Mercurial > repos > bgruening > plotly_ml_performance_plots
diff plot_ml_performance.py @ 3:1c5dcef5ce0f draft default tip
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
author | bgruening |
---|---|
date | Tue, 07 May 2024 14:11:16 +0000 |
parents | 62e3a4e8c54c |
children |
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--- a/plot_ml_performance.py Thu Jan 16 13:49:49 2020 -0500 +++ b/plot_ml_performance.py Tue May 07 14:11:16 2024 +0000 @@ -1,9 +1,12 @@ import argparse + import pandas as pd import plotly -import pickle import plotly.graph_objs as go -from sklearn.metrics import confusion_matrix, precision_recall_fscore_support, roc_curve, auc +from galaxy_ml.model_persist import load_model_from_h5 +from galaxy_ml.utils import clean_params +from sklearn.metrics import (auc, confusion_matrix, + precision_recall_fscore_support, roc_curve) from sklearn.preprocessing import label_binarize @@ -16,8 +19,8 @@ infile_trained_model: str, input trained model file (zip) """ - df_input = pd.read_csv(infile_input, sep='\t', parse_dates=True) - df_output = pd.read_csv(infile_output, sep='\t', parse_dates=True) + df_input = pd.read_csv(infile_input, sep="\t", parse_dates=True) + df_output = pd.read_csv(infile_output, sep="\t", parse_dates=True) true_labels = df_input.iloc[:, -1].copy() predicted_labels = df_output.iloc[:, -1].copy() axis_labels = list(set(true_labels)) @@ -27,47 +30,40 @@ z=c_matrix, x=axis_labels, y=axis_labels, - colorscale='Portland', + colorscale="Portland", ) ] layout = go.Layout( - title='Confusion Matrix between true and predicted class labels', - xaxis=dict(title='Predicted class labels'), - yaxis=dict(title='True class labels') + title="Confusion Matrix between true and predicted class labels", + xaxis=dict(title="Predicted class labels"), + yaxis=dict(title="True class labels"), ) fig = go.Figure(data=data, layout=layout) plotly.offline.plot(fig, filename="output_confusion.html", auto_open=False) # plot precision, recall and f_score for each class label - precision, recall, f_score, _ = precision_recall_fscore_support(true_labels, predicted_labels) + precision, recall, f_score, _ = precision_recall_fscore_support( + true_labels, predicted_labels + ) trace_precision = go.Scatter( - x=axis_labels, - y=precision, - mode='lines+markers', - name='Precision' + x=axis_labels, y=precision, mode="lines+markers", name="Precision" ) trace_recall = go.Scatter( - x=axis_labels, - y=recall, - mode='lines+markers', - name='Recall' + x=axis_labels, y=recall, mode="lines+markers", name="Recall" ) trace_fscore = go.Scatter( - x=axis_labels, - y=f_score, - mode='lines+markers', - name='F-score' + x=axis_labels, y=f_score, mode="lines+markers", name="F-score" ) layout_prf = go.Layout( - title='Precision, recall and f-score of true and predicted class labels', - xaxis=dict(title='Class labels'), - yaxis=dict(title='Precision, recall and f-score') + title="Precision, recall and f-score of true and predicted class labels", + xaxis=dict(title="Class labels"), + yaxis=dict(title="Precision, recall and f-score"), ) data_prf = [trace_precision, trace_recall, trace_fscore] @@ -75,8 +71,8 @@ plotly.offline.plot(fig_prf, filename="output_prf.html", auto_open=False) # plot roc and auc curves for different classes - with open(infile_trained_model, 'rb') as model_file: - model = pickle.load(model_file) + classifier_object = load_model_from_h5(infile_trained_model) + model = clean_params(classifier_object) # remove the last column (label column) test_data = df_input.iloc[:, :-1] @@ -84,9 +80,9 @@ try: # find the probability estimating method - if 'predict_proba' in model_items: + if "predict_proba" in model_items: y_score = model.predict_proba(test_data) - elif 'decision_function' in model_items: + elif "decision_function" in model_items: y_score = model.decision_function(test_data) true_labels_list = true_labels.tolist() @@ -104,43 +100,44 @@ trace = go.Scatter( x=fpr[i], y=tpr[i], - mode='lines+markers', - name='ROC curve of class {0} (AUC = {1:0.2f})'.format(i, roc_auc[i]) + mode="lines+markers", + name="ROC curve of class {0} (AUC = {1:0.2f})".format( + i, roc_auc[i] + ), ) data_roc.append(trace) else: try: y_score_binary = y_score[:, 1] - except: + except Exception: y_score_binary = y_score fpr, tpr, _ = roc_curve(one_hot_labels, y_score_binary, pos_label=1) roc_auc = auc(fpr, tpr) trace = go.Scatter( x=fpr, y=tpr, - mode='lines+markers', - name='ROC curve (AUC = {0:0.2f})'.format(roc_auc) + mode="lines+markers", + name="ROC curve (AUC = {0:0.2f})".format(roc_auc), ) data_roc.append(trace) - trace_diag = go.Scatter( - x=[0, 1], - y=[0, 1], - mode='lines', - name='Chance' - ) + trace_diag = go.Scatter(x=[0, 1], y=[0, 1], mode="lines", name="Chance") data_roc.append(trace_diag) layout_roc = go.Layout( - title='Receiver operating characteristics (ROC) and area under curve (AUC)', - xaxis=dict(title='False positive rate'), - yaxis=dict(title='True positive rate') + title="Receiver operating characteristics (ROC) and area under curve (AUC)", + xaxis=dict(title="False positive rate"), + yaxis=dict(title="True positive rate"), ) fig_roc = go.Figure(data=data_roc, layout=layout_roc) plotly.offline.plot(fig_roc, filename="output_roc.html", auto_open=False) except Exception as exp: - print("Plotting the ROC-AUC graph failed. This exception was raised: {}".format(exp)) + print( + "Plotting the ROC-AUC graph failed. This exception was raised: {}".format( + exp + ) + ) if __name__ == "__main__":