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Merge pull request #663 from EducationalTestingService/release/11.2.0
Release/11.2.0
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README.rst

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conda-recipe/rsmtool/meta.yaml

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package:
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name: rsmtool
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version: 11.1.1
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version: 11.2.0
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source:
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path: ../../../rsmtool
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build:
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number: 1
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number: 0
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noarch: python
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script:
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- cd $SRC_DIR

rsmtool/notebooks/builtin_model.ipynb

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"grey_colors = sns.color_palette('Greys', len(features_used))[::-1]\n",
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"with sns.axes_style('whitegrid'):\n",
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" ax1=fig.add_subplot(121)\n",
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" sns.barplot(x=\"feature\", y=\"standardized\", data=df_betas_sorted, \n",
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" sns.barplot(x=\"feature\", y=\"standardized\", hue=\"feature\",\n",
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" legend=False, data=df_betas_sorted, \n",
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" order=df_betas_sorted['feature'].values,\n",
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" palette=sns.color_palette(\"Greys\", 1), ax=ax1)\n",
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" ax1.set_xticklabels(df_betas_sorted['feature'].values, rotation=90)\n",
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" palette=sns.color_palette(\"Greys\", len(features_used)), ax=ax1)\n",
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" tick_positions = np.arange(len(features_used))\n",
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" ax1.set_xticks(tick_positions, df_betas_sorted['feature'].values, rotation=90)\n",
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" ax1.set_title('Values of standardized coefficients')\n",
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" ax1.set_xlabel('')\n",
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" ax1.set_ylabel('')\n",

rsmtool/notebooks/comparison/header.ipynb

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"log_msgs = []\n",
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"# first check the training set\n",
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"if not (outputs_old['df_descriptives'].empty or outputs_new['df_descriptives'].empty):\n",
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" oldTrainN = outputs_old['df_descriptives']['N'][0] # take the N from the descriptive stats for the first feature\n",
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" newTrainN = outputs_new['df_descriptives']['N'][0]\n",
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" oldTrainN = outputs_old['df_descriptives']['N'].iloc[0] # take the N from the descriptive stats for the first feature\n",
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" newTrainN = outputs_new['df_descriptives']['N'].iloc[0]\n",
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" if oldTrainN != newTrainN:\n",
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" log_msg = \"WARNING: the training sets were different sizes. old N: {}, new N: {}.\".format(oldTrainN, newTrainN)\n",
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" log_msgs.append(log_msg)\n",

rsmtool/version.py

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in one place. Based on the suggestion `here. <http://bit.ly/16LbuJF>`_
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"""
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__version__ = "11.1.1"
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__version__ = "11.2.0"
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VERSION = tuple(int(x) for x in __version__.split("."))

tests/data/experiments/knn-explain/output/knn_rsmexplain.json

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"num_features_to_display": 15,
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"show_auto_cohorts": true,
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"standardize_features": true,
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"truncate_outliers": true,
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"general_sections": [
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"all"
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],

tests/data/experiments/linearsvr-self-compare/linearsvr/output/LinearSVR_rsmtool.json

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"use_scaled_predictions": false,
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"select_transformations": false,
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"standardize_features": true,
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"truncate_outliers": true,
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"use_thumbnails": false,
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"use_truncation_thresholds": false,
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"predict_expected_scores": false,

tests/data/experiments/linearsvr-self-summary/linearsvr/output/LinearSVR_rsmtool.json

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"use_scaled_predictions": false,
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"select_transformations": false,
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"standardize_features": true,
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"truncate_outliers": true,
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"use_thumbnails": false,
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"use_truncation_thresholds": false,
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"predict_expected_scores": false,

tests/data/experiments/lr-different-compare/lr-subset-features/output/lr_subset_rsmtool.json

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"exclude_zero_scores": true,
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"select_transformations": false,
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"standardize_features": true,
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"truncate_outliers": true,
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"use_thumbnails": false,
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"use_truncation_thresholds": false,
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"predict_expected_scores": false,

tests/data/experiments/lr-different-compare/lr/output/lr_rsmtool.json

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"exclude_zero_scores": true,
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"select_transformations": false,
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"standardize_features": true,
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"truncate_outliers": true,
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"use_thumbnails": false,
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"use_truncation_thresholds": false,
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"predict_expected_scores": false,

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