Mercurial > repos > bgruening > sklearn_train_test_eval
changeset 2:e23cfe4be9d4 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 02087ce2966cf8b4aac9197a41171e7f986c11d1-dirty"
| author | bgruening |
|---|---|
| date | Wed, 02 Oct 2019 03:46:45 -0400 |
| parents | cc49634df38f |
| children | 20bb2a45f922 |
| files | main_macros.xml ml_visualization_ex.py stacking_ensembles.py train_test_eval.xml |
| diffstat | 4 files changed, 42 insertions(+), 22 deletions(-) [+] |
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--- a/main_macros.xml Fri Sep 13 12:08:44 2019 -0400 +++ b/main_macros.xml Wed Oct 02 03:46:45 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 12:08:44 2019 -0400 +++ b/ml_visualization_ex.py Wed Oct 02 03:46:45 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/stacking_ensembles.py Fri Sep 13 12:08:44 2019 -0400 +++ b/stacking_ensembles.py Wed Oct 02 03:46:45 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')
--- a/train_test_eval.xml Fri Sep 13 12:08:44 2019 -0400 +++ b/train_test_eval.xml Wed Oct 02 03:46:45 2019 -0400 @@ -76,7 +76,7 @@ </section> </when> </conditional> - <expand macro="sl_mixed_input"/> + <expand macro="sl_mixed_input_plus_sequence"/> <param name="save" type="select" label="Save the fitted model" help="For security reason, deep learning models will be saved into two datasets, model skeleton and weights."> <option value="nope" selected="true">Nope, save is unnecessary</option> <option value="save_estimator">Fitted whole estimator (excluding deep learning)</option>