* Fix: Replace pylab with matplotlib.pyplot #6754
- one instance of 22 occurrences of pylab replaced with matplotlib.pyplot
- bench_glm.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- one instance of 21 remaining occurrences of pylab replaced with
matplotlib.pyplot
- bench_glmnet.py now free of pylab references
- code does not execute for extraneous reason: ImportError: No module named
glmnet.elastic_net
* Fix: Replace pylab with matplotlib.pyplot #6754
- one instance of 19 occurrences of pylab replaced with matplotlib.pyplot
- bench_lasso.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- one instance of 18 occurrences of pylab replaced with matplotlib.pyplot
- bench_plot_neighbors.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- one instance of 17 occurrences of pylab replaced with matplotlib.pyplot
- bench_plot_omp_lars.py now free of pylab references
- code does not execute for extraneous reasons:
- File "bench_plot_omp_lars.py", line 111, in <module>
- ax = fig.add_subplot(1, 2, i)
- ValueError: num must be 1 <= num <= 2, not 0
- line 111 should probably be ax = fig.add_subplot(1, 2, i+1)
* Fix: Replace pylab with matplotlib.pyplot #6754
- bench_plot_parallel_pairwise.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- bench_plot_ward.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- bench_sgd_regression.py now free of pylab references
- code executes properly
* Fix: Replace pylab with matplotlib.pyplot #6754
- bench_tree.py now free of pylab references
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_glm.py clean
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_glm.py clean of pl
- code does not execute for extraneous reasons
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_lasso.py clean of pl
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_plot_neighbors.py clean of pl
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_plot_omp_lars.py clean of pl
- code does not execute for extraneous reasons
* fix: Fix bug that prevented graphs from displaying
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_plot_parallel_pairwise.py clean of pl
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_plot_ward.py clean of pl
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_sgd_regression.py clean of pl
- code executes properly
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- bench_tree.py clean of pl
- code executes properly
* docs: removed pylab references from comments
* docs: removed all pylab references
- replaced with matplotlib.pyplot
- pl --> plt
* docs: removed pylab references from comments
- replaced with matplotlib.pyplot
- pl --> plt
* docs: removed all pylab references
- replaced with matplotlib.pyplot
- pl --> plt
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- mlcomp_sparse_document_classification.py clean of pl
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- plot_gpr_noisy_targets.py clean of pl
- code does not execute for extraneous reasons
- File "examples/gaussian_process/plot_gpr_noisy_targets.py", line 31, in
<module>
- from sklearn.gaussian_process import GaussianProcessRegressor
- ImportError: cannot import name GaussianProcessRegressor
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- plot_gpc_isoprobability.py clean of pl
- code does not execute for extraneous reasons
- File "examples/gaussian_process/plot_gpc_isoprobability.py", line 24, in
<module>
- from sklearn.gaussian_process import GaussianProcessClassifier
- ImportError: cannot import name GaussianProcessClassifier
* docs: removed all pylab references
- replaced with matplotlib.pyplot
- pl --> plt
* docs: removed all pylab references
- replaced with matplotlib.pyplot
* refactor: Replace pl with plt
- replace instances of pl (as on import pylab as pl)
with plt (as in import matplotlib.pyplot as plt)
- plot_sparse_coding.py clean of pl
- code executes properly
* docs: removed all pylab references
- replaced with matplotlib.pyplot
* docs: removed all pylab references
- replaced with matplotlib.pyplot
* style: Indent properly
* style: indent properly
* style: Indent properly
* docs: Add missing .pyplot
* docs: Fix typo
* style: Indent properly
* add a title to all benchmark figures/windows
* "bench" is non-standard slang for benchmarking
* remove calls to pyplot.clf because the previous call
to pyplot.figure sets the current figure
* standardize on "Time (s)" as time axis label
* move some benchmark plot legends to the upper left corner of the plot:
most benchmarks expand from (0,0) up and to the right
* simplified plot titles and moved constant info from legend into title
* fixed typos
* made some labels more viewer-friendly
Fixes#2023.
Notable changes:
1) Supports classification and regression
2) 3 classification criteria, 1 regression criterion
3) A new dataset is provided to test regression (Boston House Prices)
4) Weights are removed from the algorithm entirely. If the need for weights can be justified, I would welcome reintroducing them, but for the refactoring I left them out.
5) The subset of dimensions (F) to split on is fixed for the entire tree, not at each node. This is more in line with CART and RandomForests.
6) A max_depth parameter is offered to limit the size of the constructed trees.
7) Randomisation is fixed with python's random module, but can be seeded.
8) For classification, the number of classes must be provided when the tree is constructed. This is because the tree cannot necessarily infer the correct number of labels at the time of training if only a subset of the data is used for individual trees.
9) For classification, labels are not normalised internally. Labels must be provided to the algorithm in the range [0, ..., K)
10) For classification, the leaf nodes retain the distribution of classes. This means that it is possible to query the tree for the probability distribution of a test sample
Thomas J. Fan