1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
|
%global _empty_manifest_terminate_build 0
Name: python-yellowbrick
Version: 1.5
Release: 1
Summary: A suite of visual analysis and diagnostic tools for machine learning.
License: Apache 2
URL: http://scikit-yb.org/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/76/6c/91ae3fcc99173f3b90642eb8b9eb6faac4e21dba6020566c751535e72554/yellowbrick-1.5.tar.gz
BuildArch: noarch
Requires: python3-matplotlib
Requires: python3-scipy
Requires: python3-scikit-learn
Requires: python3-numpy
Requires: python3-cycler
%description
# Yellowbrick
[](https://www.scikit-yb.org/)
Yellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with scikit-learn. The library implements a new core API object, the `Visualizer` that is an scikit-learn estimator — an object that learns from data. Similar to transformers or models, visualizers learn from data by creating a visual representation of the model selection workflow.
Visualizer allow users to steer the model selection process, building intuition around feature engineering, algorithm selection and hyperparameter tuning. For instance, they can help diagnose common problems surrounding model complexity and bias, heteroscedasticity, underfit and overtraining, or class balance issues. By applying visualizers to the model selection workflow, Yellowbrick allows you to steer predictive models toward more successful results, faster.
The full documentation can be found at [scikit-yb.org](https://scikit-yb.org/) and includes a [Quick Start Guide](https://www.scikit-yb.org/en/latest/quickstart.html) for new users.
## Visualizers
Visualizers are estimators — objects that learn from data — whose primary objective is to create visualizations that allow insight into the model selection process. In scikit-learn terms, they can be similar to transformers when visualizing the data space or wrap a model estimator similar to how the `ModelCV` (e.g. [`RidgeCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html), [`LassoCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html)) methods work. The primary goal of Yellowbrick is to create a sensical API similar to scikit-learn. Some of our most popular visualizers include:
### Classification Visualization
- **Classification Report**: a visual classification report that displays a model's precision, recall, and F1 per-class scores as a heatmap
- **Confusion Matrix**: a heatmap view of the confusion matrix of pairs of classes in multi-class classification
- **Discrimination Threshold**: a visualization of the precision, recall, F1-score, and queue rate with respect to the discrimination threshold of a binary classifier
- **Precision-Recall Curve**: plot the precision vs recall scores for different probability thresholds
- **ROCAUC**: graph the receiver operator characteristic (ROC) and area under the curve (AUC)
### Clustering Visualization
- **Intercluster Distance Maps**: visualize the relative distance and size of clusters
- **KElbow Visualizer**: visualize cluster according to the specified scoring function, looking for the "elbow" in the curve.
- **Silhouette Visualizer**: select `k` by visualizing the silhouette coefficient scores of each cluster in a single model
### Feature Visualization
- **Manifold Visualization**: high-dimensional visualization with manifold learning
- **Parallel Coordinates**: horizontal visualization of instances
- **PCA Projection**: projection of instances based on principal components
- **RadViz Visualizer**: separation of instances around a circular plot
- **Rank Features**: single or pairwise ranking of features to detect relationships
### Model Selection Visualization
- **Cross Validation Scores**: display the cross-validated scores as a bar chart with the average score plotted as a horizontal line
- **Feature Importances**: rank features based on their in-model performance
- **Learning Curve**: show if a model might benefit from more data or less complexity
- **Recursive Feature Elimination**: find the best subset of features based on importance
- **Validation Curve**: tune a model with respect to a single hyperparameter
### Regression Visualization
- **Alpha Selection**: show how the choice of alpha influences regularization
- **Cook's Distance**: show the influence of instances on linear regression
- **Prediction Error Plots**: find model breakdowns along the domain of the target
- **Residuals Plot**: show the difference in residuals of training and test data
### Target Visualization
- **Balanced Binning Reference**: generate a histogram with vertical lines showing the recommended value point to the bin data into evenly distributed bins
- **Class Balance**: show the relationship of the support for each class in both the training and test data by displaying how frequently each class occurs as a bar graph the frequency of the classes' representation in the dataset
- **Feature Correlation**: visualize the correlation between the dependent variables and the target
### Text Visualization
- **Dispersion Plot**: visualize how key terms are dispersed throughout a corpus
- **PosTag Visualizer**: plot the counts of different parts-of-speech throughout a tagged corpus
- **Token Frequency Distribution**: visualize the frequency distribution of terms in the corpus
- **t-SNE Corpus Visualization**: uses stochastic neighbor embedding to project documents
- **UMAP Corpus Visualization**: plot similar documents closer together to discover clusters
... and more! Yellowbrick is adding new visualizers all the time so be sure to check out our [examples gallery]https://github.com/DistrictDataLabs/yellowbrick/tree/develop/examples) — or even the [develop](https://github.com/districtdatalabs/yellowbrick/tree/develop) branch — and feel free to contribute your ideas for new Visualizers!
## Affiliations
[](https://www.districtdatalabs.com/) [](https://numfocus.org/)
%package -n python3-yellowbrick
Summary: A suite of visual analysis and diagnostic tools for machine learning.
Provides: python-yellowbrick
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-yellowbrick
# Yellowbrick
[](https://www.scikit-yb.org/)
Yellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with scikit-learn. The library implements a new core API object, the `Visualizer` that is an scikit-learn estimator — an object that learns from data. Similar to transformers or models, visualizers learn from data by creating a visual representation of the model selection workflow.
Visualizer allow users to steer the model selection process, building intuition around feature engineering, algorithm selection and hyperparameter tuning. For instance, they can help diagnose common problems surrounding model complexity and bias, heteroscedasticity, underfit and overtraining, or class balance issues. By applying visualizers to the model selection workflow, Yellowbrick allows you to steer predictive models toward more successful results, faster.
The full documentation can be found at [scikit-yb.org](https://scikit-yb.org/) and includes a [Quick Start Guide](https://www.scikit-yb.org/en/latest/quickstart.html) for new users.
## Visualizers
Visualizers are estimators — objects that learn from data — whose primary objective is to create visualizations that allow insight into the model selection process. In scikit-learn terms, they can be similar to transformers when visualizing the data space or wrap a model estimator similar to how the `ModelCV` (e.g. [`RidgeCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html), [`LassoCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html)) methods work. The primary goal of Yellowbrick is to create a sensical API similar to scikit-learn. Some of our most popular visualizers include:
### Classification Visualization
- **Classification Report**: a visual classification report that displays a model's precision, recall, and F1 per-class scores as a heatmap
- **Confusion Matrix**: a heatmap view of the confusion matrix of pairs of classes in multi-class classification
- **Discrimination Threshold**: a visualization of the precision, recall, F1-score, and queue rate with respect to the discrimination threshold of a binary classifier
- **Precision-Recall Curve**: plot the precision vs recall scores for different probability thresholds
- **ROCAUC**: graph the receiver operator characteristic (ROC) and area under the curve (AUC)
### Clustering Visualization
- **Intercluster Distance Maps**: visualize the relative distance and size of clusters
- **KElbow Visualizer**: visualize cluster according to the specified scoring function, looking for the "elbow" in the curve.
- **Silhouette Visualizer**: select `k` by visualizing the silhouette coefficient scores of each cluster in a single model
### Feature Visualization
- **Manifold Visualization**: high-dimensional visualization with manifold learning
- **Parallel Coordinates**: horizontal visualization of instances
- **PCA Projection**: projection of instances based on principal components
- **RadViz Visualizer**: separation of instances around a circular plot
- **Rank Features**: single or pairwise ranking of features to detect relationships
### Model Selection Visualization
- **Cross Validation Scores**: display the cross-validated scores as a bar chart with the average score plotted as a horizontal line
- **Feature Importances**: rank features based on their in-model performance
- **Learning Curve**: show if a model might benefit from more data or less complexity
- **Recursive Feature Elimination**: find the best subset of features based on importance
- **Validation Curve**: tune a model with respect to a single hyperparameter
### Regression Visualization
- **Alpha Selection**: show how the choice of alpha influences regularization
- **Cook's Distance**: show the influence of instances on linear regression
- **Prediction Error Plots**: find model breakdowns along the domain of the target
- **Residuals Plot**: show the difference in residuals of training and test data
### Target Visualization
- **Balanced Binning Reference**: generate a histogram with vertical lines showing the recommended value point to the bin data into evenly distributed bins
- **Class Balance**: show the relationship of the support for each class in both the training and test data by displaying how frequently each class occurs as a bar graph the frequency of the classes' representation in the dataset
- **Feature Correlation**: visualize the correlation between the dependent variables and the target
### Text Visualization
- **Dispersion Plot**: visualize how key terms are dispersed throughout a corpus
- **PosTag Visualizer**: plot the counts of different parts-of-speech throughout a tagged corpus
- **Token Frequency Distribution**: visualize the frequency distribution of terms in the corpus
- **t-SNE Corpus Visualization**: uses stochastic neighbor embedding to project documents
- **UMAP Corpus Visualization**: plot similar documents closer together to discover clusters
... and more! Yellowbrick is adding new visualizers all the time so be sure to check out our [examples gallery]https://github.com/DistrictDataLabs/yellowbrick/tree/develop/examples) — or even the [develop](https://github.com/districtdatalabs/yellowbrick/tree/develop) branch — and feel free to contribute your ideas for new Visualizers!
## Affiliations
[](https://www.districtdatalabs.com/) [](https://numfocus.org/)
%package help
Summary: Development documents and examples for yellowbrick
Provides: python3-yellowbrick-doc
%description help
# Yellowbrick
[](https://www.scikit-yb.org/)
Yellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with scikit-learn. The library implements a new core API object, the `Visualizer` that is an scikit-learn estimator — an object that learns from data. Similar to transformers or models, visualizers learn from data by creating a visual representation of the model selection workflow.
Visualizer allow users to steer the model selection process, building intuition around feature engineering, algorithm selection and hyperparameter tuning. For instance, they can help diagnose common problems surrounding model complexity and bias, heteroscedasticity, underfit and overtraining, or class balance issues. By applying visualizers to the model selection workflow, Yellowbrick allows you to steer predictive models toward more successful results, faster.
The full documentation can be found at [scikit-yb.org](https://scikit-yb.org/) and includes a [Quick Start Guide](https://www.scikit-yb.org/en/latest/quickstart.html) for new users.
## Visualizers
Visualizers are estimators — objects that learn from data — whose primary objective is to create visualizations that allow insight into the model selection process. In scikit-learn terms, they can be similar to transformers when visualizing the data space or wrap a model estimator similar to how the `ModelCV` (e.g. [`RidgeCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html), [`LassoCV`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html)) methods work. The primary goal of Yellowbrick is to create a sensical API similar to scikit-learn. Some of our most popular visualizers include:
### Classification Visualization
- **Classification Report**: a visual classification report that displays a model's precision, recall, and F1 per-class scores as a heatmap
- **Confusion Matrix**: a heatmap view of the confusion matrix of pairs of classes in multi-class classification
- **Discrimination Threshold**: a visualization of the precision, recall, F1-score, and queue rate with respect to the discrimination threshold of a binary classifier
- **Precision-Recall Curve**: plot the precision vs recall scores for different probability thresholds
- **ROCAUC**: graph the receiver operator characteristic (ROC) and area under the curve (AUC)
### Clustering Visualization
- **Intercluster Distance Maps**: visualize the relative distance and size of clusters
- **KElbow Visualizer**: visualize cluster according to the specified scoring function, looking for the "elbow" in the curve.
- **Silhouette Visualizer**: select `k` by visualizing the silhouette coefficient scores of each cluster in a single model
### Feature Visualization
- **Manifold Visualization**: high-dimensional visualization with manifold learning
- **Parallel Coordinates**: horizontal visualization of instances
- **PCA Projection**: projection of instances based on principal components
- **RadViz Visualizer**: separation of instances around a circular plot
- **Rank Features**: single or pairwise ranking of features to detect relationships
### Model Selection Visualization
- **Cross Validation Scores**: display the cross-validated scores as a bar chart with the average score plotted as a horizontal line
- **Feature Importances**: rank features based on their in-model performance
- **Learning Curve**: show if a model might benefit from more data or less complexity
- **Recursive Feature Elimination**: find the best subset of features based on importance
- **Validation Curve**: tune a model with respect to a single hyperparameter
### Regression Visualization
- **Alpha Selection**: show how the choice of alpha influences regularization
- **Cook's Distance**: show the influence of instances on linear regression
- **Prediction Error Plots**: find model breakdowns along the domain of the target
- **Residuals Plot**: show the difference in residuals of training and test data
### Target Visualization
- **Balanced Binning Reference**: generate a histogram with vertical lines showing the recommended value point to the bin data into evenly distributed bins
- **Class Balance**: show the relationship of the support for each class in both the training and test data by displaying how frequently each class occurs as a bar graph the frequency of the classes' representation in the dataset
- **Feature Correlation**: visualize the correlation between the dependent variables and the target
### Text Visualization
- **Dispersion Plot**: visualize how key terms are dispersed throughout a corpus
- **PosTag Visualizer**: plot the counts of different parts-of-speech throughout a tagged corpus
- **Token Frequency Distribution**: visualize the frequency distribution of terms in the corpus
- **t-SNE Corpus Visualization**: uses stochastic neighbor embedding to project documents
- **UMAP Corpus Visualization**: plot similar documents closer together to discover clusters
... and more! Yellowbrick is adding new visualizers all the time so be sure to check out our [examples gallery]https://github.com/DistrictDataLabs/yellowbrick/tree/develop/examples) — or even the [develop](https://github.com/districtdatalabs/yellowbrick/tree/develop) branch — and feel free to contribute your ideas for new Visualizers!
## Affiliations
[](https://www.districtdatalabs.com/) [](https://numfocus.org/)
%prep
%autosetup -n yellowbrick-1.5
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-yellowbrick -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.5-1
- Package Spec generated
|
