--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/ml_visualization_ex.py	Fri Sep 13 12:29:52 2019 -0400
@@ -0,0 +1,305 @@
+import argparse
+import json
+import numpy as np
+import pandas as pd
+import plotly
+import plotly.graph_objs as go
+import warnings
+
+from keras.models import model_from_json
+from keras.utils import plot_model
+from sklearn.feature_selection.base import SelectorMixin
+from sklearn.metrics import precision_recall_curve, average_precision_score
+from sklearn.metrics import roc_curve, auc
+from sklearn.pipeline import Pipeline
+from galaxy_ml.utils import load_model, read_columns, SafeEval
+
+
+safe_eval = SafeEval()
+
+
+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):
+    """
+    Parameter
+    ---------
+    inputs : str
+        File path to galaxy tool parameter
+
+    infile_estimator : str, default is None
+        File path to estimator
+
+    infile1 : str, default is None
+        File path to dataset containing features or true labels.
+
+    infile2 : str, default is None
+        File path to dataset containing target values or predicted
+        probabilities.
+
+    outfile_result : str, default is None
+        File path to save the results, either cv_results or test result
+
+    outfile_object : str, default is None
+        File path to save searchCV object
+
+    groups : str, default is None
+        File path to dataset containing groups labels
+
+    ref_seq : str, default is None
+        File path to dataset containing genome sequence file
+
+    intervals : str, default is None
+        File path to dataset containing interval file
+
+    targets : str, default is None
+        File path to dataset compressed target bed file
+
+    fasta_path : str, default is None
+        File path to dataset containing fasta file
+
+    model_config : str, default is None
+        File path to dataset containing JSON config for neural networks
+    """
+    warnings.simplefilter('ignore')
+
+    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']
+    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'])
+        else:
+            c = None
+
+        _, 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
+
+        if isinstance(estimator, Pipeline):
+            for st in estimator.steps[:-1]:
+                if isinstance(st[-1], SelectorMixin):
+                    mask = st[-1].get_support()
+                    feature_names = feature_names[mask]
+            estimator = estimator.steps[-1][-1]
+
+        if hasattr(estimator, 'coef_'):
+            coefs = estimator.coef_
+        else:
+            coefs = getattr(estimator, 'feature_importances_', None)
+        if coefs is None:
+            raise RuntimeError('The classifier does not expose '
+                               '"coef_" or "feature_importances_" '
+                               'attributes')
+
+        threshold = params['plotting_selection']['threshold']
+        if threshold is not None:
+            mask = (coefs > threshold) | (coefs < -threshold)
+            coefs = coefs[mask]
+            feature_names = feature_names[mask]
+
+        # sort
+        indices = np.argsort(coefs)[::-1]
+
+        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)
+
+    elif plot_type == 'pr_curve':
+        df1 = pd.read_csv(infile1, sep='\t', header=None)
+        df2 = pd.read_csv(infile2, sep='\t', header=None)
+
+        precision = {}
+        recall = {}
+        ap = {}
+
+        pos_label = params['plotting_selection']['pos_label'].strip() \
+            or None
+        for col in df1.columns:
+            y_true = df1[col].values
+            y_score = df2[col].values
+
+            precision[col], recall[col], _ = precision_recall_curve(
+                y_true, y_score, pos_label=pos_label)
+            ap[col] = average_precision_score(
+                y_true, y_score, pos_label=pos_label or 1)
+
+        if len(df1.columns) > 1:
+            precision["micro"], recall["micro"], _ = precision_recall_curve(
+                df1.values.ravel(), df2.values.ravel(), pos_label=pos_label)
+            ap['micro'] = average_precision_score(
+                df1.values, df2.values, average='micro', pos_label=pos_label or 1)
+
+        data = []
+        for key in precision.keys():
+            trace = go.Scatter(
+                x=recall[key],
+                y=precision[key],
+                mode='lines',
+                name='%s (area = %.2f)' % (key, ap[key]) if key == 'micro'
+                     else 'column %s (area = %.2f)' % (key, ap[key])
+            )
+            data.append(trace)
+
+        layout = go.Layout(
+            title=title or "Precision-Recall curve",
+            xaxis=dict(title='Recall'),
+            yaxis=dict(title='Precision')
+        )
+
+        fig = go.Figure(data=data, layout=layout)
+
+    elif plot_type == 'roc_curve':
+        df1 = pd.read_csv(infile1, sep='\t', header=None)
+        df2 = pd.read_csv(infile2, sep='\t', header=None)
+
+        fpr = {}
+        tpr = {}
+        roc_auc = {}
+
+        pos_label = params['plotting_selection']['pos_label'].strip() \
+            or None
+        for col in df1.columns:
+            y_true = df1[col].values
+            y_score = df2[col].values
+
+            fpr[col], tpr[col], _ = roc_curve(
+                y_true, y_score, pos_label=pos_label)
+            roc_auc[col] = auc(fpr[col], tpr[col])
+
+        if len(df1.columns) > 1:
+            fpr["micro"], tpr["micro"], _ = roc_curve(
+                df1.values.ravel(), df2.values.ravel(), pos_label=pos_label)
+            roc_auc['micro'] = auc(fpr["micro"], tpr["micro"])
+
+        data = []
+        for key in fpr.keys():
+            trace = go.Scatter(
+                x=fpr[key],
+                y=tpr[key],
+                mode='lines',
+                name='%s (area = %.2f)' % (key, roc_auc[key]) if key == 'micro'
+                     else 'column %s (area = %.2f)' % (key, roc_auc[key])
+            )
+            data.append(trace)
+
+        trace = go.Scatter(x=[0, 1], y=[0, 1], 
+                           mode='lines', 
+                           line=dict(color='black', dash='dash'),
+                           showlegend=False)
+        data.append(trace)
+
+        layout = go.Layout(
+            title=title or "Receiver operating characteristic curve",
+            xaxis=dict(title='False Positive Rate'),
+            yaxis=dict(title='True Positive Rate')
+        )
+
+        fig = go.Figure(data=data, layout=layout)
+
+    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 = safe_eval(steps)
+
+        data = go.Scatter(
+            x=list(range(len(scores))),
+            y=scores,
+            text=[str(_) for _ in steps] if steps else None,
+            mode='lines'
+        )
+        layout = go.Layout(
+            xaxis=dict(title="Number of features selected"),
+            yaxis=dict(title="Cross validation score"),
+            title=title or None
+        )
+
+        fig = go.Figure(data=[data], layout=layout)
+
+    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',
+            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',
+            name="Test Scores",
+        )
+        layout = dict(
+            xaxis=dict(
+                title='No. of samples'
+            ),
+            yaxis=dict(
+                title='Performance Score'
+            ),
+            title=title or 'Learning Curve'
+        )
+        fig = go.Figure(data=[data1, data2], layout=layout)
+
+    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")
+        __import__('os').rename('output.png', 'output')
+
+        return 0
+
+    plotly.offline.plot(fig, filename="output.html",
+                        auto_open=False)
+    # to be discovered by `from_work_dir`
+    __import__('os').rename('output.html', 'output')
+
+
+if __name__ == '__main__':
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-e", "--estimator", dest="infile_estimator")
+    aparser.add_argument("-X", "--infile1", dest="infile1")
+    aparser.add_argument("-y", "--infile2", dest="infile2")
+    aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
+    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
+    aparser.add_argument("-g", "--groups", dest="groups")
+    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
+    aparser.add_argument("-b", "--intervals", dest="intervals")
+    aparser.add_argument("-t", "--targets", dest="targets")
+    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
+    aparser.add_argument("-c", "--model_config", dest="model_config")
+    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)