os.path.join(folder, "output"))
 
 
-def visualize_roc_curve_plotly(df1, df2, pos_label,
-                               drop_intermediate=True,
-                               title=None):
+def visualize_roc_curve_plotly(df1, df2, pos_label, drop_intermediate=True, title=None):
     """output roc-curve in html using plotly
 
     df1 : pandas.DataFrame
@@ -169,45 +156,31 @@
         y_true = df1.iloc[:, idx].values
         y_score = df2.iloc[:, idx].values
 
-        fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label,
-                                drop_intermediate=drop_intermediate)
+        fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label, drop_intermediate=drop_intermediate)
         roc_auc = auc(fpr, tpr)
 
         trace = go.Scatter(
             x=fpr,
             y=tpr,
-            mode='lines',
-            marker=dict(
-                color=default_colors[idx % len(default_colors)]
-            ),
-            name='%s (area = %.3f)' % (idx, roc_auc)
+            mode="lines",
+            marker=dict(color=default_colors[idx % len(default_colors)]),
+            name="%s (area = %.3f)" % (idx, roc_auc),
         )
         data.append(trace)
 
     layout = go.Layout(
-        xaxis=dict(
-            title='False Positive Rate',
-            linecolor='lightslategray',
-            linewidth=1
-        ),
-        yaxis=dict(
-            title='True Positive Rate',
-            linecolor='lightslategray',
-            linewidth=1
-        ),
+        xaxis=dict(title="False Positive Rate", linecolor="lightslategray", linewidth=1),
+        yaxis=dict(title="True Positive Rate", linecolor="lightslategray", linewidth=1),
         title=dict(
-            text=title or 'Receiver Operating Characteristic (ROC) Curve',
+            text=title or "Receiver Operating Characteristic (ROC) Curve",
             x=0.5,
             y=0.92,
-            xanchor='center',
-            yanchor='top'
+            xanchor="center",
+            yanchor="top",
         ),
-        font=dict(
-            family="sans-serif",
-            size=11
-        ),
+        font=dict(family="sans-serif", size=11),
         # control backgroud colors
-        plot_bgcolor='rgba(255,255,255,0)'
+        plot_bgcolor="rgba(255,255,255,0)",
     )
     """
     # legend=dict(
@@ -229,66 +202,84 @@
 
     plotly.offline.plot(fig, filename="output.html", auto_open=False)
     # to be discovered by `from_work_dir`
-    os.rename('output.html', 'output')
+    os.rename("output.html", "output")
 
 
-def visualize_roc_curve_matplotlib(df1, df2, pos_label,
-                                   drop_intermediate=True,
-                                   title=None):
-    """visualize roc-curve using matplotlib and output svg image
-    """
+def visualize_roc_curve_matplotlib(df1, df2, pos_label, drop_intermediate=True, title=None):
+    """visualize roc-curve using matplotlib and output svg image"""
     backend = matplotlib.get_backend()
     if "inline" not in backend:
         matplotlib.use("SVG")
-    plt.style.use('seaborn-colorblind')
+    plt.style.use("seaborn-colorblind")
     plt.figure()
 
     for idx in range(df1.shape[1]):
         y_true = df1.iloc[:, idx].values
         y_score = df2.iloc[:, idx].values
 
-        fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label,
-                                drop_intermediate=drop_intermediate)
+        fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label, drop_intermediate=drop_intermediate)
         roc_auc = auc(fpr, tpr)
 
-        plt.step(fpr, tpr, 'r-', color="black", alpha=0.3, lw=1,
-                 where="post", label='%s (area = %.3f)' % (idx, roc_auc))
+        plt.step(
+            fpr,
+            tpr,
+            "r-",
+            color="black",
+            alpha=0.3,
+            lw=1,
+            where="post",
+            label="%s (area = %.3f)" % (idx, roc_auc),
+        )
 
     plt.xlim([0.0, 1.0])
     plt.ylim([0.0, 1.05])
-    plt.xlabel('False Positive Rate')
-    plt.ylabel('True Positive Rate')
-    title = title or 'Receiver Operating Characteristic (ROC) Curve'
+    plt.xlabel("False Positive Rate")
+    plt.ylabel("True Positive Rate")
+    title = title or "Receiver Operating Characteristic (ROC) Curve"
     plt.title(title)
     folder = os.getcwd()
     plt.savefig(os.path.join(folder, "output.svg"), format="svg")
-    os.rename(os.path.join(folder, "output.svg"),
-              os.path.join(folder, "output"))
+    os.rename(os.path.join(folder, "output.svg"), os.path.join(folder, "output"))
 
 
 def get_dataframe(file_path, plot_selection, header_name, column_name):
-    header = 'infer' if plot_selection[header_name] else None
+    header = "infer" if plot_selection[header_name] else None
     column_option = plot_selection[column_name]["selected_column_selector_option"]
-    if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
+    if column_option in [
+        "by_index_number",
+        "all_but_by_index_number",
+        "by_header_name",
+        "all_but_by_header_name",
+    ]:
         col = plot_selection[column_name]["col1"]
     else:
         col = None
     _, input_df = read_columns(file_path, c=col,
-                                   c_option=column_option,
-                                   return_df=True,
-                                   sep='\t', header=header,
-                                   parse_dates=True)
+                               c_option=column_option,
+                               return_df=True,
+                               sep='\t', header=header,
+                               parse_dates=True)
     return input_df
 
 
-def main(inputs, infile_estimator=None, infile1=None,
-         infile2=None, outfile_result=None,
-         outfile_object=None, groups=None,
-         ref_seq=None, intervals=None,
-         targets=None, fasta_path=None,
-         model_config=None, true_labels=None,
-         predicted_labels=None, plot_color=None,
-         title=None):
+def main(
+    inputs,
+    infile_estimator=None,
+    infile1=None,
+    infile2=None,
+    outfile_result=None,
+    outfile_object=None,
+    groups=None,
+    ref_seq=None,
+    intervals=None,
+    targets=None,
+    fasta_path=None,
+    model_config=None,
+    true_labels=None,
+    predicted_labels=None,
+    plot_color=None,
+    title=None,
+):
     """
     Parameter
     ---------
@@ -341,34 +332,39 @@
     title : str, default is None
         Title of the confusion matrix heatmap
     """
-    warnings.simplefilter('ignore')
+    warnings.simplefilter("ignore")
 
-    with open(inputs, 'r') as param_handler:
+    with open(inputs, "r") as param_handler:
         params = json.load(param_handler)
 
-    title = params['plotting_selection']['title'].strip()
-    plot_type = params['plotting_selection']['plot_type']
-    plot_format = params['plotting_selection']['plot_format']
+    title = params["plotting_selection"]["title"].strip()
+    plot_type = params["plotting_selection"]["plot_type"]
+    plot_format = params["plotting_selection"]["plot_format"]
 
-    if plot_type == 'feature_importances':
-        with open(infile_estimator, 'rb') as estimator_handler:
+    if plot_type == "feature_importances":
+        with open(infile_estimator, "rb") as estimator_handler:
             estimator = load_model(estimator_handler)
 
-        column_option = (params['plotting_selection']
-                               ['column_selector_options']
-                               ['selected_column_selector_option'])
-        if column_option in ['by_index_number', 'all_but_by_index_number',
-                             'by_header_name', 'all_but_by_header_name']:
-            c = (params['plotting_selection']
-                       ['column_selector_options']['col1'])
+        column_option = params["plotting_selection"]["column_selector_options"]["selected_column_selector_option"]
+        if column_option in [
+            "by_index_number",
+            "all_but_by_index_number",
+            "by_header_name",
+            "all_but_by_header_name",
+        ]:
+            c = params["plotting_selection"]["column_selector_options"]["col1"]
         else:
             c = None
 
-        _, input_df = read_columns(infile1, c=c,
-                                   c_option=column_option,
-                                   return_df=True,
-                                   sep='\t', header='infer',
-                                   parse_dates=True)
+        _, input_df = read_columns(
+            infile1,
+            c=c,
+            c_option=column_option,
+            return_df=True,
+            sep="\t",
+            header="infer",
+            parse_dates=True,
+        )
 
         feature_names = input_df.columns.values
 
@@ -379,16 +375,14 @@
                     feature_names = feature_names[mask]
             estimator = estimator.steps[-1][-1]
 
-        if hasattr(estimator, 'coef_'):
+        if hasattr(estimator, "coef_"):
             coefs = estimator.coef_
         else:
-            coefs = getattr(estimator, 'feature_importances_', None)
+            coefs = getattr(estimator, "feature_importances_", None)
         if coefs is None:
-            raise RuntimeError('The classifier does not expose '
-                               '"coef_" or "feature_importances_" '
-                               'attributes')
+            raise RuntimeError("The classifier does not expose " '"coef_" or "feature_importances_" ' "attributes")
 
-        threshold = params['plotting_selection']['threshold']
+        threshold = params["plotting_selection"]["threshold"]
         if threshold is not None:
             mask = (coefs > threshold) | (coefs < -threshold)
             coefs = coefs[mask]
@@ -397,80 +391,74 @@
         # sort
         indices = np.argsort(coefs)[::-1]
 
-        trace = go.Bar(x=feature_names[indices],
-                       y=coefs[indices])
+        trace = go.Bar(x=feature_names[indices], y=coefs[indices])
         layout = go.Layout(title=title or "Feature Importances")
         fig = go.Figure(data=[trace], layout=layout)
 
-        plotly.offline.plot(fig, filename="output.html",
-                            auto_open=False)
+        plotly.offline.plot(fig, filename="output.html", auto_open=False)
         # to be discovered by `from_work_dir`
-        os.rename('output.html', 'output')
+        os.rename("output.html", "output")
 
         return 0
 
-    elif plot_type in ('pr_curve', 'roc_curve'):
-        df1 = pd.read_csv(infile1, sep='\t', header='infer')
-        df2 = pd.read_csv(infile2, sep='\t', header='infer').astype(np.float32)
+    elif plot_type in ("pr_curve", "roc_curve"):
+        df1 = pd.read_csv(infile1, sep="\t", header="infer")
+        df2 = pd.read_csv(infile2, sep="\t", header="infer").astype(np.float32)
 
-        minimum = params['plotting_selection']['report_minimum_n_positives']
+        minimum = params["plotting_selection"]["report_minimum_n_positives"]
         # filter out columns whose n_positives is beblow the threhold
         if minimum:
             mask = df1.sum(axis=0) >= minimum
             df1 = df1.loc[:, mask]
             df2 = df2.loc[:, mask]
 
-        pos_label = params['plotting_selection']['pos_label'].strip() \
-            or None
+        pos_label = params["plotting_selection"]["pos_label"].strip() or None
 
-        if plot_type == 'pr_curve':
-            if plot_format == 'plotly_html':
+        if plot_type == "pr_curve":
+            if plot_format == "plotly_html":
                 visualize_pr_curve_plotly(df1, df2, pos_label, title=title)
             else:
                 visualize_pr_curve_matplotlib(df1, df2, pos_label, title)
-        else:          # 'roc_curve'
-            drop_intermediate = (params['plotting_selection']
-                                       ['drop_intermediate'])
-            if plot_format == 'plotly_html':
-                visualize_roc_curve_plotly(df1, df2, pos_label,
-                                           drop_intermediate=drop_intermediate,
-                                           title=title)
+        else:  # 'roc_curve'
+            drop_intermediate = params["plotting_selection"]["drop_intermediate"]
+            if plot_format == "plotly_html":
+                visualize_roc_curve_plotly(
+                    df1,
+                    df2,
+                    pos_label,
+                    drop_intermediate=drop_intermediate,
+                    title=title,
+                )
             else:
                 visualize_roc_curve_matplotlib(
-                    df1, df2, pos_label,
+                    df1,
+                    df2,
+                    pos_label,
                     drop_intermediate=drop_intermediate,
-                    title=title)
+                    title=title,
+                )
 
         return 0
 
-    elif plot_type == 'rfecv_gridscores':
-        input_df = pd.read_csv(infile1, sep='\t', header='infer')
+    elif plot_type == "rfecv_gridscores":
+        input_df = pd.read_csv(infile1, sep="\t", header="infer")
         scores = input_df.iloc[:, 0]
-        steps = params['plotting_selection']['steps'].strip()
+        steps = params["plotting_selection"]["steps"].strip()
         steps = safe_eval(steps)
 
         data = go.Scatter(
             x=list(range(len(scores))),
             y=scores,
             text=[str(_) for _ in steps] if steps else None,
-            mode='lines'
+            mode="lines",
         )
         layout = go.Layout(
             xaxis=dict(title="Number of features selected"),
             yaxis=dict(title="Cross validation score"),
-            title=dict(
-                text=title or None,
-                x=0.5,
-                y=0.92,
-                xanchor='center',
-                yanchor='top'
-            ),
-            font=dict(
-                family="sans-serif",
-                size=11
-            ),
+            title=dict(text=title or None, x=0.5, y=0.92, xanchor="center", yanchor="top"),
+            font=dict(family="sans-serif", size=11),
             # control backgroud colors
-            plot_bgcolor='rgba(255,255,255,0)'
+            plot_bgcolor="rgba(255,255,255,0)",
         )
         """
         # legend=dict(
@@ -489,55 +477,43 @@
         """
 
         fig = go.Figure(data=[data], layout=layout)
-        plotly.offline.plot(fig, filename="output.html",
-                            auto_open=False)
+        plotly.offline.plot(fig, filename="output.html", auto_open=False)
         # to be discovered by `from_work_dir`
-        os.rename('output.html', 'output')
+        os.rename("output.html", "output")
 
         return 0
 
-    elif plot_type == 'learning_curve':
-        input_df = pd.read_csv(infile1, sep='\t', header='infer')
-        plot_std_err = params['plotting_selection']['plot_std_err']
+    elif plot_type == "learning_curve":
+        input_df = pd.read_csv(infile1, sep="\t", header="infer")
+        plot_std_err = params["plotting_selection"]["plot_std_err"]
         data1 = go.Scatter(
-            x=input_df['train_sizes_abs'],
-            y=input_df['mean_train_scores'],
-            error_y=dict(
-                array=input_df['std_train_scores']
-            ) if plot_std_err else None,
-            mode='lines',
+            x=input_df["train_sizes_abs"],
+            y=input_df["mean_train_scores"],
+            error_y=dict(array=input_df["std_train_scores"]) if plot_std_err else None,
+            mode="lines",
             name="Train Scores",
         )
         data2 = go.Scatter(
-            x=input_df['train_sizes_abs'],
-            y=input_df['mean_test_scores'],
-            error_y=dict(
-                array=input_df['std_test_scores']
-            ) if plot_std_err else None,
-            mode='lines',
+            x=input_df["train_sizes_abs"],
+            y=input_df["mean_test_scores"],
+            error_y=dict(array=input_df["std_test_scores"]) if plot_std_err else None,
+            mode="lines",
             name="Test Scores",
         )
         layout = dict(
-            xaxis=dict(
-                title='No. of samples'
-            ),
-            yaxis=dict(
-                title='Performance Score'
-            ),
+            xaxis=dict(title="No. of samples"),
+            yaxis=dict(title="Performance Score"),
             # modify these configurations to customize image
             title=dict(
-                text=title or 'Learning Curve',
+                text=title or "Learning Curve",
                 x=0.5,
                 y=0.92,
-                xanchor='center',
-                yanchor='top'
+                xanchor="center",
+                yanchor="top",
             ),
-            font=dict(
-                family="sans-serif",
-                size=11
-            ),
+            font=dict(family="sans-serif", size=11),
             # control backgroud colors
-            plot_bgcolor='rgba(255,255,255,0)'
+            plot_bgcolor="rgba(255,255,255,0)",
         )
         """
         # legend=dict(
@@ -556,27 +532,26 @@
         """
 
         fig = go.Figure(data=[data1, data2], layout=layout)
-        plotly.offline.plot(fig, filename="output.html",
-                            auto_open=False)
+        plotly.offline.plot(fig, filename="output.html", auto_open=False)
         # to be discovered by `from_work_dir`
-        os.rename('output.html', 'output')
+        os.rename("output.html", "output")
 
         return 0
 
-    elif plot_type == 'keras_plot_model':
-        with open(model_config, 'r') as f:
+    elif plot_type == "keras_plot_model":
+        with open(model_config, "r") as f:
             model_str = f.read()
         model = model_from_json(model_str)
         plot_model(model, to_file="output.png")
-        os.rename('output.png', 'output')
+        os.rename("output.png", "output")
 
         return 0
 
-    elif plot_type == 'classification_confusion_matrix':
+    elif plot_type == "classification_confusion_matrix":
         plot_selection = params["plotting_selection"]
         input_true = get_dataframe(true_labels, plot_selection, "header_true", "column_selector_options_true")
-        header_predicted = 'infer' if plot_selection["header_predicted"] else None
-        input_predicted = pd.read_csv(predicted_labels, sep='\t', parse_dates=True, header=header_predicted)
+        header_predicted = "infer" if plot_selection["header_predicted"] else None
+        input_predicted = pd.read_csv(predicted_labels, sep="\t", parse_dates=True, header=header_predicted)
         true_classes = input_true.iloc[:, -1].copy()
         predicted_classes = input_predicted.iloc[:, -1].copy()
         axis_labels = list(set(true_classes))
@@ -586,15 +561,15 @@
         for i in range(len(c_matrix)):
             for j in range(len(c_matrix)):
                 ax.text(j, i, c_matrix[i, j], ha="center", va="center", color="k")
-        ax.set_ylabel('True class labels')
-        ax.set_xlabel('Predicted class labels')
+        ax.set_ylabel("True class labels")
+        ax.set_xlabel("Predicted class labels")
         ax.set_title(title)
         ax.set_xticks(axis_labels)
         ax.set_yticks(axis_labels)
         fig.colorbar(im, ax=ax)
         fig.tight_layout()
         plt.savefig("output.png", dpi=125)
-        os.rename('output.png', 'output')
+        os.rename("output.png", "output")
 
         return 0
 
@@ -603,7 +578,7 @@
     # fig.write_image("image.pdf", format='pdf', width=340*2, height=226*2)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     aparser = argparse.ArgumentParser()
     aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
     aparser.add_argument("-e", "--estimator", dest="infile_estimator")
@@ -623,11 +598,21 @@
     aparser.add_argument("-pt", "--title", dest="title")
     args = aparser.parse_args()
 
-    main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
-         args.outfile_result, outfile_object=args.outfile_object,
-         groups=args.groups, ref_seq=args.ref_seq, intervals=args.intervals,
-         targets=args.targets, fasta_path=args.fasta_path,
-         model_config=args.model_config, true_labels=args.true_labels,
-         predicted_labels=args.predicted_labels,
-         plot_color=args.plot_color,
-         title=args.title)
+    main(
+        args.inputs,
+        args.infile_estimator,
+        args.infile1,
+        args.infile2,
+        args.outfile_result,
+        outfile_object=args.outfile_object,
+        groups=args.groups,
+        ref_seq=args.ref_seq,
+        intervals=args.intervals,
+        targets=args.targets,
+        fasta_path=args.fasta_path,
+        model_config=args.model_config,
+        true_labels=args.true_labels,
+        predicted_labels=args.predicted_labels,
+        plot_color=args.plot_color,
+        title=args.title,
+    )