Mercurial > repos > bgruening > ml_visualization_ex
changeset 1:09efff9a5765 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 02087ce2966cf8b4aac9197a41171e7f986c11d1-dirty"
| author | bgruening |
|---|---|
| date | Wed, 02 Oct 2019 03:50:11 -0400 |
| parents | f96efab83b65 |
| children | dd13740e8fdc |
| files | main_macros.xml ml_visualization_ex.py ml_visualization_ex.xml stacking_ensembles.py |
| diffstat | 4 files changed, 47 insertions(+), 27 deletions(-) [+] |
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--- a/main_macros.xml Fri Sep 13 12:23:39 2019 -0400 +++ b/main_macros.xml Wed Oct 02 03:50:11 2019 -0400 @@ -421,27 +421,46 @@ <xml name="sl_mixed_input"> <conditional name="input_options"> - <param name="selected_input" type="select" label="Select input type:"> - <option value="tabular" selected="true">tabular data</option> - <option value="sparse">sparse matrix</option> - <option value="seq_fasta">sequnences in a fasta file</option> - <option value="refseq_and_interval">reference genome and intervals</option> - </param> - <when value="tabular"> - <expand macro="samples_tabular" multiple1="true" multiple2="false"/> - </when> - <when value="sparse"> - <expand macro="sparse_target"/> - </when> - <when value="seq_fasta"> - <expand macro="inputs_seq_fasta"/> - </when> - <when value="refseq_and_interval"> - <expand macro="inputs_refseq_and_interval"/> - </when> + <expand macro="data_input_options"/> + <expand macro="data_input_whens"/> </conditional> </xml> + <xml name="sl_mixed_input_plus_sequence"> + <conditional name="input_options"> + <expand macro="data_input_options"> + <option value="seq_fasta">sequnences in a fasta file</option> + <option value="refseq_and_interval">reference genome and intervals</option> + </expand> + <expand macro="data_input_whens"> + <when value="seq_fasta"> + <expand macro="inputs_seq_fasta"/> + </when> + <when value="refseq_and_interval"> + <expand macro="inputs_refseq_and_interval"/> + </when> + </expand> + </conditional> + </xml> + + <xml name="data_input_options"> + <param name="selected_input" type="select" label="Select input type:"> + <option value="tabular" selected="true">tabular data</option> + <option value="sparse">sparse matrix</option> + <yield/> + </param> + </xml> + + <xml name="data_input_whens"> + <when value="tabular"> + <expand macro="samples_tabular" multiple1="true" multiple2="false"/> + </when> + <when value="sparse"> + <expand macro="sparse_target"/> + </when> + <yield/> + </xml> + <xml name="input_tabular_target"> <param name="infile2" type="data" format="tabular" label="Dataset containing class labels or target values:"/> <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" />
--- a/ml_visualization_ex.py Fri Sep 13 12:23:39 2019 -0400 +++ b/ml_visualization_ex.py Wed Oct 02 03:50:11 2019 -0400 @@ -146,7 +146,8 @@ 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) + df1.values, df2.values, average='micro', + pos_label=pos_label or 1) data = [] for key in precision.keys(): @@ -201,7 +202,7 @@ ) data.append(trace) - trace = go.Scatter(x=[0, 1], y=[0, 1], + trace = go.Scatter(x=[0, 1], y=[0, 1], mode='lines', line=dict(color='black', dash='dash'), showlegend=False)
--- a/ml_visualization_ex.xml Fri Sep 13 12:23:39 2019 -0400 +++ b/ml_visualization_ex.xml Wed Oct 02 03:50:11 2019 -0400 @@ -35,8 +35,8 @@ <conditional name="plotting_selection"> <param name="plot_type" type="select" label="Select a plotting type"> <option value="learning_curve" selected="true">Learning curve</option> - <option value="pr_curve">2-class Precison Recall curve</option> - <option value="roc_curve">2-class Receiver Operating Characteristic (ROC) curve</option> + <option value="pr_curve">2-class / multpi-label Precison Recall curve</option> + <option value="roc_curve">2-class / multi-label Receiver Operating Characteristic (ROC) curve</option> <option value="rfecv_gridscores">Number of features vs. Recursive Feature Elimination gridscores with corss-validation</option> <option value="feature_importances">Feature Importances plot</option> <option value="keras_plot_model">keras plot model - plot configuration of a neural network model</option> @@ -47,14 +47,14 @@ <param name="title" type="text" value="" optional="true" label="Plot title" help="Optional. If change is desired."/> </when> <when value="pr_curve"> - <param name="infile1" type="data" format="tabular" label="Select the dataset containing true labels." help="No headers. Each column corresponds to one class."/> - <param name="infile2" type="data" format="tabular" label="Select the dataset containing predicted probabilities." help="No headers. Each column corresponds to one class."/> + <param name="infile1" type="data" format="tabular" label="Select the dataset containing true labels." help="No headers. For 2-class, single column contains both class labels (e.g. True and False). For multi-label, each column, hot-encoded, corresponds to one label."/> + <param name="infile2" type="data" format="tabular" label="Select the dataset containing predicted probabilities." help="No headers. For 2-class, sinle column or the first column contains scores for the positive label. For multi-label, each column corresponds to one label."/> <param name="pos_label" type="text" value="" optional="true" label="pos_label" help="The label of positive class. If not specified, it will be 1 by default."/> <param name="title" type="text" value="" optional="true" label="Plot title" help="Optional. If change is desired."/> </when> <when value="roc_curve"> - <param name="infile1" type="data" format="tabular" label="Select the dataset containing true labels." help="No headers. Each column corresponds to one class."/> - <param name="infile2" type="data" format="tabular" label="Select the dataset containing predicted probabilities." help="No headers. Each column corresponds to one class."/> + <param name="infile1" type="data" format="tabular" label="Select the dataset containing true labels." help="No headers. For 2-class, single column contains both class labels (e.g. True and False). For multi-label, each column, hot-encoded, corresponds to one label."/> + <param name="infile2" type="data" format="tabular" label="Select the dataset containing predicted probabilities." help="No headers. For 2-class, sinle column or the first column contains scores for the positive label. For multi-label, each column corresponds to one label."/> <param name="pos_label" type="text" value="" optional="true" label="pos_label" help="The label of positive class. If not specified, it will be 1 by default."/> <param name="drop_intermediate" type="boolean" truevalue="booltrue" falsevalue="boolfalse" optional="true" checked="true" label="drop_intermediate" help="Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve."/> <param name="title" type="text" value="" optional="true" label="Plot title" help="Optional. If change is desired."/>
--- a/stacking_ensembles.py Fri Sep 13 12:23:39 2019 -0400 +++ b/stacking_ensembles.py Wed Oct 02 03:50:11 2019 -0400 @@ -11,7 +11,7 @@ from sklearn import ensemble from galaxy_ml.utils import (load_model, get_cv, get_estimator, - get_search_params) + get_search_params) warnings.filterwarnings('ignore')