Fix merge conflicts [skip ci] #3892
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This should resolve the confusion caused in the issue rapidsai/raft#228. Tagging @dantegd for review. Authors: - Thejaswi. N. S (https://github.com/teju85) Approvers: - Dante Gama Dessavre (https://github.com/dantegd) URL: #3875
Use floating rounding to make UMAP optimization deterministic. This is a breaking change as the batch size parameter is removed. * Add procedure for rounding the gradient updates. * Add buffer for gradient updates. * Add an internal parameter `deterministic`, which should be set to `true` when `random_state` is set. The test file is removed due to #3849 . Authors: - Jiaming Yuan (https://github.com/trivialfis) Approvers: - Corey J. Nolet (https://github.com/cjnolet) URL: #3848
This PR updates the `0.20` references in `CHANGELOG.md` to be `21.06`. Authors: - AJ Schmidt (https://github.com/ajschmidt8) Approvers: - Dillon Cullinan (https://github.com/dillon-cullinan) URL: #3883
I made the mistake and got a segmentation fault. A value error might be nicer. Authors: - Jiaming Yuan (https://github.com/trivialfis) Approvers: - Corey J. Nolet (https://github.com/cjnolet) URL: #3881
…istances from self-loops (#3824) Closes #3801 Closes #3802 Corresponding RAFT PR: rapidsai/raft#217 Authors: - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Dante Gama Dessavre (https://github.com/dantegd) URL: #3824
This PR rewrites the mean squared error objective. Mean squared error is much easier when factored mathematically into a slightly different form. This should bring regression performance in line with classification. I've also removed the MAE objective as its not correct. This can be seen from the fact that leaf predictions with MAE use the mean, where the correct minimiser is the median. Also see sklearns implementation, where streaming median calculations are required: https://github.com/scikit-learn/scikit-learn/blob/de1262c35e2aa4ee062d050281ee576ce9e35c94/sklearn/tree/_criterion.pyx#L976. Implementing this correctly for GPU would be very challenging. Performance before:  After:  Script: ```python from cuml import RandomForestRegressor as cuRF from sklearn.ensemble import RandomForestRegressor as sklRF from sklearn.datasets import make_regression from sklearn.metrics import mean_squared_error import numpy as np import pandas as pd import matplotlib import matplotlib.pyplot as plt import seaborn as sns import time matplotlib.use("Agg") sns.set() X, y = make_regression(n_samples=100000, random_state=0) X = X.astype(np.float32) y = y.astype(np.float32) rs = np.random.RandomState(92) df = pd.DataFrame(columns=["algorithm", "Time(s)", "MSE"]) d = 10 n_repeats = 5 bootstrap = False max_samples = 1.0 max_features = 0.5 n_estimators = 10 n_bins = min(X.shape[0], 128) for _ in range(n_repeats): clf = sklRF( n_estimators=n_estimators, max_depth=d, random_state=rs, max_features=max_features, bootstrap=bootstrap, max_samples=max_samples if max_samples < 1.0 else None, ) start = time.perf_counter() clf.fit(X, y) skl_time = time.perf_counter() - start pred = clf.predict(X) cu_clf = cuRF( n_estimators=n_estimators, max_depth=d, random_state=rs.randint(0, 1 << 32), n_bins=n_bins, max_features=max_features, bootstrap=bootstrap, max_samples=max_samples, use_experimental_backend=True, ) start = time.perf_counter() cu_clf.fit(X, y) cu_time = time.perf_counter() - start cu_pred = cu_clf.predict(X, predict_model="CPU") df = df.append( { "algorithm": "cuml", "Time(s)": cu_time, "MSE": mean_squared_error(y, cu_pred), }, ignore_index=True, ) df = df.append( { "algorithm": "sklearn", "Time(s)": skl_time, "MSE": mean_squared_error(y, pred), }, ignore_index=True, ) print(df) fig, ax = plt.subplots(1, 2) sns.barplot(data=df, x="algorithm", y="Time(s)", ax=ax[0]) sns.barplot(data=df, x="algorithm", y="MSE", ax=ax[1]) plt.savefig("rf_regression_perf_fix.png") ``` Authors: - Rory Mitchell (https://github.com/RAMitchell) Approvers: - Philip Hyunsu Cho (https://github.com/hcho3) - Thejaswi. N. S (https://github.com/teju85) - John Zedlewski (https://github.com/JohnZed) URL: #3845
See #3820 Authors: - Micka (https://github.com/lowener) Approvers: - Victor Lafargue (https://github.com/viclafargue) - Dante Gama Dessavre (https://github.com/dantegd) URL: #3831
See #3820 Authors: - Philip Hyunsu Cho (https://github.com/hcho3) Approvers: - Victor Lafargue (https://github.com/viclafargue) - Dante Gama Dessavre (https://github.com/dantegd) URL: #3830
ajschmidt8
added
improvement
dillon-cullinan
approved these changes
May 24, 2021
|
|
vimarsh6739
pushed a commit
to vimarsh6739/cuml
that referenced
this pull request
Oct 9, 2023
…1.06 Fix merge conflicts [skip ci]
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR fixes the merge conflicts in #3887.