diff --git a/ci/accel/scikit-learn-tests/README.md b/ci/accel/scikit-learn-tests/README.md deleted file mode 100644 index ba51b8aa5e..0000000000 --- a/ci/accel/scikit-learn-tests/README.md +++ /dev/null @@ -1,40 +0,0 @@ -# scikit-learn Acceleration Tests - -This suite provides infrastructure to run and analyze tests for scikit-learn with cuML acceleration support. - -## Components - -- `run-tests.sh` - Executes scikit-learn tests using GPU-accelerated paths. Any arguments passed to the script are forwarded directly to pytest. - - Example usage: - ```bash - ./run-tests.sh # Run all tests - ./run-tests.sh -v -k test_kmeans # Run specific test with verbosity - ./run-tests.sh -x --pdb # Stop on first failure and debug - ``` - -- `summarize-results.sh` - Analyzes test results from an XML report file and prints a summary. - Options: - - `-v, --verbose` : Display detailed failure information - - `-f, --fail-below VALUE` : Set a minimum pass rate threshold (0-100) - -## Usage - -### 1. Run tests -Run all tests: -```bash -./run-tests.sh -``` - -Run specific tests using pytest arguments: -```bash -./run-tests.sh -v -k "test_logistic" -``` - -### 2. Summarize test results -Generate a summary from the XML report with a pass rate threshold: -```bash -./summarize-results.sh -v -f 80 report.xml -``` diff --git a/ci/accel/scikit-learn-tests/summarize-results.sh b/ci/accel/scikit-learn-tests/summarize-results.sh deleted file mode 100755 index b92c4dec48..0000000000 --- a/ci/accel/scikit-learn-tests/summarize-results.sh +++ /dev/null @@ -1,95 +0,0 @@ -#!/bin/bash -# Copyright (c) 2025, NVIDIA CORPORATION. - -usage() { - echo "Usage: $0 [options] REPORT_FILE" - echo "" - echo "Options:" - echo " -h, --help Show this help message" - echo " -v, --verbose Show detailed failure information" - echo " -f, --fail-below VALUE Minimum pass rate threshold [0-100] (default: 0)" - exit 1 -} - -# Parse command line arguments -THRESHOLD=0 -VERBOSE=0 - -while [[ $# -gt 0 ]]; do - case $1 in - -h|--help) - usage - ;; - -f|--fail-below) - THRESHOLD="$2" - shift 2 - ;; - -v|--verbose) - VERBOSE=1 - shift - ;; - *) - if [ -z "${REPORT_FILE:-}" ]; then - REPORT_FILE="$1" - else - echo "Unknown option: $1" - usage - fi - shift - ;; - esac -done - -if [ -z "${REPORT_FILE:-}" ]; then - echo "Error: No report file specified" - usage -fi - -# Validate threshold is a number between 0 and 100 -if ! [[ "$THRESHOLD" =~ ^[0-9]+(\.[0-9]+)?$ ]]; then - echo "Error: Threshold must be a number" - exit 1 -fi - -if ! awk -v t="$THRESHOLD" 'BEGIN{exit !(t >= 0 && t <= 100)}'; then - echo "Error: Threshold must be between 0 and 100" - exit 1 -fi - -# Extract test statistics using xmllint -total_tests=$(xmllint --xpath "string(/testsuites/testsuite/@tests)" "${REPORT_FILE}") -failures=$(xmllint --xpath "string(/testsuites/testsuite/@failures)" "${REPORT_FILE}") -errors=$(xmllint --xpath "string(/testsuites/testsuite/@errors)" "${REPORT_FILE}") -skipped=$(xmllint --xpath "string(/testsuites/testsuite/@skipped)" "${REPORT_FILE}") -time=$(xmllint --xpath "string(/testsuites/testsuite/@time)" "${REPORT_FILE}") - -# Calculate passed tests and pass rate using awk -passed=$((total_tests - failures - errors - skipped)) -pass_rate=$(awk -v passed="$passed" -v total="$total_tests" 'BEGIN { printf "%.2f", (passed/total) * 100 }') - -# Print summary -echo "Test Summary:" -echo " Total Tests: ${total_tests}" -echo " Passed: ${passed}" -echo " Failed: ${failures}" -echo " Errors: ${errors}" -echo " Skipped: ${skipped}" -echo " Pass Rate: ${pass_rate}%" -echo " Total Time: ${time}s" - -# List failed tests only in verbose mode -if [ "$((failures + errors))" -gt 0 ] && [ "${VERBOSE}" -eq 1 ]; then - echo "" - echo "Failed Tests:" - xmllint --xpath "//testcase[failure or error]/@name" "${REPORT_FILE}" | tr ' ' '\n' | sed 's/name=//g' | sed 's/"//g' | grep . -fi - -# Check if threshold is nonzero before applying the check. -if awk -v rate="$pass_rate" -v threshold="$THRESHOLD" 'BEGIN { exit (rate >= threshold) }'; then - echo "" - echo "Error: Pass rate ${pass_rate}% is below threshold ${THRESHOLD}%" - exit 1 -fi - -# In all other cases, return with success code. -exit 0 diff --git a/ci/test_python_scikit_learn_tests.sh b/ci/test_python_scikit_learn_tests.sh index 8ba2669d96..89a40bf953 100755 --- a/ci/test_python_scikit_learn_tests.sh +++ b/ci/test_python_scikit_learn_tests.sh @@ -2,7 +2,7 @@ # Copyright (c) 2025, NVIDIA CORPORATION. # Support invoking test script outside the script directory -cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../ +cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../ || exit 1 # Common setup steps shared by Python test jobs source ./ci/test_python_common.sh @@ -15,12 +15,14 @@ set +e rapids-logger "Running scikit-learn tests with cuML acceleration" # Run the tests -./ci/accel/scikit-learn-tests/run-tests.sh \ - --junitxml="${RAPIDS_TESTS_DIR}/junit-cuml-accel-scikit-learn.xml" || true +./python/cuml/cuml/accel/tests/scikit-learn/run-tests.sh \ + --numprocesses=8 \ + --dist=worksteal \ + --junitxml="${RAPIDS_TESTS_DIR}/junit-cuml-accel-scikit-learn.xml" # Analyze results and check pass rate threshold rapids-logger "Analyzing test results" -./ci/accel/scikit-learn-tests/summarize-results.sh \ +./python/cuml/cuml/accel/tests/scikit-learn/summarize-results.py \ --fail-below 80 \ "${RAPIDS_TESTS_DIR}/junit-cuml-accel-scikit-learn.xml" diff --git a/conda/environments/all_cuda-118_arch-aarch64.yaml b/conda/environments/all_cuda-118_arch-aarch64.yaml index fd98083a3f..4aef75f26b 100644 --- a/conda/environments/all_cuda-118_arch-aarch64.yaml +++ b/conda/environments/all_cuda-118_arch-aarch64.yaml @@ -60,6 +60,7 @@ dependencies: - pytest-xdist - pytest==7.* - python>=3.10,<3.13 +- pyyaml - raft-dask==25.6.*,>=0.0.0a0 - rapids-build-backend>=0.3.0,<0.4.0.dev0 - rapids-dask-dependency==25.6.*,>=0.0.0a0 diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index a542688748..cae91746cc 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -60,6 +60,7 @@ dependencies: - pytest-xdist - pytest==7.* - python>=3.10,<3.13 +- pyyaml - raft-dask==25.6.*,>=0.0.0a0 - rapids-build-backend>=0.3.0,<0.4.0.dev0 - rapids-dask-dependency==25.6.*,>=0.0.0a0 diff --git a/conda/environments/all_cuda-128_arch-aarch64.yaml b/conda/environments/all_cuda-128_arch-aarch64.yaml index 5e721fc730..ba60712ec0 100644 --- a/conda/environments/all_cuda-128_arch-aarch64.yaml +++ b/conda/environments/all_cuda-128_arch-aarch64.yaml @@ -56,6 +56,7 @@ dependencies: - pytest-xdist - pytest==7.* - python>=3.10,<3.13 +- pyyaml - raft-dask==25.6.*,>=0.0.0a0 - rapids-build-backend>=0.3.0,<0.4.0.dev0 - rapids-dask-dependency==25.6.*,>=0.0.0a0 diff --git a/conda/environments/all_cuda-128_arch-x86_64.yaml b/conda/environments/all_cuda-128_arch-x86_64.yaml index d44f8922a5..3f71ad2ab1 100644 --- a/conda/environments/all_cuda-128_arch-x86_64.yaml +++ b/conda/environments/all_cuda-128_arch-x86_64.yaml @@ -56,6 +56,7 @@ dependencies: - pytest-xdist - pytest==7.* - python>=3.10,<3.13 +- pyyaml - raft-dask==25.6.*,>=0.0.0a0 - rapids-build-backend>=0.3.0,<0.4.0.dev0 - rapids-dask-dependency==25.6.*,>=0.0.0a0 diff --git a/dependencies.yaml b/dependencies.yaml index 6688e68802..0d1aafac84 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -486,6 +486,7 @@ dependencies: - hypothesis>=6.0,<7 - nltk - numpydoc + - pyyaml - pytest==7.* - pytest-benchmark - pytest-cases diff --git a/python/cuml/cuml/accel/__init__.py b/python/cuml/cuml/accel/__init__.py index 5143d38450..a62d8439f9 100644 --- a/python/cuml/cuml/accel/__init__.py +++ b/python/cuml/cuml/accel/__init__.py @@ -17,7 +17,11 @@ from cuml.accel.core import enabled, install from cuml.accel.estimator_proxy import is_proxy from cuml.accel.magics import load_ipython_extension -from cuml.accel.pytest_plugin import pytest_load_initial_conftests +from cuml.accel.pytest_plugin import ( + pytest_addoption, + pytest_collection_modifyitems, + pytest_load_initial_conftests, +) __all__ = ( "enabled", @@ -25,4 +29,6 @@ "is_proxy", "load_ipython_extension", "pytest_load_initial_conftests", + "pytest_collection_modifyitems", + "pytest_addoption", ) diff --git a/python/cuml/cuml/accel/pytest_plugin.py b/python/cuml/cuml/accel/pytest_plugin.py index 9c00758ab9..352d567a90 100644 --- a/python/cuml/cuml/accel/pytest_plugin.py +++ b/python/cuml/cuml/accel/pytest_plugin.py @@ -14,6 +14,13 @@ # limitations under the License. # +from collections import defaultdict +from importlib.metadata import version +from pathlib import Path + +import yaml +from packaging.requirements import Requirement + from cuml.accel.core import install @@ -26,3 +33,89 @@ def pytest_load_initial_conftests(early_config, parser, args): raise RuntimeError( "An existing plugin has already loaded sklearn. Interposing failed." ) + + +def pytest_addoption(parser): + """Add command line option for xfail list file.""" + parser.addoption( + "--xfail-list", + action="store", + help="Path to YAML file containing list of test IDs to mark as xfail", + ) + + +def create_version_condition(condition_str: str) -> bool: + """Evaluate a version condition immediately. + + Args: + condition_str: String in format 'package[comparison]version' + For example: + - 'scikit-learn>=1.5.2' + - 'numpy<2.0.0' + - 'pandas==2.1.0' + + Returns: + bool: True if the condition is met, False otherwise + """ + if not condition_str: + return True + + try: + req = Requirement(condition_str) + installed_version = version(req.name) + return req.specifier.contains(installed_version) + except Exception: + return False + + +def pytest_collection_modifyitems(config, items): + """Apply xfail markers to tests listed in the xfail list file.""" + # Import pytest lazily to avoid requiring it for normal cuml usage. + # pytest is only needed when running tests. + import pytest + + xfail_list_path = config.getoption("xfail_list") + if not xfail_list_path: + return + + xfail_list_path = Path(xfail_list_path) + if not xfail_list_path.exists(): + raise ValueError(f"Xfail list file not found: {xfail_list_path}") + + xfail_list = yaml.safe_load(xfail_list_path.read_text()) + + if not isinstance(xfail_list, list): + raise ValueError("Xfail list must be a list of test entries") + + # Convert list of dicts into dict mapping test IDs to lists of xfail configs + xfail_configs = defaultdict(list) + for entry in xfail_list: + if not isinstance(entry, dict): + raise ValueError("Xfail list entry must be a dictionary") + if "id" not in entry: + raise ValueError("Xfail list entry must contain an 'id' field") + + test_id = entry["id"] + condition = True + if "condition" in entry: + condition = create_version_condition(entry["condition"]) + + config = { + "reason": entry.get("reason", "Test listed in xfail list"), + "strict": entry.get("strict", True), + "condition": condition, + } + + xfail_configs[test_id].append(config) + + for item in items: + test_id = f"{item.module.__name__}::{item.name}" + if test_id in xfail_configs: + for config in xfail_configs[test_id]: + item.add_marker( + pytest.mark.xfail( + reason=config["reason"], + strict=config["strict"], + condition=config["condition"], + ) + ) diff --git a/python/cuml/cuml/accel/tests/scikit-learn/README.md b/python/cuml/cuml/accel/tests/scikit-learn/README.md new file mode 100644 index 0000000000..9b5b70e359 --- /dev/null +++ b/python/cuml/cuml/accel/tests/scikit-learn/README.md @@ -0,0 +1,109 @@ +# scikit-learn Acceleration Tests + +This suite provides infrastructure to run and analyze tests for scikit-learn with cuML acceleration support. + +## Components + +- `run-tests.sh` + Executes scikit-learn tests using GPU-accelerated paths. Any arguments passed to the script are forwarded directly to pytest. + + Example usage: + ```bash + ./run-tests.sh # Run all tests + ./run-tests.sh -v -k test_kmeans # Run specific test with verbosity + ./run-tests.sh -x --pdb # Stop on first failure and debug + ``` + +- `summarize-results.py` + Analyzes test results from an XML report file and prints a summary or generates an xfail list. + Options: + - `-v, --verbose` : Display detailed failure information + - `-f, --fail-below VALUE` : Set a minimum pass rate threshold (0-100) + - `--format FORMAT` : Output format (summary or xfail_list) + - `--update-xfail-list PATH` : Path to existing xfail list to update + - `-i, --in-place` : Update the xfail list file in place + - `--xpassed ACTION` : How to handle XPASS tests (keep/remove/mark-flaky) + +## Usage + +### 1. Run tests and generate report +Run tests and save the report: +```bash +./run-tests.sh --junitxml=report.xml +``` + +**Tip**: Run tests in parallel with `-n auto` to use all available CPU cores: +```bash +./run-tests.sh --junitxml=report.xml -n auto +``` + +### 2. Analyze results +Generate a summary from the report: +```bash +./summarize-results.py -v -f 80 report.xml +``` + +## Xfail List + +The xfail list (`xfail-list.yaml`) is used to mark tests that are expected to fail. This is useful for: +- Tracking known issues +- Managing test failures during development +- Handling version-specific test failures +- Managing flaky tests that occasionally fail + +### Automatic Usage +The `run-tests.sh` script automatically uses an `xfail-list.yaml` file if present in the same directory. + +### Generating an Xfail List +The `summarize-results.py` script provides several ways to manage the xfail list: + +1. Generate a new xfail list from test results: +```bash +./summarize-results.py --format=xfail_list report.xml > xfail-list.yaml +``` + +2. Update an existing xfail list (in place): +```bash +./summarize-results.py --update-xfail-list=xfail-list.yaml --in-place report.xml +``` + +The script handles XPASS tests in three ways (controlled by `--xpassed`): +- `keep`: Preserve all xpassed tests in the list (default) +- `remove`: Remove xpassed tests from the list +- `mark-flaky`: Convert strict xpassed tests to non-strict (flaky) + +Example with all options: +```bash +./summarize-results.py --update-xfail-list=xfail-list.yaml --in-place --xpassed=mark-flaky report.xml +``` + +### Format +The xfail list is a YAML file containing test IDs to mark as xfail. Each entry can include: +- `id`: Test ID in format "module::test_name" +- `reason`: Optional reason for xfail (default: "Test listed in xfail list") +- `strict`: Whether to enforce xfail (default: true) +- `condition`: Optional version requirement (e.g., "scikit-learn>=1.5.2") + +Example: +```yaml +- id: "sklearn.linear_model.tests.test_logistic::test_logistic_regression" + reason: "Known issue with sparse inputs" + strict: true +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_convergence[42-elkan]" + condition: "scikit-learn<1.5.2" + reason: "Unsupported hyperparameter for older scikit-learn version." +- id: "sklearn.ensemble.tests.test_forest::test_random_forest_classifier" + reason: "Flaky test due to random seed sensitivity" + strict: false +``` + +**Note on `strict: false`**: +The `strict` flag should be set to `true` by default. Use `strict: false` only for: +- Tests that are genuinely non-deterministic (e.g., due to floating-point arithmetic) +- Tests that fail intermittently due to external factors (e.g., network timeouts) +- Tests that are known to be flaky but cannot be fixed immediately + +Ideally, Each use of `strict: false` should include: +- A clear explanation of why the test is non-deterministic +- A plan to fix the underlying issue +- Regular review to ensure the flag is still necessary diff --git a/ci/accel/scikit-learn-tests/run-tests.sh b/python/cuml/cuml/accel/tests/scikit-learn/run-tests.sh similarity index 80% rename from ci/accel/scikit-learn-tests/run-tests.sh rename to python/cuml/cuml/accel/tests/scikit-learn/run-tests.sh index 7dfd92a3fa..3a8653ac77 100755 --- a/ci/accel/scikit-learn-tests/run-tests.sh +++ b/python/cuml/cuml/accel/tests/scikit-learn/run-tests.sh @@ -11,4 +11,6 @@ set -eu -pytest -p cuml.accel --pyargs sklearn -v "$@" +pytest -p cuml.accel --pyargs sklearn -v \ + --xfail-list="$(dirname "$0")/xfail-list.yaml" \ + "$@" diff --git a/python/cuml/cuml/accel/tests/scikit-learn/summarize-results.py b/python/cuml/cuml/accel/tests/scikit-learn/summarize-results.py new file mode 100755 index 0000000000..65a123dfc0 --- /dev/null +++ b/python/cuml/cuml/accel/tests/scikit-learn/summarize-results.py @@ -0,0 +1,419 @@ +#!/usr/bin/env python3 +# Copyright (c) 2025, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Summarize test results from a JUnit XML report file.""" + +import argparse +import sys +import xml.etree.ElementTree as ET +from pathlib import Path + +import yaml + + +class QuoteTestID(str): + """String subclass to force quoting of test IDs.""" + + pass + + +def setup_yaml(): + """Configure YAML dumper with custom string handling.""" + + def quoted_scalar(dumper, data): + scalar_tag = "tag:yaml.org,2002:str" + return dumper.represent_scalar(scalar_tag, data, style='"') + + yaml.add_representer(QuoteTestID, quoted_scalar) + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser( + description="Summarize test results from a JUnit XML report file" + ) + parser.add_argument( + "report_file", + type=Path, + help="Path to the JUnit XML report file", + ) + parser.add_argument( + "-v", + "--verbose", + action="store_true", + help="Show detailed failure information", + ) + parser.add_argument( + "-f", + "--fail-below", + type=float, + default=0.0, + help="Minimum pass rate threshold [0-100] (default: 0)", + ) + parser.add_argument( + "--format", + choices=["summary", "xfail_list"], + default="summary", + help="Output format (default: summary)", + ) + parser.add_argument( + "--update-xfail-list", + type=Path, + help="Path to existing xfail list to update", + ) + parser.add_argument( + "-i", + "--in-place", + action="store_true", + help="Update the xfail list file in place", + ) + parser.add_argument( + "--xpassed", + choices=["keep", "remove", "mark-flaky"], + default="keep", + help="How to handle XPASS tests (default: keep)", + ) + return parser.parse_args() + + +def validate_threshold(threshold): + """Validate that the threshold is between 0 and 100.""" + if not 0 <= threshold <= 100: + raise ValueError("Threshold must be between 0 and 100") + + +def load_existing_xfail_list(path): + """Load existing xfail list from file.""" + if not path.exists(): + return [] + with open(path) as f: + return yaml.safe_load(f) or [] + + +def update_xfail_list(existing_list, test_results, xpassed_action="keep"): + """Update existing xfail list based on test results. + + Args: + existing_list: List of existing xfail entries + test_results: Dict containing test results by test ID + xpassed_action: How to handle XPASS tests + ("keep", "remove", or "mark-flaky") + + Returns: + Updated xfail list + """ + # Convert existing list to dict for easier lookup + existing_by_id = {entry["id"]: entry for entry in existing_list} + updated_entries = {} + + # First add all existing entries that are still failing, + # xfailing, or marked as non-strict + for test_id, entry in existing_by_id.items(): + if test_id in test_results: + result = test_results[test_id] + if result["status"] in ("fail", "xfail"): + # Keep failing tests + updated_entries[test_id] = entry + elif result["status"] == "xpass": + if xpassed_action == "keep": + # Keep all xpassed tests + updated_entries[test_id] = entry + elif xpassed_action == "mark-flaky" and entry.get( + "strict", True + ): + # Mark strict xpassed tests as flaky + entry = entry.copy() + entry["strict"] = False + entry["reason"] = "Test is flaky with cuml.accel" + updated_entries[test_id] = entry + # For "remove", we don't add xpassed tests at all + elif entry.get("strict", True) is False: + # Always keep non-strict tests + updated_entries[test_id] = entry + else: + # Test not in results - keep it to be safe + updated_entries[test_id] = entry + + # Then add any new failing tests + for test_id, result in test_results.items(): + if test_id not in updated_entries and result["status"] in ( + "fail", + "xfail", + ): + updated_entries[test_id] = {"id": QuoteTestID(test_id)} + + # Convert back to list and sort + # Ensure all test IDs are properly quoted + final_entries = [] + for entry in sorted(updated_entries.values(), key=lambda x: x["id"]): + entry = entry.copy() + entry["id"] = QuoteTestID(entry["id"]) + final_entries.append(entry) + return final_entries + + +def get_test_results(testsuite): + """Extract test results from testsuite. + + Returns dict mapping test IDs to their results. + """ + results = {} + for testcase in testsuite.findall(".//testcase"): + classname = testcase.get("classname", "") + if not classname.startswith("sklearn."): + classname = f"sklearn.{classname}" + test_id = f"{classname}::{testcase.get('name')}" + + failure = testcase.find("failure") + error = testcase.find("error") + skipped_elem = testcase.find("skipped") + + if failure is not None: + msg = str(failure.get("message")) + if "XPASS(strict)" in msg: + status = "xpass" + elif msg == "xfail": + status = "xfail" + else: + status = "fail" + elif error is not None: + if "XPASS(strict)" in str(error.get("message")): + status = "xpass" + else: + status = "fail" + elif ( + skipped_elem is not None + and skipped_elem.get("type") == "pytest.xfail" + ): + status = "xfail" + else: + status = "pass" + + results[test_id] = { + "status": status, + } + + return results + + +def format_table(rows, col_sep=" "): + """Format a table with aligned columns. + + Args: + rows: List of rows, where each row is a list of strings + col_sep: String to separate columns + + Returns: + List of formatted row strings + """ + if not rows: + return [] + + # Calculate column widths + num_cols = len(rows[0]) + col_widths = [0] * num_cols + for row in rows: + for i, cell in enumerate(row): + col_widths[i] = max(col_widths[i], len(cell)) + + # Format each row + formatted_rows = [] + for row in rows: + formatted_cells = [] + for i, cell in enumerate(row): + # Right-align numeric values, left-align text + if i > 0 and cell.replace(".", "").replace("%", "").isdigit(): + formatted_cells.append(f"{cell:>{col_widths[i]}}") + else: + formatted_cells.append(f"{cell:<{col_widths[i]}}") + formatted_rows.append(col_sep.join(formatted_cells)) + + return formatted_rows + + +def main(): + """Main entry point.""" + args = parse_args() + validate_threshold(args.fail_below) + + if not args.report_file.exists(): + print(f"Error: Report file not found: {args.report_file}") + sys.exit(1) + + try: + tree = ET.parse(args.report_file) + except ET.ParseError as e: + print(f"Error: Invalid XML file: {e}") + sys.exit(1) + + root = tree.getroot() + testsuite = root.find("testsuite") + if testsuite is None: + print("Error: No testsuite element found in XML file") + sys.exit(1) + + # Extract test statistics + total_tests = int(testsuite.get("tests", 0)) + total_errors = int(testsuite.get("errors", 0)) + total_skipped = int(testsuite.get("skipped", 0)) + time = float(testsuite.get("time", 0)) + + # Count failures, xfails, and xpasses separately + regular_failures = 0 + xfailed = 0 + xpassed_strict = 0 + xpassed_non_strict = 0 + for testcase in testsuite.findall(".//testcase"): + failure = testcase.find("failure") + error = testcase.find("error") + skipped_elem = testcase.find("skipped") + + if failure is not None: + msg = str(failure.get("message")) + if "XPASS(strict)" in msg: + xpassed_strict += 1 + elif "XPASS" in msg: + xpassed_non_strict += 1 + elif msg == "xfail": + xfailed += 1 + else: + regular_failures += 1 + elif error is not None: + msg = str(error.get("message")) + if "XPASS(strict)" in msg: + xpassed_strict += 1 + elif "XPASS" in msg: + xpassed_non_strict += 1 + else: + regular_failures += 1 + elif ( + skipped_elem is not None + and skipped_elem.get("type") == "pytest.xfail" + ): + xfailed += 1 + + # Calculate passed tests and pass rate + passed = ( + total_tests + - regular_failures + - xfailed + - xpassed_strict + - xpassed_non_strict + - total_errors + - total_skipped + ) + pass_rate = (passed / total_tests * 100) if total_tests > 0 else 0 + + if args.format == "xfail_list" or args.update_xfail_list: + # Get test results + test_results = get_test_results(testsuite) + + if args.update_xfail_list: + if not args.update_xfail_list.exists(): + print(f"Error: Xfail list not found: {args.update_xfail_list}") + sys.exit(1) + # Update existing xfail list + existing_list = load_existing_xfail_list(args.update_xfail_list) + xfail_list = update_xfail_list( + existing_list, test_results, args.xpassed + ) + # Write to file if in-place, otherwise print + setup_yaml() + if args.in_place: + with open(args.update_xfail_list, "w") as f: + yaml.dump( + xfail_list, f, sort_keys=False, width=float("inf") + ) + print(f"Updated {args.update_xfail_list}") + else: + print( + yaml.dump(xfail_list, sort_keys=False, width=float("inf")) + ) + else: + # Generate new xfail list + xfail_list = [] + for test_id, result in test_results.items(): + if result["status"] in ("fail", "xfail"): + xfail_list.append({"id": QuoteTestID(test_id)}) + xfail_list.sort(key=lambda x: x["id"]) + # Print to stdout + setup_yaml() + print(yaml.dump(xfail_list, sort_keys=False, width=float("inf"))) + return + + # Print summary + print("Test Summary:") + rows = [ + ["Total Tests:", str(total_tests)], + ["Passed:", str(passed)], + ["Failed:", str(regular_failures)], + ["XFailed:", str(xfailed)], + ["XPassed (strict):", str(xpassed_strict)], + ["XPassed (non-strict):", str(xpassed_non_strict)], + ["Errors:", str(total_errors)], + ["Skipped:", str(total_skipped)], + ["Pass Rate:", f"{pass_rate:.2f}%"], + ["Total Time:", f"{time:.2f}s"], + ] + for row in format_table(rows, " "): + print(f" {row}") + + # List failed tests in verbose mode + if (regular_failures + total_errors) > 0 and args.verbose: + print("\nFailed Tests:") + for testcase in testsuite.findall(".//testcase"): + failure = testcase.find("failure") + error = testcase.find("error") + if failure is not None or error is not None: + msg = "" + if failure is not None and failure.get("message") is not None: + msg = failure.get("message") + elif error is not None and error.get("message") is not None: + msg = error.get("message") + if "XPASS" in msg: + continue # Skip xpassed tests in failure list + elif msg == "xfail": + print(f" {testcase.get('name')} (xfail)") + else: + print(f" {testcase.get('name')}") + + # List strict xpasses in verbose mode + if xpassed_strict > 0 and args.verbose: + print("\nPotential Improvements (Strict XPASS):") + for testcase in testsuite.findall(".//testcase"): + failure = testcase.find("failure") + error = testcase.find("error") + if failure is not None or error is not None: + msg = "" + if failure is not None and failure.get("message") is not None: + msg = failure.get("message") + elif error is not None and error.get("message") is not None: + msg = error.get("message") + if "XPASS(strict)" in msg: + print(f" {testcase.get('name')}") + + # Check threshold + if pass_rate < args.fail_below: + print( + f"\nError: Pass rate {pass_rate:.2f}% is below threshold " + f"{args.fail_below}%" + ) + sys.exit(1) + + sys.exit(0) + + +if __name__ == "__main__": + main() diff --git a/python/cuml/cuml/accel/tests/scikit-learn/xfail-list.yaml b/python/cuml/cuml/accel/tests/scikit-learn/xfail-list.yaml new file mode 100644 index 0000000000..2d869d2d8a --- /dev/null +++ b/python/cuml/cuml/accel/tests/scikit-learn/xfail-list.yaml @@ -0,0 +1,1294 @@ +- id: "sklearn.cluster.tests.test_dbscan::test_dbscan_no_core_samples[csr_array]" +- id: "sklearn.cluster.tests.test_dbscan::test_dbscan_no_core_samples[csr_matrix]" +- id: "sklearn.cluster.tests.test_dbscan::test_dbscan_sparse_precomputed[False]" +- id: "sklearn.cluster.tests.test_dbscan::test_dbscan_sparse_precomputed[True]" +- id: "sklearn.cluster.tests.test_dbscan::test_dbscan_sparse_precomputed_different_eps" +- id: "sklearn.cluster.tests.test_k_means::test_all_init[KMeans-callable-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_all_init[KMeans-callable-sparse_array]" +- id: "sklearn.cluster.tests.test_k_means::test_all_init[KMeans-callable-sparse_matrix]" +- id: "sklearn.cluster.tests.test_k_means::test_dense_sparse[42-KMeans-X_csr0]" +- id: "sklearn.cluster.tests.test_k_means::test_dense_sparse[42-KMeans-X_csr1]" +- id: "sklearn.cluster.tests.test_k_means::test_float_precision[42-KMeans-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_integer_input[42-KMeans-k-means++-int32-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_integer_input[42-KMeans-k-means++-int64-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_integer_input[42-KMeans-ndarray-int32-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_integer_input[42-KMeans-ndarray-int64-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_convergence[42-elkan]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_convergence[42-lloyd]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_elkan_results[42-0-dense-blobs]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_elkan_results[42-0-dense-normal]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_elkan_results[42-1e-100-dense-blobs]" + strict: false + reason: Test is flaky with cuml.accel +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_empty_cluster_relocated[dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_init_auto_with_initial_centroids[KMeans--default]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_init_auto_with_initial_centroids[KMeans-array-like-1]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_init_auto_with_initial_centroids[KMeans-k-means++-1]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_init_auto_with_initial_centroids[KMeans-random-default]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_predict[float64-42-100-KMeans-elkan-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_predict[float64-42-100-KMeans-lloyd-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_predict[float64-42-2-KMeans-elkan-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_predict[float64-42-2-KMeans-lloyd-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_relocated_clusters[elkan-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_relocated_clusters[lloyd-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_results[float32-elkan-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_results[float32-lloyd-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_results[float64-elkan-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_results[float64-lloyd-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_verbose[0-elkan]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_verbose[0-lloyd]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_verbose[0.01-elkan]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_verbose[0.01-lloyd]" +- id: "sklearn.cluster.tests.test_k_means::test_kmeans_warns_less_centers_than_unique_points[42]" +- id: "sklearn.cluster.tests.test_k_means::test_n_init_auto[KMeans-10]" +- id: "sklearn.cluster.tests.test_k_means::test_predict_dense_sparse[KMeans-k-means++-X_csr0]" +- id: "sklearn.cluster.tests.test_k_means::test_predict_dense_sparse[KMeans-k-means++-X_csr1]" +- id: "sklearn.cluster.tests.test_k_means::test_predict_dense_sparse[KMeans-random-X_csr0]" +- id: "sklearn.cluster.tests.test_k_means::test_predict_dense_sparse[KMeans-random-X_csr1]" +- id: "sklearn.cluster.tests.test_k_means::test_predict_does_not_change_cluster_centers[None]" +- id: "sklearn.cluster.tests.test_k_means::test_relocating_with_duplicates[elkan-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_relocating_with_duplicates[lloyd-dense]" +- id: "sklearn.cluster.tests.test_k_means::test_score_max_iter[42-KMeans]" +- id: "sklearn.cluster.tests.test_k_means::test_transform[42-KMeans]" +- id: "sklearn.cluster.tests.test_k_means::test_warning_elkan_1_cluster" +- id: "sklearn.cluster.tests.test_k_means::test_warning_n_init_precomputed_centers[KMeans]" +- id: "sklearn.cluster.tests.test_k_means::test_weighted_vs_repeated[42]" +- id: "sklearn.cluster.tests.test_k_means::test_wrong_params[param0-n_samples.* should be >= n_clusters-KMeans]" +- id: "sklearn.cluster.tests.test_k_means::test_wrong_params[param1-The shape of the initial centers .* does not match the number of clusters-KMeans]" +- id: "sklearn.cluster.tests.test_k_means::test_wrong_params[param2-The shape of the initial centers .* does not match the number of clusters-KMeans]" +- id: "sklearn.cluster.tests.test_k_means::test_wrong_params[param3-The shape of the initial centers .* does not match the number of features of the data-KMeans]" +- id: "sklearn.cluster.tests.test_k_means::test_wrong_params[param4-The shape of the initial centers .* does not match the number of features of the data-KMeans]" +- id: "sklearn.cluster.tests.test_spectral::test_precomputed_nearest_neighbors_filtering" +- id: "sklearn.cross_decomposition.tests.test_pls::test_pls_regression_fit_1d_y" +- id: "sklearn.datasets.tests.test_arff_parser::test_pandas_arff_parser_strip_no_quotes[_liac_arff_parser]" +- id: "sklearn.decomposition.tests.test_pca::test_infer_dim_by_explained_variance[X0-0.95-2]" +- id: "sklearn.decomposition.tests.test_pca::test_infer_dim_by_explained_variance[X1-0.01-1]" +- id: "sklearn.decomposition.tests.test_pca::test_infer_dim_by_explained_variance[X2-0.5-2]" +- id: "sklearn.decomposition.tests.test_pca::test_mle_simple_case" +- id: "sklearn.decomposition.tests.test_pca::test_n_components_none[data0-arpack-3]" +- id: "sklearn.decomposition.tests.test_pca::test_n_components_none[data1-arpack-3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_dtype_preservation[42-arpack]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_dtype_preservation[42-auto]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_dtype_preservation[42-covariance_eigh]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_dtype_preservation[42-full]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_dtype_preservation[42-randomized]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_n_components_mostly_explained_variance_ratio" +- id: "sklearn.decomposition.tests.test_pca::test_pca_score3" +- id: "sklearn.decomposition.tests.test_pca::test_pca_solver_equivalence[42-float64-False-True-tall-arpack]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_solver_equivalence[42-float64-False-True-tall-covariance_eigh]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_solver_equivalence[42-float64-True-True-tall-arpack]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_solver_equivalence[42-float64-True-True-tall-covariance_eigh]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_array-1-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_array-1-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_array-1-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_array-10-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_array-10-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_array-10-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_array-2-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_array-2-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_array-2-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_matrix-1-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_matrix-1-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_matrix-1-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_matrix-10-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_matrix-10-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_matrix-10-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_matrix-2-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_matrix-2-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csc_matrix-2-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_array-1-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_array-1-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_array-1-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_array-10-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_array-10-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_array-10-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_array-2-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_array-2-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_array-2-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_matrix-1-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_matrix-1-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_matrix-1-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_matrix-10-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_matrix-10-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_matrix-10-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_matrix-2-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_matrix-2-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-arpack-csr_matrix-2-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_array-1-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_array-1-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_array-1-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_array-10-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_array-10-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_array-10-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_array-2-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_array-2-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_array-2-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_matrix-1-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_matrix-1-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_matrix-1-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_matrix-10-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_matrix-10-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_matrix-10-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_matrix-2-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_matrix-2-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csc_matrix-2-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_array-1-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_array-1-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_array-1-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_array-10-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_array-10-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_array-10-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_array-2-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_array-2-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_array-2-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_matrix-1-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_matrix-1-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_matrix-1-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_matrix-10-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_matrix-10-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_matrix-10-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-1-covariance_eigh-csr_matrix-2-0.01]" +- id: 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"sklearn.decomposition.tests.test_pca::test_pca_sparse[42-100-covariance_eigh-csr_matrix-2-0.01]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-100-covariance_eigh-csr_matrix-2-0.1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse[42-100-covariance_eigh-csr_matrix-2-0.3]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse_fit_transform[42-csc_array]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse_fit_transform[42-csc_matrix]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse_fit_transform[42-csr_array]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_sparse_fit_transform[42-csr_matrix]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_svd_solver_auto[10-50-5-full]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_svd_solver_auto[1000-50-50-covariance_eigh]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_svd_solver_auto[1000-500-0.5-full]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_svd_solver_auto[1000-500-10-randomized]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_svd_solver_auto[1000-500-400-full]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_validation[arpack-0-must be between 1 and min\\\\(n_samples, n_features\\\\)-data0]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_validation[arpack-0-must be between 1 and min\\\\(n_samples, n_features\\\\)-data1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_validation[arpack-2-must be strictly less than min-data0]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_validation[arpack-2-must be strictly less than min-data1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_validation[auto-3-n_components=3 must be between 0 and min\\\\(n_samples, n_features\\\\)=2 with svd_solver='full'-data0]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_validation[auto-3-n_components=3 must be between 0 and min\\\\(n_samples, n_features\\\\)=2 with svd_solver='full'-data1]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_validation[randomized-0-must be between 1 and min\\\\(n_samples, n_features\\\\)-data0]" +- id: "sklearn.decomposition.tests.test_pca::test_pca_validation[randomized-0-must be between 1 and min\\\\(n_samples, n_features\\\\)-data1]" +- id: "sklearn.decomposition.tests.test_pca::test_sparse_pca_solver_error[42-csc_array-full]" +- id: "sklearn.decomposition.tests.test_pca::test_sparse_pca_solver_error[42-csc_array-randomized]" +- id: "sklearn.decomposition.tests.test_pca::test_sparse_pca_solver_error[42-csc_matrix-full]" +- id: "sklearn.decomposition.tests.test_pca::test_sparse_pca_solver_error[42-csc_matrix-randomized]" +- id: "sklearn.decomposition.tests.test_pca::test_sparse_pca_solver_error[42-csr_array-full]" +- id: "sklearn.decomposition.tests.test_pca::test_sparse_pca_solver_error[42-csr_array-randomized]" +- id: "sklearn.decomposition.tests.test_pca::test_sparse_pca_solver_error[42-csr_matrix-full]" +- id: "sklearn.decomposition.tests.test_pca::test_sparse_pca_solver_error[42-csr_matrix-randomized]" +- id: "sklearn.decomposition.tests.test_truncated_svd::test_explained_variance_components_10_20[arpack-sparse]" + strict: false + reason: Test is flaky with cuml.accel +- id: "sklearn.decomposition.tests.test_truncated_svd::test_explained_variance_components_10_20[randomized-sparse]" + strict: false + reason: Test is flaky with cuml.accel +- id: "sklearn.decomposition.tests.test_truncated_svd::test_truncated_svd_eq_pca" +- id: "sklearn.ensemble.tests.test_forest::test_1d_input[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_1d_input[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_backend_respected" +- id: "sklearn.ensemble.tests.test_forest::test_class_weight_balanced_and_bootstrap_multi_output[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_class_weight_errors[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_class_weights[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_classes_shape[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_decision_path[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_decision_path[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_dtype_convert" +- id: "sklearn.ensemble.tests.test_forest::test_estimators_samples[RandomForestClassifier-False-1]" +- id: "sklearn.ensemble.tests.test_forest::test_estimators_samples[RandomForestClassifier-False-None]" +- id: "sklearn.ensemble.tests.test_forest::test_estimators_samples[RandomForestClassifier-True-1]" +- id: "sklearn.ensemble.tests.test_forest::test_estimators_samples[RandomForestClassifier-True-None]" +- id: "sklearn.ensemble.tests.test_forest::test_estimators_samples[RandomForestRegressor-False-1]" +- id: "sklearn.ensemble.tests.test_forest::test_estimators_samples[RandomForestRegressor-False-None]" +- id: "sklearn.ensemble.tests.test_forest::test_estimators_samples[RandomForestRegressor-True-1]" +- id: "sklearn.ensemble.tests.test_forest::test_estimators_samples[RandomForestRegressor-True-None]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X0-y0-0.9-array-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X0-y0-0.9-sparse_csc-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X0-y0-0.9-sparse_csr-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X1-y1-0.65-array-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X1-y1-0.65-sparse_csc-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X1-y1-0.65-sparse_csr-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X2-y2-0.65-array-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X2-y2-0.65-sparse_csc-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X2-y2-0.65-sparse_csr-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X3-y3-0.18-array-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X3-y3-0.18-sparse_csc-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[True-X3-y3-0.18-sparse_csr-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X0-y0-0.9-array-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X0-y0-0.9-sparse_csc-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X0-y0-0.9-sparse_csr-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X1-y1-0.65-array-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X1-y1-0.65-sparse_csc-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X1-y1-0.65-sparse_csr-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X2-y2-0.65-array-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X2-y2-0.65-sparse_csc-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X2-y2-0.65-sparse_csr-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X3-y3-0.18-array-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X3-y3-0.18-sparse_csc-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_classifier_oob[oob_score1-X3-y3-0.18-sparse_csr-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_feature_importances_sum" +- id: "sklearn.ensemble.tests.test_forest::test_forest_multioutput_integral_regression_target[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[True-X0-y0-0.7-array-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[True-X0-y0-0.7-sparse_csc-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[True-X0-y0-0.7-sparse_csr-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[True-X1-y1-0.55-array-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[True-X1-y1-0.55-sparse_csc-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[True-X1-y1-0.55-sparse_csr-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[explained_variance_score-X0-y0-0.7-array-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[explained_variance_score-X0-y0-0.7-sparse_csc-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[explained_variance_score-X0-y0-0.7-sparse_csr-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[explained_variance_score-X1-y1-0.55-array-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[explained_variance_score-X1-y1-0.55-sparse_csc-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_regressor_oob[explained_variance_score-X1-y1-0.55-sparse_csr-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_y_sparse[csr_array]" +- id: "sklearn.ensemble.tests.test_forest::test_forest_y_sparse[csr_matrix]" +- id: "sklearn.ensemble.tests.test_forest::test_importances[RandomForestClassifier-gini-float32]" +- id: "sklearn.ensemble.tests.test_forest::test_importances[RandomForestClassifier-gini-float64]" +- id: "sklearn.ensemble.tests.test_forest::test_importances[RandomForestClassifier-log_loss-float32]" +- id: "sklearn.ensemble.tests.test_forest::test_importances[RandomForestClassifier-log_loss-float64]" +- id: "sklearn.ensemble.tests.test_forest::test_importances[RandomForestRegressor-absolute_error-float32]" +- id: "sklearn.ensemble.tests.test_forest::test_importances[RandomForestRegressor-absolute_error-float64]" +- id: "sklearn.ensemble.tests.test_forest::test_importances[RandomForestRegressor-friedman_mse-float32]" +- id: "sklearn.ensemble.tests.test_forest::test_importances[RandomForestRegressor-friedman_mse-float64]" +- id: "sklearn.ensemble.tests.test_forest::test_importances[RandomForestRegressor-squared_error-float32]" +- id: "sklearn.ensemble.tests.test_forest::test_importances[RandomForestRegressor-squared_error-float64]" +- id: "sklearn.ensemble.tests.test_forest::test_iris_criterion[log_loss-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_large_max_samples_exception[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_large_max_samples_exception[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_little_tree_with_small_max_samples[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_little_tree_with_small_max_samples[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_max_leaf_nodes_max_depth[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_max_leaf_nodes_max_depth[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_max_samples_bootstrap[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_max_samples_bootstrap[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_memory_layout[float32-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_memory_layout[float32-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_memory_layout[float64-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_memory_layout[float64-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_min_impurity_decrease" +- id: "sklearn.ensemble.tests.test_forest::test_min_samples_leaf[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_min_samples_leaf[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_min_samples_split[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_min_samples_split[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_min_weight_fraction_leaf[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_min_weight_fraction_leaf[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_missing_value_is_predictive[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_missing_value_is_predictive[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_mse_criterion_object_segfault_smoke_test[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_multioutput[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_multioutput[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_multioutput_string[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_oob_not_computed_twice[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_oob_not_computed_twice[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_poisson_y_positive_check" +- id: "sklearn.ensemble.tests.test_forest::test_probability[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_regression_criterion[friedman_mse-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_regression_criterion[squared_error-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[coo_array-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[coo_array-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[coo_matrix-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[coo_matrix-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[csc_array-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[csc_array-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[csc_matrix-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[csc_matrix-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[csr_array-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[csr_array-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[csr_matrix-RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_sparse_input[csr_matrix-RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_unfitted_feature_importances[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_unfitted_feature_importances[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_warm_start[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_warm_start[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_warm_start_equal_n_estimators[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_warm_start_equal_n_estimators[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_warm_start_oob[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_warm_start_oob[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_forest::test_warm_start_smaller_n_estimators[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_forest::test_warm_start_smaller_n_estimators[RandomForestRegressor]" +- id: "sklearn.ensemble.tests.test_stacking::test_stacking_classifier_multilabel_auto_predict[False-auto]" +- id: "sklearn.ensemble.tests.test_stacking::test_stacking_classifier_multilabel_auto_predict[False-predict]" +- id: "sklearn.ensemble.tests.test_stacking::test_stacking_classifier_multilabel_auto_predict[True-auto]" +- id: "sklearn.ensemble.tests.test_stacking::test_stacking_classifier_multilabel_auto_predict[True-predict]" +- id: "sklearn.ensemble.tests.test_stacking::test_stacking_classifier_multilabel_predict_proba[RandomForestClassifier]" +- id: "sklearn.ensemble.tests.test_stacking::test_stacking_classifier_sparse_passthrough[coo_array]" +- id: "sklearn.ensemble.tests.test_stacking::test_stacking_classifier_sparse_passthrough[csc_array]" +- id: "sklearn.ensemble.tests.test_stacking::test_stacking_classifier_sparse_passthrough[csr_array]" +- id: "sklearn.ensemble.tests.test_stacking::test_stacking_without_n_features_in[make_regression-StackingRegressor-LinearRegression]" +- id: "sklearn.ensemble.tests.test_voting::test_none_estimator_with_weights[X0-y0-voter0]" +- id: "sklearn.ensemble.tests.test_voting::test_none_estimator_with_weights[X1-y1-voter1]" +- id: "sklearn.ensemble.tests.test_voting::test_predict_on_toy_problem[42]" +- id: "sklearn.ensemble.tests.test_voting::test_sample_weight[42]" +- id: "sklearn.ensemble.tests.test_voting::test_set_estimator_drop" +- id: "sklearn.ensemble.tests.test_voting::test_tie_situation" +- id: "sklearn.ensemble.tests.test_weight_boosting::test_estimator" +- id: "sklearn.feature_selection.tests.test_from_model::test_feature_importances" +- id: "sklearn.feature_selection.tests.test_from_model::test_prefit_get_feature_names_out" +- id: "sklearn.feature_selection.tests.test_from_model::test_threshold_string" +- id: "sklearn.feature_selection.tests.test_rfe::test_multioutput[RFECV]" +- id: "sklearn.feature_selection.tests.test_rfe::test_multioutput[RFE]" +- id: "sklearn.feature_selection.tests.test_rfe::test_rfe_cv_groups" +- id: "sklearn.feature_selection.tests.test_rfe::test_rfe_features_importance" +- id: "sklearn.feature_selection.tests.test_sequential::test_unsupervised_model_fit[2]" + strict: false + reason: Test is flaky with cuml.accel +- id: "sklearn.feature_selection.tests.test_sequential::test_unsupervised_model_fit[3]" + strict: false + reason: Test is flaky with cuml.accel +- id: "sklearn.inspection.tests.test_partial_dependence::test_multiclass_multioutput[KNeighborsClassifier]" +- id: "sklearn.inspection.tests.test_partial_dependence::test_multiclass_multioutput[RandomForestClassifier]" +- id: "sklearn.inspection.tests.test_partial_dependence::test_partial_dependence_sample_weight_with_recursion" +- id: "sklearn.inspection.tests.test_partial_dependence::test_partial_dependence_unfitted[estimator0]" +- id: "sklearn.inspection.tests.test_partial_dependence::test_partial_dependence_unfitted[estimator1]" +- id: "sklearn.inspection.tests.test_partial_dependence::test_recursion_decision_tree_vs_forest_and_gbdt[0]" +- id: "sklearn.inspection.tests.test_permutation_importance::test_permutation_importance_correlated_feature_regression[ones-0.5-1]" +- id: "sklearn.inspection.tests.test_permutation_importance::test_permutation_importance_correlated_feature_regression[ones-0.5-2]" +- id: "sklearn.inspection.tests.test_permutation_importance::test_permutation_importance_correlated_feature_regression[ones-1.0-1]" +- id: "sklearn.inspection.tests.test_permutation_importance::test_permutation_importance_correlated_feature_regression[ones-1.0-2]" +- id: "sklearn.inspection.tests.test_permutation_importance::test_permutation_importance_sample_weight" +- id: "sklearn.inspection.tests.test_permutation_importance::test_robustness_to_high_cardinality_noisy_feature[0.5-1]" +- id: "sklearn.inspection.tests.test_permutation_importance::test_robustness_to_high_cardinality_noisy_feature[0.5-2]" +- id: "sklearn.inspection.tests.test_permutation_importance::test_robustness_to_high_cardinality_noisy_feature[1.0-1]" +- id: "sklearn.inspection.tests.test_permutation_importance::test_robustness_to_high_cardinality_noisy_feature[1.0-2]" +- id: "sklearn.linear_model.tests.test_base::test_inplace_data_preprocessing[42-False-None]" +- id: "sklearn.linear_model.tests.test_base::test_inplace_data_preprocessing[42-True-None]" +- id: "sklearn.linear_model.tests.test_base::test_linear_regression" +- id: "sklearn.linear_model.tests.test_base::test_linear_regression_pd_sparse_dataframe_warning" +- id: "sklearn.linear_model.tests.test_base::test_linear_regression_sample_weight_consistency[42-False-None]" +- id: "sklearn.linear_model.tests.test_base::test_linear_regression_sample_weight_consistency[42-True-None]" +- id: 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"sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed1-20-float64-0.2-lsqr-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed1-20-float64-0.2-ridgecv-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed1-20-float64-0.2-ridgecv-csr_array]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed1-20-float64-0.2-ridgecv-csr_matrix]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed1-20-float64-0.2-sag-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed1-20-float64-0.2-saga-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed1-20-float64-0.2-sparse_cg-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed1-20-float64-0.2-sparse_cg-csr_array]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed1-20-float64-0.2-sparse_cg-csr_matrix]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float32-0.1-cholesky-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float32-0.1-lsqr-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float32-0.1-ridgecv-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float32-0.1-ridgecv-csr_array]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float32-0.1-ridgecv-csr_matrix]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float32-0.1-sag-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float32-0.1-saga-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float32-0.1-sparse_cg-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float32-0.1-sparse_cg-csr_array]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float32-0.1-sparse_cg-csr_matrix]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float64-0.2-cholesky-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float64-0.2-lsqr-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float64-0.2-ridgecv-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float64-0.2-ridgecv-csr_array]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float64-0.2-ridgecv-csr_matrix]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float64-0.2-sag-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float64-0.2-saga-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float64-0.2-sparse_cg-None]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float64-0.2-sparse_cg-csr_array]" +- id: "sklearn.linear_model.tests.test_ridge::test_solver_consistency[seed2-20-float64-0.2-sparse_cg-csr_matrix]" +- id: "sklearn.linear_model.tests.test_sag::test_binary_classifier_class_weight[csr_array]" +- id: "sklearn.linear_model.tests.test_sag::test_binary_classifier_class_weight[csr_matrix]" +- id: "sklearn.linear_model.tests.test_sag::test_classifier_results[csr_array]" +- id: "sklearn.linear_model.tests.test_sag::test_classifier_single_class" +- id: "sklearn.linear_model.tests.test_sag::test_sag_classifier_computed_correctly[csr_array]" +- id: "sklearn.linear_model.tests.test_sag::test_sag_classifier_computed_correctly[csr_matrix]" +- id: "sklearn.linear_model.tests.test_sag::test_sag_classifier_raises_error[sag]" +- id: "sklearn.linear_model.tests.test_sag::test_sag_classifier_raises_error[saga]" +- id: "sklearn.linear_model.tests.test_sag::test_sag_multiclass_computed_correctly[csr_array]" +- id: "sklearn.linear_model.tests.test_sag::test_sag_multiclass_computed_correctly[csr_matrix]" +- id: "sklearn.linear_model.tests.test_sag::test_sag_pobj_matches_logistic_regression[csr_array]" +- id: "sklearn.linear_model.tests.test_sag::test_step_size_alpha_error" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_multitarget[csc_array]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_multitarget[csc_matrix]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_explicit_sparse_input[lil_array]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_explicit_sparse_input[lil_matrix]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_list_input[csc_array-False]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_list_input[csc_array-True]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_list_input[csc_matrix-False]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_enet_toy_list_input[csc_matrix-True]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_lasso_zero[csc_array]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_lasso_zero[csc_matrix]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_same_multiple_output_sparse_dense[coo_array]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_same_multiple_output_sparse_dense[coo_matrix]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-6-24-False-ElasticNet]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-6-24-False-Lasso]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-6-24-True-ElasticNet]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-False-6-24-True-Lasso]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-6-24-False-ElasticNet]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-6-24-False-Lasso]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-6-24-True-ElasticNet]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_array-True-6-24-True-Lasso]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-6-24-False-ElasticNet]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-6-24-False-Lasso]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-6-24-True-ElasticNet]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-False-6-24-True-Lasso]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-True-6-24-False-ElasticNet]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-True-6-24-False-Lasso]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-True-6-24-True-ElasticNet]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_dense_equality[csc_matrix-True-6-24-True-Lasso]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_enet_not_as_toy_dataset[0.001-False-True-csc_array]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_enet_not_as_toy_dataset[0.001-False-True-csc_matrix]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_enet_not_as_toy_dataset[0.001-True-True-csc_array]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_enet_not_as_toy_dataset[0.001-True-True-csc_matrix]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_enet_not_as_toy_dataset[0.1-False-False-csc_array]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_enet_not_as_toy_dataset[0.1-False-False-csc_matrix]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_enet_not_as_toy_dataset[0.1-True-False-csc_array]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_enet_not_as_toy_dataset[0.1-True-False-csc_matrix]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_lasso_not_as_toy_dataset[csc_array]" +- id: "sklearn.linear_model.tests.test_sparse_coordinate_descent::test_sparse_lasso_not_as_toy_dataset[csc_matrix]" +- id: "sklearn.manifold.tests.test_spectral_embedding::test_pipeline_spectral_clustering" +- id: "sklearn.manifold.tests.test_t_sne::test_accessible_kl_divergence" +- id: "sklearn.manifold.tests.test_t_sne::test_bad_precomputed_distances[D0-.* square distance matrix-barnes_hut-asarray]" +- id: "sklearn.manifold.tests.test_t_sne::test_bad_precomputed_distances[D0-.* square distance matrix-barnes_hut-csr_array]" +- id: "sklearn.manifold.tests.test_t_sne::test_bad_precomputed_distances[D0-.* square distance matrix-barnes_hut-csr_matrix]" +- id: "sklearn.manifold.tests.test_t_sne::test_bad_precomputed_distances[D0-.* square distance matrix-exact-asarray]" +- id: "sklearn.manifold.tests.test_t_sne::test_bad_precomputed_distances[D1-.* positive.*-barnes_hut-asarray]" +- id: "sklearn.manifold.tests.test_t_sne::test_bad_precomputed_distances[D1-.* positive.*-barnes_hut-csr_array]" +- id: "sklearn.manifold.tests.test_t_sne::test_bad_precomputed_distances[D1-.* positive.*-barnes_hut-csr_matrix]" +- id: "sklearn.manifold.tests.test_t_sne::test_bad_precomputed_distances[D1-.* positive.*-exact-asarray]" +- id: "sklearn.manifold.tests.test_t_sne::test_bh_match_exact" +- id: "sklearn.manifold.tests.test_t_sne::test_binary_perplexity_stability" +- id: "sklearn.manifold.tests.test_t_sne::test_early_exaggeration_used" +- id: "sklearn.manifold.tests.test_t_sne::test_exact_no_precomputed_sparse[csr_array]" +- id: "sklearn.manifold.tests.test_t_sne::test_exact_no_precomputed_sparse[csr_matrix]" +- id: "sklearn.manifold.tests.test_t_sne::test_fit_transform_csr_matrix[csr_array-barnes_hut]" +- id: "sklearn.manifold.tests.test_t_sne::test_fit_transform_csr_matrix[csr_array-exact]" +- id: "sklearn.manifold.tests.test_t_sne::test_fit_transform_csr_matrix[csr_matrix-barnes_hut]" + strict: false + reason: Test is flaky with cuml.accel +- id: "sklearn.manifold.tests.test_t_sne::test_high_perplexity_precomputed_sparse_distances[csr_array]" +- id: "sklearn.manifold.tests.test_t_sne::test_high_perplexity_precomputed_sparse_distances[csr_matrix]" +- id: "sklearn.manifold.tests.test_t_sne::test_init_ndarray" +- id: "sklearn.manifold.tests.test_t_sne::test_init_ndarray_precomputed" +- id: "sklearn.manifold.tests.test_t_sne::test_max_iter_used" +- id: "sklearn.manifold.tests.test_t_sne::test_n_components_range" +- id: "sklearn.manifold.tests.test_t_sne::test_n_iter_without_progress" +- id: "sklearn.manifold.tests.test_t_sne::test_non_positive_computed_distances" +- id: "sklearn.manifold.tests.test_t_sne::test_optimization_minimizes_kl_divergence" +- id: "sklearn.manifold.tests.test_t_sne::test_pca_initialization_not_compatible_with_precomputed_kernel" +- id: "sklearn.manifold.tests.test_t_sne::test_pca_initialization_not_compatible_with_sparse_input[csr_array]" +- id: "sklearn.manifold.tests.test_t_sne::test_pca_initialization_not_compatible_with_sparse_input[csr_matrix]" +- id: "sklearn.manifold.tests.test_t_sne::test_preserve_trustworthiness_approximately[pca-barnes_hut]" +- id: "sklearn.manifold.tests.test_t_sne::test_preserve_trustworthiness_approximately[pca-exact]" +- id: "sklearn.manifold.tests.test_t_sne::test_preserve_trustworthiness_approximately[random-barnes_hut]" +- id: "sklearn.manifold.tests.test_t_sne::test_preserve_trustworthiness_approximately[random-exact]" +- id: "sklearn.manifold.tests.test_t_sne::test_preserve_trustworthiness_approximately_with_precomputed_distances" +- id: "sklearn.manifold.tests.test_t_sne::test_reduction_to_one_component" +- id: "sklearn.manifold.tests.test_t_sne::test_sparse_precomputed_distance[csr_array]" +- id: "sklearn.manifold.tests.test_t_sne::test_sparse_precomputed_distance[csr_matrix]" +- id: "sklearn.manifold.tests.test_t_sne::test_sparse_precomputed_distance[lil_array]" +- id: "sklearn.manifold.tests.test_t_sne::test_sparse_precomputed_distance[lil_matrix]" +- id: "sklearn.manifold.tests.test_t_sne::test_tnse_n_iter_deprecated" +- id: "sklearn.manifold.tests.test_t_sne::test_tnse_n_iter_max_iter_both_set" +- id: "sklearn.manifold.tests.test_t_sne::test_tsne_n_jobs[barnes_hut]" +- id: "sklearn.manifold.tests.test_t_sne::test_tsne_n_jobs[exact]" +- id: "sklearn.manifold.tests.test_t_sne::test_tsne_perplexity_validation[20]" +- id: "sklearn.manifold.tests.test_t_sne::test_tsne_perplexity_validation[30]" +- id: "sklearn.manifold.tests.test_t_sne::test_tsne_with_different_distance_metrics[barnes_hut-cosine-cosine_distances]" +- id: "sklearn.manifold.tests.test_t_sne::test_tsne_with_different_distance_metrics[barnes_hut-manhattan-manhattan_distances]" +- id: "sklearn.manifold.tests.test_t_sne::test_tsne_with_different_distance_metrics[exact-cosine-cosine_distances]" +- id: "sklearn.manifold.tests.test_t_sne::test_tsne_with_different_distance_metrics[exact-manhattan-manhattan_distances]" +- id: "sklearn.manifold.tests.test_t_sne::test_tsne_with_mahalanobis_distance" +- id: "sklearn.manifold.tests.test_t_sne::test_uniform_grid[barnes_hut]" +- id: "sklearn.manifold.tests.test_t_sne::test_uniform_grid[exact]" +- id: "sklearn.manifold.tests.test_t_sne::test_verbose" +- id: "sklearn.metrics._plot.tests.test_common_curve_display::test_display_curve_not_fitted_errors[DetCurveDisplay-clf0]" +- id: "sklearn.metrics._plot.tests.test_common_curve_display::test_display_curve_not_fitted_errors[DetCurveDisplay-clf1]" +- id: "sklearn.metrics._plot.tests.test_common_curve_display::test_display_curve_not_fitted_errors[DetCurveDisplay-clf2]" +- id: "sklearn.metrics._plot.tests.test_common_curve_display::test_display_curve_not_fitted_errors[PrecisionRecallDisplay-clf0]" +- id: "sklearn.metrics._plot.tests.test_common_curve_display::test_display_curve_not_fitted_errors[PrecisionRecallDisplay-clf1]" +- id: "sklearn.metrics._plot.tests.test_common_curve_display::test_display_curve_not_fitted_errors[PrecisionRecallDisplay-clf2]" +- id: "sklearn.metrics._plot.tests.test_common_curve_display::test_display_curve_not_fitted_errors[RocCurveDisplay-clf0]" +- id: "sklearn.metrics._plot.tests.test_common_curve_display::test_display_curve_not_fitted_errors[RocCurveDisplay-clf1]" +- id: "sklearn.metrics._plot.tests.test_common_curve_display::test_display_curve_not_fitted_errors[RocCurveDisplay-clf2]" +- id: "sklearn.metrics._plot.tests.test_confusion_matrix_display::test_confusion_matrix_pipeline[clf]" +- id: "sklearn.metrics._plot.tests.test_precision_recall_display::test_precision_recall_display_pipeline[clf0]" +- id: "sklearn.metrics._plot.tests.test_precision_recall_display::test_precision_recall_display_pipeline[clf1]" +- id: "sklearn.metrics._plot.tests.test_predict_error_display::test_from_estimator_not_fitted" +- id: "sklearn.metrics._plot.tests.test_roc_curve_display::test_roc_curve_display_complex_pipeline[from_estimator-clf0]" +- id: "sklearn.metrics._plot.tests.test_roc_curve_display::test_roc_curve_display_complex_pipeline[from_estimator-clf1]" +- id: "sklearn.metrics._plot.tests.test_roc_curve_display::test_roc_curve_display_complex_pipeline[from_estimator-clf2]" +- id: "sklearn.metrics.tests.test_pairwise::test_euclidean_distances_extreme_values[1-float64-1e-08-0.99]" +- id: "sklearn.metrics.tests.test_pairwise::test_euclidean_distances_extreme_values[1000000-float64-1e-08-0.99]" +- id: "sklearn.metrics.tests.test_score_objects::test_get_scorer_multilabel_indicator" +- id: "sklearn.metrics.tests.test_score_objects::test_multimetric_scorer_calls_method_once_classifier_no_decision[scorers0]" +- id: "sklearn.metrics.tests.test_score_objects::test_multimetric_scorer_calls_method_once_classifier_no_decision[scorers1]" +- id: "sklearn.model_selection.tests.test_classification_threshold::test_fit_and_score_over_thresholds_sample_weight" +- id: "sklearn.model_selection.tests.test_search::test_grid_search_pipeline_steps" +- id: "sklearn.model_selection.tests.test_search::test_search_cv_pairwise_property_equivalence_of_precomputed" +- id: "sklearn.model_selection.tests.test_search::test_unsupervised_grid_search" +- id: "sklearn.model_selection.tests.test_validation::test_cross_val_predict[coo_array]" +- id: "sklearn.model_selection.tests.test_validation::test_cross_val_predict[coo_matrix]" +- id: "sklearn.model_selection.tests.test_validation::test_cross_val_predict_class_subset" +- id: "sklearn.model_selection.tests.test_validation::test_cross_val_predict_with_method_multilabel_rf" +- id: "sklearn.model_selection.tests.test_validation::test_cross_val_predict_with_method_multilabel_rf_rare_class" +- id: "sklearn.neighbors.tests.test_neighbors::test_KNeighborsClassifier_multioutput" +- id: "sklearn.neighbors.tests.test_neighbors::test_KNeighborsClassifier_raise_on_all_zero_weights" +- id: "sklearn.neighbors.tests.test_neighbors::test_KNeighborsRegressor_multioutput_uniform_weight" +- id: "sklearn.neighbors.tests.test_neighbors::test_auto_algorithm[X0-precomputed-None-brute]" +- id: "sklearn.neighbors.tests.test_neighbors::test_auto_algorithm[X3-euclidean-None-kd_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_auto_algorithm[X4-seuclidean-metric_params4-ball_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_callable_metric" +- id: "sklearn.neighbors.tests.test_neighbors::test_dtype_convert" +- id: "sklearn.neighbors.tests.test_neighbors::test_k_and_radius_neighbors_X_None[auto]" +- id: "sklearn.neighbors.tests.test_neighbors::test_k_and_radius_neighbors_X_None[ball_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_k_and_radius_neighbors_X_None[brute]" +- id: "sklearn.neighbors.tests.test_neighbors::test_k_and_radius_neighbors_X_None[kd_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_k_and_radius_neighbors_duplicates[auto]" +- id: "sklearn.neighbors.tests.test_neighbors::test_k_and_radius_neighbors_duplicates[ball_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_k_and_radius_neighbors_duplicates[brute]" +- id: "sklearn.neighbors.tests.test_neighbors::test_k_and_radius_neighbors_duplicates[kd_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_k_and_radius_neighbors_train_is_not_query" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_brute_backend[float64-42-braycurtis]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_brute_backend[float64-42-dice]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_brute_backend[float64-42-hamming]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_brute_backend[float64-42-jaccard]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_brute_backend[float64-42-nan_euclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_brute_backend[float64-42-rogerstanimoto]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_brute_backend[float64-42-russellrao]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_brute_backend[float64-42-seuclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_brute_backend[float64-42-sokalmichener]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_brute_backend[float64-42-sokalsneath]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_brute_backend[float64-42-yule]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-_weight_func-auto]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-_weight_func-ball_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-_weight_func-brute]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-_weight_func-kd_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-distance-auto]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-distance-ball_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-distance-brute]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-distance-kd_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-uniform-auto]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-uniform-ball_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-uniform-brute]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier[float64-uniform-kd_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier_predict_proba[float64]" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_classifier_sparse" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_regressor" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_regressor_multioutput" +- id: "sklearn.neighbors.tests.test_neighbors::test_kneighbors_regressor_sparse" +- id: "sklearn.neighbors.tests.test_neighbors::test_metric_params_interface" +- id: "sklearn.neighbors.tests.test_neighbors::test_nearest_neighbors_validate_params" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-chebyshev-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-cityblock-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-euclidean-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-l1-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-l2-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-manhattan-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-100-1000-minkowski-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-chebyshev-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-cityblock-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-euclidean-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-l1-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-l2-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-manhattan-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsClassifier-50-500-minkowski-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-DM_euclidean-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-1-100-DM_euclidean-1000-5-100]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-DM_euclidean-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-DM_euclidean-1000-5-100]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-chebyshev-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-cityblock-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-euclidean-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-l1-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-l2-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-manhattan-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-100-1000-minkowski-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-DM_euclidean-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-DM_euclidean-1000-5-100]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-chebyshev-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-cityblock-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-euclidean-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-l1-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-l2-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-manhattan-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neigh_predictions_algorithm_agnosticity[float64-KNeighborsRegressor-50-500-minkowski-100-100-10]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_iris" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_metrics[float64-42-DM_euclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_metrics[float64-42-braycurtis]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_metrics[float64-42-canberra]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_metrics[float64-42-haversine]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_metrics[float64-42-minkowski]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_metrics[float64-42-seuclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_error[ball_tree-100-KNeighborsClassifier]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_error[ball_tree-100-KNeighborsRegressor]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_error[ball_tree-2-KNeighborsClassifier]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_error[ball_tree-2-KNeighborsRegressor]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_error[kd_tree-100-KNeighborsClassifier]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_error[kd_tree-100-KNeighborsRegressor]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_error[kd_tree-2-KNeighborsClassifier]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_error[kd_tree-2-KNeighborsRegressor]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_warn[auto-100-KNeighborsClassifier]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_warn[auto-100-KNeighborsRegressor]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_warn[auto-2-KNeighborsClassifier]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_warn[auto-2-KNeighborsRegressor]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_warn[brute-100-KNeighborsClassifier]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_warn[brute-100-KNeighborsRegressor]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_warn[brute-2-KNeighborsClassifier]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_minkowski_semimetric_algo_warn[brute-2-KNeighborsRegressor]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_validate_parameters[csr_array-KNeighborsClassifier]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_validate_parameters[csr_array-KNeighborsRegressor]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_validate_parameters[csr_matrix-KNeighborsClassifier]" +- id: "sklearn.neighbors.tests.test_neighbors::test_neighbors_validate_parameters[csr_matrix-KNeighborsRegressor]" +- id: "sklearn.neighbors.tests.test_neighbors::test_not_fitted_error_gets_raised" +- id: "sklearn.neighbors.tests.test_neighbors::test_pairwise_boolean_distance" +- id: "sklearn.neighbors.tests.test_neighbors::test_pipeline_with_nearest_neighbors_transformer" +- id: "sklearn.neighbors.tests.test_neighbors::test_precomputed_cross_validation" +- id: "sklearn.neighbors.tests.test_neighbors::test_precomputed_dense" +- id: "sklearn.neighbors.tests.test_neighbors::test_precomputed_sparse_invalid[csr_array]" +- id: "sklearn.neighbors.tests.test_neighbors::test_precomputed_sparse_invalid[csr_matrix]" +- id: "sklearn.neighbors.tests.test_neighbors::test_precomputed_sparse_knn[csr]" +- id: "sklearn.neighbors.tests.test_neighbors::test_precomputed_sparse_knn[lil]" +- id: "sklearn.neighbors.tests.test_neighbors::test_precomputed_sparse_radius[csr]" +- id: "sklearn.neighbors.tests.test_neighbors::test_precomputed_sparse_radius[lil]" +- id: "sklearn.neighbors.tests.test_neighbors::test_query_equidistant_kth_nn[ball_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_query_equidistant_kth_nn[brute]" +- id: "sklearn.neighbors.tests.test_neighbors::test_query_equidistant_kth_nn[kd_tree]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_brute_backend[42-float64-braycurtis]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_brute_backend[42-float64-dice]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_brute_backend[42-float64-hamming]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_brute_backend[42-float64-jaccard]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_brute_backend[42-float64-nan_euclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_brute_backend[42-float64-rogerstanimoto]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_brute_backend[42-float64-russellrao]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_brute_backend[42-float64-seuclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_brute_backend[42-float64-sokalmichener]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_brute_backend[42-float64-sokalsneath]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_brute_backend[42-float64-yule]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_returns_array_of_objects[csr_array]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_returns_array_of_objects[csr_matrix]" +- id: "sklearn.neighbors.tests.test_neighbors::test_radius_neighbors_sort_results[brute-precomputed]" +- id: "sklearn.neighbors.tests.test_neighbors::test_regressor_predict_on_arraylikes" +- id: "sklearn.neighbors.tests.test_neighbors::test_sparse_metric_callable[csr_array]" +- id: "sklearn.neighbors.tests.test_neighbors::test_sparse_metric_callable[csr_matrix]" +- id: "sklearn.neighbors.tests.test_neighbors::test_unsupervised_inputs[float64-KNeighborsClassifier]" +- id: "sklearn.neighbors.tests.test_neighbors::test_unsupervised_inputs[float64-KNeighborsRegressor]" +- id: "sklearn.neighbors.tests.test_neighbors::test_unsupervised_inputs[float64-NearestNeighbors]" +- id: "sklearn.neighbors.tests.test_neighbors::test_unsupervised_kneighbors[float64-DM_euclidean-False-100-100-10-100]" +- id: "sklearn.neighbors.tests.test_neighbors::test_unsupervised_kneighbors[float64-DM_euclidean-False-1000-5-100-1]" +- id: "sklearn.neighbors.tests.test_neighbors::test_unsupervised_kneighbors[float64-DM_euclidean-True-100-100-10-100]" +- id: "sklearn.neighbors.tests.test_neighbors::test_unsupervised_kneighbors[float64-DM_euclidean-True-1000-5-100-1]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-DM_euclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-braycurtis]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-cityblock]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-cosine]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-dice]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-euclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-hamming]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-jaccard]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-l1]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-l2]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-manhattan]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-nan_euclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-precomputed]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-rogerstanimoto]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-russellrao]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-seuclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-sokalmichener]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-sokalsneath]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-yule]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-DM_euclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-braycurtis]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-dice]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-hamming]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-jaccard]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-nan_euclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-precomputed]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-rogerstanimoto]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-russellrao]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-seuclidean]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-sokalmichener]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-sokalsneath]" +- id: "sklearn.neighbors.tests.test_neighbors::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-yule]" +- id: "sklearn.neighbors.tests.test_neighbors_pipeline::test_kneighbors_regressor" +- id: "sklearn.neighbors.tests.test_neighbors_pipeline::test_tsne" +- id: "sklearn.preprocessing.tests.test_polynomial::test_csr_polynomial_expansion_index_overflow[csr_array-False-True-2-65535]" +- id: "sklearn.preprocessing.tests.test_polynomial::test_csr_polynomial_expansion_index_overflow[csr_array-False-True-3-2344]" +- id: "sklearn.semi_supervised.tests.test_self_training::test_classification[k_best-base_estimator0]" +- id: "sklearn.semi_supervised.tests.test_self_training::test_classification[threshold-base_estimator0]" +- id: "sklearn.semi_supervised.tests.test_self_training::test_zero_iterations[y1-base_estimator0]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[fit-intercept-multi-class-log-array]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[fit-intercept-multi-class-log-csr_array]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[fit-intercept-multi-class-log-csr_matrix]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[fit-intercept-two-classes-log-array]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[fit-intercept-two-classes-log-csr_array]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[fit-intercept-two-classes-log-csr_matrix]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[no-intercept-multi-class-log-array]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[no-intercept-multi-class-log-csr_array]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[no-intercept-multi-class-log-csr_matrix]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[no-intercept-two-classes-log-array]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[no-intercept-two-classes-log-csr_array]" +- id: "sklearn.svm.tests.test_bounds::test_l1_min_c[no-intercept-two-classes-log-csr_matrix]" +- id: "sklearn.svm.tests.test_sparse::test_weight[csr_array]" +- id: "sklearn.svm.tests.test_sparse::test_weight[csr_matrix]" +- id: "sklearn.tests.test_calibration::test_calibrated_classifier_cv_double_sample_weights_equivalence[False-isotonic]" +- id: "sklearn.tests.test_calibration::test_calibrated_classifier_cv_double_sample_weights_equivalence[False-sigmoid]" +- id: "sklearn.tests.test_calibration::test_calibrated_classifier_cv_double_sample_weights_equivalence[True-isotonic]" +- id: "sklearn.tests.test_calibration::test_calibrated_classifier_cv_double_sample_weights_equivalence[True-sigmoid]" +- id: "sklearn.tests.test_calibration::test_calibrated_classifier_cv_zeros_sample_weights_equivalence[False-isotonic]" +- id: "sklearn.tests.test_calibration::test_calibrated_classifier_cv_zeros_sample_weights_equivalence[False-sigmoid]" +- id: "sklearn.tests.test_calibration::test_calibrated_classifier_cv_zeros_sample_weights_equivalence[True-isotonic]" +- id: "sklearn.tests.test_calibration::test_calibrated_classifier_cv_zeros_sample_weights_equivalence[True-sigmoid]" +- id: "sklearn.tests.test_calibration::test_calibration_dict_pipeline" +- id: "sklearn.tests.test_common::test_estimators[BernoulliRBM()-check_methods_sample_order_invariance]" +- id: "sklearn.tests.test_common::test_estimators[BernoulliRBM()-check_methods_subset_invariance]" +- id: "sklearn.tests.test_common::test_estimators[BisectingKMeans()-check_sample_weights_invariance(kind=zeros)]" +- id: "sklearn.tests.test_common::test_estimators[CalibratedClassifierCV(estimator=LogisticRegression(C=1))-check_sample_weights_invariance(kind=zeros)]" +- id: "sklearn.tests.test_common::test_estimators[DummyClassifier()-check_methods_sample_order_invariance]" +- id: "sklearn.tests.test_common::test_estimators[DummyClassifier()-check_methods_subset_invariance]" +- id: "sklearn.tests.test_common::test_estimators[ElasticNetCV()-check_sample_weights_invariance(kind=zeros)]" +- id: "sklearn.tests.test_common::test_estimators[IsolationForest()-check_sample_weights_invariance(kind=zeros)]" +- id: 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"sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_classifiers_one_label]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_classifiers_regression_target]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_dont_overwrite_parameters]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_dtype_object]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_estimators_nan_inf]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_estimators_overwrite_params]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_fit2d_1sample]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_fit_check_is_fitted]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_supervised_y_2d]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_supervised_y_no_nan]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_dont_overwrite_parameters]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_estimators_nan_inf]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_estimators_overwrite_params]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_fit2d_1sample]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_fit_check_is_fitted]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_regressor_data_not_an_array]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_supervised_y_2d]" +- id: "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_supervised_y_no_nan]" +- id: "sklearn.tests.test_common::test_estimators[SGDClassifier()-check_sample_weights_invariance(kind=zeros)]" +- id: "sklearn.tests.test_common::test_estimators[SGDOneClassSVM()-check_sample_weights_invariance(kind=zeros)]" +- id: "sklearn.tests.test_common::test_estimators[SGDRegressor()-check_sample_weights_invariance(kind=zeros)]" +- id: "sklearn.tests.test_common::test_estimators[SVC()-check_sample_weights_invariance(kind=zeros)]" +- id: "sklearn.tests.test_common::test_estimators[SVR()-check_sample_weights_invariance(kind=zeros)]" +- id: "sklearn.tests.test_common::test_estimators[SpectralBiclustering()-check_estimators_dtypes]" +- id: "sklearn.tests.test_common::test_estimators[SpectralBiclustering()-check_fit2d_1feature]" +- id: "sklearn.tests.test_common::test_estimators[SpectralBiclustering()-check_fit2d_1sample]" +- id: "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_dont_overwrite_parameters]" +- id: "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_estimator_sparse_array]" +- id: "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_estimator_sparse_matrix]" +- id: "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_estimators_dtypes]" +- id: "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_fit2d_1feature]" +- id: "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_fit2d_1sample]" +- id: "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_fit2d_predict1d]" +- id: "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_methods_subset_invariance]" +- id: "sklearn.tests.test_common::test_estimators[TunedThresholdClassifierCV(estimator=LogisticRegression(C=1))-check_classifiers_train(readonly_memmap=True)]" +- id: 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"sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_dtype_object]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit1d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit2d_1feature]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_supervised_y_2d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_complex_data]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_dtype_object]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit1d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit2d_1feature]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressor_data_not_an_array]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_supervised_y_2d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_supervised_y_no_nan]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_classifier_data_not_an_array]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_classifiers_one_label]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_classifiers_regression_target]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_dtype_object]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_estimators_overwrite_params]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_fit1d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_supervised_y_2d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_complex_data]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_dtype_object]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_estimators_overwrite_params]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_fit1d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_regressor_data_not_an_array]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_supervised_y_2d]" +- id: "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_supervised_y_no_nan]" +- id: "sklearn.tests.test_docstrings::test_docstring[GridSearchCV-None]" +- id: "sklearn.tests.test_multioutput::test_base_chain_fit_and_predict_with_sparse_data_and_cv[csr_array]" +- id: "sklearn.tests.test_multioutput::test_classifier_chain_fit_and_predict_with_sparse_data[csr_array]" +- id: "sklearn.tests.test_multioutput::test_multi_output_classes_[estimator0]" +- id: "sklearn.tests.test_multioutput::test_multi_output_classification_sample_weights" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[bsr_array]" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[bsr_matrix]" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[coo_array]" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[coo_matrix]" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[csc_array]" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[csc_matrix]" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[csr_array]" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[csr_matrix]" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[dok_array]" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[dok_matrix]" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[lil_array]" +- id: "sklearn.tests.test_multioutput::test_multi_target_sparse_regression[lil_matrix]" +- id: "sklearn.tests.test_multioutput::test_multiclass_multioutput_estimator_predict_proba" +- id: "sklearn.tests.test_pipeline::test_pipeline_set_output_integration" +- id: "sklearn.tests.test_pipeline::test_pipeline_with_estimator_with_len" +- id: "sklearn.tests.test_public_functions::test_class_wrapper_param_validation[sklearn.cluster.dbscan-sklearn.cluster.DBSCAN]" +- id: "sklearn.tests.test_public_functions::test_class_wrapper_param_validation[sklearn.cluster.k_means-sklearn.cluster.KMeans]" +- id: "sklearn.tree.tests.test_monotonic_tree::test_bad_monotonic_cst_raises[RandomForestClassifier]" +- id: "sklearn.tree.tests.test_monotonic_tree::test_monotonic_constraints_classifications[42-csc_array-False-False-RandomForestClassifier]" +- id: "sklearn.tree.tests.test_monotonic_tree::test_monotonic_constraints_classifications[42-csc_array-False-True-RandomForestClassifier]" +- id: "sklearn.tree.tests.test_monotonic_tree::test_monotonic_constraints_classifications[42-csc_matrix-False-False-RandomForestClassifier]" +- id: "sklearn.tree.tests.test_monotonic_tree::test_monotonic_constraints_classifications[42-csc_matrix-False-True-RandomForestClassifier]" +- id: "sklearn.tree.tests.test_monotonic_tree::test_monotonic_constraints_regressions[42-csc_array-squared_error-False-False-RandomForestRegressor]" +- id: "sklearn.tree.tests.test_monotonic_tree::test_monotonic_constraints_regressions[42-csc_array-squared_error-False-True-RandomForestRegressor]" +- id: "sklearn.tree.tests.test_monotonic_tree::test_monotonic_constraints_regressions[42-csc_matrix-squared_error-False-False-RandomForestRegressor]" +- id: "sklearn.tree.tests.test_monotonic_tree::test_monotonic_constraints_regressions[42-csc_matrix-squared_error-False-True-RandomForestRegressor]" +- id: "sklearn.tree.tests.test_monotonic_tree::test_multiclass_raises[RandomForestClassifier]" +- id: "sklearn.tree.tests.test_monotonic_tree::test_multiple_output_raises[RandomForestClassifier]" +- id: "sklearn.utils.tests.test_estimator_checks::test_check_dataframe_column_names_consistency" +- id: "sklearn.utils.tests.test_estimator_checks::test_check_estimator" +- id: "sklearn.utils.tests.test_estimator_checks::test_check_estimator_clones" +- id: "sklearn.utils.tests.test_estimator_checks::test_check_estimator_pairwise" +- id: "sklearn.utils.tests.test_estimator_html_repr::test_estimator_html_repr_fitted_icon[estimator0]" +- id: "sklearn.utils.tests.test_estimator_html_repr::test_estimator_html_repr_fitted_icon[estimator1]" +- id: "sklearn.utils.tests.test_estimator_html_repr::test_estimator_html_repr_fitted_icon[estimator2]" +- id: "sklearn.utils.tests.test_estimator_html_repr::test_show_arrow_pipeline" +- id: "sklearn.utils.tests.test_validation::test_has_fit_parameter" diff --git a/python/cuml/pyproject.toml b/python/cuml/pyproject.toml index b6d1e78941..72a884bc7b 100644 --- a/python/cuml/pyproject.toml +++ b/python/cuml/pyproject.toml @@ -138,6 +138,7 @@ test = [ "pytest-cov", "pytest-xdist", "pytest==7.*", + "pyyaml", "scikit-learn==1.5.*", "seaborn", "statsmodels",