| Next changeset 1:85da91bbdbfb (2019-06-18) |
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Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88 |
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added:
plot_ml_performance.py plotly_ml_performance_plots.xml test-data/binary_prediction_sgd.tabular test-data/binary_test_label.tabular test-data/cnf_binary_knn.html test-data/cnf_binary_linearsvm.html test-data/cnf_binary_rfc.html test-data/cnf_binary_sgd.html test-data/cnf_multi_lr.html test-data/model_binary_knn.zip test-data/model_binary_linearsvm.zip test-data/model_binary_rfc.zip test-data/model_binary_sgd.zip test-data/model_multi_lr.zip test-data/multi_test_label.tabular test-data/multi_test_label.tsv.tabular test-data/prediction_binary_knn.tabular test-data/prediction_binary_linearsvm.tabular test-data/prediction_binary_rfc.tabular test-data/prediction_multi_lr.tabular test-data/prf_binary_knn.html test-data/prf_binary_linearsvm.html test-data/prf_binary_rfc.html test-data/prf_binary_sgd.html test-data/prf_multi_lr.html test-data/roc_auc_binary_knn.html test-data/roc_auc_binary_linearsvm.html test-data/roc_auc_binary_rfc.html test-data/roc_auc_binary_sgd.html test-data/roc_auc_multi_lr.html |
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| diff -r 000000000000 -r 4fac53da862f plot_ml_performance.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plot_ml_performance.py Thu Oct 11 14:37:54 2018 -0400 |
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| @@ -0,0 +1,152 @@ +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 sklearn.preprocessing import label_binarize + + +def main(infile_input, infile_output, infile_trained_model): + """ + Produce an interactive confusion matrix (heatmap), precision, recall, fscore and auc plots + Args: + infile_input: str, input tabular file with true labels + infile_output: str, input tabular file with predicted labels + 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) + true_labels = df_input.iloc[:, -1].copy() + predicted_labels = df_output.iloc[:, -1].copy() + axis_labels = list(set(true_labels)) + c_matrix = confusion_matrix(true_labels, predicted_labels) + data = [ + go.Heatmap( + z=c_matrix, + x=axis_labels, + y=axis_labels, + colorscale='Portland', + ) + ] + + layout = go.Layout( + title='Confusion Matrix between true and predicted class labels', + xaxis=dict(title='True class labels'), + yaxis=dict(title='Predicted 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) + + trace_precision = go.Scatter( + x=axis_labels, + y=precision, + mode='lines+markers', + name='Precision' + ) + + trace_recall = go.Scatter( + 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' + ) + + 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') + ) + + data_prf = [trace_precision, trace_recall, trace_fscore] + fig_prf = go.Figure(data=data_prf, layout=layout_prf) + 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) + + # remove the last column (label column) + test_data = df_input.iloc[:, :-1] + model_items = dir(model) + + try: + # find the probability estimating method + if 'predict_proba' in model_items: + y_score = model.predict_proba(test_data) + elif 'decision_function' in model_items: + y_score = model.decision_function(test_data) + + true_labels_list = true_labels.tolist() + one_hot_labels = label_binarize(true_labels_list, classes=axis_labels) + data_roc = list() + + if len(axis_labels) > 2: + fpr = dict() + tpr = dict() + roc_auc = dict() + for i in axis_labels: + fpr[i], tpr[i], _ = roc_curve(one_hot_labels[:, i], y_score[:, i]) + roc_auc[i] = auc(fpr[i], tpr[i]) + for i in range(len(axis_labels)): + 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]) + ) + data_roc.append(trace) + else: + try: + y_score_binary = y_score[:, 1] + except: + 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) + ) + data_roc.append(trace) + + 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') + ) + + 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: + pass + + +if __name__ == "__main__": + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--input", dest="infile_input", required=True) + aparser.add_argument("-j", "--output", dest="infile_output", required=True) + aparser.add_argument("-k", "--model", dest="infile_trained_model", required=True) + args = aparser.parse_args() + main(args.infile_input, args.infile_output, args.infile_trained_model) |
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| diff -r 000000000000 -r 4fac53da862f plotly_ml_performance_plots.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plotly_ml_performance_plots.xml Thu Oct 11 14:37:54 2018 -0400 |
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| @@ -0,0 +1,86 @@ +<tool id="plotly_ml_performance_plots" name="Plot confusion matrix, precision, recall and ROC and AUC curves" version="0.1"> + <description>of tabular data</description> + <requirements> + <requirement type="package" version="0.22.0">pandas</requirement> + <requirement type="package" version="3.1.1">plotly</requirement> + <requirement type="package" version="0.19.1">scikit-learn</requirement> + <requirement type="package" version="1.1.0">scipy</requirement> + </requirements> + <version_command>echo $version</version_command> + <command detect_errors="aggressive"><![CDATA[ + python '$__tool_directory__/plot_ml_performance.py' + -i '$infile_input' + -j '$infile_output' + -k '$infile_trained_model' +]]> + </command> + <inputs> + <param name="infile_input" type="data" format="tabular" label="Select input data file :" help="Input data is a matrix (tabular) where each column is a feature and the last column contains the (true or original) class labels."/> + <param name="infile_output" type="data" format="tabular" label="Select predicted data file :" help="Predicted data is a matrix (tabular) where each column is a feature and the last column contains the predicted class labels."/> + <param name="infile_trained_model" type="data" format="zip" label="Select trained model :" help="This file is a final model trained on training data."/> + </inputs> + + <outputs> + <data name="output_confusion" format="html" from_work_dir="output_confusion.html" label="Confusion matrix of tabular data on ${on_string}"/> + <data name="output_prf" format="html" from_work_dir="output_prf.html" label="Precision, recall and f-score of tabular data on ${on_string}"/> + <data name="output_roc" format="html" from_work_dir="output_roc.html" label="ROC and AUC curves of tabular data on ${on_string}"/> + </outputs> + + <tests> + <test> + <param name="infile_input" value="binary_test_label.tabular" ftype="tabular"/> + <param name="infile_output" value="binary_prediction_sgd.tabular" ftype="tabular"/> + <param name="infile_trained_model" value="model_binary_sgd.zip" ftype="zip"/> + <output name="output_confusion" file="cnf_binary_sgd.html" compare="sim_size"/> + <output name="output_prf" file="prf_binary_sgd.html" compare="sim_size"/> + <output name="output_roc" file="roc_auc_binary_sgd.html" compare="sim_size"/> + </test> + + <test> + <param name="infile_input" value="binary_test_label.tabular" ftype="tabular"/> + <param name="infile_output" value="prediction_binary_linearsvm.tabular" ftype="tabular"/> + <param name="infile_trained_model" value="model_binary_linearsvm.zip" ftype="zip"/> + <output name="output_confusion" file="cnf_binary_linearsvm.html" compare="sim_size"/> + <output name="output_prf" file="prf_binary_linearsvm.html" compare="sim_size"/> + <output name="output_roc" file="roc_auc_binary_linearsvm.html" compare="sim_size"/> + </test> + + <test> + <param name="infile_input" value="binary_test_label.tabular" ftype="tabular"/> + <param name="infile_output" value="prediction_binary_rfc.tabular" ftype="tabular"/> + <param name="infile_trained_model" value="model_binary_rfc.zip" ftype="zip"/> + <output name="output_confusion" file="cnf_binary_rfc.html" compare="sim_size"/> + <output name="output_prf" file="prf_binary_rfc.html" compare="sim_size"/> + <output name="output_roc" file="roc_auc_binary_rfc.html" compare="sim_size"/> + </test> + + <test> + <param name="infile_input" value="binary_test_label.tabular" ftype="tabular"/> + <param name="infile_output" value="prediction_binary_knn.tabular" ftype="tabular"/> + <param name="infile_trained_model" value="model_binary_knn.zip" ftype="zip"/> + <output name="output_confusion" file="cnf_binary_knn.html" compare="sim_size"/> + <output name="output_prf" file="prf_binary_knn.html" compare="sim_size"/> + <output name="output_roc" file="roc_auc_binary_knn.html" compare="sim_size"/> + </test> + + <test> + <param name="infile_input" value="multi_test_label.tabular" ftype="tabular"/> + <param name="infile_output" value="prediction_multi_lr.tabular" ftype="tabular"/> + <param name="infile_trained_model" value="model_multi_lr.zip" ftype="zip"/> + <output name="output_confusion" file="cnf_multi_lr.html" compare="sim_size"/> + <output name="output_prf" file="prf_multi_lr.html" compare="sim_size"/> + <output name="output_roc" file="roc_auc_multi_lr.html" compare="sim_size"/> + </test> + + </tests> + <help><![CDATA[ +**What it does** + + +Produce a `heatmap plot (confusion matrix) <https://plot.ly/python/heatmaps/>`_ from tabular files. The input data contains the original/true class labels (last column) and the predicted data contains the predicted class labels (last column). The true and predicted class labels are plotted against each other. The diagonal of this heatmap shows the correctly predicted data. The plot is buried in a html file which +provides rich interactive features. Image can be saved in various format, such as 'png', 'svg', 'jpeg' and so on. + + + ]]> + </help> +</tool> |
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| diff -r 000000000000 -r 4fac53da862f test-data/binary_prediction_sgd.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/binary_prediction_sgd.tabular Thu Oct 11 14:37:54 2018 -0400 |
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|
| b |
| diff -r 000000000000 -r 4fac53da862f test-data/binary_test_label.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/binary_test_label.tabular Thu Oct 11 14:37:54 2018 -0400 |
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| diff -r 000000000000 -r 4fac53da862f test-data/cnf_binary_linearsvm.html --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cnf_binary_linearsvm.html Thu Oct 11 14:37:54 2018 -0400 |
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|
| b |
| diff -r 000000000000 -r 4fac53da862f test-data/multi_test_label.tsv.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/multi_test_label.tsv.tabular Thu Oct 11 14:37:54 2018 -0400 |
| b |
| b'@@ -0,0 +1,501 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|
| b |
| diff -r 000000000000 -r 4fac53da862f test-data/prediction_binary_knn.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/prediction_binary_knn.tabular Thu Oct 11 14:37:54 2018 -0400 |
| b |
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|
| b |
| diff -r 000000000000 -r 4fac53da862f test-data/prediction_binary_linearsvm.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/prediction_binary_linearsvm.tabular Thu Oct 11 14:37:54 2018 -0400 |
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|
| b |
| diff -r 000000000000 -r 4fac53da862f test-data/prediction_binary_rfc.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/prediction_binary_rfc.tabular Thu Oct 11 14:37:54 2018 -0400 |
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|
| b |
| diff -r 000000000000 -r 4fac53da862f test-data/prediction_multi_lr.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/prediction_multi_lr.tabular Thu Oct 11 14:37:54 2018 -0400 |
| b |
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| b |
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