Mercurial > repos > bgruening > sklearn_nn_classifier
comparison ml_visualization_ex.py @ 14:9871a634540f draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 02087ce2966cf8b4aac9197a41171e7f986c11d1-dirty"
| author | bgruening |
|---|---|
| date | Wed, 02 Oct 2019 03:58:00 -0400 |
| parents | fbc38059bb8f |
| children | 699024d5c451 |
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| 13:fbc38059bb8f | 14:9871a634540f |
|---|---|
| 144 | 144 |
| 145 if len(df1.columns) > 1: | 145 if len(df1.columns) > 1: |
| 146 precision["micro"], recall["micro"], _ = precision_recall_curve( | 146 precision["micro"], recall["micro"], _ = precision_recall_curve( |
| 147 df1.values.ravel(), df2.values.ravel(), pos_label=pos_label) | 147 df1.values.ravel(), df2.values.ravel(), pos_label=pos_label) |
| 148 ap['micro'] = average_precision_score( | 148 ap['micro'] = average_precision_score( |
| 149 df1.values, df2.values, average='micro', pos_label=pos_label or 1) | 149 df1.values, df2.values, average='micro', |
| 150 pos_label=pos_label or 1) | |
| 150 | 151 |
| 151 data = [] | 152 data = [] |
| 152 for key in precision.keys(): | 153 for key in precision.keys(): |
| 153 trace = go.Scatter( | 154 trace = go.Scatter( |
| 154 x=recall[key], | 155 x=recall[key], |
| 199 name='%s (area = %.2f)' % (key, roc_auc[key]) if key == 'micro' | 200 name='%s (area = %.2f)' % (key, roc_auc[key]) if key == 'micro' |
| 200 else 'column %s (area = %.2f)' % (key, roc_auc[key]) | 201 else 'column %s (area = %.2f)' % (key, roc_auc[key]) |
| 201 ) | 202 ) |
| 202 data.append(trace) | 203 data.append(trace) |
| 203 | 204 |
| 204 trace = go.Scatter(x=[0, 1], y=[0, 1], | 205 trace = go.Scatter(x=[0, 1], y=[0, 1], |
| 205 mode='lines', | 206 mode='lines', |
| 206 line=dict(color='black', dash='dash'), | 207 line=dict(color='black', dash='dash'), |
| 207 showlegend=False) | 208 showlegend=False) |
| 208 data.append(trace) | 209 data.append(trace) |
| 209 | 210 |